Paper Digest: WWW 2020 Highlights

May 12, 2020June 25, 2021 admin

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The Web Conference (WWW) is one of the top internet conferences in the world. In 2020, it is to be held virtually due to covid-19 pandemic.

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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TABLE 1: WWW 2020 Papers

	Title	Authors	Highlight
1	Relation Adversarial Network for Low Resource Knowledge Graph Completion	Ningyu Zhang, Shumin Deng, Zhanlin Sun, Jiaoyan Chen, Wei Zhang, Huajun Chen	In this work, we aim at predicting new facts under a challenging setting where only limited training instances are available.
2	Learning to Classify: A Flow-Based Relation Network for Encrypted Traffic Classification	Wenbo Zheng, Chao Gou, Lan Yan, Shaocong Mo	In this paper, we propose an application of a meta-learning approach to address these problems in encrypted traffic classification, named Flow-Based Relation Network (RBRN).
3	FiDo: Ubiquitous Fine-Grained WiFi-based Localization for Unlabelled Users via Domain Adaptation	Xi Chen, Hang Li, Chenyi Zhou, Xue Liu, Di Wu, Gregory Dudek	To address this issue, we propose a WiFi-based Domain-adaptive system FiDo, which is able to localize many different users with labelled data from only one or two example users.
4	An Empirical Study of the Use of Integrity Verification Mechanisms for Web Subresources	Bertil Chapuis, Olamide Omolola, Mauro Cherubini, Mathias Humbert, Kévin Huguenin	In this paper, we conduct the first large-scale longitudinal study of the use of SRI on the Web by analyzing massive crawls (≈ 3B URLs) of the Web over the last 3.5 years.
5	Power-Law Graphs Have Minimal Scaling of Kemeny Constant for Random Walks	Wanyue Xu, Yibin Sheng, Zuobai Zhang, Haibin Kan, Zhongzhi Zhang	Here we study numerically or analytically the Kemeny constant on many sparse real-world and model networks with scale-free small-world topology, and show that their Kemeny constant also behaves linearly with N. Thus, sparse networks with scale-free and small-world topology are favorable architectures with optimal scaling of Kemeny constant.
6	Metric Learning with Equidistant and Equidistributed Triplet-based Loss for Product Image Search	Furong Xu, Wei Zhang, Yuan Cheng, Wei Chu	In this paper, we propose a novel Equidistant and Equidistributed Triplet-based (EET) loss function to adjust the distance between samples with relative distance constraints.
7	”What Apps Did You Use?”: Understanding the Long-term Evolution of Mobile App Usage	Tong Li, Mingyang Zhang, Hancheng Cao, Yong Li, Sasu Tarkoma, Pan Hui	In this paper, we study how mobile app usage evolves over a long-term period.
8	OutfitNet: Fashion Outfit Recommendation with Attention-Based Multiple Instance Learning	Yusan Lin, Maryam Moosaei, Hao Yang	We propose OutfitNet, a fashion outfit recommendation framework that includes two stages.
9	Towards Fine-grained Flow Forecasting: A Graph Attention Approach for Bike Sharing Systems	Suining He, Kang G. Shin	To remedy this problem, we propose a novel data-driven spatio-temporal Graph attention convolutional neural network for Bikestation-level flow prediction (GBikes).
10	Reinforced Negative Sampling over Knowledge Graph for Recommendation	Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Meng Wang, Tat-Seng Chua	In this work, we hypothesize that item knowledge graph (KG), which provides rich relations among items and KG entities, could be useful to infer informative and factual negative samples. We release the codes and datasets at https://github.com/xiangwang1223/kgpolicy.
11	Crowd Teaching with Imperfect Labels	Yao Zhou, Arun Reddy Nelakurthi, Ross Maciejewski, Wei Fan, Jingrui He	In this paper, we aim to answer both questions via a novel interactive teaching framework, which uses visual explanations to simultaneously teach and gauge the confidence level of the crowd workers.
12	Directional and Explainable Serendipity Recommendation	Xueqi Li, Wenjun Jiang, Weiguang Chen, Jie Wu, Guojun Wang, Kenli Li	To address these limitations, we propose a Directional and Explainable Serendipity Recommendation method named DESR.
13	Dynamic Flow Distribution Prediction for Urban Dockless E-Scooter Sharing Reconfiguration	Suining He, Kang G. Shin	To meet this need, we propose GCScoot, a novel dynamic flow distribution prediction for reconfiguring urban DES systems.
14	Graph Attention Topic Modeling Network	Liang Yang, Fan Wu, Junhua Gu, Chuan Wang, Xiaochun Cao, Di Jin, Yuanfang Guo	In this paper, we provide a new method to overcome the overfitting issue of pLSI by using the amortized inference with word embedding as input, instead of the Dirichlet prior in LDA.
15	Measurements, Analyses, and Insights on the Entire Ethereum Blockchain Network	Xi Tong Lee, Arijit Khan, Sourav Sen Gupta, Yu Hann Ong, Xuan Liu	Our analyses on the networks reveal new insights by combining information from the four networks.
16	The Representativeness of Automated Web Crawls as a Surrogate for Human Browsing	David Zeber, Sarah Bird, Camila Oliveira, Walter Rudametkin, Ilana Segall, Fredrik Wollsén, Martin Lopatka	In this paper, we quantify the repeatability and representativeness of Web crawls in terms of common tracking and fingerprinting metrics, considering both variation across crawls and divergence from human browser usage.
17	Client Insourcing: Bringing Ops In-House for Seamless Re-engineering of Full-Stack JavaScript Applications	Kijin An, Eli Tilevich	Our approach is enabled by Client Insourcing, a novel automatic refactoring that creates a semantically equivalent centralized version of a distributed application.
18	Privacy-preserving AI Services Through Data Decentralization	Christian Meurisch, Bekir Bayrak, Max Mühlhäuser	This paper presents PrivAI, a new decentralized and privacy-by-design platform for overcoming the need for sharing user data to benefit from personalized AI services.
19	ASER: A Large-scale Eventuality Knowledge Graph	Hongming Zhang, Xin Liu, Haojie Pan, Yangqiu Song, Cane Wing-Ki Leung	To fill this gap, we develop ASER (activities, states, events, and their relations), a large-scale eventuality knowledge graph extracted from more than 11-billion-token unstructured textual data.
20	Nowhere to Hide: Cross-modal Identity Leakage between Biometrics and Devices	Chris Xiaoxuan Lu, Yang Li, Yuanbo Xiangli, Zhengxiong Li	In this work, we explore the feasibility of the compound identity leakage across cyber-physical spaces and unveil that co-located smart device IDs (e.g., smartphone MAC addresses) and physical biometrics (e.g., facial/vocal samples) are side channels to each other.
21	Facebook Ads Monitor: An Independent Auditing System for Political Ads on Facebook	Márcio Silva, Lucas Santos de Oliveira, Athanasios Andreou, Pedro Olmo Vaz de Melo, Oana Goga, Fabricio Benevenuto	Concerned with the risk of the same kind of abuse to happen in the 2018 Brazilian elections, we designed and deployed an independent auditing system to monitor political ads on Facebook in Brazil.
22	Keyword Search over Knowledge Graphs via Static and Dynamic Hub Labelings	Yuxuan Shi, Gong Cheng, Evgeny Kharlamov	In this paper, we propose practical approximation algorithms with a guaranteed quality of computed answers and very low run time.
23	AutoMAP: Diagnose Your Microservice-based Web Applications Automatically	Meng Ma, Jingmin Xu, Yuan Wang, Pengfei Chen, Zonghua Zhang, Ping Wang	In AutoMAP, we propose the concept of anomaly behavior graph to describe the correlations between services associated with different types of metrics.
24	Graph Representation Learning via Graphical Mutual Information Maximization	Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu, Junzhou Huang	To this end, we propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations.
25	Apophanies or Epiphanies? How Crawlers Impact Our Understanding of the Web	Syed Suleman Ahmad, Muhammad Daniyal Dar, Muhammad Fareed Zaffar, Narseo Vallina-Rodriguez, Rishab Nithyanand	In this paper, we conduct a systematic study of the trade-offs presented by different crawlers and the impact that these can have on various types of measurement studies.
26	Generating Multi-hop Reasoning Questions to Improve Machine Reading Comprehension	Jianxing Yu, Xiaojun Quan, Qinliang Su, Jian Yin	Guided by the chain, we propose a holistic generator-evaluator network to form the questions, where such guidance helps to ensure the rationality of generated questions which need multi-hop deduction to correspond to the answers.
27	Hierarchical Adaptive Contextual Bandits for Resource Constraint based Recommendation	Mengyue Yang, Qingyang Li, Zhiwei Qin, Jieping Ye	In this paper, we propose a hierarchical adaptive contextual bandit method (HATCH) to conduct the policy learning of contextual bandits with a budget constraint.
28	Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation	Fajie Yuan, Xiangnan He, Haochuan Jiang, Guibing Guo, Jian Xiong, Zhezhao Xu, Yilin Xiong	To this end, we propose a new encoder-decoder framework named Gap-filling based Recommender (GRec), which trains the encoder and decoder by a gap-filling mechanism.
29	The Fast and The Frugal: Tail Latency Aware Provisioning for Coping with Load Variations	Adithya Kumar, Iyswarya Narayanan, Timothy Zhu, Anand Sivasubramaniam	In this paper, we address the problem of capacity provisioning under fixed budget constraints with the goal of minimizing tail latency.
30	Facebook Ads as a Demographic Tool to Measure the Urban-Rural Divide	Daniele Rama, Yelena Mejova, Michele Tizzoni, Kyriaki Kalimeri, Ingmar Weber	In this study, we examine the usefulness of the Facebook Advertising platform, which offers a digital “census” of over two billions of its users, in measuring potential rural-urban inequalities.
31	Efficient Maximal Balanced Clique Enumeration in Signed Networks	Zi Chen, Long Yuan, Xuemin Lin, Lu Qin, Jianye Yang	Motivated by this, we propose the balanced clique model that considers the most fundamental and dominant theory, structural balance theory, for signed networks, and study the maximal balanced clique enumeration problem which computes all the maximal balanced cliques in a given signed network.
32	Text-to-SQL Generation for Question Answering on Electronic Medical Records	Ping Wang, Tian Shi, Chandan K. Reddy	Based on the widely used publicly available electronic medical database, we create a new large-scale Question-SQL pair dataset, named MIMICSQL, in order to perform the Question-to-SQL generation task in healthcare domain.
33	Searching for polarization in signed graphs: a local spectral approach	Han Xiao, Bruno Ordozgoiti, Aristides Gionis	Instead, in this paper we are interested in finding polarized communities that are related to a small set of seed nodes provided as input.
34	Multi-Objective Ranking Optimization for Product Search Using Stochastic Label Aggregation	David Carmel, Elad Haramaty, Arnon Lazerson, Liane Lewin-Eytan	In this work we explore several label aggregation methods for MORO in product search.
35	Open Knowledge Enrichment for Long-tail Entities	Ermei Cao, Difeng Wang, Jiacheng Huang, Wei Hu	In this paper, we propose a full-fledged approach to knowledge enrichment, which predicts missing properties and infers true facts of long-tail entities from the open Web.
36	Dirty Clicks: A Study of the Usability and Security Implications of Click-related Behaviors on the Web	Iskander Sanchez-Rola, Davide Balzarotti, Christopher Kruegel, Giovanni Vigna, Igor Santos	In this paper, we look at one of the simplest but also most representative aspect that captures the struggle between these opposite demands: a mouse click.
37	Adversarial Bandits Policy for Crawling Commercial Web Content	Shuguang Han, Michael Bendersky, Przemek Gajda, Sergey Novikov, Marc Najork, Bernhard Brodowsky, Alexandrin Popescul	In this paper, we demonstrate that the effectiveness of LambdaCrawl is governed in large part by how well future content change rate can be estimated.
38	Generating Clarifying Questions for Information Retrieval	Hamed Zamani, Susan Dumais, Nick Craswell, Paul Bennett, Gord Lueck	Generating Clarifying Questions for Information Retrieval
39	MetaNER: Named Entity Recognition with Meta-Learning	Jing Li, Shuo Shang, Ling Shao	In this paper, we investigate the problem of domain adaptation for NER under homogeneous and heterogeneous settings.
