Paper Digest: CIKM 2024 Papers & Highlights
The ACM Conference on Information and Knowledge Management (CIKM) is an annual computer science research conference dedicated to information management and knowledge management. To help the community quickly catch up on the work presented in this conference, Paper Digest Team processed all accepted papers, and generated one highlight sentence (typically the main topic) for each paper. Readers are encouraged to read these machine generated highlights to quickly get the main idea of each paper.
To search or review papers within CIKM-2024 related to a specific topic, please use the search by venue (CIKM-2024), review by venue (CIKM-2024) and question answering by venue (CIKM-2024) services. To browse papers by author, here is a list of all 2,800 authors (CIKM-2024). You may also like to explore our “Best Paper” Digest (CIKM), which lists the most influential CIKM papers since 1993.
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TABLE 1: Paper Digest: CIKM 2024 Papers & Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | A Geometric Perspective for High-Dimensional Multiplex Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, our study reveals that increasing the number of graph dimensions can cause further distortions to the highly curved manifolds. To address this problem, we propose a novel multiplex graph embedding method that harnesses hierarchical dimension embedding and Hyperbolic Graph Neural Networks. |
Kamel Abdous; Nairouz Mrabah; Mohamed Bouguessa; |
| 2 | Neural Additive Tensor Decomposition for Sparse Tensors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, this has come at the cost of interpretability: neural tensor models entangle interactions across and within latent structures in a black-box manner, making it difficult to readily understand the discovered structures. Understanding these structures, however, is crucial in applications such as healthcare, which requires transparency in critical decision-making processes.To overcome this major limitation and bridge the gap between the classical multi-linear models and neural tensor models, we propose Neural Additive Tensor Decomposition (NeAT), an accurate and interpretable neural tensor model for sparse tensors. |
Dawon Ahn; Uday Singh Saini; Evangelos E. Papalexakis; Ali Payani; |
| 3 | Navigating The Landscape of Reproducible Research: A Predictive Modeling Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Typically, a paper’s reproducibility is inferred based on the availability of artifacts such as code, data, or supplemental information, often without extensive empirical investigation. To address these issues, we utilized artifacts of papers as fundamental units to develop a novel, dual-spectrum framework that focuses on author-centric and external-agent perspectives |
Akhil Pandey Akella; Sagnik Ray Choudhury; David Koop; Hamed Alhoori; |
| 4 | Can LLMs Reason Like Humans? Assessing Theory of Mind Reasoning in LLMs for Open-Ended Questions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To enhance LLM capabilities, we implement a prompt tuning method that incorporates human intentions and emotions, resulting in improvements in ToM reasoning performance. |
Maryam Amirizaniani; Elias Martin; Maryna Sivachenko; Afra Mashhadi; Chirag Shah; |
| 5 | Spatio-temporal Graph Normalizing Flow for Probabilistic Traffic Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite their good performance, most traffic prediction models are point estimation models, lacking the capability to estimate the uncertainties of future traffic data, which is crucial in practical traffic decision-making. Aiming at this problem, we combine the probabilistic estimation capabilities of conditional normalizing flows with the spatio-temporal relationship learning of spatio-temporal graphs, leading to a Spatio-Temporal Graph Normalizing Flow (STGNF) model to estimate the distribution of future traffic data. |
Yang An; Zhibin Li; Wei Liu; Haoliang Sun; Meng Chen; Wenpeng Lu; Yongshun Gong; |
| 6 | Advances in Citation Text Generation: Leveraging Multi-Source Seq2Seq Models and Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Citation Text Generation (CTG) in scientific documents often relies on standard summarization techniques, which may not fully capture the nuanced relationship between the citing and cited papers. To address this, we present a Multi-Source Citation Text Generation (M-CTG) architecture, leveraging a Seq2Seq transformer framework enhanced with keyphrase embeddings, graph embeddings, and text representations. |
Avinash Anand; Ashwin R Nair; Kritarth Prasad; Vrinda Narayan; Naman Lal; Debanjan Mahata; Yaman K Singla; Rajiv Ratn Shah; |
| 7 | Out-of-Distribution Aware Classification for Tabular Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we address OOD-aware classification for tabular data, where an unrelated dataset cannot be used as OOD training data. |
Amirhossein Ansari; Ke Wang; Pulei Xiong; |
| 8 | Aligning Large Language Model with Direct Multi-Preference Optimization for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, directly applying LLMs to recommendation tasks has proven to be less effective due to the significant gap between the data used for pre-training LLMs and the specific requirements of recommendation tasks. In this study, we propose Direct Multi-Preference Optimization (DMPO), a streamlined framework to bridge this gap and enhance the alignment of LLMs for recommendation tasks. |
Zhuoxi Bai; Ning Wu; Fengyu Cai; Xinyi Zhu; Yun Xiong; |
| 9 | City Foundation Models for Learning General Purpose Representations from OpenStreetMap Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present CityFM, a self-supervised framework to train a foundation model within a selected geographical area. |
Pasquale Balsebre; Weiming Huang; Gao Cong; Yi Li; |
| 10 | A Learning-based Approach for Explaining Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present Learning Attributions (LA), a novel method for explaining language models. |
Oren Barkan; Yonatan Toib; Yehonatan Elisha; Noam Koenigstein; |
| 11 | Covering A Graph with Dense Subgraph Families, Via Triangle-Rich Sets Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We design a provable algorithm, RTRExtractor, that can discover RTR families that approximately cover any RTR set. |
Sabyasachi Basu; Daniel Paul-Pena; Kun Qian; C. Seshadhri; Edward W Huang; Karthik Subbian; |
| 12 | Discovering Denial Constraints Based on Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, these methods overlook the intent of users, which requires the discovered DCs to be succinct, relevant, and diverse concurrently. To address these limitations, we introduce DCMiner, a deep reinforcement learning (DRL)-based framework that produces rules satisfying user preferences. |
Lingfeng Bian; Weidong Yang; Jingyi Xu; Zijing Tan; |
| 13 | Hierarchical Graph Latent Diffusion Model for Conditional Molecule Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces the Hierarchical Graph Latent Diffusion Model (HGLDM), a novel variant of latent diffusion models that overcomes the problem of applying continuous diffusion models directly to discrete graph data. |
Tian Bian; Yifan Niu; Heng Chang; Divin Yan; Junzhou Huang; Yu Rong; Tingyang Xu; Jia Li; Hong Cheng; |
| 14 | Finding MIDDLE Ground: Scalable and Secure Distributed Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In practice, edge devices have varying computational and memory constraints, which may not allow certain devices to have the space or speed to train a specific model. To overcome these issues, we propose MIDDLE, a model-independent distributed learning algorithm that allows heterogeneous edge devices to assist each other in training while communicating only non-sensitive information. |
Marco Bornstein; Nawaf Nazir; Jan Drgona; Soumya Kundu; Veronica Adetola; |
| 15 | 3M-Health: Multimodal Multi-Teacher Knowledge Distillation for Mental Health Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce a Multimodal and Multi-Teacher Knowledge Distillation model for Mental Health Classification, leveraging insights from cross-modal human understanding. |
Rina Carines Cabral; Siwen Luo; Josiah Poon; Soyeon Caren Han; |
| 16 | Wise Fusion: Group Fairness Enhanced Rank Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Yet, real-world fusion tasks often combine rankings of varying candidate sets, may also contain relevance scores, or are better suited to equal representation. To address fairness in these settings, we present a new plug-and-play fairness-aware fusion strategy: WISE fusion. |
Kathleen Cachel; Elke Rundensteiner; |
| 17 | Hypergraph Hash Learning for Efficient Trajectory Similarity Computation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel hash learning method to encode the trajectories into binary hash codes and compute trajectory similarities by Hamming distances which is much more efficient. |
Yuan Cao; Lei Li; Xiangru Chen; Xue Xu; Zuojin Huang; Yanwei Yu; |
| 18 | MATCC: A Novel Approach for Robust Stock Price Prediction Incorporating Market Trends and Cross-time Correlations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Prior research has predominantly concentrated on time-aligned feature correlations, with limited exploration of cross-time stock correlations. To address these issues, we propose a novel framework, MATCC (Market Trend and Cross-time Correlation model). |
Zhiyuan Cao; Jiayu Xu; Chengqi Dong; Peiwen Yu; Tian Bai; |
| 19 | DiHAN: A Novel Dynamic Hierarchical Graph Attention Network for Fake News Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: With the support of News-DyHIN, we propose a novel fake news detection framework, named <u> D </u>ynam<u> i </u>c <u> H </u>ierarchical <u> A </u>ttention <u> N </u>etwork (DiHAN), which learns news representations via a hierarchical attention mechanism to fuse temporal interactions among news articles. |
Ya-Ting Chang; Zhibo Hu; Xiaoyu Li; Shuiqiao Yang; Jiaojiao Jiang; Nan Sun; |
| 20 | FCS-HGNN: Flexible Multi-type Community Search in Heterogeneous Information Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose FCS-HGNN, a novel method for flexibly identifying both single-type and multi-type communities in HINs. |
Guoxin Chen; Fangda Guo; Yongqing Wang; Yanghao Liu; Peiying Yu; Huawei Shen; Xueqi Cheng; |
| 21 | Towards Online and Safe Configuration Tuning with Semi-supervised Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the aforementioned issues, we propose SafeTune, an online tuning system that adapts to dynamic workloads. |
Haitian Chen; Xu Chen; Zibo Liang; Xiushi Feng; Jiandong Xie; Han Su; Kai Zheng; |
| 22 | Improving Message-Passing GNNs By Asynchronous Aggregation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Finally, vanilla MPNNs fail to meet the ability of training in heterophilic graphs. In this paper, we provide a unified insight into these defects: node embeddings are sent to neighbors at a constant pace and are aggregated immediately. |
Jialong Chen; Tianchi Liao; Chuan Chen; Zibin Zheng; |
| 23 | PTSR: Prefix-Target Graph-based Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Prefix-Target Graph-based Sequential Recommendation Approach (PTSR), which constructs a prefix-target graph (PTG) to collect observed correlations among prefixes and targets. |
Jiayu Chen; Xiaoyu Du; Yonghua Pan; Jinhui Tang; |
| 24 | PACIFIC: Enhancing Sequential Recommendation Via Preference-aware Causal Intervention and Counterfactual Data Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, these methods rely on extensive interaction sequences, but sequential data often suffers from sparsity issues. To address these limitations, this paper proposes a <u> P </u>reference-<u> a </u>ware <u> C </u>ausal <u> I </u>ntervention and Counter<u> f </u>a<u> c </u>tual Data Augmentation ( Pacific ) framework to enhance sequential recommendation. |
Jinpeng Chen; Huachen Guan; Huan Li; Fan Zhang; Liwei Huang; Guangyao Pang; Xiongnan Jin; |
| 25 | ELCoRec: Enhance Language Understanding with Co-Propagation of Numerical and Categorical Features for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose ELCoRec to Enhance Language understanding with Co-Propagation of numerical and categorical features for Recommendation. |
Jizheng Chen; Kounianhua Du; Jianghao Lin; Bo Chen; Ruiming Tang; Weinan Zhang; Yong Yu; |
| 26 | ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Unfortunately, these labels often come with noise, compromising the generalization performance of deep networks. To tackle this challenge and enhance the robustness of deep learning models against label noise in graph-based tasks, we propose a method called ERASE (Error-Resilient representation learning on graphs for lAbel noiSe tolerancE). |
Ling-Hao Chen; Yuanshuo Zhang; Taohua Huang; Liangcai Su; Zeyi Lin; Xi Xiao; Xiaobo Xia; Tongliang Liu; |
| 27 | Urban Traffic Accident Risk Prediction Revisited: Regionality, Proximity, Similarity and Sparsity Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel multi-granularity hierarchical spatio-temporal network. |
Minxiao Chen; Haitao Yuan; Nan Jiang; Zhifeng Bao; Shangguang Wang; |
| 28 | Assessing Image Inpainting Via Re-Inpainting Self-Consistency Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Traditional evaluation methods, heavily dependent on the existence of unmasked reference images, inherently favor certain inpainting outcomes, introducing biases. Addressing this issue, we introduce an innovative evaluation paradigm that utilizes a self-supervised metric based on multiple re-inpainting passes. |
Tianyi Chen; Jianfu Zhang; Yan Hong; Yiyi Zhang; Liqing Zhang; |
| 29 | DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel representation learning method \o{}urs~for DTDGs, pivoting from the traditional GNN+RNN framework to a Transformer-based architecture. |
Xi Chen; Yun Xiong; Siwei Zhang; Jiawei Zhang; Yao Zhang; Shiyang Zhou; Xixi Wu; Mingyang Zhang; Tengfei Liu; Weiqiang Wang; |
| 30 | Social Influence Learning for Recommendation Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, the potential of social influence is still under-explored. In this paper, we will fill this gap by proposing a novel model for social influence learning to derive the essential influence patterns within the user relationships. |
Ximing Chen; Pui Ieng Lei; Yijun Sheng; Yanyan Liu; Zhiguo Gong; |
| 31 | Domain Alignment with Large Vision-language Models for Cross-domain Remote Sensing Image Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: (2) They suffer from severe performance degradation when the data distribution between the source domain and target domain becomes highly inconsistent. To address these challenges, we propose <u> D </u>omain <u> A </u>lignment with <u> L </u>arge <u> V </u>ision-language models for cross-domain remote sensing image retrieval (termed as DALV). |
Yan Chen; Guocan Cai; Fufang Li; Yangtao Wang; Xin Tan; Xiaocui Li; |
| 32 | Hyperedge Importance Estimation Via Identity-aware Hypergraph Attention Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For example, in a co-authorship hypergraph, a paper (hyperedge) is co-authored by multiple authors (nodes). The number of citations a paper receives can be regarded as the importance value of its corresponding hyperedge, reflecting its academic influence and significance.In this work, we introduce hyperedge importance estimation as a new problem in hypergraph learning. |
Yin Chen; Xiaoyang Wang; Chen Chen; |
| 33 | Honest-Majority Maliciously Secure Skyline Queries on Outsourced Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, query outcomes are vulnerable to potential malicious cloud services. To circumvent these limitations, this work presents the <u>H</u>onest-<u>M</u>ajority and <u>M</u>aliciously <u>S</u>kyline <u>Q</u>uery scheme (HMMSQ), which facilitates efficient skyline queries while safeguarding the privacy of datasets, queries, and skylines, as well as detecting malevolent activities. |
Yu Chen; Lin Liu; Rongmao Chen; Shaojing Fu; Yuexiang Yang; |
| 34 | Empowering Private Tutoring By Chaining Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we explore the development of a full-fledged intelligent tutoring system based on large language models (LLMs). |
Yulin Chen; Ning Ding; Hai-Tao Zheng; Zhiyuan Liu; Maosong Sun; Bowen Zhou; |
| 35 | SVIPTR: Fast and Efficient Scene Text Recognition with Vision Permutable Extractor Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose a VIsion Permutable extractor for fast and efficient Scene Text Recognition (SVIPTR), which achieves an impressive balance between high performance and rapid inference speeds in the domain of STR. |
Xianfu Cheng; Weixiao Zhou; Xiang Li; Jian Yang; Hang Zhang; Tao Sun; Wei Zhang; Yuying Mai; Tongliang Li; Xiaoming Chen; Zhoujun Li; |
| 36 | TESSM: Tree-based Selective State Space Models for Efficient Join Order Selection Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: As more tables join, the complexity surges, turning it into an NP-hard problem due to the exponential growth of possible orders. Deep reinforcement learning (DRL) has recently made significant strides, outperforming traditional algorithms by treating join selection as a Markov Decision Process to devise more effective strategies.Current methods struggle with integrating query semantics and plan structures, as well as encountering issues with complex joins where bottom-up learning can lead to information loss.To tackle these issues, we present the Tree-based Selective State Space Models for Efficient Join Order Selection Learning(TESSM). |
Yaohui Chu; Yizhe Liu; Yue Zhang; Xuan Hou; Longfei Yu; Zhaohui Peng; |
| 37 | Automatic Large Language Model Evaluation Via Peer Review Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these paradigms often suffer from high cost, low generalizability, and inherited biases in practice, which make them incapable of supporting the sustainable development of LLMs in the long term. In order to address these issues, inspired by the peer review systems widely used in the academic publication process, we propose a novel framework that can automatically evaluate LLMs through a peer-review process. |
Zhumin Chu; Qingyao Ai; Yiteng Tu; Haitao Li; Yiqun Liu; |
| 38 | Time Is Not Enough: Time-Frequency Based Explanation for Time-Series Black-Box Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we present Spectral eXplanation (SpectralX), an XAI framework that provides time-frequency explanations for time-series black-box classifiers. |
Hyunseung Chung; Sumin Jo; Yeonsu Kwon; Edward Choi; |
| 39 | Context Matters: Enhancing Sequential Recommendation with Context-aware Diffusion-based Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, pulling semantically inconsistent sequences closer in the representation space can render the user sequence embeddings insensitive to variations in user preferences, which contradicts the primary objective of sequential recommendation. To address these limitations, we propose the Context-aware Diffusion-based Contrastive Learning for Sequential Recommendation, named CaDiRec. |
Ziqiang Cui; Haolun Wu; Bowei He; Ji Cheng; Chen Ma; |
| 40 | ByGCN: Spatial Temporal Byroad-Aware Graph Convolution Network for Traffic Flow Prediction in Road Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Meaning that the existing methods suffer the non-similar forgetting and hard to gain the multi-hop correlation. To overcome these problems, we propose a novel Spatial Temporal <u>By</u>road-Aware <u>G</u>raph <u>C</u>onvolution <u>N</u>etwork (ByGCN) in this paper. |
Tangpeng Dan; Xiao Pan; Bolong Zheng; Xiaofeng Meng; |
| 41 | PROSPECT: Learn MLPs on Graphs Robust Against Adversarial Structure Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To mitigate potential convergence failure caused by inductive bias conflicts between the heterogeneous MLP and GNN, we propose the Quasi-Alternating Cosine Annealing (QACA) learning rate scheduler, inspired by our convergence analysis of the involved MLP. |
Bowen Deng; Jialong Chen; Yanming Hu; Zhiyong Xu; Chuan Chen; Tao Zhang; |
| 42 | ALDF: An Adaptive Logical Decision Framework for Multimodal Named Entity Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes an Adaptive Logical Decision Framework (ALDF) capable of determining the sufficiency of textual information in NER tasks, deciding whether to introduce image information, avoiding unnecessary noise, and focusing more on information-scarce entities when introducing image information. |
Guohui Ding; Tianhao Jiang; Rui Zhou; Qian Gao; |
| 43 | DRFormer: Multi-Scale Transformer Utilizing Diverse Receptive Fields for Long Time-Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a dynamic tokenizer with a dynamic sparse learning algorithm to capture diverse receptive fields and sparse patterns of time series data. |
Ruixin Ding; Yuqi Chen; Yu-Ting Lan; Wei Zhang; |
| 44 | Effective Illicit Account Detection on Large Cryptocurrency MultiGraphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present DIAM, an effective method for detecting illicit accounts in cryptocurrency transaction networks modeled by directed multi-graphs with attributed edges. |
Zhihao Ding; Jieming Shi; Qing Li; Jiannong Cao; |
| 45 | SGOOD: Substructure-enhanced Graph-Level Out-of-Distribution Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: A trustworthy model should be able to detect OOD graphs to avoid unreliable predictions, while producing accurate in-distribution (ID) predictions. To achieve this, we present SGOOD, a novel graph-level OOD detection framework. |
Zhihao Ding; Jieming Shi; Shiqi Shen; Xuequn Shang; Jiannong Cao; Zhipeng Wang; Zhi Gong; |
| 46 | Boosting Certificate Robustness for Time Series Classification with Efficient Self-Ensemble Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our review found that Randomized Smoothing performs modestly in TSC, struggling to provide effective assurances on datasets with poor robustness. Therefore, we propose a self-ensemble method to enhance the lower bound of the probability confidence of predicted labels by reducing the variance of classification margins, thereby certifying a larger radius. |
Chang George Dong; Zhengyang David Li; Liangwei Nathan Zheng; Weitong Chen; Wei Emma Zhang; |
| 47 | PIXEL: Prompt-based Zero-shot Hashing Via Visual and Textual Semantic Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most existing methods focus on leveraging the extracted attribute numerical values, without exploring the textual semantics in attribute descriptions. To bridge this gap, in this paper, we propose Prompt-based zero-shot hashing via vIsual and teXtual sEmantic aLignment, namely PIXEL. |
Zeyu Dong; Qingqing Long; Yihang Zhou; Pengfei Wang; Zhihong Zhu; Xiao Luo; Yidong Wang; Pengyang Wang; Yuanchun Zhou; |
| 48 | FZR: Enhancing Knowledge Transfer Via Shared Factors Composition in Zero-Shot Relational Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our study, we aim to identify and utilize shared factors that widely exist in the prior knowledge of classes to learn enhanced semantic representations via shared factors composition, and develop our Factor-based ZSRL framework (FZR) with Generative Adversarial Networks (GANs) to bridge inequality between seen and unseen classes. |
Zhijun Dong; Likang Wu; Kai Zhang; Ye Liu; Yanghai Zhang; Zhi Li; Hongke Zhao; Enhong Chen; |
| 49 | Enhancing Deep Entity Resolution with Integrated Blocker-Matcher Training: Balancing Consensus and Discrepancy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they often fail to balance the consensus and discrepancy between the blocker and matcher, emphasizing the consensus while neglecting the discrepancy. This paper proposes MutualER, a deep entity resolution framework that integrates and jointly trains the blocker and matcher, balancing both the consensus and discrepancy between them. |
Wenzhou Dou; Derong Shen; Xiangmin Zhou; Hui Bai; Yue Kou; Tiezheng Nie; Hang Cui; Ge Yu; |
| 50 | Towards Uncertainty Quantification for Time Series Segmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose UQ-TSS, a framework to quantify uncertainties surrounding TSS. |
Erick Draayer; Huiping Cao; |
| 51 | Explainable Stock Price Movement Prediction Using Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel triplet network for contrastive learning to enhance the explainability of stock movement prediction by considering instances of integrated textual information and quantitative indicators. |
Kelvin Du; Rui Mao; Frank Xing; Erik Cambria; |
| 52 | IMIRACLE: An Iterative Multi-View Graph Neural Network to Model Intercellular Gene Regulation From Spatial Transcriptomic Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, few methods were developed to connect them to provide biological insights into intercellular regulation. To fill this gap, we propose iMiracle, an iterative multi-view graph neural network that models each cell’s intercellular regulation with three key features. |
Ziheng Duan; Siwei Xu; Cheyu Lee; Dylan Riffle; Jing Zhang; |
| 53 | Low Carbon Footprint Training for 1D-CNNs with Temporal Max-Pooling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose HotConv, a low GPU memory and low carbon footprint learning strategy for training the class of 1D CNNs that have a temporal max-pooling layer. |
Anandharaju Durai Raju; Ke Wang; |
| 54 | Integrating Fair Representation Learning with Fairness Regularization for Intersectional Group Fairness Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a method dubbed FairReg which integrates fairness regularization with fair representation learning. |
David Quashigah Dzakpasu; Jixue Liu; Jiuyong Li; Lin Liu; |
| 55 | Probabilistic Path Integration with Mixture of Baseline Distributions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work examines the performance of different baseline distributions on explainability metrics and proposes a probabilistic path integration approach where the baseline distribution is modeled as a mixture of distributions, learned for each combination of model architecture and explanation metric. |
Yehonatan Elisha; Oren Barkan; Noam Koenigstein; |
| 56 | Precision Meets Resilience: Cross-Database Generalization with Uncertainty Quantification for Robust Cost Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we take a step towards a practical cost estimation model, named Tosure, which can quantify the uncer<u>T</u> ainty for c<u>o</u>st estimation and generalizes to un<u>s</u>een databases acc<u>ur</u>ately and <u>e</u>fficiently. |
Shuhuan Fan; Mengshu Hou; Rui Xi; Wenwen Ma; |
| 57 | Progressive Multimodal Pivot Learning: Towards Semantic Discordance Understanding As Humans Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: With the motivation of improving the robustness of multimodal recognition models in practical scenarios, this work poses a new challenge in multimodal recognition, which is coined as Semantic Discordance Understanding. |
Junlin Fang; Wenya Wang; Tianze Luo; Yanyong Huang; Fengmao Lv; |
| 58 | A Spatio-Temporal Diffusion Model for Missing and Real-Time Financial Data Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we focus on the issue of empirical asset pricing, where the cross-section of future asset returns is a function of lagged firm characteristics that vary in time frequencies and missing ratios. |
Yupeng Fang; Ruirui Liu; Huichou Huang; Peilin Zhao; Qingyao Wu; |
| 59 | PARs: Predicate-based Association Rules for Efficient and Accurate Anomaly Explanation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a novel approach for efficient and accurate model-agnostic anomaly explanation for tabular data using Predicate-based Association Rules (PARs). |
Cheng Feng; |
| 60 | CHDAER:Consistent Hashing-based Data Allocation for Efficient Recommendation in Edge Environment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This issue is further exacerbated by the need to process large amounts of data within edge storage systems. To address this challenge, we propose an efficient recommendation method based on data allocation. |
Zhikang Feng; Chao Yan; Rong Jiang; Xiaolong Xu; Xuyun Zhang; Xiaokang Zhou; Wanchun Dou; Lianyong Qi; |
| 61 | SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a <u>S</u>tructure-aware <u>IN</u>ductive <u>K</u>nowledge <u>T</u>racing model with large language model (dubbed SINKT), which, for the first time, introduces large language models (LLMs) and realizes inductive knowledge tracing. |
Lingyue Fu; Hao Guan; Kounianhua Du; Jianghao Lin; Wei Xia; Weinan Zhang; Ruiming Tang; Yasheng Wang; Yong Yu; |
| 62 | ACDM: An Effective and Scalable Active Clustering with Pairwise Constraint Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an <u>A</u> ctive <u>C</u> lustering with <u>D</u> iffusion <u>M</u> odel (ACDM). |
Xun Fu; Wen-Bo Xie; Bin Chen; Tao Deng; Tian Zou; Xin Wang; |
| 63 | HierRec: Scenario-Aware Hierarchical Modeling for Multi-scenario Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing multi-scenario models only consider coarse-grained explicit scenario modeling that depends on pre-defined scenario identification from manual prior rules, which is biased and sub-optimal. To address these limitations, we propose a Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendations (HierRec), which perceives implicit patterns adaptively, and conducts explicit and implicit scenario modeling jointly. |
Jingtong Gao; Bo Chen; Menghui Zhu; Xiangyu Zhao; Xiaopeng Li; Yuhao Wang; Yichao Wang; Huifeng Guo; Ruiming Tang; |
| 64 | Compositional and Hierarchical Semantic Learning Model for Hospital Readmission Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In particular, predicting hospital 30-day readmission is important to improve healthcare outcomes and reduce cost. Previous works on outcome prediction using clinical notes overlook complex semantic compositions and syntactic structure when learning the note level embedding, which may fail to capture the note semantics and make accurate predictions.To address these limitations, we propose a Compositional and Hierarchical Semantic Learning Model (CHSLM). |
Weiting Gao; Xiangyu Gao; Yi Chen; |
| 65 | Tiled Bit Networks: Sub-Bit Neural Network Compression Through Reuse of Learnable Binary Vectors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a new form of quantization to tile neural network layers with sequences of bits to achieve sub-bit compression of binary-weighted neural networks. |
Matt Gorbett; Hossein Shirazi; Indrakshi Ray; |
| 66 | MSTEM: Masked Spatiotemporal Event Series Modeling for Urban Undisciplined Events Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing supervised methods struggle to perform well on sparse UUEs while self-supervised MAE-based methods adopt a traditional random masking strategy which leads to limited performance on UUE forecasting. Fortunately, we have designed an innovative spatiotemporal masking strategy and its corresponding pre-training task called <u>M</u>asked <u>S</u>patio-<u>T</u>emporal <u>E</u>vent Series <u>M</u>odeling (MSTEM). |
Zehao Gu; Shiyang Zhou; Yun Xiong; Yang Luo; Hongrun Ren; Qiang Wang; Xiaofeng Gao; Philip Yu; |
| 67 | Look Globally and Reason: Two-stage Path Reasoning Over Sparse Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This approach introduces unexplainable factors or necessitates meticulous rule design. In light of this, this paper proposes an alternative approach by looking inward instead of seeking external assistance. |
Saiping Guan; Jiyao Wei; Xiaolong Jin; Jiafeng Guo; Xueqi Cheng; |
| 68 | Graph Local Homophily Network for Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we find that local homophily is a valuable metric for assessing the weights of high- and low-frequency information at the node level, and explicitly point out that the accuracy of local homophily is positively correlated with the accuracy of anomaly detection. |
Ronghui Guo; Minghui Zou; Sai Zhang; Xiaowang Zhang; Zhizhi Yu; Zhiyong Feng; |
| 69 | Information Retrieval Optimization for Non-Exemplar Class Incremental Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, we propose a new NECIL method based on the IB framework. |
Shuai Guo; Yang Gu; Yuan Ma; Yingwei Zhang; Weining Weng; Jun Liu; Weiwei Dai; Yiqiang Chen; |
| 70 | Mitigating Cold-Start Problems in Knowledge Tracing with Large Language Models: An Attribute-aware Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore leveraging the power of Large Language Models (LLMs) to help understand questions for knowledge tracing, which benefits mitigating cold-start and sparse problems and modeling the transfer of students’ knowledge states in a sophisticated manner. |
Yuxiang Guo; Shuanghong Shen; Qi Liu; Zhenya Huang; Linbo Zhu; Yu Su; Enhong Chen; |
| 71 | Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our experiments show that both our novel safe doubly robust method and PRPO provide higher performance than the existing safe inverse propensity scoring approach. |
Shashank Gupta; Harrie Oosterhuis; Maarten de Rijke; |
| 72 | Contrastive Learning on Medical Intents for Sequential Prescription Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The goal of this study can be motivated from two perspectives. |
Arya Hadizadeh Moghaddam; Mohsen Nayebi Kerdabadi; Mei Liu; Zijun Yao; |
| 73 | Fragment Allocations for Partially Replicated Databases Considering Data Modifications and Changing Workloads Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For larger problems, users often fall back to simple heuristics, which can lose optimization potential. This paper demonstrates that scalable heuristics can be built on ILP, preserving its strengths. |
Stefan Halfpap; Rainer Schlosser; |
| 74 | HeckmanCD: Exploiting Selection Bias in Cognitive Diagnosis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we formulate cognitive diagnosis with a sample selection problem where observations are sampled through non-random probabilities that correlate with both the student’s response correctness and the features of the student and exercise. We proposed a simple but effective method called HeckmanCD, adapting the Heckman two-stage approach to mitigate this endogeneity issue. |
Dongxuan Han; Qi Liu; Siqi Lei; Shiwei Tong; Wei Huang; |
| 75 | Quantum Cognition-Inspired EEG-based Recommendation Via Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To explore the utility of EEG-based recommendation, we propose a novel neural network model, QUARK, combining Quantum Cognition Theory and Graph Convolutional Networks for accurate item recommendations. |
Jinkun Han; Wei Li; Yingshu Li; Zhipeng Cai; |
| 76 | Multi-Modal Sarcasm Detection Via Graph Convolutional Network and Dynamic Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods rely on static networks to capture incongruous features, which makes them inflexible in adapting to diverse groups of text and image, or neglect important information due to inadequate use of text and image. To address these limitations, we propose a multi-modal sarcasm detection model based on the combination of Graph Convolutional Network and Dynamic Network. |
Jiaqi Hao; Junfeng Zhao; Zhigang Wang; |
| 77 | A General Strategy Graph Collaborative Filtering for Recommendation Unlearning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose A General Strategy Graph Collaborative Filtering for Recommendation Unlearning (GSGCF-RU), which is a novel model-agnostic learnable delete operator that optimizes unlearning edge consistency and feature representation consistency. |
Yongjing Hao; Fuzhen Zhuang; Deqing Wang; Guanfeng Liu; Victor S. Sheng; Pengpeng Zhao; |
| 78 | Interpretable Triplet Importance for Personalized Ranking Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To tackle these, we propose the Triplet Shapley —a Shapely value-based method to measure the triplet importance in an interpretable manner. |
Bowei He; Chen Ma; |
| 79 | Spatio-Temporal Transformer Network with Physical Knowledge Distillation for Weather Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, it is challenging for spatio-temporal methods to capture the physical knowledge of meteorological dynamics. To address this problem, we propose in this paper a spatio-temporal Transformer network with physical knowledge distillation (PKD-STTN) for weather forecasting. |
