OpenRaiser/Intern-Atlas huggingface dataset:
Focusing Attention Network for Answer Ranking
Authors: Yufei Xie, Shuchun Liu, Tangren Yao, Yao Peng, Zhao Lu
Venue: WWW
Year: 2019

Answer ranking is an important task in Community Question Answering (CQA), by which “Good” answers should be ranked in the front of “Bad” or “Potentially Useful” answers. The state of the art is the attention-based classification framework that learns the mapping between the questions and the answers. However, we observe that existing attention-based methods perform poorly on complicated question-answer pairs. One major reason is that existing methods cannot get accurate alignments between questions and answers for such pairs. We call the phenomenon “attention divergence”. In this paper, we propose a new attention mechanism, called Focusing Attention Network(FAN), which can automatically draw back the divergent attention by adding the semantic, and metadata features. Our Model can focus on the most important part of the sentence and therefore improve the answer ranking performance. Experimental results on the CQA dataset of SemEval-2016 and SemEval-2017 demonstrate that our method respectively attains 79.38 and 88.72 on MAP and outperforms the Top-1 system in the shared task by 0.19 and 0.29.
No More than What I Post: Preventing Linkage Attacks on Check-in Services
Authors: Fengli Xu, Zhen Tu, Hongjia Huang, Shuhao Chang, Funing Sun, Diansheng Guo, Yong Li
Venue: WWW
Year: 2019

With the flourishing of location based social networks, posting check-ins has become a common practice to document one's daily life. Users usually do not consider check-in records as violations of their privacy. However, through analyzing two real-world check-in datasets, our study shows that check-in records are vulnerable to linkage attacks. To address this problem, we design a partition-and-group framework to integrate the information of check-ins and additional mobility data to attain a novel privacy criterion - kt, l-anonymity. It ensures adversaries with arbitrary background knowledge cannot use check-ins to re-identify users in other anonymous datasets or learning unreported mobility records. The proposed framework achieves favorable performance against state-of-art baseline in terms of improving check-in utility by 24% ~ 57% while providing stronger privacy guarantee at the same time. We believe this study will open a new angle in attaining both privacy-preserving and useful check-in services.
Achieving Differential Privacy and Fairness in Logistic Regression
Authors: Depeng Xu, Shuhan Yuan, Xintao Wu
Venue: WWW
Year: 2019

Machine learning algorithms are used to make decisions in various applications. These algorithms rely on large amounts of sensitive individual information to work properly. Hence, there are sociological concerns about machine learning algorithms on matters like privacy and fairness. Currently, many studies focus on only protecting individual privacy or ensuring fairness of algorithms. However, how to meet both privacy and fairness requirements simultaneously in machine learning algorithms is under exploited. In this paper, we focus on one classic machine learning model, logistic regression, and develop differentially private and fair logistic regression models by combining functional mechanism and decision boundary fairness in a joint form. Theoretical analysis and empirical evaluations demonstrate our approaches effectively achieve both differential privacy and fairness while preserving good utility.
Recurrent Convolutional Neural Network for Sequential Recommendation
Authors: Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Jiajie Xu, Victor S. Sheng, Zhiming Cui, Xiaofang Zhou, Hui Xiong
Venue: WWW
Year: 2019

The sequential recommendation, which models sequential behavioral patterns among users for the recommendation, plays a critical role in recommender systems. However, the state-of-the-art Recurrent Neural Networks (RNN) solutions rarely consider the non-linear feature interactions and non-monotone short-term sequential patterns, which are essential for user behavior modeling in sparse sequence data. In this paper, we propose a novel Recurrent Convolutional Neural Network model (RCNN). It not only utilizes the recurrent architecture of RNN to capture complex long-term dependencies, but also leverages the convolutional operation of Convolutional Neural Network (CNN) model to extract short-term sequential patterns among recurrent hidden states. Specifically, we first generate a hidden state at each time step with the recurrent layer. Then the recent hidden states are regarded as an “image”, and RCNN searches non-linear feature interactions and non-monotone local patterns via intra-step horizontal and inter-step vertical convolutional filters, respectively. Moreover, the output of convolutional filters and the hidden state are concatenated and fed into a fully-connected layer to generate the recommendation. Finally, we evaluate the proposed model using four real-world datasets from various application scenarios. The experimental results show that our model RCNN significantly outperforms the state-of-the-art approaches on sequential recommendation.
Constrained Local Graph Clustering by Colored Random Walk
Authors: Yaowei Yan, Yuchen Bian, Dongsheng Luo, Dongwon Lee, Xiang Zhang
Venue: WWW
Year: 2019

