Paper Digest: ICDM 2019 Highlights

November 7, 2019February 8, 2020 admin

IEEE International Conference on Data Mining (ICDM) is a premier research conference in data mining. In 2019, it is to be held in Beijing, China.

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.

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Paper Digest Team
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TABLE 1: ICDM 2019 Papers

	Title	Authors	Highlight
1	Collaborative Graph Walk for Semi-Supervised Multi-label Node Classification	U. Akujuobi, H. Yufei, Q. Zhang and X. Zhang	In this work, we study semi-supervised multi-label node classification problem in attributed graphs.
2	Dataset Recommendation via Variational Graph Autoencoder	B. Altaf, U. Akujuobi, L. Yu and X. Zhang	We propose to learn representations of research papers and datasets in the two-layer network using heterogeneous variational graph autoencoder, and then compute the relevance of the query to the dataset candidates based on the learned representations.
3	CUDA: Contradistinguisher for Unsupervised Domain Adaptation	S. Balgi and A. Dukkipati	Motivated by this supervised-unsupervised joint learning, we propose a simple model referred as Contradistinguisher (CTDR) for unsupervised domain adaptation whose objective is to jointly learn to contradistinguish on unlabeled target domain in a fully unsupervised manner along with prior knowledge acquired by supervised learning on an entirely different domain.
4	An Efficient Policy Gradient Method for Conditional Dialogue Generation	L. Cai and S. Ji	To overcome the bottleneck of excessive time complexity incurred by the Monte Carlo search for training, we propose a local discriminator network to compute the individual reward in one forward propagation, thereby dramatically accelerating the training procedure.
5	Supervised Class Distribution Learning for GANs-Based Imbalanced Classification	Z. Cai, X. Wang, M. Zhou, J. Xu and L. Jing	To overcome this issue, we propose a novel imbalanced classification framework with two stages.
6	NVSRN: A Neural Variational Scaling Reasoning Network for Initiative Response Generation	J. Chang et al.	In this paper, we propose a novel Neural Variational Scaling Reasoning Network (NVSRN) for initiative response generation.
7	InBEDE: Integrating Contextual Bandit with TD Learning for Joint Pricing and Dispatch of Ride-Hailing Platforms	H. Chen et al.	In this paper, we show that these two processes are in fact intrinsically interrelated.
8	Neural Feature Search: A Neural Architecture for Automated Feature Engineering	X. Chen et al.	In this paper, we present Neural Feature Search (NFS), a novel neural architecture for automated feature engineering.
9	Preference Relationship-Based CrossCMN Scheme for Answer Ranking in Community QA	Q. Chen, J. Wang, X. Lan and N. Zheng	To improve such problem, we design a novel scheme, named PW-CrossCMN. The scheme ranks the candidate answers by pair-wise approach based on numerous historical documents.
10	Automatic Clustering by Detecting Significant Density Dips in Multiple Dimensions	P. Chronis, S. Athanasiou and S. Skiadopoulos	In this paper, we present M-Dip, an automatic clustering algorithm that works directly on multi-dimensional space.
11	Generative Oversampling with a Contrastive Variational Autoencoder	W. Dai, K. Ng, K. Severson, W. Huang, F. Anderson and C. Stultz	In this work, we use an oversampling method that leverages information in both the majority and minority classes to mitigate the class imbalance problem.
12	MUSE-RNN: A Multilayer Self-Evolving Recurrent Neural Network for Data Stream Classification	M. Das, M. Pratama, S. Savitri and J. Zhang	In this paper, we propose MUSE-RNN, a multilayer self-evolving recurrent neural network model for real-time classification of streaming data.
13	Learning Dynamic Author Representations with Temporal Language Models	E. Delasalles, S. Lamprier and L. Denoyer	We propose a neural model, based on recurrent language modeling, which aims at capturing language diffusion tendencies in author communities through time.
14	Closed Form Word Embedding Alignment	S. Dev, S. Hassan and J. M. Phillips	We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e.g., GloVe or word2vec).
15	Reinforcement Learning Based Monte Carlo Tree Search for Temporal Path Discovery	P. Ding, G. Liu, P. Zhao, A. Liu, Z. Li and K. Zheng	In order to deliver an efficient and effective temporal path discovery method to be used in real-time environment, we propose a Reinforcement Learning (RL) based, Monte Carlo Tree Search algorithm (RLMCTS).
16	Learning Credible Deep Neural Networks with Rationale Regularization	M. Du, N. Liu, F. Yang and X. Hu	In pursuit of developing more credible DNNs, in this paper we propose CREX, which encourages DNN models to focus more on evidences that actually matter for the task at hand, and to avoid overfitting to data-dependent bias and artifacts.
17	Beyond Geo-First Law: Learning Spatial Representations via Integrated Autocorrelations and Complementarity	J. Du, Y. Zhang, P. Wang, J. Leopold and Y. Fu	In this paper, we study the problem of improving spatial representation learning using spatial structure knowledge.
18	Improving Spectral Clustering with Deep Embedding and Cluster Estimation	L. Duan, C. Aggarwal, S. Ma and S. Sathe	To address these two problems, in this paper, we propose an approach to extending spectral clustering with deep embedding and estimation of the number of clusters.
19	Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory	S. Dutta, D. Das and T. Chakraborty	We propose RGNet (Relativistic Gravitational Nerwork), a novel algorithm that uses Einstein Field Equations of gravity to model online discussions as ‘cloud of dust’ hovering over a user spacetime manifold, attracting users of different groups at different rates over time.
20	Large-Scale Personalized Delivery for Guaranteed Display Advertising with Real-Time Pacing	Z. Fang, Y. Li, C. Liu, W. Zhu, Y. Zheng and W. Zhou	In this paper, we present an large-scale system for personalized delivery in GD advertising services.
21	DCMN: Double Core Memory Network for Patient Outcome Prediction with Multimodal Data	Y. Feng et al.	In this paper, we propose a method called Double Core Memory Networks (DCMN) to integrate information from different modalities of the longitudinal patient data and learn a joint patient representation effective for downstream analytical tasks such as risk prediction.
22	Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text	O. Feyisetan, T. Diethe and T. Drake	In this work, we explore word representations in Hyperbolic space as a means of preserving privacy in text.
23	Discovering Subdimensional Motifs of Different Lengths in Large-Scale Multivariate Time Series	Y. Gao and J. Lin	In this paper, we introduce an approximate variable-length subdimensional motif discovery algorithm called Collaborative HIerarchy based Motif Enumeration (CHIME) to efficiently detect variable-length subdimensional motifs given a minimum motif length in large-scale multivariate time series.
24	Tabular Cell Classification Using Pre-Trained Cell Embeddings	M. Ghasemi Gol, J. Pujara and P. Szekely	In this paper, we propose a method to embed the semantic and contextual information about tabular cells in a low dimension cell embedding space.
