OpenRaiser/Intern-Atlas huggingface dataset:
Response Selection for a Virtual Counsellor
Authors: John Lee, Baikun Liang
Venue: WWW
Year: 2021

Chatbots are increasingly used for delivering mental health assistance. As part of our effort to develop a chatbot on academic and social issues for Cantonese-speaking students, we have constructed a dataset of 1,028 post-reply pairs on test anxiety and loneliness. The posts, harvested from Cantonese social media, are manually classified to a symptom category drawn from counselling literature; the replies are human-crafted, offering brief advice for each post. For response selection, the chatbot predicts the quality of a candidate post-reply pair with a regression model. During training, the symptom categories were used as proxies of reply relevance. In experiments, this approach improved response selection accuracy over a binary classification model and a weakly supervised regression model. This result suggests that manual annotation of symptom category can help boost the performance of a counsellor chatbot.
Collocating News Articles with Structured Web Tables✱
Authors: Alyssa Lees, Luciano Barbosa, Flip Korn, Levy de Souza Silva, You Wu, Cong Yu
Venue: WWW
Year: 2021

In today’s news deluge, it can often be overwhelming to understand the significance of a news article or verify the facts within. One approach to address this challenge is to identify relevant data so that crucial statistics or facts can be highlighted for the user to easily digest, and thus improve the user’s comprehension of the news story in a larger context. In this paper, we look toward structured tables on the Web, especially the high quality data tables from Wikipedia, to assist in news understanding. Specifically, we aim to automatically find tables related to a news article. For that, we leverage the content and entities extracted from news articles and their matching tables to fine-tune a Bidirectional Transformers (BERT) model. The resulting model is, therefore, an encoder tailored for article-to-table match. To find the matching tables for a given news article, the fine-tuned BERT model encodes each table in the corpus and the news article into their respective embedding vectors. The tables with the highest cosine similarities to the news article in this new representation space are considered the possible matches. Comprehensive experimental analyses show that the new approach significantly outperforms the baselines over a large, weakly-labeled, dataset obtained from Web click logs as well as a small, crowdsourced, evaluation set. Specifically, our approach achieves near 90% accuracy@5 as opposed to baselines varying between 30% and 64%.
Counterfactual-Augmented Data for Multi-Hop Knowledge Base Question Answering
Authors: Yingting Li
Venue: WWW
Year: 2021

The rise of the counterfactual concept promoted the study of reasoning, and we applied it to Knowledge Base Question Answering (KBQA) multi-hop reasoning as a way of data augmentation for the first time. Intuitively, we propose a model-agnostic Counterfactual Samples Synthesizing(CSS) training scheme. The CSS uses two augmentation methods Q-CSS and T-CSS to augment the training set. That is, for each training instance, we create two augmented instances, one per augmentation method. Furthermore, perform the Dynamic Answer Equipment(DAE) algorithm to dynamically assign ground-truth answers for the expanded question, constructing counterfactual examples. After training with the supplemented examples, the KBQA model can focus on all key entities and words, which significantly improved model’s sensitivity. Experimental verified the effectiveness of CSS and achieved consistent improvements across settings with different extents of KB incompleteness.
What Happens Behind the Scene? Towards Fraud Community Detection in E-Commerce from Online to Offline
Authors: Zhao Li, Pengrui Hui, Peng Zhang, Jiaming Huang, Biao Wang, Ling Tian, Ji Zhang, Jianliang Gao, Xing Tang
Venue: WWW
Year: 2021

Fraud behavior poses a severe threat to e-commerce platforms and anti-fraud systems have become indispensable infrastructure of these platforms. Recently, there have been a large number of fraud detection models proposed to monitor online purchasing transactions and extract hidden fraud patterns. Thanks to these fraud detection models, we have observed a significant reduction of committed frauds in the last several years. However, there have been an increasing number of malicious sellers on e-commerce platforms, according to our recent statistics, who purposely circumvent these online fraud detection systems by transferring their fake purchasing behaviors from online to offline. This way, the effectiveness of our existing fraud detection system built based upon online transactions is compromised. To solve this problem, we study in this paper a new problem, called offline fraud community detection, which can greatly strengthen our existing fraud detection systems. We propose a new FRaud COmmunity Detection from Online to Offline (FRODO) framework which combines the strength of both online and offline data views, especially the offline spatial-temporal data, for fraud community discovery. Moreover, a new Multi-view Heterogeneous Graph Neural Network model is proposed within our new FRODO framework which can find anomalous graph patterns such as biclique communities through only a small number of black seeds, i.e., a small number of labeled fraud users. The seeds are processed by a streamlined pipeline of three components comprised of label propagation for a high coverage, multi-view heterogeneous graph neural networks for high-risky fraud user recognition, and spatial-temporal network reconstruction and mining for offline fraud community detection. The extensive experimental results on a large real-life Taobao network, with 20 millions of users, 5 millions of product items and 30 millions of transactions, demonstrate the good effectiveness of the proposed methods.
Consistent Sampling Through Extremal Process
Authors: Ping Li, Xiaoyun Li, Gennady Samorodnitsky, Weijie Zhao
Venue: WWW
Year: 2021