40	HTML: Hierarchical Transformer-based Multi-task Learning for Volatility Prediction	Linyi Yang, Tin Lok James Ng, Barry Smyth, Riuhai Dong	This paper proposes a novel hierarchical, transformer, multi-task architecture designed to harness the text and audio data from quarterly earnings conference calls to predict future price volatility in the short and long term.
41	Beyond Rank-1: Discovering Rich Community Structure in Multi-Aspect Graphs	Ekta Gujral, Ravdeep Pasricha, Evangelos Papalexakis	In this paper we bridge that gap by empowering tensor-based methods to extract rich community structure from multi-aspect graphs.
42	Off-policy Learning in Two-stage Recommender Systems	Jiaqi Ma, Zhe Zhao, Xinyang Yi, Ji Yang, Minmin Chen, Jiaxi Tang, Lichan Hong, Ed H. Chi	In this work, we propose a two-stage off-policy policy gradient method, and showcase that ignoring the interaction between the two stages leads to a sub-optimal policy in two-stage recommender systems.
43	Selective Weak Supervision for Neural Information Retrieval	Kaitao Zhang, Chenyan Xiong, Zhenghao Liu, Zhiyuan Liu	We revisit the classic IR intuition that anchor-document relations approximate query-document relevance and propose a reinforcement weak supervision selection method, ReInfoSelect, which learns to select anchor-document pairs that best weakly supervise the neural ranker (action), using the ranking performance on a handful of relevance labels as the reward.
44	TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network	Jiaming Shen, Zhihong Shen, Chenyan Xiong, Chi Wang, Kuansan Wang, Jiawei Han	In this paper, we study how to expand an existing taxonomy by adding a set of new concepts.
45	A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems	Ye Yuan, Xin Luo, Mingsheng Shang, Di Wu	For addressing these issues, this study proposes a generalized and fast-converging non-negative latent factor (GFNLF) model.
46	Deep Adversarial Completion for Sparse Heterogeneous Information Network Embedding	Kai Zhao, Ting Bai, Bin Wu, Bai Wang, Youjie Zhang, Yuanyu Yang, Jian-Yun Nie	To address this problem, we propose a novel and principled approach: a Multi-View Adversarial Completion Model (MV-ACM).
47	Learning the Structure of Auto-Encoding Recommenders	Farhan Khawar, Leonard Poon, Nevin L. Zhang	In this paper, we introduce structure learning for autoencoder recommenders by taking advantage of the inherent item groups present in the collaborative filtering domain.
48	StageNet: Stage-Aware Neural Networks for Health Risk Prediction	Junyi Gao, Cao Xiao, Yasha Wang, Wen Tang, Lucas M. Glass, Jimeng Sun	To fill the gap, we propose a Stage-aware neural Network (StageNet) model to extract disease stage information from patient data and integrate it into risk prediction.
49	CLARA: Clinical Report Auto-completion	Siddharth Biswal, Cao Xiao, Lucas M. Glass, Brandon Westover, Jimeng Sun	We propose CLinicAl Report Auto-completion (, an interactive method that generates reports in a sentence by sentence fashion based on doctors’ anchor words and partially completed sentences.
50	Deep Global and Local Generative Model for Recommendation	Huafeng Liu, Liping Jing, Jingxuan Wen, Zhicheng Wu, Xiaoyi Sun, Jiaqi Wang, Lin Xiao, Jian Yu	In this paper, thus, we propose a Deep Global and Local Generative Model for recommendation to consider both local and global structure among users (DGLGM) under the Wasserstein auto-encoder framework.
51	Comparing the Effects of DNS, DoT, and DoH on Web Performance	Austin Hounsel, Kevin Borgolte, Paul Schmitt, Jordan Holland, Nick Feamster	In this paper, we measure the effect of Do53, DoT, and DoH on query response times and page load times from five global vantage points.
52	Flowless: Extracting Densest Subgraphs Without Flow Computations	Digvijay Boob, Yu Gao, Richard Peng, Saurabh Sawlani, Charalampos Tsourakakis, Di Wang, Junxing Wang	In this paper we design Greedy++, an iterative peeling algorithm that improves upon the performance of Charikar’s greedy algorithm significantly.
53	CellRep: Usage Representativeness Modeling and Correction Based on Multiple City-Scale Cellular Networks	Zhihan Fang, Guang Wang, Shuai Wang, Chaoji Zuo, Fan Zhang, Desheng Zhang	In this paper, we conduct the first comprehensive investigation of multiple cellular networks in a city with a 100% user penetration rate.
54	Why Do Competitive Markets Converge to First-Price Auctions?	Renato Paes Leme, Balasubramanian Sivan, Yifeng Teng	Our analysis aims to shed light on the recent change in the Display Ads market landscape: here, ad exchanges (sellers) were mostly running second-price auctions earlier and over time they switched to variants of the first-price auction, culminating in Google’s Ad Exchange moving to a first-price auction in 2019.
55	Frozen Binomials on the Web: Word Ordering and Language Conventions in Online Text	Katherine Van Koevering, Austin R. Benson, Jon Kleinberg	In this work, we expand the view of binomials to include a large-scale analysis of both frozen and non-frozen binomials in a quantitative way.
56	Snippext: Semi-supervised Opinion Mining with Augmented Data	Zhengjie Miao, Yuliang Li, Xiaolan Wang, Wang-Chiew Tan	In this paper, we study the problem of how to significantly reduce the amount of labeled training data required in fine-tuning language models for opinion mining.
57	paper2repo: GitHub Repository Recommendation for Academic Papers	Huajie Shao, Dachun Sun, Jiahao Wu, Zecheng Zhang, Aston Zhang, Shuochao Yao, Shengzhong Liu, Tianshi Wang, Chao Zhang, Tarek Abdelzaher	Motivated by this trend, we describe a novel item-item cross-platform recommender system, paper2repo, that recommends relevant repositories on GitHub that match a given paper in an academic search system such as Microsoft Academic.
58	High Quality Candidate Generation and Sequential Graph Attention Network for Entity Linking	Zheng Fang, Yanan Cao, Ren Li, Zhenyu Zhang, Yanbing Liu, Shi Wang	To address the first problem, we propose a multi-strategy based CG method to generate high recall candidate sets.
59	Adaptive Probabilistic Word Embedding	Shuangyin Li, Yu Zhang, Rong Pan, Kaixiang Mo	To address this problem, we propose a novel Adaptive Probabilistic Word Embedding (APWE) model, where the word polysemy is defined over a latent interpretable semantic space.
60	Conversational Contextual Bandit: Algorithm and Application	Xiaoying Zhang, Hong Xie, Hang Li, John C.S. Lui	Contextual bandit algorithms provide principled online learning solutions to balance the exploitation-exploration trade-off in various applications such as recommender systems.
61	Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach	Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung-Yu Hsieh, Vasant Honavar	We propose a novel reinforcement learning method for Node Injection Poisoning Attacks (NIPA), to sequentially modify the labels and links of the injected nodes, without changing the connectivity between existing nodes.
62	Efficient Implicit Unsupervised Text Hashing using Adversarial Autoencoder	Khoa D. Doan, Chandan K. Reddy	In this paper, we propose a Denoising Adversarial Binary Autoencoder (DABA) model which presents a novel representation learning framework that captures structured representation of text documents in the learned hash function.
63	LightRec: A Memory and Search-Efficient Recommender System	Defu Lian, Haoyu Wang, Zheng Liu, Jianxun Lian, Enhong Chen, Xing Xie	To overcome both limitations, we propose LightRec, a lightweight recommender system which enjoys fast online inference and economic memory consumption.
64	Clustering in graphs and hypergraphs with categorical edge labels	Ilya Amburg, Nate Veldt, Austin Benson	When there are only two label types, our objective can be optimized in polynomial time, using an algorithm based on minimum cuts.
65	Few-Sample and Adversarial Representation Learning for Continual Stream Mining	Zhuoyi Wang, Yigong Wang, Yu Lin, Evan Delord, Khan Latifur	In this paper, we focus on improving the generalization of the model on the novel classes, and making the model continually learn from only a few samples from the novel categories.
66	Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions	Feiyang Pan, Xiang Ao, Pingzhong Tang, Min Lu, Dapeng Liu, Lei Xiao, Qing He	To this end, we propose Neural Calibration, a simple yet powerful post-hoc calibration method that learns to calibrate by making full use of the field-aware information over the validation set.
67	Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning	Gaole He, Junyi Li, Wayne Xin Zhao, Peiju Liu, Ji-Rong Wen	In this paper, we take a new perspective that aims to leverage rich user-item interaction data (user interaction data for short) for improving the KGC task.
68	Multiple Knowledge Syncretic Transformer for Natural Dialogue Generation	Xiangyu Zhao, Longbiao Wang, Ruifang He, Ting Yang, Jinxin Chang, Ruifang Wang	In this paper, we propose a novel universal transformer-based architecture for dialogue system, the Multiple Knowledge Syncretic Transformer (MKST), which fuses multi-knowledge in open-domain conversation.
69	JSCleaner: De-Cluttering Mobile Webpages Through JavaScript Cleanup	Moumena Chaqfeh, Yasir Zaki, Jacinta Hu, Lakshmi Subramanian	In this paper, we propose JSCleaner, a JavaScript de-cluttering engine that aims at simplifying webpages without compromising their content or functionality.
70	Conquering Cross-source Failure for News Credibility: Learning Generalizable Representations beyond Content Embedding	Yen-Hao Huang, Ting-Wei Liu, Ssu-Rui Lee, Fernando Henrique Calderon Alvarado, Yi-Shin Chen	To overcome this challenge, we propose a syntactic network for news credibility (SYNC), which focuses on function words and syntactic structure to learn generalizable representations for news credibility and further reinforce the cross-source robustness for different media.
71	Financial Defaulter Detection on Online Credit Payment via Multi-view Attributed Heterogeneous Information Network	Qiwei Zhong, Yang Liu, Xiang Ao, Binbin Hu, Jinghua Feng, Jiayu Tang, Qing He	In this paper, we consider default users, a more general concept in credit risk, and propose a multi-view attributed heterogeneous information network based approach coined MAHINDER to remedy the special challenges.
72	Fast Generating A Large Number of Gumbel-Max Variables	Yiyan Qi, Pinghui Wang, Yuanming Zhang, Junzhou Zhao, Guangjian Tian, Xiaohong Guan	To solve this problem, we propose a novel algorithm, FastGM, that reduces the time complexity from O(kn+) to O(kln k + n+), where n+ is the number of positive elements in the vector of interest.
73	Don’t Count Me Out: On the Relevance of IP Address in the Tracking Ecosystem	Vikas Mishra, Pierre Laperdrix, Antoine Vastel, Walter Rudametkin, Romain Rouvoy, Martin Lopatka	In this paper, we study the stability of the public IP addresses a user device uses to communicate with our server.
74	Go See a Specialist? Predicting Cybercrime Sales on Online Anonymous Markets from Vendor and Product Characteristics	Rolf van Wegberg, Fieke Miedema, Ugur Akyazi, Arman Noroozian, Bram Klievink, Michel van Eeten	In this paper, we investigate which factors determine the performance of cybercrime products.
75	Adversarial Multimodal Representation Learning for Click-Through Rate Prediction	Xiang Li, Chao Wang, Jiwei Tan, Xiaoyi Zeng, Dan Ou, Dan Ou, Bo Zheng	We propose a novel Multimodal Adversarial Representation Network (MARN) for the CTR prediction task.
76	Dual Learning for Explainable Recommendation: Towards Unifying User Preference Prediction and Review Generation	Peijie Sun, Le Wu, Kun Zhang, Yanjie Fu, Richang Hong, Meng Wang	In this paper, we make a further study of unifying these two tasks for explainable recommendation.
77	The Chameleon Attack: Manipulating Content Display in Online Social Media	Aviad Elyashar, Sagi Uziel, Abigail Paradise, Rami Puzis	In this article, we discuss the chameleon attack technique, a new type of OSN-based trickery where malicious posts and profiles change the way they are displayed to OSN users to conceal themselves before the attack or avoid detection.
