Paper Digest: WWW 2021 Highlights
Download WWW-2021-Paper-Digests.pdf– highlights of all WWW-2021 papers. Readers can also choose to read this highlight article on our console, which allows users to filter out papers using keywords. The Web Conference (WWW) is one of the top internet conferences in the world. In 2021, it is to be held online.
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 / summaries to quickly get the main idea of each paper. For users searching for papers/patents/grants with highlights, related papers, patents, grants, experts and organizations, please try our search console. We also provide an exclusive professor search service to find more than 400K professors across the US using their research work.
If you do not want to miss any interesting academic paper, you are welcome to sign up our free daily paper digest service to get updates on new papers published in your area every day. You are also welcome to follow us on Twitter and Linkedin to get updated with new conference digests.
Paper Digest Team
team@paperdigest.org
TABLE 1: Paper Digest: WWW 2021 Highlights
| Paper | Author(s) | |
|---|---|---|
| 1 | REST: Relational Event-driven Stock Trend Forecasting Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods. |
Wentao Xu; Weiqing Liu; Chang Xu; Jiang Bian; Jian Yin; Tie-Yan Liu; |
| 2 | Exploring The Scale-Free Nature of Stock Markets: Hyperbolic Graph Learning for Algorithmic Trading Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To capture the scale-free spatial and temporal dependencies in stock prices, we propose HyperStockGAT: Hyperbolic Stock Graph Attention Network, the first model on the Riemannian Manifolds for stock selection. |
Ramit Sawhney; Shivam Agarwal; Arnav Wadhwa; Rajiv Shah; |
| 3 | Detecting and Quantifying Wash Trading on Decentralized Cryptocurrency Exchanges Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we illustrate how wash trading activity can be identified on two of the first popular limit order book-based decentralized exchanges on the Ethereum blockchain, IDEX and EtherDelta. |
Friedhelm Victor; Andrea Marie Weintraud; |
| 4 | Towards Understanding and Demystifying Bitcoin Mixing Services Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we propose a generic abstraction model for mixing services and observe that there are two mixing mechanisms in the wild, i.e. swapping and obfuscating. |
Lei Wu; Yufeng Hu; Yajin Zhou; Haoyu Wang; Xiapu Luo; Zhi Wang; Fan Zhang; Kui Ren; |
| 5 | Towards Understanding Cryptocurrency Derivatives:A Case Study of BitMEX Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we explore this new paradigm through a study of BitMEX, one of the first and most successful derivatives platforms for leveraged cryptocurrency trading. |
Kyle Soska; Jin-Dong Dong; Alex Khodaverdian; Ariel Zetlin-Jones; Bryan Routledge; Nicolas Christin; |
| 6 | On The Feasibility of Automated Built-in Function Modeling for PHP Symbolic Execution Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore the feasibility of automating the process of modeling PHP built-in functions for symbolic execution. |
Penghui Li; Wei Meng; Kangjie Lu; Changhua Luo; |
| 7 | TLS 1.3 in Practice:How TLS 1.3 Contributes to The Internet Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we take a closer look at TLS 1.3 deployments in practice regarding adoption rate, security, performance, and implementation by applying temporal, spatial, and platform-based approaches on 687M connections. |
Hyunwoo Lee; Doowon Kim; Yonghwi Kwon; |
| 8 | Towards Realistic and ReproducibleWeb Crawl Measurements Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our work improves the state of web privacy and security by investigating how key measurements differ when using naive crawling tool defaults vs. careful attempts to match “real” users across the Tranco top 25k web domains. |
Jordan Jueckstock; Shaown Sarker; Peter Snyder; Aidan Beggs; Panagiotis Papadopoulos; Matteo Varvello; Benjamin Livshits; Alexandros Kapravelos; |
| 9 | #Twiti: Social Listening for Threat Intelligence Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present Twiti, a system that automatically extracts various forms of malware IOCs from Twitter. |
Hyejin Shin; WooChul Shim; Saebom Kim; Sol Lee; Yong Goo Kang; Yong Ho Hwang; |
| 10 | An Investigation of Identity-Account Inconsistency in Single Sign-On Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the identity-account inconsistency threat, a new SSO vulnerability that can cause the compromise of online accounts. |
Guannan Liu; Xing Gao; Haining Wang; |
| 11 | An Alternative Cross Entropy Loss for Learning-to-Rank Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a cross entropy-based learning-to-rank loss function that is theoretically sound, is a convex bound on NDCG—a popular ranking metric—and is consistent with NDCG under learning scenarios common in information retrieval. |
Sebastian Bruch; |
| 12 | Diversification-Aware Learning to Rank Using Distributed Representation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a soft version of the “next document” paradigm in which we associate each document with an approximate rank, and thus the subtopics covered prior to a document can also be estimated. |
Le Yan; Zhen Qin; Rama Kumar Pasumarthi; Xuanhui Wang; Michael Bendersky; |
| 13 | Maximizing Marginal Fairness for Dynamic Learning to Rank Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this problem, we propose a fair and unbiased ranking method named Maximal Marginal Fairness (MMF). |
Tao Yang; Qingyao Ai; |
| 14 | PairRank: Online Pairwise Learning to Rank By Divide-and-Conquer Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to estimate a pairwise learning to rank model online. |
Yiling Jia; Huazheng Wang; Stephen Guo; Hongning Wang; |
| 15 | Robust Generalization and Safe Query-Specializationin Counterfactual Learning to Rank Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce the Generalization and Specialization (GENSPEC) algorithm, a robust feature-based counterfactual LTR method that pursues per-query memorization when it is safe to do so. |
Harrie Oosterhuis; Maarten de de Rijke; |
| 16 | Communication Efficient Federated Generalized Tensor Factorization for Collaborative Health Data Analytics Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a communication efficient federated generalized tensor factorization, which is flexible enough to choose from a variate of losses to best suit different types of data in practice. |
Jing Ma; Qiuchen Zhang; Jian Lou; Li Xiong; Joyce C. Ho; |
| 17 | Completing Missing Prevalence Rates for Multiple Chronic Diseases By Jointly Leveraging Both Intra- and Inter-Disease Population Health Data Correlations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this problem, this paper proposes a deep-learning-based approach, called Compressive Population Health (CPH), to infer and recover (to complete) the missing prevalence rate entries of multiple chronic diseases. |
Yujie Feng; Jiangtao Wang; Yasha Wang; Sumi Helal; |
| 18 | Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we work towards improving empathy in online mental health support conversations. |
Ashish Sharma; Inna W. Lin; Adam S. Miner; David C. Atkins; Tim Althoff; |
| 19 | Search Engines Vs. Symptom Checkers: A Comparison of Their Effectiveness for Online Health Advice Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we conducted a user study with 64 real-world users performing 8 simulated self-diagnosis tasks. |
Sebastian Cross; Ahmed Mourad; Guido Zuccon; Bevan Koopman; |
| 20 | UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To fill the gap, we propose UNcertaInTy-based hEalth risk prediction (UNITE) model. |
Chacha Chen; Junjie Liang; Fenglong Ma; Lucas Glass; Jimeng Sun; Cao Xiao; |
| 21 | MIRA:Leveraging Multi-Intention Co-click Information in Web-scale Document Retrieval Using Deep Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we aim to leverage the additional information for documents from their co-click neighbours to help document retrieval. |
Yusi Zhang; Chuanjie Liu; Angen Luo; Hui Xue; Xuan Shan; Yuxiang Luo; Yiqian Xia; Yuanchi Yan; Haidong Wang; |
| 22 | Long Short-Term Session Search: Joint Personalized Reranking and Next Query Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a personalized session search model, called Long short-term session search, Network (LostNet), that jointly learns to rerank documents for the current query and predict the next query. |
Qiannan Cheng; Zhaochun Ren; Yujie Lin; Pengjie Ren; Zhumin Chen; Xiangyuan Liu; Maarten de de Rijke; |
| 23 | On The Value of Wikipedia As A Gateway to The Web Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To date, however, little is known about the amount of traffic generated by Wikipedia’s external links. We fill this gap in a detailed analysis of usage logs gathered from Wikipedia users’ client devices. |
Tiziano Piccardi; Miriam Redi; Giovanni Colavizza; Robert West; |
| 24 | Projected Hamming Dissimilarity for Bit-Level Importance Coding in Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a variational hashing model for learning hash codes optimized for this projected Hamming dissimilarity, and experimentally evaluate it in collaborative filtering experiments. |
Christian Hansen; Casper Hansen; Jakob Grue Simonsen; Christina Lioma; |
| 25 | Constructing A Comparison-based Click Model for Web Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This finding leads to the design of a novel click model named Comparison-based Click Model (CBCM). |
Ruizhe Zhang; Xiaohui Xie; Jiaxin Mao; Yiqun Liu; Min Zhang; Shaoping Ma; |
| 26 | WiseTrans: Adaptive Transport Protocol Selection for Mobile Web Service Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present WiseTrans to adaptively switch transport protocols for mobile web service online and improve the completion time of web requests. |
Jia Zhang; Enhuan Dong; Zili Meng; Yuan Yang; Mingwei Xu; Sijie Yang; Miao Zhang; Yang Yue; |
| 27 | Surrounded By The Clouds: A Comprehensive Cloud Reachability Study Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we provide a comprehensive cloud reachability study as we perform extensive global client-to-cloud latency measurements towards 189 datacenters from all major cloud providers. |
Lorenzo Corneo; Maximilian Eder; Nitinder Mohan; Aleksandr Zavodovski; Suzan Bayhan; Walter Wong; Per Gunningberg; Jussi Kangasharju; Jörg Ott; |
| 28 | BrowseLite: A Private Data Saving Solution for The Web Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that data-saving is still possible without impacting either users privacy or Web-compat. |
Conor Kelton; Matteo Varvello; Andrius Aucinas; Ben Livshits; |
| 29 | Superways: A Datacenter Topology for Incast-heavy Workloads Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Superways, a heterogeneous datacenter topology that provides higher bandwidth for some servers to absorb incasts, as incasts occur only at a small number of servers that aggregate responses from other senders. |
Hamed Rezaei; Balajee Vamanan; |
| 30 | BBR Bufferbloat in DASH Video Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our investigation reveals that BBR under deep buffers and high network burstiness severely overestimates available bandwidth and does not converge to steady state, both of which results in BBR sending substantially more data into the network, causing a queue buildup. |
Santiago Vargas; Rebecca Drucker; Aiswarya Renganathan; Aruna Balasubramanian; Anshul Gandhi; |
| 31 | Graph Structure Estimation Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose graph estimation neural networks GEN, which estimates graph structure for GNNs. |
Ruijia Wang; Shuai Mou; Xiao Wang; Wanpeng Xiao; Qi Ju; Chuan Shi; Xing Xie; |
| 32 | Efficient Probabilistic Truss Indexing on Uncertain Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the problem of (k, ?)-truss indexing and querying over an uncertain graph . |
Zitan Sun; Xin Huang; Jianliang Xu; Francesco Bonchi; |
| 33 | Random Graphs with Prescribed K-Core Sequences: A New Null Model for Network Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Guided by this challenge, we establish a new family of network null models that operate on the k-core decomposition. |
Katherine Van Koevering; Austin Benson; Jon Kleinberg; |
| 34 | Motif-driven Dense Subgraph Discovery in Directed and Labeled Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose quark decomposition framework to locate dense subgraphs that are rich with a given motif. |
Ahmet Erdem Sarıyüce; |
| 35 | Heterogeneous Graph Neural Network Via Attribute Completion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we hold that missing attributes can be acquired by a learnable manner, and propose a general framework for Heterogeneous Graph Neural Network via Attribute Completion (HGNN-AC), including pre-learning of topological embedding and attribute completion with attention mechanism. |
Di Jin; Cuiying Huo; Chundong Liang; Liang Yang; |
| 36 | DGCN: Diversified Recommendation with Graph Convolutional Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim at pushing the diversification to the upstream candidate generation stage, with the help of Graph Convolutional Networks (GCN). |
Yu Zheng; Chen Gao; Liang Chen; Depeng Jin; Yong Li; |
| 37 | Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we fill this gap and propose a multi-channel hypergraph convolutional network to enhance social recommendation by leveraging high-order user relations. |
Junliang Yu; Hongzhi Yin; Jundong Li; Qinyong Wang; Nguyen Quoc Viet Hung; Xiangliang Zhang; |
| 38 | Reinforcement Recommendation with User Multi-aspect Preference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider how to model user multi-aspect preferences in the context of RL-based recommender system. |
Xu Chen; Yali Du; Long Xia; Jun Wang; |
| 39 | Variation Control and Evaluation for Generative Slate Recommendations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to enhance the accuracy-based evaluation with slate variation metrics to estimate the stochastic behavior of generative models. |
Shuchang Liu; Fei Sun; Yingqiang Ge; Changhua Pei; Yongfeng Zhang; |
| 40 | Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve the above problem, in this work, we propose a novel method called Adversarial and Contrastive Variational Autoencoder (ACVAE) for sequential recommendation. |
Zhe Xie; Chengxuan Liu; Yichi Zhang; Hongtao Lu; Dong Wang; Yue Ding; |
| 41 | “Is It A Qoincidence?”: An Exploratory Study of QAnon on Voat Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper provides an empirical exploratory analysis of the QAnon community on Voat.co, a Reddit-esque news aggregator, which has captured the interest of the press for its toxicity and for providing a platform to QAnon followers. |
Antonis Papasavva; Jeremy Blackburn; Gianluca Stringhini; Savvas Zannettou; Emiliano De Cristofaro; |
| 42 | Chinese Wall or Swiss Cheese? Keyword filtering in the Great Firewall of China Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We report on a detailed investigation of the GFW’s application-layer understanding of HTTP. |
Zachary Weinberg; Diogo Barradas; Nicolas Christin; |
| 43 | Understanding The Impact of Encrypted DNS on Internet Censorship Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the impact of the encrypted DNS on Internet censorship in two aspects. |
Lin Jin; Shuai Hao; Haining Wang; Chase Cotton; |
| 44 | Improving Cyberbullying Detection with User Interaction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we observe that user comments in the same session may be inherently related, e.g., discussing similar topics, and their interaction may evolve over time. |
Suyu Ge; Lu Cheng; Huan Liu; |