The1 Jaccard similarity has been widely used in search and machine learning, especially in industrial practice. For binary (0/1) data, the Jaccard similarity is often called the “resemblance” and the method of minwise hashing has been the standard tool for computing resemblances in massive data. For general weighted data, the commonly used sampling algorithm for computing the (weighted) Jaccard similarity is the Consistent Weighted Sampling (CWS). A convenient (and perhaps also mysterious) implementation of CWS is the so-called “0-bit CWS” published in KDD 2015 [31], which, in this paper, we refer to as the “relaxed CWS” and was purely an empirical observation without theoretical justification. The difficulty in the analysis of the “relaxed CWS” is due to the complicated probability problem, which we could not resolve at this point. In this paper, we propose using extremal processes to generate samples for estimating the Jaccard similarity. Surprisingly, the proposed “extremal sampling” (ES) scheme makes it possible to analyze the “relaxed ES” variant. Through some novel probability endeavours, we are able to rigorously compute the bias of the “relaxed ES” which, to a good extent, explains why the “relaxed ES” works so well and when it does not in extreme corner cases. Interestingly, compared with CWS, the resultant algorithm only involves counting and does not need sophisticated mathematical operations (as required by CWS). It is therefore not surprising that the proposed ES scheme is actually noticeably faster than CWS. Although ES is different from CWS (and other algorithms in the literature for estimating the Jaccard similarity), in retrospect ES is indeed closely related to CWS. This paper provides the much needed insight which connects CWS with extremal processes. This insight may help understand CWS (and variants), and might help develop new algorithms for similarity estimation, in future research.
LChecker: Detecting Loose Comparison Bugs in PHP
Authors: Penghui Li, Wei Meng
Venue: WWW
Year: 2021

Weakly-typed languages such as PHP support loosely comparing two operands by implicitly converting their types and values. Such a language feature is widely used but can also pose severe security threats. In certain conditions, loose comparisons can cause unexpected results, leading to authentication bypass and other functionality problems. In this paper, we present the first in-depth study of such loose comparison bugs. We develop LChecker, a system to statically detect PHP loose comparison bugs. It employs a context-sensitive inter-procedural data-flow analysis together with several new techniques. We also enhance the PHP interpreter to help dynamically validate the detected bugs. Our evaluation shows that LChecker can both effectively and efficiently detect PHP loose comparison bugs with a reasonably low false-positive rate. It also successfully detected all previously known bugs in our evaluation dataset with no false negative. Using LChecker, we discovered 42 new loose comparison bugs and were assigned 9 new CVE IDs.
On the Feasibility of Automated Built-in Function Modeling for PHP Symbolic Execution
Authors: Penghui Li, Wei Meng, Kangjie Lu, Changhua Luo
Venue: WWW
Year: 2021

Symbolic execution has been widely applied in detecting vulnerabilities in web applications. Modeling language-specific built-in functions is essential for symbolic execution. Since built-in functions tend to be complicated and are typically implemented in low-level languages, a common strategy is to manually translate them into the SMT-LIB language for constraint solving. Such translation requires an excessive amount of human effort and deep understandings of the function behaviors. Incorrect translation can invalidate the final results. This problem aggravates in PHP applications because of their cross-language nature, i.e., , the built-in functions are written in C, but the rest code is in PHP. In this paper, we explore the feasibility of automating the process of modeling PHP built-in functions for symbolic execution. We synthesize C programs by transforming the constraint solving task in PHP symbolic execution into a C-compliant format and integrating them with C implementations of the built-in functions. We apply symbolic execution on the synthesized C program to find a feasible path, which gives a solution that can be applied to the original PHP constraints. In this way, we automate the modeling of built-in functions in PHP applications. We thoroughly compare our automated method with the state-of-the-art manual modeling tool. The evaluation results demonstrate that our automated method is more accurate with a higher function coverage, and can exploit a similar number of vulnerabilities. Our empirical analysis also shows that the manual and automated methods have different strengths, which complement each other in certain scenarios. Therefore, the best practice is to combine both of them to optimize the accuracy, correctness, and coverage of symbolic execution.
Effective Named Entity Recognition with Boundary-aware Bidirectional Neural Networks
Authors: Fei Li, Zheng Wang, Siu Cheung Hui, Lejian Liao, Dandan Song, Jing Xu
Venue: WWW
Year: 2021