78	A Generic Solver Combining Unsupervised Learning and Representation Learning for Breaking Text-Based Captchas	Sheng Tian, Tao Xiong	This paper proposes a generic solver combining unsupervised learning and representation learning to automatically remove the noisy background of captchas and solve text-based captchas.
79	Dynamic Composition for Conversational Domain Exploration	Idan Szpektor, Deborah Cohen, Gal Elidan, Michael Fink, Avinatan Hassidim, Orgad Keller, Sayali Kulkarni, Eran Ofek, Sagie Pudinsky, Asaf Revach, Shimi Salant, Yossi Matias	To address these dialogue characteristics, we introduce a novel approach termed dynamic composition that decouples candidate content generation from the flexible composition of bot responses.
80	Deconstructing Google’s Web Light Service	Ammar Tahir, Muhammad Tahir Munir, Shaiq Munir Malik, Zafar Ayyub Qazi, Ihsan Ayyub Qazi	In this paper, we perform the first independent, empirical analysis of Google’s Web Light service to shed light on these concerns.
81	A First Look at Commercial 5G Performance on Smartphones	Arvind Narayanan, Eman Ramadan, Jason Carpenter, Qingxu Liu, Yu Liu, Feng Qian, Zhi-Li Zhang	We conduct to our knowledge a first measurement study of commercial 5G performance on smartphones by closely examining 5G networks of three carriers (two mmWave carriers, one mid-band carrier) in three U.S. cities. We have released the data collected from our study (referred to as 5Gophers) at https://fivegophers.umn.edu/www20.
82	Next Point-of-Interest Recommendation on Resource-Constrained Mobile Devices	Qinyong Wang, Hongzhi Yin, Tong Chen, Zi Huang, Hao Wang, Yanchang Zhao, Nguyen Quoc Viet Hung	To bypass these defects, we propose a novel Light Location Recommender System (LLRec) to perform next POI recommendation locally on resource-constrained mobile devices.
83	Adversarial Attack on Community Detection by Hiding Individuals	Jia Li, Honglei Zhang, Zhichao Han, Yu Rong, Hong Cheng, Junzhou Huang	In this paper, we extend adversarial graphs to the problem of community detection which is much more difficult.
84	Modeling Users’ Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection	Yongchun Zhu, Dongbo Xi, Bowen Song, Fuzhen Zhuang, Shuai Chen, Xi Gu, Qing He	Recently, some approaches have been proposed to solve this sequence-based fraud detection problem.
85	Valve: Securing Function Workflows on Serverless Computing Platforms	Pubali Datta, Prabuddha Kumar, Tristan Morris, Michael Grace, Amir Rahmati, Adam Bates	As a practical means of addressing this problem, we present Valve, a serverless platform that enables developers to exert complete fine-grained control of information flows in their applications.
86	Experimental Evidence Extraction System in Data Science with Hybrid Table Features and Ensemble Learning	Wenhao Yu, Wei Peng, Yu Shu, Qingkai Zeng, Meng Jiang	In this work, we build an experimental evidence extraction system to automate the integration of tables (in the paper PDFs) into a database of experimental results.
87	Identifying Referential Intention with Heterogeneous Contexts	Wenhao Yu, Mengxia Yu, Tong Zhao, Meng Jiang	In this work, we propose to identify the referential intention which motivates the action of using the referred (e.g., cited, quoted, and retweeted) source and content to support their claims.
88	PG2S+: Stack Distance Construction Using Popularity, Gap and Machine Learning	Jiangwei Zhang, Y.C. Tay	This paper introduces a new approximation technique PG2S that is based on reference popularity and gap distance.
89	SMART-KG: Hybrid Shipping for SPARQL Querying on the Web	Amr Azzam, Javier D. Fernández, Maribel Acosta, Martin Beno, Axel Polleres	In this paper we present smart-KG, a novel approach to share the load between servers and clients, while significantly reducing data transfer volume, by combining TPF with shipping compressed KG partitions.
90	Learning from Cross-Modal Behavior Dynamics with Graph-Regularized Neural Contextual Bandit	Xian Wu, Suleyman Cetintas, Deguang Kong, Miao Lu, Jian Yang, Nitesh Chawla	To address the above challenges, we develop a Graph Regularized Cross-modal (GRC) learning model, a general framework to exploit transferable knowledge learned from user-item interactions as well as the external features of users and items in online personalized recommendations.
91	Read Between the Lines: An Empirical Measurement of Sensitive Applications of Voice Personal Assistant Systems	Faysal Hossain Shezan, Hang Hu, Jiamin Wang, Gang Wang, Yuan Tian	In this paper, we perform an empirical analysis of the third-party applications of Amazon Alexa and Google Home to systematically assess the attack surfaces.
92	A Kernel of Truth: Determining Rumor Veracity on Twitter by Diffusion Pattern Alone	Nir Rosenfeld, Aron Szanto, David C. Parkes	In this work, we investigate an alternative modality that is naturally robust: the pattern in which information propagates.
93	DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction	Xingyao Zhang, Cao Xiao, Lucas M. Glass, Jimeng Sun	To address these challenges, we proposed a cross-modal inference learning model to jointly encode enrollment criteria (text) and patients records (tabular data) into a shared latent space for matching inference.
94	Weakly Supervised Attention for Hashtag Recommendation using Graph Data	Amin Javari, Zhankui He, Zijie Huang, Raj Jeetu, Kevin Chen-Chuan Chang	Our core idea is to build a graph-based profile of users and incorporate it into hashtag recommendation.
95	Real-Time Clustering for Large Sparse Online Visitor Data	Gromit Yeuk-Yin Chan, Fan Du, Ryan A. Rossi, Anup B. Rao, Eunyee Koh, Cláudio T. Silva, Juliana Freire	In this paper, we propose a real-time clustering algorithm, sparse density peaks, for large-scale sparse data.
96	Smaller, Faster & Lighter KNN Graph Constructions	Rachid Guerraoui, Anne-Marie Kermarrec, Olivier Ruas, François Taïani	We propose GoldFinger, a new compact and fast-to-compute binary representation of datasets to approximate Jaccard’s index.
97	Automatic Boolean Query Formulation for Systematic Review Literature Search	Harrisen Scells, Guido Zuccon, Bevan Koopman, Justin Clark	In this paper, we investigate the possibility of automatically formulating a Boolean query from the systematic review protocol.
98	Traffic Flow Prediction via Spatial Temporal Graph Neural Network	Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, Jian Yu	In this paper, we propose a novel spatial temporal graph neural network for traffic flow prediction, which can comprehensively capture spatial and temporal patterns.
99	Personalized Ranking with Importance Sampling	Defu Lian, Qi Liu, Enhong Chen	Optimizing ranking loss aligns better with the ultimate goal of item recommendation, so many ranking-based methods were proposed for item recommendation, such as collaborative filtering with Bayesian Personalized Ranking (BPR) loss, and Weighted Approximate-Rank Pairwise (WARP) loss.
100	Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases	Yu Liu, Quanming Yao, Yong Li	To generalize tensor decomposition for n-ary relational KBs, in this work, we propose GETD, a generalized model based on Tucker decomposition and Tensor Ring decomposition.
101	What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization	Caleb Belth, Xinyi Zheng, Jilles Vreeken, Danai Koutra	In this work, we introduce a unified solution to KG characterization by formulating the problem as unsupervised KG summarization with a set of inductive, soft rules, which describe what is normal in a KG, and thus can be used to identify what is abnormal, whether it be strange or missing.
102	Intention Modeling from Ordered and Unordered Facets for Sequential Recommendation	Xueliang Guo, Chongyang Shi, Chuanming Liu	To this end, in this paper, we propose a novel intention modeling from ordered and unordered facets (IMfOU) for sequential recommendation.
103	Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog	Shen Gao, Xiuying Chen, Chang Liu, Li Liu, Dongyan Zhao, Rui Yan	Hence, in this paper, we propose to recommend an appropriate sticker to user based on multi-turn dialog context history without any external labels. To evaluate our proposed method, we collect a large-scale real-world dialog dataset with stickers from one of the most popular online chatting platform.
104	Dynamic Graph Convolutional Networks for Entity Linking	Junshuang Wu, Richong Zhang, Yongyi Mao, Hongyu Guo, Masoumeh Soflaei, Jinpeng Huai	In this paper, we propose a dynamic GCN architecture to effectively cope with this challenge.
105	Leading Conversational Search by Suggesting Useful Questions	Corbin Rosset, Chenyan Xiong, Xia Song, Daniel Campos, Nick Craswell, Saurabh Tiwary, Paul Bennett	The weak supervision signals help ground the suggestions to users’ information-seeking trajectories: we identify more coherent and informative sessions using encodings, and then weakly supervise our models to imitate how users transition to the next state of search. We first establish a novel evaluation metric, usefulness, which goes beyond relevance and measures whether the suggestions provide valuable information for the next step of a user’s journey, and construct a public benchmark for useful question suggestion.
106	De-Kodi: Understanding the Kodi Ecosystem	Marc Anthony Warrior, Yunming Xiao, Matteo Varvello, Aleksandar Kuzmanovic	We address these challenges with de-Kodi, a full fledged crawling system capable of discovering and crawling large cross-sections of Kodi’s decentralized ecosystem.
107	Attention Please: Your Attention Check Questions in Survey Studies Can Be Automatically Answered	Weiping Pei, Arthur Mayer, Kaylynn Tu, Chuan Yue	We propose AC-EasyPass, an attack framework with a concrete model, that combines convolutional neural network and weighted feature reconstruction to easily pass attention check questions. We construct the first attention check question dataset that consists of both original and augmented questions, and demonstrate the effectiveness of AC-EasyPass.
108	FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms	Gourab K Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi, Abhijnan Chakraborty	We investigate the problem of fair recommendation in the context of two-sided online platforms, comprising customers on one side and producers on the other.
109	Complex Factoid Question Answering with a Free-Text Knowledge Graph	Chen Zhao, Chenyan Xiong, Xin Qian, Jordan Boyd-Graber	We introduce delft, a factoid question answering system which combines the nuance and depth of knowledge graph question answering approaches with the broader coverage of free-text.
110	Leveraging Sentiment Distributions to Distinguish Figurative From Literal Health Reports on Twitter	Rhys Biddle, Aditya Joshi, Shaowu Liu, Cecile Paris, Guandong Xu	Since the experience of a disease is associated with a negative sentiment, we present a method that utilises sentiment information to improve health mention classification.
111	Reputation Agent: Prompting Fair Reviews in Gig Markets	Carlos Toxtli, Angela Richmond-Fuller, Saiph Savage	Our study presents a new tool, Reputation Agent, to promote fairer reviews from requesters (employers or customers) on gig markets.
112	Becoming the Super Turker:Increasing Wages via a Strategy from High Earning Workers	Saiph Savage, Chun Wei Chiang, Susumu Saito, Carlos Toxtli, Jeffrey Bigham	In this paper, we explore how novice workers can improve their earnings by following the transparency criteria of Super Turkers, i.e., crowd workers who earn higher salaries on Amazon Mechanical Turk (MTurk).
113	GraphGen: A Scalable Approach to Domain-agnostic Labeled Graph Generation	Nikhil Goyal, Harsh Vardhan Jain, Sayan Ranu	In this work, we develop a domain-agnostic technique called GraphGen to overcome all of these limitations.
114	A Category-Aware Deep Model for Successive POI Recommendation on Sparse Check-in Data	Fuqiang Yu, Lizhen Cui, Wei Guo, Xudong Lu, Qingzhong Li, Hua Lu	In this work, we focus on predicting POIs that will be visited by users in the next 24 hours.
115	Beyond the Front Page:Measuring Third Party Dynamics in the Field	Tobias Urban, Martin Degeling, Thorsten Holz, Norbert Pohlmann	In this paper, we present a large-scale measurement study to analyze the magnitude of these new challenges.
116	Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks	Adam Breuer, Roee Eilat, Udi Weinsberg	In this paper, we study the problem of early detection of fake user accounts on social networks based solely on their network connectivity with other users.
117	Novel Entity Discovery from Web Tables	Shuo Zhang, Edgar Meij, Krisztian Balog, Ridho Reinanda	We propose a feature-based method and on two public test collections we demonstrate substantial improvements over the state-of-the-art in terms of precision whilst also improving recall.