25	Streaming Random Patches for Evolving Data Stream Classification	H. M. Gomes, J. Read and A. Bifet	In this work, we introduce the Streaming Random Patches (SRP) algorithm, an ensemble method specially adapted to stream classification which combines random subspaces and online bagging.
26	Deep Multi-attributed Graph Translation with Node-Edge Co-Evolution	X. Guo, L. Zhao, C. Nowzari, S. Rafatirad, H. Homayoun and S. M. Pudukotai Dinakarrao	In this paper, we termed this generic problem “multi-attributed graph translation” and developed a novel framework integrating both node and edge translations seamlessly.
27	Efficient Data Representation by Selecting Prototypes with Importance Weights	K. S. Gurumoorthy, A. Dhurandhar, G. Cecchi and C. Aggarwal	In this paper, we present algorithms with strong theoretical guarantees to mine these data sets and select prototypes, a.k.a. representatives that optimally describes them.
28	Interpretable Feature Learning of Graphs using Tensor Decomposition	S. M. Hamdi and R. Angryk	In this paper, we present two novel tensor decomposition-based node embedding algorithms, that can learn node features from arbitrary types of graphs: undirected, directed, and/or weighted, without relying on eigendecomposition or word embedding-based hyperparameters.
29	Discriminatively Relabel for Partial Multi-label Learning	S. He, K. Deng, L. Li, S. Shu and L. Liu	To alleviate this problem, a coupled framework is established in this paper to learn the desired model and perform the relabeling procedure alternatively.
30	Distribution of Node Embeddings as Multiresolution Features for Graphs	M. Heimann, T. Safavi and D. Koutra	To address such challenges, we propose Randomized Grid Mapping (RGM), a fast-to-compute feature map that represents a graph via the distribution of its node embeddings in feature space.
31	Multi-aspect Mining of Complex Sensor Sequences	T. Honda, Y. Matsubara, R. Neyama, M. Abe and Y. Sakurai	In this paper we present CUBEMARKER, an efficient and effective method for capturing such multi-aspect features in sensor sequences.
32	Online Budgeted Least Squares with Unlabeled Data	C. Huang, P. Li, C. Gao, Q. Yang and J. Shao	In this paper, we propose an efficient and effective online semi-supervised learning approach via Budgeted Least Square (BLS).
33	Bi-directional Causal Graph Learning through Weight-Sharing and Low-Rank Neural Network	H. Huang, C. Xu and S. Yoo	In this paper, we present a Bi-directional neural network for Causal Graph Learning (Bi-CGL) through weight-sharing and low-rank neural network.
34	Matrix Profile XIX: Time Series Semantic Motifs: A New Primitive for Finding Higher-Level Structure in Time Series	S. Imani and E. Keogh	In this work we generalize the definition of motifs to one which allows us to capture higher level semantic structure.
35	Forest Distance Closeness Centrality in Disconnected Graphs	Y. Jin, Q. Bao and Z. Zhang	In this paper, we use forest distance to assess the importance of nodes in a graph, whether connected or disconnected.
36	Computing Optimal Assignments in Linear Time for Approximate Graph Matching	N. M. Kriege, P. Giscard, F. Bause and R. C. Wilson	In this paper, we develop an algorithm which can find an optimal assignment in linear time when the cost function between objects is represented by a tree distance.
37	Multi-hop Knowledge Base Question Answering with an Iterative Sequence Matching Model	Y. Lan, S. Wang and J. Jiang	In this paper, we propose a novel iterative sequence matching model to address several limitations of previous methods for multi-hop KBQA.
38	Collaborative Distillation for Top-N Recommendation	J. Lee, M. Choi, J. Lee and H. Shim	To address the issues, we propose a new KD model for the collaborative filtering approach, namely collaborative distillation (CD).
39	HierCon: Hierarchical Organization of Technical Documents Based on Concepts	K. Li, S. Li, S. Yavuz, H. Zha, Y. Su and X. Yan	In this work we study the hierarchical organization of technical documents, where given a set of documents and a hierarchy of categories, the goal is to assign documents to their corresponding categories.
40	Classify EEG and Reveal Latent Graph Structure with Spatio-Temporal Graph Convolutional Neural Network	X. Li, B. Qian, J. Wei, A. Li, X. Liu and Q. Zheng	In our work, we treat EEG signals as frames of graph, and propose an end-to-end edge-aware spatio-temporal graph convolutional neural network for EEG classification.
41	Learning Classifiers on Positive and Unlabeled Data with Policy Gradient	T. Li et al.	In this paper, we propose to alternatively train the two steps using reinforcement learning.
42	Learning a Low-Rank Tensor of Pharmacogenomic Multi-relations from Biomedical Networks	Z. Li, W. Zhang, R. S. Huang and R. Kuang	To achieve the goal, we propose a general tensor-based optimization framework and a scalable Graph-Regularized Tensor Completion from Observed Pairwise Relations (GT-COPR) algorithm to infer the multi-relations among the entities across multiple networks in a low-rank tensor, based on manifold regularization with the graph Laplacian of a Cartesian, tensor or strong product of the networks, and consistencies between the collapsed tensors and the observed bipartite relations.
43	One-Stage Deep Instrumental Variable Method for Causal Inference from Observational Data	A. Lin, J. Lu, J. Xuan, F. Zhu and G. Zhang	This paper presents a one-stage approach to jointly estimate the treatment distribution and the outcome generating function through a cleverly designed deep neural network structure.
44	Guiding Cross-lingual Entity Alignment via Adversarial Knowledge Embedding	X. Lin, H. Yang, J. Wu, C. Zhou and B. Wang	To this end, we present a new Adversarial Knowledge Embedding framework (AKE for short) that jointly learns the representation, mapping and adversarial modules in an end-to-end manner.
45	Exploring Semantic Relationships for Image Captioning without Parallel Data	F. Liu, M. Gao, T. Zhang and Y. Zou	In this work, we achieve unpaired image captioning by bridging the vision and the language domains with high-level semantic information.
46	Cross-Modal Zero-Shot Hashing	X. Liu, Z. Li, J. Wang, G. Yu, C. Domenicon and X. Zhang	To address these issues, we propose a general Cross-modal Zero-shot Hashing (CZHash) solution to effectively leverage unlabeled and labeled multi-modality data with different label spaces.
47	Performing Co-membership Attacks Against Deep Generative Models	K. S. Liu, C. Xiao, B. Li and J. Gao	In this paper we propose a new membership attack method called co-membership attacks against deep generative models including Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs).
48	Matching Novelty While Training: Novel Recommendation Based on Personalized Pairwise Loss Weighting	K. Lo and T. Ishigaki	In this work, we propose a personalized pairwise novelty weighting for BPR loss function, which covers the limitations of BPR and effectively improves novelty with marginal loss in accuracy.
