OpenRaiser/Intern-Atlas huggingface dataset:
Reinforced Negative Sampling over Knowledge Graph for Recommendation
Authors: Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Meng Wang, Tat-Seng Chua
Venue: WWW
Year: 2020

Properly handling missing data is a fundamental challenge in recommendation. Most present works perform negative sampling from unobserved data to supply the training of recommender models with negative signals. Nevertheless, existing negative sampling strategies, either static or adaptive ones, are insufficient to yield high-quality negative samples — both informative to model training and reflective of user real needs. 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. Towards this end, we develop a new negative sampling model, Knowledge Graph Policy Network (KGPolicy), which works as a reinforcement learning agent to explore high-quality negatives. Specifically, by conducting our designed exploration operations, it navigates from the target positive interaction, adaptively receives knowledge-aware negative signals, and ultimately yields a potential negative item to train the recommender. We tested on a matrix factorization (MF) model equipped with KGPolicy, and it achieves significant improvements over both state-of-the-art sampling methods like DNS [39] and IRGAN [30], and KG-enhanced recommender models like KGAT [32]. Further analyses from different angles provide insights of knowledge-aware sampling. We release the codes and datasets at https://github.com/xiangwang1223/kgpolicy.
Hybrid-Voting: A Hybrid Structured Electronic Voting System
Authors: Ping-Lun Wang, Shao-Hong Yang, Hsu-Chun Hsiao
Venue: WWW
Year: 2020

Recent development of electronic voting (e-voting) systems has been focusing on blockchain-based design [4]. Despite blockchain’s advantages in public verifiability, existing blockchain-based voting systems are impractical due to the high block time and transaction costs of the underlying blockchain. To achieve verifiability and efficiency simultaneously, we propose Hybrid-Voting, a hybrid structured e-voting system that combines an untrusted centralized server with smart contracts on Ethereum blockchain. In addition, voter anonymity and privacy are guaranteed by using short linkable ring signature and ElGamal encryption. Our evaluation shows that Hybrid-Voting can support 10k voters by using one commodity computer, and the cost per voter is less than one US dollar, much lower than the cost per voter in today’s elections [1].
Q-BERT: A BERT-based Framework for Computing SPARQL Similarity in Natural Language
Authors: Chunpei Wang, Xiaowang Zhang
Venue: WWW
Year: 2020

In this paper, we present a pre-trained transformer network Q-BERT, in which siamese network architecture is employed to produce semantically meaningful embeddings of SPARQL queries to be compared via cosine-similarity. A core idea of Q-BERT is to put SPARQL query into the category of natural language to ensure that each entity mention or relation phrase in different knowledge bases has the same vector representation. Moreover, based on Q-BERT, we present a practical approach for the SPARQL query-empty-answer problem by accessing the RDF repository. The experiments on real datasets show the effectiveness and efficiency of Q-BERT.
Earn More Social Attention: User Popularity Based Tag Recommendation System
Authors: Xueting Wang, Yiwei Zhang, Toshihiko Yamasaki
Venue: WWW
Year: 2020

Enhancing social popularity of a post (i.e., the number of views or likes) on social network services is important for both ordinary users and companies who want to promote themselves. In this paper, we have implemented an online tagging support system to achieve this using an algorithm that recommends appropriate hashtags considering not only content popularity but also user popularity. The effectiveness of this technology has been verified by actually uploading photos with recommended hashtags on a real social network service.
DyCRS: Dynamic Interpretable Postoperative Complication Risk Scoring
Authors: Wen Wang, Han Zhao, Honglei Zhuang, Nirav Shah, Rema Padman
Venue: WWW
Year: 2020

Early identification of patients at risk for postoperative complications can facilitate timely workups and treatments and improve health outcomes. Currently, a widely-used surgical risk calculator online web system developed by the American College of Surgeons (ACS) uses patients’ static features, e.g. gender, age, to assess the risk of postoperative complications. However, the most crucial signals that reflect the actual postoperative physical conditions of patients are usually real-time dynamic signals, including the vital signs of patients (e.g., heart rate, blood pressure) collected from postoperative monitoring. 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. DyCRS is based on adaptations of the Hidden Markov Model (HMM) that captures hidden states as well as observable states to generate a real-time, probabilistic, complication risk score. Evaluating our model using electronic health record (EHR) on elective Colectomy surgery from a major health system, we show that DyCRS significantly outperforms the state-of-the-art ACS calculator and real-time predictors with 50.16% area under precision-recall curve (AUCPRC) gain on average in terms of detection effectiveness. In terms of earliness, our DyCRS can predict 15hrs55mins earlier on average than clinician’s diagnosis with the recall of 60% and precision of 55%. Furthermore, Our DyCRS can extract interpretable patients’ stages, which are consistent with previous medical postoperative complication studies. We believe that our contributions demonstrate significant promise for developing a more accurate, robust and interpretable postoperative complication risk scoring system, which can benefit more than 50 million annual surgeries in the US by substantially lowering adverse events and healthcare costs.
De-Kodi: Understanding the Kodi Ecosystem
Authors: Marc Anthony Warrior, Yunming Xiao, Matteo Varvello, Aleksandar Kuzmanovic
Venue: WWW
Year: 2020