Detecting local graph clusters is an important problem in big graph analysis. Given seed nodes in a graph, local clustering aims at finding subgraphs around the seed nodes, which consist of nodes highly relevant to the seed nodes. However, existing local clustering methods either allow only a single seed node, or assume all seed nodes are from the same cluster, which is not true in many real applications. Moreover, the assumption that all seed nodes are in a single cluster fails to use the crucial information of relations between seed nodes. In this paper, we propose a method to take advantage of such relationship. With prior knowledge of the community membership of the seed nodes, the method labels seed nodes in the same (different) community by the same (different) color. To further use this information, we introduce a color-based random walk mechanism, where colors are propagated from the seed nodes to every node in the graph. By the interaction of identical and distinct colors, we can enclose the supervision of seed nodes into the random walk process. We also propose a heuristic strategy to speed up the algorithm by more than 2 orders of magnitude. Experimental evaluations reveal that our clustering method outperforms state-of-the-art approaches by a large margin.
GraviTIE: Exploratory Analysis of Large-Scale Heterogeneous Image Collections
Authors: Sean T. Yang, Luke Rodriguez, Jevin D. West, Bill Howe
Venue: WWW
Year: 2019

We present GraviTIE (Global Representation and Visualization of Text and Image Embeddings, pronounced ”gravity”), an interactive visualization system for large-scale image datasets. GraviTIE operates on datasets consisting of images equipped with unstructured and semi-structured text, relying on multi-modal unsupervised learning methods to produce an interactive similarity map. Users interact with the similarity map through pan and zoom operations, as well as keyword-oriented queries. GraviTIE makes no assumptions about the form, scale, or content of the data, allowing it to be used for exploratory analysis, assessment of unsupervised learning methods, data curation and quality control, data profiling, and other purposes where flexibility and scalability are paramount. We demonstrate GraviTIE on three real datasets: 500k images from the Russian misinformation dataset from Twitter, 2 million art images, and 5 million scientific figures. A screencast video is available at https://vimeo.com/310511187.
Robust Detection of Cyberbullying in Social Media
Authors: Mengfan Yao
Venue: WWW
Year: 2019

The potentially detrimental effects of cyberbullying have led to the development of numerous automated, data–driven approaches, with an emphasis on classification accuracy. Cyberbullying, as a form of abusive online behavior, although not well–defined, is a repetitive process, i.e., a sequence of aggressive messages sent from a bully to a victim over a period of time with the intent to harm the victim. Existing work has focused on aggression (i.e., using profanity to classify toxic comments independently) as an indicator of cyberbullying, disregarding the repetitive nature of this harassing process. However, raising a cyberbullying alert immediately after an aggressive comment is detected can lead to a high number of false positives. At the same time, three key practical challenges remain unaddressed: (i) detection timeliness, which is necessary to support victims as early as possible, (ii) scalability to the staggering rates at which content is generated in online social networks, (iii) reliance on high quality annotations from human experts for training of highly accurate supervised classifiers. To overcome the challenges associated with cyberbullying detection in online social networks, my PhD thesis focuses on a novel formulation of the online classification problem as sequential hypothesis testing that seeks to drastically reduce the number of features used while maintaining high classification accuracy. To reduce the dependency on labeled datasets, I seek to develop efficient semisupervised methods that extrapolate from a small seed set of expert annotations. Preliminary results are very encouraging, showing significant improvements over the state–of–the–art.
Lightning Talk-Towards Robust Detection of Cyberbullying in Social Media
Authors: Mengfan Yao, Charalampos Chelmis, Daphney-Stavroula Zois
Venue: WWW
Year: 2019