49	RiWalk: Fast Structural Node Embedding via Role Identification	X. Ma, G. Qin, Z. Qiu, M. Zheng and Z. Wang	Here we propose RiWalk, a flexible paradigm for learning structural node representations as it decouples the structural embedding problem into a role identification procedure and a network embedding procedure.
50	Sharp Characterization of Optimal Minibatch Size for Stochastic Finite Sum Convex Optimization	A. Nitanda, T. Murata and T. Suzuki	In this study, we give a sharp characterization of the minimax optimal minibatch size to achieve the optimal iteration complexity by providing a reachable lower bound for minimizing finite sum of convex functions and, surprisingly, show that the optimal method with the minimax optimal minibatch size can achieve both of the optimal iteration complexity and the optimal total computational complexity simultaneously.
51	Temporal Self-Attention Network for Medical Concept Embedding	X. Peng, G. Long, T. Shen, S. Wang, J. Jiang and M. Blumenstein	In this paper, we propose a medical concept embedding method based on applying a self-attention mechanism to represent each medical concept.
52	Efficient Sketching Algorithm for Sparse Binary Data	R. Pratap, D. Bera and K. Revanuru	In this work, we address this problem and propose a sketching (alternatively, dimensionality reduction) algorithm ? BinSketch (Binary Data Sketch) ? for sparse binary datasets.
53	Exploiting Multi-domain Visual Information for Fake News Detection	P. Qi, J. Cao, T. Yang, J. Guo and J. Li	Therefore, we propose a novel framework Multi-domain Visual Neural Network (MVNN) to fuse the visual information of frequency and pixel domains for detecting fake news.
54	Personalized Knowledge Graph Summarization: From the Cloud to Your Pocket	T. Safavi, C. Belth, L. Faber, D. Mottin, E. M?ller and D. Koutra	Motivated by the disparity between individuals’ limited information needs and the massive scale of KGs, in this paper we propose a new problem called personalized knowledge graph summarization.
55	Learning to Sample: An Active Learning Framework	J. Shao, Q. Wang and F. Liu	In our paper, we tackle these issues by proposing a novel learning-based active learning framework, called Learning To Sample (LTS).
56	Reinforced Molecule Generation with Heterogeneous States	F. Shi, S. You and C. Xu	In this paper, we propose to augment the original graph states with the SMILES context vectors.
57	Modeling Graphs with Vertex Replacement Grammars	S. Sikdar, J. Hibshman and T. Weninger	We show that a variant of the VRG called Clustering-based Node Replacement Grammar (CNRG) can be efficiently extracted from many hierarchical clusterings of a graph.
58	M-estimation in Low-Rank Matrix Factorization: A General Framework	W. Tu et al.	In this paper, we present a general framework of low-rank matrix factorization based on M-estimation in statistics.
59	Towards Making Deep Transfer Learning Never Hurt	R. Wan, H. Xiong, X. Li, Z. Zhu and J. Huan	In this paper, we consider deep transfer learning as minimizing a linear combination of empirical loss and regularizer based on pre-trained weights, where the regularizer would restrict the training procedure from lowering the empirical loss, with conflicted descent directions (e.g., derivatives).
60	Generative Correlation Discovery Network for Multi-label Learning	L. Wang, Z. Ding, S. Han, J. Han, C. Choi and Y. Fu	To this end, we propose an end-to-end Generative Correlation Discovery Network (GCDN) method for multi-label learning in this paper.
61	A Semi-Supervised Graph Attentive Network for Financial Fraud Detection	D. Wang et al.	To address the problem, we expand the labeled data through their social relations to get the unlabeled data and propose a semi-supervised attentive graph neural network, named SemiGNN to utilize the multi-view labeled and unlabeled data for fraud detection.
62	DMFP: A Dynamic Multi-faceted Fine-Grained Preference Model for Recommendation	H. Wang, G. Liu, Y. Zhao, B. Zheng, P. Zhao and K. Zheng	In this paper, we propose a Dynamic Multi-faceted Fine-grained Preference model (DMFP), where the multi-hops attention mechanism and the feature-level attention mechanism together with a vertical convolution operation are adopted to capture users’ multi-faceted long-term preference and fine-grained short-term preference, respectively.
63	DeepTrust: A Deep User Model of Homophily Effect for Trust Prediction	Q. Wang, W. Zhao, J. Yang, J. Wu, W. Hu and Q. Xing	Inspired by the homophily theory, which shows a pervasive feature of social and economic networks that trust relations tend to be developed among similar people, we propose a novel deep user model for trust prediction based on user similarity measurement.
64	A Coarse-to-Fine Multi-stream Hybrid Deraining Network for Single Image Deraining	Y. Wei, Z. Zhang, H. Zhang, R. Hong and M. Wang	To handle this issue, this paper proposes a new coarse-to-fine single image deraining framework termed Multi-stream Hybrid Deraining Network (shortly, MH-DerainNet).
65	XOR-Based Boolean Matrix Decomposition	J. Wicker, Y. C. Hua, R. Rebello and B. Pfahringer	In this paper, we introduce a new algorithm named XBMaD (XOR-based Boolean Matrix Decomposition) where the addition operator is defined as the exclusive OR (XOR).
66	Domain-Adversarial Graph Neural Networks for Text Classification	M. Wu, S. Pan, X. Zhu, C. Zhou and L. Pan	This paper proposes an end-to-end, domain-adversarial graph neural networks (DAGNN), for cross-domain text classification.
67	Discriminative Regularized Deep Generative Models for Semi-Supervised Learning	Q. Xie, J. Huang, M. Peng, Y. Zhang, K. Peng and H. Wang	In this paper, we propose a novel discriminative regularized deep generative method for SSL, which fully exploits the discriminative and geometric information of data to address the aforementioned issue.
68	Privacy-Preserving Auto-Driving: A GAN-Based Approach to Protect Vehicular Camera Data	Z. Xiong, W. Li, Q. Han and Z. Cai	In this paper, we intend to fill this blank by developing a GAN-based image-toimage translation method named Auto-Driving GAN (ADGAN).
69	Social Trust Network Embedding	P. Xu, W. Hu, J. Wu, W. Liu, B. Du and J. Yang	In this study, we propose a novel social trust network embedding method (STNE) to address these issues.
70	VSB-DVM: An End-to-End Bayesian Nonparametric Generalization of Deep Variational Mixture Model	X. Yang, Y. Yan, K. Huang and R. Zhang	In this paper, we propose an end-to-end Bayesian nonparametric generalization of deep mixture model with a Variational Auto-Encoder (VAE) framework.
71	Neural Embedding Propagation on Heterogeneous Networks	C. Yang, J. Zhang and J. Han	In this work, we focus on the more general and powerful heterogeneous networks, which accommodate multi-typed objects and links, and thus endure multi-typed complex interactions.