Jing He; Junzhong Ji; Minglong Lei; |
| 80 | On The Sensitivity of Individual Fairness: Measures and Robust Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Consequently, the similarity or distance measure used in almost any individually fair algorithm is likely to be imperfect due to various reasons such as imprecise prior/domain knowledge, noise, or even adversaries. In this paper, we take an important step towards resolving this fundamental challenge and ask: how sensitive is the individually fair learning algorithm with respect to the given similarities? |
Xinyu He; Jian Kang; Ruizhong Qiu; Fei Wang; Jose Sepulveda; Hanghang Tong; |
| 81 | New Localization Frameworks: User-centric Approaches to Source Localization in Real-world Propagation Scenarios Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite all the recent advancements, existing methods do not consider the dynamic and heterogeneous propagation behaviors of users and are developed based on simulated data with strong model assumptions, limiting the application in real-world scenarios. This research addresses this limitation by presenting a novel framework for source localization, grounded in real-world propagation cascades from platforms like Weibo and Twitter. |
Dongpeng Hou; Yuchen Wang; Chao Gao; Xianghua Li; Zhen Wang; |
| 82 | NC2D: Novel Class Discovery for Node Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, the implementation of NCD on continuously scalable graph-structured data remains an under-explored area. In response to these challenges, we introduce for the first time a more practical NCD scenario for node classification (i.e., NC-NCD), and propose a novel self-training framework with prototype replay and distillation called SWORD, adopted to our NC-NCD setting. |
Yue Hou; Xueyuan Chen; He Zhu; Ruomei Liu; Bowen Shi; Jiaheng Liu; Junran Wu; Ke Xu; |
| 83 | Accurate Neural Network Option Pricing Methods with Control Variate Techniques and Data Synthesis/Cleaning with Financial Rationality Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We develop a learnable data-cleaning method to remove potentially irrational quotes spotted by no-arbitrage constraints properly. |
Chia-Wei Hsu; Tian-Shyr Dai; Chuan-Ju Wang; Ying-Ping Chen; |
| 84 | PIECE: Protagonist Identification and Event Chronology Extraction for Enhanced Timeline Summarization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This oversight can result in the extraction of sensationalized events unrelated to the topic’s progression, distracting readers from tracking the topic’s development. To address this limitation, we propose a novel strategy that identifies protagonists through dependency relations and tracks changes in the context surrounding them over time using a multi-faceted temporal graph. |
Tz-Huan Hsu; Li-Hsuan Chin; Yen-Hao Huang; Yi-Shin Chen; |
| 85 | The Devil Is in The Sources! Knowledge Enhanced Cross-Domain Recommendation in An Information Bottleneck Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, previously proposed CDR models pursue an imprudent assumption that the entire information from the source domain is equally contributed to the target domain, neglecting the evil part that is completely irrelevant to users’ intrinsic interest. To address this concern, in this paper, we propose a novel knowledge enhanced cross-domain recommendation framework named CoTrans, which remolds the core procedures of CDR models with: Compression on the knowledge from the source domain and Transfer of the purity to the target domain. |
Binbin Hu; Weifan Wang; Shuhan Wang; Ziqi Liu; Bin Shen; Yong He; Jiawei Chen; |
| 86 | Prompt-Based Spatio-Temporal Graph Transfer Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To bridge the gap, we propose Spatio-Temporal Graph Prompting (STGP), a prompt-based framework capable of adapting to multi-diverse tasks in a data-scarce domain. |
Junfeng Hu; Xu Liu; Zhencheng Fan; Yifang Yin; Shili Xiang; Savitha Ramasamy; Roger Zimmermann; |
| 87 | APTNESS: Incorporating Appraisal Theory and Emotion Support Strategies for Empathetic Response Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: By incorporating emotional support strategies, we aim to enrich the model’s capabilities in both cognitive and affective empathy, leading to a more nuanced and comprehensive empathetic response. |
Yuxuan Hu; Minghuan Tan; Chenwei Zhang; Zixuan Li; Xiaodan Liang; Min Yang; Chengming Li; Xiping Hu; |
| 88 | DIIT: A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Therefore, simply applying existing CDR methods in the industrial RS environment may lead to low effectiveness and efficiency. To fill this gap, we propose DIIT, an end-to-end Domain-Invariant Information Transfer method for industrial cross-domain recommendation. |
Heyuan Huang; Xingyu Lou; Chaochao Chen; Pengxiang Cheng; Yue Xin; Chengwei He; Xiang Liu; Jun Wang; |
| 89 | From Retrieval to Generation: Efficient and Effective Entity Set Expansion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most existing ESE methods are retrieval-based frameworks that need to extract contextual features of entities and calculate the similarity between seed entities and candidate entities. To achieve the two purposes, they iteratively traverse the corpus and the entity vocabulary, resulting in poor efficiency and scalability. |
Shulin Huang; Shirong Ma; Yangning Li; Yinghui Li; Hai-Tao Zheng; |
| 90 | A Payment Transaction Pre-training Model for Fraud Transaction Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Detecting daily fraudulent payment transactions, a challenging task for current methods, requires efficient transformation of transactions into embeddings, especially in representing merchants based on their behavioral transactions. To address this, we propose the Grouping Sampling-based Sequence Generation (GSSG) method to generate meaningful sequences, enabling interactions among correlated transactions. |
Wenxi Huang; Zhangyi Zhao; Xiaojun Chen; Qin Zhang; Mark Junjie Li; Hanjing Su; Qingyao Wu; |
| 91 | RD-P: A Trustworthy Retrieval-Augmented Prompter with Knowledge Graphs for LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel method called Retrieve-and-Discriminate Prompter (RD-P), which leverages knowledge graphs (KGs) for trustworthy RAG by synchronizing knowledge retrieval and discrimination in a unified model. |
Yubo Huang; Guosun Zeng; |
| 92 | Understanding GNNs for Boolean Satisfiability Through Approximation Algorithms Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper delves into the interpretability of Graph Neural Networks in the context of Boolean Satisfiability. The goal is to demystify the internal workings of these models and provide insightful perspectives into their decision-making processes. |
Jan H\r{u}la; David Moj\'{\i}ek; Mikol\'{a} Janota; |
| 93 | Fast and Accurate PARAFAC2 Decomposition for Time Range Queries on Irregular Tensors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: How can we efficiently analyze a specific time range on an irregular tensor? |
Jun-Gi Jang; Yong-chan Park; U Kang; |
| 94 | Calibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Previous methods typically leverage reranking algorithms to calibrate recommendations after training a model without considering the effect of calibration and do not effectively tackle the conflict between relevance and calibration during the reranking process. In this work, we propose LeapRec (Calibration-Disentangled <u>Lea</u>rning and Relevance-<u>P</u>rioritized Reranking), a novel approach for the calibrated sequential recommendation that addresses these challenges. |
Hyunsik Jeon; Se-eun Yoon; Julian McAuley; |
| 95 | HiLite: Hierarchical Level-implemented Architecture Attaining Part-Whole Interpretability Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the imaginary system GLOM, we present HiLite : <u>Hi</u>erarchical <u>L</u>evel-<u>i</u>mplemented Archi<u>te</u>cture attaining Part-Whole Interpretability, where islands of identical vectors can provide unprecedented interpretability. |
Yoo Hyun Jeong; Sunghyun Hwang; Dong-Kyu Chae; |
| 96 | GameTrail: Probabilistic Lifecycle Process Model for Deep Game Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the GameTrail – Probabilistic Lifecycle Process Model, designed to construct the complete lifecycle and stage representation for games and users through long-term repeated interactions. |
Shanyang Jiang; Lan Zhang; Hui Xu; Jiahui Huang; Qi He; Xing Zhou; Lei Huang; Jie Jiang; |
| 97 | Physics-guided Active Sample Reweighting for Urban Flow Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To overcome the challenges, we develop a discretized physics-guided network (PN), and propose a data-aware framework <u>P</u>hysics-<u>g</u>uided <u>A</u>ctive <u>S</u>ample <u>R</u>eweighting (P-GASR) to enhance PN. |
Wei Jiang; Tong Chen; Guanhua Ye; Wentao Zhang; Lizhen Cui; Zi Huang; Hongzhi Yin; |
| 98 | Tackling Noisy Clients in Federated Learning with End-to-end Label Correction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Intuitively, the performance degradation is dominated by clients with higher noise rates since their trained models contain more misinformation from data, thus it is necessary to devise an effective optimization scheme to mitigate the negative impacts of these noisy clients. In this work, we propose a two-stage framework FedELC to tackle this complicated label noise issue. |
Xuefeng Jiang; Sheng Sun; Jia Li; Jingjing Xue; Runhan Li; Zhiyuan Wu; Gang Xu; Yuwei Wang; Min Liu; |
| 99 | Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Based on the results of frontdoor adjustment, we introduce a novel <u>C</u>ausality-<u>A</u>ware <u>Sp</u>atiot<u>e</u>mpo<u>r</u>al Graph Neural Network (Casper), which contains a novel Prompt Based Decoder (PBD) and a Spatiotemporal Causal Attention (SCA). |
Baoyu Jing; Dawei Zhou; Kan Ren; Carl Yang; |
| 100 | Federated Heterogeneous Contrastive Distillation for Molecular Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These methods also fail to leverage multi-modal molecular representations effectively. To address the above issues, we propose a novel framework, Federated Heterogeneous Contrastive Distillation (FedHCD), which enables to jointly train global models from clients with heterogeneous data modalities, learning tasks, and molecular models. |
Jinjia Feng; Zhen Wang; Zhewei Wei; Yaliang Li; Bolin Ding; Hongteng Xu; |
| 101 | HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces HiQuE (Hierarchical Question Embedding network), a novel depression detection framework that leverages the hierarchical relationship between primary and follow-up questions in clinical interviews. |
Juho Jung; Chaewon Kang; Jeewoo Yoon; Seungbae Kim; Jinyoung Han; |
| 102 | Effectively Capturing Label Correlation for Tabular Multi-Label Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our approach jointly learns the representation of feature space and the correlation among labels within a unified network. |
Sajjad Kamali Siahroudi; Zahra Ahmadi; Daniel Kudenko; |
| 103 | Embedding Knowledge Graphs in Function Spaces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a novel embedding method diverging from conventional approaches by operating within function spaces of finite dimension rather than finite vector space, thus departing significantly from standard knowledge graph embedding techniques. |
Louis Mozart Kamdem Teyou; Caglar Demir; Axel-Cyrille Ngonga Ngomo; |
| 104 | Transformer for Point Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces Transformer for Point Anomaly Detection (TransPAD), a novel Transformer-based AutoEncoder framework specifically designed for point anomaly detection. |
Harim Kim; Chang Ha Lee; Charmgil Hong; |
| 105 | Enhancing Anomaly Detection Via Generating Diversified and Hard-to-distinguish Synthetic Anomalies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these approaches often encounter limitations when domain-specific transformations are not well-specified such as in tabular data, or when it becomes trivial to distinguish between them. To address these issues, we introduce a novel domain-agnostic method that employs a set of conditional perturbators and a discriminator. |
Hyuntae Kim; Changhee Lee; |
| 106 | PolarDSN: An Inductive Approach to Learning The Evolution of Network Polarization in Dynamic Signed Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While existing DSNE methods are useful for understanding polarization between users in diverse domains, they fail to consider the concept of a community boundary that contributes to network-wide polarization and lack inductive ability due to their reliance on homophily bias. To address these limitations, we propose a novel DSNE method, named PolarDSN, which learns the evolution of network <u>POLAR</u>ization and enhances inductive ability for <u>D</u>ynamic <u>S</u>igned <u>N</u>etworks. |
Min-Jeong Kim; Yeon-Chang Lee; Sang-Wook Kim; |
| 107 | Discrepancy-guided Channel Dropout for Domain Generalization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Discrepancy-guided Channel Dropout (DgCD) for DG that explicitly derives the discrepancy between domains and drops the channels with significant distribution discrepancy. |
Seonggyeom Kim; Byeongtae Park; Harim Lee; Dong-Kyu Chae; |
| 108 | FaDE: A Face Segment Driven Identity Anonymization Framework For Fair Face Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study an identity protected fair FR problem where the goal is to augment the datasets with external face images while ensuring the anonymity of the corresponding face identities. |
Ziyi Kou; Yijun Tian; Meng Jiang; Xiangliang Zhang; |
| 109 | Federated Deep Equilibrium Learning: Harnessing Compact Global Representations to Enhance Personalization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite enhancing privacy and efficiency in information retrieval and knowledge management contexts, training and deploying FL models confront significant challenges such as communication bottlenecks, data heterogeneity, and memory limitations. To comprehensively address these challenges, we introduce FeDEQ, a novel FL framework that incorporates deep equilibrium learning and consensus optimization to harness compact global data representations for efficient personalization. |
Long Tan Le; Tuan Dung Nguyen; Tung-Anh Nguyen; Choong Seon Hong; Suranga Seneviratne; Wei Bao; Nguyen H. Tran; |
| 110 | FastSimiFeat: A Fast and Generalized Approach Utilizing K-NN for Noisy Data Handling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In response, we introduce FastSimiFeat, a novel algorithm that utilizes the k-nearest neighbors (k-NN) technique on feature vectors derived from pre-trained models efficiently. |
Jungi Lee; Hwiwoo Park; Myounghwan Kim; Jiseong Yoon; Kwangsun Yoo; Seok-Joo Byun; |
| 111 | Vision Language Model Is NOT All You Need: Augmentation Strategies for Molecule Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose AMOLE, which 1) augments molecule-text pairs with structural similarity preserving loss, and 2) transfers the expertise between the molecules. |
Namkyeong Lee; Siddhartha Laghuvarapu; Chanyoung Park; Jimeng Sun; |
| 112 | MuLe: Multi-Grained Graph Learning for Multi-Behavior Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While previous methods have made strides in leveraging such interactions with advanced machine learning methods, they still face challenges in adequately using multi-faceted relationships among behaviors and handling uncertain auxiliary interactions that could potentially lead to purchases or not. In this paper, we propose MuLe (Multi-Grained Graph Learning), a novel graph-based model designed to address these limitations. |
Seunghan Lee; Geonwoo Ko; Hyun-Je Song; Jinhong Jung; |
| 113 | Learning Fair Invariant Representations Under Covariate and Correlation Shifts Simultaneously Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods address the problem of fairness-aware domain generalization, focusing on either covariate shift or correlation shift, but rarely consider both at the same time. In this paper, we introduce a novel approach that focuses on learning a fairness-aware domain-invariant predictor within a framework addressing both covariate and correlation shifts simultaneously, ensuring its generalization to unknown test domains inaccessible during training. |
Dong Li; Chen Zhao; Minglai Shao; Wenjun Wang; |
| 114 | Inferring Visualization Intent from Conversation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider a conversational approach to visualization, where users specify their needs at each step in natural language, with a visualization being returned in turn. |
Haotian Li; Nithin Chalapathi; Huamin Qu; Alvin Cheung; Aditya G. Parameswaran; |
| 115 | MoTTo: Scalable Motif Counting with Time-aware Topology Constraint for Large-scale Temporal Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Despite their significance, counting temporal motifs efficiently remains a challenge, particularly on moderately sized datasets with millions of motif instances. To address this challenge, we propose a novel algorithm called Scalable <u>Mo</u>tif Counting with <u>T</u>ime-aware <u>T</u>opology C<u>o</u>nstraint (MoTTo). |
Jiantao Li; Jianpeng Qi; Yueling Huang; Lei Cao; Yanwei Yu; Junyu Dong; |
| 116 | Efficient and Secure Contribution Estimation in Vertical Federated Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a Vertically Federated Contribution Estimation (VF-CE) method. |
Juan Li; Rui Deng; Tianzi Zang; Mingqi Kong; Kun Zhu; |
| 117 | Dynamic Neural Control Flow Execution: An Agent-Based Deep Equilibrium Approach for Binary Vulnerability Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, the size of programs often does not allow a graph neural network with fixed layers to aggregate global information. To address these issues, we propose DeepEXE, an agent-based implicit neural network that mimics the execution path of a program. |
Litao Li; Steven H. H. Ding; Andrew Walenstein; Philippe Charland; Benjamin C. M. Fung; |
| 118 | Integrating Structure and Text for Enhancing Hyper-relational Knowledge Graph Representation Via Structure Soft Prompt Tuning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing HKG embedding methods mainly rely on structural information but overlook textual information in HKGs, which are less effective in representing entities with limited structural information. To address this issue, the paper proposes HIST (Hyper-relational Knowledge Graph Encoder Integrating Structure and Text), which incorporates textual information and structural information in HKGs to enhance representations of entities and relations. |
Lijie Li; Hui Wang; Jiahang Li; Xiaodi Xu; Ye Wang; Tao Ren; |
| 119 | LagCNN: A Fast Yet Effective Model for Multivariate Long-term Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unfortunately, existing works lack consideration of this classic but important information. Motivated by these factors, we propose a fast yet effective CNN model with time lags for multivariate long-term time series forecasting, named LagCNN. |
Linsen Li; Chunfei Jian; Feng Wan; Dongdong Geng; Ziquan Fang; Lu Chen; Yunjun Gao; Weihao Jiang; Jiang Zhu; |
| 120 | Privacy-preserving Spatial Dataset Search in Cloud Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The density distribution-based similarity model is proposed to measure the similarity between spatial datasets, and then the order-preserving encrypted similarity is designed to achieve secure similarity calculation. |
Pengyue Li; Hua Dai; Sheng Wang; Wenzhe Yang; Geng Yang; |
| 121 | Noise-Resilient Unsupervised Graph Representation Learning Via Multi-Hop Feature Quality Estimation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: With empirical analysis, we reveal that feature propagation, the essential operation in GNNs, acts as a double-edged sword in handling noisy features – it can both denoise and diffuse noise, leading to varying feature quality across nodes, even within the same node at different hops. Building on this insight, we propose a novel UGRL method based on <u>M</u>ulti-hop feature <u>Q</u>uality <u>E</u>stimation (MQE for short). |
Shiyuan Li; Yixin Liu; Qingfeng Chen; Geoffrey I. Webb; Shirui Pan; |
| 122 | Seeing The Forest for The Trees: Road-Level Insights Assisted Lane-Level Traffic Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Lane-level traffic prediction is crucial for refined smart city applications, yet the scarcity and quality issues of datasets hinder its development. To overcome these challenges, this study introduces a novel <u> M </u>ulti-<u> c </u>hannel <u> g </u>raph-structured <u> V </u>ariational <u> A </u>uto<u> E </u>ncoder model, McgVAE. |
Shuhao Li; Yue Cui; Jingyi Xu; Jing Zhao; Fan Zhang; Weidong Yang; Xiaofang Zhou; |
| 123 | CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient Health State Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, these methods are limited to capturing personalized patient representations due to the following primary limitations: (i) unable to capture the differences in the impact of diseases/procedures on patients across various patient health states; (ii) fail to model the direct causal relationships between medications and specific health state of patients, resulting in an inability to determine which specific disease each medication is treating. To address these limitations, we propose CausalMed, a patient health state-centric model capable of enhancing the personalization of patient representations. |
Xiang Li; Shunpan Liang; Yu Lei; Chen Li; Yulei Hou; Dashun Zheng; Tengfei Ma; |
| 124 | On Evaluation Metrics for Diversity-enhanced Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, existing diversity metrics disregard the feature distribution of ground-truth items, potentially skewing the assessment of diversity performance. To address these limitations, we design three new accuracy-aware metrics: DCC, FDCC, and DILAD, and conduct a re-evaluation using these metrics. |
Xueqi Li; Gao Cong; Guoqing Xiao; Yang Xu; Wenjun Jiang; Kenli Li; |
| 125 | Design Element Aware Poster Layout Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the meantime, we propose a new evaluation metric called AspDiff to measure whether the generated layout matches the given design elements. |
Yinan Li; Jia Chen; Yin Bai; Jia Cheng; Jun Lei; |
| 126 | LLM-Empowered Few-Shot Node Classification on Incomplete Graphs with Real Node Degrees Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new few-shot node classification problem on incomplete graphs with real node degrees. |
Yun Li; Yi Yang; Jiaqi Zhu; Hui Chen; Hongan Wang; |
| 127 | Privacy-Preserving Graph Embedding Based on Local Differential Privacy Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We introduce a novel privacy-preserving graph embedding framework, named PrivGE, to protect node data privacy. |
Zening Li; Rong-Hua Li; Meihao Liao; Fusheng Jin; Guoren Wang; |
| 128 | Learning from Novel Knowledge: Continual Few-shot Knowledge Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel framework designed to equip the few-shot model with the ability to learn sequentially from novel relations. |
Zhuofeng Li; Haoxiang Zhang; Qiannan Zhang; Ziyi Kou; Shichao Pei; |
| 129 | Spectral and Geometric Spaces Representation Regularization for Multi-Modal Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we first identify and formalize three properties i.e., diversity, compactness, and consistency from the geometric space and spectrum perspective. Building upon this foundation, we devise tailored loss functions to regularize the above three properties for representation optimization. |
Zihao Li; Xuekong Xu; Zuoli Tang; Lixin Zou; Qian Wang; Chenliang Li; |
| 130 | RecDiff: Diffusion Model for Social Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, this assumption is not always valid due to the presence of irrelevant and false social ties, which can contaminate user embeddings and adversely affect recommendation accuracy. To address this challenge, we propose a novel diffusion-based social denoising framework for recommendation (RecDiff). |
Zongwei Li; Lianghao Xia; Chao Huang; |
| 131 | Higher-order Spatio-temporal Physics-incorporated Graph Neural Network for Multivariate Time Series Imputation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Additionally, calculating the high-order neighbor nodes in these models is of high computational complexity. To address these problems, we propose a novel higher-order spatio-temporal physics-incorporated GNN (HSPGNN). |
Guojun Liang; Prayag Tiwari; S\l{}awomir Nowaczyk; Stefan Byttner; |
| 132 | Aligning Large Language Models to A Domain-specific Graph Database for NL2GQL Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nonetheless, in the realm of NL2GQL tasks tailored to a particular domain, the absence of domain-specific NL-GQL data pairs adds complexity to aligning LLMs with the graph DB. To tackle this challenge, we present a well-defined pipeline. |
Yuanyuan Liang; Keren Tan; Tingyu Xie; Wenbiao Tao; Siyuan Wang; Yunshi Lan; Weining Qian; |
| 133 | ITIU: Intention Understanding Via Interactive Table in Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The pioneering study tackles intention understanding through iteratively interacting with users to enhance response quality; however, it fails to identify the notorious challenges associated with the task, where efficiency and accuracy are paramount for ensuring optimal user experience. To address these challenges, we introduce a new interactive table based intention understanding (ITIU) framework, which refers to and implements non-linear thinking in psychology such that details of intention are parallelly generated. |
Zenghua Liao; Jinzhi Liao; Xiang Zhao; |
| 134 | Towards Robust Vision Transformer Via Masked Adaptive Ensemble Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, the prominent AT solutions are still vulnerable to adaptive attacks. To tackle such shortcomings, this paper proposes a novel ViT architecture, including a detector and a classifier bridged by our newly developed adaptive ensemble. |
Fudong Lin; Jiadong Lou; Xu Yuan; Nian-Feng Tzeng; |
| 135 | GUME: Graphs and User Modalities Enhancement for Long-Tail Multimodal Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, previous research has primarily focused on learning item modality representations, while user modality representations have remained relatively simplistic. To address these challenges, we propose a novel <u>G</u>raphs and <u>U</u>ser <u>M</u>odalities <u>E</u>nhancement (GUME) for long-tail multimodal recommendation. |
Guojiao Lin; Meng Zhen; Dongjie Wang; Qingqing Long; Yuanchun Zhou; Meng Xiao; |
| 136 | Hierarchical Spatio-Temporal Graph Learning Based on Metapath Aggregation for Emergency Supply Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a hierarchical spatio-temporal graph learning model to predict the emergency supply capacity of IWDSN based on micro and macro graphs. |
Li Lin; Kaiwen Xia; Anqi Zheng; Shijie Hu; Shuai Wang; |
| 137 | PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, they have to execute n (n is the number of nodes) Local Push subroutines to obtain a provable PPR matrix, resulting in prohibitively high computational costs for large n. (2) The PPR matrix has limited power in capturing the structural similarity between vertices, leading to performance degradation. To overcome these dilemmas, we propose PSNE, an efficient spectral sParsification method for Scaling Network Embedding, which can fast obtain the embedding vectors that retain strong structural similarities. |
Longlong Lin; Yunfeng Yu; Zihao Wang; Zeli Wang; Yuying Zhao; Jin Zhao; Tao Jia; |
| 138 | Behavior-Dependent Linear Recurrent Units for Efficient Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose RecBLR, an Efficient Sequential Recommendation Model based on Behavior-Dependent Linear Recurrent Units to accomplish the impossible triangle of the three principles. |
Chengkai Liu; Jianghao Lin; Hanzhou Liu; Jianling Wang; James Caverlee; |
| 139 | Retrieval-Oriented Knowledge for Click-Through Rate Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, they face challenges including inference inefficiency and high resource consumption due to the retrieval process, which hinder their practical application in industrial settings. To address this, we propose a universal plug-and-play <u>r</u>etrieval-<u>o</u>riented <u>k</u>nowledge (ROK) framework that bypasses the real retrieval process. |
Huanshuo Liu; Bo Chen; Menghui Zhu; Jianghao Lin; Jiarui Qin; Hao Zhang; Yang Yang; Ruiming Tang; |
| 140 | Efficient and Robust Regularized Federated Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper addresses these challenges by reformulating the federated recommendation problem as a convex optimization issue, ensuring convergence to the global optimum. Based on this, we devise a novel method, RFRec, to tackle this optimization problem efficiently. |
Langming Liu; Wanyu Wang; Xiangyu Zhao; Zijian Zhang; Chunxu Zhang; Shanru Lin; Yiqi Wang; Lixin Zou; Zitao Liu; Xuetao Wei; Hongzhi Yin; Qing Li; |
| 141 | Two Heads Are Better Than One: Zero-shot Cognitive Reasoning Via Multi-LLM Knowledge Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We argue that the power of a single LLM is typically finite since it may not have learned some relevant knowledge about the question. To address these issues, we propose a Multi-LLM Knowledge Fusion (MLKF) approach, which resorts to heterogeneous knowledge emerging from multiple LLMs, for zero-shot cognitive reasoning tasks. |
Liang Liu; Dong Zhang; Shoushan Li; Guodong Zhou; Erik Cambria; |
| 142 | Collaborative Fraud Detection on Large Scale Graph Using Secure Multi-Party Computation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel approach, SecureFD (Secure Fraud Detector), aimed at detecting fraud in multi-party graph data, ensuring privacy, accuracy, and scalability. |
Xin Liu; Xiaoyu Fan; Rong Ma; Kun Chen; Yi Li; Guosai Wang; Wei Xu; |
| 143 | UniRec: A Dual Enhancement of Uniformity and Frequency in Sequential Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose UniRec, a novel bidirectional enhancement sequential recommendation method. |
Yang Liu; Yitong Wang; Chenyue Feng; |
| 144 | Confidence-aware Self-Semantic Distillation on Knowledge Graph Embedding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While several recent efforts utilize knowledge distillation or non-Euclidean representation learning to augment the effectiveness of low-dimensional KGE, they either necessitate a pre-trained high-dimensional teacher model or involve complex non-Euclidean operations, thereby incurring considerable additional computational costs. To address this, this work proposes Confidence-aware Self-Knowledge Distillation (CSD) that learns from the model itself to enhance KGE in a low-dimensional space. |
Yichen Liu; Jiawei Chen; Defang Chen; Zhehui Zhou; Yan Feng; Can Wang; |
| 145 | AlignRec: Aligning and Training in Multimodal Recommendations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Each alignment is characterized by a specific objective function and is integrated into our multimodal recommendation framework. To effectively train AlignRec, we propose starting from pre-training the first alignment to obtain unified multimodal features and subsequently training the following two alignments together with these features as input. |
Yifan Liu; Kangning Zhang; Xiangyuan Ren; Yanhua Huang; Jiarui Jin; Yingjie Qin; Ruilong Su; Ruiwen Xu; Yong Yu; Weinan Zhang; |
| 146 | Self-Supervision Improves Diffusion Models for Tabular Data Imputation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Additionally, the sparsity inherent in tabular data poses challenges for diffusion models in accurately modeling the data manifold, impacting the robustness of these models for data imputation. To tackle these challenges, this paper introduces an advanced diffusion model named <u>S</u> elf-supervised <u>imp</u> utation <u>D</u> iffusion <u>M</u> odel (SimpDM for brevity), specifically tailored for tabular data imputation tasks. |
Yixin Liu; Thalaiyasingam Ajanthan; Hisham Husain; Vu Nguyen; |
| 147 | KMCT: K-Means Clustering of Trajectories Efficiently in Location-Based Services Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: With the widespread use of GPS devices and the advancement of location-based services, a vast amount of trajectory data has been collected and mined for various applications. … |
Yuanjun Liu; Guanfeng Liu; Qingzhi Ma; Zhixu Li; Shiting Wen; Lei Zhao; An Liu; |
| 148 | A Universal and Interpretable Method for Enhancing Stock Price Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods have inherent limitations, such as low accuracy, lack of transparency, and failure to consider the interactions among stock factors. To address these issues, we propose a UNIversal and interpretable framework for enhancing Stock Price Prediction (abbreviated to UniSPP), which is capable of modeling the interactions among stock factors. |
Yuchen Liu; Shimin Di; Lei Chen; Xiaofang Zhou; Fei Lin; |
| 149 | A Universal Sets-level Optimization Framework for Next Set Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this research, we endeavor to unveil a universal and Sets-level optimization framework for Next Set Recommendation (SNSRec), offering a holistic fusion of diversity distribution and intricate dependency relationships within temporal sets. |
Yuli Liu; Min Liu; Christian Walder; Lexing Xie; |
| 150 | Multivariate Time-Series Anomaly Detection Based on Enhancing Graph Attention Networks with Topological Analysis Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces a novel temporal model built on an enhanced Graph Attention Network (GAT) for multivariate time series anomaly detection called TopoGDN. |
Zhe Liu; Xiang Huang; Jingyun Zhang; Zhifeng Hao; Li Sun; Hao Peng; |
| 151 | Collaborative Cross-modal Fusion with Large Language Model for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Some simply instruct-tune a language model, while others directly inject the embeddings of a CF-based model, lacking a synergistic fusion of different modalities. To address these issues, we propose a framework of Collaborative Cross-modal Fusion with Large Language Models, termed CCF-LLM, for recommendation. |
Zhongzhou Liu; Hao Zhang; Kuicai Dong; Yuan Fang; |
| 152 | Multi-Behavior Generative Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose MBGen, a novel Multi-Behavioral sequential Generative recommendation framework. |
Zihan Liu; Yupeng Hou; Julian McAuley; |
| 153 | MOAT: Graph Prompting for 3D Molecular Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing approaches tend to overlook the unique properties inherent in molecular graphs. To address this gap, our paper introduces a novel approach named 3D <u>MO</u> lecul <u>A</u> rpromp <u>T</u> (MOAT) designed specifically for geometric molecules. |
Qingqing Long; Yuchen Yan; Wentao Cui; Wei Ju; Zhihong Zhu; Yuanchun Zhou; Xuezhi Wang; Meng Xiao; |
| 154 | Hierarchical Structure Construction on Hypergraphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We observed that directly extending hierarchical frameworks from pairwise graphs to hypergraphs overlooks high-order interactions and can result in either high computational complexity or sparse hierarchy structure. To address this challenge, we introduce a dual-layer hypergraph hierarchy consisting of a primary hierarchy and a secondary hierarchy, enabling the construction of a refined hypergraph hierarchy in linear time. |
Qi Luo; Wenjie Zhang; Zhengyi Yang; Dong Wen; Xiaoyang Wang; Dongxiao Yu; Xuemin Lin; |
| 155 | A Knowledge-Enhanced Transformer-FL Method for Fault Root Cause Localization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Meanwhile, it is a big challenge to extract as many feature details as possible from limited information and then fully utilize them. Therefore, this paper proposes a Knowledge-Enhanced Transformer-FL method, namely, KETrans-FL, to address the problem of root cause localization, by treating it as a multi-class classification problem. |
Zhe Lv; Yaqiong Liu; Xidian Wang; Peng Gao; Zhouyuan Li; Yuanzhen Jiang; |