118	Abstractive Snippet Generation	Wei-Fan Chen, Shahbaz Syed, Benno Stein, Matthias Hagen, Martin Potthast	We propose a bidirectional abstractive snippet generation model and assess the quality of both our corpus and the generated abstractive snippets with standard measures, crowdsourcing, and in comparison to the state of the art.
119	AutoNav: Evaluation and Automatization of Web Navigation Policies	Benjamin Eriksson, Andrei Sabelfeld	We identify both specification- and implementation-level vulnerabilities and propose countermeasures to mitigate both.
120	Learning Contextualized Document Representations for Healthcare Answer Retrieval	Sebastian Arnold, Betty van Aken, Paul Grundmann, Felix A. Gers, Alexander Löser	We present Contextual Discourse Vectors (CDV), a distributed document representation for efficient answer retrieval from long healthcare documents.
121	Broccoli: Sprinkling Lightweight Vocabulary Learning into Everyday Information Diets	Roland Aydin, Lars Klein, Arnaud Miribel, Robert West	In this paper, we propose Broccoli, a new paradigm aimed at reducing the required effort by seamlessly embedding vocabulary learning into users’ everyday information diets.
122	What is the Human Mobility in a New City: Transfer Mobility Knowledge Across Cities	Tianfu He, Jie Bao, Ruiyuan Li, Sijie Ruan, Yanhua Li, Li Song, Hui He, Yu Zheng	Realizing this, in this paper, based on the intuition that the human mobility is driven by the mobility intentions reflected by the origin and destination (or OD) features, as well as the preference to select the path between them, we investigate the problem to generate mobility data for a new target city, by transferring knowledge from mobility data and multi-source data of the source cities.
123	The Automated Copywriter: Algorithmic Rephrasing of Health-Related Advertisements to Improve their Performance	Brit Youngmann, Elad Yom-Tov, Ran Gilad-Bachrach, Danny Karmon	Here we develop an algorithm which builds on past advertising data to train a sequence-to-sequence Deep Neural Network which “translates” advertisements into optimized ads that are more likely to be clicked.
124	Finding large balanced subgraphs in signed networks	Bruno Ordozgoiti, Antonis Matakos, Aristides Gionis	In this paper we propose an efficient algorithm for finding large balanced subgraphs in signed networks.
125	Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative Framework	Han Zhang, Wenhao Zheng, Charley Chen, Kevin Gao, Yao Hu, Ling Huang, Wei Xu	To address the above issues, we propose a novel unsupervised generative framework called FIRD, which utilizes adversarial distributions to fit and disentangle the heterogeneous statistical patterns.
126	Structural Deep Clustering Network	Deyu Bo, Xiao Wang, Chuan Shi, Meiqi Zhu, Emiao Lu, Peng Cui	Motivated by the great success of Graph Convolutional Network (GCN) in encoding the graph structure, we propose a Structural Deep Clustering Network (SDCN) to integrate the structural information into deep clustering.
127	Traveling the token world: A graph analysis of Ethereum ERC20 token ecosystem	Weili Chen, Tuo Zhang, Zhiguang Chen, Zibin Zheng, Yutong Lu	Besides, we propose an algorithm to discover potential relationships between tokens and other accounts.
128	Towards IP-based Geolocation via Fine-grained and Stable Webcam Landmarks	Zhihao Wang, Qiang Li, Jinke Song, Haining Wang, Limin Sun	In this paper, we leverage the availability of numerous online webcams that are used to monitor physical surroundings as a rich source of promising high-quality landmarks for serving IP-based geolocation.
129	Keeping out the Masses: Understanding the Popularity and Implications of Internet Paywalls	Panagiotis Papadopoulos, Peter Snyder, Dimitrios Athanasakis, Benjamin Livshits	Despite the potential significance of a move from an “advertising-but-open” web to a “paywalled” web, we find this issue understudied.
130	Discovering Mathematical Objects of Interest—A Study of Mathematical Notations	André Greiner-Petter, Moritz Schubotz, Fabian Müller, Corinna Breitinger, Howard Cohl, Akiko Aizawa, Bela Gipp	In this paper, we present the first in-depth study on the distributions of mathematical notation in two large scientific corpora: the open access arXiv (2.5B mathematical objects) and the mathematical reviewing service for pure and applied mathematics zbMATH (61M mathematical objects).
131	Unsupervised Domain Adaptive Graph Convolutional Networks	Man Wu, Shirui Pan, Chuan Zhou, Xiaojun Chang, Xingquan Zhu	In this paper, we present a novel approach, unsupervised domain adaptive graph convolutional networks (UDA-GCN), for domain adaptation learning for graphs.
132	Query-Efficient Correlation Clustering	David García-Soriano, Konstantin Kutzkov, Francesco Bonchi, Charalampos Tsourakakis	In this paper we study query-efficient algorithms for correlation clustering.
133	Stop tracking me Bro! Differential Tracking of User Demographics on Hyper-Partisan Websites	Pushkal Agarwal, Sagar Joglekar, Panagiotis Papadopoulos, Nishanth Sastry, Nicolas Kourtellis	In this paper, we take a first step to shed light and measure such potential differences in tracking imposed on users when visiting specific party-line’s websites.
134	How Do We Create a Fantabulous Password?	Simon S. Woo	To measure the strength of our approach, we use attacker models, where attackers have complete knowledge of our password generation algorithms.
135	What Changed Your Mind: The Roles of Dynamic Topics and Discourse in Argumentation Process	Jichuan Zeng, Jing Li, Yulan He, Cuiyun Gao, Michael Lyu, Irwin King	Specifically, we propose a novel neural model that is able to dynamically track the changes of latent topics and discourse in argumentative conversations, allowing the investigation of their roles in influencing the outcomes of persuasion.
136	Ten Social Dimensions of Conversations and Relationships	Minje Choi, Luca Maria Aiello, Krisztián Zsolt Varga, Daniele Quercia	We show that social dimensions can be predicted purely from conversations with an AUC up to 0.98, and that the combination of the predicted dimensions suggests both the types of relationships people entertain (conflict vs. support) and the types of real-world communities (wealthy vs. deprived) they shape.
137	Social Interactions or Business Transactions?What customer reviews disclose about Airbnb marketplace	Giovanni Quattrone, Antonino Nocera, Licia Capra, Daniele Quercia	To answer these questions, we propose a novel market analysis approach that exploits customers’ reviews.
138	Correcting Knowledge Base Assertions	Jiaoyan Chen, Xi Chen, Ian Horrocks, Erik B. Myklebust, Ernesto Jimenez-Ruiz	We study the problem of correcting such assertions, and present a general correction framework which combines lexical matching, semantic embedding, soft constraint mining and semantic consistency checking.
139	The POLAR Framework: Polar Opposites Enable Interpretability of Pre-Trained Word Embeddings	Binny Mathew, Sandipan Sikdar, Florian Lemmerich, Markus Strohmaier	We introduce ‘POLAR’ — a framework that adds interpretability to pre-trained word embeddings via the adoption of semantic differentials.
140	Designing Fairly Fair Classifiers Via Economic Fairness Notions	Safwan Hossain, Andjela Mladenovic, Nisarg Shah	We propose novel relaxations of these fairness notions which apply to groups rather than individuals, and are compelling in a broad range of settings.
141	Stable Model Semantics for Recursive SHACL	Medina Andresel, Julien Corman, Magdalena Ortiz, Juan L. Reutter, Ognjen Savkovic, Mantas Simkus	In contrast, we propose in this paper a stricter, more constructive semantics for SHACL, based on stable models, which are well-known in Answer Set Programming (ASP).
142	Task-Oriented Genetic Activation for Large-Scale Complex Heterogeneous Graph Embedding	Zhuoren Jiang, Zheng Gao, Jinjiong Lan, Hongxia Yang, Yao Lu, Xiaozhong Liu	To address these challenges, in this study, we propose a novel solution, Genetic hEterogeneous gRaph eMbedding (GERM), which enables flexible and efficient task-driven vertex embedding in a complex heterogeneous graph.
143	Factoring Fact-Checks: Structured Information Extraction from Fact-Checking Articles	Shan Jiang, Simon Baumgartner, Abe Ittycheriah, Cong Yu	In this paper, we propose the task of factoring fact-checks for automatically extracting structured information from fact-checking articles.
144	Keywords Generation Improves E-Commerce Session-based Recommendation	Yuanxing Liu, Zhaochun Ren, Wei-Nan Zhang, Wanxiang Che, Ting Liu, Dawei Yin	In this paper, we propose the e-commerce session-based recommendation model with keywords generation (abbreviated as ESRM-KG) to integrate keywords generation into e-commerce session-based recommendation.
145	TRAP: Two-level Regularized Autoencoder-based Embedding for Power-law Distributed Data	Dongmin Park, Hwanjun Song, Minseok Kim, Jae-Gil Lee	In this paper, we propose TRAP that leverages two-level regularizers to effectively alleviate the polarization problem.
146	An Intent-Based Automation Framework for Securing Dynamic Consumer IoT Infrastructures	Vasudevan Nagendra, Arani Bhattacharya, Vinod Yegneswaran, Amir Rahmati, Samir Das	In this paper, we introduce VISCR, a Vendor-Independent policy Specification and Conflict Resolution engine that enables intent-based conflict-free policy specification and enforcement in IoT environments.
147	Inferring Passengers’ Interactive Choices on Public Transits via MA-AL: Multi-Agent Apprenticeship Learning	Mingzhou Yang, Yanhua Li, Xun Zhou, Hui Lu, Zhihong Tian, Jun Luo	We propose an iterative algorithm inspired by single-agent apprenticeship learning algorithms and the cyclic coordinate descent approach.
148	Personalized Employee Training Course Recommendation with Career Development Awareness	Chao Wang, Hengshu Zhu, Chen Zhu, Xi Zhang, Enhong Chen, Hui Xiong	To this end, in this paper, we propose an explainable personalized online course recommender system for enhancing employee training and development.
149	Efficient Neural Interaction Function Search for Collaborative Filtering	Quanming Yao, Xiangning Chen, James T. Kwok, Yong Li, Cho-Jui Hsieh	We propose an one-shot search algorithm that simultaneously updates both the architecture and learning parameters.
150	Early Detection of User Exits from Clickstream Data: A Markov Modulated Marked Point Process Model	Tobias Hatt, Stefan Feuerriegel	In this paper, we develop a novel Markov modulated marked point process (M3PP) model for detecting users at risk of exiting with no purchase from clickstream data.
151	Filter List Generation for Underserved Regions	Alexander Sjösten, Peter Snyder, Antonio Pastor, Panagiotis Papadopoulos, Benjamin Livshits	We apply our unique two-step filter list generation pipeline to three regions of the web that currently have poorly maintained filter lists: Sri Lanka, Hungary, and Albania.
152	Improving Learning Outcomes with Gaze Tracking and Automatic Question Generation	Rohail Syed, Kevyn Collins-Thompson, Paul N. Bennett, Mengqiu Teng, Shane Williams, Dr. Wendy W. Tay, Shamsi Iqbal	We aim to expand the known pedagogical benefits of adjunct questions to more general reading scenarios, by investigating the benefits of adjunct questions generated after participants attend to passages in an article, based on their gaze behavior.
153	REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild	Rahul Duggal, Scott Freitas, Cao Xiao, Duen Horng Chau, Jimeng Sun	We propose Rest , a new method that simultaneously tackles both issues via 1) adversarial training and controlling the Lipschitz constant of the neural network through spectral regularization while 2) enabling neural network compression through sparsity regularization.
154	MadDroid: Characterizing and Detecting Devious Ad Contents for Android Apps	Tianming Liu, Haoyu Wang, Li Li, Xiapu Luo, Feng Dong, Yao Guo, Liu Wang, Tegawendé Bissyandé, Jacques Klein	To understand the practice of these devious ad behaviors, we perform a large-scale study on the app contents harvested through automated app testing.
155	Mobile App Squatting	Yangyu Hu, Haoyu Wang, Ren He, Li Li, Gareth Tyson, Ignacio Castro, Yao Guo, Lei Wu, Guoai Xu	In this paper, we explore the presence of squatting attacks in the mobile app ecosystem.
156	Sub-linear RACE Sketches for Approximate Kernel Density Estimation on Streaming Data	Benjamin Coleman, Anshumali Shrivastava	We propose RACE, an efficient sketching algorithm for kernel density estimation on high-dimensional streaming data.