72	Discrete Overlapping Community Detection with Pseudo Supervision	F. Ye, C. Chen, Z. Zheng, R. Li and J. X. Yu	To circumvent the cumbersome post-processing step, we propose a novel discrete overlapping community detection approach, i.e., Discrete Nonnegative Matrix Factorization (DNMF), which seeks for a discrete (binary) community membership matrix directly.
73	Identifying High Potential Talent: A Neural Network Based Dynamic Social Profiling Approach	Y. Ye, H. Zhu, T. Xu, F. Zhuang, R. Yu and H. Xiong	To this end, in this paper, we propose a neural network based dynamic social profiling approach for quantitatively identifying HIPOs from the newly-enrolled employees by modeling the dynamics of their behaviors in organizational social networks.
74	Automatic Generation of Medical Imaging Diagnostic Report with Hierarchical Recurrent Neural Network	C. Yin et al.	We present a new framework to accurately detect the abnormalities and automatically generate medical reports.
75	Domain Knowledge Guided Deep Learning with Electronic Health Records	C. Yin, R. Zhao, B. Qian, X. Lv and P. Zhang	In the study, we propose a new model, Domain Knowledge Guided Recurrent Neural Networks (DG-RNN), by directly introducing domain knowledge from the medical knowledge graph into an RNN architecture, as well as taking the irregular time intervals into account.
76	Fast LSTM Inference by Dynamic Decomposition on Cloud Systems	Y. You et al.	In this paper we aim to minimize the latency of LSTM inference on cloud systems without losing accuracy.
77	Self-Attentive Attributed Network Embedding Through Adversarial Learning	W. Yu, W. Cheng, C. Aggarwal, B. Zong, H. Chen and W. Wang	To address the above issues, in this paper, we propose Nettention, a self-attentive network embedding approach that can efficiently learn vertex embeddings on attributed network.
78	Generating Reliable Friends via Adversarial Training to Improve Social Recommendation	J. Yu, M. Gao, H. Yin, J. Li, C. Gao and Q. Wang	Concretely, in this paper, we propose an end-to-end social recommendation framework based on Generative Adversarial Nets (GAN).
79	Transfer Learning with Dynamic Adversarial Adaptation Network	C. Yu, J. Wang, Y. Chen and M. Huang	In this paper, we propose a novel Dynamic Adversarial Adaptation Network (DAAN) to dynamically learn domain-invariant representations while quantitatively evaluate the relative importance of global and local domain distributions.
80	Mining Audio, Text and Visual Information for Talking Face Generation	L. Yu, J. Yu and Q. Ling	To overcome the problems above, we present a data-mining framework to learn the synchronous pattern between different channels from large recorded audio/text dataset and visual dataset, and apply it to generate realistic talking face animations.
81	Jointly Embedding the Local and Global Relations of Heterogeneous Graph for Rumor Detection	C. Yuan, Q. Ma, W. Zhou, J. Han and S. Hu	In this paper, we present a novel global-local attention network (GLAN) for rumor detection, which jointly encodes the local semantic and global structural information.
82	Learning Hierarchical and Shared Features for Improving 3D Neuron Reconstruction	H. Yuan, N. Zou, S. Zhang, H. Peng and S. Ji	In this work, we propose a deep learning approach for improving the accuracy of 3D neuron reconstruction.
83	PERCeIDs: PERiodic CommunIty Detection	L. Zhang, A. Gorovits and P. Bogdanov	We propose PERCeIDs, a framework for periodic overlapping community detection from temporal interaction data.
84	Generalized Adversarial Training in Riemannian Space	S. Zhang, K. Huang, R. Zhang and A. Hussain	In this paper, we extend the learning of adversarial examples to the more general Riemannian space over DNNs.
85	Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification	Z. Zhang, Y. Sun, Z. Zhang, Y. Wang, G. Liu and M. Wang	In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence.
86	Adaptive Structure-Constrained Robust Latent Low-Rank Coding for Image Recovery	Z. Zhang et al.	In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data.
87	Machine Comprehension-Incorporated Relevance Matching	C. Zhang, H. Wang, L. Zhou, Y. Wang and C. Chen	This paper proposes a unified model of Machine Comprehension-incorporated Relevance Matching (MCRM).
88	Boosted Trajectory Calibration for Traffic State Estimation	X. Zhang, L. Xie, Z. Wang and J. Zhou	To address the aforementioned challenges, in this paper, we propose a Boosted Trajectory Calibration (BTRAC) framework to model traffic states of complex road networks that effectively integrates trajectory information.
89	HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories	Y. Zhang et al.	In recognition of these challenges, we propose the HiGitClass framework, comprising of three modules: heterogeneous information network embedding; keyword enrichment; topic modeling and pseudo document generation.
90	Aftershock Detection with Multi-scale Description Based Neural Network	Q. Zhang et al.	To that end, in this paper, we propose a novel framework named Multi-Scale Description based Neural Network (MSDNN) for enhancing aftershock detection.
91	Know Your Mind: Adaptive Cognitive Activity Recognition with Reinforced CNN	X. Zhang, L. Yao, X. Wang, W. Zhang, S. Zhang and Y. Liu	In this regard, we propose a generic EEG-based cognitive activity recognition framework that can adaptively support a wide range of cognitive applications to address the above issues.
92	A Parallel Simulated Annealing Enhancement of the Optimal-Matching Heuristic for Ridesharing	L. Zhang, Z. Ye, K. Xiao and B. Jin	In this paper, we develop an efficient parallelheuristic method for solving the global optimization problemassociated with the ridesharing system.
93	Rank-Based Multi-task Learning for Fair Regression	C. Zhao and F. Chen	In this work, we develop a novel fairness learning approach for multi-task regression models based on a biased training dataset, using a popular rank-based non-parametric independence test, i.e., Mann Whitney U statistic, for measuring the dependency between target variable and protected variables.
94	Adversarial Robustness of Similarity-Based Link Prediction	K. Zhou, T. P. Michalak and Y. Vorobeychik	We propose a novel approach for increasing robustness of similarity-based link prediction by endowing the analyst with a restricted set of reliable queries which accurately measure the existence of queried links.
95	Matrix Profile XVIII: Time Series Mining in the Face of Fast Moving Streams using a Learned Approximate Matrix Profile	Z. Zimmerman et al.	In this work we introduce LAMP, a model that predicts, in constant time, the Matrix Profile value that would have been assigned to an incoming subsequence.
96	ChainNet: Learning on Blockchain Graphs with Topological Features	N. C. Abay et al.	We show that standard graph features such as degree distribution of the transaction graph may not be sufficient to capture network dynamics and its potential impact on fluctuations of Bitcoin price.
97	MTEX-CNN: Multivariate Time Series EXplanations for Predictions with Convolutional Neural Networks	R. Assaf, I. Giurgiu, F. Bagehorn and A. Schumann	In this work we present MTEX-CNN, a novel explainable convolutional neural network architecture which can not only be used for making predictions based on multivariate time series data, but also for explaining these predictions.