| 156 | Unveiling Intellectual Property Vulnerabilities of GAN-Based Distributed Machine Learning Through Model Extraction Attacks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present the first investigation into model extraction attacks against GANs in distributed settings. |
Mengyao Ma; Shuofeng Liu; Mohan Baruwal Chhetri; Guangdong Bai; |
| 157 | Data Void Exploits: Tracking \& Mitigation Strategies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle data voids, we introduce an adversarial game model involving two agents: a disinformer and a mitigator. |
Miro Mannino; Junior Garcia; Reem Hazim; Azza Abouzied; Paolo Papotti; |
| 158 | Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we study exposure bias in a class of well-known contextual bandit algorithms known as Linear Cascading Bandits,. |
Masoud Mansoury; Bamshad Mobasher; Herke van Hoof; |
| 159 | Veracity Estimation for Entity-Oriented Search with Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we discuss the potential costs that emerge from using a Knowledge Graph (KG) in entity-oriented search without considering its data veracity. |
Stefano Marchesin; Gianmaria Silvello; Omar Alonso; |
| 160 | Revisiting Optimal Window Aggregation in Data Streams: The Prefix-Sum Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The article presents a simple yet optimal approach to compute aggregates of window queries over data streams. |
Jos\'{e} Martinez; Guillaume Raschia; |
| 161 | PIP: Prototypes-Injected Prompt for Federated Class Incremental Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a novel rehearsal-free method for FCIL named prototypes-injected prompt (PIP) that involves 3 main ideas: a) prototype injection on prompt learning, b) prototype augmentation, and c) weighted Gaussian aggregation on the server side. |
Muhammad Anwar Ma’sum; Mahardhika Pratama; Savitha Ramasamy; Lin Liu; Habibullah Habibullah; Ryszard Kowalczyk; |
| 162 | Semantic Prototypes: Enhancing Transparency Without Black Boxes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a novel framework that utilizes semantic descriptions to define prototypes and provide clear explanations, effectively addressing the shortcomings of conventional methods. |
Orfeas Menis Mastromichalakis; Giorgos Filandrianos; Jason Liartis; Edmund Dervakos; Giorgos Stamou; |
| 163 | Link Polarity Prediction from Sparse and Noisy Labels Via Multiscale Social Balance Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our model-agnostic approach can seamlessly integrate with any SGNN architecture, dynamically reweighting the importance of each data sample while making strategic use of the structural information from unlabeled edges combined with social balance theory.Empirical validation demonstrates that our approach outperforms established baseline models, effectively addressing the limitations imposed by noisy and sparse data. |
Marco Minici; Federico Cinus; Francesco Bonchi; Giuseppe Manco; |
| 164 | Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we leverage both rewritten queries and relevance judgments in the conversational search data to train a better query representation model. |
Fengran Mo; Chen Qu; Kelong Mao; Yihong Wu; Zhan Su; Kaiyu Huang; Jian-Yun Nie; |
| 165 | LLaVA-Chef: A Multi-modal Generative Model for Food Recipes Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Recent advancements in large language models (LLMs) like GPT-2 and LLaVA have paved the way for Natural Language Processing (NLP) approaches to delve deeper into various facets of food-related tasks, encompassing ingredient recognition and comprehensive recipe generation. |
Fnu Mohbat; Mohammed J. Zaki; |
| 166 | Inductive Knowledge Graph Embedding Via Exploring Interaction Patterns of Relations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel inductive knowledge graph embedding model that effectively handles unknown entities and relations by capturing their local structural features. |
Chong Mu; Lizong Zhang; Jinchuan Zhang; Qian Huang; Zhiguo Wang; |
| 167 | Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we explore the possibility of finding an alternative source of comments to guarantee the availability of diverse comments, especially those from silent users. |
Qiong Nan; Qiang Sheng; Juan Cao; Beizhe Hu; Danding Wang; Jintao Li; |
| 168 | Saliency Detection in Educational Videos: Analyzing The Performance of Current Models, Identifying Limitations and Advancement Directions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To the best of our knowledge, there is currently no study that evaluates saliency detection approaches in educational videos. In this paper, we address this gap by evaluating four state-of-the-art saliency detection approaches for educational videos. |
Evelyn Navarrete; Ralph Ewerth; Anett Hoppe; |
| 169 | Towards Fair Graph Anomaly Detection: Problem, Benchmark Datasets, and Evaluation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the current literature does not comprehensively discuss this problem, nor does it provide realistic datasets that encompass actual graph structures, anomaly labels, and sensitive attributes. To bridge this gap, we introduce a formal definition of the FairGAD problem and present two novel datasets constructed from the social media platforms Reddit and Twitter. |
Neng Kai Nigel Neo; Yeon-Chang Lee; Yiqiao Jin; Sang-Wook Kim; Srijan Kumar; |
| 170 | Adaptive Cascading Network for Continual Test-Time Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing methods on test-time training suffer from several limitations: (1) Mismatch between the feature extractor and classifier; (2) Interference between the main and self-supervised tasks; (3) Lack of the ability to quickly adapt to the current distribution. In light of these challenges, we propose a cascading paradigm that simultaneously updates the feature extractor and classifier at test time, mitigating the mismatch between them and enabling long-term model adaptation. |
Kien X. Nguyen; Fengchun Qiao; Xi Peng; |
| 171 | Cultural Commonsense Knowledge for Intercultural Dialogues Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents Mango, a methodology for distilling high-accuracy, high-recall assertions of cultural knowledge. |
Tuan-Phong Nguyen; Simon Razniewski; Gerhard Weikum; |
| 172 | Exploring Robustness of GNN Against Universal Injection Attack from A Worst-case Perspective Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nevertheless, these strategies are often effective only against limited attack scenarios, and prevailing certification methods prove inadequate when confronted with injection attacks. In this paper, we propose a method named CERT_UIA to enhance the robustness of GNN models against worst-case attacks, specifically targeting the scenario of <u>U</u>niversal node <u>I</u>njection <u>A</u>ttacks (UIA), thereby filling a gap in the existing literature on certified robustness in this context. |
Dandan Ni; Sheng Zhang; Cong Deng; Han Liu; Gang Chen; Minhao Cheng; Hongyang Chen; |
| 173 | CADIF-OSN: Detecting Cloned Accounts with Missing Profile Attributes on Online Social Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose cloned account detection with imputation framework for online social networks (CADIF-OSN) to accurately find potential cloned accounts on OSNs. |
Dewei Ning; Yong-Feng Ge; Hua Wang; Changjun Zhou; |
| 174 | Adversarial Text Rewriting for Text-aware Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, we argue that the dependency on item descriptions makes the recommender system vulnerable to manipulation by adversarial sellers on e-commerce platforms. In this paper, we explore the possibility of such manipulation by proposing a new text rewriting framework to attack text-aware recommender systems. |
Sejoon Oh; Gaurav Verma; Srijan Kumar; |
| 175 | The Impact of External Sources on The Friedkin–Johnsen Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we generalize the popular Friedkin–Johnsen model to include the effects of external media sources on opinion formation. |
Charlotte Out; Sijing Tu; Stefan Neumann; Ahad N. Zehmakan; |
| 176 | Reviving The Context: Camera Trap Species Classification As Link Prediction on Multimodal Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we exploit the structured context linked to camera trap images to boost out-of-distribution generalization for species classification tasks in camera traps. |
Vardaan Pahuja; Weidi Luo; Yu Gu; Cheng-Hao Tu; Hong-You Chen; Tanya Berger-Wolf; Charles Stewart; Song Gao; Wei-Lun Chao; Yu Su; |
| 177 | Distilling Large Language Models for Text-Attributed Graph Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Large language models (LLMs) have recently demonstrated remarkable capabilities in few-shot and zero-shot TAG learning, but they suffer from scalability, cost, and privacy issues. Therefore, in this work, we focus on synergizing LLMs and graph models with their complementary strengths by distilling the power of LLMs into a local graph model on TAG learning. |
Bo Pan; Zheng Zhang; Yifei Zhang; Yuntong Hu; Liang Zhao; |
| 178 | Novelty-aware Graph Traversal and Expansion for Hierarchical Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a new method, Novelty-aware Graph Traversal and Expansion (NGTE), which selects an optimal node at the graph boundary, termed an Outpost Subgoal, as a direct path toward the final goal. |
Jongchan Park; Seungjun Oh; Yusung Kim; |
| 179 | Exploiting Pre-trained Models for Drug Target Affinity Prediction with Nearest Neighbors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, inspired by the recent success of retrieval methods, we propose kNN-DTA, a non-parametric embedding-based retrieval method adopted on a pre-trained DTA prediction model, which can extend the power of the DTA model with no or negligible cost. |
Qizhi Pei; Lijun Wu; Zhenyu He; Jinhua Zhu; Yingce Xia; Shufang Xie; Rui Yan; |
| 180 | Towards Deconfounded Visual Question Answering Via Dual-causal Intervention Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While a variety of solutions have been proposed, they solely focus on the shortcuts in the language modality, leaving other kinds of shortcut biases untouched. In this paper, we shift our lens to all kinds of shortcuts and resort to causal inference to circumvent these issues. |
Daowan Peng; Wei Wei; |
| 181 | Beyond Over-smoothing: Uncovering The Trainability Challenges in Deep Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We theoretically prove that the difficult training problem of deep MLPs is actually the main challenge, and various existing methods that supposedly tackle Over-smoothing actually improve the trainability of MLPs, which is the main reason for their performance gains. |
Jie Peng; Runlin Lei; Zhewei Wei; |
| 182 | Table-Filling Via Mean Teacher for Cross-domain Aspect Sentiment Triplet Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This allows the model to benefit from the OD extraction paradigm and region-level alignment. Building upon this premise, we propose a novel method named Table-Filling via Mean Teacher (TFMT). |
Kun Peng; Lei Jiang; Qian Li; Haoran Li; Xiaoyan Yu; Li Sun; Shuo Sun; Yanxian Bi; Hao Peng; |
| 183 | Bi-directional Learning of Logical Rules with Type Constraints for Knowledge Graph Completion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Accordingly, we propose an end-to-end approach to effectively learn TC-rules, by parameterizing a neural model to simulate the inference of TC-rules. |
Kunxun Qi; Jianfeng Du; Hai Wan; |
| 184 | UniMEL: A Unified Framework for Multimodal Entity Linking with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To this end, we propose UniMEL, a <u>uni</u>fied framework which establishes a new paradigm to process <u>m</u>ultimodal <u>e</u>ntity <u>l</u>inking tasks using LLMs. |
Qi Liu; Yongyi He; Tong Xu; Defu Lian; Che Liu; Zhi Zheng; Enhong Chen; |
| 185 | SGFL-Attack: A Similarity-Guidance Strategy for Hard-Label Textual Adversarial Attack Based on Feedback Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Meanwhile, geometry-based strategies are prone to falling into local optima. To address these limitations, in this paper, we introduce SGFL-Attack, a novel approach that leverages a <u>S</u>imilarity-<u>G</u>uidance strategy based on <u>F</u>eedback <u>L</u>earning for hard-label textual adversarial attack, with limited query budget. |
Panjia Qiu; Guanghao Zhou; Mingyuan Fan; Cen Chen; Yaliang Li; Wenming Zhou; |
| 186 | Towards Completeness-Oriented Tool Retrieval for Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Subsequently, we construct three bipartite graphs among queries, scenes, and tools and introduce a dual-view graph collaborative learning framework to capture the intricate collaborative relationships among tools during the collaborative learning stage. |
Changle Qu; Sunhao Dai; Xiaochi Wei; Hengyi Cai; Shuaiqiang Wang; Dawei Yin; Jun Xu; Ji-Rong Wen; |
| 187 | Scalable Dynamic Embedding Size Search for Streaming Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although a few methods attempt to mitigate this by employing embedding size search strategies to assign different embedding dimensions in streaming recommendations, they assume that the embedding size grows with the frequency of users/items, which eventually still exceeds the predefined memory budget over time. To address this issue, this paper proposes to learn Scalable Lightweight Embeddings for streaming recommendation, called SCALL, which can adaptively adjust the embedding sizes of users/items within a given memory budget over time. |
Yunke Qu; Liang Qu; Tong Chen; Xiangyu Zhao; Quoc Viet Hung Nguyen; Hongzhi Yin; |
| 188 | PISeL: <u>Pi</u>pelining DNN <u>I</u>nference for <u>Se</u>rver<u>l</u>ess Computing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a layer-grouping mechanism and policy to pipeline model download, model deserialization and copy and request execution. |
Masoud Rahimi Jafari; Jianchang Su; Yifan Zhang; Oliver Wang; Wei Zhang; |
| 189 | No Query Left Behind: Query Refinement Via Backtranslation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To fill the gap, little work has been proposed to generate benchmark datasets of (query ’ refined query) pairs through an overwhelming application of unsupervised or supervised modifications to the original query while controlling topic drifts. |
Delaram Rajaei; Zahra Taheri; Hossein Fani; |
| 190 | Periormer: Periodic Transformer for Seasonal and Irregularly Sampled Time Series Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current Transformer-based models consider time steps as discrete tokens, thereby failing to account for periodicity and temporal intervals when selecting relevant time steps in the past. To address this limitation, we propose an end-to-end framework called Periormer for forecasting irregularly sampled time series. |
Xiaobin Ren; Kaiqi Zhao; Katerina Takova; Patricia Riddle; Lianyan Li; |
| 191 | SmartHash: Perceptual Hashing for Image Tampering Detection and Authentication Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Perceptual hashing algorithms have been used extensively to detect duplicate images, similar images for reverse image search, inappropriate and explicit images, and child sexual abuse (CSAM) images. |
Priyanka Samanta; Shweta Jain; |
| 192 | Mining Path Association Rules in Large Property Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the problem of path association rule mining (PARM). |
Yuya Sasaki; Panagiotis Karras; |
| 193 | Leveraging Trustworthy Node Attributes for Effective Network Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we aim to design augmented attributes that enhance network alignment by reflecting three key structural <u> C </u>haracteristics: (C1) global structural characteristic, reflects the global network structure; (C2) seed-based structural characteristic, leverages cross-network structural information associated with seed nodes; (C3) multi-aspect structural characteristic, employs diverse structural relationship measures. |
Dong-Hyuk Seo; Jae-Hwan Lim; Won-Yong Shin; Sang-Wook Kim; |
| 194 | Structural Representation Learning and Disentanglement for Evidential Chinese Patent Approval Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel framework called DiSPat, which focuses on structural representation learning and disentanglement to predict the approval of Chinese patents and offer decision-making evidence. |
Jinzhi Shan; Qi Zhang; Chongyang Shi; Mengting Gui; Shoujin Wang; Usman Naseem; |
| 195 | Fast Human Action Recognition Via Millimeter Wave Radar Point Cloud Sequences Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a fast human action recognition framework based on 3D point cloud sequences generated by commercial 4D millimeter wave imaging radar systems. |
Tongfei Shao; Zheyu Du; Chuanyou Li; Tianxing Wu; Meng Wang; |
| 196 | Robust Federated Unlearning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce a robust federated unlearning framework (robustFU) which notably enhances the resilience of FU algorithms against a wide range of adversarial attacks. |
Xinyi Sheng; Wei Bao; Liming Ge; |
| 197 | AgentRE: An Agent-Based Framework for Navigating Complex Information Landscapes in Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The relation extraction (RE) in complex scenarios faces challenges such as diverse relation types and ambiguous relations between entities within a single sentence, leading to the poor performance of pure text-in, text-out language models (LMs). To address these challenges, in this paper, we propose an agent-based RE framework, namely AgentRE, which fully leverages the potential of large language models (LLMs) including memory, retrieval and reflection, to achieve RE in complex scenarios. |
Yuchen Shi; Guochao Jiang; Tian Qiu; Deqing Yang; |
| 198 | Retrieval-enhanced Knowledge Editing in Language Models for Multi-Hop Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To tackle the problem, we propose the Retrieval-Augmented model Editing (RAE) framework for multi-hop question answering. |
Yucheng Shi; Qiaoyu Tan; Xuansheng Wu; Shaochen Zhong; Kaixiong Zhou; Ninghao Liu; |
| 199 | Discovering Graph Generating Dependencies for Property Graph Profiling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, GGDs can be difficult to set manually. To solve this issue, we propose a framework for discovering GGDs automatically from the property graph to profile graph data. |
Larissa C. Shimomura; Nikolay Yakovets; George Fletcher; |
| 200 | Self-supervised One-Stage Learning for RF-based Multi-Person Pose Estimation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes an efficient and lightweight one-stage MPPE model based on raw RF signals. |
Seunghwan Shin; Yusung Kim; |
| 201 | When LLM Meets Hypergraph: A Sociological Analysis on Personality Via Online Social Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Many psychological studies have observed that personalities are strongly reflected in their social behaviors and social environments. Unfortunately, psychological traits like one’s personality are high-level and hidden in the innermost corner of data, which is intractable to be uncovered by traditional data mining approaches; The data quality of online social networks is far from sufficient to support such profound psychological analysis, because user behavior records and their attributes are usually very fragmented, missing lots of key information to understand a person in depth; In addition, the social environments in online networks are very complicated, making the interaction patterns between users and their environments underexplored.In light of these problems, this paper proposes a sociological analysis framework for one’s personality in an environment-based view instead of individual-level data mining. |
Zhiyao Shu; Xiangguo Sun; Hong Cheng; |
| 202 | XCrowd: Combining Explainability and Crowdsourcing to Diagnose Models in Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a new method for diagnosing model predictions and detecting potential inaccuracies. |
Alisa Smirnova; Jie Yang; Philippe Cudre-Mauroux; |
| 203 | DFLStar: A Decentralized Federated Learning Framework with Self-Knowledge Distillation and Participant Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, each client in DFL needs to communicate with multiple neighbors, yielding a heavy communication load. To tackle these challenges, we propose a novel DFL framework called DFLStar, which can improve DFL from two perspectives. |
Behnaz Soltani; Venus Haghighi; Yipeng Zhou; Quan Z. Sheng; Lina Yao; |
| 204 | HTFabric: A Fast Re-ordering and Parallel Re-execution Method for A High-Throughput Blockchain Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We address two critical performance bottlenecks that hinder their throughput: the high cost of transaction re-ordering and the high cost of re-execution of invalid transactions. To address these challenges, we propose HTFabric, a method that combines fast re-ordering and parallel re-execution to achieve exceptionally high successful throughput. |
Jaeyub Song; Juyeong Jeong; Jemin Lee; Inju Na; Min-Soo Kim; |
| 205 | How Much Do Prompting Methods Help LLMs on Quantitative Reasoning with Irrelevant Information? Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We evaluated SOTA prompting methods using MPN. We propose Noise Reduction Prompting (NRP) and its variant (NRP+) to reduce the impact of IR noise. |
Seok Hwan Song; Wallapak Tavanapong; |
| 206 | Breaking The Bottleneck on Graphs with Structured State Spaces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we observe that the sensitivity between nodes in MPNNs decreases exponentially with the shortest path distance. |
Yunchong Song; Siyuan Huang; Jiacheng Cai; Xinbing Wang; Chenghu Zhou; Zhouhan Lin; |
| 207 | MultiLoRA: Multi-Directional Low Rank Adaptation for Multi-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose MultiLoRA, a Multi-directional Low Rank Adaptation paradigm for multi-domain recommendation. |
Zijian Song; Wenhan Zhang; Lifang Deng; Jiandong Zhang; Kaigui Bian; Bin Cui; |
| 208 | FABLE: Approximate Butterfly Counting in Bipartite Graph Stream with Duplicate Edges Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose FABLE, a Fixed-size memory Approximate Butterfly counting algorithm for dupLicate Edges in bipartite graph stream. |
Guozhang Sun; Yuhai Zhao; Yuan Li; |
| 209 | A Learning-path Based Supervised Method for Concept Prerequisite Relations Extraction in Educational Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: (2) A disregard for temporal context, essential for learning, limits both the performance and the application of these methods. To address these issues, we propose a novel graph-based approach, called Learning-path based Concept Prerequisite Relations Extraction (LCPRE). |
Jingwen Sun; Yu He; Yiyu Xu; Jingwei Sun; Guangzhong Sun; |
| 210 | Large Language Models Enhanced Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, drawing inspiration from the in-context learning and chain of thought reasoning in LLMs, we propose the Large Language Models enhanced Collaborative Filtering (LLM-CF) framework, which distills the world knowledge and reasoning capabilities of LLMs into collaborative filtering. |
Zhongxiang Sun; Zihua Si; Xiaoxue Zang; Kai Zheng; Yang Song; Xiao Zhang; Jun Xu; |
| 211 | Multimodal Misinformation Detection Using Large Vision-Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we investigate the potential of LLMs for misinformation detection in a zero-shot setting. |
Sahar Tahmasebi; Eric M\{u}ller-Budack; Ralph Ewerth; |
| 212 | Natural Language-Assisted Multi-modal Medication Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, the textual knowledge derived from the EHRs of patients remains largely underutilized. To address these issues, we introduce the Natural Language-Assisted Multi-modal Medication Recommendation (NLA-MMR), a multimodal alignment framework designed to learn knowledge from the patient view and medication view jointly. |
Jie Tan; Yu Rong; Kangfei Zhao; Tian Bian; Tingyang Xu; Junzhou Huang; Hong Cheng; Helen Meng; |
| 213 | Factor Model-Based Large Covariance Estimation from Streaming Data Using A Knowledge-Based Sketch Matrix Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The existing methods either assume sparsity, ignoring any possible common factor among the variables, or obtain poor performance in recovering the covariance matrix directly from sketched data. To address these issues, we propose a novel method – KEEF: <u>K</u>nowledge-based Time and Memory <u>E</u>fficient Covariance <u>E</u>stimator in <u>F</u>actor Model and its extended variation. |
Xiao Tan; Zhaoyang Wang; Hao Qian; Jun Zhou; Peibo Duan; Dian Shen; Meng Wang; Beilun Wang; |
| 214 | EasyST: A Simple Framework for Spatio-Temporal Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, long-spanning large-scale spatio-temporal data introduce distribution shifts, necessitating improved generalization performance. To address these challenges, we propose a simple framework for spatio-temporal prediction – EasyST paradigm. |
Jiabin Tang; Wei Wei; Lianghao Xia; Chao Huang; |
| 215 | LAMRec: Label-aware Multi-view Drug Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most models do not explicitly establish a mapping relationship between drug labels and patients’ historical visits. To address these two problems, we proposed a label-aware multi-view drug recommendation model named LAMRec. |
Yunsen Tang; Ning Liu; Haitao Yuan; Yonghe Yan; Lei Liu; Weixing Tan; Lizhen Cui; |
| 216 | TEXT CAN BE FAIR: Mitigating Popularity Bias with PLMs By Learning Relative Preference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, analogous to the training of large language models (LLMs), we introduce a pre-training and a fair supervised fine-tuning with a decoupled layer to build the ranker model. |
Zuoli Tang; Zhaoxin Huan; Zihao Li; Shirui Hu; Xiaolu Zhang; Jun Zhou; Lixin Zou; Chenliang Li; |
| 217 | Retrieval Augmented Deep Anomaly Detection for Tabular Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we investigate using retrieval-augmented models for anomaly detection on tabular data. |
Hugo Thimonier; Fabrice Popineau; Arpad Rimmel; Bich-Li\^{e}n Doan; |
| 218 | Re-evaluating The Command-and-Control Paradigm in Conversational Search Interactions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present a first look at an extensive collection of conversational queries, aiming to identify limitations and improvement opportunities specifically related to information access (i.e., search interactions). |
Johanne R. Trippas; Luke Gallagher; Joel Mackenzie; |
| 219 | LTBoost: Boosted Hybrids of Ensemble Linear and Gradient Algorithms for The Long-term Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This shift is critical as we confront three principal challenges in deep-learning-based forecasting frameworks: (i) the inherent limitations of transformers, which, despite their attempts to preserve ordering information, the temporal information loss due to the permutation-invariant nature of self-attention mechanisms is inevitable, (ii) the inefficacy of linear models in capturing the dynamic interactions within swiftly evolving signals; and (iii) the incapacity of tree-based approaches to extrapolating beyond values present in the training set. In response to these challenges, we introduce LTBoost, an innovative boosted hybrid of linear and tree-based ensemble gradient algorithms tailored for long-term time series forecasting (LTSF) tasks, scalable to high data dimensions. |
Hubert Truchan; Christian Kalfar; Zahra Ahmadi; |
| 220 | GAS-Norm: Score-Driven Adaptive Normalization for Non-Stationary Time Series Forecasting in Deep Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The proposed approach is model-agnostic and can be applied to any DNN forecasting model. |
Edoardo Urettini; Daniele Atzeni; Reshawn J. Ramjattan; Antonio Carta; |
| 221 | Causal Probing for Dual Encoders Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unlike existing works that have probed cross-encoders to show query-document interactions, we provide a principled approach to probe dual-encoders. |
Jonas Wallat; Hauke Hinrichs; Avishek Anand; |
| 222 | Why Misinformation Is Created? Detecting Them By Integrating Intent Features Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we are inspired by the opposition of intents between misinformation and real information. |
Bing Wang; Ximing Li; Changchun Li; Bo Fu; Songwen Pei; Shengsheng Wang; |
| 223 | Collaborative Alignment for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce CARec, a novel model that adeptly integrates collaborative filtering signals with semantic representations, ensuring alignment within the semantic space while maintaining essential semantics. |
Chen Wang; Liangwei Yang; Zhiwei Liu; Xiaolong Liu; Mingdai Yang; Yueqing Liang; Philip S. Yu; |
| 224 | HC-GST: Heterophily-aware Distribution Consistency Based Graph Self-training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle them, we propose a novel Heterophily-aware Distribution Consistency-based Graph Self-Training (HC-GST) framework, which estimates homophily ratios using soft labels and optimizes a selection vector to align pseudo-nodes with the global homophily ratio distribution. |
Fali Wang; Tianxiang Zhao; Junjie Xu; Suhang Wang; |
| 225 | MMPolymer: A Multimodal Multitask Pretraining Framework for Polymer Property Prediction Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we propose MMPolymer, a novel multimodal multitask pretraining framework incorporating polymer 1D sequential and 3D structural information to encourage downstream polymer property prediction tasks. |
Fanmeng Wang; Wentao Guo; Minjie Cheng; Shen Yuan; Hongteng Xu; Zhifeng Gao; |
| 226 | Trojan Activation Attack: Red-Teaming Large Language Models Using Steering Vectors for Safety-Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we study a different attack scenario, called Trojan Activation Attack (TA2), which injects trojan steering vectors into the activation layers of LLMs. |
Haoran Wang; Kai Shu; |
| 227 | Sparks of Surprise: Multi-objective Recommendations with Hierarchical Decision Transformers for Diversity, Novelty, and Serendipity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing PSR methods face two limitations: (1) treating offline sessions uniformly as static data and relying on user embeddings to represent personalized information overlook the dynamic evolution of interests over time, which can change significantly as sessions progress in practical application. (2) focusing on accuracy, i.e., recommending items relevant to recent interactions, ignores the balance of multi-faceted requirements for user satisfaction, i.e., diversity, novelty, and serendipity.Therefore, we introduce Multi-objective PSR (MOPSR) task and propose Hierarchical Decision Transformers (HDT) framework, which models strictly sequential preference transitions of users across and within sessions to balance recommendation accuracy with the mentioned objectives. |
Jie Wang; Alexandros Karatzoglou; Ioannis Arapakis; Xin Xin; Xuri Ge; Joemon M. Jose; |
| 228 | Bots Shield Fake News: Adversarial Attack on User Engagement Based Fake News Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we introduce an attack problem that perturbs user-news engagements by injecting bots to shield the targeted fake news from being detected by GNN-based fake news detection models. |
Lanjun Wang; Zehao Wang; Le Wu; An-An Liu; |
| 229 | MANA-Net: Mitigating Aggregated Sentiment Homogenization with News Weighting for Enhanced Market Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Consequently, the aggregated sentiment representations lose much predictive value of news data. To address this problem, we introduce the Market Attention-weighted News Aggregation Network (MANA-Net), a novel method that leverages a dynamic market-news attention mechanism to aggregate news sentiments for market prediction. |
Mengyu Wang; Tiejun Ma; |
| 230 | On Causally Disentangled State Representation Learning for Reinforcement Learning Based Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This issue is exacerbated by the evolving nature of user preferences and behaviors, requiring the recommender system to adaptively focus on the most relevant information for decision-making while preserving generaliability. To tackle this problem, we introduce an innovative causal approach for decomposing the state and extracting Causal-InDispensable State Representations (CIDS) in RLRS. |
Siyu Wang; Xiaocong Chen; Lina Yao; |
| 231 | Learnable Item Tokenization for Generative Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Current approaches, such as ID, textual, and codebook-based identifiers, exhibit shortcomings in encoding semantic information, incorporating collaborative signals, or handling code assignment bias. To address these limitations, we propose LETTER (a LEarnable Tokenizer for generaTivE Recommendation), which integrates hierarchical semantics, collaborative signals, and code assignment diversity to satisfy the essential requirements of identifiers. |
Wenjie Wang; Honghui Bao; Xinyu Lin; Jizhi Zhang; Yongqi Li; Fuli Feng; See-Kiong Ng; Tat-Seng Chua; |
| 232 | Improving Adversarial Transferability Via Frequency-Guided Sample Relevance Attack Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We observe a correlation between the sharpness of decision boundaries in model sensitive regions and overfitting during adversarial training, which hampers the adversarial examples’ transferability. To address this issue, we propose a novel approach termed Frequency-Guided Sample Relevance Attack (FGSRA). |
Xinyi Wang; Zhibo Jin; Zhiyu Zhu; Jiayu Zhang; Huaming Chen; |
| 233 | Content-Based Collaborative Generation for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: How to integrate content knowledge and collaborative interaction signals in a generative framework tailored for item recommendation is still an open research challenge.In this paper, we propose <u>co</u> ntent-based col <u>la</u> borative generation for <u>rec</u> ommender systems, namely ColaRec. |
Yidan Wang; Zhaochun Ren; Weiwei Sun; Jiyuan Yang; Zhixiang Liang; Xin Chen; Ruobing Xie; Su Yan; Xu Zhang; Pengjie Ren; Zhumin Chen; Xin Xin; |
| 234 | DDIPrompt: Drug-Drug Interaction Event Prediction Based on Graph Prompt Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In response, we offer DDIPrompt, an innovative solution inspired by the recent advancements in graph prompt learning. Our framework aims to address these issues by leveraging the intrinsic knowledge from pre-trained models, which can be efficiently deployed with minimal downstream data. |
Yingying Wang; Yun Xiong; Xixi Wu; Xiangguo Sun; Jiawei Zhang; GuangYong Zheng; |
| 235 | Topology-aware Retrieval Augmentation for Text Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We empirically verify their correlation to text relations, which motivates us to propose the framework of Topology-aware Retrieval-augmented Generation for text generation, which consists of a retrieval module to retrieve texts by their topological relations and an aggregation module to compose retrieved texts into prompts triggering LLMs for text generation. |
Yu Wang; Nedim Lipka; Ruiyi Zhang; Alexa Siu; Yuying Zhao; Bo Ni; Xin Wang; Ryan Rossi; Tyler Derr; |
| 236 | Inferring Information Diffusion Networks Without Timestamps Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, it requires high costs to acquire extensive temporal information, and the performance of network inference may decrease due to potential observational errors. Therefore, this paper specifically focuses on the time-independent scenario to address these limitations. |
Yuchen Wang; Dongpeng Hou; Chao Gao; Xianghua Li; Zhen Wang; |
| 237 | Enhancing Click-through Rate Prediction in Recommendation Domain with Search Query Representation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Since users and items are often shared between the search and recommendation domains, there is a valuable opportunity to enhance the recommendation domain by leveraging user preferences extracted from the search domain. Existing approaches either overlook the shift in user intention between these domains or fail to capture the significant impact of learning from users’ search queries on understanding their interests.In this paper, we propose a framework that learns from user search query embeddings within the context of user preferences in the recommendation domain. |