| 45 | IFSpard: An Information Fusion-based Framework for Spam Review Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve these problems, we propose IFSpard, a novel information fusion-based framework that aims at exploring and exploiting useful information from various aspects for spam review detection. |
Yao Zhu; Hongzhi Liu; Yingpeng Du; Zhonghai Wu; |
| 46 | Dr.Emotion: Disentangled Representation Learning for Emotion Analysis on Social Media to Improve Community Resilience in The COVID-19 Era and Beyond Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To help combat the prolonged pandemic that has exposed vulnerabilities impacting community resilience, in this paper, based on our established large-scale COVID-19 related social media data, we propose and develop an integrated framework (named Dr.Emotion) to learn disentangled representations of social media posts (i.e., tweets) for emotion analysis and thus to gain deep insights into public perceptions towards COVID-19. |
Mingxuan Ju; Wei Song; Shiyu Sun; Yanfang Ye; Yujie Fan; Shifu Hou; Kenneth Loparo; Liang Zhao; |
| 47 | Modeling Human Motives and Emotions from Personal Narratives Using External Knowledge And Entity Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a Transformer-based architecture, referred to as , to model characters’ motives and emotions from personal narratives. |
Prashanth Vijayaraghavan; Deb Roy; |
| 48 | Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel instance-level MDA framework, named curriculum cycle-consistent generative adversarial network (C-CycleGAN), to address the above issues. |
Sicheng Zhao; Yang Xiao; Jiang Guo; Xiangyu Yue; Jufeng Yang; Ravi Krishna; Pengfei Xu; Kurt Keutzer; |
| 49 | Latent Target-Opinion As Prior for Document-Level Sentiment Classification: A Variational Approach from Fine-Grained Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we draw inspiration from fine-grained sentiment analysis, proposing to first learn the latent target-opinion distribution behind the documents, and then leverage such fine-grained prior knowledge into the classification process. |
Hao Fei; Yafeng Ren; Shengqiong Wu; Bobo Li; Donghong Ji; |
| 50 | Contrastive Lexical Diffusion Coefficient: Quantifying The Stickiness of The Ordinary Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study we introduce a new metric, contrastive lexical diffusion (CLD) coefficient, which attempts to measure the degree to which ordinary language (here clusters of common words) catch on over friendship connections over time. |
Mohammadzaman Zamani; H. Andrew Schwartz; |
| 51 | High-dimensional Sparse Embeddings for Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new recommendation model based on a full-rank factorization of the inverse Gram matrix. |
Jan Van Balen; Bart Goethals; |
| 52 | Sinkhorn Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop a unified view for the existing latent factor models from a probabilistic perspective. |
Xiucheng Li; Jin Yao Chin; Yile Chen; Gao Cong; |
| 53 | HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we combine these frameworks in a novel way, by proposing a hyperbolic GCN model for collaborative filtering. |
Jianing Sun; Zhaoyue Cheng; Saba Zuberi; Felipe Perez; Maksims Volkovs; |
| 54 | Collaborative Filtering with Preferences Inferred from Brain Signals Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we use brain-computer interfacing to infer preferences directly from the human brain. |
Keith M. Davis III; Michiel Spapé; Tuukka Ruotsalo; |
| 55 | Variable Interval Time Sequence Modeling for Career Trajectory Prediction: Deep Collaborative Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper, we propose a unified time-aware career trajectory prediction framework, namely TACTP, which is capable of jointly providing the above three abilities for better understanding the career trajectories of talents. |
Chao Wang; Hengshu Zhu; Qiming Hao; Keli Xiao; Hui Xiong; |
| 56 | User-oriented Fairness in Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we address the unfairness problem in recommender systems from the user perspective. |
Yunqi Li; Hanxiong Chen; Zuohui Fu; Yingqiang Ge; Yongfeng Zhang; |
| 57 | Mitigating Gender Bias in Captioning Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To alleviate the unwanted bias, we propose a new Guided Attention Image Captioning model (GAIC) which provides self-guidance on visual attention to encourage the model to capture correct gender visual evidence. |
Ruixiang Tang; Mengnan Du; Yuening Li; Zirui Liu; Na Zou; Xia Hu; |
| 58 | Fair Partitioning of Public Resources: Redrawing District Boundary to Minimize Spatial Inequality in School Funding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on the spatial geometry/distribution of such inequality, i.e., how the highly funded and lesser funded school districts are located relative to each other. |
Nuno Mota; Negar Mohammadi; Palash Dey; Krishna P. Gummadi; Abhijnan Chakraborty; |
| 59 | Understanding User Sensemaking in Machine Learning Fairness Assessment Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we seek to inform the design of these systems by examining how individuals make sense of fairness issues as they use different de-biasing affordances. |
Ziwei Gu; Jing Nathan Yan; Jeffrey M. Rzeszotarski; |
| 60 | Not All Features Are Equal: Discovering Essential Features for Preserving Prediction Privacy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We formulate this problem as a gradient-based perturbation maximization method that discovers this subset in the input feature space with respect to the functionality of the prediction model used by the provider. |
Fatemehsadat Mireshghallah; Mohammadkazem Taram; Ali Jalali; Ahmed Taha Taha Elthakeb; Dean Tullsen; Hadi Esmaeilzadeh; |
| 61 | Twin Peaks, A Model for Recurring Cascades Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the first mathematical model that provably explains this interesting phenomenon, besides exhibiting other fundamental properties of information cascades. |
Matteo Almanza; Silvio Lattanzi; Alessandro Panconesi; Giuseppe Re; |
| 62 | TEDIC: Neural Modeling of Behavioral Patterns in Dynamic Social Interaction Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we fill this gap by proposing a novel framework, termed TEmporal network-DIffusion Convolutional networks (TEDIC), for generic representation learning on dynamic social interaction networks. |
Yanbang Wang; Pan Li; Chongyang Bai; Jure Leskovec; |
| 63 | Modeling Sparse Information Diffusion at Scale Via Lazy Multivariate Hawkes Processes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel model and computational approach to overcome this important limitation. |
Maximilian Nickel; Matthew Le; |
| 64 | DYMOND: DYnamic MOtif-NoDes Network Generative Model Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, in this work we propose DYnamic MOtif-NoDes (DYMOND)—a generative model that considers (i) the dynamic changes in overall graph structure using temporal motif activity and (ii) the roles nodes play in motifs (e.g., one node plays the hub role in a wedge, while the remaining two act as spokes). |
Giselle Zeno; Timothy La Fond; Jennifer Neville; |
| 65 | Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new model for networks of time series that influence each other. |
Alasdair Tran; Alexander Mathews; Cheng Soon Ong; Lexing Xie; |
| 66 | Towards A Better Understanding of Query Reformulation Behavior in Web Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Different from existing efforts that rely on external assessors to make judgments, in the field study we collect both implicit behavior signals and explicit user feedback information. |
Jia Chen; Jiaxin Mao; Yiqun Liu; Fan Zhang; Min Zhang; Shaoping Ma; |
| 67 | Topic-enhanced Knowledge-aware Retrieval Model for Diverse Relevance Estimation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Topic Enhanced Knowledge-aware retrieval Model (TEKM) that jointly learns semantic similarity, knowledge relevance and topical relatedness to estimate relevance between query and document. |
Xiangsheng Li; Jiaxin Mao; Weizhi Ma; Yiqun Liu; Min Zhang; Shaoping Ma; Zhaowei Wang; Xiuqiang He; |
| 68 | Controllable Gradient Item Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we identify and study an important problem of gradient item retrieval. |
Haonan Wang; Chang Zhou; Carl Yang; Hongxia Yang; Jingrui He; |
| 69 | Graph-based Hierarchical Relevance Matching Signals for Ad-hoc Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we propose a Graph-based Hierarchical Relevance Matching model (GHRM) for ad-hoc retrieval, by which we can capture the subtle and general hierarchical matching signals simultaneously. |
Xueli Yu; Weizhi Xu; Zeyu Cui; Shu Wu; Liang Wang; |
| 70 | Cross-Positional Attention for Debiasing Clicks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a fully data-driven neural model for the examination bias, Cross-Positional Attention (XPA), which is more flexible in fitting complex user behaviors. |
Honglei Zhuang; Zhen Qin; Xuanhui Wang; Michael Bendersky; Xinyu Qian; Po Hu; Dan Chary Chen; |
| 71 | Inductive Entity Representations from Text Via Link Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, in this work, we propose a holistic evaluation protocol for entity representations learned via a link prediction objective. |
Daniel Daza; Michael Cochez; Paul Groth; |
| 72 | Revisiting The Evaluation Protocol of Knowledge Graph Completion Methods for Link Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we contribute to the evaluation of link prediction as follows: 1) We propose a variation of the mean rank that considers the number of negative counterparts. |
Sudhanshu Tiwari; Iti Bansal; Carlos R. Rivero; |
| 73 | Boosting The Speed of Entity Alignment 10 ×: Dual Attention Matching Network with Normalized Hard Sample Mining Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel KG encoder — Dual Attention Matching Network (Dual-AMN), which not only models both intra-graph and cross-graph information smartly, but also greatly reduces computational complexity. |
Xin Mao; Wenting Wang; Yuanbin Wu; Man Lan; |
| 74 | Progressive, Holistic Geospatial Interlinking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we introduce a batch algorithm that simultaneously computes all topological relations and define the task of Progressive Geospatial Interlinking, which produces results in a pay-as-you-go manner when the available computational or temporal resources are limited. |
George Papadakis; Georgios Mandilaras; Nikos Mamoulis; Manolis Koubarakis; |
| 75 | RETA: A Schema-Aware, End-to-End Solution for Instance Completion in Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an end-to-end solution called RETA (as it suggests the Relation and Tail for a given head entity) consisting of two components: a RETA-Filter and RETA-Grader. |
Paolo Rosso; Dingqi Yang; Natalia Ostapuk; Philippe Cudré-Mauroux; |
| 76 | A Recommender System for Crowdsourcing Food Rescue Platforms Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the first machine learning based model to improve volunteer engagement in the food waste and security domain. |
Zheyuan Ryan Shi; Leah Lizarondo; Fei Fang; |
| 77 | A Workflow Analysis of Context-driven Conversational Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we aim to understand a general workflow of natural context-driven conversational recommendation that arises from a pairwise study of a human user interacting with a human simulating the role of a recommender. |
Shengnan Lyu; Arpit Rana; Scott Sanner; Mohamed Reda Bouadjenek; |
| 78 | Learning Intents Behind Interactions with Knowledge Graph for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN). |
Xiang Wang; Tinglin Huang; Dingxian Wang; Yancheng Yuan; Zhenguang Liu; Xiangnan He; Tat-Seng Chua; |
| 79 | Rabbit Holes and Taste Distortion: Distribution-Aware Recommendation with Evolving Interests Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We empirically identify this taste distortion problem through a data-driven study over multiple datasets. |
Xing Zhao; Ziwei Zhu; James Caverlee; |
| 80 | DeepRec: On-device Deep Learning for Privacy-Preserving Sequential Recommendation in Mobile Commerce Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the issue, this paper proposes DeepRec, an on-device deep learning framework of mining interaction behaviors for sequential recommendation without sending any raw data or intermediate results out of the device, preserving user privacy maximally. |
Jialiang Han; Yun Ma; Qiaozhu Mei; Xuanzhe Liu; |
| 81 | Meta-HAR: Federated Representation Learning for Human Activity Recognition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Meta-HAR, a federated representation learning framework, in which a signal embedding network is meta-learned in a federated manner, while the learned signal representations are further fed into a personalized classification network at each user for activity prediction. |
Chenglin Li; Di Niu; Bei Jiang; Xiao Zuo; Jianming Yang; |
| 82 | PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we attempt to realize personalization beyond a single client. |
Bingyan Liu; Yao Guo; Xiangqun Chen; |
| 83 | Characterizing Impacts of Heterogeneity in Federated Learning Upon Large-Scale Smartphone Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we carry out the first empirical study to characterize the impacts of heterogeneity in FL. |
Chengxu Yang; Qipeng Wang; Mengwei Xu; Zhenpeng Chen; Kaigui Bian; Yunxin Liu; Xuanzhe Liu; |
| 84 | Incentive Mechanism for Horizontal Federated Learning Based on Reputation and Reverse Auction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, this paper proposes a federated learning incentive mechanism based on reputation and reverse auction theory. |
Jingwen Zhang; Yuezhou Wu; Rong Pan; |
| 85 | Hierarchical Personalized Federated Learning for User Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel client-server architecture framework, namely Hierarchical Personalized Federated Learning (HPFL) to serve federated learning in user modeling with inconsistent clients. |
Jinze Wu; Qi Liu; Zhenya Huang; Yuting Ning; Hao Wang; Enhong Chen; Jinfeng Yi; Bowen Zhou; |
| 86 | Data Poisoning Attacks and Defenses to Crowdsourcing Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we show that crowdsourcing is vulnerable to data poisoning attacks, in which malicious clients provide carefully crafted data to corrupt the aggregated data. |
Minghong Fang; Minghao Sun; Qi Li; Neil Zhenqiang Gong; Jin Tian; Jia Liu; |
| 87 | Deepfake Videos in The Wild: Analysis and Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To bridge this gap in knowledge, we make the following contributions: First, we collect and present the largest dataset of deepfake videos in the wild, containing 1,869 videos from YouTube and Bilibili, and extract over 4.8M frames of content. Second, we present a comprehensive analysis of the growth patterns, popularity, creators, manipulation strategies, and production methods of deepfake content in the real-world. |
Jiameng Pu; Neal Mangaokar; Lauren Kelly; Parantapa Bhattacharya; Kavya Sundaram; Mobin Javed; Bolun Wang; Bimal Viswanath; |
| 88 | RIGA: Covert and Robust White-Box Watermarking of Deep Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we generalize white-box watermarking algorithms for DNNs, where the data owner needs white-box access to the model to extract the watermark. |
Tianhao Wang; Florian Kerschbaum; |
| 89 | CoResident Evil: Covert Communication In The Cloud With Lambdas Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we explore the feasibility of constructing a practical covert channel from lambdas. |
Anil Yelam; Shibani Subbareddy; Keerthana Ganesan; Stefan Savage; Ariana Mirian; |
| 90 | DAPter: Preventing User Data Abuse in Deep Learning Inference Services Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose, to our best knowledge, the first data abuse prevention mechanism called DAPter. |