Free and open source media centers are currently experiencing a boom in popularity for the convenience and flexibility they offer users seeking to remotely consume digital content. This newfound fame is matched by increasing notoriety—for their potential to serve as hubs for illegal content—and a presumably ever-increasing network footprint. It is fair to say that a complex ecosystem has developed around Kodi, composed of millions of users, thousands of “add-ons”—Kodi extensions from 3rd-party developers—and content providers. Motivated by these observations, this paper conducts the first analysis of the Kodi ecosystem. Our approach is to build “crawling” software around Kodi which can automatically install an addon, explore its menu, and locate (video) content. This is challenging for many reasons. First, Kodi largely relies on visual information and user input which intrinsically complicates automation. Second, no central aggregators for Kodi addons exist. Third, the potential sheer size of this ecosystem requires a highly scalable crawling solution. 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. With de-Kodi, we discovered and tested over 9,000 distinct Kodi addons. Our results demonstrate de-Kodi, which we make available to the general public, to be an essential asset in studying one of the largest multimedia platforms in the world. Our work further serves as the first ever transparent and repeatable analysis of the Kodi ecosystem at large.
The Pod People: Understanding Manipulation of Social Media Popularity via Reciprocity Abuse
Authors: Janith Weerasinghe, Bailey Flanigan, Aviel J. Stein, Damon McCoy, Rachel Greenstadt
Venue: WWW
Year: 2020

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. One method of subversion that has recently emerged occurs when users form groups, called pods, to facilitate reciprocity abuse, where each member reciprocally interacts with content posted by other members of the group. We collect 1.8 million Instagram posts that were posted in pods hosted on Telegram. We first summarize the properties of these pods and how they are used, uncovering that they are easily discoverable by Google search and have a low barrier to entry. We then create two machine learning models for detecting Instagram posts that have gained interaction through two different kinds of pods, achieving 0.91 and 0.94 AUC, respectively. Finally, we find that pods are effective tools for increasing users’ Instagram popularity, we estimate that pod utilization leads to a significantly increased level of likely organic comment interaction on users’ subsequent posts.
Go See a Specialist? Predicting Cybercrime Sales on Online Anonymous Markets from Vendor and Product Characteristics
Authors: Rolf van Wegberg, Fieke Miedema, Ugur Akyazi, Arman Noroozian, Bram Klievink, Michel van Eeten
Venue: WWW
Year: 2020

Many cybercriminal entrepreneurs lack the skills and techniques to provision certain parts of their business model, leading them to outsource these parts to specialized criminal vendors. Online anonymous markets, from Silk Road to AlphaBay, have been used to search for these products and contract with their criminal vendors. While one listing of a product generates high sales numbers, another identical listing fails to sell. In this paper, we investigate which factors determine the performance of cybercrime products. To answer this question, we analyze scraped data on the business-to-business cybercrime segments of AlphaBay (2015-2017), consisting of 7,543 listings from 1,339 vendors, sold at least 126,934 times. We construct new variables to capture product differentiators and price. We capture the influence of vendor characteristics by identifying five distinct vendor profiles based on latent profile analysis of six properties. We leverage these product and vendor characteristics to empirically predict the performance of cybercrime products, whilst controlling for the lifespan and type of solution. Consistent with earlier insights into carding forums, we identify prevalent product differentiators to be influencing the relative success of a product. While all these product differentiators do correlate significantly with product performance, their explanatory power is lower than that of vendor profiles. When outsourcing, the vendor seems to be of more importance to the buyers than product differentiators.
Examining Protest as An Intervention to Reduce Online Prejudice: A Case Study of Prejudice Against Immigrants
Authors: Kai Wei, Yu-Ru Lin, Muheng Yan
Venue: WWW
Year: 2020