Yuening Wang; Man Chen; Yaochen Hu; Wei Guo; Yingxue Zhang; Huifeng Guo; Yong Liu; Mark Coates; |
| 238 | LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Meanwhile, though large language model (LLM) has shown great capability of reasoning and capturing semantic information, the high inference latency and high computation cost of tuning hinder its implementation in industrial recommender systems. To fill these gaps, we propose an LLM-enhanced paradigm LLM4MSR in this work. |
Yuhao Wang; Yichao Wang; Zichuan Fu; Xiangyang Li; Wanyu Wang; Yuyang Ye; Xiangyu Zhao; Huifeng Guo; Ruiming Tang; |
| 239 | A Mixed-Curvature Graph Diffusion Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In light of the aforementioned issues, we restore the notion of product space, and propose a generic graph generation method, called mixed-curvature Product Space Graph Diffusion Model (ProGDM). |
Yujie Wang; Shuo Zhang; Junda Ye; Hao Peng; Li Sun; |
| 240 | Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Sample Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose to use similar sequences (with different target items) as additional positive samples and introduce a Relative Contrastive Learning (RCL) framework for sequential recommendation. |
Zhikai Wang; Yanyan Shen; Zexi Zhang; Li He; Yichun Li; Hao Gu; Yinghua Zhang; |
| 241 | Learning to Differentiate Pairwise-Argument Representations for Implicit Discourse Relation Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To enable encoders to produce clearly distinguishable representations, we propose a joint learning framework. |
Zhipang Wang; Yu Hong; Yuxiang Lu; Xiabing Zhou; Jianmin Yao; Guodong Zhou; |
| 242 | GAD: A Generalized Framework for Anomaly Detection at Different Risk Levels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For this purpose, we present a generalized anomaly detection framework flexible in addressing a broader range of anomaly detection scenarios. |
Rulan Wei; Zewei He; Martin Pavlovski; Fang Zhou; |
| 243 | OptDist: Learning Optimal Distribution for Customer Lifetime Value Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel optimal distribution selection model (OptDist) for CLTV prediction, which utilizes an adaptive optimal sub-distribution selection mechanism to improve the accuracy of complex distribution modeling. |
Yunpeng Weng; Xing Tang; Zhenhao Xu; Fuyuan Lyu; Dugang Liu; Zexu Sun; Xiuqiang He; |
| 244 | Identifying Contemporaneous and Lagged Dependence Structures By Promoting Sparsity in Continuous-time Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, employing neural networks for parameterizing dynamics makes it challenging for humans to identify dependence structures, especially in the presence of delayed effects. In consequence, these models are not an attractive option when capturing dependence carries more importance than accurate modeling, e.g., in tsunami forecasting.In this paper, we present a novel method for identifying dependence structures in continuous-time dynamics models. |
Fan Wu; Woojin Cho; David Korotky; Sanghyun Hong; Donsub Rim; Noseong Park; Kookjin Lee; |
| 245 | Time-Sensitve Retrieval-Augmented Generation for Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, a benchmark query dataset is constructed to retrieve documents containing time-evolving facts, and the results show that current embedding-based similarity-matching methods struggle to handle queries with explicit temporal constraints. Therefore, we propose a novel approach that integrates supervised contrastive learning with tailored negative sample pairs for temporal constraints to train the retriever of an RAG system, along with query-side fine-tuning and routing techniques. |
Feifan Wu; Lingyuan Liu; Wentao He; Ziqi Liu; Zhiqiang Zhang; Haofen Wang; Meng Wang; |
| 246 | Teach Harder, Learn Poorer: Rethinking Hard Sample Distillation for GNN-to-MLP Knowledge Distillation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we revisit the knowledge samples (nodes) in teacher GNNs from the perspective of hardness, and identify that hard sample distillation may be a major performance bottleneck of existing graph KD algorithms. |
Lirong Wu; Yunfan Liu; Haitao Lin; Yufei Huang; Stan Z. Li; |
| 247 | Bridge The Gap Between Past and Future: Siamese Model Optimization for Context-Aware Document Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we explore the integration of future contextual information into the session context to enhance document ranking. |
Songhao Wu; Quan Tu; Mingjie Zhong; Hong Liu; Jia Xu; Jinjie Gu; Rui Yan; |
| 248 | StatioCL: Contrastive Learning for Time Series Via Non-Stationary and Temporal Contrast Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing CL methods often introduce false negative pairs (FNPs) by neglecting inherent characteristics and then randomly selecting distinct segments as dissimilar pairs, leading to erroneous representation learning, reduced model performance, and overall inefficiency. To address these issues, we systematically define and categorize FNPs in time series into semantic false negative pairs and temporal false negative pairs for the first time: the former arising from overlooking similarities in label categories, which correlates with similarities in non-stationarity and the latter from neglecting temporal proximity. |
Yu Wu; Ting Dang; Dimitris Spathis; Hong Jia; Cecilia Mascolo; |
| 249 | MemoCRS: Memory-enhanced Sequential Conversational Recommender Systems with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we leverage memory-enhanced LLMs to model the preference continuity, addressing two key issues: (1) redundancy and noise in historical dialogue sessions, and (2) the cold-start users problem. |
Yunjia Xi; Weiwen Liu; Jianghao Lin; Bo Chen; Ruiming Tang; Weinan Zhang; Yong Yu; |
| 250 | Advancing Certified Robustness of Explanation Via Gradient Quantization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we certify the robustness of explanations motivated by the success of randomized smoothing. |
Yang Xiao; Zijie Zhang; Yuchen Fang; Da Yan; Yang Zhou; Wei-Shinn Ku; Bo Hui; |
| 251 | Federated Node Classification Over Distributed Ego-Networks with Secure Contrastive Embedding Sharing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: FGL in such novel yet realistic ego-network settings faces the unique challenge of incomplete neighborhood information for non-ego local nodes since they likely appear and have different sets of neighbors in multiple ego-networks. To address this challenge, we propose an FGL method for distributed ego-networks in which clients obtain complete neighborhood information of local nodes through sharing node embeddings with other clients. |
Han Xie; Li Xiong; Carl Yang; |
| 252 | Bridging User Dynamics: Transforming Sequential Recommendations with Schr\{o}dinger Bridge and Diffusion Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing sequential recommendation methods based on diffusion models are constrained by a prior distribution limited to Gaussian distribution, hindering the possibility of introducing user-specific information for each recommendation and leading to information loss. To address these issues, we introduce the Schr\{o}dinger Bridge into diffusion-based sequential recommendation models, creating the SdifRec model. |
Wenjia Xie; Rui Zhou; Hao Wang; Tingjia Shen; Enhong Chen; |
| 253 | Image-text Retrieval with Main Semantics Consistency Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the rich semantic content contained in each image may bring false matches, resulting in the matched text ignoring the main semantics but focusing on the secondary or other semantics of this image. To address this issue, this paper proposes a semantically optimized approach with a novel Main Semantics Consistency (MSC) loss function, which aims to rank the semantically most similar images (or texts) corresponding to the given query at the top position during the retrieval process. |
Yi Xie; Yangtao Wang; Yanzhao Xie; Xin Tan; Jingjing Li; Xiaocui Li; Weilong Peng; Maobin Tang; Meie Fang; |
| 254 | UniMPC: Towards A Unified Framework for Multi-Party Conversations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, dialogue evolves through diverse meta-information, where knowledge from specific subtasks can influence others. To address this, we propose UniMPC, a unified framework that consolidates common MPC subtasks. |
Yunhe Xie; Chengjie Sun; Yifan Liu; Zhenzhou Ji; Bingquan Liu; |
| 255 | GetCom: An Efficient and Generalizable Framework for Community Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduce GetCom, a novel three-phase pre-train, generate, prompt framework that integrates traditional methods and deep learning techniques. |
Kaiyu Xiong; Yucheng Jin; Yun Xiong; Jiawei Zhang; |
| 256 | Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our observations indicate that current methods face significant challenges in dealing with specialized and complex knowledge in medical domain. Therefore, we propose MedLaSA, a novel Layer-wise Scalable Adapter strategy for medical model editing. |
Derong Xu; Ziheng Zhang; Zhihong Zhu; Zhenxi Lin; Qidong Liu; Xian Wu; Tong Xu; Wanyu Wang; Yuyang Ye; Xiangyu Zhao; Enhong Chen; Yefeng Zheng; |
| 257 | Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Specifically, the datasets are severely impacted by the noises caused by distribution shifts across sub-populations and the neglect of node identities, both obstruct the identification of disease-specific patterns. To tackle these challenges, we propose Contrasformer, a novel contrastive brain network Transformer. |
Jiaxing Xu; Kai He; Mengcheng Lan; Qingtian Bian; Wei Li; Tieying Li; Yiping Ke; Miao Qiao; |
| 258 | AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel group recommendation method AlignGroup, which focuses on both group consensus and individual preferences of group members to infer the group decision-making. |
Jinfeng Xu; Zheyu Chen; Jinze Li; Shuo Yang; Hewei Wang; Edith C. H. Ngai; |
| 259 | Shape-aware Graph Spectral Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To mitigate this gap, we conduct theoretical and empirical analyses revealing a positive correlation between low-frequency importance and the homophily ratio, and a negative correlation between high-frequency importance and the homophily ratio. Motivated by this, we propose shape-aware regularization on a Newton Interpolation-based spectral filter that can (i) learn an arbitrary polynomial spectral filter; and (ii) incorporate prior knowledge about the desired shape of the corresponding homophily level. |
Junjie Xu; Enyan Dai; Dongsheng Luo; Xiang Zhang; Suhang Wang; |
| 260 | Post-Quantum Searchable Encryption Supporting User-Authorization for Outsourced Data Management Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a lattice-based variant of PEAKS (L-PEAKS) that enables keyword dataset authorization for outsourced data management. |
Shiyuan Xu; Yibo Cao; Xue Chen; Yu Guo; Yuer Yang; Fangda Guo; Siu-Ming Yiu; |
| 261 | Identifying Disinformation from Online Social Media Via Dynamic Modeling Across Propagation Stages Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we conceive and implement a novel framework called DMPS for identifying disinformation, which Dynamically Models diverse topological structures of reposting trees as well as the textual content streams across different Propagation Stages. |
Shuai Xu; Jianqiu Xu; Shuo Yu; Bohan Li; |
| 262 | ScACT: Accurate Cross-modality Translation Via Cycle-consistent Training from Unpaired Single-cell Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Many methods have been developed for cross-modality translation, but their reliance on scarce high-quality co-assay data limits their applicability. Addressing this, we introduce scACT, a deep generative model designed to extract cross-modality biological insights from unpaired single-cell data. |
Siwei Xu; Junhao Liu; Jing Zhang; |
| 263 | Source Prompt: Coordinated Pre-training of Language Models on Diverse Corpora from Multiple Sources Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we identify the disadvantage of heterogeneous corpora from multiple sources for pre-training PLMs. |
Yipei Xu; Dakuan Lu; Jiaqing Liang; Jin Zhao; Xintao Wang; Hengkui Wu; Ken Chen; Liujiang Liu; Yingsi Xin; Xuepeng Liu; Yanghua Xiao; Zhixu Li; |
| 264 | CLR2G: Cross Modal Contrastive Learning on Radiology Report Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods still struggle to identify rare anomalies in the images. To address this issue, we propose a two-stage training model, named CLR2G, based on cross-modal contrastive learning. |
Hongchen Xue; Qingzhi Ma; Guanfeng Liu; Jianfeng Qu; Yuanjun Liu; An Liu; |
| 265 | Enhancing The Completeness of Rationales for Multi-Step Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, drawing inspiration from human-like reasoning processes in answering multi-step questions, we explicitly plan the rationales to ensure their completeness. |
Shangzi Xue; Zhenya Huang; Xin Lin; Jiayu Liu; Longhu Qin; Tianhuang Su; Haifeng Liu; Qi Liu; |
| 266 | Predicting Scientific Impact Through Diffusion, Conformity, and Contribution Disentanglement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this research, we introduce a novel model, DPPDCC, which Disentangles the Potential impacts of Papers into Diffusion, Conformity, and Contribution values. |
Zhikai Xue; Guoxiu He; Zhuoren Jiang; Sichen Gu; Yangyang Kang; Star Zhao; Wei Lu; |
| 267 | Buffalo: Biomedical Vision-Language Understanding with Cross-Modal Prototype and Federated Foundation Model Collaboration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: With privacy considerations, data transfer cannot be permitted, restricting knowledge exchange among different clients. To trickle these issues, we propose a cross-modal prototype imputation method for visual-language understanding (Buffalo) with only a slight increase in communication cost, which can improve the performance of fine-tuning general foundation models for downstream biomedical tasks. |
Bingjie Yan; Qian Chen; Yiqiang Chen; Xinlong Jiang; Wuliang Huang; Bingyu Wang; Zhirui Wang; Chenlong Gao; Teng Zhang; |
| 268 | Multi-Task Recommendation with Task Information Decoupling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such issues undermine the generalization capability of MTL models across various tasks. To address these limitations, we introduce the Task Information Decoupling Model (TIDM), designed to alleviate negative transfer by decoupling task knowledge. |
Ruiran Yan; Rui Fan; Defu Lian; |
| 269 | Topological Anonymous Walk Embedding: A New Structural Node Embedding Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although numerous structural node embedding methods are designed to encode such structural information, most, if not all, of these methods cannot simultaneously achieve the following three desired properties: (1) bijective mapping between embedding and local structure of node; (2) inductive capability; and (3) good interpretability of node embedding. To address this challenge, in this paper, we propose a novel structural node embedding algorithm named topological anonymous walk embedding (TAWE). |
Yuchen Yan; Yongyi Hu; Qinghai Zhou; Shurang Wu; Dingsu Wang; Hanghang Tong; |
| 270 | ST-ECP: A Novel Spatial-Temporal Framework for Energy Consumption Prediction of Vehicle Trajectory Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Second, current models predict driver behavior preferences solely from vehicle operational states in historical trajectories, often overlooking the influence of external environmental factors. To overcome these limitations, we introduce a Spatial-Temporal Framework for Energy Consumption Prediction of Vehicle Trajectories (ST-ECP). |
Biao Yang; Yun Xiong; Xi Chen; Xuejing Feng; Meng Wang; Jun Ma; |
| 271 | SGES: A General and Space-efficient Framework for Graphlet Counting in Graph Streams Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose SGES algorithm to estimate more complex graphlets in graph streams. |
Chen Yang; Lailong Luo; Yuliang Lu; Chu Huang; Qianzhen Zhang; Guozheng Yang; Deke Guo; |
| 272 | Behavior-Aware Hypergraph Convolutional Network for Illegal Parking Prediction with Multi-Source Contextual Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel behavior-aware hypergraph convolutional network named BHIPP for city-wide illegal parking prediction. |
Guang Yang; Meiqi Tu; Zelong Li; Jinquan Hang; Taichi Liu; Ruofeng Liu; Yi Ding; Yu Yang; Desheng Zhang; |
| 273 | Spectral-Aware Augmentation for Enhanced Graph Representation Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present GASSER, a model that applies tailored perturbations to specific frequencies of graph structures in the spectral domain, guided by spectral hints. |
Kaiqi Yang; Haoyu Han; Wei Jin; Hui Liu; |
| 274 | Efficient Pruned Top-K Subgraph Matching with Topology-Aware Bounds Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose PTAB, an efficient algorithm for top-k subgraph matching. |
Linglin Yang; Yuqi Zhou; Yue Pang; Lei Zou; |
| 275 | Decoupled Behavior-based Contrastive Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: b) Existing CL-based methods mostly construct contrastive views through heuristic-based embedding or structure perturbation, which are prone to introduce noise or discard important information, leading to a decreased representation quality. To address these issues, we propose a Decoupled Behavior-based Contrastive Recommendation model (DBCR) that effectively decouples user behaviors from binary datasets for better user-item interaction modeling. |
Mengduo Yang; Jie Zhou; Meng Xi; Xiaohua Pan; Yi Yuan; Ying Li; Yangyang Wu; Jinshan Zhang; Jianwei Yin; |
| 276 | A New Framework for Evaluating Faithfulness of Video Moment Retrieval Against Multiple Distractors Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the MVMR (Massive Videos Moment Retrieval for Faithfulness Evaluation) task that aims to retrieve video moments within a massive video set, including multiple distractors, to evaluate the faithfulness of VMR models. |
Nakyeong Yang; Minsung Kim; Seunghyun Yoon; Joongbo Shin; Kyomin Jung; |
| 277 | MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The primary objective of this research is to mitigate the adverse effects of traffic signal malfunction, such as traffic congestion and collision, by optimizing the control of neighboring functioning signals. |
Qinchen Yang; Zejun Xie; Hua Wei; Desheng Zhang; Yu Yang; |
| 278 | Leveraging Local Structure for Improving Model Explanations: An Information Propagation Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a method called IProp, which models each pixel’s individual attribution score as a source of explanatory information and explains the image prediction through the dynamic propagation of information across all pixels. |
Ruo Yang; Binghui Wang; Mustafa Bilgic; |
| 279 | Attacking Visually-aware Recommender Systems with Transferable and Imperceptible Adversarial Styles Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we propose a novel Style perturbation-based Practical Attack Framework (SPAF). |
Shiyi Yang; Chen Wang; Xiwei Xu; Liming Zhu; Lina Yao; |
| 280 | TrafCL: Robust Encrypted Malicious Traffic Detection Via Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, prior works have not adequately addressed the task due to challenges of encrypted traffics with misleading contents, incomplete sessions, and limited labels. To overcome these limitations, in this paper, we propose TrafCL, a contrastive learning framework for robust encrypted malicious traffic detection. |
Xiaodu Yang; Sijie Ruan; Jinyu Li; Yinliang Yue; Bo Sun; |
| 281 | Hyperbolic Contrastive Learning for Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a new framework called Hyperbolic Contrastive Learning (HCTS), designed to capture the unique features of each domain while enabling efficient knowledge transfer between domains. |
Xin Yang; Heng Chang; Zhijian Lai; Jinze Yang; Xingrun Li; Yu Lu; Shuaiqiang Wang; Dawei Yin; Erxue Min; |
| 282 | Breaking State-of-the-Art Poisoning Defenses to Federated Learning: An Optimization-Based Attack Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show that these novel robust AGRs are also vulnerable to carefully designed poisoning attacks. Specifically, we observe that breaking these robust AGRs reduces to bypassing the clipping or/and filtering of malicious clients, and propose an optimization-based attack framework to leverage this observation. |
Yuxin Yang; Qiang Li; Chenfei Nie; Yuan Hong; Binghui Wang; |
| 283 | What A Surprise! Computing Rewritten Modules Can Be As Efficient As Computing Subset Modules Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, realizing its full potential demands highly optimized techniques for generating such modules. Previous studies have identified notable challenges in generating uniform interpolants for EL-ontologies, where their computation is substantially more complex and computationally demanding than standard subset modules.Despite these obstacles, this paper introduces an advanced forgetting method tailored for computing uniform interpolants of ELIO-ontologies with ABoxes. |
Zhihao Yang; Yizheng Zhao; |
| 284 | Adaptive Differentially Private Structural Entropy Minimization for Unsupervised Social Event Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel social event detection framework, ADP-SEMEvent, an unsupervised social event detection method that prioritizes privacy. |
Zhiwei Yang; Yuecen Wei; Haoran Li; Qian Li; Lei Jiang; Li Sun; Xiaoyan Yu; Chunming Hu; Hao Peng; |
| 285 | Combining Incomplete Observational and Randomized Data for Heterogeneous Treatment Effects Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our paper, we propose a resilient approach to Combine Incomplete Observational data and randomized data for HTE estimation, which we abbreviate as CIO. |
Dong Yao; Caizhi Tang; Qing Cui; Longfei Li; |
| 286 | Distilling Multi-Scale Knowledge for Event Temporal Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present MulCo : Distilling Mul ti-Scale Knowledge via Co ntrastive Learning, a knowledge co-distillation approach that shares knowledge across multiple event pair proximity bands to improve performance on all types of temporal datasets. |
Hao-Ren Yao; Luke Breitfeller; Aakanksha Naik; Chunxiao Zhou; Carolyn Rose; |
| 287 | A Cause-Focused Query Optimizer Alert System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce an alert system for query optimization, which is built upon cost models to reduce the selection of regressed plans. |
Runfan Ye; Zibo Liang; Xu Chen; Shuncheng Liu; Kai Zheng; |
| 288 | Guaranteeing Accuracy and Fairness Under Fluctuating User Traffic: A Bankruptcy-Inspired Re-ranking Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Inspired by the bankruptcy problem in economics, we propose a novel fairness-aware re-ranking approach named BankFair. |
Xiaopeng Ye; Chen Xu; Jun Xu; Xuyang Xie; Gang Wang; Zhenhua Dong; |
| 289 | CKNN: Cleansed K-Nearest Neighbor for Unsupervised Video Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we address the problem of unsupervised video anomaly detection (UVAD). |
Jihun Yi; Sungroh Yoon; |
| 290 | Debiased Graph Poisoning Attack Via Contrastive Surrogate Objective Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Based on our analysis, we propose a Meta-gradient-based attack method using contrastive surrogate objective (Metacon), which alleviates the bias in meta-gradient using a new surrogate loss. |
Kanghoon Yoon; Yeonjun In; Namkyeong Lee; Kibum Kim; Chanyoung Park; |
| 291 | GraphCBAL: Class-Balanced Active Learning for Graph Neural Networks Via Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This further adversely affects the classification performance. To tackle this issue, in this paper, we propose a novel reinforced class-balanced active learning framework for GNNs, namely, GraphCBAL. |
Chengcheng Yu; Jiapeng Zhu; Xiang Li; |
| 292 | Rethinking Attention Mechanism for Spatio-Temporal Modeling: A Decoupling Perspective in Traffic Flow Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We rethink the role of the attention mechanism during spatio-temporal modeling from a decoupling perspective, and propose DEC-Former for traffic flow prediction. |
Qi Yu; Weilong Ding; Hao Zhang; Yang Yang; Tianpu Zhang; |
| 293 | Time-Series Representation Learning Via Dual Reference Contrasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, they usually employ only a single positive sample for comparative learning, which is insufficient to model the diversity and hurts the robustness. To address these issues, this paper proposes a Time Series representation learning framework via Dual Reference Contrasting (TS-DRC). |
Rui Yu; Yongshun Gong; Shoujin Wang; Jiasheng Si; Xueping Peng; Bing Xu; Wenpeng Lu; |
| 294 | DAMe: Personalized Federated Social Event Detection with Dual Aggregation Mechanism Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper proposes a personalized federated learning framework with a dual aggregation mechanism for social event detection, namely DAMe. |
Xiaoyan Yu; Yifan Wei; Pu Li; Shuaishuai Zhou; Hao Peng; Li Sun; Liehuang Zhu; Philip S. Yu; |
| 295 | MMLRec: A Unified Multi-Task and Multi-Scenario Learning Benchmark for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Furthermore, our experimental analysis indicates that more complex methods exhibit better robustness when there are significant differences between tasks or scenarios. By providing a unified framework (MMLRec), our goal is to promote rapid evaluation and inspire innovative research in this continuously evolving field. |
Guanghu Yuan; Jieyu Yang; Shujie Li; Mingjie Zhong; Ang Li; Ke Ding; Yong He; Min Yang; Liang Zhang; Xiaolu Zhang; Linjian Mo; |
| 296 | Transformer Based Bayesian Network Embedding for Efficient Multiple Probabilistic Inferences Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The efficiency and accuracy of multiple probabilistic inferences in BN could not be guaranteed by most of the existing approximate inference methods. To address this issue, we propose the methods of Transformer based BN embedding (TBNE) and TBNE based probabilistic inferences. |
Kun Yue; Zhiwei Qi; Liang Duan; |
| 297 | Using Distributed Ledgers To Build Knowledge Graphs For Decentralized Computing Ecosystems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel system design that utilizes Distributed Ledger Technology to build knowledge graphs. |
Tarek Zaarour; Ahmed Khalid; Preeja Pradeep; Ahmed Zahran; |
| 298 | Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy Induction from Limited Examples Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this work, we introduce Chain-of-Layer which is an in-context learning framework designed to induct taxonomies from a given set of entities. |
Qingkai Zeng; Yuyang Bai; Zhaoxuan Tan; Shangbin Feng; Zhenwen Liang; Zhihan Zhang; Meng Jiang; |
| 299 | Benchmarking Challenges for Temporal Knowledge Graph Alignment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the aforementioned issues, in this work, we propose to benchmark challenges for temporal knowledge graph alignment by establishing a new dataset, i.e., BETA, which features multi-granular temporal information, more realistic quadruple distribution, and new challenging alignment scenarios. |
Weixin Zeng; Jie Zhou; Xiang Zhao; |
| 300 | M2ConceptBase: A Fine-Grained Aligned Concept-Centric Multimodal Knowledge Base Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a context-aware multimodal symbol grounding approach to align concept-image and concept-description pairs using context information from image-text datasets. |
Zhiwei Zha; Jiaan Wang; Zhixu Li; Xiangru Zhu; Wei Song; Yanghua Xiao; |
| 301 | Cost-Effective Framework with Optimized Task Decomposition and Batch Prompting for Medical Dialogue Summary Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In-context learning (ICL) methods improve accuracy and reduce data requirements but still produce unstructured notes and require high time and cost. To tackle the above challenges, we propose a three-module framework, called CE-DEPT, for accurate, efficient and cost-effective medical note generation. |
Chi Zhang; Tao Chen; Jiehao Chen; Hao Wang; Jiyun Shi; Zhaojing Luo; Meihui Zhang; |
| 302 | Reformulating Conversational Recommender Systems As Tri-Phase Offline Policy Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This limitation frequently leads to models that perform well in simulated environments but falter in actual deployment. Addressing these challenges, this paper introduces the Tri-Phase Offline Policy Learning-based Conversational Recommender System (TPCRS), which significantly reduces dependency on real-time interactions and mitigates overfitting issues prevalent in traditional approaches. |
Gangyi Zhang; Chongming Gao; Hang Pan; Runzhe Teng; Ruizhe Li; |
| 303 | Revisit Orthogonality in Graph-Regularized MLPs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces OrthoReg, a simple yet effective Graph-regularized MLP model for semi-supervised node representation learning. |
Hengrui Zhang; Shen Wang; Vassilis N. Ioannidis; Soji Adeshina; Jiani Zhang; Xiao Qin; Christos Faloutsos; Da Zheng; George Karypis; Philip S. Yu; |
| 304 | InfoMLP: Unlocking The Potential of MLPs for Semi-Supervised Learning with Structured Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce InfoMLP, an innovative model structured like a Multilayer Perceptron (MLP) for semi-supervised classification of structured data, e.g., graphs. |
Hengrui Zhang; Qitian Wu; Chenxiao Yang; Philip S. Yu; |
| 305 | SAQRec: Aligning Recommender Systems to User Satisfaction Via Questionnaire Feedback Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we aim to leverage the questionnaire feedback to align the recommendation model with users’ true preferences. |
Kepu Zhang; Teng Shi; Sunhao Dai; Xiao Zhang; Yinfeng Li; Jing Lu; Xiaoxue Zang; Yang Song; Jun Xu; |
| 306 | EFVAE: Efficient Federated Variational Autoencoder for Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This leads to significant communication cost during the model’s transmission phases (distribution and upload), making FedVAE’s implementation extremely challenging. To address these challenges, we propose an Efficient Federated Variational AutoEncoder for collaborative filtering, EFVAE, which core is the Federated Collaborative Importance Sampling (FCIS) method. |
Lu Zhang; Qian Rong; Xuanang Ding; Guohui Li; Ling Yuan; |
| 307 | HGCH: A Hyperbolic Graph Convolution Network Model for Heterogeneous Collaborative Graph Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing HGCN methods have several drawbacks: they fail to fully leverage hyperbolic space properties due to arbitrary embedding initialization and imprecise tangent space aggregation; they overlook auxiliary information that could enrich the collaborative graph; and their training convergence is slow due to margin ranking loss and random negative sampling. To overcome these challenges, we propose Hyperbolic Graph Collaborative for Heterogeneous Recommendation (HGCH), an enhanced HGCN-based model for collaborative filtering that integrates diverse side information into a heterogeneous collaborative graph and improves training convergence speed. |
Lu Zhang; Ning Wu; |
| 308 | CYCLE: Cross-Year Contrastive Learning in Entity-Linking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These changes lead to temporal degradation in entity linking models, characterized as a decline in model performance over time. To address this issue, we propose leveraging graph relationships to aggregate information from neighboring entities across different time periods. |
Pengyu Zhang; Congfeng Cao; Klim Zaporojets; Paul Groth; |
| 309 | MSKR: Advancing Multi-modal Structured Knowledge Representation with Synergistic Hard Negative Samples Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For instance, when distinguishing between texts or images that are generally similar but have distinct structured knowledge (such as entities and relationships in text, or objects and object attributes in images), the model’s capabilities are limited. In this paper, we propose a advancing Multi-modal Structured Knowledge Representation with synergistic hard negative samples (MSKR), thereby significantly improving the model’s matching capability for such data. |
Shuili Zhang; Hongzhang Mu; Tingwen Liu; Qianqian Tong; Jiawei Sheng; |
| 310 | Watermarking Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: While model watermarking has emerged as a potent defense mechanism in various domains, its direct application to recommender systems remains unexplored and non-trivial. In this paper, we address this gap by introducing Autoregressive Out-of-distribution Watermarking (AOW), a novel technique tailored specifically for recommender systems. |
Sixiao Zhang; Cheng Long; Wei Yuan; Hongxu Chen; Hongzhi Yin; |
| 311 | Language Models-enhanced Semantic Topology Representation Learning For Temporal Knowledge Graph Extrapolation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Until recently, few Language Models (LM) based methods have attempted to model the semantic representations of TKGs, however, lacking specific designs for the topology information. Therefore, we propose a Semantic TOpology REpresentation learning (STORE) framework enhanced by LMs to bridge the gap between the semantics and topology of TKGs. |
Tianli Zhang; Tongya Zheng; Zhenbang Xiao; Zulong Chen; Liangyue Li; Zunlei Feng; Dongxiang Zhang; Mingli Song; |
| 312 | Data Imputation from The Perspective of Graph Dirichlet Energy Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Existing refinement techniques, such as the Graph Convolutional Network (GCN), often result in further energy reduction. To address this, we introduce a new framework, the Graph Laplacian Pyramid Network (GLPN). |
Weiqi Zhang; Guanlue Li; Jianheng Tang; Jia Li; Fugee Tsung; |
| 313 | Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper presents a critical examination of the necessity of graph convolutions during the training phase and introduces an innovative alternative: the Light Post-Training Graph Ordinary-Differential-Equation (LightGODE). |
Weizhi Zhang; Liangwei Yang; Zihe Song; Henry Peng Zou; Ke Xu; Liancheng Fang; Philip S. Yu; |
| 314 | DPCAG: A Community Affiliation Graph Generation Model for Preserving Group Relationships Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper aims to propose an effective graph synthesis algorithm by using differential privacy named DPCAG (Differentially Private Community Affiliation Graph Generation Model) for protecting user group relationships. |
Xinjian Zhang; Bo Ning; Chengfei Liu; |
| 315 | ROLeR: Effective Reward Shaping in Offline Reinforcement Learning for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, a novel model-based Reward Shaping in Offline Reinforcement Learning for Recommender Systems, ROLeR, is proposed for reward and uncertainty estimation in recommendation systems. |
Yi Zhang; Ruihong Qiu; Jiajun Liu; Sen Wang; |
| 316 | Multi-modal Food Recommendation with Health-aware Knowledge Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Notably, our preliminary investigation on two datasets unveiled that the semantic divergence between health-related knowledge and collaborative filtering signals is more pronounced in comparison to other metadata information, thereby potentially impeding the efficacy of food recommendation systems. To address these limitations, we propose HealthRec, a novel multi-modal food recommendation framework with health-aware knowledge distillation. |
Yixin Zhang; Xin Zhou; Fanglin Zhu; Ning Liu; Wei Guo; Yonghui Xu; Zhiqi Shen; Lizhen Cui; |
| 317 | Preference Prototype-Aware Learning for Universal Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Preference Prototype-Aware (PPA) learning method to quantitatively learn user preferences while minimizing disturbances from the source domain. |
Yuxi Zhang; Ji Zhang; Feiyang Xu; Lvying Chen; Bohan Li; Lei Guo; Hongzhi Yin; |