98	Efficient Approximate Solution Path Algorithm for Order Weight L_1-Norm with Accuracy Guarantee	R. Bao, B. Gu and H. Huang	To address this challenge, in this paper, we propose an efficient approximate solution path algorithm (OWLAGPath) to solve the OWL model with accuracy guarantee.
99	A Wasserstein Subsequence Kernel for Time Series	C. Bock, M. Togninalli, E. Ghisu, T. Gumbsch, B. Rieck and K. Borgwardt	We therefore propose a meaningful approach based on optimal transport theory that simultaneously captures local and global characteristics of time series.
100	Nearest Neighbor Ensembles: An Effective Method for Difficult Problems in Streaming Classification with Emerging New Classes	X. Cai, P. Zhao, K. Ting, X. Mu and Y. Jiang	To solve low-alpha SENC problems effectively, we propose an approach using nearest neighbor ensembles or SENNE.
101	VASE: A Twitter-Based Vulnerability Analysis and Score Engine	H. Chen, J. Liu, R. Liu, N. Park and V. S. Subrahmanian	We present VASE (Vulnerability Analysis and Scoring Engine) that uses Twitter discussions about CVEs to predict CVSS scores before the official assessments from NIST.
102	Scalable Explanation of Inferences on Large Graphs	C. Chen, Y. Liu, X. Zhang and S. Xie	We propose a beam search algorithm to find trees to enhance the explanation interpretability and diversity.
103	AMENDER: An Attentive and Aggregate Multi-layered Network for Dataset Recommendation	Y. Chen, Y. Wang, Y. Zhang, J. Pu and X. Zhang	In this paper, we study the problem of recommending the appropriate datasets for authors, which is implemented to infer the proximity between authors and datasets by leveraging the information from a three-layered network, composed by authors, papers and datasets.
104	Session-Based Recommendation with Local Invariance	T. Chen and R. C. Wong	To this end, we propose a novel model to automatically ignore the insignificant detailed ordering information in some sub-sessions, while keeping the high-level sequential information of the whole sessions.
105	Alpha-Beta Sampling for Pairwise Ranking in One-Class Collaborative Filtering	M. Cheng et al.	This paper introduces Alpha-Beta Sampling (ABS) strategy, which is particularly intended for the sampling problem of pairwise ranking in one-class collaborative filtering (PROCCF).
106	Counterfactual Attention Supervision	S. Choi, H. Park and S. Hwang	To this end, we propose a counterfactual method to estimate such missing observations and debias the existing supervisions.
107	Inductive Embedding Learning on Attributed Heterogeneous Networks via Multi-task Sequence-to-Sequence Learning	Y. Chu, C. Guo, T. He, Y. Wang, J. Hwang and C. Feng	In the paper, we study the problem of inductive embedding learning on attributed heterogeneous networks, and propose a Multi-task sequence-to-sequence learning based Inductive Network Embedding framework (MINE) capturing the attribute similarity, network proximity, and partial label information simultaneously.
108	A Factorized Version Space Algorithm for “Human-In-the-Loop” Data Exploration	L. Di Palma, Y. Diao and A. Liu	Our work offers theoretical results on optimality and approximation for this algorithm, as well as optimizations for better performance.
109	Optimal Timelines for Network Processes	D. J. DiTursi, C. S. Kaminski and P. Bogdanov	We introduce the general problem of inferring the optimal temporal resolution for network event data.
110	Intervention-Aware Early Warning	D. Eswaran, C. Faloutsos, N. Mishra and Y. Naamad	We consider the problem of learning to interpretably early warn from labeled data tainted by interventions.
111	CAMP: Co-Attention Memory Networks for Diagnosis Prediction in Healthcare	J. Gao et al.	To tackle these challenges, we propose a model called Co-Attention Memory networks for diagnosis Prediction (CAMP), which tightly integrates historical records, fine-grained patient conditions, and demographics with a three-way interaction architecture built on co-attention.
112	DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums	Y. Gao, L. Wu, H. Homayoun and L. Zhao	In this paper, we first formulate the transition of user activities as a dynamic graph with multi-attributed nodes, then formalize the health stage inference task as a dynamic graph-to-sequence learning problem, and hence propose a novel dynamic graph-to-sequence neural networks architecture (DynGraph2Seq) to address all the challenges.
113	DRCGR: Deep Reinforcement Learning Framework Incorporating CNN and GAN-Based for Interactive Recommendation	R. Gao, H. Xia, J. Li, D. Liu, S. Chen and G. Chun	Therefore, to solve the problems mentioned above, a novel Deep Q-Network based recommendation framework incorporating CNN and GAN-based models is proposed to acquire robust performance, named DRCGR.
114	An Integrated Model for Urban Subregion House Price Forecasting: A Multi-source Data Perspective	C. Ge, Y. Wang, X. Xie, H. Liu and Z. Zhou	In this work, we propose an effective and fine-grained model for urban subregion housing price predictions.
115	Fair Adversarial Gradient Tree Boosting	V. Grari, B. Ruf, S. Lamprier and M. Detyniecki	For this reason, we have developed a novel approach of adversarial gradient tree boosting.
116	Quantized Adversarial Training: An Iterative Quantized Local Search Approach	Y. Guo, T. Ji, Q. Wang, L. Yu and P. Li	In this paper, we first develop an Iterative Quantized Local Search (IQLS) algorithm that finds strong perturbation noises by quantizing both the input space and perturbation space. Then, we theoretically analyze and prove the upper bound on the number of iterations needed for the IQLS algorithm, based on which we devise an efficient and effective Quantized Adversarial Training (QAT) scheme.
117	A Weighted Aggregating SGD for Scalable Parallelization in Deep Learning	P. Guo, Z. Ye and K. Xiao	We propose a novel update rule, named weighted aggregating stochastic gradient decent, after theoretically analyzing the characteristics of the newly formalized objective function.
118	Improving Disentangled Representation Learning with the Beta Bernoulli Process	P. Gyawali et al.	In this paper, we investigate the little-explored effect of the modeling capacity of a posterior density on the disentangling ability of the VAE.
119	Diversity-Aware Recommendation by User Interest Domain Coverage Maximization	Y. He, H. Zou, H. Yu, Q. Wang and S. Gao	Aiming at improving the adaptability and efficiency of diversity-aware recommendations, we propose a coverage-based approach according to the concepts of user-coverage and users’ interest domain we have defined in this paper.
120	Deep Reinforcement Learning for Multi-driver Vehicle Dispatching and Repositioning Problem	J. Holler et al.	In this paper we present a deep reinforcement learning approach for tackling the full fleet management and dispatching problems.