The potentially detrimental effects of cyberbullying have led to the development of numerous automated, data–driven approaches, with emphasis on classification accuracy. Cyberbullying, as a form of abusive online behavior, although not well–defined, is a repetitive process, i.e., a sequence of aggressive messages sent from a bully to a victim over a period of time with the intent to harm the victim. Existing work has focused on harassment (i.e., using profanity to classify toxic comments independently) as an indicator of cyberbullying, disregarding the repetitive nature of this harassing process. However, raising a cyberbullying alert immediately after an aggressive comment is detected can lead to a high number of false positives. At the same time, two key practical challenges remain unaddressed: (i) timeliness: the state–of–the–art relies on a fixed set of features learned during training for offline detection (i.e., after all correspondence has become available), hindering the ability to respond in a timely manner (i.e., as soon as possible) to cyberbullying events. (ii) scalabilty: the scalability of existing methods to the staggering rates at which content is generated (e.g., 95 million photos and videos are shared on Instagram per day1) has largely remained unaddressed. In my lightning talk, I will introduce CONcISE, a novel approach for timely and accurate Cyberbullying detectiON on Instagram media SEssions, that has been accepted for presentation at the main conference [1]. Specifically, I will present a novel two–stage online approach (illustrated in Figure 1) designed to reduce the time to raise a cyberbullying alert by (i) sequentially examining comments as they become available over time, and (ii) minimizing the number of feature evaluations necessary for a decision to be made for each comment. By formalizing the problem as a sequential hypothesis testing problem, a novel algorithm has been developed that satisfies four key properties: accuracy, repetitiveness, timeliness, and efficiency. Extensive experiments on a real–world Instagram dataset with ∼ 4M users and ∼ 10M comments demonstrate the effectiveness of the proposed approach with respect to accuracy, timeliness, efficiency, and robustness, and show that it consistently outperforms the stat–of–the–art, often by a considerable margin.
Snapshot-based Loading Acceleration of Web Apps with Nondeterministic JavaScript Execution
Authors: Ji Hwan Yeo, Chang Hyun Shin, Soo-Mook Moon
Venue: WWW
Year: 2019

JavaScript execution is heavily used during the loading of web apps, taking a substantial portion of the app loading time. To accelerate JavaScript execution, snapshot-based app loading has been proposed [5, 17]. We take a snapshot of the JavaScript objects in the heap at some point during app loading (which we call snapshot point) and save them in a file in advance. Then, we start app loading by copying the objects in the snapshot to the heap directly, skipping JavaScript execution to create those objects. One issue is that the JavaScript execution state at the snapshot point should be the same at every app loading. If JavaScript execution included in the snapshot is involved with some nondeterminism (e.g., use random function or current time/location), snapshot-based app loading might be inapplicable since the loaded state might differ each time. In this paper, we perform an empirical study for the nondeterministic behavior of web apps during app loading. The result shows that nondeterminism is used frequently, but its impact is small from the user's perspectives. Based on this observation, we propose two techniques that can increase the applicability of snapshot. First, we propose two types of snapshot point, which allows saving as much execution state as possible in the snapshot, just before the nondeterministic execution affects the user's perception. Second, we develop a tool to identify those snapshot points by static and dynamic analysis, so that the developer can easily identify them. Our experimental results show that the techniques can accelerate the app loading by 1.81 times, by applying the snapshot that would otherwise be inapplicable, achieving a performance comparable to that of a snapshot that completely ignores the nondeterminism issues.
Doppelgängers on the Dark Web: A Large-scale Assessment on Phishing Hidden Web Services
Authors: Changhoon Yoon, Kwanwoo Kim, Yongdae Kim, Seungwon Shin, Sooel Son
Venue: WWW
Year: 2019