| 318 | A GAIL Fine-Tuned LLM Enhanced Framework for Low-Resource Knowledge Graph Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present an enhancement framework that utilizes Generative Adversarial Imitation Learning (GAIL) to fine-tune LLMs, which can address the challenges inherent in the low-resource KGQA task. |
Zhiqiang Zhang; Liqiang Wen; Wen Zhao; |
| 319 | NeutronCache: An Efficient Cache-Enhanced Distributed Graph Neural Network Training System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, we also found that after a certain iterations of training, the parameter updates during each iteration had minimal effect on the parameters. Based on these findings, we have improved the original implementation and proposed a cache-enhanced distributed graph training system, NeutronCache. |
Chu Zhao; Shengjie Dong; Yuhai Zhao; Yuan Li; |
| 320 | Towards Coarse-grained Visual Language Navigation Task Planning Enhanced By Event Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most existing work focuses on the former kind of instruction in VLN tasks, ignoring the latter abstract instructions belonging to daily life scenarios. To overcome the above challenge in abstract instruction, we attempt to consider coarse-grained instruction in VLN by event knowledge enhancement. |
Kaichen Zhao; Yaoxian Song; Haiquan Zhao; Haoyu Liu; Tiefeng Li; Zhixu Li; |
| 321 | Correcting Biases of Shapley Value Attributions for Informative Machine Learning Model Explanations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These explanation errors can be decomposed into two components: 1) observation bias which stems from data sparsity and leads to over-informativeness; and 2) structural bias which stems from distributional assumptions and leads to under-informativeness. To alleviate these biases, in this paper, we propose a series of refinement methods that combine out-of-distribution (OOD) detection and importance sampling. |
Ningsheng Zhao; Jia Yuan Yu; Trang Bui; Krzysztof Dzieciolowski; |
| 322 | Zero-shot Knowledge Graph Question Generation Via Multi-agent LLMs and Small Models Synthesis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although extensively explored in recent years, prevailing models predominantly depend on labelled data for training deep learning models or employ large parametric frameworks, e.g., Large Language Models (LLMs), which can incur significant deployment costs and pose practical implementation challenges. To address these issues, in this work, we put forward a zero-shot, multi-agent KGQG framework. |
Runhao Zhao; Jiuyang Tang; Weixin Zeng; Ziyang Chen; Xiang Zhao; |
| 323 | HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current methods often overlook the multi-scale nature of time series, which is essential for accurate forecasting. To address this, we propose HiMTM, a hierarchical multi-scale masked time series modeling with self-distillation for long-term forecasting. |
Shubao Zhao; Ming Jin; Zhaoxiang Hou; Chengyi Yang; Zengxiang Li; Qingsong Wen; Yi Wang; |
| 324 | Multi-Task Modeling of Student Knowledge and Behavior Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel multi-task model, the Multi-Task Student Knowledge and Behavior Model (KTBM), which combines KT and BM to improve both performance and interoperability. |
Siqian Zhao; Sherry Sahebi; |
| 325 | Aligning Explanations for Recommendation with Rating and Feature Via Maximizing Mutual Information Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, as current explanation generation methods are commonly trained with an objective to mimic existing user reviews, the generated explanations are often not aligned with the predicted ratings or some important features of the recommended items, and thus, are suboptimal in helping users make informed decision on the recommendation platform. To tackle this problem, we propose a flexible model-agnostic method named MMI (Maximizing Mutual Information) framework to enhance the alignment between the generated natural language explanations and the predicted rating/important item features. |
Yurou Zhao; Yiding Sun; Ruidong Han; Fei Jiang; Lu Guan; Xiang Li; Wei Lin; Weizhi Ma; Jiaxin Mao; |
| 326 | Generating Intent-aware Clarifying Questions in Conversational Information Retrieval Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we define the ”intent” of a query as a verb representing the potential behavior, action, or task the user may take. |
Ziliang Zhao; Zhicheng Dou; Yujia Zhou; |
| 327 | Devil in The Tail: A Multi-Modal Framework for Drug-Drug Interaction Prediction in Long Tail Distinction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, a novel multi-modal deep learning-based framework, namely TFDM, is introduced to leverage multiple properties of a drug to achieve DDI classification. |
Liangwei Nathan Zheng; Chang George Dong; Wei Emma Zhang; Xin Chen; Lin Yue; Weitong Chen; |
| 328 | Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We proposed a novel transformer-based framework for general irregular time series data that treats IRTS from four views: Locality, Time, Spatio and Irregularity to motivate the data usage to the highest potential. |
Liangwei Nathan Zheng; Zhengyang Li; Chang George Dong; Wei Emma Zhang; Lin Yue; Miao Xu; Olaf Maennel; Weitong Chen; |
| 329 | FGITrans: Cross-City Transformer for Fine-grained Urban Flow Inference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, many cities still suffer from the data scarcity issue due to the unbalanced city development levels. To mitigate this issue, we propose a novel cross-city fine-grained urban flow inference model named FGITrans, which aims to effectively transfer the knowledge from the data-rich cities to the data-scarce cities. |
Yuhao Zheng; Yishuo Cai; Zihao Cai; Changjun Fan; Senzhang Wang; Jianxin Wang; |
| 330 | AdaTM: Fine-grained Urban Flow Inference with Adaptive Knowledge Transfer Across Multiple Cities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel fine-grained urban flow inference model named AdaTM, which leverages the city-specific and city-invariant knowledge extracted from multiple cities. |
Yuhao Zheng; Jinyang Wu; Zihao Cai; Senzhang Wang; Jianxin Wang; |
| 331 | Interaction-level Membership Inference Attack Against Recommender Systems with Long-tailed Distribution Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces MINER, a new IMIA designed to enhance attack performance against RSs with long-tailed item distribution. |
Da Zhong; Xiuling Wang; Zhichao Xu; Jun Xu; Wendy Hui Wang; |
| 332 | AdaTrans: Adaptive Transfer Time Prediction for Multi-modal Transportation Modes Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These factors are dynamic and vary with location and time, presenting a significant challenge. To address this, we introduce an adaptive transfer time prediction framework, AdaTrans, to forecast personalized transfer times between upstream and downstream transportation modes. |
Shuxin Zhong; Hua Wei; Wenjun Lyu; Guang Yang; Zhiqing Hong; Guang Wang; Yu Yang; Desheng Zhang; |
| 333 | Learning Cross-modal Knowledge Reasoning and Heuristic-prompt for Visual-language Navigation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Most existing research focuses on the fusion of visual features and semantic space, which ignoring the importance of local highlight features and semantic knowledge alignment in images for agent navigation. Therefore, this paper proposes a novel visual language model combining Knowledge-augmented Reasoning and Soft-Prompt (KRSP) learning. |
Dongming Zhou; Zhengbin Pang; Wei Li; |
| 334 | LST2A: Lexical-Syntactic Targeted Adversarial Attack for Texts Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Meanwhile, existing textual adversarial attack methods primarily rely on word substitution operations to maintain semantic similarity between the adversarial and original examples, which greatly limits the search space for adversarial examples. To address these issues, we propose a novel <u>L</u>exical-<u>S</u>yntactic <u>T</u>argeted <u>A</u>dversarial <u>A</u>ttack method tailored for the black-box settings, referred to as LST2A. |
Guanghao Zhou; Panjia Qiu; Mingyuan Fan; Cen Chen; Yaliang Li; Wenmeng Zhou; |
| 335 | MTSCI: A Conditional Diffusion Model for Multivariate Time Series Consistent Imputation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Diffusion models, known for their powerful generative abilities, prefer to generate consistent results based on available observations. Therefore, we propose a conditional diffusion model for Multivariate Time Series Consistent Imputation (MTSCI). |
Jianping Zhou; Junhao Li; Guanjie Zheng; Xinbing Wang; Chenghu Zhou; |
| 336 | A Power Method to Alleviate Over-smoothing for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: After theoretical analysis, we can effectively solve the problem of difficulty in decreasing the loss value by adding only a hyperparameter, called power. |
Peng Zhou; Yachao Cui; Han Cao; |
| 337 | Graph Anomaly Detection with Adaptive Node Mixup Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the data scarcity issue in GAD, in this paper, we propose, gADAM, a novel graph neural network-based GAD framework, which consolidates (1) an innovative mixup approach to augment the original training data by adaptively interpolating data instances in the embedding space, and (2) an efficacious sampling method to obtain high-quality negative samples for model training. |
Qinghai Zhou; Yuzhong Chen; Zhe Xu; Yuhang Wu; Menghai Pan; Mahashweta Das; Hao Yang; Hanghang Tong; |
| 338 | REDI: Recurrent Diffusion Model for Probabilistic Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose REDI, a recurrent diffusion model that achieves effective probabilistic time series prediction with recurrent forward diffusion process and step-aware guidance in backward denoising process. |
Shiyang Zhou; Zehao Gu; Yun Xiong; Yang Luo; Qiang Wang; Xiaofeng Gao; |
| 339 | Scalable Transformer for High Dimensional Multivariate Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our analysis reveals that two primary issues lie in the introduced noise from unrelated series that increases the difficulty of capturing the crucial inter-channel dependencies, and challenges in training strategies due to high-dimensional data. To address these issues, we propose STHD, the Scalable Transformer for High-Dimensional Multivariate Time Series Forecasting. |
Xin Zhou; Weiqing Wang; Wray Buntine; Shilin Qu; Abishek Sriramulu; Weicong Tan; Christoph Bergmeir; |
| 340 | GLFNet: Global and Local Frequency-domain Network for Long-term Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the Global and Local Frequency-domain Network (GLFNet), a novel architecture that efficiently learns global time dependencies and local time relationships in the frequency domain. |
Xucheng Zhou; Yuwen Liu; Lianyong Qi; Xiaolong Xu; Wanchun Dou; Xuyun Zhang; Yang Zhang; Xiaokang Zhou; |
| 341 | Regularized Unconstrained Weakly Submodular Maximization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the problem of maximizing functions of the form h = f-c, where f is a monotone, non-negative, weakly submodular set function and c is a modular function. |
Yanhui Zhu; Samik Basu; A. Pavan; |
| 342 | EMERGE: Enhancing Multimodal Electronic Health Records Predictive Modeling with Retrieval-Augmented Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In response, we propose EMERGE, a Retrieval-Augmented Generation (RAG) driven framework to enhance multimodal EHR predictive modeling. |
Yinghao Zhu; Changyu Ren; Zixiang Wang; Xiaochen Zheng; Shiyun Xie; Junlan Feng; Xi Zhu; Zhoujun Li; Liantao Ma; Chengwei Pan; |
| 343 | PRISM: Mitigating EHR Data Sparsity Via Learning from Missing Feature Calibrated Prototype Patient Representations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Conventional imputation methods inadequately distinguish between real and imputed data, leading to potential inaccuracies of patient representations. To address these issues, we introduce PRISM, a framework that indirectly imputes data through prototype representations of similar patients, thus ensuring denser and more accurate embeddings. |
Yinghao Zhu; Zixiang Wang; Long He; Shiyun Xie; Xiaochen Zheng; Liantao Ma; Chengwei Pan; |
| 344 | SaLa: Scenario-aware Label Graph Interaction for Multi-intent Spoken Language Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new framework dubbed SALA (short for Scenario- a ware Label gr a ph interaction), which effectively captures the dynamic co-occurrence relationships among labels across various scenarios, employing a strategy akin to a divide-and-conquer approach. |
Zhihong Zhu; Xuxin Cheng; Zhanpeng Chen; Zhichang Wang; Zhiqi Huang; Yuexian Zou; |
| 345 | L-APPLE: Language-agnostic Prototype Prefix Learning for Cross-lingual Event Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In contrast, prefix-tuning is a more lightweight alternative, but it relies solely on the labeled source language data during training, limiting its performance. To address the above problems, we propose a novel framework for CLED with Language-agnostic Prototypical Prefix-Learning (L-APPLE), which can integrate language-agnostic event information with prefix-tuning. |
Ziqin Zhu; Xutan Peng; Qian Li; Cheng Ji; Qingyun Sun; Jianxin Li; |
| 346 | Not All Negatives Are Equally Negative: Soft Contrastive Learning for Unsupervised Sentence Representations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods mostly treat all negative examples equally and overlook the different similarities between the negative examples and the anchors, which thus fail to capture the fine-grained semantic information of the sentences. To address this issue, we explicitly differentiate the negative examples by their similarities with the anchor, and thus propose a simple yet effective method SoftCSE that individualizes either the weight or temperature of each negative pair in the standard InfoNCE loss according to the similarities of the negative examples and the anchors. |
Haojie Zhuang; Wei Emma Zhang; Jian Yang; Weitong Chen; Quan Z. Sheng; |
| 347 | MV-BART: Multi-view BART for Multi-modal Sarcasm Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: From this perspective, we propose a new framework called Multi-view BART(MV-BART), which is capable of exploiting multi-granularity cues from multiple viewpoints and dynamically adjusting the view weights, applied to different sarcastic scenarios. |
Xingjie Zhuang; Fengling Zhou; Zhixin Li; |
| 348 | Enhancing Event Detection with Inter-Event Dependencies in Large Ontologies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: One of the hindrances for this purpose is the lack of resources to encode event-event dependencies for large ontologies. This study introduces a novel approach that leverages existing inter-event dependency resources to provide this information for extensive ontologies. |
Samireh Abdi; |
| 349 | Accurate Path Prediction of Provenance Traces Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We formulate the path prediction problem for provenance traces and take a machine learning approach to solve the problem. |
Raza Ahmad; Hee Young Jung; Yuta Nakamura; Tanu Malik; |
| 350 | COSCO: A Sharpness-Aware Training Framework for Few-shot Multivariate Time Series Classification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In few-shot settings, i.e. only a limited number of samples per class are available in training data, DNNs show a significant drop in testing accuracy and poor generalization ability. In this paper, we propose to address these problems from an optimization and a loss function perspective. |
Jesus Barreda; Ashley Gomez; Ruben Puga; Kaixiong Zhou; Li Zhang; |
| 351 | Fractional Budget Allocation for Influence Maximization Under General Marketing Strategies Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In general, the activation likelihood could be any non-decreasing function of the discount, whereas, our focus lies on the case when the activation likelihood is an affine function of the discount, potentially varying across different users. As this problem is shown to be NP-hard, we propose and analyze an efficient (1-1/e)-approximation algorithm. |
Akhil Bhimaraju; Eliot W. Robson; Lav R. Varshney; Abhishek K. Umrawal; |
| 352 | IEcons: A New Consensus Approach Using Multi-Text Representations for Clustering Task Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We compare our algorithm with others from the literature on five different textual datasets using several algorithm performance criteria. |
Karima Boutalbi; Rafika Boutalbi; Herv\'{e} Verjus; Kave Salamatian; David Telisson; Olivier Le Van; |
| 353 | Facets of Disparate Impact: Evaluating Legally Consistent Bias in Machine Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Leveraging current legal standards, we define bias through the lens of marginal benefits and objective testing with the novel metric Objective Fairness Index. |
Jarren Briscoe; Assefaw Gebremedhin; |
| 354 | Pairing Clustered Inverted Indexes with Κ-NN Graphs for Fast Approximate Retrieval Over Learned Sparse Representations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: At query time, each inverted list associated with a query term is traversed one block at a time in an arbitrary order, with the inner product between the query and summaries determining if a block must be evaluated. When a block is deemed promising, its documents are fully evaluated with a forward index. |
Sebastian Bruch; Franco Maria Nardini; Cosimo Rulli; Rossano Venturini; |
| 355 | Early Exit Strategies for Approximate K-NN Search in Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: A popular technique for making A-kNN search efficient is based on a two-level index, where the embeddings of documents are clustered offline and, at query processing, a fixed number N of clusters closest to the query is visited exhaustively to compute the result set.In this paper, we build upon state-of-the-art for early exit A-kNN and propose an unsupervised method based on the notion of patience, which can reach competitive effectiveness with large efficiency gains. |
Francesco Busolin; Claudio Lucchese; Franco Maria Nardini; Salvatore Orlando; Raffaele Perego; Salvatore Trani; |
| 356 | Bubble Sketch: A High-performance and Memory-efficient Sketch for Finding Top-k Items in Data Streams Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present Bubble Sketch, a compact algorithm that excels in both performance and accuracy. |
Lu Cao; Qilong Shi; Yuxi Liu; Hanyue Zheng; Yao Xin; Wenjun Li; Tong Yang; Yangyang Wang; Yang Xu; Weizhe Zhang; Mingwei Xu; |
| 357 | Scalable Unsupervised Feature Selection with Reconstruction Error Guarantees Via QMR Decomposition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, their scalability to large datasets remains a challenge, rendering common UFS methods impractical for such applications. To address this issue, we introduce QMR-FS, a greedy forward filtering approach that selects linearly independent features up to a specified relative tolerance, ensuring that any excluded features can be reconstructed from the retained set within this tolerance. |
Ciwan Ceylan; Kambiz Ghoorchian; Danica Kragic; |
| 358 | End-to-End Aspect Based Sentiment Analysis Using Graph Attention Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we investigate the capability of Graph Attention Network for extracting aspect and opinion terms. |
Abir Chakraborty; |
| 359 | Deep Noise-Aware Quality Loss for Speaker Verification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a Noise-Aware Quality Network designed to estimate a score based on speech quality and the presence of speech obscured by noise in real-world environments. |
Pantid Chantangphol; Theerat Sakdejayont; Monchai Lertsutthiwong; Tawunrat Chalothorn; |
| 360 | Professionalism-Aware Pre-Finetuning for Profitability Ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel approach to overcome current limitations in assessing and ranking investor opinions based on profitability. |
Chung-Chi Chen; Hiroya Takamura; Ichiro Kobayashi; Yusuke Miyao; |
| 361 | Empowering LLMs for Multi-Page Layout Generation Via Consistency-Oriented In-Context Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a novel framework called Multi-Page Layout Generation via Consistency-Oriented modeling (MuLCO) that capitalizes on in-context learning of LLMs without the need for training or fine-tuning. |
Mengyao Chen; Xinghua Zhang; Junhao Zhang; Quangang Li; Tingwen Liu; |
| 362 | PP4RNR: Popularity- and Position-Aware Contrastive Learning for Retrieval-Driven News Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing news recommendation systems often overlook the diversity of recommended content and exhibit popularity bias, resulting in suboptimal performance. To address this issue, this paper introduces a novel news recommendation approach, Popularity- and Position-Aware Contrastive Learning for Retrieval-Driven News Recommendation (PP4RNR). |
Wenwei Chen; Yewang Chen; |
| 363 | Distributed Boosting: An Enhancing Method on Dataset Distillation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Squeeze, Recover and Relabel (SRe2L) and Adversarial Prediction Matching (APM) are two advanced and efficient DD methods, yet their performance is moderate with lower volumes of distilled data. This paper proposes an ingenious improvement method, Distributed Boosting (DB), capable of significantly enhancing the performance of these two algorithms at low distillation volumes, leading to DB-SRe2L and DB-APM. |
Xuechao Chen; Wenchao Meng; Peiran Wang; Qihang Zhou; |
| 364 | MODRL-TA: A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the aforementioned issues, this paper propose a multi-objective deep reinforcement learning framework consisting of multi-objective Q-learning (MOQ), a decision fusion algorithm (DFM) based on the cross-entropy method(CEM), and a progressive data augmentation system (PDA). |
Peng Cheng; Huimu Wang; Jinyuan Zhao; Yihao Wang; Enqiang Xu; Yu Zhao; Zhuojian Xiao; Songlin Wang; Guoyu Tang; Lin Liu; Sulong Xu; |
| 365 | CMG: A Causality-enhanced Multi-view Graph Model for Stock Trend Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, current methods also overlook the commonalities and differences between stock relations. To address these shortcomings, we propose a causal-enhanced multi-view temporal graph model, named CMG. |
Xi Cheng; Liang Wang; Yunan Zeng; Qiang Liu; |
| 366 | Exploiting Preferences in Loss Functions for Sequential Recommendation Via Weak Transitivity Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Such a binary label assignment is generally limited to assuring a higher recommendation score of the positive item, neglecting potential structures induced by varying preferences between other unobserved items. To alleviate this issue, we propose a novel method that extends original objectives to explicitly leverage the different levels of preferences as relative orders between their scores. |
Hyunsoo Chung; Jungtaek Kim; Hyungeun Jo; Hyungwon Choi; |
| 367 | MSG-Chart: Multimodal Scene Graph for ChartQA Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Automatic Chart Question Answering (ChartQA) is challenging due to the complex distribution of chart elements with patterns of the underlying data not explicitly displayed in charts. To address this challenge, we design a joint multimodal scene graph for charts to explicitly represent the relationships between chart elements and their patterns. |
Yue Dai; Soyeon Caren Han; Wei Liu; |
| 368 | Quantifying Uncertainty in Neural Networks Through Residuals Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose the posthoc HetGP on the residuals of the trained deterministic neural network to obtain both epistemic and aleatoric uncertainty. |
Dalavai Udbhav Mallanna; Rini Smita Thakur; Rajeev Ranjan Dwivedi; Vinod K. Kurmi; |
| 369 | A Systematic Evaluation of Generated Time Series and Their Effects in Self-Supervised Pretraining Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Self-supervised Pretrained Models (PTMs) have demonstrated remarkable performance in computer vision and natural language processing tasks. |
Audrey Der; Chin-Chia Michael Yeh; Xin Dai; Huiyuan Chen; Yan Zheng; Yujie Fan; Zhongfang Zhuang; Vivian Lai; Junpeng Wang; Liang Wang; Wei Zhang; Eamonn Keogh; |
| 370 | Quantum Inverse Contextual Vision Transformers (Q-ICVT): A New Frontier in 3D Object Detection for AVs Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, the fusion process encounters challenges in detecting distant objects due to the disparity between the high resolution of cameras and the sparse data from LiDAR. Insufficient integration of global perspectives with local-level details results in sub-optimal fusion performance.To address this issue, we have developed an innovative two-stage fusion process called Quantum Inverse Contextual Vision Transformers (Q-ICVT). |
Sanjay Bhargav Dharavath; Tanmoy Dam; Supriyo Chakraborty; Prithwiraj Roy; Aniruddha Maiti; |
| 371 | Boosting Large Language Models with Socratic Method for Conversational Mathematics Teaching Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we focus on improving the capability of mathematics teaching via a Socratic teaching-based LLM (SocraticLLM), which guides learners toward profound thinking with clarity and self-discovery via conversation. |
Yuyang Ding; Hanglei Hu; Jie Zhou; Qin Chen; Bo Jiang; Liang He; |
| 372 | Efficient Global Message Passing for Heterophilous Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In addition, earlier studies show that GNNs can be outperformed by Multi-Layer Perceptrons on heterophilous graphs, indicating insufficient exploitation of node feature information. To address these limitations, we propose Prototype Mediated GNN (PM-GNN), a novel framework which efficiently captures global feature information using class prototypes. |
Yanfei Dong; Mohammed Haroon Dupty; Lambert Deng; Yong Liang Goh; Wee Sun Lee; |
| 373 | The Factuality of Large Language Models in The Legal Domain Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper investigates the factuality of large language models (LLMs) as knowledge bases in the legal domain, in a realistic usage scenario: we allow for acceptable variations in the answer, and let the model abstain from answering when uncertain. |
Rajaa El Hamdani; Thomas Bonald; Fragkiskos D. Malliaros; Nils Holzenberger; Fabian Suchanek; |
| 374 | Accurate Embedding-based Log Determinant Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Many tangible and intangible objects are represented as itemsets; i.e., composition of individual items. In this paper, we address the problem of finding the embedding of such items so as to use those embeddings in tasks like missing item prediction. |
Daye Eun; Byungkon Kang; |
| 375 | Effective Clean-Label Backdoor Attacks on Graph Neural Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Specifically, we employ a novel method to select effective poisoned samples belonging to the target class. |
Xuanhao Fan; Enyan Dai; |
| 376 | General Time Transformer: An Encoder-only Foundation Model for Zero-Shot Multivariate Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present General Time Transformer (GTT), an encoder-only style foundation model for zero-shot multivariate time series forecasting. |
Cheng Feng; Long Huang; Denis Krompass; |
| 377 | Retrogressive Document Manipulation of US Federal Environmental Websites Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present an analysis of the intersection of the EDGI dataset and the ORCAS dataset, matching changes on federal environmental webpages with their associated queries. |
Lesley Frew; Michael L. Nelson; Michele C. Weigle; |
| 378 | Beyond Language Bias: Overcoming Multimodal Shortcut and Distribution Biases for Robust Visual Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we address the issue of bias in VQA task by targeting the various sources of bias. |
Jingliang Gu; Zhixin Li; |
| 379 | RECE: Reduced Cross-Entropy Loss for Large-Catalogue Sequential Recommenders Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Using a GPU-efficient locality-sensitive hashing-like algorithm for approximating large tensor of logits, this paper introduces a novel RECE (REduced Cross-Entropy) loss. |
Danil Gusak; Gleb Mezentsev; Ivan Oseledets; Evgeny Frolov; |
| 380 | Enhancing CTR Prediction Through Sequential Recommendation Pre-training: Introducing The SRP4CTR Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these approaches tend to ignore the additional inference costs and do not consider how to transfer the effective information from the pre-trained models for specific estimated items in CTR prediction. In this paper, we propose a Sequential Recommendation Pre-training framework for CTR prediction (SRP4CTR) to tackle the above problems. |
Ruidong Han; Qianzhong Li; He Jiang; Rui Li; Yurou Zhao; Xiang Li; Wei Lin; |
| 381 | A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Bottleneck identification is a challenging task in network analysis, especially when the network is not fully specified. To address this task, we develop a unified online learning framework based on combinatorial semi-bandits that performs bottleneck identification alongside learning the specifications of the underlying network. |
Fazeleh Hoseini; Niklas \r{A}kerblom; Morteza Haghir Chehreghani; |
| 382 | Nonparametric Estimation of Non-Smooth Divergences Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we show empirically that the ensemble estimation approach for smooth functionals can be applied to less smooth functionals and obtain good convergence rates, suggesting a gap in current theory. |
Mina Mahbub Hossain; Alan Wisler; Kevin R. Moon; |
| 383 | Enhanced Retrieval Effectiveness Through Selective Query Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a two-step query reformulation framework for generating and selecting optimal target query variants which not only achieve higher retrieval performance but also preserve the original query’s information need. |
Seyed Mohammad Hosseini; Negar Arabzadeh; Morteza Zihayat; Ebrahim Bagheri; |
| 384 | Application of Large Language Models in Chemistry Reaction Data Extraction and Cleaning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a paradigm that leverages prompt-tuning, fine-tuning techniques, and a verifier to check the extracted information. |
Xiaobao Huang; Mihir Surve; Yuhan Liu; Tengfei Luo; Olaf Wiest; Xiangliang Zhang; Nitesh V. Chawla; |
| 385 | LEX-GNN: Label-Exploring Graph Neural Network for Accurate Fraud Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we propose the Label-Exploring Graph Neural Network (LEX-GNN), designed to enhance fraud detection by actively leveraging labeled node information. |
Woochang Hyun; Insoo Lee; Bongwon Suh; |
| 386 | GraphVAE: Unveiling Dynamic Stock Relationships with Variational Autoencoder-based Factor Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, traditional factor models and recent deep learning approaches either overlook the relationships among stocks or rely on static, predefined ones, which hampers their representational power and hinders their ability to dynamically adapt to market changes. To overcome this limitation, we introduce a novel dynamic factor model named GraphVAE. |
Yulong Jia; Guanxing Li; Ganlong Zhao; Xiangru Lin; Guanbin Li; |
| 387 | Covariate Ordered Systematic Sampling As An Improvement to Randomized Controlled Trials Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a variant of systematic sampling called Covariate Ordered Systematic Sampling (COSS). |
Deddy Jobson; Yilin Li; Naoki Nishimura; Koya Ohashi; Jie Yang; Takeshi Matsumoto; |
| 388 | CXSimulator: A User Behavior Simulation Using LLM Embeddings for Web-Marketing Campaign Assessment Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents the Customer Experience (CX) Simulator, a novel framework designed to assess the effects of untested web-marketing campaigns through user behavior simulations. |
Akira Kasuga; Ryo Yonetani; |
| 389 | MARS: Matching Attribute-aware Representations for Text-based Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, existing text-based recommender models still struggle with two key challenges: (i) representing users and items with multiple attributes, and (ii) matching items with complex user interests. To address these challenges, we propose a novel model, Matching Attribute-aware Representations for Text-based Sequential Recommendation (MARS). |
Hyunsoo Kim; Junyoung Kim; Minjin Choi; Sunkyung Lee; Jongwuk Lee; |
| 390 | Towards Better Utilization of Multiple Views for Bundle Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our examination of different role (main or sub-views) combinations of the views reveals two key observations: (1) the best combination varies across target users (i.e., who receive recommendations), and (2) the U-I view is relatively weak as the main role. Driven by these observations, we propose PET, which synergizes the three views through (1) personalized view weighting, (2) U-I view enhancement, and (3) two-pronged contrastive learning. |
Kyungho Kim; Sunwoo Kim; Geon Lee; Kijung Shin; |
| 391 | Flexi-clique: Exploring Flexible and Sub-linear Clique Structures Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel degree-based relaxation model called Flexi-clique, where the degree constraint is adjusted sub-linearly based on the subgraph size. |
Song Kim; Junghoon Kim; Susik Yoon; Jungeun Kim; |
| 392 | Intricate Object Detection in Self Driving Environments with Edge-Adaptive Depth Estimation(EADE) Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This has limitations for reliable object identification in autonomous driving environments containing a variety of objects, which is a challenge for clear criteria-based object avoidance and collision protection. To overcome these limitations, this paper proposes Edge-Adaptive Depth Estimation(EADE). |
Subi Kim; Jieun Kang; Yongik Yoon; |
| 393 | Error Detection and Constraint Recovery in Hierarchical Multi-Label Classification Without Prior Knowledge Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, such work assumes the existence of such constraints a-priori. In this paper, we relax this strong assumption and present an approach based on Error Detection Rules (EDR) that allow for learning explainable rules about the failure modes of machine learning models. |
Joshua Shay Kricheli; Khoa Vo; Aniruddha Datta; Spencer Ozgur; Paulo Shakarian; |
| 394 | Learning Prompt-Level Quality Variance for Cost-Effective Text-to-Image Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper explores the relationship between the generation quality of text-to-image models and the linguistic features of input prompts by measuring the performance of state-of-the-art models using five different prompt datasets each with its distinctive features. Motivated by our empirical observations, we propose a novel approach that assigns each prompt to its best-performing model based on quality prediction. |
Dongkeun Lee; Wonjun Lee; |
| 395 | HypMix: Hyperbolic Representation Learning for Graphs with Mixed Hierarchical and Non-hierarchical Structures Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a new hyperbolic representation learning model that can handle complex hierarchical structures and also learn the representation of both hierarchical and non-hierarchic structures. |
Eric W. Lee; Bo Xiong; Carl Yang; Joyce C. Ho; |
| 396 | Post-Training Embedding Enhancement for Long-Tail Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This disparity results in high-quality embeddings for popular (head) items, but lower-quality embeddings for unpopular (tail) items, leading to less accurate recommendations for the latter. Our observations confirm that embeddings of tail items often exhibit (1) magnitudes (i.e., norms) that are less reflective of actual popularity and (2) directions that are less effective in capturing user preferences, compared to those of head items.To address this issue, we propose EDGE, a post-training embedding enhancement method for long-tail recommendations. |
Geon Lee; Kyungho Kim; Kijung Shin; |