Hao Wu; Xuejin Tian; Yuhang Gong; Xing Su; Minghao Li; Fengyuan Xu; |
| 91 | Cross-lingual Language Model Pretraining for Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce two novel retrieval-oriented pretraining tasks to further pretrain cross-lingual language models for downstream retrieval tasks such as cross-lingual ad-hoc retrieval (CLIR) and cross-lingual question answering (CLQA). |
Puxuan Yu; Hongliang Fei; Ping Li; |
| 92 | Match Plan Generation in Web Search with Parameterized Action Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we formulate the match plan generation as a Partially Observable Markov Decision Process (POMDP) with a parameterized action space, and propose a novel reinforcement learning algorithm Parameterized Action Soft Actor-Critic (PASAC) to effectively enhance the exploration in both spaces. |
Ziyan Luo; Linfeng Zhao; Wei Cheng; Sihao Chen; Qi Chen; Hui Xue; Haidong Wang; Chuanjie Liu; Mao Yang; Lintao Zhang; |
| 93 | A Linguistic Study on Relevance Modeling in Information Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We proposed three intervention methods to investigate how to leverage different modeling focuses of relevance to improve these IR tasks. |
Yixing Fan; Jiafeng Guo; Xinyu Ma; Ruqing Zhang; Yanyan Lan; Xueqi Cheng; |
| 94 | Estimation of Fair Ranking Metrics with Incomplete Judgments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In order to address this problem, we propose a sampling strategy and estimation technique for four fair ranking metrics. |
Ömer Kırnap; Fernando Diaz; Asia Biega; Michael Ekstrand; Ben Carterette; Emine Yilmaz; |
| 95 | Pivot-based Candidate Retrieval for Cross-lingual Entity Linking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a pivot-based approach which inherits the advantages of the aforementioned two approaches while avoiding their limitations. |
Qian Liu; Xiubo Geng; Jie Lu; Daxin Jiang; |
| 96 | The Structure of Toxic Conversations on Twitter Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the relationship between structure and toxicity in conversations on Twitter. |
Martin Saveski; Brandon Roy; Deb Roy; |
| 97 | Interventions for Softening Can Lead to Hardening of Opinions: Evidence from a Randomized Controlled Trial Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by the goal of designing interventions for softening polarized opinions on the Web, and building on results from psychology, we hypothesized that people would be moved more easily towards opposing opinions when the latter were voiced by a celebrity they like, rather than by a celebrity they dislike. |
Andreas Spitz; Ahmad Abu-Akel; Robert West; |
| 98 | “Short Is The Road That Leads from Fear to Hate”: Fear Speech in Indian WhatsApp Groups Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we perform the first large scale study on fear speech across thousands of public WhatsApp groups discussing politics in India. |
Punyajoy Saha; Binny Mathew; Kiran Garimella; Animesh Mukherjee; |
| 99 | “Go Eat A Bat, Chang!”: On The Emergence of Sinophobic Behavior on Web Communities in The Face of COVID-19 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we make a first attempt to study the emergence of Sinophobic behavior on the Web during the outbreak of the COVID-19 pandemic. |
Fatemeh Tahmasbi; Leonard Schild; Chen Ling; Jeremy Blackburn; Gianluca Stringhini; Yang Zhang; Savvas Zannettou; |
| 100 | Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We introduce a series of new theory-inspired metrics to define prosocial outcomes such as mentoring and esteem enhancement. |
Jiajun Bao; Junjie Wu; Yiming Zhang; Eshwar Chandrasekharan; David Jurgens; |
| 101 | DF-TAR: A Deep Fusion Network for Citywide Traffic Accident Risk Prediction with Dangerous Driving Behavior Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Deep Fusion network for citywide Traffic Accident Risk prediction (DF-TAR) with dangerous driving statistics that contain the frequencies of various dangerous driving offences in each region. |
Patara Trirat; Jae-Gil Lee; |
| 102 | Dissecting Performance of Production QUIC Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we conduct one of the first comparative studies on the performance of QUIC and TCP against production endpoints hosted by Google, Facebook, and Cloudflare under various dimensions: network conditions, workloads, and client implementations. |
Alexander Yu; Theophilus A. Benson; |
| 103 | XY-Sketch: on Sketching Data Streams at Web Scale Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel structure, called XY-sketch, which estimates the frequency of a data item by estimating the probability of this item appearing in the data stream. |
Yongqiang Liu; Xike Xie; |
| 104 | NTAM: Neighborhood-Temporal Attention Model for Disk Failure Prediction in Cloud Platforms Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Neighborhood-Temporal Attention Model (NTAM), a novel deep learning based approach to disk failure prediction. |
Chuan Luo; Pu Zhao; Bo Qiao; Youjiang Wu; Hongyu Zhang; Wei Wu; Weihai Lu; Yingnong Dang; Saravanakumar Rajmohan; Qingwei Lin; Dongmei Zhang; |
| 105 | WebSocket Adoption and The Landscape of The Real-Time Web Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper investigates this ecosystem and reports on the prevalence, benefits, and drawbacks of these technologies, with a particular focus on the adoption of WebSockets. |
Paul Murley; Zane Ma; Joshua Mason; Michael Bailey; Amin Kharraz; |
| 106 | Theoretically Improving Graph Neural Networks Via Anonymous Walk Graph Kernels Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose GSKN1, a GNN model with a theoretically stronger ability to distinguish graph structures. |
Qingqing Long; Yilun Jin; Yi Wu; Guojie Song; |
| 107 | Interpreting and Unifying Graph Neural Networks with An Optimization Framework Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we establish a surprising connection between different propagation mechanisms with a unified optimization problem, showing that despite the proliferation of various GNNs, in fact, their proposed propagation mechanisms are the optimal solution optimizing a feature fitting function over a wide class of graph kernels with a graph regularization term. |
Meiqi Zhu; Xiao Wang; Chuan Shi; Houye Ji; Peng Cui; |
| 108 | Extract The Knowledge of Graph Neural Networks and Go Beyond It: An Effective Knowledge Distillation Framework Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a framework based on knowledge distillation to address the above issues. |
Cheng Yang; Jiawei Liu; Chuan Shi; |
| 109 | CurGraph: Curriculum Learning for Graph Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we present the CurGraph (Curriculum Learning for Graph Classification) framework, that analyzes the graph difficulty in the high-level semantic feature space. |
Yiwei Wang; Wei Wang; Yuxuan Liang; Yujun Cai; Bryan Hooi; |
| 110 | Lorentzian Graph Convolutional Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel hyperbolic GCN named Lorentzian graph convolutional network (LGCN), which rigorously guarantees the learned node features follow the hyperbolic geometry. |
Yiding Zhang; Xiao Wang; Chuan Shi; Nian Liu; Guojie Song; |
| 111 | Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Inspired by the idea of vector quantization that uses cluster centroids to approximate items, we propose LISA (LInear-time Self Attention), which enjoys both the effectiveness of vanilla self-attention and the efficiency of sparse attention. |
Yongji Wu; Defu Lian; Neil Zhenqiang Gong; Lu Yin; Mingyang Yin; Jingren Zhou; Hongxia Yang; |
| 112 | Learning Heterogeneous Temporal Patterns of User Preference for Timely Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we first define the unique characteristics of the two kinds of temporal pattern of user preference that should be considered in time-aware recommender systems. Then we propose a novel recommender system for timely recommendations, called TimelyRec, which jointly learns the heterogeneous temporal patterns of user preference considering all of the defined characteristics. |
Junsu Cho; Dongmin Hyun; Seongku Kang; Hwanjo Yu; |
| 113 | Drug Package Recommendation Via Interaction-aware Graph Induction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To that end, in this paper, we target at developing a new paradigm for drug package recommendation with considering the interaction effect within drugs, in which the interaction effects could be affected by patient conditions. |
Zhi Zheng; Chao Wang; Tong Xu; Dazhong Shen; Penggang Qin; Baoxing Huai; Tongzhu Liu; Enhong Chen; |
| 114 | Interest-aware Message-Passing GCN for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Interest-aware Message-Passing GCN (IMP-GCN) recommendation model, which performs high-order graph convolution inside subgraphs. |
Fan Liu; Zhiyong Cheng; Lei Zhu; Zan Gao; Liqiang Nie; |
| 115 | Task-adaptive Neural Process for User Cold-Start Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop a novel meta-learning recommender called task-adaptive neural process (TaNP). |
Xixun Lin; Jia Wu; Chuan Zhou; Shirui Pan; Yanan Cao; Bin Wang; |
| 116 | Consistent Sampling Through Extremal Process Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose using extremal processes to generate samples for estimating the Jaccard similarity. |
Ping Li; Xiaoyun Li; Gennady Samorodnitsky; Weijie Zhao; |
| 117 | Beyond Outlier Detection: Outlier Interpretation By Attention-Guided Triplet Deviation Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Accordingly, this paper proposes a novel Attention-guided Triplet deviation network for Outlier interpretatioN (ATON). |
Hongzuo Xu; Yijie Wang; Songlei Jian; Zhenyu Huang; Yongjun Wang; Ning Liu; Fei Li; |
| 118 | Fair and Representative Subset Selection from Data Streams Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we consider the setting where data items in the stream belong to one of several disjoint groups and investigate the optimization problem with an additional fairness constraint that limits selection to a given number of items from each group. |
Yanhao Wang; Francesco Fabbri; Michael Mathioudakis; |
| 119 | CLEAR: Contrastive-Prototype Learning with Drift Estimation for Resource Constrained Stream Mining Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we focus on improving the performance of the stream mining approach under the constrained resources, where both the memory resource of old data and labeled new instances are limited/scarce. |
Zhuoyi Wang; Yuqiao Chen; Chen Zhao; Yu Lin; Xujiang Zhao; Hemeng Tao; Yigong Wang; Latifur Khan; |
| 120 | Diversity on The Go! Streaming Determinantal Point Processes Under A Maximum Induced Cardinality Objective Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we give the first streaming algorithm for optimizing DPPs under the Maximum Induced Cardinality (MIC) objective of Gillenwater et al. [15]. |
Paul Liu; Akshay Soni; Eun Yong Kang; Yajun Wang; Mehul Parsana; |
| 121 | Self-Supervised Hyperboloid Representations from Logical Queries Over Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose Hyperboloid Embeddings (HypE), a novel self-supervised dynamic reasoning framework, that utilizes positive first-order existential queries on a KG to learn representations of its entities and relations as hyperboloids in a Poincaré ball. |
Nurendra Choudhary; Nikhil Rao; Sumeet Katariya; Karthik Subbian; Chandan K. Reddy; |
| 122 | ColChain: Collaborative Linked Data Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we therefore propose ColChain (COLlaborative knowledge CHAINs), a novel decentralized architecture based on blockchains that not only lowers the burden for the data providers but at the same time also allows users to propose updates to faulty or outdated data, trace updates back to their origin, and query older versions of the data. |
Christian Aebeloe; Gabriela Montoya; Katja Hose; |
| 123 | MedPath: Augmenting Health Risk Prediction Via Medical Knowledge Paths Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose MedPath to solve these challenges and augment existing risk prediction models with the ability to use personalized information and provide reliable interpretations inferring from disease progression paths. |
Muchao Ye; Suhan Cui; Yaqing Wang; Junyu Luo; Cao Xiao; Fenglong Ma; |
| 124 | Efficient Computation of Semantically Cohesive Subgraphs for Keyword-Based Knowledge Graph Exploration Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we formulate it as a quadratic group Steiner tree problem (QGSTP) by extending the classical minimum-weight GST problem which is NP-hard. |
Yuxuan Shi; Gong Cheng; Trung-Kien Tran; Evgeny Kharlamov; Yulin Shen; |
| 125 | WiseKG: Balanced Access to Web Knowledge Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, to the best of our knowledge, we present the first work that combines both client-side and server-side query optimization techniques in a truly dynamic fashion: we introduce WiseKG, a system that employs a cost model that dynamically delegates the load between servers and clients by combining client-side processing of shipped partitions with efficient server-side processing of star-shaped sub-queries, based on current server workload and client capabilities. |
Amr Azzam; Christian Aebeloe; Gabriela Montoya; Ilkcan Keles; Axel Polleres; Katja Hose; |
| 126 | A Longitudinal Study of Removed Apps in IOS App Store Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To fill the void, in this paper, we present a large-scale and longitudinal study of removed apps in iOS app store. |
Fuqi Lin; Haoyu Wang; Liu Wang; Xuanzhe Liu; |
| 127 | Demystifying Illegal Mobile Gambling Apps Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: However, to the best of our knowledge, mobile gambling apps have not been investigated by our research community. In this paper, we take the first step to fill the void. |
Yuhao Gao; Haoyu Wang; Li Li; Xiapu Luo; Guoai Xu; Xuanzhe Liu; |
| 128 | ReACt: A Resource-centric Access Control System for Web-app Interactions on Android Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose ReACt, a novel Resource-centric Access Control design that can coherently work with all the web-app interaction mechanisms while addressing the above-mentioned limitations. |
Xin Zhang; Yifan Zhang; |
| 129 | NeuroPose: 3D Hand Pose Tracking Using EMG Wearables Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents NeuroPose, a system that shows the feasibility of 3D finger motion tracking using a platform of wearable ElectroMyoGraphy (EMG) sensors. |
Yilin Liu; Shijia Zhang; Mahanth Gowda; |
| 130 | Whale Watching in Inland Indonesia: Analyzing A Small, Remote, Internet-Based Community Cellular Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Through tight integration with a local operator’s infrastructure, we gather a unique dataset to characterize and report a year of interaction between finances, utilization, and performance of a novel, remote, data-only Community LTE Network in Bokondini, Indonesia. |
Matthew Johnson; Jenny Liang; Michelle Lin; Sudheesh Singanamalla; Kurtis Heimerl; |
| 131 | Learning Neural Point Processes with Latent Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Because these two tasks are nested, we propose to optimize the model parameters through bilevel programming, and develop an efficient solution based on truncated gradient back-propagation. |
Qiang Zhang; Aldo Lipani; Emine Yilmaz; |
| 132 | OCT-GAN: Neural ODE-based Conditional Tabular GANs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we significantly improve the utility by designing our generator and discriminator based on neural ordinary differential equations (NODEs). |
Jayoung Kim; Jinsung Jeon; Jaehoon Lee; Jihyeon Hyeong; Noseong Park; |