There has been a growing concern about online users using social media to incite prejudice and hatred against other individuals or groups. While there has been research in developing automated techniques to identify online prejudice acts and hate speech, how to effectively counter online prejudice remains a societal challenge. Social protests, on the other hand, have been frequently used as an intervention for countering prejudice. However, research to date has not examined the relationship between protests and online prejudice. Using large-scale panel data collected from Twitter, we examine the changes in users’ tweeting behaviors relating to prejudice against immigrants following recent protests in the U.S. on immigration related topics. This is the first empirical study examining the effect of protests on reducing online prejudice. Our results show that there were both negative and positive changes in the measured prejudice after a protest, suggesting protest might have a mixed effect on reducing prejudice. We further identify users who are likely to change (or resist change) after a protest. This work contributes to the understanding of online prejudice and its intervention effect. The findings of this research have implications for designing targeted intervention.
Scalable Anti-TrustRank with Qualified Site-level Seeds for Link-based Web Spam Detection
Authors: Joyce Jiyoung Whang, Yeonsung Jung, Seonggoo Kang, Dongho Yoo, Inderjit S. Dhillon
Venue: WWW
Year: 2020

Web spam detection is one of the most important and challenging tasks in web search. Since web spam pages tend to have a lot of spurious links, many web spam detection algorithms exploit the hyperlink structure between the web pages to detect the spam pages. In this paper, we conduct a comprehensive analysis of the link structure of web spam using real-world web graphs to systemically investigate the characteristics of the link-based web spam. By exploring the structure of the page-level graph as well as the site-level graph, we propose a scalable site-level seeding methodology for the Anti-TrustRank (ATR) algorithm. The key idea is to map a website into a feature space where we learn a classifier to prioritize the websites so that we can effectively select a set of good seeds for the ATR algorithm. This seeding method enables the ATR algorithm to detect the largest number of spam pages among the competitive baseline methods. Furthermore, we design work-efficient asynchronous ATR algorithms which are able to significantly reduce the computational cost of the traditional ATR algorithm without degrading the performance in detecting spam pages while guaranteeing the convergence.
Designing Social Machines for Tackling Online Disinformation
Authors: Antonia Wild, Andrei Ciortea, Simon Mayer
Venue: WWW
Year: 2020

Traditional news outlets as carriers and distributors of information have been challenged by online social networks with regards to their gate-keeping function. We believe that only a combined effort of people and machines will be able to curb so-called “fake news” at scale in a decentralized Web. In this position paper, we propose an approach to design social machines that coordinate human- and machine-driven credibility assessment of information on a decentralized Web. To this end, we defined a fact-checking process that draws upon ongoing efforts for tackling disinformation on the Web, and we formalized this process as a multi-agent organisation for curating W3C Web Annotations. We present the current state of our prototypical implementation in the form of a browser plugin that builds on the Hypothesis annotation platform and the JaCaMo multi-agent platform. Our social machines would span across the Web to enable collaboration in form of public discourse, thereby increasing the transparency and accountability of information.
DeepEncyclolink: A Cross-Encyclopedia, Cross-language Article-Linking System Based on Deep Learning
Authors: Ka Ming Wong, Chia-Min Chuang, Chun-Kai Wu, Yu-Chun Wang, Richard Tzong-Han Tsai
Venue: WWW
Year: 2020

DeepEncyclolink is a web-based system for linking pairs of corresponding articles from different encyclopedias in different languages. The core technology of DeepEncyclolink is based on paragraph embeddings calculated by a long-short-term-memory network with attention. Compared to our previous feature-based machine learning model, DeepEncyclolink has made great strides in coverage and accuracy. DeepEncyclolink offers an easy-to-use user interface and is supported by a powerful service backend for retrieving and selecting equivalent articles between English Wikipedia and Chinese Baidu Encyclopedia. DeepEncyclolink breaks the barriers of different encyclopedias and different languages, making it easy for users to access knowledge written in various languages.
How Do We Create a Fantabulous Password?
Authors: Simon S. Woo
Venue: WWW
Year: 2020

Although pronounceability can improve password memorability, most existing password generation approaches have not properly integrated the pronounceability of passwords in their designs. In this work, we demonstrate several shortfalls of current pronounceable password generation approaches, and then propose, ProSemPass, a new method of generating passwords that are pronounceable and semantically meaningful. In our approach, users supply initial input words and our system improves the pronounceability and meaning of the user-provided words by automatically creating a portmanteau. To measure the strength of our approach, we use attacker models, where attackers have complete knowledge of our password generation algorithms. We measure strength in guess numbers and compare those with other existing password generation approaches. Using a large-scale IRB-approved user study with 1,563 Amazon MTurkers over 9 different conditions, our approach achieves a 30% higher recall than those from current pronounceable password approaches, and is stronger than the offline guessing attack limit.
I've Got Your Packages: Harvesting Customers' Delivery Order Information using Package Tracking Number Enumeration Attacks
Authors: Simon S. Woo, Hanbin Jang, Woojung Ji, Hyoungshick Kim
Venue: WWW
Year: 2020