| 397 | Document-Level Relation Extraction Based on Heterogeneous Graph Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an efficient Document-Level Relation Extraction Model based on Heterogeneous Graph Reasoning (HGR-DREM), which enables relation extraction more accurate. |
Dong Li; Miao Li; Zhilei Lei; Baoyan Song; Xiaohuan Shan; |
| 398 | Beyond Aggregation: Efficient Federated Model Consolidation with Heterogeneity-Adaptive Weights Diffusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In response, we introduce a novel method, FedDiff, which utilizes diffusion models for generating model weights on FL servers, replacing traditional aggregation methods. |
Jiaqi Li; Xiaoyang Qu; Wenbo Ding; Zihao Zhao; Jianzong Wang; |
| 399 | ChefFusion: Multimodal Foundation Model Integrating Recipe and Food Image Generation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: None of these approaches integrate all modalities simultaneously. To address this gap, we introduce a novel food computing foundation model that achieves true multimodality, encompassing tasks such as t2t, t2i, i2t, it2t, and t2ti. |
Peiyu Li; Xiaobao Huang; Yijun Tian; Nitesh V. Chawla; |
| 400 | Coresets for Deletion-Robust K-Center Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we study the deletion-robust version of the problem. |
Ruien Li; Yanhao Wang; Michael Mathioudakis; |
| 401 | Knowledge-enhanced Dynamic Modeling Framework for Multi-Behavior Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These limitations can lead to model performance degradation and affect user satisfaction. To address these issues, we propose a Knowledge-enhanced Dynamic Modeling framework for Multi-Behavior Recommendation (KDMBR). |
Xiujuan Li; Nan Wang; Jin Zeng; Yingli Zhong; Zhonghui Shen; |
| 402 | Improving Prompt-based News Recommendation with Individual Template and Customized Answer Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose an Automatic Prompt based NR (AutoPNR) scheme, which automatically generates individual templates for users according to their potential interests, and customized answer words w.r.t. the topics of candidate news. |
Yijiang Li; Jun Wu; |
| 403 | Ocean Significant Wave Height Estimation with Spatio-temporally Aware Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, due to limited observational technology and high costs, the scarcity of real-world data restricts the potential of machine learning models. To overcome these limitations, we propose an ocean SWH estimation framework, namely Orca. |
Zhe Li; Ronghui Xu; Jilin Hu; Zhong Peng; Xi Lu; Chenjuan Guo; Bin Yang; |
| 404 | Effective Job-market Mobility Prediction with Attentive Heterogeneous Knowledge Learning and Synergy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we push forward to exploit the heterogeneous relational knowledge among the job market structures by proposing a model namely Attentive Heterogeneous Knowledge Learning and Synergy (AHKLS). |
Sida Lin; Zhouyi Zhang; Yankai Chen; Chenhao Ma; Yixiang Fang; Shan Dai; Guangli Lu; |
| 405 | RecPrompt: A Self-tuning Prompting Framework for News Recommendation Using Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces RecPrompt, the first self-tuning prompting framework for news recommendation, leveraging the capabilities of LLMs to perform complex news recommendation tasks. |
Dairui Liu; Boming Yang; Honghui Du; Derek Greene; Neil Hurley; Aonghus Lawlor; Ruihai Dong; Irene Li; |
| 406 | Enhanced Privacy Bound for Shuffle Model with Personalized Privacy Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We develop a more precise analysis, which yields a general and tighter bound for arbitrary DP mechanisms. |
Yixuan Liu; Yuhan Liu; Li Xiong; Yujie Gu; Hong Chen; |
| 407 | Scalable and Adaptive Spectral Embedding for Attributed Graph Clustering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce Scalable and Adaptive Spectral Embedding (SASE), a simple attributed graph clustering method devoid of parameter learning. |
Yunhui Liu; Tieke He; Qing Wu; Tao Zheng; Jianhua Zhao; |
| 408 | Multi-DSI: Non-deterministic Identifier and Concept Alignment for Differentiable Search Index Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing methods face two issues: (1) when a document is represented by a single semantic ID, the retrieval model may fail to capture the multifaceted and complex content of the document; and (2) when the generated training data exhibits semantic ambiguity, the retrieval model may struggle to distinguish the differences in the content of similar documents. To address these issues, we propose Multi-DSI to (1) offer multiple non-deterministic semantic identifiers and (2) align the concepts of queries and documents to avoid ambiguity. |
Yu-Ze Liu; Jyun-Yu Jiang; Pu-Jen Cheng; |
| 409 | The Elusiveness of Detecting Political Bias in Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study challenges the prevailing approach of measuring political leanings in Large Language Models (LLMs) through direct questioning. |
Riccardo Lunardi; David La Barbera; Kevin Roitero; |
| 410 | Ask or Recommend: An Empirical Study on Conversational Product Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To fill the gap, we conduct an empirical study in this paper. |
Heli Ma; Jie Zou; Mohammad Aliannejadi; Evangelos Kanoulas; Yi Bin; Yang Yang; |
| 411 | An Explainable Multi-atlas Fusion Model Based on Spatial Overlap for ASD Diagnosis Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, current research either focus on a single atlas or a simple matrix concatenation combination, neglecting the complex and spatial relationship among the brain regions in different atlases. To tackle this weakness, in this paper, we propose a novel multi-atlas time-series feature fusion model with three steps based on spatial overlap proportion of brain regions to obtain an explainable representation of brain networks, which aims to achieve excellent diagnosis of ASD/TC. |
Yuefeng Ma; Xiaochen Mu; Tengfei Zhang; |
| 412 | ToxVI: A Multimodal LLM-based Framework for Generating Intervention in Toxic Code-Mixed Videos Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We are introducing a Toxic Code-Mixed Intervention Video benchmark dataset (ToxCMI), comprising 1697 code-mixed toxic video utterances sourced from YouTube. |
Krishanu Maity; A.S. Poornash; Sriparna Saha; Kitsuchart Pasupa; |
| 413 | Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation? Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Abstract: Generally, items with missing modalities are dropped in multimodal recommendation. However, with this work, we question this procedure, highlighting that it would further damage … |
Daniele Malitesta; Emanuele Rossi; Claudio Pomo; Tommaso Di Noia; Fragkiskos D. Malliaros; |
| 414 | SOUP: A Unified Shopping Query Suggestion Framework to Optimize Language Model with User Preference Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a comprehensive framework for the shopping query suggestion that effectively addresses the shortcomings of existing approaches. |
Xu Meng; Zhaohui Luo; Xinxin Wang; Wen Jiang; Wei Ning; Shuhan Qi; |
| 415 | How to Leverage Personal Textual Knowledge for Personalized Conversational Information Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we explore and test several ways to select knowledge from PTKB and use it for query reformulation by using a large language model (LLM). |
Fengran Mo; Longxiang Zhao; Kaiyu Huang; Yue Dong; Degen Huang; Jian-Yun Nie; |
| 416 | Channel-Aware Low-Rank Adaptation in Time Series Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: To balance the two strategies, we present a channel-aware low-rank adaptation method to condition CD models on identity-aware individual components. |
Tong Nie; Yuewen Mei; Guoyang Qin; Jian Sun; Wei Ma; |
| 417 | Extended Japanese Commonsense Morality Dataset with Masked Token and Label Enhancement Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The Extended JCM (eJCM) has grown from the original 13,975 sentences to 31,184 sentences using our proposed sentence expansion method called Masked Token and Label Enhancement (MTLE). |
Takumi Ohashi; Tsubasa Nakagawa; Hitoshi Iyatomi; |
| 418 | Progressive Label Disambiguation for Partial Label Learning in Homogeneous Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Subsequently, we propose a new PLD-Graph algorithm for PLL in homogeneous graphs with scarce labels. |
Rajat Patel; Aakarsh Malhotra; Sudipta Modak; Siddharth Yeramsetty; |
| 419 | MEDFuse: Multimodal EHR Data Fusion with Masked Lab-Test Modeling and Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose MEDFuse, a Multimodal EHR Data Fusion framework that incorporates masked lab-test modeling and large language models (LLMs) to effectively integrate structured and unstructured medical data. |
Phan Nguyen Minh Thao; Cong-Tinh Dao; Chenwei Wu; Jian-Zhe Wang; Shun Liu; Jun-En Ding; David Restrepo; Feng Liu; Fang-Ming Hung; Wen-Chih Peng; |
| 420 | Improving German News Clustering with Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose text augmentation methods and use contrastive learning to cluster daily growing full-length German news articles. |
Piriyakorn Piriyatamwong; Saikishore Kalloori; Fabio Z\{u}nd; |
| 421 | Hol-Light: A Holistic Framework for Efficient and Dynamic Traffic Signal Management Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The question arises: Can a single approach effectively tackle all these issues concurrently? We propose a holistic framework, Hol-Light, that can effectively solve all these issues. |
Siyao Qiao; Jia Wu; |
| 422 | ILTS: Inducing Intention Propagation in Decentralized Multi-Agent Tasks with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Conventional decentralized approaches have struggled to effectively induce cooperative behaviors between agents. We propose a novel hierarchical framework that synergistically combines large language models (LLMs) and deep reinforcement learning to address this challenge. |
Xihe Qiu; Haoyu Wang; Xiaoyu Tan; Chao Qu; |
| 423 | ExPrompt: Augmenting Prompts Using Examples As Modern Baseline for Stance Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we avoid the costly training or fine-tuning of models by reusing pre-trained large language models together with few-shot in-context learning. |
Umair Qudus; Michael R\{o}der; Daniel Vollmers; Axel-Cyrille Ngonga Ngomo; |
| 424 | A Mixture of Experts in Forecasting Student Performance in Classroom Programming Activities Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Forecasting student scores based on their programming activities is challenging because the accuracy of different predictive models often varies throughout these activities. To address this challenge, we introduce a novel framework utilizing Mixture of Experts (MoE). |
Moqsadur Rahman; Monika Akbar; Justice T. Walker; M. Shahriar Hossain; |
| 425 | Compressed Models Are NOT Miniature Versions of Large Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We compare compressed models with corresponding large neural models using four model characteristics: prediction errors, data representation, data distribution, and vulnerability to adversarial attack. |
Rohit Raj Rai; Rishant Pal; Amit Awekar; |
| 426 | LayerPlexRank: Exploring Node Centrality and Layer Influence Through Algebraic Connectivity in Multiplex Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces LayerPlexRank, an algorithm that simultaneously assesses node centrality and layer influence in multiplex networks using algebraic connectivity metrics. |
Hao Ren; Jiaojiao Jiang; |
| 427 | Generative AI for Energy: Multi-Horizon Power Consumption Forecasting Using Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We leverage generative NLP-based models, specifically Transformer-Based models, for multi-horizon univariate and multivariate power consumption forecasting. |
Kevin Roitero; Gianluca D’Abrosca; Andrea Zancola; Vincenzo Della Mea; Stefano Mizzaro; |
| 428 | Scalable Expressiveness Through Preprocessed Graph Perturbations Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Despite their expressive power, this approach does not scale well on larger graphs. To address this scalability issue, we introduce Scalable Expressiveness through Preprocessed Graph Perturbation (SE2P). |
Danial Saber; Amirali Salehi-Abari; |
| 429 | EDGE: Evaluation Framework for Logical Vs. Subgraph Explanations for Node Classifiers on Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose a framework called EDGE to evaluate diverse knowledge graph explanations, assessing logical rule-based and subgraph-based explanations by various explainers in terms of prediction accuracy and fidelity to the Graph Neural Network (GNN) model. |
Rupesh Sapkota; Dominik K\{o}hler; Stefan Heindorf; |
| 430 | Empowering Traffic Speed Prediction with Auxiliary Feature-Aided Dependency Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our study, we present the individual spatio-temporal (IST) dependencies on flow and speed, and characterize three types of IST-dependencies with the flow-to-flow, speed-to-speed, and flow-to-speed graphs. |
Dong-hyuk Seo; Jiwon Son; Namhyuk Kim; Won-Yong Shin; Sang-Wook Kim; |
| 431 | QuestGen: Effectiveness of Question Generation Methods for Fact-Checking Applications Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent approaches have demonstrated that decomposing claims into relevant questions to gather evidence enhances the efficiency of the fact-checking process. In this paper, we provide empirical evidence showing that this question decomposition can be effectively automated. |
Ritvik Setty; Vinay Setty; |
| 432 | M2IoU: A Min-Max Distance-based Loss Function for Bounding Box Regression in Medical Imaging Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Min-Max IoU (M2IoU) loss function by introducing a new min-max-based penalty term in the loss equation, between the predicted box and the ground truth coordinates. |
Anurag Kumar Shandilya; Kalash Shah; Bhavik Kanekar; Akshat Gautam; Pavni Tandon; Ganesh Ramakrishnan; Kshitij Jadhav; |
| 433 | Learning Links for Adaptable and Explainable Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a framework for constructing a graph that integrates human knowledge with user activity data analysis. |
Jianqiang Shen; Yuchin Juan; Ping Liu; Wen Pu; Shaobo Zhang; Qianqi Shen; Liangjie Hong; Wenjing Zhang; |
| 434 | Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Instead of dismissing the role of incremental learning, we attribute the lack of anticipated performance enhancement to a mismatch between the LLM4Rec architecture and incremental learning: LLM4Rec employs a single adaptation module for learning recommendations, limiting its ability to simultaneously capture long-term and short-term user preferences in the incremental learning context. To test this speculation, we introduce a Long- and Short-term Adaptation-aware Tuning (LSAT) framework for incremental learning in LLM4Rec. |
Tianhao Shi; Yang Zhang; Zhijian Xu; Chong Chen; Fuli Feng; Xiangnan He; Qi Tian; |
| 435 | Tabularis Revilio: Converting Text to Tables Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present Revilio, a novel neurosymbolic system for reconstructing tables when their column boundaries have been lost. |
Mukul Singh; Gust Verbruggen; Vu Le; Sumit Gulwani; |
| 436 | Enhancing SPARQL Generation By Triplet-order-sensitive Pre-training Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although pre-trained language models like T5 have achieved significant success in the Text-to-SPARQL task, their generated outputs still exhibit notable errors specific to the SPARQL language, such as triplet flips. To address this challenge and further improve the performance, we propose an additional pre-training stage with a new objective, Triplet Order Correction (TOC), along with the commonly used Masked Language Modeling (MLM), to collectively enhance the model’s sensitivity to triplet order and SPARQL syntax. |
Chang Su; Jiexing Qi; He Yan; Kai Zou; Zhouhan Lin; |
| 437 | Improved Estimation of Ranks for Learning Item Recommenders with Negative Sampling Related Papers Related Patents Related Grants Related Venues Related Experts View Abstract: In recommendation systems, there has been a growth in the number of recommendable items (# of movies, music, products). When the set of recommendable items is large, training and … |
Anushya Subbiah; Steffen Rendle; Vikram Aggarwal; |
| 438 | Revealing The Power of Masked Autoencoders in Traffic Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, this field faces challenges related to data scarcity and model stability, which results in limited performance improvement. To address these issues, we propose Spatial-Temporal Masked AutoEncoders (STMAE), a plug-and-play framework designed to enhance existing spatial-temporal models on traffic prediction. |
Jiarui Sun; Yujie Fan; Chin-Chia Michael Yeh; Wei Zhang; Girish Chowdhary; |
| 439 | Spatio-Temporal Sequence Modeling for Traffic Signal Control Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, in this paper, we present an innovative formulation of the offline TSC problem by introducing a spatio-temporal graph to model the historical Markov Decision Process sequences across all traffic signals within the road network. Along this line, we propose STLight, a novel spatio-temporal sequence modeling approach to predict optimal actions for the signals from historical data, accounting for the inherent inter-dependencies among them. |
Qian Sun; Le Zhang; Jingbo Zhou; Rui Zha; Yu Mei; Chujie Tian; Hui Xiong; |
| 440 | SurvReLU: Inherently Interpretable Survival Analysis Via Deep ReLU Networks Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we bridge the gap between previous deep survival models and traditional tree-based survival models through deep rectified linear unit (ReLU) networks. |
Xiaotong Sun; Peijie Qiu; Shengfan Zhang; |
| 441 | STAR: Sparse Text Approach for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we propose to adapt Learned Sparse Retrieval, an emerging approach in IR, to text-centric content-based recommendations, leveraging the strengths of transformer models for an efficient and interpretable user-item matching. |
Anna Tigunova; Ghazaleh Haratinezhad Torbati; Andrew Yates; Gerhard Weikum; |
| 442 | Harnessing Empathy and Ethics for Relevance Detection and Information Categorization in Climate and COVID-19 Tweets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we aim to understand the general public perception of societal issues related to the current climate crisis and the COVID-19 pandemic on Twitter (X). |
Apoorva Upadhyaya; Wolfgang Nejdl; Marco Fisichella; |
| 443 | Over-penalization for Extra Information in Neural IR Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents our analysis of neural IR models, particularly focusing on over-penalization for extra information (OPEX) – a phenomenon where addition of a sentence to a document causes an unreasonable decline in the document rank. |
Kota Usuha; Makoto P. Kato; Sumio Fujita; |
| 444 | Osprey 🪶: A Reference Framework for Online Grooming Detection Via Neural Models and Conversation Features Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we foremost contribute Osprey, an open-source library to support a standard pipeline and experimental details, incorporating canonical neural models and a variety of vector representation learning for conversations while accommodating new models and training datasets. |
Hamed Waezi; Reza Barzegar; Hossein Fani; |
| 445 | Enhancing Temporal and Geographical Named Entity Recognition in Chinese Ancient Texts with External Time-series Knowledge Bases Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a temporal and geographic extraction model for ancient Chinese text, enhanced by time-series external knowledge base. |
Xiaotong Wang; Xuanning Liu; Shuai Zhong; Xinming Chen; Bin Wu; |
| 446 | FashionLOGO: Prompting Multimodal Large Language Models for Fashion Logo Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The emerging Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in both visual and textual understanding. Inspired by this, we propose an approach, FashionLOGO, to explore how to prompt MLLMs to generate appropriate text for product images, which can help visual models achieve better logo embeddings. |
Zhen Wang; Da Li; Yulin Su; Min Yang; Minghui Qiu; Walton Wang; |
| 447 | Does Knowledge Localization Hold True? Surprising Differences Between Entity and Relation Perspectives in Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To further elucidate the differences between entity and relational knowledge, we employ causal analysis to investigate how relational knowledge is stored in pre-trained models. |
Yifan Wei; Xiaoyan Yu; Yixuan Weng; Huanhuan Ma; Yuanzhe Zhang; Jun Zhao; Kang Liu; |
| 448 | DP-FedFace: Privacy-Preserving Facial Recognition in Real Federated Scenarios Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose DP-FedFace, a privacy framework specifically designed for a realistic scenario where each client contains only the owner’s face images (one identity per client). |
Wenjing Wang; Si Li; |
| 449 | Attentional Neural Integral Equation for Temporal Knowledge Graph Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle the above limitations, this paper utilizes Attentional Neural Integral Equation for TKGF (tIE), enabling the global interaction between query-related historical graph sequences. To achieve this, we employ the Relational Graph Convolutional Network and Fourier-type Transformer to model the graph structure and temporal evolution of TKG. |
Likang Xiao; Zijie Chen; Richong Zhang; Junfan Chen; |
| 450 | MPHDetect: Multi-View Prompting and Hypergraph Fusion for Malevolence Detection in Dialogues Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although existing studies have shown promising performance by modeling interaction patterns from dialogue history, various malevolence-invoking factors, such as fine-grained emotions, evolving topics and user profiles, are often overlooked. To comprehensively consider these factors, we propose a hypergraph fusion model by employing multi-view LLM-driven prompts for malevolence detection in dialogues. |
Bo Xu; Xuening Qiao; Hongfei Lin; Linlin Zong; |
| 451 | Exploring High-Order User Preference with Knowledge Graph for Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recent knowledge-aware recommendation methods, which utilize graph neural networks and contrastive learning, underestimate two issues: 1) The neglect of modeling the latent relationships between users and entities; 2) The insufficiency of traditional cross-view contrastive learning whose domain is incapable of covering all nodes in a graph. To address these issues, we propose a novel model named Knowledge-aware User Preference Network (KUPN). |
Caijun Xu; Fuwei Zhang; Zhao Zhang; Fuzhen Zhuang; Rui Liu; |
| 452 | SparseBF: Enhancing Scalability and Efficiency for Sparsely Filled Privacy-Preserving Record Linkage Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address this challenge, we have observed that BF encodings often exhibit sparsely filled patterns. Leveraging this insight, we introduce SparseBF, a scalable data structure that is space-optimized and maintains fast computation speed for PPRL. |
Han Xu; Yuhong Shao; Kareem Benaissa; Yutong Li; |
| 453 | CrossPred: A Cross-City Mobility Prediction Framework for Long-Distance Travelers Via POI Feature Matching Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, due to the mixture of mobility pattern from both cities, directly applying the model trained in the source city may lead to negative transfer in the target city. To tackle these issues, in this paper, we conceive and implement a novel framework called CrossPred to predict the cross-city mobility of long-distance travelers in the target city. |
Shuai Xu; Donghai Guan; |
| 454 | Enhancing Content-based Recommendation Via Large Language Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a ‘plugin’ semantic knowledge transferring method LoID, which includes two major components: (1) LoRA-based large language model pretraining to extract multi-aspect semantic information; (2) ID-based contrastive objective to align their feature spaces. |
Wentao Xu; Qianqian Xie; Shuo Yang; Jiangxia Cao; Shuchao Pang; |
| 455 | Learning Counterfactual Explanations with Intervals for Time-series Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Since features in the time series are temporally dependent, interpretability is improved by considering intervals where the counterfactual can deviate from the original instance. |
Akihiro Yamaguchi; Ken Ueno; Ryusei Shingaki; Hisashi Kashima; |
| 456 | GeoReasoner: Reasoning On Geospatially Grounded Context For Natural Language Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, these methods face two significant challenges: i) they do not generalize well to unseen geospatial scenarios, and ii) they overlook the importance of integrating geospatial context from geographical databases with linguistic information from the Internet. To handle these challenges, we propose GeoReasoner, a language model capable of reasoning on geospatially grounded natural language. |
Yibo Yan; Joey Lee; |
| 457 | The Effect of Icon Semantic Distance on Preschool Children’s Information Search: Evidence from An Eye-Tracking Study Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we apply semantic distance to measure the explicitness of icons in children-oriented book search, utilizing self-developed icons tailored for indexing picture books. |
Jiaqi Yang; Pianran Wang; |
| 458 | Multi-Scale Contrastive Attention Representation Learning for Encrypted Traffic Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes MsETC, a multi-scale contrastive attention representation learning method for encrypted traffic classification. |
Shuo Yang; Xinran Zheng; Jinze Li; Jinfeng Xu; Edith C. H. Ngai; |
| 459 | You Can’t Ignore Either: Unifying Structure and Feature Denoising for Robust Graph Learning Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we develop a unified graph denoising (UGD) framework to unravel the deadlock between structure and feature denoising. |
Tianmeng Yang; Jiahao Meng; Min Zhou; Yaming Yang; Yujing Wang; Xiangtai Li; Yunhai Tong; |
| 460 | CAG: A Consistency-Adaptive Text-Image Alignment Generation for Joint Multimodal Entity-Relation Extraction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Consistency-adaptive text-image Alignment Generation (CAG) framework for various text-image consistency scenarios. |
Xinjie Yang; Xiaocheng Gong; Binghao Tang; Yang Lei; Yayue Deng; Huan Ouyang; Gang Zhao; Lei Luo; Yunling Feng; Bin Duan; Si Li; Yajing Xu; |
| 461 | Contrastive Disentangled Representation Learning for Debiasing Recommendation with Uniform Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel disentangled framework, named CDLRec, for learning unbiased representations, leveraging uniform data as supervisory signal for disentangling. |
Xinxin Yang; Zhen Liu; Xiaoman Lu; Yafan Yuan; Sibo Lu; Yibo Gao; |
| 462 | Robust Heterophily Graph Learning Via Uniformity Augmentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel method, Robust Heterophily Graph Learning via Uniformity Augmentation (RHGL-UA), which incorporates uniformity in the augmentation process through controlled random perturbations. |
Xusheng Yang; Zhengyu Chen; Yuexian Zou; |
| 463 | Multi-Stage Refined Visual Captioning for Baidu Ad Creatives Generation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Large Language Models (LLM) and Large Multimodal Models (LMM) has demonstrated promising performance in cross-modal understanding and generation. In light of this, we propose a Chinese visual captioning pipeline for the synthesis of high-quality data. |
Yi Yang; Xinyu Zhao; Kang Zhao; Zhipeng Jin; Wen Tao; Lin Liu; Shuanglong Li; |
| 464 | BART-based Hierarchical Attentional Network for Sentence Ordering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a novel BART-based Hierarchical Attentional Ordering Network (BHAONet), aiming to address the coherence modeling challenge within paragraphs, which stands as a cornerstone in comprehension, generation, and reasoning tasks. |
Yiping Yang; Baiyun Cui; Yingming Li; |
| 465 | Span Confusion Is All You Need for Chinese Spelling Correction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we contribute a novel approach of constructing confusion corpus, which can automatically generate high-quality spelling errors. |
Dezhi Ye; Haomei Jia; Bowen Tian; Jie Liu; Haijin Liang; Jin Ma; Wenmin Wang; |
| 466 | Automation of Text-Based Economic Indicator Construction: A Pilot Exploration on Economic Policy Uncertainty Index Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper seeks to explore the reliability of LLM-suggested keywords in the automatic construction of the Economic Policy Uncertainty (EPU) index. |
Hsiu-Hsuan Yeh; Yu-Lieh Huang; Ziho Park; Chung-Chi Chen; |
| 467 | Prioritized Binary Transformation Method for Efficient Multi-label Classification of Data Streams with Many Labels Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel online approach: the Prioritized Binary Transformation (PBT) method, which can classify data with large numbers of labels by ordering the labels using Principal Component Analysis (PCA) within a fixed-size window. |
Onur Yildirim; Sepehr Bakhshi; Fazli Can; |
| 468 | Forecasting Live Chat Intent from Browsing History Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the problem of predicting user intent from browsing history and address it through a two-stage approach. |
Se-eun Yoon; Ahmad Bin Rabiah; Zaid Alibadi; Surya Kallumadi; Julian McAuley; |
| 469 | GaQR: An Efficient Generation-augmented Question Rewriter Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we introduce an efficient GaQR to reformulate a question into several queries using Chain of Thought (CoT) and make it more efficient through knowledge distillation. |
Oliver Young; Yixing Fan; Ruqing Zhang; Jiafeng Guo; Maarten de Rijke; Xueqi Cheng; |
| 470 | XRDMamba: Large-scale Crystal Material Space Group Identification with Selective State Space Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our approach XRDMamba integrates chemical knowledge and presents a fresh crystal planes perspective on XRD data. |
Liheng Yu; Pengkun Wang; Zhe Zhao; Zhongchao Yi; Sun Nan; Di Wu; Yang Wang; |
| 471 | Dual-level Intents Modeling for Knowledge-aware Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we utilize the rich interpretable knowledge information in the knowledge graph to design a novel dual-level intents modeling framework called DIM. |
Jin Zeng; Nan Wang; Jinbao Li; |
| 472 | Learn From Mistakes: Guidance on Zero-shot Conversational Text-to-SQL Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel approach that provides guidance through learning from mistakes. |
Wenshuo Zhai; Xiang Zhao; Jinzhi Liao; Ziyang Chen; |
| 473 | Distilling Knowledge Based on Curriculum Learning for Temporal Knowledge Graph Embeddings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new framework for distilling TKGE models via an easy to hard pedagogical principle. |
Bin Zhang; Jiayin Li; Yuanfei Dai; |
| 474 | Momentum Contrastive Bidirectional Encoding with Self-Distillation for Sequential Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new Momentum Contrastive Bidirectional Encoding network with S elf-D istillation (MoCoBE-SD) to alleviate the data sparsity and noise issues in sequential recommendation by providing rich informative supervisions from both sequence-level and item-level perspectives. |
Dingyi Zhang; Haoyu Wenren; Yue Wang; Yingming Li; |
| 475 | Meta-Prompt Tuning Vision-Language Model for Multi-Label Few-Shot Image Recognition Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Tuning a shared prompt is insufficient for all samples especially when the tasks are complex and tuning specific prompts for each class is inevitable to lose generalization ability, thus failing to capture diverse visual knowledge. To address these issues, we propose to meta-tune a generalized prompt pool, enabling each prompt to act as an expert for multi-label few-shot image recognition. |
Feng Zhang; Wei Chen; Fei Ding; Tengjiao Wang; Dawei Lu; Jiabin Zheng; |
| 476 | Multi-view Temporal Knowledge Graph Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To make better predictions on both facts, we introduce a novel TKG reasoning model, named Multi-view Recurrent Network (MV-NET), which generates different views to capture reasoning patterns for both recurring and unknown facts. |
Fuwei Zhang; Zhao Zhang; Fuzhen Zhuang; Zhiqiang Zhang; Jun Zhou; Deqing Wang; |
| 477 | Mamba Retriever: Utilizing Mamba for Effective and Efficient Dense Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In the information retrieval (IR) area, dense retrieval (DR) models use deep learning techniques to encode queries and passages into embedding space to compute their semantic relations. |
Hanqi Zhang; Chong Chen; Lang Mei; Qi Liu; Jiaxin Mao; |
| 478 | Evolving to The Future: Unseen Event Adaptive Fake News Detection on Social Media Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Unfortunately, most of the existing approaches can hardly handle this challenge since they rely heavily on event-specific features for prediction and cannot generalize to unseen events. To address this, we introduce Future AD aptive Event-based Fake news Detection (FADE) framework. |
Jiajun Zhang; Zhixun Li; Qiang Liu; Shu Wu; Zilei Wang; Liang Wang; |
| 479 | P-Rank+: A Scalable Efficient P-Rank Search Algorithm Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose P-Rank+, a fast and efficient algorithm for computing P-Rank similarities, which scales well on large graphs with billions of edges. |
Maoyin Zhang; Weiren Yu; |
| 480 | H2D: Hierarchical Heterogeneous Graph Learning Framework for Drug-Drug Interaction Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, most existing methods oversimplify the complex hierarchical structure within molecules and overlook the multi-source heterogeneous information external to molecules, limiting their modeling and predictive capabilities. To address this, we propose a <u>H</u> ierarchical <u>H</u> eterogeneous graph learning framework for <u>D</u> DI prediction, namely H2D. |
Ran Zhang; Xuezhi Wang; Sheng Wang; Kunpeng Liu; Yuanchun Zhou; Pengfei Wang; |
| 481 | Learning The Dynamics in Sequential Recommendation By Exploiting Real-time Information Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing approaches typically fail to adequately describe the dynamics of the entire recommender system, including shifts in both user interest and item availability. To address this, we propose a simple yet effective framework with three key perspectives, tailored to the dynamics of recommender system by fully exploiting the time information. |
Rujiao Zhang; Hao Zhang; Yucong Luo; Zhiding Liu; Mingyue Cheng; Qi Liu; Enhong Chen; |
| 482 | VIER: Visual Imagination Enhanced Retrieval in Sponsored Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Visual Imagination Enhanced Retrieval model (VIER) to explore the implicit imagery of users. |
Yadong Zhang; Yuqing Song; Siyu Lu; Qiang Liu; Xingxing Wang; |
| 483 | In Situ Answer Sentence Selection at Web-scale Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present Passage-based Extracting Answer Sentence In-place (PEASI), a novel answer selection model optimized for Web-scale setting. |
Zeyu Zhang; Thuy Vu; Alessandro Moschitti; |
| 484 | Generating Cross-model Analytics Workloads Using LLMs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we take a step toward filling this research gap by generating new query workloads spanning relational and graph data, which are ubiquitous in analytics applications. |
Xiuwen Zheng; Arun Kumar; Amarnath Gupta; |
| 485 | Long-Term Hydrologic Time Series Prediction with LSPM Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the use of additional input data in these studies has often been insufficient, resulting in predictions with low accuracy. In this paper, we address these issues and propose LSPM, a Long Short-term Polar-Learning time series forecasting Model. |
Sicheng Zhou; David C. Anastasiu; |