157	LOVBench: Ontology Ranking Benchmark	Niklas Kolbe, Pierre-Yves Vandenbussche, Sylvain Kubler, Yves Le Traon	With inferred relevance judgments for more than 7000 queries, LOVBench is large enough to perform a comparison study using learning to rank (LTR) with complex ontology ranking models. In this paper, we first introduce the LOVBench dataset as a benchmark for ontology term ranking.
158	Adaptive Low-level Storage of Very Large Knowledge Graphs	Jacopo Urbani, Ceriel Jacobs	We propose Trident, a novel storage architecture for very large KGs on centralized systems.
159	Generating Representative Headlines for News Stories	Xiaotao Gu, Yuning Mao, Jiawei Han, Jialu Liu, You Wu, Cong Yu, Daniel Finnie, Hongkun Yu, Jiaqi Zhai, Nicholas Zukoski	In this work, we study the problem of generating representative headlines for news stories.
160	Adversarial Cooperative Imitation Learning for Dynamic Treatment Regimes✱	Lu Wang, Wenchao Yu, Xiaofeng He, Wei Cheng, Martin Renqiang Ren, Wei Wang, Bo Zong, Haifeng Chen, Hongyuan Zha	To address these limitations, in this paper, we propose the adversarial cooperative imitation learning model, ACIL, to deduce the optimal dynamic treatment regimes that mimics the positive trajectories while differs from the negative trajectories.
161	A Data-Driven Metric of Incentive Compatibility	Yuan Deng, Sébastien Lahaie, Vahab Mirrokni, Song Zuo	We introduce a new metric to quantify incentive compatibility in both static and dynamic environments.
162	Modeling and Aggregation of Complex Annotations via Annotation Distances	Alexander Braylan, Matthew Lease	To obviate the need for task-specific modeling, we propose to model distances between labels, rather than the labels themselves.
163	#Outage: Detecting Power and Communication Outages from Social Networks	Udit Paul, Alexander Ermakov, Michael Nekrasov, Vivek Adarsh, Elizabeth Belding	In this research, we investigate the use of tweets posted on the Twitter social media platform to detect power and communication outages during natural disasters. We annotate the gathered data set to separate the tweets into different types of outage-related events: power outage, communication outage and both power-communication outage.
164	LOREM: Language-consistent Open Relation Extraction from Unstructured Text	Tom Harting, Sepideh Mesbah, Christoph Lofi	We introduce a Language-consistent multi-lingual Open Relation Extraction Model (LOREM) for finding relation tuples of any type between entities in unstructured texts.
165	DyCRS: Dynamic Interpretable Postoperative Complication Risk Scoring	Wen Wang, Han Zhao, Honglei Zhuang, Nirav Shah, Rema Padman	In this paper, we develop a dynamic postoperative complication risk scoring framework (DyCRS) to detect the “at-risk” patients in a real-time way based on postoperative sequential vital signs and static features.
166	OpenCrowd: A Human-AI Collaborative Approach for Finding Social Influencers via Open-Ended Answers Aggregation	Ines Arous, Jie Yang, Mourad Khayati, Philippe Cudré-Mauroux	To tackle those issues, we present OpenCrowd, a unified Bayesian framework that seamlessly incorporates machine learning and crowdsourcing for effectively finding social influencers.
167	Correcting for Selection Bias in Learning-to-rank Systems	Zohreh Ovaisi, Ragib Ahsan, Yifan Zhang, Kathryn Vasilaky, Elena Zheleva	Here, we propose new counterfactual approaches which adapt Heckman’s two-stage method and accounts for selection and position bias in LTR systems.
168	The Pod People: Understanding Manipulation of Social Media Popularity via Reciprocity Abuse	Janith Weerasinghe, Bailey Flanigan, Aviel Stein, Damon McCoy, Rachel Greenstadt	Online Social Network (OSN) Users’ demand to increase their account popularity has driven the creation of an underground ecosystem that provides services or techniques to help users manipulate content curation algorithms.
169	Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction	Paolo Rosso, Dingqi Yang, Philippe Cudré-Mauroux	To address this issue, we propose HINGE, a hyper-relational KG embedding model, which directly learns from hyper-relational facts in a KG.
170	Context-Aware Document Term Weighting for Ad-Hoc Search	Zhuyun Dai, Jamie Callan	This paper proposes HDCT, a context-aware document term weighting framework for document indexing and retrieval.
171	NetTaxo: Automated Topic Taxonomy Construction from Text-Rich Network	Jingbo Shang, Xinyang Zhang, Liyuan Liu, Sha Li, Jiawei Han	In this paper, we propose NetTaxo, a novel automatic topic taxonomy construction framework, which goes beyond the existing paradigm and allows text data to collaborate with network structure.
172	Do podcasts and music compete with one another? Understanding users’ audio streaming habits	Ang Li, Alice Wang, Zahra Nazari, Praveen Chandar, Benjamin Carterette	Taking all the differences as input features to a machine learning model, we demonstrate that a podcast listening session is predictable at the start of a new listening session.
173	Near-Perfect Recovery in the One-Dimensional Latent Space Model	Yu Chen, Sampath Kannan, Sanjeev Khanna	We initiate our study with the weaker goal of recovering only the order in which vertices appear on the line segment.
174	Finding a Choice in a Haystack: Automatic Extraction of Opt-Out Statements from Privacy Policy Text	Vinayshekhar Bannihatti Kumar, Roger Iyengar, Namita Nisal, Yuanyuan Feng, Hana Habib, Peter Story, Sushain Cherivirala, Margaret Hagan, Lorrie Cranor, Shomir Wilson, Florian Schaub, Norman Sadeh	We describe a method for the automated detection of opt-out choices in privacy policy text and their presentation to users through a web browser extension.
175	eDarkFind: Unsupervised Multi-view Learning for Sybil Account Detection	Ramnath Kumar, Shweta Yadav, Raminta Daniulaityte, Francois Lamy, Krishnaprasad Thirunarayan, Usha Lokala, Amit Sheth	In this paper, we present a multi-view unsupervised framework (eDarkFind) that helps modeling vendor characteristics and facilitates Sybil account detection.
176	Provably and Efficiently Approximating Near-cliques using the Turán Shadow: PEANUTS	Shweta Jain, C. Seshadhri	We exploit the fact that a near-clique contains a smaller clique, and use techniques for clique sampling to count near-cliques.
177	Differentially Private Stream Processing for the Semantic Web	Daniele Dell’Aglio, Abraham Bernstein	Specifically, we propose SihlQL, a query language that processes RDF streams in a privacy-preserving fashion.
178	Learning to Hash with Graph Neural Networks for Recommender Systems	Qiaoyu Tan, Ninghao Liu, Xing Zhao, Hongxia Yang, Jingren Zhou, Xia Hu	In this work, we investigate the problem of hashing with graph neural networks (GNNs) for high quality retrieval, and propose a simple yet effective discrete representation learning framework to jointly learn continuous and discrete codes.
179	Edge formation in Social Networks to Nurture Content Creators	Chun Lo, Emilie de Longueau, Ankan Saha, Shaunak Chatterjee	In this work, we focus on the creator experience and specifically on improving edge recommendations to better serve creators in such ecosystems.
180	Open Intent Extraction from Natural Language Interactions	Nikhita Vedula, Nedim Lipka, Pranav Maneriker, Srinivasan Parthasarathy	We propose a novel domain-agnostic approach, OPINE, which formulates the problem as a sequence tagging task under an open-world setting.
181	Efficient Algorithms towards Network Intervention	Hui-Ju Hung, Wang-Chien Lee, De-Nian Yang, Chih-Ya Shen, Zhen Lei, Sy-Miin Chow	We propose the Candidate Re-selection with Preserved Dependency (CRPD) algorithm for NILD-S, and the Objective-aware Intervention edge Selection and Adjustment (OISA) algorithm for NILD-M.
182	Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus	Bang Liu, Haojie Wei, Di Niu, Haolan Chen, Yancheng He	In this paper, we propose Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions.
183	Expanding Taxonomies with Implicit Edge Semantics	Emaad Manzoor, Rui Li, Dhananjay Shrouty, Jure Leskovec	In this work, we propose Arborist, an approach to automatically expand textual taxonomies by predicting the parents of new taxonomy nodes.
184	Herding a Deluge of Good Samaritans: How GitHub Projects Respond to Increased Attention	Danaja Maldeniya, Ceren Budak, Lionel P. Robert Jr., Daniel M. Romero	To understand these dynamics, we analyze millions of actions of thousands of contributors in over 1100 open source software projects that topped the GitHub Trending Projects page and thus experienced a large increase in attention, in comparison to a control group of projects identified through propensity score matching.
185	Don’t Let Me Be Misunderstood:Comparing Intentions and Perceptions in Online Discussions	Jonathan P. Chang, Justin Cheng, Cristian Danescu-Niculescu-Mizil	In this work, we present a computational framework for exploring and comparing both perspectives in online public discussions.
186	Discovering Strategic Behaviors for Collaborative Content-Production in Social Networks	Yuxin Xiao, Adit Krishnan, Hari Sundaram	Therefore, in this paper, we are motivated to investigate if resource-limited individuals discover strategic behaviors associated with high payoffs when producing collaborative/interactive content in social networks.
187	Seeding Network Influence in Biased Networks and the Benefits of Diversity	Ana-Andreea Stoica, Jessy Xinyi Han, Augustin Chaintreau	Through a theoretical model of biased networks, we characterize the intricate relationship between diversity and efficiency, which sometimes may be at odds but may also reinforce each other.
188	Liquidity in Credit Networks with Constrained Agents	Geoffrey Ramseyer, Ashish Goel, David Mazières	In this paper, we introduce constraints that bound the total amount of loss that the rest of the network can suffer if an agent (or a set of agents) were to default – equivalently, how the network changes if agents can support limited solvency guarantees.
189	Dark Matter: Uncovering the DarkComet RAT Ecosystem	Brown Farinholt, Mohammad Rezaeirad, Damon McCoy, Kirill Levchenko	Using a known method for collecting victim log databases from DarkComet controllers, we present novel techniques for tracking RAT controllers across hostname changes and improve on established techniques for filtering spurious victim records caused by scanners and sandboxed malware executions.
190	Discriminative Topic Mining via Category-Name Guided Text Embedding	Yu Meng, Jiaxin Huang, Guangyuan Wang, Zihan Wang, Chao Zhang, Yu Zhang, Jiawei Han	We propose a new task, discriminative topic mining, which leverages a set of user-provided category names to mine discriminative topics from text corpora.
191	Designing for Trust: A Behavioral Framework for Sharing Economy Platforms	Natã M. Barbosa, Emily Sun, Judd Antin, Paolo Parigi	In this work, we present the design and evaluation of a behavioral framework to measure a user’s propensity to trust others on Airbnb.
192	A Generic Edge-Empowered Graph Convolutional Network via Node-Edge Mutual Enhancement	Pengyang Wang, Jiaping Gui, Zhengzhang Chen, Junghwan Rhee, Haifeng Chen, Yanjie Fu	In this paper, we propose a novel framework EE-GCN that achieves node-edge enhancement.
193	Algorithmic Effects on the Diversity of Consumption on Spotify	Ashton Anderson, Lucas Maystre, Ian Anderson, Rishabh Mehrotra, Mounia Lalmas	In this work, we study the user experience on Spotify, a popular music streaming service, through the lens of diversity—the coherence of the set of songs a user listens to.
194	NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction	Wenxuan Zhou, Hongtao Lin, Bill Yuchen Lin, Ziqi Wang, Junyi Du, Leonardo Neves, Xiang Ren	In this paper, we present a neural approach to ground rules for RE, named Nero, which jointly learns a relation extraction module and a soft matching module.
195	Clustering and Constructing User Coresets to Accelerate Large-scale Top-K Recommender Systems	Jyun-Yu Jiang, Patrick H. Chen, Cho-Jui Hsieh, Wei Wang	In this paper, we propose a novel model for clustering and navigating for top-K recommenders (CANTOR) to expedite the computation of top-K recommendations based on latent factor models.