| 133 | Neural Collaborative Reasoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to advance Collaborative Filtering (CF) to Collaborative Reasoning (CR), which means that each user knows part of the reasoning space, and they collaborate for reasoning in the space to estimate preferences for each other. |
Hanxiong Chen; Shaoyun Shi; Yunqi Li; Yongfeng Zhang; |
| 134 | Deep Co-Attention Network for Multi-View Subspace Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, in this paper, we propose a deep co-attention network for multi-view subspace learning, which aims to extract both the common information and the complementary information in an adversarial setting and provide robust interpretations behind the prediction to the end-users via the co-attention mechanism. |
Lecheng Zheng; Yu Cheng; Hongxia Yang; Nan Cao; Jingrui He; |
| 135 | ATJ-Net: Auto-Table-Join Network for Automatic Learning on Relational Databases Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: For automatic learning on relational databases, we propose an auto-table-join network (ATJ-Net). |
Jinze Bai; Jialin Wang; Zhao Li; Donghui Ding; Ji Zhang; Jun Gao; |
| 136 | A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This is unrealistic because In this paper, we study personalized TDSs without assuming that user profiles are complete. |
Jiahuan Pei; Pengjie Ren; Maarten de Rijke; |
| 137 | Stimuli-Sensitive Hawkes Processes for Personalized Student Procrastination Modeling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To resolve these problems, we introduce a new personalized stimuli-sensitive Hawkes process model (SSHP), by jointly modeling all student-assignment pairs and utilizing their similarities, to predict students’ next activity times even when there are no historical observations. |
Mengfan Yao; Siqian Zhao; Shaghayegh Sahebi; Reza Feyzi Behnagh; |
| 138 | Personalized Treatment Selection Using Causal Heterogeneity Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we develop a framework for personalization through (i) estimation of heterogeneous treatment effect at either a cohort or member-level, followed by (ii) selection of optimal treatment variants for cohorts (or members) obtained through (deterministic or stochastic) constrained optimization. |
Ye Tu; Kinjal Basu; Cyrus DiCiccio; Romil Bansal; Preetam Nandy; Padmini Jaikumar; Shaunak Chatterjee; |
| 139 | Incremental Spatio-Temporal Graph Learning for Online Query-POI Matching Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper, we propose an Incremental Spatio-Temporal Graph Learning (IncreSTGL) framework for intelligent online query-POI matching. |
Zixuan Yuan; Hao Liu; Junming Liu; Yanchi Liu; Yang Yang; Renjun Hu; Hui Xiong; |
| 140 | Slot Self-Attentive Dialogue State Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a slot self-attention mechanism that can learn the slot correlations automatically. |
Fanghua Ye; Jarana Manotumruksa; Qiang Zhang; Shenghui Li; Emine Yilmaz; |
| 141 | Dynamic Embeddings for Interaction Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Building on the success of these studies, we propose a novel method called DeePRed that addresses some of their limitations. |
Zekarias Kefato; Sarunas Girdzijauskas; Nasrullah Sheikh; Alberto Montresor; |
| 142 | Knowledge Embedding Based Graph Convolutional Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper addresses these limitations by proposing a novel framework, namely the Knowledge Embedding based Graph Convolutional Network (KE-GCN), which combines the power of GCNs in graph-based belief propagation and the strengths of advanced knowledge embedding (a.k.a. knowledge graph embedding) methods, and goes beyond. |
Donghan Yu; Yiming Yang; Ruohong Zhang; Yuexin Wu; |
| 143 | Motif-Preserving Dynamic Attributed Network Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we present Motif-preserving Temporal Shift Network (MTSN), a novel dynamic network embedding framework that simultaneously models the local high-order structures and temporal evolution for dynamic attributed networks. |
Zhijun Liu; Chao Huang; Yanwei Yu; Junyu Dong; |
| 144 | Highly Liquid Temporal Interaction Graph Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose HILI (Highly Liquid Temporal Interaction Graph Embeddings) to predict highly liquid embeddings on temporal interaction graphs. |
Huidi Chen; Yun Xiong; Yangyong Zhu; Philip S. Yu; |
| 145 | Multiplex Bipartite Network Embedding Using Dual Hypergraph Convolutional Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop an unsupervised Dual HyperGraph Convolutional Network (DualHGCN) model that scalably transforms the multiplex bipartite network into two sets of homogeneous hypergraphs and uses spectral hypergraph convolutional operators, along with intra- and inter-message passing strategies to promote information exchange within and across domains, to learn effective node embeddings. |
Hansheng Xue; Luwei Yang; Vaibhav Rajan; Wen Jiang; Yi Wei; Yu Lin; |
| 146 | Semi-Open Information Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we define a new task, namely Semi-Open Information Extraction (SOIE), to address this need. |
Bowen Yu; Zhenyu Zhang; Jiawei Sheng; Tingwen Liu; Yubin Wang; Yucheng Wang; Bin Wang; |
| 147 | RECON: Relation Extraction Using Knowledge Graph Context in A Graph Neural Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG). |
Anson Bastos; Abhishek Nadgeri; Kuldeep Singh; Isaiah Onando Mulang; Saeedeh Shekarpour; Johannes Hoffart; Manohar Kaul; |
| 148 | GNEM: A Generic One-to-Set Neural Entity Matching Framework Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a generic one-to-set neural framework named GNEM for entity matching. |
Runjin Chen; Yanyan Shen; Dongxiang Zhang; |
| 149 | Effective Named Entity Recognition with Boundary-aware Bidirectional Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Boundary-aware Bidirectional Neural Networks (Ba-BNN) model to tackle these problems for neural-based NER. |
Fei Li; Zheng Wang; Siu Cheung Hui; Lejian Liao; Dandan Song; Jing Xu; |
| 150 | A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we present a Trigger-Sense Memory Flow Framework (TriMF) for joint entity and relation extraction. |
Yongliang Shen; Xinyin Ma; Yechun Tang; Weiming Lu; |
| 151 | MulDE: Multi-teacher Knowledge Distillation for Low-dimensional Knowledge Graph Embeddings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, instead of training high-dimensional models, we propose MulDE, a novel knowledge distillation framework, which includes multiple low-dimensional hyperbolic KGE models as teachers and two student components, namely Junior and Senior. |
Kai Wang; Yu Liu; Qian Ma; Quan Z. Sheng; |
| 152 | Efficient Non-Sampling Knowledge Graph Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To avoid such deficiency, we propose a new framework for KG embedding—Efficient Non-Sampling Knowledge Graph Embedding (NS-KGE). |
Zelong Li; Jianchao Ji; Zuohui Fu; Yingqiang Ge; Shuyuan Xu; Chong Chen; Yongfeng Zhang; |
| 153 | Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we follow the textual encoding paradigm and aim to alleviate its drawbacks by augmenting it with graph embedding techniques – a complementary hybrid of both paradigms. |
Bo Wang; Tao Shen; Guodong Long; Tianyi Zhou; Ying Wang; Yi Chang; |
| 154 | An Adversarial Transfer Network for Knowledge Representation Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose an adversarial embedding transfer network ATransN, which transfers knowledge from one or more teacher knowledge graphs to a target one through an aligned entity set without explicit data leakage. |
Huijuan Wang; Shuangyin Li; Rong Pan; |
| 155 | Mixed-Curvature Multi-Relational Graph Neural Network for Knowledge Graph Completion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address this issue, we propose Mixed-Curvature Multi-Relational Graph Neural Network (M2GNN), a generic approach that embeds multi-relational KGs in a mixed-curvature space for knowledge graph completion. |
Shen Wang; Xiaokai Wei; Cicero Nogueira Nogueira dos Santos; Zhiguo Wang; Ramesh Nallapati; Andrew Arnold; Bing Xiang; Philip S. Yu; Isabel F. Cruz; |
| 156 | Elo-MMR: A Rating System for Massive Multiplayer Competitions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel Bayesian rating system for contests with many participants. |
Aram Ebtekar; Paul Liu; |
| 157 | DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In light of the pros/cons of DCN and existing feature interaction learning approaches, we propose an improved framework DCN-V2 to make DCN more practical in large-scale industrial settings. |
Ruoxi Wang; Rakesh Shivanna; Derek Cheng; Sagar Jain; Dong Lin; Lichan Hong; Ed Chi; |
| 158 | A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, based on the key insight that adaptive shrinkage on singular values improve empirical performance, we propose a new nonconvex low-rank regularizer called ”nuclear norm minus Frobenius norm” regularizer, which is scalable, adaptive and sound. |
Yaqing Wang; Quanming Yao; James Kwok; |
| 159 | An Adversarial Imitation Click Model for Information Retrieval Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel framework, Adversarial Imitation Click Model (AICM), based on imitation learning. |
Xinyi Dai; Jianghao Lin; Weinan Zhang; Shuai Li; Weiwen Liu; Ruiming Tang; Xiuqiang He; Jianye Hao; Jun Wang; Yong Yu; |
| 160 | CrowdGP: A Gaussian Process Model for Inferring Relevance from Crowd Annotations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we relax the independence assumption to model task correlation in terms of relevance. |
Dan Li; Zhaochun Ren; Evangelos Kanoulas; |
| 161 | Fine-Grained Urban Flow Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these two challenges, we present a Spatio-Temporal Relation Network (STRN) to predict fine-grained urban flows. |
Yuxuan Liang; Kun Ouyang; Junkai Sun; Yiwei Wang; Junbo Zhang; Yu Zheng; David Rosenblum; Roger Zimmermann; |
| 162 | AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph✱ Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle these challenges, we propose a novel framework, entitled AutoSTG, for automated spatio-temporal graph prediction. |
Zheyi Pan; Songyu Ke; Xiaodu Yang; Yuxuan Liang; Yong Yu; Junbo Zhang; Yu Zheng; |
| 163 | Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a framework, named Multi-Agent Spatio-Temporal Reinforcement Learning (Master), for intelligently recommending public accessible charging stations by jointly considering various long-term spatiotemporal factors. |
Weijia Zhang; Hao Liu; Fan Wang; Tong Xu; Haoran Xin; Dejing Dou; Hui Xiong; |
| 164 | STUaNet: Understanding Uncertainty in Spatiotemporal Collective Human Mobility Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To provide high-quality uncertainty quantification for spatiotemporal forecasting, we propose an uncertainty learning mechanism to simultaneously estimate internal data quality and quantify external uncertainty regarding various contextual interactions. |
Zhengyang Zhou; Yang Wang; Xike Xie; Lei Qiao; Yuantao Li; |
| 165 | DeepFEC: Energy Consumption Prediction Under Real-World Driving Conditions for Smart Cities Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a novel framework that identifies vehicle/driving environment-dependent factors to predict energy consumption over a road network based on historical consumption data for different vehicle types. |
Sayda Elmi; Kian-Lee Tan; |
| 166 | Separ: Towards Regulating Future of Work Multi-Platform Crowdworking Environments with Privacy Guarantees Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an overall vision exploring the regulation, privacy, and architecture dimensions for the future of work multi-platform crowdworking environments. |
Mohammad Javad Amiri; Joris Duguépéroux; Tristan Allard; Divyakant Agrawal; Amr El Abbadi; |
| 167 | Online Label Aggregation: A Variational Bayesian Approach Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel online label aggregation framework, BiLA , which employs variational Bayesian inference method and designs a novel stochastic optimization scheme for incremental training. |
Chi Hong; Amirmasoud Ghiassi; Yichi Zhou; Robert Birke; Lydia Y. Chen; |
| 168 | Peer Grading The Peer Reviews: A Dual-Role Approach for Lightening The Scholarly Paper Review Process Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a human-AI approach that estimates the conformity of reviews to the conference standards. |
Ines Arous; Jie Yang; Mourad Khayati; Philippe Cudre-Mauroux; |
| 169 | Multi-Session Diversity to Improve User Satisfaction in Web Applications Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Given the hardness of those problems, we propose to formulate a constrained optimization problem that optimizes inter diversity, subject to the constraint of intra diversity. |
Mohammadreza Esfandiari; Ria Mae Borromeo; Sepideh Nikookar; Paras Sakharkar; Sihem Amer-Yahia; Senjuti Basu Roy; |
| 170 | What Do You Mean? Interpreting Image Classification with Crowdsourced Concept Extraction and Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce a scalable human-in-the-loop approach for global interpretability. |
Agathe Balayn; Panagiotis Soilis; Christoph Lofi; Jie Yang; Alessandro Bozzon; |
| 171 | FINN: Feedback Interactive Neural Network for Intent Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we propose a feedback interactive neural network (FINN) to estimate user’s potential search intent more accurately, by making full use of the feedback interaction with the following three parts: 1) Both positive feedback (PF) and negative feedback (NF) information are collected simultaneously. |
Yatao Yang; Biyu Ma; Jun Tan; Hongbo Deng; Haikuan Huang; Zibin Zheng; |
| 172 | Weakly-Supervised Question Answering with Effective Rank and Weighted Loss Over Candidates Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present an effective method to learn a question answering model in a weak supervision way. |
Haozhe Qin; Jiangang Zhu; Beijun Shen; |
| 173 | Controlling The Risk of Conversational Search Via Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a risk-aware conversational search agent model to balance the risk of answering user’s query and asking clarifying questions. |
Zhenduo Wang; Qingyao Ai; |
| 174 | Joint Spatio-Textual Reasoning for Answering Tourism Questions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our goal is to answer real-world tourism questions that seek Points-of-Interest (POI) recommendations. |
Danish Contractor; Shashank Goel; Mausam; Parag Singla; |
| 175 | Adapting to Context-Aware Knowledge in Natural Conversation for Multi-Turn Response Selection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we define Adaptive Knowledge-Grounded Conversations (AKGCs) where the knowledge is to ground the conversation within a multi-turn context by adapting to three modes. |
Chen Zhang; Hao Wang; Feijun Jiang; Hongzhi Yin; |
| 176 | Understanding The Complexity of Detecting Political Ads Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We take an empirical approach to analyze what kind of ads are deemed political by ordinary people and what kind of ads lead to disagreement. |
Vera Sosnovik; Oana Goga; |
| 177 | War of Words II: Enriched Models of Law-Making Processes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We mine a rich dataset of law edits and introduce models predicting their adoption by parliamentary committees. |
Victor Kristof; Aswin Suresh; Matthias Grossglauser; Patrick Thiran; |
| 178 | Assessing The Effects of Friend-to-Friend Texting OnTurnout in The 2018 US Midterm Elections Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We address these challenges, which are likely to impinge upon any study that seeks to randomize authentic friend-to-friend interactions, by tailoring the statistical analysis to make use of additional data about both users and subjects. |
Aaron Schein; Keyon Vafa; Dhanya Sridhar; Victor Veitch; Jeffrey Quinn; James Moffet; David M. Blei; Donald P. Green; |