121	Deep-Aligned Convolutional Neural Network for Skeleton-Based Action Recognition and Segmentation	B. Hosseini, R. Montagne and B. Hammer	In this work, we propose a novel deep-aligned convolutional neural network (DACNN) to tackle the above challenges for the particular problem of SBARS.
122	A Distributed Fair Machine Learning Framework with Private Demographic Data Protection	H. Hu, Y. Liu, Z. Wang and C. Lan	In this paper, we propose a distributed fair learning framework for protecting the privacy of demographic data.
123	Constructing Educational Concept Maps with Multiple Relationships from Multi-Source Data	X. Huang et al.	To this end, we propose a novel framework, named Extracting Multiple Relationships Concept Map (EMRCM), to construct multiple relations concept maps from Multi-source Data.
124	Unsupervised Qualitative Scoring for Binary Item Features	K. Ichikawa and H. Tamano	In this paper, we propose a novel approach to estimate the qualitative score from the binary features of products.
125	Semi-Supervised Adversarial Domain Adaptation for Seagrass Detection in Multispectral Images	K. A. Islam, V. Hill, B. Schaeffer, R. Zimmerman and J. Li	In this paper, we developed a semi-supervised domain adaptation method to generalize a trained deep CNN model to other locations for seagrass detection.
126	CSNN: Contextual Sentiment Neural Network	T. Ito, K. Tsubouchi, H. Sakaji, K. Izumi and T. Yamashita	In response, we propose a novel neural network model called contextual sentiment neural network (CSNN) model that can explain the process of its sentiment analysis prediction in a way that humans find natural and agreeable.
127	Multi-view Outlier Detection in Deep Intact Space	Y. Ji et al.	We propose a new algorithm termed Multi-view Outlier Detection in Deep Intact Space (MODDIS) to find all the three types of outliers simultaneously and avoid comparing different views in a pairwise manner.
128	Block-Structured Optimization for Anomalous Pattern Detection in Interdependent Networks	F. Jie, C. Wang, F. Chen, L. Li and X. Wu	We propose a generalized optimization framework for detecting anomalous patterns (subgraphs that are interesting or unexpected) in interdependent networks, such as multi-layer networks, temporal networks, networks of networks, and many others.
129	Network Identification and Authentication	S. Jin, V. Phoha and R. Zafarani	Using these identities, we propose two approaches for network identification.
130	Discovering Robustly Connected Subgraphs with Simple Descriptions	J. Kalofolias, M. Boley and J. Vreeken	To do so efficiently we propose a non-redundant iterative deepening approach, which we equip with a linear-time tight optimistic estimator that allows pruning large parts of the search space.
131	Matrix Profile XV: Exploiting Time Series Consensus Motifs to Find Structure in Time Series Sets	K. Kamgar, S. Gharghabi and E. Keogh	In this work we introduce a definition of time series consensus motifs and a scalable algorithm to discover them in large data collections.
132	Iterative Graph Alignment via Supermodular Approximation	A. Konar and N. Sidiropoulos	In this paper, we approach the task of designing an efficient polynomial-time approximation algorithm for graph matching from a previously unconsidered perspective.
133	Transfer Metric Learning for Unseen Domains	A. Kumagai, T. Iwata and Y. Fujiwara	We propose a transfer metric learning method to infer domain-specific data embeddings for unseen domains, from which no data are given in the training phase, by using knowledge transferred from related domains.
134	SENTI2POP: Sentiment-Aware Topic Popularity Prediction on Social Media	J. Li, Y. Gao, X. Gao, Y. Shi and G. Chen	In this paper, we propose a novel framework, SENTI2POP, to predict topic popularity utilizing sentiment information.
135	To Be or Not to Be: Analyzing & Modeling Social Recommendation in Online Social Networks	Y. Li, H. Xie, Y. Lin and J. C. S. Lui	In particular, our model captures how users decide whether to recommend an item, which is a key factor but often ignored by previous social recommendation models such as the “Independent Cascade model”.
136	Dense Semantic Matching Network for Multi-turn Conversation	Y. Li, J. Yu and Z. Wang	To address the problem, we propose a dense semantic matching network (DMN).
137	Contrast Feature Dependency Pattern Mining for Controlled Experiments with Application to Driving Behavior	Q. Li, L. Zhao, Y. Lee, Y. Ye, J. Lin and L. Wu	In this paper, we propose a generative model with partial correlation-based feature dependency regularization to help analysts understand the CMTS data by jointly 1) characterizing a set of comparable multivariate Gaussian distributions from CMTS, and 2) determining whether the intervention causes the changes between two comparable distributions.
138	Mining Maximal Clique Summary with Effective Sampling	X. Li et al.	In this paper, we study how to report a summary of less overlapping maximal cliques.
139	Consistency Meets Inconsistency: A Unified Graph Learning Framework for Multi-view Clustering	Y. Liang, D. Huang and C. Wang	To address this, this paper presents a new graph learning-based multi-view clustering approach, which for the first time, to our knowledge, simultaneously and explicitly formulates the multi-view consistency and the multi-view inconsistency in a unified optimization model.
140	Predicting Water Quality for the Woronora Delivery Network with Sparse Samples	B. Liang et al.	This paper details an approach that provides TC prediction within the entire Woronora delivery network in Sydney in the next 24 hours.
141	First Index-Free Manifold Ranking-Based Image Retrieval with Output Bound	D. Lin, V. J. Wei and R. C. Wong	In this paper, we study the image retrieval with manifold ranking (MR) which considers both the local similarity and the global similarity, which could give more accurate results.
142	What is the Value of Experimentation & Measurement?	C. H. B. Liu and B. P. Chamberlain	We tackle this problem by analyzing how, by decreasing estimation uncertainty, E&M platforms allow for better prioritization.
143	Spatio-Temporal GRU for Trajectory Classification	H. Liu, H. Wu, W. Sun and I. Lee	In this study, we propose a trajectory classifier called Spatio-Temporal GRU to better model the spatio-temporal correlations and irregular temporal intervals prevalently present in spatio-temporal trajectories.
144	Learning Local and Global Multi-context Representations for Document Classification	Y. Liu, H. Yuan and S. Ji	In this work, we address these limitations by proposing novel attention-based approaches.
145	I-CARS: An Interactive Context-Aware Recommender System	R. Lumbantoruan, X. Zhou, Y. Ren and L. Chen	In this work, we propose an Interactive Context-Aware Recommender System (I-CARS), which allows users to interact and present their needs, so the system can personalize and refine user preferences.
146	Triple-Shapelet Networks for Time Series Classification	Q. Ma, W. Zhuang and G. Cottrell	In this paper, we propose a novel end-to-end shapelet learning model called Triple Shapelet Networks (TSNs) to extract multi-level feature representations.
147	Discovering Reliable Correlations in Categorical Data	P. Mandros, M. Boley and J. Vreeken	In particular, we propose a corrected-for-chance, consistent, and efficient estimator for normalized total correlation, in order to obtain a reliable, interpretable, and non-parametric measure for correlation over multivariate sets.