Named Entity Recognition (NER) is a fundamental problem in Natural Language Processing and has received much research attention. Although the current neural-based NER approaches have achieved the state-of-the-art performance, they still suffer from one or more of the following three problems in their architectures: (1) boundary tag sparsity, (2) lacking of global decoding information; and (3) boundary error propagation. In this paper, we propose a novel Boundary-aware Bidirectional Neural Networks (Ba-BNN) model to tackle these problems for neural-based NER. The proposed Ba-BNN model is constructed based on the structure of pointer networks for tackling the first problem on boundary tag sparsity. Moreover, we also use a boundary-aware binary classifier to capture the global decoding information as input to the decoders. In the Ba-BNN model, we propose to use two decoders to process the information in two different directions (i.e., from left-to-right and right-to-left). The final hidden states of the left-to-right decoder are obtained by incorporating the hidden states of the right-to-left decoder in the decoding process. In addition, a boundary retraining strategy is also proposed to help reduce boundary error propagation caused by the pointer networks in boundary detection and entity classification. We have conducted extensive experiments based on three NER benchmark datasets. The performance results have shown that the proposed Ba-BNN model has outperformed the current state-of-the-art models.
Robust Android Malware Detection against Adversarial Example Attacks
Authors: Heng Li, Shiyao Zhou, Wei Yuan, Xiapu Luo, Cuiying Gao, Shuiyan Chen
Venue: WWW
Year: 2021

Adversarial examples pose severe threats to Android malware detection because they can render the machine learning based detection systems useless. How to effectively detect Android malware under various adversarial example attacks becomes an essential but very challenging issue. Existing adversarial example defense mechanisms usually rely heavily on the instances or the knowledge of adversarial examples, and thus their usability and effectiveness are significantly limited because they often cannot resist the unseen-type adversarial examples. In this paper, we propose a novel robust Android malware detection approach that can resist adversarial examples without requiring their instances or knowledge by jointly investigating malware detection and adversarial example defenses. More precisely, our approach employs a new VAE (variational autoencoder) and an MLP (multi-layer perceptron) to detect malware, and combines their detection outcomes to make the final decision. In particular, we share a feature extraction network between the VAE and the MLP to reduce model complexity and design a new loss function to disentangle the features of different classes, hence improving detection performance. Extensive experiments confirm our model’s advantage in accuracy and robustness. Our method outperforms 11 state-of-the-art robust Android malware detection models when resisting 7 kinds of adversarial example attacks.
Fine-Grained Urban Flow Prediction
Authors: Yuxuan Liang, Kun Ouyang, Junkai Sun, Yiwei Wang, Junbo Zhang, Yu Zheng, David S. Rosenblum, Roger Zimmermann
Venue: WWW
Year: 2021

Urban flow prediction benefits smart cities in many aspects, such as traffic management and risk assessment. However, a critical prerequisite for these benefits is having fine-grained knowledge of the city. Thus, unlike previous works that are limited to coarse-grained data, we extend the horizon of urban flow prediction to fine granularity which raises specific challenges: 1) the predominance of inter-grid transitions observed in fine-grained data makes it more complicated to capture the spatial dependencies among grid cells at a global scale; 2) it is very challenging to learn the impact of external factors (e.g., weather) on a large number of grid cells separately. To address these two challenges, we present a Spatio-Temporal Relation Network (STRN) to predict fine-grained urban flows. First, a backbone network is used to learn high-level representations for each cell. Second, we present a Global Relation Module (GloNet) that captures global spatial dependencies much more efficiently compared to existing methods. Third, we design a Meta Learner that takes external factors and land functions (e.g., POI density) as inputs to produce meta knowledge and boost model performances. We conduct extensive experiments on two real-world datasets. The results show that STRN reduces the errors by 7.1% to 11.5% compared to the state-of-the-art method while using much fewer parameters. Moreover, a cloud-based system called UrbanFlow 3.0 has been deployed to show the practicality of our approach.
Multi-domain Dialogue State Tracking with Recursive Inference
Authors: Lizi Liao, Tongyao Zhu, Le Hong Long, Tat-Seng Chua
Venue: WWW
Year: 2021