Anonymous network services on the World Wide Web have emerged as a new web architecture, called the Dark Web. The Dark Web has been notorious for harboring cybercriminals abusing anonymity. At the same time, the Dark Web has been a last resort for people who seek freedom of the press as well as avoid censorship. This anonymous nature allows website operators to conceal their identity and thereby leads users to have difficulties in determining the authenticity of websites. Phishers abuse this perplexing authenticity to lure victims; however, only a little is known about the prevalence of phishing attacks on the Dark Web. We conducted an in-depth measurement study to demystify the prevalent phishing websites on the Dark Web. We analyzed the text content of 28,928 HTTP Tor hidden services hosting 21 million dark webpages and confirmed 901 phishing domains. We also discovered a trend on the Dark Web in which service providers perceive dark web domains as their service brands. This trend exacerbates the risk of phishing for their service users who remember only a partial Tor hidden service address. Our work facilitates a better understanding of the phishing risks on the Dark Web and encourages further research on establishing an authentic and reliable service on the Dark Web.
TaxVis: a Visual System for Detecting Tax Evasion Group
Authors: Hongchao Yu, Huan He, Qinghua Zheng, Bo Dong
Venue: WWW
Year: 2019

The demo presents TaxVis, a visual detection system for tax auditor. The system supports tax evasion group detection based on a two-phase detection approach. Different from the pattern matching based methods, this two-phase method can analyze the suspicious groups automatically without artificial extraction of tax evasion patterns. In the first phase, we use a network embedding method node2vec to learn representations that embed corporations from a Corporation Associated Network (CANet), and use LightGBM to calculate a suspicious score for each corporation. In the second phase, the system use three detection rules to analyze the transaction anomaly around the suspicious corporations. According to these transaction anomalies, we can discover potential suspicious tax evasion groups. We demonstrate TaxVis on tax data of Shaanxi province in China to verify the usefulness of the system.
Detecting Low Self-Esteem in Youths from Web Search Data
Authors: Anis Zaman, Rupam Acharyya, Henry A. Kautz, Vincent Silenzio
Venue: WWW
Year: 2019

Online behavior leaves a digital footprint that can be analyzed to reveal our cognitive and psychological state through time. Recognizing these subtle cues can help identify different aspects of mental health, such as low self-esteem, depression, and anxiety. Google's web search engine, used daily by millions of people, logs every search query made by a user, which is accessible through a platform called Google Takeout. Previous researchers have made efforts to detect and predict behaviors associated with depression and anxiety from web data, but only at a population level. This paper fills in the gap of looking into signs of low self-esteem, a condition that work in a vicious cycle with depression and anxiety, at an individual level by looking into Google search history data. We target college students, a population prone to depression, anxiety, and low self-esteem, and ask to take mental health assessment survey along with their individual search history. Textual analysis show that search logs contain strong signals that can identify individuals with current low self-esteem. For example, participants with low self-esteem have fewer searches pertaining to family, friend, and money attributes; and we also observed differences in the search category distribution, over time, when compared with individuals with moderate to high self-esteem. Using these markers we were able to build a probabilistic classifier that can identify low self-esteem conditions, based on search history, with an average F1 score of 0.86.
Your Style Your Identity: Leveraging Writing and Photography Styles for Drug Trafficker Identification in Darknet Markets over Attributed Heterogeneous Information Network
Authors: Yiming Zhang, Yujie Fan, Wei Song, Shifu Hou, Yanfang Ye, Xin Li, Liang Zhao, Chuan Shi, Jiabin Wang, Qi Xiong
Venue: WWW
Year: 2019