148	Deep Embedded Cluster Tree	D. Mautz, C. Plant and C. B?hm	In this paper, we propose the Deep Embedded Cluster Tree (DeepECT), the first divisive hierarchical embedded clustering method.
149	Recognizing Variables from Their Data via Deep Embeddings of Distributions	J. Mueller and A. Smola	Here, we present a computationally efficient method to identify high-confidence variable matches between a given set of data values and a large repository of previously encountered datasets.
150	Efficient Bayesian Optimization for Uncertainty Reduction Over Perceived Optima Locations	V. Nguyen, S. Gupta, S. Rana, M. Thai, C. Li and S. Venkatesh	In this paper, we propose an alternative scheme – predictive variance reduction search (PVRS) ? to find a point that maximally reduces the uncertainty at the perceived optima locations.
151	Efficient Mining and Exploration of Multiple Axis-Aligned Intersecting Objects	T. Pechlivanoglou, V. Chu and M. Papagelis	With that mind, we design and implement a novel, exact, fast and scalable yet versatile, sweep-line based algorithm, named SLIG.
152	An Integrated Multimodal Attention-Based Approach for Bank Stress Test Prediction	F. Razzak, F. Yi, Y. Yang and H. Xiong	In this paper, we propose an Integrated Multimodal Bank Stress Test Prediction (IMBSTP) model framework consisting of a two-stages; (1) economic conditions estimator to approximate joint representation among the exogenous factors using generative models, (2) bank capital & loss forecaster to project stress test measures based on dimensional & temporal features selected from the exogenous economic conditions & banking performance profiles using a dual-attention recurrent neural network.
153	Dual Adversarial Learning Based Network Alignment	J. Ren, Y. Zhou, R. Jin, Z. Zhang, D. Dou and P. Wang	This paper presents a novel network alignment framework, RANA, that combines dual generative adversarial network (GAN) techniques to match the distributions of two networks based on two dimensions of distance and shape.
154	On Privacy of Socially Contagious Attributes	A. Rezaei and J. Gao	In this paper, we take a closer look at the validity of Differential Privacy guarantees, when sensitive attributes are subject to social contagion.
155	Nearest Neighbor Classifiers Versus Random Forests and Support Vector Machines	S. Sathe and C. C. Aggarwal	In this paper, we examine this obvious disconnect between the great theoretical promise and empirical reality of the nearest neighbor classifier.
156	Multi-graph Convolution Collaborative Filtering	J. Sun et al.	In this work, we develop a graph convolution-based recommendation framework, named Multi-Graph Convolution Collaborative Filtering (Multi-GCCF), which explicitly incorporates multiple graphs in the embedding learning process.
157	Space-Efficient Feature Maps for String Alignment Kernels	Y. Tabei, Y. Yamanishi and R. Pagh	Thus, we present novel space-efficient feature maps (SFMs) of RFFs for a space reduction from O(dD) of the original FMs to O(d) of SFMs with a theoretical guarantee with respect to concentration bounds.
158	An Arm-Wise Randomization Approach to Combinatorial Linear Semi-Bandits	K. Takemura and S. Ito	In this paper, we clarify why such a shortcoming occurs, and we introduce a key technique of arm-wise randomization to overcome it.
159	User Response Driven Content Understanding with Causal Inference	F. Tan, Z. Wei, A. Pani and Z. Yan	We propose to revisit the content understanding problem in digital marketing from three novel perspectives.
160	Permutation Strategies for Mining Significant Sequential Patterns	A. Tonon and F. Vandin	We present PROMISE, an algorithm for identifying significant sequential patterns while guaranteeing that the probability that one or more false discoveries are reported in output (i.e., the Family-Wise Error Rate – FWER) is less than a user-defined threshold.
161	Curve Fitting from Probabilistic Emissions and Applications to Dynamic Item Response Theory	A. Tripathi and B. Domingue	We propose DynAEsti, an augmentation of the traditional IRT Expectation Maximization algorithm that allows ability to be a continuously varying curve over time.
162	Adaptive Teacher-and-Student Model for Heterogeneous Domain Adaptation	Y. Wang, X. Chen, S. Chen and H. Xue	In this paper, we develop an adaptive teacher-and-student model for heterogeneous domain adaptation (AtsHDA).
163	Fast Semantic Preserving Hashing for Large-Scale Cross-Modal Retrieval	X. Wang, X. Liu, S. Peng, Y. Cheung, Z. Hu and N. Wang	To tackle these issues, we first formulate the learning of similarity-preserving hash codes in terms of orthogonally rotating the semantic data to hamming space, and then propose a novel Fast Semantic Preserving Hashing (FSePH) approach to large-scale cross-modal retrieval.
164	Fast Classification Algorithms via Distributed Accelerated Alternating Direction Method of Multipliers	H. Wang, S. Meng, Y. Qiao and J. Zhang	In this paper, we focus regularized empirical risk minimization problems, and propose two novel Distributed Accelerated Alternating Direction Method of Multipliers (D-A2DM2) algorithms for distributed classification.
165	Learning to Hash for Efficient Search Over Incomplete Knowledge Graphs	M. Wang et al.	In this paper, we introduce a novel framework for encoding incomplete KGs and graph queries in Hamming space.
166	Competitive Multi-agent Deep Reinforcement Learning with Counterfactual Thinking	Y. Wang, Y. Wan, C. Zhang, L. Bai, L. Cui and P. Yu	In particular, we propose a multi-agent deep reinforcement learning model with a structure which mimics the human-psychological counterfactual thinking process to improve the competitive abilities for agents.
167	TMDA: Task-Specific Multi-source Domain Adaptation via Clustering Embedded Adversarial Training	H. Wang, W. Yang, Z. Lin and Y. Yu	Accordingly, we propose a novel task-specific multi-source domain adaptation method (TMDA) with a clustering embedded adversarial training process.
168	Learning Robust Representations with Graph Denoising Policy Network	L. Wang et al.	In this paper, we propose Graph Denoising Policy Network (short for GDPNet) to learn robust representations from noisy graph data through reinforcement learning.
169	Personalized Neural Usefulness Network for Rating Prediction	Q. Wang and M. Zhang	To address these problems, we propose to model reviews in a view of sentence usefulness, since different sentences have varied importance for reviews modeling.
170	Collaborative Label Correction via Entropy Thresholding	H. Wu, J. Yao, J. Wang, Y. Chen, Y. Zhang and Y. Wang	We examine this behavior in light of the Shannon entropy of the predictions and demonstrate the low entropy predictions determined by a given threshold are much more reliable as the supervision than the original noisy labels.
171	Deep Technology Tracing for High-Tech Companies	H. Wu et al.	To this end, in this paper, we develop a novel data-driven solution, i.e., Deep Technology Forecasting (DTF) framework, to automatically find the most possible technology directions customized to each high-tech company.