Multi-domain dialogue state tracking (DST) is a critical component for monitoring user goals during the course of an interaction. Existing approaches have relied on dialogue history indiscriminately or updated on the most recent turns incrementally. However, in spite of modeling it based on fixed ontology or open vocabulary, the former setting violates the interactive and progressing nature of dialogue, while the later easily gets affected by the error accumulation conundrum. Here, we propose a Recursive Inference mechanism (ReInf) to resolve DST in multi-domain scenarios that call for more robust and accurate tracking capability. Specifically, our agent reversely reviews the dialogue history until the agent has pinpointed sufficient turns confidently for slot value prediction. It also recursively factors in potential dependencies among domains and slots to further solve the co-reference and value sharing problems. The quantitative and qualitative experimental results on the MultiWOZ 2.1 corpus demonstrate that the proposed ReInf not only outperforms the state-of-the-art methods, but also achieves reasonable turn reference and interpretable slot co-reference.
REST: Reciprocal Framework for Spatiotemporal-coupled Predictions
Authors: Haozhe Lin, Yushun Fan, Jia Zhang, Bing Bai
Venue: WWW
Year: 2021

In recent years, Graph Convolutional Networks (GCNs) have been applied to benefit spatiotemporal predictions. The current shell for spatiotemporal predictions often relies heavily on the quality of handcraft, fixed graphical structures, however, we argue that such a paradigm could be expensive and sub-optimal in many applications. To raise the bar, this paper proposes to jointly mine the spatial dependencies and model temporal patterns in a coupled framework, i.e., to make spatiotemporal-coupled predictions. We come up with a novel Reciprocal SpatioTemporal (REST) framework, which introduces Edge Inference Networks (EINs) to couple with GCNs. From the temporal side to the spatial side, EINs infer spatial dependencies among time series vertices and generate multi-modal directed weighted graphs to serve GCNs. And from the temporal side to the spatial side, GCNs utilize these spatial dependencies to make predictions and then introduce feedback to optimize EINs. The REST framework is incrementally trained for higher performance of spatiotemporal prediction, powered by the reciprocity between its comprised two components from such an iterative joint learning process. Additionally, to maximize the power of the REST framework, we design a phased heuristic approach, which effectively stabilizes training procedure and prevents early-stop. Extensive experiments on two real-world datasets have demonstrated that the proposed REST framework significantly outperforms baselines, and can learn meaningful spatial dependencies beyond predefined graphical structures.
An Investigation of Identity-Account Inconsistency in Single Sign-On
Authors: Guannan Liu, Xing Gao, Haining Wang
Venue: WWW
Year: 2021

Single Sign-On (SSO) has been widely adopted for online authentication due to its favorable usability and security. However, it also introduces a single point of failure since all service providers fully trust the identity of a user created by the SSO identity provider. In this paper, we investigate the identity-account inconsistency threat, a new SSO vulnerability that can cause the compromise of online accounts. The vulnerability exists because current SSO systems highly rely on a user’s email address to bind an account with a real identity, but ignore the fact that email addresses might be reused by other users. We reveal that under the SSO authentication, such inconsistency allows an adversary controlling a reused email address to take over associated online accounts without knowing any credentials like passwords. Specifically, we first conduct a measurement study on the account management policies for multiple cloud email providers, showing the feasibility of acquiring previously used email accounts. We further perform a systematic study on 100 popular websites using the Google business email service with our own domain address and demonstrate that most online accounts can be compromised by exploiting this inconsistency vulnerability. To shed light on email reuse in the wild, we analyze the commonly used naming conventions that lead to a wide existence of potential email address collisions, and conduct a case study on the account policies of U.S. universities. Finally, we propose several useful practices for end-users, service providers, and identity providers to protect against this identity-account inconsistency threat.
Bridging the Gap between von Neumann Graph Entropy and Structural Information: Theory and Applications
Authors: Xuecheng Liu, Luoyi Fu, Xinbing Wang
Venue: WWW
Year: 2021

The von Neumann graph entropy (VNGE) is a measure of graph complexity based on the Laplacian spectrum. It has recently found applications in various learning tasks driven by networked data. However, it is computationally demanding and hard to interpret using simple structural patterns. Due to the close relation between Lapalcian spectrum and degree sequence, we conjecture that the structural information, defined as the Shannon entropy of the normalized degree sequence, might approximate VNGE well. In this work, we thereby study the difference between the structural information and VNGE named as entropy gap. Based on the knowledge that the degree sequence is majorized by the Laplacian spectrum, we for the first time prove the entropy gap is between 0 and log 2e in any undirected unweighted graphs. Consequently we certify that the structural information is a good approximation of VNGE that achieves provable accuracy, scalability, and interpretability simultaneously. This approximation is further applied to two entropy-related tasks: network design and graph similarity measure, where novel graph similarity measure and fast algorithms are proposed. Our experimental results on graphs of various scales and types show that the very small entropy gap readily applies to a wide range of graphs and weighted graphs. As an approximation of VNGE, the structural information is the only one that achieves both high efficiency and high accuracy among the prominent methods. It is at least two orders of magnitude faster than SLaQ [40] with comparable accuracy. Our structural information based methods also exhibit superior performance in two entropy-related tasks.