Due to its anonymity, there has been a dramatic growth of underground drug markets hosted in the darknet (e.g., Dream Market and Valhalla). To combat drug trafficking (a.k.a. illicit drug trading) in the cyberspace, there is an urgent need for automatic analysis of participants in darknet markets. However, one of the key challenges is that drug traffickers (i.e., vendors) may maintain multiple accounts across different markets or within the same market. To address this issue, in this paper, we propose and develop an intelligent system named uStyle-uID leveraging both writing and photography styles for drug trafficker identification at the first attempt. At the core of uStyle-uID is an attributed heterogeneous information network (AHIN) which elegantly integrates both writing and photography styles along with the text and photo contents, as well as other supporting attributes (i.e., trafficker and drug information) and various kinds of relations. Built on the constructed AHIN, to efficiently measure the relatedness over nodes (i.e., traffickers) in the constructed AHIN, we propose a new network embedding model Vendor2Vec to learn the low-dimensional representations for the nodes in AHIN, which leverages complementary attribute information attached in the nodes to guide the meta-path based random walk for path instances sampling. After that, we devise a learning model named vIdentifier to classify if a given pair of traffickers are the same individual. Comprehensive experiments on the data collections from four different darknet markets are conducted to validate the effectiveness of uStyle-uID which integrates our proposed method in drug trafficker identification by comparisons with alternative approaches.
WWW'19 Tutorial on Explainable Recommendation and Search
Authors: Yongfeng Zhang, Jiaxin Mao, Qingyao Ai
Venue: WWW
Year: 2019

Explainable recommendation and search attempt to develop models or methods that not only generate high-quality recommendation or search results, but also intuitive explanations of the results for users or system designers, where the explanations can be either post-hoc or directly come from an explainable model. Explainable recommendation and search can help to improve the system transparency, persuasiveness, trustworthiness, and effectiveness. This is even more important in personalized search and recommendation scenarios, where users would like to know why a particular product, web page, news report, or friend suggestion exists in his or her own search and recommendation lists. The tutorial focuses on the research and application of explainable recommendation and search algorithms, as well as their application in real-world systems such as search engine, e-commerce and social networks. The tutorial aims at introducing and communicating explainable recommendation and search methods to the community, as well as gathering researchers and practitioners interested in this research direction for discussions, idea communications, and research promotions.
Multilevel Network Alignment
Authors: Si Zhang, Hanghang Tong, Ross Maciejewski, Tina Eliassi-Rad
Venue: WWW
Year: 2019

Network alignment, which aims to find the node correspondence across multiple networks, is a fundamental task in many areas, ranging from social network analysis to adversarial activity detection. The state-of-the-art in the data mining community often view the node correspondence as a probabilistic cross-network node similarity, and thus inevitably introduce an O(n2) lower bound on the computational complexity. Moreover, they might ignore the rich patterns (e.g., clusters) accompanying the real networks. In this paper, we propose a multilevel network alignment algorithm (Moana) which consists of three key steps. It first efficiently coarsens the input networks into their structured representations, and then aligns the coarsest representations of the input networks, followed by the interpolations to obtain the alignment at multiple levels including the node level at the finest granularity. The proposed coarsen-align-interpolate method bears two key advantages. First, it overcomes the O(n2) lower bound, achieving a linear complexity. Second, it helps reveal the alignment between rich patterns of the input networks at multiple levels (e.g., node, clusters, super-clusters, etc.). Extensive experimental evaluations demonstrate the efficacy of the proposed algorithm on both the node-level alignment and the alignment among rich patterns (e.g., clusters) at different granularities.
Neural IR Meets Graph Embedding: A Ranking Model for Product Search
Authors: Yuan Zhang, Dong Wang, Yan Zhang
Venue: WWW
Year: 2019

Recently, neural models for information retrieval are becoming increasingly popular. They provide effective approaches for product search due to their competitive advantages in semantic matching. However, it is challenging to use graph-based features, though proved very useful in IR literature, in these neural approaches. In this paper, we leverage the recent advances in graph embedding techniques to enable neural retrieval models to exploit graph-structured data for automatic feature extraction. The proposed approach can not only help to overcome the long-tail problem of click-through data, but also incorporate external heterogeneous information to improve search results. Extensive experiments on a real-world e-commerce dataset demonstrate significant improvement achieved by our proposed approach over multiple strong baselines both as an individual retrieval model and as a feature used in learning-to-rank frameworks.