| 179 | Fast Evaluation for Relevant Quantities of Opinion Dynamics Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, by reducing the problem of computing relevant quantities to evaluating ?2 norms of some vectors, we present a nearly linear time algorithm to estimate all these quantities. |
Wanyue Xu; Qi Bao; Zhongzhi Zhang; |
| 180 | Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this context, we develop a novel recommendation framework with a goal of improving information diversity using a modified random walk exploration of the user-item graph. |
Bibek Paudel; Abraham Bernstein; |
| 181 | Soft-mask: Adaptive Substructure Extractions for Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose to learn graph representations from a sequence of subgraphs of the original graph to better capture task-relevant substructures or hierarchical structures and skip noisy parts. |
Mingqi Yang; Yanming Shen; Heng Qi; Baocai Yin; |
| 182 | Graph Contrastive Learning with Adaptive Augmentation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel graph contrastive representation learning method with adaptive augmentation that incorporates various priors for topological and semantic aspects of the graph. |
Yanqiao Zhu; Yichen Xu; Feng Yu; Qiang Liu; Shu Wu; Liang Wang; |
| 183 | SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a novel hierarchical subgraph-level selection and embedding-based graph neural network for graph classification, namely SUGAR, to learn more discriminative subgraph representations and respond in an explanatory way. |
Qingyun Sun; Jianxin Li; Hao Peng; Jia Wu; Yuanxing Ning; Philip S. Yu; Lifang He; |
| 184 | Strongly Local Hypergraph Diffusions for Clustering and Semi-supervised Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle these issues, this paper proposes a new diffusion-based hypergraph clustering algorithm that solves a quadratic hypergraph cut based objective akin to a hypergraph analog of Andersen-Chung-Lang personalized PageRank clustering for graphs. |
Meng Liu; Nate Veldt; Haoyu Song; Pan Li; David F. Gleich; |
| 185 | TG-GAN: Continuous-time Temporal Graph Deep Generative Models with Time-Validity Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we propose the “Temporal Graph Generative Adversarial Network” (TG-GAN) for continuous-time graph generation with time-validity constraints 1. |
Liming Zhang; Liang Zhao; Shan Qin; Dieter Pfoser; Chen Ling; |
| 186 | Cookie Swap Party: Abusing First-Party Cookies for Web Tracking Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we shed light into the increasingly used practice of relying on first-party cookies that are set by third-party JavaScript code to implement user tracking and other potentially unwanted capabilities. |
Quan Chen; Panagiotis Ilia; Michalis Polychronakis; Alexandros Kapravelos; |
| 187 | User Tracking in The Post-cookie Era: How Websites Bypass GDPR Consent to Track Users Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore whether websites use more persistent and sophisticated forms of tracking in order to track users who said they do not want cookies. |
Emmanouil Papadogiannakis; Panagiotis Papadopoulos; Nicolas Kourtellis; Evangelos P. Markatos; |
| 188 | It’s Not Just The Site, It’s The Contents: Intra-domain Fingerprinting Social Media Websites Through CDN Bursts Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate the intra-domain WPF among social media websites, against the realistic on-path passive attack scenario. |
Kailong Wang; Junzhe Zhang; Guangdong Bai; Ryan Ko; Jin Song Dong; |
| 189 | Have You Been Properly Notified? Automatic Compliance Analysis of Privacy Policy Text with GDPR Article 13 Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we target to solve the problem of compliance analysis between GDPR (Article 13) and privacy policies. |
Shuang Liu; Baiyang Zhao; Renjie Guo; Guozhu Meng; Fan Zhang; Meishan Zhang; |
| 190 | Privacy Policies Over Time: Curation and Analysis of A Million-Document Dataset Related Papers Related Patents Related Grants Related Orgs Related Experts Details |
Ryan Amos; Gunes Acar; Elena Lucherini; Mihir Kshirsagar; Arvind Narayanan; Jonathan Mayer; Ryan Amos; Gunes Acar; Elena Lucherini; Mihir Kshirsagar; Arvind Narayanan; Jonathan Mayer; |
| 191 | STAN: Spatio-Temporal Attention Network for Next Location Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To aggregate all relevant visits from user trajectory and recall the most plausible candidates from weighted representations, here we propose a Spatio-Temporal Attention Network (STAN) for location recommendation. |
Yingtao Luo; Qiang Liu; Zhaocheng Liu; |
| 192 | Session-aware Linear Item-Item Models for Session-based Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose simple-yet-effective linear models for considering the holistic aspects of the sessions. |
Minjin Choi; Jinhong Kim; Joonseok Lee; Hyunjung Shim; Jongwuk Lee; |
| 193 | Learning Fair Representations for Recommendation: A Graph-based Perspective Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel graph based technique for ensuring fairness of any recommendation models. |
Le Wu; Lei Chen; Pengyang Shao; Richang Hong; Xiting Wang; Meng Wang; |
| 194 | Leveraging Review Properties for Effective Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose to model the reviews with their associated available properties. |
Xi Wang; Iadh Ounis; Craig Macdonald; |
| 195 | A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, we propose a novel dual transfer learning framework that jointly learns the knowledge transfer from both model-level and item-level: 1. |
Yin Zhang; Derek Zhiyuan Cheng; Tiansheng Yao; Xinyang Yi; Lichan Hong; Ed H. Chi; |
| 196 | DeepVista: 16K Panoramic Cinema on Your Mobile Device Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we design, implement, and evaluate , which is to our knowledge the first consumer-class system that streams panoramic videos far beyond the ultra high-definition resolution (up to 16K) to mobile devices, offering truly immersive experiences. |
Wenxiao Zhang; Feng Qian; Bo Han; Pan Hui; |
| 197 | CoopEdge: A Decentralized Blockchain-based Platform for Cooperative Edge Computing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle these challenges systematically, this paper proposes CoopEdge, a novel blockchain-based decentralized platform, to drive and support cooperative edge computing. |
Liang Yuan; Qiang He; Siyu Tan; Bo Li; Jiangshan Yu; Feifei Chen; Hai Jin; Yun Yang; |
| 198 | Temporal Analysis of The Entire Ethereum Blockchain Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate the evolutionary nature of Ethereum interaction networks from a temporal graphs perspective. |
Lin Zhao; Sourav Sen Gupta; Arijit Khan; Robby Luo; |
| 199 | SrVARM: State Regularized Vector Autoregressive Model for Joint Learning of Hidden State Transitions and State-Dependent Inter-Variable Dependencies from Multi-variate Time Series Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve this problem, we introduce the State-Regularized Vector Autoregressive Model (SrVARM) which combines a state-regularized recurrent neural network to learn the dynamics of transitions between discrete hidden states with an augmented autoregressive model which models the inter-variable dependencies in each state using a state-dependent directed acyclic graph (DAG). |
Tsung-Yu Hsieh; Yiwei Sun; Xianfeng Tang; Suhang Wang; Vasant G. Honavar; |
| 200 | Equilibrium Inverse Reinforcement Learning for Ride-hailing Vehicle Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we formulate the problem of passenger-vehicle matching in a sparsely connected graph and proposed an algorithm to derive an equilibrium policy in a multi-agent environment. |
Takuma Oda; |
| 201 | Unifying Offline Causal Inference and Online Bandit Learning for Data Driven Decision Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome the aforementioned limitations, we propose a framework to unify offline causal inference algorithms (e.g., weighting, matching) and online learning algorithms (e.g., UCB, LinUCB). |
Ye Li; Hong Xie; Yishi Lin; John C.S. Lui; |
| 202 | Automated Creative Optimization for E-Commerce Advertising Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Specifically, motivated by AutoML, we propose one-shot search algorithms for searching effective interaction functions between elements. |
Jin Chen; Ju Xu; Gangwei Jiang; Tiezheng Ge; Zhiqiang Zhang; Defu Lian; Kai Zheng; |
| 203 | GuideBoot: Guided Bootstrap for Deep Contextual Banditsin Online Advertising Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we introduce Guided Bootstrap (GuideBoot), combining the best of both worlds. |
Feiyang Pan; Haoming Li; Xiang Ao; Wei Wang; Yanrong Kang; Ao Tan; Qing He; |
| 204 | A Hybrid Bandit Model with Visual Priors for Creative Ranking in Display Advertising Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our contributions are three-fold: 1) We present a visual-aware ranking model (called VAM) that incorporates a list-wise ranking loss for ordering the creatives according to the visual appearance. |
Shiyao Wang; Qi Liu; Tiezheng Ge; Defu Lian; Zhiqiang Zhang; |
| 205 | Local Clustering in Contextual Multi-Armed Bandits Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In particular, we define the problem of cluster detection in contextual MAB, and propose a bandit algorithm, LOCB, embedded with local clustering procedure. |
Yikun Ban; Jingrui He; |
| 206 | Learning from Graph Propagation Via Ordinal Distillation for One-Shot Automated Essay Scoring Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a Transductive Graph-based Ordinal Distillation (TGOD) framework to tackle the task of one-shot AES. |
Zhiwei Jiang; Meng Liu; Yafeng Yin; Hua Yu; Zifeng Cheng; Qing Gu; |
| 207 | Wiki2Prop: A Multimodal Approach for Predicting Wikidata Properties from Wikipedia Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we focus on entities with a dedicated Wikipedia page in any language to make predictions directly based on textual content. |
Michael Luggen; Julien Audiffren; Djellel Difallah; Philippe Cudré-Mauroux; |
| 208 | FANCY: Human-centered, Deep Learning-based Framework for Fashion Style Analysis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present FANCY (Fashion Attributes detectioN for Clustering stYle), a human-centered, deep learning-based framework to support fashion professionals’ analytic tasks using a computational method integrated with their insights. |
Youngseung Jeon; Seungwan Jin; Kyungsik Han; |
| 209 | PARIMA: Viewport Adaptive 360-Degree Video Streaming Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We develop PARIMA, a fast and efficient online viewport prediction model that uses past viewports of users along with the trajectories of prime objects as a representative of video content to predict future viewports. |
Lovish Chopra; Sarthak Chakraborty; Abhijit Mondal; Sandip Chakraborty; |
| 210 | Controllable and Diverse Text Generation in E-commerce Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To remedy these problems, we propose a fine-grained controllable generative model, called Apex, that uses an algorithm borrowed from automatic control (namely, a variant of the proportional, integral, and derivative (PID) controller) to precisely manipulate the diversity/accuracy trade-off of generated text. |
Huajie Shao; Jun Wang; Haohong Lin; Xuezhou Zhang; Aston Zhang; Heng Ji; Tarek Abdelzaher; |
| 211 | Nonlinear Higher-Order Label Spreading Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we add nonlinearity to label spreading via nonlinear functions involving higher-order network structure, namely triangles in the graph. |
Francesco Tudisco; Austin R. Benson; Konstantin Prokopchik; |
| 212 | HDMI: High-order Deep Multiplex Infomax Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the above-mentioned problems, we propose a novel framework, called High-order Deep Multiplex Infomax (HDMI), for learning node embedding on multiplex networks in a self-supervised way. |
Baoyu Jing; Chanyoung Park; Hanghang Tong; |
| 213 | Network of Tensor Time Series Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel model called Network of Tensor Time Series (NeT3), which is comprised of two modules, including Tensor Graph Convolutional Network (TGCN) and Tensor Recurrent Neural Network (TRNN). |
Baoyu Jing; Hanghang Tong; Yada Zhu; |
| 214 | Improving Graph Neural Networks with Structural Adaptive Receptive Fields Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Graph Neural Networks with STructural Adaptive Receptive fields (STAR-GNN), which adaptively construct a receptive field for each node with structural information and further achieve better aggregation of information. |
Xiaojun Ma; Junshan Wang; Hanyue Chen; Guojie Song; |
| 215 | Few-shot Network Anomaly Detection Via Cross-network Meta-learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Taking advantage of this potential, in this work, we tackle the problem of few-shot network anomaly detection by (1) proposing a new family of graph neural networks – Graph Deviation Networks (GDN) that can leverage a small number of labeled anomalies for enforcing statistically significant deviations between abnormal and normal nodes on a network; and (2) equipping the proposed GDN with a new cross-network meta-learning algorithm to realize few-shot network anomaly detection by transferring meta-knowledge from multiple auxiliary networks. |
Kaize Ding; Qinghai Zhou; Hanghang Tong; Huan Liu; |
| 216 | Learning Dynamic User Behavior Based on Error-driven Event Representation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Thus in this paper, we provide a new dynamic network dataset with evolving labels called Arxiv and make it publicly available. |
Honglian Wang; Peiyan Li; Wujun Tao; Bailin Feng; Junming Shao; |
| 217 | Using Prior Knowledge to Guide BERT’s Attention in Semantic Textual Matching Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the problem of incorporating prior knowledge into a deep Transformer-based model, i.e., Bidirectional Encoder Representations from Transformers (BERT), to enhance its performance on semantic textual matching tasks. |
Tingyu Xia; Yue Wang; Yuan Tian; Yi Chang; |
| 218 | Wait, Let’s Think About Your Purchase Again: A Study on Interventions for Supporting Self-Controlled Online Purchases Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we conducted an online survey with 118 consumers in their 20s to investigate their impulse buying behaviors and self-control strategies. |
Yunha Han; Hwiyeon Kim; Hyeshin Chu; Joohee Kim; Hyunwook Lee; Seunghyeong Choe; Dooyoung Jung; Dongil Chung; Bum Chul Kwon; Sungahn Ko; |
| 219 | Online Mobile App Usage As An Indicator of Sleep Behavior and Job Performance Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we demonstrate that people’s everyday interactions with online mobile apps can reveal insights into their job performance in real-world contexts. |
Chunjong Park; Morelle Arian; Xin Liu; Leon Sasson; Jeffrey Kahn; Shwetak Patel; Alex Mariakakis; Tim Althoff; |
| 220 | Quiz-Style Question Generation for News Stories Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Using this dataset, we propose a series of novel techniques for applying large pre-trained Transformer encoder-decoder models, namely PEGASUS and T5, to the tasks of question-answer generation and distractor generation. |
Adam D. Lelkes; Vinh Q. Tran; Cong Yu; |
| 221 | Large-scale Comb-K Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To solve the promotion recommendation problem, we propose the comb-K recommendation model, a constrained combinatorial optimization model which seamlessly integrates the selection phase and delivery phase with delicately designed constraints. |
Houye Ji; Junxiong Zhu; Chuan Shi; Xiao Wang; Bai Wang; Chaoyu Zhang; Zixuan Zhu; Feng Zhang; Yanghua Li; |