196	Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion	Jiaxin Huang, Yiqing Xie, Yu Meng, Jiaming Shen, Yunyi Zhang, Jiawei Han	In this paper we demonstrate that by generating auxiliary sets, we can guide the expansion process of target set to avoid touching those ambiguous areas around the border with auxiliary sets, and we show that Set-CoExpan outperforms strong baseline methods significantly.
197	Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation	Jibang Wu, Renqin Cai, Hongning Wang	In this paper, we argue that the influence from the past events on a user’s current action should vary over the course of time and under different context.
198	An End-to-end Topic-Enhanced Self-Attention Network for Social Emotion Classification	Chang Wang, Bang Wang	In this paper, we propose an end-to-end topic-enhanced self-attention network (TESAN) that jointly encodes document semantics and extracts document topics.
199	When Recommender Systems Meet Fleet Management: Practical Study in Online Driver Repositioning System	Zhe Xu, Chang Men, Peng Li, Bicheng Jin, Ge Li, Yue Yang, Chunyang Liu, Ben Wang, Xiaohu Qie	In this paper, we describe a novel framework of driver repositioning system, which meets various requirements in practical situations, including robust driver experience satisfaction and multi-driver collaboration.
200	Domain Adaptive Multi-Modality Neural Attention Network for Financial Forecasting	Dawei Zhou, Lecheng Zheng, Yada Zhu, Jianbo Li, Jingrui He	To answer this question, in this paper, we propose a generic time series forecasting framework named Dandelion, which leverages the consistency of multiple modalities and explores the relatedness of multiple tasks using a deep neural network.
201	Collective Multi-type Entity Alignment Between Knowledge Graphs	Qi Zhu, Hao Wei, Bunyamin Sisman, Da Zheng, Christos Faloutsos, Xin Luna Dong, Jiawei Han	In this paper, we present a Collective Graph neural network for Multi-type entity Alignment, called CG-MuAlign.
202	Characterizing Search-Engine Traffic to Internet Research Agency Web Properties	Alexander Spangher, Gireeja Ranade, Besmira Nushi, Adam Fourney, Eric Horvitz	In this paper, we focus on IRA activities that received exposure through search engines, by joining data from Facebook and Twitter with logs from the Internet Explorer 11 and Edge browsers and the Bing.com search engine.
203	Understanding Electricity-Theft Behavior via Multi-Source Data	Wenjie Hu, Yang Yang, Jianbo Wang, Xuanwen Huang, Ziqiang Cheng	In this paper, we propose to recognize electricity-theft behavior via multi-source data.
204	Understanding the Performance Costs and Benefits of Privacy-focused Browser Extensions	Kevin Borgolte, Nick Feamster	In this paper, we analyze how eight popular privacy-focused browser extensions for Google Chrome and Mozilla Firefox, the two desktop browsers with the highest market share, affect browser performance.
205	Estimate the Implicit Likelihoods of GANs with Application to Anomaly Detection	Shaogang Ren, Dingcheng Li, Zhixin Zhou, Ping Li	In this paper, we propose an approach to estimate the implicit likelihoods of GAN models.
206	RLPer: A Reinforcement Learning Model for Personalized Search	Jing Yao, Zhicheng Dou, Jun Xu, Ji-Rong Wen	In this paper, we propose a reinforcement learning based personalization model, referred to as RLPer, to track the sequential interactions between the users and search engine with a hierarchical Markov Decision Process (MDP).
207	Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning	Wei Ye, Rui Xie, Jinglei Zhang, Tianxiang Hu, Xiaoyin Wang, Shikun Zhang	In this paper, we propose a novel end-to-end model for the two tasks by introducing an additional code generation task.
208	Hierarchically Structured Transformer Networks for Fine-Grained Spatial Event Forecasting	Xian Wu, Chao Huang, Chuxu Zhang, Nitesh V. Chawla	To tackle this challenge, we develop a hierarchically structured Spatial-Temporal ransformer network (STtrans) which leverages a main embedding space to capture the inter-dependencies across time and space for alleviating the data imbalance issue.
209	MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding	Xinyu Fu, Jiani Zhang, Ziqiao Meng, Irwin King	To address these three limitations, we propose a new model named Metapath Aggregated Graph Neural Network (MAGNN) to boost the final performance.
210	Mining Points-of-Interest for Explaining Urban Phenomena: A Scalable Variational Inference Approach	Christof Naumzik, Patrick Zoechbauer, Stefan Feuerriegel	In order to overcome this shortcoming, the present paper proposes a novel spatial model for explaining spatial distributions based on web-mined POIs.
211	Large-Scale Talent Flow Embedding for Company Competitive Analysis	Le Zhang, Tong Xu, Hengshu Zhu, Chuan Qin, Qingxin Meng, Hui Xiong, Enhong Chen	Instead, in this paper, we aim to develop a new paradigm for studying the competition among companies through the analysis of talent flows.
212	Quantifying Engagement with Citations on Wikipedia	Tiziano Piccardi, Miriam Redi, Giovanni Colavizza, Robert West	To close this gap, we built client-side instrumentation for logging all interactions with links leading from English Wikipedia articles to cited references during one month, and conducted the first analysis of readers’ interactions with citations.
213	Condition Aware and Revise Transformer for Question Answering	Xinyan Zhao, Feng Xiao, Haoming Zhong, Jun Yao, Huanhuan Chen	To alleviate the effect of defective condition values, this paper proposes condition aware and revise Transformer (CAR-Transformer).
214	In Opinion Holders’ Shoes: Modeling Cumulative Influence for View Change in Online Argumentation	Zhen Guo, Zhe Zhang, Munindar Singh	(2) We present a sequential model for cumulative influence that captures the interplay between comments as both local and nonlocal dependencies, and demonstrate its capability of selecting the most effective information for changing views.
215	Efficient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation	Chong Chen, Min Zhang, Weizhi Ma, Yiqun Liu, Shaoping Ma	In this paper, we propose to learn FM without sampling for ranking tasks that helps context-aware recommendation particularly.
216	Domain-Guided Task Decomposition with Self-Training for Detecting Personal Events in Social Media	Payam Karisani, Joyce C. Ho, Eugene Agichtein	To reduce the burden of creating extensive labeled data and improve classification performance, we propose to perform these tasks in two steps: 1.
217	Leveraging Passage-level Cumulative Gain for Document Ranking	Zhijing Wu, Jiaxin Mao, Yiqun Liu, Jingtao Zhan, Yukun Zheng, Min Zhang, Shaoping Ma	In this paper, we investigate how information gain accumulates with passages when users sequentially read a document.
218	Towards Hybrid Human-AI Workflows for Unknown Unknown Detection	Anthony Liu, Santiago Guerra, Isaac Fung, Gabriel Matute, Ece Kamar, Walter Lasecki	Instead, this paper presents an approach that leverages people’s ability to find patterns to retrain classifiers more effectively with fewer examples.
219	Examining Protest as An Intervention to Reduce Online Prejudice: A Case Study of Prejudice Against Immigrants	Kai Wei, Yu-Ru Lin, Muheng Yan	This work contributes to the understanding of online prejudice and its intervention effect.
220	Interpretable Complex Question Answering	Soumen Chakrabarti	We will review cross-community co-evolution of question answering (QA) with the advent of large-scale knowledge graphs (KGs), continuous representations of text and graphs, and deep sequence analysis.
221	Practical Data Poisoning Attack against Next-Item Recommendation	Hengtong Zhang, Yaliang Li, Bolin Ding, Jing Gao	In this paper, we focus on a general next-item recommendation setting and propose a practical poisoning attack approach named LOKI against blackbox recommendation systems.
222	Efficient Online Multi-Task Learning via Adaptive Kernel Selection	Peng Yang, Ping Li	To overcome this issue, we propose a randomized kernel sampling technique across multiple tasks.
223	Few-Shot Learning for New User Recommendation in Location-based Social Networks	Ruirui Li, Xian Wu, Xian Wu, Wei Wang	In this work, we investigate the recommendation problem in the context of identifying potential new customers for businesses in LBSNs.
224	Ad Hoc Table Retrieval using Intrinsic and Extrinsic Similarities	Roee Shraga, Haggai Roitman, Guy Feigenblat, Mustafa Canim	In this work, we make a novel use of intrinsic (passage-based) and extrinsic (manifold-based) table similarities for enhanced retrieval.
225	Leveraging Context for Neural Question Generation in Open-domain Dialogue Systems	Yanxiang Ling, Fei Cai, Honghui Chen, Maarten de Rijke	We propose a Context-augmented Neural Question Generation (CNQG) model that leverages the conversational context to generate questions for promoting interactivity and persistence of multi-turn dialogues.
226	Higher-Order Label Homogeneity and Spreading in Graphs	Dhivya Eswaran, Srijan Kumar, Christos Faloutsos	To this end, we propose Higher-Order Label Spreading (HOLS) to spread labels using higher-order structures.
227	Enhanced-RCNN: An Efficient Method for Learning Sentence Similarity	Shuang Peng, Hengbin Cui, Niantao Xie, Sujian Li, Jiaxing Zhang, Xiaolong Li	Learning sentence similarity is a fundamental research topic and has been explored using various deep learning methods recently.
228	MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection	Mi Luo, Fei Chen, Pengxiang Cheng, Zhenhua Dong, Xiuqiang He, Jiashi Feng, Zhenguo Li	We propose a meta-learning framework to facilitate user-level adaptive model selection in recommender systems.
229	TransModality: An End2End Fusion Method with Transformer for Multimodal Sentiment Analysis	Zilong Wang, Zhaohong Wan, Xiaojun Wan	Enlightened by recent success of Transformer in the area of machine translation, we propose a new fusion method, TransModality, to address the task of multimodal sentiment analysis.
230	Recommending Themes for Ad Creative Design via Visual-Linguistic Representations	Yichao Zhou, Shaunak Mishra, Manisha Verma, Narayan Bhamidipati, Wei Wang	To automatically infer ad themes via such multimodal sources of information in past ad campaigns, we propose a theme (keyphrase) recommender system for ad creative strategists.
231	Attentive Sequential Models of Latent Intent for Next Item Recommendation	Md Mehrab Tanjim, Congzhe Su, Ethan Benjamin, Diane Hu, Liangjie Hong, Julian McAuley	To discover such latent intents, and use them effectively for recommendation, in this paper we propose an Attentive Sequential model of Latent Intent (ASLI in short).
232	Latent Linear Critiquing for Conversational Recommender Systems	Kai Luo, Scott Sanner, Ga Wu, Hanze Li, Hojin Yang	In this paper, we revisit the critiquing approach in the era of recommendation methods based on latent embeddings with subjective item descriptions (i.e., keyphrases from user reviews).
233	A Multimodal Variational Encoder-Decoder Framework for Micro-video Popularity Prediction	Jiayi Xie, Yaochen Zhu, Zhibin Zhang, Jian Peng, Jing Yi, Yaosi Hu, Hongyi Liu, Zhenzhong Chen	In this paper, we propose a multimodal variational encoder-decoder (MMVED) framework that considers the uncertain factors as the randomness for the mapping from the multimodal features to the popularity.
234	Graph-Query Suggestions for Knowledge Graph Exploration	Matteo Lissandrini, Davide Mottin, Themis Palpanas, Yannis Velegrakis	To achieve this result, we propose a model that can bridge graph search paradigms with well-established techniques for information-retrieval.
235	Visual Concept Naming: Discovering Well-Recognized Textual Expressions of Visual Concepts	Masayasu Muraoka, Tetsuya Nasukawa, Rudy Raymond, Bishwaranjan Bhattacharjee	We propose a task called Visual Concept Naming to associate visual concepts with the corresponding textual expressions, i.e., names of visual concepts found in real-world multimodal data. To tackle the task, we create a dataset consisting of 3.4 million tweets in total in three languages.
236	Hierarchical Visual-aware Minimax Ranking Based on Co-purchase Data for Personalized Recommendation	Xiaoya Chong, Qing Li, Howard Leung, Qianhui Men, Xianjin Chao	In this paper, we diminish this limitation by proposing a novel learning method called Hierarchical Visual-aware Minimax Ranking (H-VMMR), in which a new concept of predictive sampling is proposed to sample items in a close relationship with the positive items (e.g., substitutes, compliments).