172	Adaptive Neural Network for Node Classification in Dynamic Networks	D. Xu et al.	In this paper, we study the problem of classifying the nodes in dynamic networks.
173	MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type Data	H. Xu, Y. Wang, Y. Wang and Z. Wu	MIX constructs a joint learning framework to establish a cooperation mechanism to make separate outlier scoring constantly communicate and sufficiently grasp the behaviours of data objects in another feature space.
174	From Joint Feature Selection and Self-Representation Learning to Robust Multi-view Subspace Clustering	H. Yan, S. Liu and P. S. Yu	These unique challenges and properties motivate us to develop a novel robust multi-view subspace clustering framework (RMSC), which learns a consensus affinity matrix with the ideal subspace structure, by extending our joint feature selection and self-representation model (JFSSR).
175	On the Robust Splitting Criterion of Random Forest	B. Yang, W. Gao and M. Li	This work presents a unified framework on various splitting criteria from the perspective of loss functions, and most classical splitting criteria can be viewed essentially as the optimizations of loss functions in this framework.
176	Scene Text Recognition with Auto-Aligned Feature Generator	Q. Yang, H. Jin, M. Cheng, W. Zhou, J. Huang and W. Lin	In this paper, we present a novel text feature alignment network to solve this problem.
177	ACE: Adaptively Similarity-Preserved Representation Learning for Individual Treatment Effect Estimation	L. Yao, S. Li, Y. Li, M. Huai, J. Gao and A. Zhang	Motivated by the above observations, we propose a novel representation learning method, which adaptively extracts fine-grained similarity information from the original feature space and minimizes the distance between different treatment groups as well as the similarity loss during the representation learning procedure.
178	EDiT: Interpreting Ensemble Models via Compact Soft Decision Trees	J. Yoo and L. Sael	In this work, we propose Ensemble to Distilled Tree (EDiT), a novel distilling method that generates compact soft decision trees from ensemble models.
179	Learning Review Representations from user and Product Level Information for Spam Detection	C. Yuan, W. Zhou, Q. Ma, S. Lv, J. Han and S. Hu	In this paper, we propose a Hierarchical Fusion Attention Network (HFAN) to automatically learn the semantics of reviews from user and product level.
180	Learning Attentional Temporal Cues of Brainwaves with Spatial Embedding for Motion Intent Detection	D. Zhang, K. Chen, D. Jian, L. Yao, S. Wang and P. Li	Regarding this gap, this paper proposes a Graph-based Convolutional Recurrent Attention Model (G-CRAM) to explore EEG features across different subjects for movement intention recognition.
181	Dynamic News Recommendation with Hierarchical Attention Network	H. Zhang, X. Chen and S. Ma	In this paper, we propose a novel dynamic model for news recommendation.
182	Fast Sparse Coding Inference with Historical Information	Z. Zhang, F. Jiang and R. Shen	In this paper, we propose a novel RNN-based SC inference framework with attention mechanism to efficiently incorporate the related historical information.
183	Discovering Relevant Reviews for Answering Product-Related Queries	S. Zhang, J. H. Lau, X. Zhang, J. Chan and C. Paris	To address this, we propose two neural models that discover relevant reviews for answering product queries.
184	TrafficGAN: Off-Deployment Traffic Estimation with Traffic Generative Adversarial Networks	Y. Zhang, Y. Li, X. Zhou, X. Kong and J. Luo	In this paper, we define the off-deployment traffic estimation problem as a traffic generation problem, and develop a novel deep generative model TrafficGAN that captures the shared patterns across spatial regions of how traffic conditions evolve according to travel demand changes and underlying road network structures. We propose a novel “off-deployment traffic estimation problem”, namely, to foresee the traffic condition changes of a region prior to the deployment of a construction plan.
185	Unveiling Taxi Drivers’ Strategies via cGAIL: Conditional Generative Adversarial Imitation Learning	X. Zhang, Y. Li, X. Zhou and J. Luo	In this paper, we make the first attempt to develop conditional generative adversarial imitation learning (cGAIL) model, as a unifying collective inverse reinforcement learning framework that learns the driver’s decision-making preferences and policies by transferring knowledge across taxi driver agents and across locations.
186	Generation of Low Distortion Adversarial Attacks via Convex Programming	T. Zhang, S. Liu, Y. Wang and M. Fardad	In this paper, we present an innovative method which generates adversarial examples via convex programming.
187	KnowRisk: An Interpretable Knowledge-Guided Model for Disease Risk Prediction	X. Zhang, B. Qian, Y. Li, C. Yin, X. Wang and Q. Zheng	In this paper, we propose an interpretable and knowledge-guided deep model to address these challenges.
188	Collective Protection: Preventing Sensitive Inferences via Integrative Transformation	D. Zhang, L. Yao, K. Chen, G. Long and S. Wang	Considering this gap, we propose a novel user sensitive information protection framework without using a sensitive training dataset or being validated on protecting only one specific sensitive information.
189	Elastic Bulk Synchronous Parallel Model for Distributed Deep Learning	X. Zhao, M. Papagelis, A. An, B. X. Chen, J. Liu and Y. Hu	To ameliorate this shortcoming of classic BSP, we propose ELASTICBSP a model that aims to relax its strict synchronization requirement.
190	Constrained Matrix Factorization for Course Score Prediction	S. Zhong, L. Huang, C. Wang and J. Lai	Therefore, we propose a Constrained Matrix Factorization (ConMF) algorithm to predict sophomores’ elective course scores, which integrates the course average score into the objective function so as to make up the prediction deviation caused by the unbalanced course selection rate and make more accurate prediction than the traditional Matrix Factorization (MF) approach.
191	Inquiry Spam Detection via Jointly Exploiting Temporal-Categorical Behavior and Linguistics	Q. Zhong, J. Tang, J. Feng and J. Chi	In this paper, we propose a system coined ISTBEL to detect spam inquiries.
192	ADMIRING: Adversarial Multi-network Mining	Q. Zhou, L. Li, N. Cao, L. Ying and H. Tong	In this paper, we address the problem of attacking multi-network mining through the way of deliberately perturbing the networks to alter the mining results.
193	A Model-Agnostic Approach for Explaining the Predictions on Clustered Data	Z. Zhou, M. Sun and J. Chen	In this paper, we address this deficiency and propose to use a linear mixed model to mimic the local behavior of any complex model on clustered data, which can also improve the fidelity of the explanation method to the complex models.
194	Relation Structure-Aware Heterogeneous Graph Neural Network	S. Zhu, C. Zhou, S. Pan, X. Zhu and B. Wang	In this paper, we propose Relation Structure-Aware Heterogeneous Graph Neural Network (RSHN), a unified model that integrates graph and its coarsened line graph to embed both nodes and edges in heterogeneous graphs without requiring any prior knowledge such as metapath.