| 222 | Dual Side Deep Context-aware Modulation for Social Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the above issues, we proposed DICER (Dual sIde deepContext-awarEmodulation for socialRecommendation). |
Bairan Fu; Wenming Zhang; Guangneng Hu; Xinyu Dai; Shujian Huang; Jiajun Chen; |
| 223 | Graph Neural Networks for Friend Ranking in Large-scale Social Platforms Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the application of GNNs for friend suggestion, providing the first investigation of GNN design for this task, to our knowledge. |
Aravind Sankar; Yozen Liu; Jun Yu; Neil Shah; |
| 224 | Pathfinder Discovery Networks for Neural Message Passing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work we propose Pathfinder Discovery Networks (PDNs), a method for jointly learning a message passing graph over a multiplex network with a downstream semi-supervised model. |
Benedek Rozemberczki; Peter Englert; Amol Kapoor; Martin Blais; Bryan Perozzi; |
| 225 | Few-Shot Graph Learning for Molecular Property Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose Meta-MGNN, a novel model for few-shot molecular property prediction. |
Zhichun Guo; Chuxu Zhang; Wenhao Yu; John Herr; Olaf Wiest; Meng Jiang; Nitesh V. Chawla; |
| 226 | Multi-domain Dialogue State Tracking with Recursive Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we propose a Recursive Inference mechanism (ReInf) to resolve DST in multi-domain scenarios that call for more robust and accurate tracking capability. |
Lizi Liao; Tongyao Zhu; Le Hong Long; Tat Seng Chua; |
| 227 | Automatic Intent-Slot Induction for Dialogue Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle these challenges, we explore a new task of automatic intent-slot induction and propose a novel domain-independent tool. |
Zengfeng Zeng; Dan Ma; Haiqin Yang; Zhen Gou; Jianping Shen; |
| 228 | Multilingual COVID-QA: Learning Towards Global Information Sharing Via Web Question Answering in Multiple Languages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a multilingual COVID-QA model to answer people’s questions in their own languages while the model is able to absorb knowledge from other languages. |
Rui Yan; Weiheng Liao; Jianwei Cui; Hailei Zhang; Yichuan Hu; Dongyan Zhao; |
| 229 | ComQA: Compositional Question Answering Via Hierarchical Graph Neural Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a large-scale compositional question answering dataset containing more than 120k human-labeled questions. |
Bingning Wang; Ting Yao; Weipeng Chen; Jingfang Xu; Xiaochuan Wang; |
| 230 | Cross-domain Knowledge Distillation for Retrieval-based Question Answering Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Hence in this work, we provide a new perspective on the potential of the teacher-student paradigm facilitating cross-domain transfer learning, where the teacher and student tasks belong to heterogeneous domains, with the goal to improve the student model’s performance in the target domain. |
Cen Chen; Chengyu Wang; Minghui Qiu; Dehong Gao; Linbo Jin; Wang Li; |
| 231 | Computing Views of OWL Ontologies for The Semantic Web Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a forgetting-based method for computing views of OWL ontologies specified in the description logic , the basic extended with role hierarchy, nominals and inverse roles. |
Jiaqi Li; Xuan Wu; Chang Lu; Wenxing Deng; Yizheng Zhao; |
| 232 | Advanced Semantics for Commonsense Knowledge Extraction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a methodology, called Ascent, to automatically build a large-scale knowledge base (KB) of CSK assertions, with advanced expressiveness and both better precision and recall than prior works. |
Tuan-Phong Nguyen; Simon Razniewski; Gerhard Weikum; |
| 233 | DISCOS: Bridging The Gap Between Discourse Knowledge and Commonsense Knowledge Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address the limitations of previous approaches, in this paper, we propose an alternative commonsense knowledge acquisition framework DISCOS (from DIScourse to COmmonSense), which automatically populates expensive complex commonsense knowledge to more affordable linguistic knowledge resources. |
Tianqing Fang; Hongming Zhang; Weiqi Wang; Yangqiu Song; Bin He; |
| 234 | Role-Aware Modeling for N-ary Relational Knowledge Bases Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Therefore, we start from the role level, and propose a Role-Aware Modeling, RAM for short, for facts in n-ary relational KBs. |
Yu Liu; Quanming Yao; Yong Li; |
| 235 | Biomedical Vocabulary Alignment at Scale In the UMLS Metathesaurus Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper aims to improve the UMLS Metathesaurus construction process by developing a novel supervised learning approach for improving the task of suggesting synonymous pairs that can scale to the size and diversity of the UMLS source vocabularies. |
Vinh Nguyen; Hong Yung Yip; Olivier Bodenreider; |
| 236 | Towards A Lightweight, Hybrid Approach for Detecting DOM XSS Vulnerabilities with Machine Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We experiment with a range of hyperparameters and present a low-latency, high-recall classifier that could serve as a pre-filter to taint tracking, reducing the cost of stand-alone taint tracking by 3.43 × while detecting 94.5% of unique vulnerabilities. |
William Melicher; Clement Fung; Lujo Bauer; Limin Jia; |
| 237 | An Empirical Study of Real-World WebAssembly Binaries: Security, Languages, Use Cases Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a comprehensive empirical study of 8,461 unique WebAssembly binaries gathered from a wide range of sources, including source code repositories, package managers, and live websites. |
Aaron Hilbig; Daniel Lehmann; Michael Pradel; |
| 238 | Security of Alerting Authorities in The WWW: Measuring Namespaces, DNSSEC, and Web PKI Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we take a first look at Alerting Authorities (AA) in the US and investigate security measures related to trustworthy and secure communication. |
Pouyan Fotouhi Tehrani; Eric Osterweil; Jochen H. Schiller; Thomas C. Schmidt; Matthias Wählisch; |
| 239 | LChecker: Detecting Loose Comparison Bugs in PHP Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present the first in-depth study of such loose comparison bugs. |
Penghui Li; Wei Meng; |
| 240 | SEPAL: Towards A Large-scale Analysis of SEAndroid Policy Customization Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To investigate the status quo of SEAndroid policy customization, we propose SEPAL, a universal tool to automatically retrieve and examine the customized policy rules. |
Dongsong Yu; Guangliang Yang; Guozhu Meng; Xiaorui Gong; Xiu Zhang; Xiaobo Xiang; Xiaoyu Wang; Yue Jiang; Kai Chen; Wei Zou; Wenke Lee; Wenchang Shi; |
| 241 | From Personal Data to Digital Legacy: Exploring Conflicts in The Sharing, Security and Privacy of Post-mortem Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We report on two workshops carried out with users of password managers to explore their views on the post-mortem sharing, security and privacy of a range of common digital assets. |
Jack Holt; James Nicholson; Jan David Smeddinck; |
| 242 | ConceptGuide: Supporting Online Video Learning with Concept Map-based Recommendation of Learning Path Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present ConceptGuide, a prototype system for learning orientations to support ad hoc online learning from unorganized video materials. |
Chien-Lin Tang; Jingxian Liao; Hao-Chuan Wang; Ching-Ying Sung; Wen-Chieh Lin; |
| 243 | An Experimental Study to Understand User Experience and Perception Bias Occurred By Fact-checking Messages Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This research brings attention to the unexpected and diminished effect of fact-checking due to cognitive biases. |
Sungkyu Park; Jamie Yejean Park; Hyojin Chin; Jeong-han Kang; Meeyoung Cha; |
| 244 | Touch Screen Exploration of Visual Artwork for Blind People Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper investigates how touchscreen exploration and verbal feedback can be used to support blind people to access visual artwork. |
Dragan Ahmetovic; Nahyun Kwon; Uran Oh; Cristian Bernareggi; Sergio Mascetti; |
| 245 | Generating Accurate Caption Units for Figure Captioning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We formulate the caption unit generation problem as a controlled captioning problem. Given a caption unit type as a control signal, a model generates an accurate caption unit of that type. |
Xin Qian; Eunyee Koh; Fan Du; Sungchul Kim; Joel Chan; Ryan A. Rossi; Sana Malik; Tak Yeon Lee; |
| 246 | Stochastic Bandits for Multi-platform Budget Optimization in Online Advertising Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We study the problem of an online advertising system that wants to optimally spend an advertiser’s given budget for a campaign across multiple platforms, without knowing the value for showing an ad to the users on those platforms. |
Vashist Avadhanula; Riccardo Colini Baldeschi; Stefano Leonardi; Karthik Abinav Sankararaman; Okke Schrijvers; |
| 247 | Incrementality Testing in Programmatic Advertising: Enhanced Precision with Double-Blind Designs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present a novel randomized design solution for incrementality testing based on ghost bidding with improved measurement precision. |
Joel Barajas; Narayan Bhamidipati; |
| 248 | FM2: Field-matrixed Factorization Machines for Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we proposed a novel approach to model the field information effectively and efficiently. |
Yang Sun; Junwei Pan; Alex Zhang; Aaron Flores; |
| 249 | Integrating Floor Plans Into Hedonic Models for Rent Price Appraisal Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we investigate to what extent an automated visual analysis of apartment floor plans on online real estate platforms can enhance hedonic rent price appraisal. |
Kirill Solovev; Nicolas Pröllochs; |
| 250 | TextGNN: Improving Text Encoder Via Graph Neural Network in Sponsored Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose the TextGNN model that naturally extends the strong twin tower structured encoders with the complementary graph information from user historical behaviors, which serves as a natural guide to help us better understand the intents and hence generate better language representations. |
Jason Zhu; Yanling Cui; Yuming Liu; Hao Sun; Xue Li; Markus Pelger; Tianqi Yang; Liangjie Zhang; Ruofei Zhang; Huasha Zhao; |
| 251 | Leveraging User Behavior History for Personalized Email Search Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Accordingly, we propose a context-dependent neural ranking model (CNRM) that encodes the ranking features in users’ search history as query context and show that it can significantly outperform the baseline neural model without using the context. |
Keping Bi; Pavel Metrikov; Chunyuan Li; Byungki Byun; |
| 252 | Partial-Softmax Loss Based Deep Hashing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this difficulty, in this paper, we propose a novel Partial-Softmax Loss based Deep Hashing, called PSLDH, to generate high-quality hash codes. |
Rong-Cheng Tu; Xian-Ling Mao; Jia-Nan Guo; Wei Wei; Heyan Huang; |
| 253 | Unsupervised Multi-Index Semantic Hashing Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose Multi-Index Semantic Hashing (MISH), an unsupervised hashing model that learns hash codes that are both effective and highly efficient by being optimized for multi-index hashing. |
Christian Hansen; Casper Hansen; Jakob Grue Simonsen; Stephen Alstrup; Christina Lioma; |
| 254 | Learning A Product Relevance Model from Click-Through Data in E-Commerce Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a new relevance learning framework that concentrates on how to train a relevance model from the weak supervision of click-through data. |
Shaowei Yao; Jiwei Tan; Xi Chen; Keping Yang; Rong Xiao; Hongbo Deng; Xiaojun Wan; |
| 255 | High-Dimensional Sparse Cross-Modal Hashing with Fine-Grained Similarity Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by these issues, in this paper, we present an efficient sparse hashing method, i.e., High-dimensional Sparse Cross-modal Hashing, HSCH for short. |
Yongxin Wang; Zhen-Duo Chen; Xin Luo; Xin-Shun Xu; |
| 256 | Hashing-Accelerated Graph Neural Networks for Link Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a simple and effective model called #GNN, which balances the trade-off between accuracy and efficiency. |
Wei Wu; Bin Li; Chuan Luo; Wolfgang Nejdl; |
| 257 | Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This study presents a new method, multi-view graph contrastive representation learning for drug-drug interaction prediction, MIRACLE for brevity, to capture inter-view molecule structure and intra-view interactions between molecules simultaneously. |
Yingheng Wang; Yaosen Min; Xin Chen; Ji Wu; |
| 258 | Multi-level Hyperedge Distillation for Social Linking Prediction on Sparsely Observed Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we go beyond pair-wise relations and propose a new and novel framework using hypergraph neural networks with multi-level hyperedge distillation strategies. |
Xiangguo Sun; Hongzhi Yin; Bo Liu; Hongxu Chen; Qing Meng; Wang Han; Jiuxin Cao; |
| 259 | Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To tackle this challenge, we develop , a framework for bridging static representation learning methods using global information from the entire graph with localized attention driven mechanisms to learn contextual node representations. |
Ping Wang; Khushbu Agarwal; Colby Ham; Sutanay Choudhury; Chandan K. Reddy; |
| 260 | Community Value Prediction in Social E-commerce Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To bridge this gap, we develop a Multi-scale Structure-aware Community value prediction network (MSC) that jointly models the structural information of different scales, including peer relations, community structure, and inter-community connections, to predict the value of given communities. |
Guozhen Zhang; Yong Li; Yuan Yuan; Fengli Xu; Hancheng Cao; Yujian Xu; Depeng Jin; |
| 261 | RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we aim to present the holistic SR that simultaneously accommodates conventional, inductive, and transferable settings. |
Cheng Hsu; Cheng-Te Li; |
| 262 | Disentangling User Interest and Conformity for Recommendation with Causal Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present DICE, a general framework that learns representations where interest and conformity are structurally disentangled, and various backbone recommendation models could be smoothly integrated. |
Yu Zheng; Chen Gao; Xiang Li; Xiangnan He; Yong Li; Depeng Jin; |
| 263 | Future-Aware Diverse Trends Framework for Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we bridge the gap between the past preference and potential future preference by proposing the future-aware diverse trends (FAT) framework. |
Yujie Lu; Shengyu Zhang; Yingxuan Huang; Luyao Wang; Xinyao Yu; Zhou Zhao; Fei Wu; |
| 264 | Graph Embedding for Recommendation Against Attribute Inference Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In our paper, we propose GERAI, a novel differentially private graph convolutional network to address such limitations. |
Shijie Zhang; Hongzhi Yin; Tong Chen; Zi Huang; Lizhen Cui; Xiangliang Zhang; |
| 265 | AutoDim: Field-aware Embedding Dimension Searchin Recommender Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, we propose an AutoML-based framework (AutoDim) in this paper, which can automatically select dimensions for different feature fields in a data-driven fashion. |
Xiangyu Zhao; Haochen Liu; Hui Liu; Jiliang Tang; Weiwei Guo; Jun Shi; Sida Wang; Huiji Gao; Bo Long; |