237	A Cue Adaptive Decoder for Controllable Neural Response Generation	Weichao Wang, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang	In this paper, we propose a Cue Adaptive Decoder (CueAD) that aims to dynamically determine the involvement of a cue at each generation step in the decoding.
238	Unsupervised Dual-Cascade Learning with Pseudo-Feedback Distillation for Query-Focused Extractive Summarization	Haggai Roitman, Guy Feigenblat, Doron Cohen, Odellia Boni, David Konopnicki	We propose Dual-CES – a novel unsupervised, query-focused, multi-document extractive summarizer.
239	Extracting Knowledge from Web Text with Monte Carlo Tree Search	Guiliang Liu, Xu Li, Jiakang Wang, Mingming Sun, Ping Li	This paper proposes to apply Monte-Carlo Tree Search (MCTS) to accomplish OIE.
240	IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems	Liu Yang, Minghui Qiu, Chen Qu, Cen Chen, Jiafeng Guo, Yongfeng Zhang, W. Bruce Croft, Haiqing Chen	In this paper, we analyze user intent patterns in information-seeking conversations and propose an intent-aware neural response ranking model “IART”, which refers to “Intent-Aware Ranking with Transformers”.
241	ResQueue: A Smarter Datacenter Flow Scheduler	Hamed Rezaei, Balajee Vamanan	In this paper, we introduce ResQueue, which uses a combination of flow size and packet history to calculate the priority of each flow.
242	PARS: Peers-aware Recommender System	Huiqiang Mao, Yanzhi Li, Chenliang Li, Di Chen, Xiaoqing Wang, Yuming Deng	In this paper, we develop a peers-aware recommender system, named PARS.
243	Fast Computation of Explanations for Inconsistency in Large-Scale Knowledge Graphs	Trung-Kien Tran, Mohamed H. Gad-Elrab, Daria Stepanova, Evgeny Kharlamov, Jannik Strötgen	In this paper, we present a novel approach to dramatically speed up the process of detecting and explaining inconsistency in large KGs by exploiting KG abstractions that capture prominent data patterns.
244	Review-guided Helpful Answer Identification in E-commerce	Wenxuan Zhang, Wai Lam, Yang Deng, Jing Ma	In this paper, we propose the Review-guided Answer Helpfulness Prediction (RAHP) model that not only considers the interactions between QA pairs but also investigates the opinion coherence between the answer and crowds’ opinions reflected in the reviews, which is another important factor to identify helpful answers.
245	How Much and When Do We Need Higher-order Informationin Hypergraphs? A Case Study on Hyperedge Prediction	Se-eun Yoon, Hyungseok Song, Kijung Shin, Yung Yi	To this end, we propose a method of incrementally representing group interactions using a notion of n-projected graph whose accumulation contains information on up to n-way interactions, and quantify the accuracy of solving a task as n grows for various datasets.
246	Multi-Context Attention for Entity Matching	Dongxiang Zhang, Yuyang Nie, Sai Wu, Yanyan Shen, Kian-Lee Tan	In this paper, we fully exploit the semantic context of embedding vectors for the pair of entity text descriptions.
247	Dolphin: A Spoken Language Proficiency Assessment System for Elementary Education	Zitao Liu, Guowei Xu, Tianqiao Liu, Weiping Fu, Yubi Qi, Wenbiao Ding, Yujia Song, Chaoyou Guo, Cong Kong, Songfan Yang, Gale Yan Huang	To alleviate this problem, we develop Dolphin, a spoken language proficiency assessment system for Chinese elementary education.
248	Twitter User Location Inference Based on Representation Learning and Label Propagation	Hechan Tian, Meng Zhang, Xiangyang Luo, Fenlin Liu, Yaqiong Qiao	In this paper, a Twitter user location inference method based on representation learning and label propagation is proposed.
249	The Structure of Social Influence in Recommender Networks	Pantelis Pipergias Analytis, Daniel Barkoczi, Philipp Lorenz-Spreen, Stefan Herzog	Using the weighted k-nearest neighbors algorithm (k-nn) to represent an array of social learning strategies, we show—leveraging methods from network science—how the k-nn algorithm gives rise to networks of social influence in six real-world domains of taste.
250	A Multi-task Learning Framework for Road Attribute Updating via Joint Analysis of Map Data and GPS Traces	Yifang Yin, Jagannadan Varadarajan, Guanfeng Wang, Xueou Wang, Dhruva Sahrawat, Roger Zimmermann, See-Kiong Ng	To model the relations between the target road attributes and other contextual information that is available from a digital map, we propose to leverage map tiles at road centers as visual features that capture the information of the surrounding geographic objects around the roads.
251	Predicting Drug Demand with Wikipedia Views: Evidence from Darknet Markets.	Sam Miller, Abeer El-Bahrawy, Martin Dittus, Mark Graham, Joss Wright	We present a novel method to predict drug use based on high-frequency sales data from darknet markets.
252	Private Data Manipulation in Optimal Sponsored Search Auction	Xiaotie Deng, Tao Lin, Tao Xiao	In this paper, We revisit the sponsored search auction as a repeated auction.
253	Active Domain Transfer on Network Embedding	Lichen Jin, Yizhou Zhang, Guojie Song, Yilun Jin	Consequently, we propose in this paper a method for active transfer learning on networks named active-transfer network embedding, abbreviated ATNE.
254	To be Tough or Soft: Measuring the Impact of Counter-Ad-blocking Strategies on User Engagement	Shuai Zhao, Achir Kalra, Cristian Borcea, Yi Chen	We utilize the difference-in-differences method to estimate the causal effects.
255	Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs	Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi	In this work, we propose SLaQ , an efficient and effective approximation technique for computing spectral distances between graphs with billions of nodes and edges.
256	Heterogeneous Graph Transformer	Ziniu Hu, Yuxiao Dong, Kuansan Wang, Yizhou Sun	In this paper, we present the Heterogeneous Graph Transformer (HGT) architecture for modeling Web-scale heterogeneous graphs.
257	On the Robustness of Cascade Diffusion under Node Attacks	Alvis Logins, Yuchen Li, Panagiotis Karras	We introduce novel algorithms from building blocks found in previous works to evaluate the proposed measures.
258	Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing	Jinyuan Jia, Binghui Wang, Xiaoyu Cao, Neil Zhenqiang Gong	In this work, we aim to bridge this gap.
259	Stabilizing Neural Search Ranking Models	Ruilin Li, Zhen Qin, Xuanhui Wang, Suming J. Chen, Donald Metzler	We propose two heuristics and one theory-guided stabilization method to solve the optimization problem.
260	Evolution of a Web-Scale Near Duplicate Image Detection System	Andrey Gusev, Jiajing Xu	In this paper, we present an efficient system for detecting near duplicate images across 8 billion images. Finally, we are releasing a human-labeled dataset of ~53,000 pairs of images introduced in this paper.
261	A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback	Shota Yasui, Gota Morishita, Fujita Komei, Masashi Shibata	We address this problem by using an importance weight approach typically used for covariate shift correction.
262	Deconstruct Densest Subgraphs	Lijun Chang, Miao Qiao	In this paper, we aim to understand the distribution of the densest subgraphs of a given graph under the density notion of average-degree.
263	Anchored Model Transfer and Soft Instance Transfer for Cross-Task Cross-Domain Learning: A Study Through Aspect-Level Sentiment Classification	Yaowei Zheng, Richong Zhang, Suyuchen Wang, Samuel Mensah, Yongyi Mao	In this paper, we propose two transfer learning methods Anchored Model Transfer (AMT) and Soft Instance Transfer (SIT), which are both based on multi-task learning, and account for model transfer and instance transfer, and can be combined into a common framework.
264	Scaling PageRank to 100 Billion Pages	Stergios Stergiou	Distributed graph processing frameworks formulate tasks as sequences of supersteps within which communication is performed asynchronously by sending messages over the graph edges.
265	Exploiting Aesthetic Preference in Deep Cross Networks for Cross-domain Recommendation	Jian Liu, Pengpeng Zhao, Fuzhen Zhuang, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Xiaofang Zhou, Hui Xiong	To this end, in this paper, we propose a new deep Aesthetic Cross-Domain Networks (ACDN), in which parameters characterizing personal aesthetic preferences are shared across networks to transfer knowledge between domains.
266	Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning	Wenhao Zhang, Wentian Bao, Xiao-Yang Liu, Keping Yang, Quan Lin, Hong Wen, Ramin Ramezani	In this paper, we propose two principled, efficient and highly effective CVR estimators for industrial CVR estimation, namely, Multi-IPW and Multi-DR.
267	ROSE: Role-based Signed Network Embedding	Amin Javari, Tyler Derr, Pouya Esmailian, Jiliang Tang, Kevin Chen-Chuan Chang	Thus, we propose network transformation based embedding to address these shortcomings.
268	Natural Key Discovery in Wikipedia Tables	Leon Bornemann, Tobias Bleifuß, Dmitri V. Kalashnikov, Felix Naumann, Divesh Srivastava	To address this challenge, we formally define the notion of natural keys and propose a supervised learning approach to automatically detect natural keys in Wikipedia tables using carefully engineered features.
269	Negative Purchase Intent Identification in Twitter	Samed Atouati, Xiao Lu, Mauro Sozio	We develop a binary classifier for such a task, which consists of a generalization of logistic regression leveraging the locality of purchase intent in posts from Twitter.
270	War of Words: The Competitive Dynamics of Legislative Processes	Victor Kristof, Matthias Grossglauser, Patrick Thiran	We propose a model for predicting the success of such edits, in the face of both the inertia of the status quo and the competition between overlapping edits.
271	Deep Rating Elicitation for New Users in Collaborative Filtering	Wonbin Kweon, Seongku Kang, Junyoung Hwang, Hwanjo Yu	This paper proposes a novel end-to-end Deep learning framework for Rating Elicitation (DRE), that chooses all the seed items at a time with consideration of the non-linear interactions.
272	Searching for Embeddings in a Haystack: Link Prediction on Knowledge Graphs with Subgraph Pruning	Unmesh Joshi, Jacopo Urbani	To counter this problem, we propose a technique to reduce the search space by identifying smaller subsets of promising entities.
273	Asymptotic Behavior of Sequence Models	Flavio Chierichetti, Ravi Kumar, Andrew Tomkins	In this paper we study the limiting dynamics of a sequential process that generalizes Pólya’s urn.
274	Clustering with a faulty oracle	Kasper Green Larsen, Michael Mitzenmacher, Charalampos Tsourakakis	In this work, we provide a polynomial time algorithm that recovers all signs correctly with high probability in the presence of noise with queries.
275	Matching Cross Network for Learning to Rank in Personal Search	Zhen Qin, Zhongliang Li, Michael Bendersky, Donald Metzler	Based on the above observations, we propose a simple but effective matching cross network (MCN) method for learning to rank with side information.
276	RLIRank: Learning to Rank with Reinforcement Learning for Dynamic Search	Jianghong Zhou, Eugene Agichtein	To address this problem, we introduce a novel reinforcement learning-based approach, RLIRank.
277	Reducing Disparate Exposure in Ranking: A Learning To Rank Approach	Meike Zehlike, Carlos Castillo	In this paper we explore a new in-processing approach: DELTR, a learning-to-rank framework that addresses potential issues of discrimination and unequal opportunity in rankings at training time.
278	Natural Language Annotations for Search Engine Optimization	Porter Jenkins, Jennifer Zhao, Heath Vinicombe, Anant Subramanian, Arun Prasad, Atillia Dobi, Eileen Li, Yunsong Guo	In this work, we build a framework for generating natural language content annotations and show how they can be used for search engine optimization.
279	Graph Enhanced Representation Learning for News Recommendation	Suyu Ge, Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang	Here we propose a news recommendation method which can enhance the representation learning of users and news by modeling their relatedness in a graph setting.
280	End-to-End Deep Attentive Personalized Item Retrieval for Online Content-sharing Platforms	Jyun-Yu Jiang, Tao Wu, Georgios Roumpos, Heng-Tze Cheng, Xinyang Yi, Ed Chi, Harish Ganapathy, Nitin Jindal, Pei Cao, Wei Wang	In this paper, we propose the end-to-end deep attentive model (EDAM) to deal with personalized item retrieval for online content-sharing platforms using only discrete personal item history and queries.