A package tracking number (PTN) is widely used to monitor and track a shipment. Through the lenses of security and privacy, however, a package tracking number can possibly reveal certain personal information, leading to security and privacy breaches. 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. Moreover, we discovered that PTNs are highly structured and predictable. Therefore, customers’ personal data can be massively collected via PTN enumeration attacks. We analyzed more than one million package tracking records obtained from Fedex, DHL, and UPS, and showed that within 5 attempts, an attacker can efficiently guess more than 90% of PTNs for FedEx and DHL, and close to 50% of PTNs for UPS. In addition, we present two practical attack scenarios: 1) to infer business transactions information and 2) to uniquely identify recipients. Also, we found that more than 109 recipients can be uniquely identified with less than 10 comparisons by linking the PTN information with the online people search service, Whitepages.
Hierarchically Structured Transformer Networks for Fine-Grained Spatial Event Forecasting
Authors: Xian Wu, Chao Huang, Chuxu Zhang, Nitesh V. Chawla
Venue: WWW
Year: 2020

Spatial event forecasting is challenging and crucial for urban sensing scenarios, which is beneficial for a wide spectrum of spatial-temporal mining applications, ranging from traffic management, public safety, to environment policy making. In spite of significant progress has been made to solve spatial-temporal prediction problem, most existing deep learning based methods based on a coarse-grained spatial setting and the success of such methods largely relies on data sufficiency. In many real-world applications, predicting events with a fine-grained spatial resolution do play a critical role to provide high discernibility of spatial-temporal data distributions. However, in such cases, applying existing methods will result in weak performance since they may not well capture the quality spatial-temporal representations when training triple instances are highly imbalanced across locations and time. 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. In our STtrans framework, the first-stage transformer module discriminates different types of region and time-wise relations. To make the latent spatial-temporal representations be reflective of the relational structure between categories, we further develop a cross-category fusion transformer network to endow STtrans with the capability to preserve the semantic signals in a fully dynamic manner. Finally, an adversarial training strategy is introduced to yield a robust spatial-temporal learning under data imbalance. Extensive experiments on real-world imbalanced spatial-temporal datasets from NYC and Chicago demonstrate the superiority of our method over various state-of-the-art baselines.
RNR: A Generic Bayesian-based Framework for Enhancing Top-N Recommender Systems
Authors: Rong-Qin Xu, Ming-Yang Zhou, Hao Liao
Venue: WWW
Year: 2020

In personalized top-N recommender systems, a core task is to design effective methods to measure user-item preference scores and then to suggest, for each user, a small set of personalized items with high scores. However, little attention was paid to the recommendation of low-score user-item links. In this work, based on the Bayesian estimation theory, we propose a novel metric RNR (Recall-to-Noise Ratio) to characterize the ability to recommend both high-score and low-score user-item links. Then we propose a generic framework that leverages RNR to transfer the link scores of the state-of-the-art recommendation methods. Empirical experiments show that the proposed framework could optimally improve the recommendation accuracy.
Learning Topic Map from Large Scale Social Media Data
Authors: Hui-Kuo Yang
Venue: WWW
Year: 2020

Geo-tagged social media data are hugely generated every day. Those content provide rich sources to explore keywords and topics in any region of the world thanks to the widely popularized mobile internet services. The association between geographic regions and their keywords/topics in social media has raised a lot of research attentions. Such association provides important information to event prediction, source detection, and news propagation in various applications. For instance, the disaster control and management, and evaluation of sales marketing campaign are just two of the many examples. The association between regions and keywords/topics are analogous to that between geographic coordinates and spatial features, such as streets intersections, building compounds and so on in a conventional street map, and we propose a new model, called “topic map” to mimic such an analogy and to encode and represent text features with geographic regions that reside in Geo-tagged social media data. Applications based on topic map will be explored during my research, and extensions to temporal data will be investigated further.
Efficient Online Multi-Task Learning via Adaptive Kernel Selection
Authors: Peng Yang, Ping Li
Venue: WWW
Year: 2020

Conventional multi-task model restricts the task structure to be linearly related, which may not be suitable when data is linearly nonseparable. To remedy this issue, we propose a kernel algorithm for online multi-task classification, as the large approximation space provided by reproducing kernel Hilbert spaces often contains an accurate function. Specifically, it maintains a local-global Gaussian distribution over each task model that guides the direction and scale of parameter updates. Nonetheless, optimizing over this space is computationally expensive. Moreover, most multi-task learning methods require accessing to the entire training instances, which is luxury unavailable in the large-scale streaming learning scenario. To overcome this issue, we propose a randomized kernel sampling technique across multiple tasks. Instead of requiring all inputs’ labels, the proposed algorithm determines whether to query a label or not via considering the confidence from the related tasks over label prediction. Theoretically, the algorithm trained on actively sampled labels can achieve a comparable result with one learned on all labels. Empirically, the proposed algorithm is able to achieve promising learning efficacy, while reducing the computational complexity and labeling cost simultaneously.