| 486 | CNN to GNN: Unsupervised Multi-level Knowledge Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Besides, lacking of the graph structure limits the extension of GNNs on non-graph datasets. To solve these problems, we propose a novel unsupervised multi-level knowledge fusion network. |
Ziheng Jiao; Hongyuan Zhang; Xuelong Li; |
| 487 | A Structural Information Guided Hierarchical Reconstruction for Graph Anomaly Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing GNN-based detection methods often merely focus on anomalies of single nodes or neighborhoods, making it hard to cope with complex and organized networks. Towards this, we propose SI-HGAD, a novel Graph Anomaly Detection (GAD) approach that utilizes hierarchical information to detect anomalies. |
Dongcheng Zou; Hao Peng; Chunyang Liu; |
| 488 | Boosting Entity Recognition By Leveraging Cross-task Domain Models for Weak Supervision Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose WeSDoM (Weak Supervision with Domain Models), which leverages pretrained encoder models from the same domain but different tasks to create domain ontologies that can enable the creation of less noisy weakly labeled data. |
Sanjay Agrawal; Srujana Merugu; Vivek Sembium; |
| 489 | UGAD: Universal Generative AI Detector Utilizing Frequency Fingerprints Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our study introduces a novel multi-modal approach to detect AI-generated images amidst the proliferation of new generation methods such as Diffusion models. |
Inzamamul Alam; Muhammad Shahid Muneer; Simon S. Woo; |
| 490 | IRAG: Advancing RAG for Videos with An Incremental Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Since user queries are not known apriori, developing a system for video to text conversion and interactive querying of video data is challenging. To address these limitations, we propose an incremental RAG system called iRAG, which augments RAG with a novel incremental workflow to enable interactive querying of a large corpus of videos. |
Md Adnan Arefeen; Biplob Debnath; Md Yusuf Sarwar Uddin; Srimat Chakradhar; |
| 491 | Leveraging Large Language Models for Improving Keyphrase Generation for Contextual Targeting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present an empirical study to facilitate a more informed use of LLMs for keyphrase generation. |
Xiao Bai; Xue Wu; Ivan Stojkovic; Kostas Tsioutsiouliklis; |
| 492 | Deep Journey Hierarchical Attention Networks for Conversion Predictions in Digital Marketing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, digital marketing agencies often struggle with incomplete user profiles and interaction details from Advertising Identifier (ADID) data in user behavior modeling. To address this, we introduce the Deep Journey Hierarchical Attention Networks (DJHAN). |
Girim Ban; Hyeonseok Yun; Banseok Lee; David Sung; Simon S. Woo; |
| 493 | LiNR: Model Based Neural Retrieval on GPUs at LinkedIn Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces LiNR, LinkedIn’s large-scale, GPU-based retrieval system. |
Fedor Borisyuk; Qingquan Song; Mingzhou Zhou; Ganesh Parameswaran; Madhu Arun; Siva Popuri; Tugrul Bingol; Zhuotao Pei; Kuang-Hsuan Lee; Lu Zheng; Qizhan Shao; Ali Naqvi; Sen Zhou; Aman Gupta; |
| 494 | LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose LLP-Bench – a web scale benchmark with ~ 70 datasets and 45 million datapoints. |
Anand Brahmbhatt; Mohith Pokala; Rishi Saket; Aravindan Raghuveer; |
| 495 | Personalized Video Summarization By Multimodal Video Understanding Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Adapting video summarization to various types of video and user preferences requires significant training data and expensive human labeling. To facilitate such research, we proposed a new benchmark for video summarization that captures various user preferences. |
Brian Chen; Xiangyuan Zhao; Yingnan Zhu; |
| 496 | Missing Interest Modeling with Lifelong User Behavior Data for Retrieval Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose the concept of missing interest, leveraging the idea of complementarity, which serves as a supplement to short-term interest based on lifelong behavior data in the retrieval stage. |
Gaode Chen; Yuezihan Jiang; Rui Huang; Kuo Cai; Yunze Luo; Ruina Sun; Qi Zhang; Han Li; Kun Gai; |
| 497 | Feedback Reciprocal Graph Collaborative Filtering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: On the other hand, disregarding the interacted but unfascinating items during graph collaborative filtering will result in an incomplete representation of users’ interaction intent, leading to a decline in the model’s recommendation capabilities. To address this seesaw problem, we propose Feedback Reciprocal Graph Collaborative Filtering (FRGCF), which emphasizes the recommendation of fascinating items while attenuating the recommendation of unfascinating items. |
Weijun Chen; Yuanchen Bei; Qijie Shen; Hao Chen; Xiao Huang; Feiran Huang; |
| 498 | PlayBest: Professional Basketball Player Behavior Synthesis Via Planning with Diffusion Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this study, we formulate the sequential decision-making process as a conditional trajectory generation process. |
Xiusi Chen; Wei-Yao Wang; Ziniu Hu; David Reynoso; Kun Jin; Mingyan Liu; P. Jeffrey Brantingham; Wei Wang; |
| 499 | DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce DeepClair, a novel framework for portfolio selection. |
Donghee Choi; Jinkyu Kim; Mogan Gim; Jinho Lee; Jaewoo Kang; |
| 500 | Blind-Match: Efficient Homomorphic Encryption-Based 1:N Matching for Privacy-Preserving Biometric Identification Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present Blind-Match, a novel biometric identification system that leverages homomorphic encryption (HE) for efficient and privacy-preserving 1:N matching. |
Hyunmin Choi; Jiwon Kim; Chiyoung Song; Simon S. Woo; Hyoungshick Kim; |
| 501 | Causal Interventional Prediction System for Robust and Explainable Effect Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we explore the robustness and explainability of AI-based forecasting systems. |
Zhixuan Chu; Hui Ding; Guang Zeng; Shiyu Wang; Yiming Li; |
| 502 | Enhancing E-Commerce Query Rewriting: A Large Language Model Approach with Domain-Specific Pre-Training and Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This research introduces a query rewriting framework predicated on large language models (LLM), encompassing three phases of training: domain-specific pre-training, supervised fine-tuning (SFT) and reinforcement learning (RL) for objective alignment. |
Aijun Dai; Zhenyu Zhu; Haiqing Hu; Guoyu Tang; Lin Liu; Sulong Xu; |
| 503 | UniEmbedding: Learning Universal Multi-Modal Multi-Domain Item Embeddings Via User-View Contrastive Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we aim to learn universal item embeddings (dubbed UniEmbedding) that capture multi-modal semantics, generalize across multiple domains, and serve different downstream tasks. |
Boqi Dai; Zhaocheng Du; Jieming Zhu; Jintao Xu; Deqing Zou; Quanyu Dai; Zhenhua Dong; Rui Zhang; Hai-Tao Zheng; |
| 504 | Transforming Location Retrieval at Airbnb: A Journey from Heuristics to Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The purpose of this paper is to demonstrate the methodology, challenges, and impact of building a machine learning based location retrieval product from the ground up. |
Dillon Davis; Huiji Gao; Thomas Legrand; Malay Haldar; Alex Deng; Han Zhao; Liwei He; Sanjeev Katariya; |
| 505 | Automated Nanoparticle Image Processing Pipeline for AI-Driven Materials Characterization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we show that adding a custom image preprocessing step before model training can produce significantly higher-performing models in a fraction of the time and make the model more robust to different image noise levels and microscope acquisition settings. |
Alexandra L. Day; Carolin B. Wahl; Roberto dos Reis; Wei-keng Liao; Vinayak P. Dravid; Alok Choudhary; Ankit Agrawal; |
| 506 | Parallel-friendly Spatio-Temporal Graph Learning for Photovoltaic Degradation Analysis at Scale Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces a novel Spatio-Temporal Graph Neural Network empowered, long-term Trend analysis system (ST-GTrend), to estimate PLR of PV systems at fleet-level. |
Yangxin Fan; Raymond Wieser; Laura S. Bruckman; Roger H. French; Yinghui Wu; |
| 507 | GraphWeaver: Billion-Scale Cybersecurity Incident Correlation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce GraphWeaver, an industry-scale framework that shifts the traditional incident correlation process to a data-optimized, geo-distributed graph based approach. |
Scott Freitas; Amir Gharib; |
| 508 | PODTILE: Facilitating Podcast Episode Browsing with Auto-generated Chapters Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Second, the transcripts are usually lengthy, averaging about 16,000 tokens, which necessitates efficient processing that can preserve context. To address these challenges, we introduce PODTILE, a fine-tuned encoder-decoder transformer to segment conversational data. |
Azin Ghazimatin; Ekaterina Garmash; Gustavo Penha; Kristen Sheets; Martin Achenbach; Oguz Semerci; Remi Galvez; Marcus Tannenberg; Sahitya Mantravadi; Divya Narayanan; Ofeliya Kalaydzhyan; Douglas Cole; Ben Carterette; Ann Clifton; Paul N. Bennett; Claudia Hauff; Mounia Lalmas; |
| 509 | CancerKG.ORG – A Web-scale, Interactive, Verifiable Knowledge Graph-LLM Hybrid for Assisting with Optimal Cancer Treatment and Care Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Here, we describe one of the first Web-scale hybrid Knowledge Graph (KG)-Large Language Model (LLM), populated with the latest peer-reviewed medical knowledge on colorectal Cancer. |
Michael Gubanov; Anna Pyayt; Aleksandra Karolak; |
| 510 | A Bayesian Multi-Armed Bandit Algorithm for Bid Shading in Online Display Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, advertisers are consistently facing a trade-off between the probability and cost-saving of winning, due to the information asymmetry, where advertisers lack knowledge about their competitors’ bids in the market. To address this challenge, we propose a Bayes ian Multi-Armed Bandit (BayesMAB) algorithm for bid shading when the winning price is unknown to advertisers who lose the impression opportunity. |
Mengzhuo Guo; Wuqi Zhang; Congde Yuan; Binfeng Jia; Guoqing Song; Hua Hua; Shuangyang Wang; Qingpeng Zhang; |
| 511 | GraphScale: A Framework to Enable Machine Learning Over Billion-node Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The key insight in our design is the separation of workers who store data and those who perform the training. This separation allows us to decouple computing and storage in graph training, thus effectively building a pipeline where data fetching and data computation can overlap asynchronously. |
Vipul Gupta; Xin Chen; Ruoyun Huang; Fanlong Meng; Jianjun Chen; Yujun Yan; |
| 512 | Quality Prediction in Arc Welding: Leveraging Transformer Models and Discrete Representations from Vector Quantised-VAE Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel learning-based approach relying on a vector quantised variational autoencoder (VQ-VAE) for data representation. |
Yannik Hahn; Robert Maack; Hasan Tercan; Tobias Meisen; Marion Purrio; Guido Buchholz; Matthias Angerhausen; |
| 513 | Cryptocurrency Price Forecasting Using Variational Autoencoder with Versatile Quantile Modeling Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel variational autoencoder learning framework for multivariate distributional forecasting. |
Sungchul Hong; Seunghwan An; Jong-June Jeon; |
| 514 | Reinforcement Feature Transformation for Polymer Property Performance Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This raises two issues: 1) automatic transformation and 2) explainable enhancement. To tackle these issues, we propose our unique Traceable Group-wise Reinforcement Generation Perspective. |
Xuanming Hu; Dongjie Wang; Wangyang Ying; Yanjie Fu; |
| 515 | DAMOCRO: A Data Migration Framework Using Online Classification and Reordering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces DAMOCRO, a <u>da</u>ta <u>m</u>igration framework using <u>o</u>nline <u>c</u>lassification and tuple <u>r</u>e<u>o</u>rdering to improve throughput and decrease the costs of data migration. |
Zhongxin Hu; Kaiyu Li; Xingjian Mao; Jingfeng Pan; Yunfei Peng; Aijun An; Xiaohui Yu; Dariusz Jania; |
| 516 | Optimizing Numerical Estimation and Operational Efficiency in The Legal Domain Through Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Leveraging LLMs’ mathematical reasoning capabilities, we propose a novel approach integrating LLM-based methodologies with specially designed prompts to address precision requirements in legal Artificial Intelligence (LegalAI) applications. |
Jia-Hong Huang; Chao-Chun Yang; Yixian Shen; Alessio M. Pacces; Evangelos Kanoulas; |
| 517 | EXIT: An EXplicit Interest Transfer Framework for Cross-Domain Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a simple and effective EXplicit Interest Transfer framework named EXIT to address the stated challenge. |
Lei Huang; Weitao Li; Chenrui Zhang; Jinpeng Wang; Xianchun Yi; Sheng Chen; |
| 518 | Robust Sequence-Based Self-Supervised Representation Learning for Anti-Money Laundering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present CLeAR, a novel robust sequence-based self-supervised Representation Learning framework for Anti-Money Laundering. |
Shuaibin Huang; Yun Xiong; Yi Xie; Tianyu Qiu; Guangzhong Wang; |
| 519 | To Explore or Exploit? A Gradient-informed Framework to Address The Feedback Loop for Graph Based Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To forge an effective E&E approach tailored to GRSs, we introduce a novel framework, the <u>GRAD</u>ient-informed <u>E</u>xploration and Exploitation (GRADE), designed to adaptively seek out underrepresented or new items with promising rewards. |
Zhigang Huangfu; Binbin Hu; Zhengwei Wu; Fengyu Han; Gong-Duo Zhang; Gong-Duo Zhang; Lihong Gu; Lihong Gu; Zhiqiang Zhang; Zhiqiang Zhang; |
| 520 | Deep Uncertainty-Based Explore for Index Construction and Retrieval in Recommendation System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The matching model serves as the starting point of the pipeline and determines the upper bound of the subsequent stages. |
Xin Jiang; Kaiqiang Wang; Yinlong Wang; Fengchang Lv; Taiyang Peng; Shuai Yang; Xianteng Wu; Pengye Zhang; Shuo Yuan; Yifan Zeng; |
| 521 | A Multi-Node Multi-GPU Distributed GNN Training Framework for Large-Scale Online Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents PGLBox-Cluster, a robust distributed graph learning framework constructed atop the PaddlePaddle platform, implemented to efficiently process graphs comprising billions of nodes and edges. |
Xuewu Jiao; Xinsheng Luo; Miao Li; Jiang Bian; Junchao Yang; Wei Hu; Mingqing Hu; Weipeng Lu; Shikun Feng; Danlei Feng; Dongxu Yang; Haoyi Xiong; Shuanglong Li; Lin Liu; |
| 522 | Pareto-based Multi-Objective Recommender System with Forgetting Curve Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recommender systems with cascading architecture play an increasingly significant role in online recommendation platforms, where the approach to dealing with negative feedback is a vital issue. For instance, in short video ad platforms, users tend to quickly slip away from ad candidates that they feel aversive, and recommender systems are expected to receive these explicit negative feedback and make adjustments to avoid these recommendations.Considering recency effect in memories, we propose a forgetting model based on Ebbinghaus Forgetting Curve to cope with negative feedback. |
Jipeng Jin; Zhaoxiang Zhang; Zhiheng Li; Xiaofeng Gao; Xiongwen Yang; Lei Xiao; Jie Jiang; |
| 523 | Automated Contrastive Learning Strategy Search for Time Series Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present an Automated Machine Learning (AutoML) practice at Microsoft, which automatically learns CLS for time series datasets and tasks, namely Automated Contrastive Learning (AutoCL). |
Baoyu Jing; Yansen Wang; Guoxin Sui; Jing Hong; Jingrui He; Yuqing Yang; Dongsheng Li; Kan Ren; |
| 524 | REAPER: Reasoning Based Retrieval Planning for Complex RAG Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Multi-agent systems may classify the query to a single Agent associated with a retrieval source, which means that a (small) classification model dictates the performance of a large language model. To address this problem, we present REAPER (REAsoning-based PlannER), an LLM-based retrieval planner that we evaluate on a conversational shopping assistant, which shows significant gains in latency over Agent-based systems and scalability to new and unseen use cases when compared to classification-based planning. |
Ashutosh Joshi; Sheikh Muhammad Sarwar; Samarth Varshney; Sreyashi Nag; Shrivats Agrawal; Juhi Naik; |
| 525 | XCapsUTL: Cross-domain Unsupervised Transfer Learning Framework Using A Capsule Neural Network Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In many industries, however, tabular data is a predominant and crucial data type. Our work introduces XCapsUTL, a novel unsupervised transfer learning framework specifically designed for tabular data, aiming to fill this significant gap. |
Naman Khetan; Sanyog Dewani; Gokul Swamy; Vikalp Gajbhiye; |
| 526 | LAPIS: Language Model-Augmented Police Investigation System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce LAPIS (Language Model Augmented Police Investigation System), an automated system that assists police officers to perform rational and legal investigative actions. |
Heedou Kim; Dain Kim; Jiwoo Lee; Chanwoong Yoon; Donghee Choi; Mogan Gim; Jaewoo Kang; |
| 527 | Sequential Optimum Test with Multi-armed Bandits for Online Experimentation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To save the cost during the long experimental process, we propose a more efficient sequential test framework named Soptima that can work with general reward types. |
Fang Kong; Penglei Zhao; Shichao Han; Yong Wang; Shuai Li; |
| 528 | XploitSQL: Advancing Adversarial SQL Injection Attack Generation with Language Models and Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce XploitSQL-an innovative approach to advance adversarial SQL injection generation by leveraging language models and reinforcement learning. |
Daniel Leung; Omar Tsai; Kourosh Hashemi; Bardia Tayebi; Mohammad A. Tayebi; |
| 529 | RL-ISLAP: A Reinforcement Learning Framework for Industrial-Scale Linear Assignment Problems at Alipay Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Additionally, the resulting parameter rules are often inefficient. To alleviate this issue, we propose RL-ISLAP, an efficient and lightweight Reinforcement Learning framework for Industrial-Scale Linear Assignment Problems. |
Hanjie Li; Yue Ning; Yang Bao; Changsheng Li; Boxiao Chen; Xingyu Lu; Ye Yuan; Guoren Wang; |
| 530 | RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle this problem, in this paper we investigate temporal causal discovery in industrial scenarios, which faces two critical challenges: how to discover causal relations without the interventional targets that are costly to obtain in practice, and how to discover causal relations via leveraging the textual information in systems which can be complex yet abundant in industrial contexts. To address these challenges, we propose the RealTCD framework, which is able to leverage domain knowledge to discover temporal causal relations without interventional targets. |
Peiwen Li; Xin Wang; Zeyang Zhang; Yuan Meng; Fang Shen; Yue Li; Jialong Wang; Yang Li; Wenwu Zhu; |
| 531 | Explainable and Coherent Complement Recommendation Based on Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce the concept of coherent complement recommendation, where coherent implies that recommended item pairs are compatible and relevant. |
Zelong Li; Yan Liang; Ming Wang; Sungro Yoon; Jiaying Shi; Xin Shen; Xiang He; Chenwei Zhang; Wenyi Wu; Hanbo Wang; Jin Li; Jim Chan; Yongfeng Zhang; |
| 532 | Bridging Dynamic Factor Models and Neural Controlled Differential Equations for Nowcasting GDP Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: However, DFMs face two main challenges: i) the lack of capturing economic uncertainties such as sudden recessions or booms, and ii) the limitation of capturing irregular dynamics from mixed-frequency data. To address these challenges, we introduce NCDENow, a novel GDP nowcasting framework that integrates neural controlled differential equations (NCDEs) with DFMs. |
Seonkyu Lim; Jeongwhan Choi; Noseong Park; Sang-Ha Yoon; ShinHyuck Kang; Young-Min Kim; Hyunjoong Kang; |
| 533 | Enhancing Relevance of Embedding-based Retrieval at Walmart Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Factors contributing to these degradations include false positives/negatives in the training data and the inability to handle query misspellings. To address these issues, we present several approaches to further strengthen the capabilities of our EBR model in terms of retrieval relevance. |
Juexin Lin; Sachin Yadav; Feng Liu; Nicholas Rossi; Praveen R. Suram; Satya Chembolu; Prijith Chandran; Hrushikesh Mohapatra; Tony Lee; Alessandro Magnani; Ciya Liao; |
| 534 | Hierarchical Information Propagation and Aggregation in Disentangled Graph Networks for Audience Expansion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our work introduces a novel heterogeneous graph-based model, named Hi-DGN, which concentrates on the Hierarchical information propagation and aggregation in Disentangled Graph Networks for audience expansion. |
Li Lin; Xinyao Chen; Kaiwen Xia; Shuai Wang; Desheng Zhang; Tian He; |
| 535 | DIFN: A Dual Intention-aware Network for Repurchase Recommendation with Hierarchical Spatio-temporal Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Although these methods represent user interests and preference features effectively, they still fail to model repurchase behaviors because (i) the environment causing repurchase intention change is neglected and (ii) the lack of feedback after purchasing makes it difficult to learn the impacts of diverse behaviors. To comprehensively consider these limitations, we design a <u>D</u> ual <u>I</u> ntention-aware <u>F</u> usion <u>N</u> etwork framework (DIFN) to understand the effects of environment and after-purchasing feedback on users’ intentions. |
Li Lin; Xin Xu; Hai Wang; Tian He; Desheng Zhang; Shuai Wang; |
| 536 | Boosting LLM-based Relevance Modeling with Distribution-Aware Robust Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel Distribution-Aware Robust Learning framework (DaRL) for relevance modeling in Alipay Search. |
Hong Liu; Saisai Gong; Yixin Ji; Kaixin Wu; Jia Xu; Jinjie Gu; |
| 537 | A Self-Adaptive Fairness Constraint Framework for Industrial Recommender System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Due to the group’s personalized selection tendencies and the non-uniform population distributions, existing industrial recommenders tend to make unfair predictions towards the preferences of minority groups. To alleviate this unfairness, we propose a model-agnostic self-adaptive fairness constraint framework (SaFair) based on the posterior preferences of different groups. |
Zhiqiang Liu; Xiaoxiao Xu; Jiaqi Yu; Han Xu; Lantao Hu; Han Li; Kun Gai; |
| 538 | DECO: Cooperative Order Dispatching for On-Demand Delivery with Real-Time Encounter Detection Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we leverage courier-courier encounter events as an opportunity to enable cooperative order dispatching (i.e., conducting order transfers among couriers during delivery) for better delivery efficiency. |
Yao Lu; Shuai Wang; Yu Yang; Hai Wang; Baoshen Guo; Desheng Zhang; Shuai Wang; Tian He; |
| 539 | Modeling User Intent Beyond Trigger: Incorporating Uncertainty for Trigger-Induced Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Moreover, users’ short-term intent shows uncertainty and is affected by various factors such as browsing context and historical behaviors, which poses challenges to user intent modeling. To address these challenges, we propose a novel model called Deep Uncertainty Intent Network (DUIN), comprising three essential modules: i) Explicit Intent Exploit Module extracting explicit user intent using the contrastive learning paradigm; ii) Latent Intent Explore Module exploring latent user intent by leveraging the multi-view relationships between items; iii) Intent Uncertainty Measurement Module offering a distributional estimation and capturing the uncertainty associated with user intent. |
Jianxing Ma; Zhibo Xiao; Luwei Yang; Hansheng Xue; Xuanzhou Liu; Wen Jiang; Wei Ning; Guannan Zhang; |
| 540 | Combat Greenwashing with GoalSpotter: Automatic Sustainability Objective Detection in Heterogeneous Reports Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To combat greenwashing, society needs effective automated approaches to identify the sustainability claims of companies in their heterogeneous reports.In this paper, we present a new sustainability objective detection system, named GoalSpotter, that automatically identifies the environmental and social claims of companies in their heterogeneous reports. |
Mohammad Mahdavi; Ramin Baghaei Mehr; Tom Debus; |
| 541 | Multi-view Causal Graph Fusion Based Anomaly Detection in Cyber-Physical Infrastructures Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the two challenges, we propose a multi-view causal graph perspective, where 1) We build causal graphs to capture invariant anomaly patterns in varying streams; and 2) Introduce multi-view fusion for robust attack pattern representation. |
Arun Vignesh Malarkkan; Dongjie Wang; Yanjie Fu; |
| 542 | Building Natural Language Interface for Product Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we implement a novel dataset generation pipeline (LLM-API) that leverages Large Language Models (LLMs), search logs and proprietary product information data from an ecommerce website to create a high quality dataset. |
Vijit Malik; Vinayak Puranik; Anirban Majumder; Vivek Sembium; |
| 543 | GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a novel approach for predicting Power Conversion Efficiency (PCE) of Organic Photovoltaic (OPV) devices, called GLaD: synergizing molecular Graphs and Language Descriptors for enhanced PCE prediction. |
Thao Nguyen; Tiara Torres-Flores; Changhyun Hwang; Carl Edwards; Ying Diao; Heng Ji; |
| 544 | Towards Better Seach Query Classification with Distribution-Diverse Multi-Expert Knowledge Distillation in JD Ads Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The proposed Distribution-Diverse Multi-Expert (DDME) framework employs multiple teacher models trained from diverse data distributions. |
Kun-Peng Ning; Ming Pang; Zheng Fang; Xue Jiang; Xi-Wei Zhao; Chang-Ping Peng; Zhan-Gang Lin; Jing-He Hu; Jing-Ping Shao; Li Yuan; |
| 545 | Ericsogate: Advancing Analytics and Management of Data from Diverse Sources Within Ericsson Using Knowledge Graphs Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces a novel, multi-layered approach to managing interlinked data for Cloud Radio Access Network (CloudRAN) at Ericsson, utilizing Knowledge Graphs (KGs). |
Abdelghny Orogat; Sri Lakshmi Vadlamani; Dimple Thomas; Ahmed El-Roby; |
| 546 | COKE: Causal Discovery with Chronological Order and Expert Knowledge in High Proportion of Missing Manufacturing Data Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Conversely, prior methods that can incorporate expert knowledge struggle with datasets that exhibit missing values. Therefore, we propose COKE to construct causal graphs in manufacturing datasets by leveraging expert knowledge and chronological order among sensors without imputing missing data. |
Ting-Yun Ou; Ching Chang; Wen-Chih Peng; |
| 547 | Cross-contextual Sequential Optimization Via Deep Reinforcement Learning for Algorithmic Trading Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Existing works mainly focus on capturing temporal relations while ignoring deriving essential factors across features. Therefore, we propose a DRL-based cross-contextual sequential optimization (CCSO) method for algorithmic trading. |
Kaiming Pan; Yifan Hu; Li Han; Haoyu Sun; Dawei Cheng; Yuqi Liang; |
| 548 | EASE: Learning Lightweight Semantic Feature Adapters from Large Language Models for CTR Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose the EASE framework, which enriches and aligns semantic feature embeddings using LLMs during the training phase while establishing a lightweight inference pipeline that does not directly involve LLMs. |
Zexuan Qiu; Jieming Zhu; Yankai Chen; Guohao Cai; Weiwen Liu; Zhenhua Dong; Irwin King; |
| 549 | Relevance Filtering for Embedding-based Retrieval Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This issue is prominent in product search, where the number of relevant products is often small. This paper introduces a novel relevance filtering component (called Cosine Adapter) for embedding-based retrieval to address this challenge. |
Nicholas Rossi; Juexin Lin; Feng Liu; Zhen Yang; Tony Lee; Alessandro Magnani; Ciya Liao; |
| 550 | EFfECT-RL: Enabling Framework for Establishing Causality and Triggering Engagement Through RL Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we aim to address two key questions: (1) What factors are driving users to increase their engagement? |
Debanjan Sadhukhan; Deepanshi Seth; Sanjay Agrawal; Tridib Mukherjee; |
| 551 | Mitigating Extreme Cold Start in Graph-based RecSys Through Re-ranking Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recommender systems based on Graph Neural Networks (GNN) have become the state-of-the-art approach in recommendation, but they struggle with in extreme cold-start settings, where most users or items lack interaction data. This paper proposes a novel framework to address this challenge in four steps: (i) a propensity model to predict item purchase behaviour, with associated explainability to identify the most relevant features, (ii) a link augmentation module to connect users based on previously obtained similarities, (iii) a GNN-based link prediction step on the obtained dense graph and (iv) a final re-ranking stage to increase diversity in predictions leveraging users embeddings. |
Alessandro Sbandi; Federico Siciliano; Fabrizio Silvestri; |
| 552 | STIR: Siamese Transformer for Image Retrieval Postprocessing Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: The resulting approach defines a new state of the art on standard image retrieval datasets: Stanford Online Products and DeepFashion In-shop. |
Aleksei Shabanov; Aleksei Tarasov; Sergey Nikolenko; |
| 553 | Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and Insights Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we explore approaches to leverage multimodal data to enhance the recommendation accuracy. |
Xiang-Rong Sheng; Feifan Yang; Litong Gong; Biao Wang; Zhangming Chan; Yujing Zhang; Yueyao Cheng; Yong-Nan Zhu; Tiezheng Ge; Han Zhu; Yuning Jiang; Jian Xu; Bo Zheng; |
| 554 | CPFD: Confidence-aware Privileged Feature Distillation for Short Video Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In industrial applications, prioritizing end-to-end multi-modal features, can enhance efficiency but often leads to the loss of valuable information from historical privileged dense features.To integrate both features while maintaining efficiency and manageable resource costs, we present Confidence-aware Privileged Feature Distillation (CPFD), which empowers features of an end-to-end multi-modal model by adaptively distilling privileged features during training.Unlike existing privileged feature distillation (PFD) methods, which apply uniform weights to all instances during distillation, potentially causing unstable performance across different business scenarios and a notable performance gap between teacher model (Dense Feature enhanced multimodal-model DF-X-VLM) and student model (multimodal-model only X-VLM), our CPFD leverages confidence scores derived from the teacher model to adaptively mitigate the performance variance with the student model. |
Jinghao Shi; Xiang Shen; Kaili Zhao; Xuedong Wang; Vera Wen; Zixuan Wang; Yifan Wu; Zhixin Zhang; |
| 555 | Ads Supply Personalization Via Doubly Robust Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a streamlined framework for personalized ad supply. |
Wei Shi; Chen Fu; Qi Xu; Sanjian Chen; Jizhe Zhang; Qinqin Zhu; Zhigang Hua; Shuang Yang; |
| 556 | LawLLM: Law Large Language Model for The US Legal System Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Researchers often conflate these concepts, making it difficult to develop specialized techniques to effectively address these nuanced tasks. In this paper, we introduce the Law Large Language Model (LawLLM), a multi-task model specifically designed for the US legal domain to address these challenges. |
Dong Shu; Haoran Zhao; Xukun Liu; David Demeter; Mengnan Du; Yongfeng Zhang; |
| 557 | TWIN V2: Scaling Ultra-Long User Behavior Sequence Modeling for Enhanced CTR Prediction at Kuaishou Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Given the extensive length of user behavior sequences spanning the entire life cycle, potentially reaching up to 10^6 in scale, there is currently no effective solution for fully modeling such expansive user interests. To overcome this issue, we introduced TWIN-V2, an enhancement of TWIN, where a divide-and-conquer approach is applied to compress life-cycle behaviors and uncover more accurate and diverse user interests. |
Zihua Si; Lin Guan; Zhongxiang Sun; Xiaoxue Zang; Jing Lu; Yiqun Hui; Xingchao Cao; Zeyu Yang; Yichen Zheng; Dewei Leng; Kai Zheng; Chenbin Zhang; Yanan Niu; Yang Song; Kun Gai; |
| 558 | Dynamic Graph-based Deep Reinforcement Learning with Long and Short-term Relation Modeling for Portfolio Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a Dynamic Graph-based Deep Reinforcement Learning (DGDRL) for optimal portfolio decisions. |
Haoyu Sun; Yuxuan Bian; Li Han; Peng Zhu; Dawei Cheng; Yuqi Liang; |
| 559 | A Real-Time Adaptive Multi-Stream GPU System For Online Approximate Nearest Neighborhood Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce a novel Real-Time Adaptive Multi-Stream GPU ANNS System (RTAMS-GANNS). |
Yiping Sun; Yang Shi; Jiaolong Du; |
| 560 | MERLIN: Multimodal \& Multilingual Embedding for Recommendations at Large-scale Via Item Associations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, the humongous scale of products, implicit co-purchase asymmetry and variation in co-purchase behavior across different categories, are orthogonal problems to solve. To address these problems, we propose MERLIN (Multimodal \& Multilingual Embedding for Recommendations at Large-scale via Item associations), a Graph Neural Network that generates product recommendations from a heterogeneous and directed product graph. |
Sambeet Tiady; Arihant Jain; Dween Rabius Sanny; Khushi Gupta; Srinivas Virinchi; Swapnil Gupta; Anoop Saladi; Deepak Gupta; |
| 561 | Reasoning Before Responding: Towards Legal Long-form Question Answering with Interpretability Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The endeavor to generate detailed answers to contextually rich legal questions has faced challenges, primarily due to the limited availability of specialized datasets involving intensive manual effort or incapability of existing LFQA models to produce informative responses. Addressing this, our research introduces a semi-synthetic dataset, Legal-LFQA (L2FQA) created by exploiting a large language model (LLM) and utilizing contexts derived from existing legal datasets. |
Utkarsh Ujwal; Sai Sri Harsha Surampudi; Sayantan Mitra; Tulika Saha; |
| 562 | Behavior-aware Sparse Trajectory Recovery in Last-mile Delivery with Multi-scale Attention Fusion Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our approach starts with a behavior-aware graph network that generates detailed spatial features. Following this, we propose a multi-scale attention fusion mechanism to extract intra- and inter-trajectory features. |
Hai Wang; Shuai Wang; Li Lin; Yu Yang; Shuai Wang; Hongkai Wen; |