281	Multimodal Post Attentive Profiling for Influencer Marketing	Seungbae Kim, Jyun-Yu Jiang, Masaki Nakada, Jinyoung Han, Wei Wang	This paper proposes a multimodal deep learning model that uses text and image information from social media posts (i) to classify influencers into specific interests/topics (e.g., fashion, beauty) and (ii) to classify their posts into certain categories. We release our influencer dataset of 33,935 influencers labeled with specific topics based on 10,180,500 posts to facilitate future research.
282	Voice-based Reformulation of Community Answers	Simone Filice, Nachshon Cohen, David Carmel	In this paper, we define the Answer Reformulation task and propose a novel solution to automatically reformulate a community provided answer making it suitable for a voice interaction.
283	VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text	Mingxi Cheng, Shahin Nazarian, Paul Bogdan	In order to improve the state-of-the-art in rumor detection, tracking, and verification, we propose VRoC, a tweet-level variational autoencoder-based rumor classification system.
284	Detecting Undisclosed Paid Editing in Wikipedia	Nikesh Joshi, Francesca Spezzano, Mayson Green, Elijah Hill	We propose a machine learning-based framework using a set of features based on both the content of the articles as well as the patterns of edit history of users who create them. To test our approach, we collected and curated a new dataset from English Wikipedia with ground truth on undisclosed paid articles.
285	Bursts of Activity: Temporal Patterns of Help-Seeking and Support in Online Mental Health Forums	Taisa Kushner, Amit Sharma	Using data from Talklife, a mental health platform, we find that user activity follows a distinct pattern of high activity periods with interleaving periods of no activity, and propose a method for identifying such bursts & breaks in activity.
286	Envy, Regret, and Social Welfare Loss	Riccardo Colini-Baldeschi, Stefano Leonardi, Okke Schrijvers, Eric Sodomka	In this work, we show that similar results can be obtained using the notion of IC-Envy.
287	ShapeVis: High-dimensional Data Visualization at Scale	Nupur Kumari, Siddarth R., Akash Rupela, Piyush Gupta, Balaji Krishnamurthy	We present ShapeVis, a scalable visualization technique for point cloud data inspired from topological data analysis.
288	Using Cliques with Higher-order Spectral Embeddings Improves Graph Visualizations	Huda Nassar, Caitlin Kennedy, Shweta Jain, Austin R. Benson, David Gleich	Here, we show that adding higher-order information based on cliques to a classic eigenvector based graph visualization technique enables it to produce meaningful plots of large graphs.
289	Distant Supervision for Multi-Stage Fine-Tuning in Retrieval-Based Question Answering	Yuqing Xie, Wei Yang, Luchen Tan, Kun Xiong, Nicholas Jing Yuan, Baoxing Huai, Ming Li, Jimmy Lin	In the context of this architecture, we present a data augmentation technique using distant supervision to automatically annotate paragraphs as either positive or negative examples to supplement existing training data, which are then used together to fine-tune BERT.
290	NCVis: Noise Contrastive Approach for Scalable Visualization	Aleksandr Artemenkov, Maxim Panov	In this work, we propose NCVis – a high-performance dimensionality reduction method built on a sound statistical basis of noise contrastive estimation.
291	I’ve Got Your Packages: Harvesting Customers’ Delivery Order Information using Package Tracking Number Enumeration Attacks	Simon Woo, Hanbin Jang, Woojung Ji, Hyoungshick Kim	In this work, we examine the privacy issues associated with online package tracking systems used in the top three most popular package delivery service providers (FedEx, DHL, and UPS) in the world and found that those websites inadvertently leak users’ personal data with a PTN.
292	Crowdsourcing Detection of Sampling Biases in Image Datasets	Xiao Hu, Haobo Wang, Anirudh Vegesana, Somesh Dube, Kaiwen Yu, Gore Kao, Shuo-Han Chen, Yung-Hsiang Lu, George K. Thiruvathukal, Ming Yin	In this paper, we propose a three-step crowdsourcing workflow to get humans into the loop for facilitating bias discovery in image datasets.
293	Don’t Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing	Subendhu Rongali, Luca Soldaini, Emilio Monti, Wael Hamza	In this work, we propose a unified architecture based on Sequence to Sequence models and Pointer Generator Network to handle both simple and complex queries.
294	Addressing the Target Customer Distortion Problem in Recommender Systems	Xing Zhao, Ziwei Zhu, Majid Alfifi, James Caverlee	In this paper, we conduct a data-driven study to reveal several distortions that arise from conventional recommenders.
295	Quantifying Community Characteristics of Maternal Mortality Using Social Media	Rediet Abebe, Salvatore Giorgi, Anna Tedijanto, Anneke Buffone, H. Andrew Andrew Schwartz	In this work, we explore the role that social media language can play in providing insights into such community characteristics.
296	Adaptive Hierarchical Translation-based Sequential Recommendation	Yin Zhang, Yun He, Jianling Wang, James Caverlee	We propose an adaptive hierarchical translation-based sequential recommendation called HierTrans that first extends traditional item-level relations to the category-level, to help capture dynamic sequence patterns that can generalize across users and time.
297	What Sparks Joy: The AffectVec Emotion Database	Shahab Raji, Gerard de Melo	In this work, we show that we can obtain a database that goes beyond the common binary scores for emotion classification provided by past work.
298	LSF-Join: Locality Sensitive Filtering for Distributed All-Pairs Set Similarity Under Skew	Cyrus Rashtchian, Aneesh Sharma, David Woodruff	To address this problem, we present a new distributed algorithm, LSF-Join, for approximate all-pairs set similarity.
299	Analyzing the Use of Audio Messages in WhatsApp Groups	Alexandre Maros, Jussara Almeida, Fabrício Benevenuto, Marisa Vasconcelos	In this paper, we build upon those prior efforts by taking a first look into the use of audio messages in WhatsApp groups, a type of content that is becoming increasingly important in the platform.
300	Understanding User Behavior For Document Recommendation	Xuhai Xu, Ahmed Hassan Awadallah, Susan T. Dumais, Farheen Omar, Bogdan Popp, Robert Rounthwaite, Farnaz Jahanbakhsh	Previous work explored various methods for better recommendations and better explanations in different domains.
301	Influence Function based Data Poisoning Attacks to Top-N Recommender Systems	Minghong Fang, Neil Zhenqiang Gong, Jia Liu	In this work, we show that an attacker can launch a data poisoning attack to a recommender system to make recommendations as the attacker desires via injecting fake users with carefully crafted user-item interaction data.
302	Continuous-Time Link Prediction via Temporal Dependent Graph Neural Network	Liang Qu, Huaisheng Zhu, Qiqi Duan, Yuhui Shi	To address this problem, we propose a Temporal Dependent Graph Neural Network (TDGNN), a simple yet effective dynamic network representation learning framework which incorporates the network temporal information into GNNs.
303	Are These Comments Triggering? Predicting Triggers of Toxicity in Online Discussions	Hind Almerekhi, Haewoon Kwak, Joni Salminen, Bernard J. Jansen	In this research, we define toxicity triggers in online discussions as a non-toxic comment that lead to toxic replies.
304	Sampling Query Variations for Learning to Rank to Improve Automatic Boolean Query Generation in Systematic Reviews	Harrisen Scells, Guido Zuccon, Mohamed A. Sharaf, Bevan Koopman	To overcome this, we propose novel query variation sampling methods for training Learning to Rank models to rank queries.
305	Learning Temporal Interaction Graph Embedding via Coupled Memory Networks	Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Chengwei Yao, Zhao Li, Can Wang	In this paper, we propose a novel framework named TigeCMN to learn node representations from a sequence of temporal interactions.
306	Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction	Wen Wang, Wei Zhang, Shukai Liu, Qi Liu, Bo Zhang, Leyu Lin, Hongyuan Zha	To overcome these limitations, we propose a novel Multi-relational Graph Neural Network model for Session-based target behavior Prediction, namely MGNN-SPred for short.
307	An Empirical Study of Android Security Bulletins in Different Vendors	Sadegh Farhang, Mehmet Bahadir Kirdan, Aron Laszka, Jens Grossklags	In this paper, we perform a comprehensive study of 3,171 Android-related vulnerabilities and study to which degree they are reflected in the Android security bulletin, as well as in the security bulletins of three leading vendors: Samsung, LG, and Huawei.
308	One2Multi Graph Autoencoder for Multi-view Graph Clustering	Shaohua Fan, Xiao Wang, Chuan Shi, Emiao Lu, Ken Lin, Bai Wang	In this paper, we make the first attempt to employ deep learning technique for attributed multi-view graph clustering, and propose a novel task-guided One2Multi graph autoencoder clustering framework.
309	Improved Touch-screen Inputting Using Sequence-level Prediction Generation	Xin Wang, Xu Li, Jinxing Yu, Mingming Sun, Ping Li	In this work, we formally discuss the general problem of input expectation prediction with a touch-screen input method editor (IME).
310	P-Simrank: Extending Simrank to Scale-Free Bipartite Networks	Prasenjit Dey, Kunal Goel, Rahul Agrawal	In this paper, we introduce P-Simrank which extends the idea of Simrank to Scale-free bipartite networks.
311	Using Facebook Data to Measure Cultural Distance between Countries: The Case of Brazilian Cuisine	Carolina Vieira, Filipe Ribeiro, Pedro Olmo Vaz de Melo, Fabricio Benevenuto, Emilio Zagheni	In this study, we measure the global spread of Brazilian culture across countries by exploring Facebook user’s preferences for typical Brazilian dishes through the Facebook Advertising Platform.
312	Structure-Feature based Graph Self-adaptive Pooling	Liang Zhang, Xudong Wang, Hongsheng Li, Guangming Zhu, Peiyi Shen, Ping Li, Xiaoyuan Lu, Syed Afaq Ali Shah, Mohammed Bennamoun	To solve these problems mentioned above, we propose a novel graph self-adaptive pooling method with the following objectives: (1) to construct a reasonable pooled graph topology, structure and feature information of the graph are considered simultaneously, which provide additional veracity and objectivity in node selection; and (2) to make the pooled nodes contain sufficiently effective graph information, node feature information is aggregated before discarding the unimportant nodes; thus, the selected nodes contain information from neighbor nodes, which can enhance the use of features of the unselected nodes.
313	Solving Billion-Scale Knapsack Problems	Xingwen Zhang, Feng Qi, Zhigang Hua, Shuang Yang	This paper examines KPs in a slightly generalized form and shows that they can be solved nearly optimally at scale via distributed algorithms.
314	MineThrottle: Defending against Wasm In-Browser Cryptojacking	Weikang Bian, Wei Meng, Mingxue Zhang	In this work, we propose MineThrottle, a browser-based defense mechanism against Wasm cryptojacking.
315	One Picture Is Worth a Thousand Words? The Pricing Power of Images in e-Commerce	Christof Naumzik, Stefan Feuerriegel	To close this research gap, we suggest a tailored web mining framework, since one must quantify the relative contribution of image content in describing prices ceteris paribus.
316	Learning Model-Agnostic Counterfactual Explanations for Tabular Data	Martin Pawelczyk, Klaus Broelemann, Gjergji Kasneci	Our contribution is twofold.
317	Domain Adaptation with Category Attention Network for Deep Sentiment Analysis	Dongbo Xi, Fuzhen Zhuang, Ganbin Zhou, Xiaohu Cheng, Fen Lin, Qing He	On the one hand, the model regards pivots and non-pivots as unified category attribute words and can automatically capture them to improve the domain adaptation performance; on the other hand, the model makes an attempt at interpretability to learn the transferred category attribute words.
318	Embedding the Scientific Record on the Web: Towards Automating Scientific Discoveries	Yolanda Gil	In this talk, I will describe guidelines for writing scientific papers of the future that embed the scientific record on the Web, and our progress on AI systems capable of using them to systematically explore experiments.
319	Architectures for Autonomy: Towards an Equitable Web of Data in the Age of AI	Sir Nigel Shadbolt	Recent work will be described that seeks to promote an equitable and balanced Web environment in which privacy can be upheld and better mutualities realised.
320	Democratizing Content Creation and Dissemination through AI Technology	Wei-Ying Ma	I will share the current research efforts at ByteDance connected to this emerging new platform through products such as Douyin and TikTok, and discuss the challenges and the direction of our future research.