| 266 | Verdi: Quality Estimation and Error Detection for Bilingual Corpora Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose Verdi, a novel framework for word-level and sentence-level post-editing effort estimation for bilingual corpora. |
Mingjun Zhao; Haijiang Wu; Di Niu; Zixuan Wang; Xiaoli Wang; |
| 267 | Crosslingual Topic Modeling with WikiPDA Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present Wikipedia-based Polyglot Dirichlet Allocation (WikiPDA), a crosslingual topic model that learns to represent Wikipedia articles written in any language as distributions over a common set of language-independent topics. |
Tiziano Piccardi; Robert West; |
| 268 | Keyword-aware Abstractive Summarization By Extracting Set-level Intermediate Summaries Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a extractor-abstractor framework in which the keyword-based extractor selects a few sets of salient sentences from the input document and then the abstractor paraphrases these sets of sentences in parallel, which are more aligned to the summary, to generate the final summary. |
Yizhu Liu; Qi Jia; Kenny Zhu; |
| 269 | Graph Topic Neural Network for Document Representation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Graph Topic Neural Network (GTNN) model to mine latent topic semantics for interpretable document representation learning, taking into account the document-document, document-word, and word-word relationships in the graph. |
Qianqian Xie; Jimin Huang; Pan Du; Min Peng; Jian-Yun Nie; |
| 270 | Insightful Dimensionality Reduction with Very Low Rank Variable Subsets Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper we explore an alternative approach to interpretable dimensionality reduction. |
Bruno Ordozgoiti; Sachith Pai; Marta Kołczyńska; |
| 271 | SDFVAE: Static and Dynamic Factorized VAE for Anomaly Detection of Multivariate CDN KPIs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a robust and noise-resilient anomaly detection mechanism using multivariate KPIs. |
Liang Dai; Tao Lin; Chang Liu; Bo Jiang; Yanwei Liu; Zhen Xu; Zhi-Li Zhang; |
| 272 | MicroRank: End-to-End Latency Issue Localization with Extended Spectrum Analysis in Microservice Environments Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a novel system, named MicroRank, which analyzes clues provided by normal and abnormal traces to locate root causes of latency issues. |
Guangba Yu; Pengfei Chen; Hongyang Chen; Zijie Guan; Zicheng Huang; Linxiao Jing; Tianjun Weng; Xinmeng Sun; Xiaoyun Li; |
| 273 | Outlier-Resilient Web Service QoS Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome this limitation, we propose an outlier-resilient QoS prediction method in this paper. |
Fanghua Ye; Zhiwei Lin; Chuan Chen; Zibin Zheng; Hong Huang; |
| 274 | Autodidactic Neurosurgeon: Collaborative Deep Inference for Mobile Edge Intelligence Via Online Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper builds a collaborative deep inference system between a resource-constrained mobile device and a powerful edge server, aiming at joining the power of both on-device processing and computation offloading. |
Letian Zhang; Lixing Chen; Jie Xu; |
| 275 | Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a novel approach for self-supervised Time Series Change Point detection method based on Contrastive Predictive coding (TS ? CP2). |
Shohreh Deldari; Daniel V. Smith; Hao Xue; Flora D. Salim; |
| 276 | REST: Reciprocal Framework for Spatiotemporal-coupled Predictions Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To raise the bar, this paper proposes to jointly mine the spatial dependencies and model temporal patterns in a coupled framework, i.e., to make spatiotemporal-coupled predictions. |
Haozhe Lin; Yushun Fan; Jia Zhang; Bing Bai; |
| 277 | Predicting Customer Value with Social Relationships Via Motif-based Graph Attention Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we fill this gap with a novel framework — Motif-based Multi-view Graph Attention Networks with Gated Fusion (MAG), which jointly considers customer demographics, past behaviors, and social network structures. |
Jinghua Piao; Guozhen Zhang; Fengli Xu; Zhilong Chen; Yong Li; |
| 278 | HINTS: Citation Time Series Prediction for New Publications Via Dynamic Heterogeneous Information Network Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we tackle a new problem: predicting a new paper’s citation time series from the date of publication (i.e., without leading values). |
Song Jiang; Bernard Koch; Yizhou Sun; |
| 279 | Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To remedy the class imbalance problem of graph-based fraud detection, we propose a Pick and Choose Graph Neural Network (PC-GNN for short) for imbalanced supervised learning on graphs. |
Yang Liu; Xiang Ao; Zidi Qin; Jianfeng Chi; Jinghua Feng; Hao Yang; Qing He; |
| 280 | Rumor Detection with Field of Linear and Non-Linear Propagation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Accordingly, in this paper, we propose a novel model Rumor Detection with Field of Linear and Non-Linear Propagation (RDLNP) to automatically detect rumors from the above two fields by taking advantage of claim content, social context and temporal information. |
An Lao; Chongyang Shi; Yayi Yang; |
| 281 | Situation and Behavior Understanding By Trope Detection on Films Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here, we present a challenging novel task, trope detection on films, in an effort to create a situation and behavior understanding for machines. |
Chen-Hsi Chang; Hung-Ting Su; Jui-Heng Hsu; Yu-Siang Wang; Yu-Cheng Chang; Zhe Yu Liu; Ya-Liang Chang; Wen-Feng Cheng; Ke-Jyun Wang; Winston H. Hsu; |
| 282 | A Novel Macro-Micro Fusion Network for User Representation Learning on Mobile Apps Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the task of user representation learning with both macro and micro interaction data on mobile apps. |
Shuqing Bian; Wayne Xin Zhao; Kun Zhou; Xu Chen; Jing Cai; Yancheng He; Xingji Luo; Ji-Rong Wen; |
| 283 | Where To Next? A Dynamic Model of User Preferences Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we argue that these models fail to anticipate changing preferences. |
Francesco Sanna Passino; Lucas Maystre; Dmitrii Moor; Ashton Anderson; Mounia Lalmas; |
| 284 | Density-Ratio Based Personalised Ranking From Implicit Feedback Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This study aims to establish an approach to learn personalised ranking from implicit feedback, which reconciles the training efficiency of the pointwise approach and ranking effectiveness of the pairwise counterpart. |
Riku Togashi; Masahiro Kato; Mayu Otani; Shin’ichi Satoh; |
| 285 | Itinerary-aware Personalized Deep Matching at Fliggy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we present a novel Fliggy ITinerary-aware deep matching NETwork (FitNET) to address these three challenges. |
Jia Xu; Ziyi Wang; Zulong Chen; Detao Lv; Yao Yu; Chuanfei Xu; |
| 286 | MATCH: Metadata-Aware Text Classification in A Large Hierarchy Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we bridge the gap by formalizing the problem of metadata-aware text classification in a large label hierarchy (e.g., with tens of thousands of labels). |
Yu Zhang; Zhihong Shen; Yuxiao Dong; Kuansan Wang; Jiawei Han; |
| 287 | Minimally-Supervised Structure-Rich Text Categorization Via Learning on Text-Rich Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We therefore propose a novel framework for minimally supervised categorization by learning from the text-rich network. |
Xinyang Zhang; Chenwei Zhang; Xin Luna Dong; Jingbo Shang; Jiawei Han; |
| 288 | Scalable Auto-weighted Discrete Multi-view Clustering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel approach for large-scale multi-view clustering to overcome the above challenges. |
Longqi Yang; Liangliang Zhang; Yuhua Tang; |
| 289 | Linguistically-Enriched and Context-AwareZero-shot Slot Filling Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new zero-shot slot filling neural model, , which works in three steps. |
A.B. Siddique; Fuad Jamour; Vagelis Hristidis; |
| 290 | Enquire One’s Parent and Child Before Decision: Fully Exploit Hierarchical Structure for Self-Supervised Taxonomy Expansion Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the Hierarchy Expansion Framework (HEF), which fully exploits the hierarchical structure’s properties to maximize the coherence of expanded taxonomy. |
Suyuchen Wang; Ruihui Zhao; Xi Chen; Yefeng Zheng; Bang Liu; |
| 291 | Typing Errors in Factual Knowledge Graphs: Severity and Possible Ways Out Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this study, we investigate the quality of these KGs, where the typing error rate is estimated to be 27% for coarse-grained types on average, and even 73% for certain fine-grained types. |
Peiran Yao; Denilson Barbosa; |
| 292 | Few-Shot Knowledge Validation Using Rules Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To address these issues, we propose Colt, a few-shot rule-based knowledge validation framework that enables the interactive quality assessment of logic rules. |
Michael Loster; Davide Mottin; Paolo Papotti; Jan Ehmüller; Benjamin Feldmann; Felix Naumann; |
| 293 | OntoZSL: Ontology-enhanced Zero-shot Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore richer and more competitive prior knowledge to model the inter-class relationship for ZSL via ontology-based knowledge representation and semantic embedding. |
Yuxia Geng; Jiaoyan Chen; Zhuo Chen; Jeff Z. Pan; Zhiquan Ye; Zonggang Yuan; Yantao Jia; Huajun Chen; |
| 294 | Trav-SHACL: Efficiently Validating Networks of SHACL Constraints Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present Trav-SHACL, a SHACL engine capable of planning the traversal and execution of a shape schema in a way that invalid entities are detected early and needless validations are minimized. |
Mónica Figuera; Philipp D. Rohde; Maria-Esther Vidal; |
| 295 | Online Disease Diagnosis with Inductive Heterogeneous Graph Convolutional Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs). |
Zifeng Wang; Rui Wen; Xi Chen; Shilei Cao; Shao-Lun Huang; Buyue Qian; Yefeng Zheng; |
| 296 | Causal Network Motifs: Identifying Heterogeneous Spillover Effects in A/B Tests Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our study provides an approach that accounts for both the local structure in a user’s social network via motifs as well as the treatment assignment conditions of neighbors. |
Yuan Yuan; Kristen Altenburger; Farshad Kooti; |
| 297 | MStream: Fast Anomaly Detection in Multi-Aspect Streams Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our work aims to define a streaming multi-aspect data anomaly detection framework, termed MStream which can detect unusual group anomalies as they occur, in a dynamic manner. |
Siddharth Bhatia; Arjit Jain; Pan Li; Ritesh Kumar; Bryan Hooi; |
| 298 | Knowledge-Preserving Incremental Social Event Detection Via Heterogeneous GNNs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel Knowledge-Preserving Incremental Heterogeneous Graph Neural Network (KPGNN) for incremental social event detection. |
Yuwei Cao; Hao Peng; Jia Wu; Yingtong Dou; Jianxin Li; Philip S. Yu; |
| 299 | How Do Hyperedges Overlap in Real-World Hypergraphs? – Patterns, Measures, and Generators Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we closely investigate thirteen real-world hypergraphs from various domains and share interesting observations of the overlaps of hyperedges. |
Geon Lee; Minyoung Choe; Kijung Shin; |
| 300 | STruD: Truss Decomposition of Simplicial Complexes Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop a new tool to study the structure of simplicial complexes: we generalize the graph notion of truss decomposition to complexes, and show that this more powerful representation gives rise to different properties compared to the graph-based one. |
Giulia Preti; Gianmarco De Francisci Morales; Francesco Bonchi; |
| 301 | Constructing Explainable Opinion Graphs from Reviews Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present ExplainIt, a system that extracts and organizes opinions into an opinion graph, which are useful for downstream applications such as generating explainable review summaries and facilitating search over opinion phrases. |
Nofar Carmeli; Xiaolan Wang; Yoshihiko Suhara; Stefanos Angelidis; Yuliang Li; Jinfeng Li; Wang-Chiew Tan; |
| 302 | The Surprising Performance of Simple Baselines for Misinformation Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we examine the performance of a broad set of modern transformer-based language models and show that with basic fine-tuning, these models are competitive with and can even significantly outperform recently proposed state-of-the-art methods. |
Kellin Pelrine; Jacob Danovitch; Reihaneh Rabbany; |
| 303 | MQuadE: A Unified Model for Knowledge Fact Embedding Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper proposes a method called MQuadE to tackle the challenge in KGE modeling. |
Jinxing Yu; Yunfeng Cai; Mingming Sun; Ping Li; |
| 304 | Target-adaptive Graph for Cross-target Stance Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents a novel approach that automatically identifies and adapts the target-dependent and target-independent roles that a word plays with respect to a specific target in stance expressions, so as to achieve cross-target stance detection. |
Bin Liang; Yonghao Fu; Lin Gui; Min Yang; Jiachen Du; Yulan He; Ruifeng Xu; |
| 305 | Mining Dual Emotion for Fake News Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we verify that dual emotion is distinctive between fake and real news and propose Dual Emotion Features to represent dual emotion and the relationship between them for fake news detection. |
Xueyao Zhang; Juan Cao; Xirong Li; Qiang Sheng; Lei Zhong; Kai Shu; |
| 306 | Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Instead, we suggest that KBQA models should have three levels of built-in generalization: i.i.d., compositional, and zero-shot. |
Yu Gu; Sue Kase; Michelle Vanni; Brian Sadler; Percy Liang; Xifeng Yan; Yu Su; |
| 307 | Improving Neural Question Generation Using Deep Linguistic Representation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, inspired by the recent achievements of text representation, we propose to utilize linguistic information via large pre-trained neural models. |
Wei Yuan; Tieke He; Xinyu Dai; |
| 308 | Diverse and Specific Clarification Question Generation with Keywords Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose a new model named KPCNet, which generates CQs with Keyword Prediction and Conditioning, to deal with the tasks. |
Zhiling Zhang; Kenny Zhu; |
| 309 | Knowledge-Aware Procedural Text Understanding with Multi-Stage Training Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we focus on the task of procedural text understanding, which aims to comprehend such documents and track entities’ states and locations during a process. |
Zhihan Zhang; Xiubo Geng; Tao Qin; Yunfang Wu; Daxin Jiang; |
| 310 | Multi-level Connection Enhanced Representation Learning for Script Event Prediction Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Here we propose a novel framework which can enhance the representation learning of events by mining their connections at multiple granularity levels, including argument level, event level and chain level. |
Lihong Wang; Juwei Yue; Shu Guo; Jiawei Sheng; Qianren Mao; Zhenyu Chen; Shenghai Zhong; Chen Li; |
| 311 | Unsupervised Lifelong Learning with Curricula Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Motivated by this situation, we propose a new paradigm named unsupervised lifelong learning with curricula (ULLC), where only one task needs to be labeled for initialization and the system then performs lifelong learning for subsequent tasks in an unsupervised fashion. |