| 563 | COIN: Chance-Constrained Imitation Learning for Safe and Adaptive Resource Oversubscription Under Uncertainty Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our objective is to enhance resource efficiency while ensuring safety against congestion risk. |
Lu Wang; Mayukh Das; Fangkai Yang; Chao Du; Bo Qiao; Hang Dong; Chetan Bansal; Si Qin; Saravan Rajmohan; Qingwei Lin; Dongmei Zhang; Qi Zhang; |
| 564 | Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload Forecasting Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper proposes a novel framework leveraging self-supervised multiscale representation learning to capture both long-term and near-term workload patterns. |
Shiyu Wang; Zhixuan Chu; Yinbo Sun; Yu Liu; Yuliang Guo; Yang Chen; Huiyang Jian; Lintao Ma; Xingyu Lu; Jun Zhou; |
| 565 | CourIRL: Predicting Couriers’ Behavior in Last-Mile Delivery Using Crossed-Attention Inverse Reinforcement Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we devote to the behavioral prediction of courier workload and quantify their workload by the working time spent at each area of interest (AOI). |
Shuai Wang; Tongtong Kong; Baoshen Guo; Li Lin; Haotian Wang; |
| 566 | RCAgent: Cloud Root Cause Analysis By Autonomous Agents with Tool-Augmented Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present RCAgent, a tool-augmented LLM autonomous agent framework for practical and privacy-aware industrial RCA usage. |
Zefan Wang; Zichuan Liu; Yingying Zhang; Aoxiao Zhong; Jihong Wang; Fengbin Yin; Lunting Fan; Lingfei Wu; Qingsong Wen; |
| 567 | Process-Informed Deep Learning for Enhanced Order Fulfillment Cycle Time Prediction in On-Demand Grocery Retailing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents an innovative deep learning model informed by a detailed comprehension of the order fulfillment process, with the objective of significantly enhancing OFCT prediction precision. |
Jiawen Wei; Ziwen Ye; Chuan Yang; Chen Chen; Guangrui Ma; |
| 568 | LLM-based Automated Web Retrieval and Text Classification of Food Sharing Initiatives Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Developed within the European CULTIVATE project, this system not only aids in comprehending the complex dynamics of the food sharing economy, but also enhances its visibility and operational efficiency. The automation of these processes plays a vital role in supporting the goals of the CULTIVATE project, notably in promoting sustainable food practices and resilient local food networks. |
Hao Wu; Hyunji Cho; Anna R. Davies; Gareth J. F. Jones; |
| 569 | G2PTL: A Geography-Graph Pre-trained Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Though promising, those NLP-based PTMs fall short of encoding geographic knowledge in addresses, which limits their application potential in geospatial tasks. To tackle the above problem, this study proposes a Geography-Graph Pre-trained model (G2PTL) that combines graph learning and text pre-training, aiming to make up for the shortcomings of traditional PTM in the geography field. |
Lixia Wu; Jianlin Liu; Junhong Lou; Minhui Deng; Jianbin Zheng; Haomin Wen; Chao Song; Shu He; |
| 570 | DivNet: Diversity-Aware Self-Correcting Sequential Recommendation Networks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In practice, however, the interaction dynamics among the recommended items, their visual appearances and meta-data such as specifications are often too complex to be captured by experts’ heuristics or simple models. To address this issue, we propose a <u>div</u> ersity-aware self-correcting sequential recommendation <u>net</u> works (DivNet) that is able to estimate utility by capturing the complex interactions among sequential items and diversify recommendations simultaneously. |
Shuai Xiao; Zaifan Jiang; |
| 571 | Advancing Re-Ranking with Multimodal Fusion and Target-Oriented Auxiliary Tasks in E-Commerce Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose Advancing Re-ranking with Multimodal Fusion and Target-Oriented Auxiliary Tasks (ARMMT), which integrates an attention-based multimodal fusion technique and an auxiliary ranking-aligned task to enhance item representation and improve targeting capabilities. |
Enqiang Xu; Xinhui Li; Zhigong Zhou; Jiahao Ji; Jinyuan Zhao; Dadong Miao; Songlin Wang; Lin Liu; Sulong Xu; |
| 572 | Sequence-level Semantic Representation Fusion for Recommender Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we propose a novel <u> Te </u>xt-I<u> D </u> semantic fusion approach for sequential <u> Rec </u>ommendation, namely TedRec. |
Lanling Xu; Zhen Tian; Bingqian Li; Junjie Zhang; Daoyuan Wang; Hongyu Wang; Jinpeng Wang; Sheng Chen; Wayne Xin Zhao; |
| 573 | Deep Learning-Based Compressed Sensing for Mobile Device-Derived Sensor Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Compressed sensing (CS) offers a promising approach, but traditional CS methods often struggle with the unique characteristics of sensor data-like variability, dynamic changes, and different sampling rates-leading to slow processing and poor reconstruction quality. To address these issues, we developed Mob-ISTA-1DNet, an innovative CS framework that integrates deep learning with the iterative shrinkage-thresholding algorithm (ISTA) to adaptively compress and reconstruct smartphone sensor data. |
Liqiang Xu; Yuuki Nishiyama; Kota Tsubouchi; Kaoru Sezaki; |
| 574 | Enhancing Playback Performance in Video Recommender Systems with An On-Device Gating and Ranking Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: It is quite a serious issue, yet easily overlooked. To tackle this issue, we propose an on-device Gating and Ranking Framework(GRF) that cooperates with server-side RS. |
Yunfei Yang; Zhenghao Qi; Honghuan Wu; Qi Song; Tieyao Zhang; Hao Li; Yimin Tu; Kaiqiao Zhan; Ben Wang; |
| 575 | Towards A Zero-Day Anomaly Detector in Cyber Physical Systems Using A Hybrid VAE-LSTM-OCSVM Model Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an Adaptive Loss Weight Adjustment Algorithm (ALWAA) to account for Domain incremental learning in our system, as required by the ISO/IEC 42001:2023 and ISO/IEC 23053:2022 standards. |
Romarick Yatagha; Betelhem Nebebe; Karl Waedt; Christoph Ruland; |
| 576 | Revolutionizing Biomarker Discovery: Leveraging Generative AI for Bio-Knowledge-Embedded Continuous Space Exploration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, we propose a new biomarker identification framework with two important modules:1) training data preparation and 2) embedding-optimization-generation. |
Wangyang Ying; Dongjie Wang; Xuanming Hu; Ji Qiu; Jin Park; Yanjie Fu; |
| 577 | An End-to-End Reinforcement Learning Based Approach for Micro-View Order-Dispatching in Ride-Hailing Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a one-stage end-to-end reinforcement learning based order-dispatching approach that solves behavior prediction and combinatorial optimization uniformly in a sequential decision-making manner. |
Xinlang Yue; Yiran Liu; Fangzhou Shi; Sihong Luo; Chen Zhong; Min Lu; Zhe Xu; |
| 578 | On The Fly Detection of Root Causes from Observed Data with Application to IT Systems Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This paper introduces a new structural causal model tailored for representing threshold-based IT systems and presents a new algorithm designed to rapidly detect root causes of anomalies in such systems. |
Lei Zan; Charles K. Assaad; Emilie Devijver; Eric Gaussier; Ali A\{\i}t-Bachir; |
| 579 | Effective Utilization of Large-scale Unobserved Data in Recommendation Systems Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose an Extract and Transfer (EAT) framework, utilizing quantities of unobserved items and other domains’ data to construct more training data for ranking models. |
Feng Zhang; Yulin Xu; Hongjie Chen; Xu Yuan; QingWen Liu; YuNing Jiang; |
| 580 | An Enhanced Batch Query Architecture in Real-time Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our contributions include optimizing hash structures with a cacheline-aware probing method to enhance coalesced hashing, as well as the implementation of a hybrid storage key-value service built upon it. |
Qiang Zhang; Zhipeng Teng; Disheng Wu; Jiayin Wang; |
| 581 | Breaking The Barrier: Utilizing Large Language Models for Industrial Recommendation Systems Through An Inferential Knowledge Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recently, Knowledge Base (KB)-based models are proposed to incorporate expert knowledge, but it struggle to adapt to new items and the evolving e-commerce environment. To address these challenges, we propose a novel Large Language Model based Complementary Knowledge Enhanced Recommendation System (LLM-KERec). |
Qian Zhao; Hao Qian; Ziqi Liu; Gong-Duo Zhang; Lihong Gu; |
| 582 | ECRT: Flexible Sequence Enhancement Framework for Cross-Domain Information Reuse in Recommendation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: So it is crucial to leverage cross-domain data from prominent domains to better supplement user behavior sequences for our targets. To tackle this problem, we propose the Enhanced Cross-domain Ralation Transfer (ECRT) framework to make flexible sequence augmentation with the assist of cross-domain information from other domains. |
Weiqiang Zhao; Zi-Yuan Wu; Yatao Yang; Lifeng Hua; Hao Xiong; |
| 583 | Scaling Vison-Language Foundation Model to 12 Billion Parameters in Baidu Dynamic Image Advertising Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, to improve multimodal content understanding in Dynamic Image adVERtising, we present a viSion-language rEpresentation model (referred to as DIVERSE) that learns on cross-view and cross-token contrastive loss. |
Xinyu Zhao; Kang Zhao; Zhipeng Jin; Yi Yang; Wen Tao; Xiaodong Chen; Cong Han; Shuanglong Li; Lin Liu; |
| 584 | Confidence-Aware Multi-Field Model Calibration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a confidence-aware multi-field calibration method, which adaptively adjusts the calibration intensity based on confidence levels derived from sample statistics. |
Yuang Zhao; Chuhan Wu; Qinglin Jia; Hong Zhu; Jia Yan; Libin Zong; Linxuan Zhang; Zhenhua Dong; Muyu Zhang; |
| 585 | A Behavior-aware Cause Identification Framework for Order Cancellation in Logistics Service Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The main challenges lie in dynamically correlating couriers’ varying behaviors with dialogue content. To tackle this challenge, we develop COCO, a cause identification framework for order cancellation in logistics, which includes: i) Multi-modal features exploration, which analyzes dialogues and couriers’ behaviors (both historical and current); ii) Multi-modal features aggregation, which uses a hierarchical attention mechanism to adaptively capture the dynamic correlations within dialogues and behaviors; iii) LLM-enhanced refinement, which leverages Large Language Models to accurately process a large number of unlabeled dialogues, significantly enhancing COCO’s generalization and performance. |
Shuxin Zhong; Yahan Gu; Wenjun Lyu; Hongyu Lin; Guang Yang; Yao Lu; Guang Wang; Yu Yang; Desheng Zhang; |
| 586 | Adaptive Cross-platform Transportation Time Prediction for Logistics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, a significant challenge arises in inferring travel-time-correlated station pairs across different companies, especially without full disclosure of station information. To address this, we design an <u>Ada</u>ptive cross-platform <u>Trans</u>portation time prediction framework built upon a hypergraph structure, named AdaTrans, comprising: i) A spatial-temporal routing graph learner employs node-centric and edge-centric hyperedges to address the complex, non-pairwise correlations among stations and station pairs within and across companies; ii) A spatial-temporal graph-based transportation time predictor that utilizes multi-task learning to enhance overall transportation time prediction by leveraging the correlations between interconnected sub-tasks (i.e., dwell and travel times prediction) Extensive evaluation with real-world data collected from JD.com, a leading e-commerce platform in China, demonstrates that consolidating records from other companies reduces RMSE, MAE, and MAPE by 12.63\%, 5.18\%, and 16.67\%, compared to state-of-the-art methods. |
Shuxin Zhong; Wenjun Lyu; Zhiqing Hong; Guang Yang; Weijian Zuo; Haotian Wang; Guang Wang; Yu Yang; Desheng Zhang; |
| 587 | LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: They often overlook the complex nonlinear dynamic characteristics and potential higher-order interaction relationships among stocks in the stock market. Therefore, we propose a stock price trend prediction model named LSR-IGRU in this paper, which is based on long short-term stock relationships and an improved GRU input. |
Peng Zhu; Yuante Li; Yifan Hu; Qinyuan Liu; Dawei Cheng; Yuqi Liang; |
| 588 | Understanding and Modeling Job Marketplace with Pretrained Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing graph neural network (GNN)-based methods have shallow understandings of the associated textual features and heterogeneous relations. To address the above challenges, we propose PLM4Job, a job marketplace foundation model that tightly couples pretrained language models (PLM) with job market graph, aiming to fully utilize the pretrained knowledge and reasoning ability to model member/job textual features as well as various member-job relations simultaneously. |
Yaochen Zhu; Liang Wu; Binchi Zhang; Song Wang; Qi Guo; Liangjie Hong; Luke Simon; Jundong Li; |
| 589 | Collaborative Scope: Encountering The Substitution Effect Within The Delivery Scope in Online Food Delivery Platform Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a novel method, named Collaborative Scope, which views the delivery scope as an assortment optimization problem, considering the substitution effect between merchants from the user’s perspective. |
Yida Zhu; Liying Chen; Chen Zheng; Jia Shi; Daping Xiong; Zewen Huang; Shihao Ren; Shuiping Chen; Jinghua Hao; Renqing He; |
| 590 | EDGE: A Conversational Interface Driven By Large Language Models for Educational Knowledge Graphs Exploration Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose EDGE (EDucational knowledge Graph Explorer), a natural language interface that uses knowledge graphs to organize educational information. |
Neda Afreen; Giacomo Balloccu; Ludovico Boratto; Gianni Fenu; Francesca Maridina Malloci; Mirko Marras; Andrea Giovanni Martis; |
| 591 | XplainScreen: Unveiling The Black Box of Graph Neural Network Drug Screening Models with A Unified XAI Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To address the interpretability issues associated with GNN-based virtual drug screening, we introduce XplainScreen: a unified explanation framework designed to evaluate various explanation methods for GNN-based models. |
Geonhee Ahn; Md Mahim Anjum Haque; Subhashis Hazarika; Soo Kyung Kim; |
| 592 | RevEx: An Online Consumer Reviews Extraction Tool Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents RevEx, an online consumer reviews extraction tool. |
Juli\'{a}n Alarte; Carlos Galindo; Carlos Mart\'{\i}n; Josep Silva; |
| 593 | AuditLLM: A Tool for Auditing Large Language Models Using Multiprobe Approach Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, a tool for performing such audits with a easy to execute workflow, and low technical threshold is lacking. In this demo, we introduce AuditLLM, a novel tool designed to audit the performance of various LLMs in a methodical way. |
Maryam Amirizaniani; Elias Martin; Tanya Roosta; Aman Chadha; Chirag Shah; |
| 594 | Preserving Old Memories in Vivid Detail: Human-Interactive Photo Restoration Framework Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we present the AI-based photo restoration framework composed of multiple stages, where each stage is tailored to enhance and restore specific types of photo damage, accelerating and automating the photo restoration process. |
Seung-Yeon Back; Geonho Son; Dahye Jeong; Eunil Park; Simon S. Woo; |
| 595 | FactCheckBureau: Build Your Own Fact-Check Analysis Pipeline Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We have built FactCheckBureau, an end-to-end solution that enables researchers to easily and interactively design and evaluate FC retrieval pipelines. |
Oana Balalau; Pablo Bertaud-Velten; Younes El Fraihi; Garima Gaur; Oana Goga; Samuel Guimaraes; Ioana Manolescu; Brahim Saadi; |
| 596 | STaR: Space and Time-aware Statistic Query Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: STaR is deployed within our system StatCheck, which we developed and shared with fact-checking journalists. |
Oana Balalau; Simon Ebel; Helena Galhardas; Th\'{e}o Galizzi; Ioana Manolescu; |
| 597 | FairRankTune: A Python Toolkit for Fair Ranking Tasks Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present FairRankTune, a multi-purpose open-source Python toolkit offering three primary services: quantifying fairness-related harms, leveraging bias mitigation algorithms, and constructing custom fairness-relevant datasets. |
Kathleen Cachel; Elke Rundensteiner; |
| 598 | Music2P: A Multi-Modal AI-Driven Tool for Simplifying Album Cover Design Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: Our ultimate goal is to provide a tool that empowers musicians and producers, especially those with limited resources or expertise, to create compelling album covers. |
Joong Ho Choi; Geonyeong Choi; Ji Eun Han; Wonjin Yang; Zhi-Qi Cheng; |
| 599 | Shaded Route Planning Using Active Segmentation and Identification of Satellite Images Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper is the first to introduce a pipeline that utilizes segmentation foundation models to extract shaded areas from high-resolution satellite images. |
Longchao Da; Rohan Chhibba; Rushabh Jaiswal; Ariane Middel; Hua Wei; |
| 600 | A Skill Proficiency Framework for Workforce Learning and Development Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a robust skill proficiency modeling framework that offers a structured method to help describe, assess and develop proficiency in key skills, facilitating individuals’ career pathways and aiding organizations in talent management and adaptability. |
Rebecca Dew; Mingzhao Li; Sandya Baratha Raj; |
| 601 | Human-in-the-Loop Feature Discovery for Tabular Data Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In recent years, researchers have developed several methods to automate discovering datasets and augmenting features for training Machine Learning (ML) models. |
Andra Ionescu; Zeger Mouw; Efthimia Aivaloglou; Rihan Hai; Asterios Katsifodimos; |
| 602 | Introducing CausalBench: A Flexible Benchmark Framework for Causal Analysis and Machine Learning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce CausalBench, a transparent, fair, and easy-to-use evaluation platform, aiming to (a) enable the advancement of research in causal learning by facilitating scientific collaboration in novel algorithms, datasets, and metrics and (b) promote scientific objectivity, reproducibility, fairness, and awareness of bias in causal learning research. |
Ahmet Kapki\c{c}; Pratanu Mandal; Shu Wan; Paras Sheth; Abhinav Gorantla; Yoonhyuk Choi; Huan Liu; K. Sel\c{c}uk Candan; |
| 603 | DirDense: A Tool for Mining Dense Subgraphs from A Big Directed Graph Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While most dense structures are defined on undirected graphs, recent efforts have generalized these notions to directed graphs. In this demonstration paper, we present DirDense, an interactive tool that makes it easy for end-users to mine dense structures from a big directed graph. |
Jalal Khalil; Ahmad Akhlaque; Da Yan; Lyuheng Yuan; Saugat Adhikari; Yang Zhou; Zhe Jiang; |
| 604 | A One-Health Platform for Antimicrobial Resistance Data Analytics Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Antimicrobial resistance (AMR) poses potentially critical health issues for human and animal populations in the near future. To meet this challenge, we need to adopt a One Health strategy, which involves studying and linking information from human and animal populations, as well as from the environment.In this demonstration, we present an early prototype of PROMISE platform, which we are developing for One Health data management and analytics, to enable experts from different fields to gain insights into AMR. |
Benoit Lange; Reza Akbarinia; Florent Masseglia; |
| 605 | DiaKoP: Dialogue-based Knowledge-oriented Programming for Neural-symbolic Knowledge Base Question Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present Dialogue-based Knowledge-oriented Programming system (DiaKoP), a system with a chat interface designed for multi-turn knowledge base question answering (KBQA). |
Zhicheng Lee; Zhidian Huang; Zijun Yao; Jinxin Liu; Amy Xin; Lei Hou; Juanzi Li; |
| 606 | LLM-PQA: LLM-enhanced Prediction Query Answering Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces LLM-PQA, a novel tool that addresses prediction queries formulated in natural language. |
Ziyu Li; Wenjie Zhao; Asterios Katsifodimos; Rihan Hai; |
| 607 | EHR-Based Mobile and Web Platform for Chronic Disease Risk Prediction Using Large Language Multimodal Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We collected five years of Electronic Health Records (EHRs) from Taiwan’s hospital database between 2017 and 2021 as an AI database. |
Chun-Chieh Liao; Wei-Ting Kuo; I-Hsuan Hu; Yen-Chen Shih; Jun-En Ding; Feng Liu; Fang-Ming Hung; |
| 608 | Demonstrating PARS: A Decision Support System for Developing Vertical Partitioning Plans Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Moreover, selecting an appropriate partitioning technique based solely on historical experimental results from research articles is challenging due to variability in storage devices, evaluation metrics, and database schemas. We propose PARS to address these issues by offering end-to-end input/output, customizable database configurations, and prioritized optimization objectives to aid database administrators (DBAs) in making informed partitioning decisions. |
Pengju Liu; Kai Zhong; Cuiping Li; Hong Chen; |
| 609 | OpenTOS: Open-source System for Transfer Learning Bayesian Optimization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we introduce OpenTOS, an open-source system designed for transfer learning in Bayesian optimization. |
Peili Mao; Ke Li; |
| 610 | GARF: A Self-supervised Data Cleaning System with SeqGAN Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce GARF, a novel data cleaning system based on sequence generative adversarial networks (SeqGAN). |
Jinfeng Peng; Hanghai Cui; Derong Shen; Yue Kou; Tiezheng Nie; Tianlong Guo; |
| 611 | Unified Argument Retrieval System from German News Articles Using Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce our unified argument retrieval system that uses our clustering model to cluster news articles and subsequently extracts the core arguments from news articles using the argument prediction model. |
Piriyakorn Piriyatamwong; Saikishore Kalloori; Fabio Z\{u}nd; |
| 612 | CourtsightTV: An Interactive Visualization Software for Labeling Key Basketball Moments Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we describe a visual interface for user-friendly and efficient labeling of key moments in basketball games aided by neural networks. |
Alexander Russakoff; Kenny Miller; Vahid Mahzoon; Parsa Esmaeilkhani; Christine Cho; Jaffar Alzeidi; Sandro Hauri; Slobodan Vucetic; |
| 613 | A Scalable Tool for Democratizing Variant Calling on Human Genomes Using Commodity Clusters Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present a scalable tool for democratizing variant calling on human genome sequences using testbeds that are available for academic research at no charge. |
Khawar Shehzad; Ajay Kumar; Matthew Schutz; Chase Webb; Polycarp Nalela; Manas Jyoti Das; Praveen Rao; |
| 614 | Demonstration of A Multi-agent Framework for Text to SQL Applications with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, the LLM needs to understand not only user input but also information from the database. In this demo, we present multi-agent SQL (MageSQL), an LLM based Text-to-SQL approach that tackles the task by orchestrating multiple agents in a pipeline. |
Chen Shen; Jin Wang; Sajjadur Rahman; Eser Kandogan; |
| 615 | Multi-Graph Explorer: A Framework for Advanced Multi-Graph Analysis and Method Development Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we present Multi-Graph Explorer: a MATLAB software designed to offer a user-friendly yet comprehensive, flexible, and extensible workflow for multi-graph analysis, aiming to break these barriers and accelerate progress in machine learning tasks involving multi-graphs. |
Yorgos Tsitsikas; Evangelos E. Papalexakis; |
| 616 | LINKin-PARK: Land Valuation Information and Knowledge in Predictive Analysis and Reporting Kit Via Dual Attention-DCCNN Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present LINKin-PARK, an innovative system that seamlessly merges geographic visualization with an advanced Dual Attention Double Channel Convolutional Neural Network with Multilayer Perceptron (Dual Attention-DCCNN+MLP) to facilitate the efficient analysis of land valuation. |
Teng-Yuan Tsou; Shih-Yu Lai; Hsuan-Ching Chen; Jung-Tsang Yeh; Pei-Xuan Li; Tzu-Chang Lee; Hsun-Ping Hsieh; |
| 617 | Empowering Shoppers with Event-focused Search Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present Event-focused Search, an automated and scalable pipeline designed to facilitate event discovery and enhance event-based search. |
Austin R. Ward; Omar Alonso; |
| 618 | DeliLaw: A Chinese Legal Counselling System Based on A Large Language Model Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we present DeliLaw, a Chinese legal counselling system based on a large language model. |
Nan Xie; Yuelin Bai; Hengyuan Gao; Ziqiang Xue; Feiteng Fang; Qixuan Zhao; Zhijian Li; Liang Zhu; Shiwen Ni; Min Yang; |
| 619 | MyCADI: My Contextual Anomaly Detection Using Isolation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: myCADI is a machine learning framework associated with a graphical interface for discovering and understanding the internal structure of an unsupervised dataset. |
V\'{e}ronne Yepmo; Gr\'{e}gory Smits; |
| 620 | GongBu: Easily Fine-tuning LLMs for Domain-specific Adaptation Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To easily and efficiently adapt LLMs to custom domains, we present a no-code fine-tuning platform, GongBu, supporting 9 PEFT methods and open-source LLMs. |
Bolin Zhang; Yimin Tian; Shengwei Wang; Zhiying Tu; Dianhui Chu; Zhiqi Shen; |
| 621 | Mastodoner: A Command-line Tool and Python Library for Public Data Collection from Mastodon Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper introduces Mastodoner, a command-line tool and Python library aimed at simplifying access to public data on Mastodon, a prominent player in the Fediverse — a decentralized network of interconnected social media platforms. |
Haris Bin Zia; Ignacio Castro; Gareth Tyson; |
| 622 | DetCat: Detecting Categorical Outliers in Relational Datasets Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we introduce DetCat for detecting categorical outliers in relational datasets, by utilizing the syntactic structure of the values. |
Arthur Zylinski; Abdulhakim A. Qahtan; |
| 623 | Understanding The User: An Intent-Based Ranking Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Web search datasets, such as MS MARCO, primarily provide short keyword queries without accompanying intent or descriptions, posing a challenge in comprehending the underlying information need. This paper proposes an approach to augmenting such datasets to annotate informative query descriptions, with a focus on two prominent benchmark datasets: TREC-DL-21 and TREC-DL-22. |
Abhijit Anand; Jurek Leonhardt; Venktesh V; Avishek Anand; |
| 624 | 3DLNews: A Three-decade Dataset of US Local News Articles Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present 3DLNews, a novel dataset with local news articles from the United States spanning the period from 1996 to 2024. |
Gangani Ariyarathne; Alexander C. Nwala; |
| 625 | Multi-turn Classroom Dialogue Dataset: Assessing Student Performance from One-on-one Conversations Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we focus on the task of automatically predicting students’ levels of mastery of math questions from teacher-student classroom dialogue data in online one-on-one classes. |
Jiahao Chen; Zitao Liu; Mingliang Hou; Xiangyu Zhao; Weiqi Luo; |
| 626 | BioMAISx: A Corpus for Aspect-Based Sentiment Analysis of Media Representations of Agricultural Biotechnologies in Africa Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we explore how aspect-based sentiment analysis might help in understanding the public discourse surrounding agricultural biotechnologies in Africa. |
Patricia Chiril; Trevor Spreadbury; Joeva Sean Rock; Brian Dowd-Uribe; David Uminsky; |
| 627 | Moving Region Representations on The Spread of A Forest Fire Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present an overview of the data acquisition and preparation steps and describe the optimization strategy implemented to establish a vertex correspondence between the regions that delimit the burned region at discrete time instants. |
Henrique Mac\'{\i}as da Silva; Tiago F. R. Ribeiro; Rog\'{e}rio Lu\'{\i}s de C. Costa; Jos\'{e} Moreira; |
| 628 | PyPANTERA: A Python PAckage for Natural Language ObfuscaTion Enforcing PRivacy \& Anonymization Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Documents, queries, posts, and reviews might pose a risk of inadvertently disclosing sensitive information. |
Francesco Luigi De Faveri; Guglielmo Faggioli; Nicola Ferro; |
| 629 | An Evaluation Framework for Attributed Information Retrieval Using Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We propose a reproducible framework to evaluate and benchmark attributed information seeking, using any backbone LLM, and different architectural designs: (1) Generate (2) Retrieve then Generate, and (3) Generate then Retrieve. |
Hanane Djeddal; Pierre Erbacher; Raouf Toukal; Laure Soulier; Karen Pinel-Sauvagnat; Sophia Katrenko; Lynda Tamine; |
| 630 | VHAKG: A Multi-modal Knowledge Graph Based on Synchronized Multi-view Videos of Daily Activities Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: In this paper, we construct an MMKG based on synchronized multi-view simulated videos of daily activities. |
Shusaku Egami; Takanori Ugai; Swe Nwe Nwe Htun; Ken Fukuda; |
| 631 | A Generative Benchmark Creation Framework for Detecting Common Data Table Versions Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Thus, this work introduces a novel framework to generate benchmarks for data versioning using Generative AI (specifically Large Language Models). |
Daniel C. Fox; Aamod Khatiwada; Roee Shraga; |
| 632 | Covid19-twitter: A Twitter-based Dataset for Discourse Analysis in Sentence-level Sentiment Classification Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a new large-scale dataset for this purpose called Covid19-twitter, which contains around 100k tweets symmetrically divided into various categories. |
Shashank Gupta; Mohamed Reda Bouadjenek; Antonio Robles-Kelly; Tsz-Kwan Lee; Thanh Thi Nguyen; Asef Nazari; Dhananjay Thiruvady; |
| 633 | Dataset Generation for Korean Urban Parks Analysis with Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This study introduces a novel dataset derived from Instagram, using 42,187 images tagged with #Seoul and #Park hashtags from 2017 to 2023. |
Honggu Kim; Minwoo Kang; Hyeyoung Choi; Yun-Gyung Cheong; |
| 634 | EUvsDisinfo: A Dataset for Multilingual Detection of Pro-Kremlin Disinformation in News Articles Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: This work introduces EUvsDisinfo, a multilingual dataset of disinformation articles originating from pro-Kremlin outlets, along with trustworthy articles from credible / less biased sources. |
Jo\~{a}o A. Leite; Olesya Razuvayevskaya; Kalina Bontcheva; Carolina Scarton; |
| 635 | LeDQA: A Chinese Legal Case Document-based Question Answering Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present LeDQA, the first Chinese legal case document-based question answering dataset to our best knowledge. |
Bulou Liu; Zhenhao Zhu; Qingyao Ai; Yiqun Liu; Yueyue Wu; |
| 636 | CH-Mits: A Cross-Modal Dataset for User Sentiment Analysis on Chinese Social Media Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Given such emotional inconsistency between images and texts, how to effectively identify users’ true emotion polarity is still challenging. Toward the above issues, in this paper, we firstly construct a Chinese social media dataset CH-Mits for multimodal user sentiment analysis. In order to evaluate the usability of the dataset, we conceive and implement a novel model called PEMNet, and compare it with state-of-the-art models based on the CH-Mits dataset. |
Juhao Ma; Shuai Xu; Yilin Liu; Xiaoming Fu; |
| 637 | Refining Wikidata Taxonomy Using Large Language Models Related Papers Related Patents Related Grants Related Venues Related Experts Related Code View Highlight: We present WiKC, a new version of Wikidata taxonomy cleaned automatically using a combination of Large Language Models (LLMs) and graph mining techniques. |
Yiwen Peng; Thomas Bonald; Mehwish Alam; |
| 638 | AnnoRank: A Comprehensive Web-Based Framework for Collecting Annotations and Assessing Rankings Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present AnnoRank, a web-based user interface (UI) framework designed to facilitate collecting crowdsource annotations in the context of information retrieval. |
Clara Rus; Gabrielle Poerwawinata; Andrew Yates; Maarten de Rijke; |
| 639 | InfinityMath: A Scalable Instruction Tuning Dataset in Programmatic Mathematical Reasoning Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce InfinityMATH, a scalable instruction tuning dataset for programmatic mathematical reasoning. |
Bo-Wen Zhang; Yan Yan; Lin Li; Guang Liu; |
| 640 | Advancing Multivariate Time Series Anomaly Detection: A Comprehensive Benchmark with Real-World Data from Alibaba Cloud Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we advance the benchmarking of time series anomaly detection by addressing datasets, evaluation metrics, and algorithm comparison. |
Chaoli Zhang; Yingying Zhang; Lanshu Peng; Qingsong Wen; Yiyuan Yang; Chongjiong Fan; Minqi Jiang; Lunting Fan; Liang Sun; |
| 641 | M3: A Multi-Image Multi-Modal Entity Alignment Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The fair comparison and development of alignment solutions may be hindered by these oversimplified scenarios. To address this problem, in this work, we first construct M3, an MMEA benchmark equipped with multiple images from different search engines in real-world scenarios. Additionally, we design a simple and universal multi-image processing module (AMIA), which assigns varying attention weights to images associated with entities to effectively model visual information. |
Shiqi Zhang; Weixin Zeng; Zhen Tan; Xiang Zhao; Weidong Xiao; |
| 642 | ELF-Gym: Evaluating Large Language Models Generated Features for Tabular Prediction Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: But despite this potential, evaluations thus far are primarily based on the end performance of a complete ML pipeline, providing limited insight into precisely how LLMs behave relative to human experts in feature engineering. To address this gap, we propose ELF-Gym, a framework for Evaluating LLM-generated Features. |
Yanlin Zhang; Ning Li; Quan Gan; Weinan Zhang; David Wipf; Minjie Wang; |
| 643 | CheckGuard: Advancing Stolen Check Detection with A Cross-Modal Image-Text Benchmark Dataset Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: While deep learning techniques show great promise in detecting objects and extracting information from images, their effectiveness in addressing check fraud is hindered by the lack of comprehensive, open-source, large training datasets specifically for check information extraction. To bridge this gap, this paper introduces CheckGuard, a large labeled image-to-text cross-modal dataset designed for check information extraction. |
Fei Zhao; Jiawen Chen; Bin Huang; Chengcui Zhang; Gary Warner; |