Yi He; Sheng Chen; Baijun Wu; Xu Yuan; Xindong Wu; |
| 312 | Taxonomy-aware Learning for Few-Shot Event Detection Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To deal with the low-resources problem, we investigate few-shot event detection in this paper and propose TaLeM, a novel taxonomy-aware learning model, consisting of two components, i.e., the taxonomy-aware self-supervised learning framework (TaSeLF) and the taxonomy-aware prototypical networks (TaPN). |
Jianming Zheng; Fei Cai; Wanyu Chen; Wengqiang Lei; Honghui Chen; |
| 313 | Distilling Knowledge from Publicly Available Online EMR Data to Emerging Epidemic for Prognosis Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a distilled transfer learning framework, which leverages the existing publicly available online Electronic Medical Records to enhance the prognosis for inpatients with emerging infectious diseases. |
Liantao Ma; Xinyu Ma; Junyi Gao; Xianfeng Jiao; Zhihao Yu; Chaohe Zhang; Wenjie Ruan; Yasha Wang; Wen Tang; Jiangtao Wang; |
| 314 | AID: Active Distillation Machine to Leverage Pre-Trained Black-Box Models in Private Data Settings Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents an active distillation method for a local institution (e.g., hospital) to find the best queries within its given budget to distill an on-server black-box model’s predictive knowledge into a local surrogate with transparent parameterization. |
Trong Nghia Hoang; Shenda Hong; Cao Xiao; Bryan Low; Jimeng Sun; |
| 315 | UserSim: User Simulation Via Supervised GenerativeAdversarial Network Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we develop a user simulator based on a Generative Adversarial Network (GAN). |
Xiangyu Zhao; Long Xia; Lixin Zou; Hui Liu; Dawei Yin; Jiliang Tang; |
| 316 | Adversarial Item Promotion: Vulnerabilities at The Core of Top-N Recommenders That Use Images to Address Cold Start Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we demonstrate how unscrupulous merchants can create item images that artificially promote their products, improving their rankings. |
Zhuoran Liu; Martha Larson; |
| 317 | Robust Android Malware Detection Against Adversarial Example Attacks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel robust Android malware detection approach that can resist adversarial examples without requiring their instances or knowledge by jointly investigating malware detection and adversarial example defenses. |
Heng Li; Shiyao Zhou; Wei Yuan; Xiapu Luo; Cuiying Gao; Shuiyan Chen; |
| 318 | Where Are You Taking Me?Understanding Abusive Traffic Distribution Systems Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We design ODIN (Observatory of Dynamic Illicit ad Networks), the first system to study cloaking, user differentiation and business integration at the same time in four different types of traffic sources: typosquatting, copyright-infringing movie streaming, ad-based URL shortening, and illicit online pharmacy websites. |
Janos Szurdi; Meng Luo; Brian Kondracki; Nick Nikiforakis; Nicolas Christin; |
| 319 | One Detector to Rule Them All: Towards A General Deepfake Attack Detection Framework Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To better cope with unknown and unseen deepfakes, we introduce a Convolutional LSTM-based Residual Network (CLRNet), which adopts a unique model training strategy and explores spatial as well as the temporal information in a deepfakes. |
Shahroz Tariq; Sangyup Lee; Simon Woo; |
| 320 | A Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we show that targeted attacks on black-box NMT systems are feasible, based on poisoning a small fraction of their parallel training data. |
Chang Xu; Jun Wang; Yuqing Tang; Francisco Guzmán; Benjamin I. P. Rubinstein; Trevor Cohn; |
| 321 | On The Equivalence of Decoupled Graph Convolution Network and Label Propagation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore the decoupled GCN for semi-supervised node classification from a novel and fundamental perspective — label propagation. |
Hande Dong; Jiawei Chen; Fuli Feng; Xiangnan He; Shuxian Bi; Zhaolin Ding; Peng Cui; |
| 322 | Mixup for Node and Graph Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose the Mixup methods for two fundamental tasks in graph learning: node and graph classification. |
Yiwei Wang; Wei Wang; Yuxuan Liang; Yujun Cai; Bryan Hooi; |
| 323 | Effective and Scalable Clustering on Massive Attributed Graphs Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose , an efficient approach to k-AGC that yields high-quality clusters with costs linear to the size of the input graph G. |
Renchi Yang; Jieming Shi; Yin Yang; Keke Huang; Shiqi Zhang; Xiaokui Xiao; |
| 324 | Mask-GVAE: Blind Denoising Graphs Via Partition Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We present Mask-GVAE, a variational generative model for blind denoising large discrete graphs, in which ”blind denoising” means we don’t require any supervision from clean graphs. |
Jia Li; Mengzhou Liu; Honglei Zhang; Pengyun Wang; Yong Wen; Lujia Pan; Hong Cheng; |
| 325 | Bridging The Gap Between Von Neumann Graph Entropy and Structural Information: Theory and Applications Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we thereby study the difference between the structural information and VNGE named as entropy gap. |
Xuecheng Liu; Luoyi Fu; Xinbing Wang; |
| 326 | Convex Surrogates for Unbiased Loss Functions in Extreme Classification With Missing Labels Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we consider common loss functions that decompose over labels, and calculate unbiased estimates that compensate missing labels according to Natarajan et al. [26]. |
Mohammadreza Qaraei; Erik Schultheis; Priyanshu Gupta; Rohit Babbar; |
| 327 | ECLARE: Extreme Classification with Label Graph Correlations Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper presents ECLARE, a scalable deep learning architecture that incorporates not only label text, but also label correlations, to offer accurate real-time predictions within a few milliseconds. |
Anshul Mittal; Noveen Sachdeva; Sheshansh Agrawal; Sumeet Agarwal; Purushottam Kar; Manik Varma; |
| 328 | GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To remedy these, GalaXC presents a framework that enables collaborative learning over joint document-label graphs at massive scales, in a way that naturally allows various auxiliary sources of information, including label metadata, to be incorporated. |
Deepak Saini; Arnav Kumar Jain; Kushal Dave; Jian Jiao; Amit Singh; Ruofei Zhang; Manik Varma; |
| 329 | Generalizing Discriminative Retrieval Models Using Generative Tasks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel multi-task learning approach which can be used to produce more effective neural ranking models. |
Binsheng Liu; Hamed Zamani; Xiaolu Lu; J. Shane Culpepper; |
| 330 | FedPS: A Privacy Protection Enhanced Personalized Search Framework Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we focus on the problem of privacy protection in personalized search, and propose a privacy protection enhanced personalized search framework, denoted with FedPS. |
Jing Yao; Zhicheng Dou; Ji-Rong Wen; |
| 331 | Auditing for Discrimination in Algorithms Delivering Job Ads Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Our first contribution is to identify the distinction between skew in ad delivery due to protected categories such as gender or race, from skew due to differences in qualification among people in the targeted audience. |
Basileal Imana; Aleksandra Korolova; John Heidemann; |
| 332 | Debiasing Career Recommendations with Neural Fair Collaborative Filtering Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we investigate gender bias in collaborative-filtering recommender systems trained on social media data. |
Rashidul Islam; Kamrun Naher Keya; Ziqian Zeng; Shimei Pan; James Foulds; |
| 333 | The Interaction Between Political Typology and Filter Bubbles in News Recommendation Algorithms Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we investigate this phenomenon in the context of political news recommendation algorithms, which have important implications for civil discourse. |
Ping Liu; Karthik Shivaram; Aron Culotta; Matthew A. Shapiro; Mustafa Bilgic; |
| 334 | Dialect Diversity in Text Summarization on Twitter Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To correct for the dialect bias, we employ a framework that takes an existing text summarization algorithm as a blackbox and, using a small set of dialect-diverse sentences, returns a summary that is relatively more dialect-diverse. |
Vijay Keswani; L. Elisa Celis; |
| 335 | Fairness-Aware PageRank Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we consider fairness for link analysis and in particular for the celebrated Pagerank algorithm. |
Sotiris Tsioutsiouliklis; Evaggelia Pitoura; Panayiotis Tsaparas; Ilias Kleftakis; Nikos Mamoulis; |
| 336 | Cost-Effective and Interpretable Job Skill Recommendation with Deep Reinforcement Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To this end, in this paper, we aim to develop a cost-effective recommendation system based on deep reinforcement learning, which can provide personalized and interpretable job skill recommendation for each talent. |
Ying Sun; Fuzhen Zhuang; Hengshu Zhu; Qing He; Hui Xiong; |
| 337 | Personalized Approximate Pareto-Efficient Recommendation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To capture users’ objective-level preferences and enhance personalization in Pareto-efficient recommendation, we propose a novel Personalized Approximate Pareto-Efficient Recommendation (PAPERec) framework for multi-objective recommendation. |
Ruobing Xie; Yanlei Liu; Shaoliang Zhang; Rui Wang; Feng Xia; Leyu Lin; |
| 338 | ELIXIR: Learning from User Feedback on Explanations To Improve Recommender Models Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of generated recommendations themselves. |
Azin Ghazimatin; Soumajit Pramanik; Rishiraj Saha Roy; Gerhard Weikum; |
| 339 | Bidirectional Distillation for Top-K Recommender System Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: Based on this observation, we propose Bidirectional Distillation (BD) framework whereby both the teacher and the student collaboratively improve with each other. |
Wonbin Kweon; Seongku Kang; Hwanjo Yu; |
| 340 | Towards Content Provider Aware Recommender Systems: A Simulation Study on The Interplay Between User and Provider Utilities Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: This paper aims to serve as a research investigation of one approach toward building a content provider aware recommender, and evaluating its impact in a simulated setup. |
Ruohan Zhan; Konstantina Christakopoulou; Ya Le; Jayden Ooi; Martin Mladenov; Alex Beutel; Craig Boutilier; Ed Chi; Minmin Chen; |
| 341 | Robust Network Alignment Via Attack Signal Scaling and Adversarial Perturbation Elimination Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a robust network alignment solution, RNA, for offering preemptive protection of existing network alignment algorithms, enhanced with the guidance of effective adversarial attacks. |
Yang Zhou; Zeru Zhang; Sixing Wu; Victor Sheng; Xiaoying Han; Zijie Zhang; Ruoming Jin; |
| 342 | Attent: Active Attributed Network Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we tackle this challenge and propose an active network alignment method (Attent) to identify the best nodes to query. |
Qinghai Zhou; Liangyue Li; Xintao Wu; Nan Cao; Lei Ying; Hanghang Tong; |
| 343 | BRIGHT: A Bridging Algorithm for Network Alignment Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To overcome these two limitations, we bridge these methods and propose a novel family of network alignment algorithms BRIGHT to handle both plain and attributed networks. |
Yuchen Yan; Si Zhang; Hanghang Tong; |
| 344 | Sketch-based Algorithms for Approximate Shortest Paths in Road Networks Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose very efficient algorithms, based on a distance oracle, for computing approximate shortest paths and alternate paths in road networks. |
Gaurav Aggarwal; Sreenivas Gollapudi; Raghavender; Ali Kemal Sinop; |
| 345 | Efficient Reductions and A Fast Algorithm of Maximum Weighted Independent Set Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we study the structural properties of this problem, giving some sufficient conditions for a vertex being or not being in a maximum weighted independent set. |
Mingyu Xiao; Sen Huang; Yi Zhou; Bolin Ding; |
| 346 | Auction Design for ROI-Constrained Buyers Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We combine theory and empirics to (i) show that some buyers in online advertising markets are financially constrained and (ii) demonstrate how to design auctions that take into account such financial constraints. |
Negin Golrezaei; Ilan Lobel; Renato Paes Leme; |
| 347 | Bid Prediction in Repeated Auctions with Learning Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose econometric approaches to simultaneously learn the parameters of a player’s utility and her learning rule, and apply these methods to a real-world dataset from the BingAds sponsored search auction marketplace. |
Gali Noti; Vasilis Syrgkanis; |
| 348 | Towards Efficient Auctions in An Auto-bidding World Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a family of auctions with boosts to improve welfare in auto-bidding environments with both return on ad spend constraints and budget constraints. |
Yuan Deng; Jieming Mao; Vahab Mirrokni; Song Zuo; |
| 349 | Information Elicitation from Rowdy Crowds Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: We initiate the study of information elicitation mechanisms for a crowd containing both self-interested agents, who respond to incentives, and adversarial agents, who may collude to disrupt the system. |
Grant Schoenebeck; Fang-Yi Yu; Yichi Zhang; |
| 350 | Evaluating The Rationales of Amateur Investors Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose an approach for capturing expert-like rationales from social media platforms without the requirement of the annotated data. |
Chung-Chi Chen; Hen-Hsen Huang; Hsin-Hsi Chen; |
| 351 | Information Extraction From Co-Occurring Similar Entities Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we explore how information extracted from similar entities that co-occur in structures like tables or lists can help to increase the coverage of such knowledge graphs. |
Nicolas Heist; Heiko Paulheim; |
| 352 | Unsupervised Semantic Association Learning with Latent Label Inference Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we unify a diverse set of learning tasks in NLP, semantic retrieval and related areas, under a common umbrella, which we call unsupervised semantic association learning (USAL). |
Yanzhao Zhang; Richong Zhang; Jaein Kim; Xudong Liu; Yongyi Mao; |
| 353 | TCN: Table Convolutional Network for Web Table Interpretation Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this work, we propose a novel relational table representation learning approach considering both the intra- and inter-table contextual information. |
Daheng Wang; Prashant Shiralkar; Colin Lockard; Binxuan Huang; Xin Luna Dong; Meng Jiang; |
| 354 | Extracting Contextualized Quantity Facts from Web Tables Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: To enable such queries over web contents, this paper develops a novel method for automatically extracting quantity facts from ad-hoc web tables. |
Vinh Thinh Ho; Koninika Pal; Simon Razniewski; Klaus Berberich; Gerhard Weikum; |
| 355 | Searching to Sparsify Tensor Decomposition for N-ary Relational Data Related Papers Related Patents Related Grants Related Orgs Related Experts Details Highlight: In this paper, we propose a novel method, i.e., S2S, for effectively and efficiently learning from the N-ary relational data. |
Shimin Di; Quanming Yao; Lei Chen; |