OpenRaiser/Intern-Atlas huggingface dataset:
PrivIdEx: Privacy Preserving and Secure Exchange of Digital Identity Assets
Authors: Hasini Gunasinghe, Ashish Kundu, Elisa Bertino, Hugo Krawczyk, Suresh Chari, Kapil Singh, Dong Su
Venue: WWW
Year: 2019

User's digital identity information has privacy and security requirements. Privacy requirements include confidentiality of the identity information itself, anonymity of those who verify and consume a user's identity information and unlinkability of online transactions which involve a user's identity. Security requirements include correctness, ownership assurance and prevention of counterfeits of a user's identity information. Such privacy and security requirements, although conflicting, are critical for identity management systems enabling the exchange of users' identity information between different parties during the execution of online transactions. Addressing all such requirements, without a centralized party managing the identity exchange transactions, raises several challenges. This paper presents a decentralized protocol for privacy preserving exchange of users' identity information addressing such challenges. The proposed protocol leverages advances in blockchain and zero knowledge proof technologies, as the main building blocks. We provide prototype implementations of the main building blocks of the protocol and assess its performance and security.
Securing the Deep Fraud Detector in Large-Scale E-Commerce Platform via Adversarial Machine Learning Approach
Authors: Qingyu Guo, Zhao Li, Bo An, Pengrui Hui, Jiaming Huang, Long Zhang, Mengchen Zhao
Venue: WWW
Year: 2019

Fraud transactions are one of the major threats faced by online e-commerce platforms. Recently, deep learning based classifiers have been deployed to detect fraud transactions. Inspired by findings on adversarial examples, this paper is the first to analyze the vulnerability of deep fraud detector to slight perturbations on input transactions, which is very challenging since the sparsity and discretization of transaction data result in a non-convex discrete optimization. Inspired by the iterative Fast Gradient Sign Method (FGSM) for the L8 attack, we first propose the Iterative Fast Coordinate Method (IFCM) for discrete L1 and L2 attacks which is efficient to generate large amounts of instances with satisfactory effectiveness. We then provide two novel attack algorithms to solve the discrete optimization. The first one is the Augmented Iterative Search (AIS) algorithm, which repeatedly searches for effective “simple” perturbation. The second one is called the Rounded Relaxation with Reparameterization (R3), which rounds the solution obtained by solving a relaxed and unconstrained optimization problem with reparameterization tricks. Finally, we conduct extensive experimental evaluation on the deployed fraud detector in TaoBao, one of the largest e-commerce platforms in the world, with millions of real-world transactions. Results show that (i) The deployed detector is highly vulnerable to attacks as the average precision is decreased from nearly 90% to as low as 20% with little perturbations; (ii) Our proposed attacks significantly outperform the adaptions of the state-of-the-art attacks. (iii) The model trained with an adversarial training process is significantly robust against attacks and performs well on the unperturbed data.
A/B Testing at Scale: Accelerating Software Innovation
Authors: Somit Gupta, Ronny Kohavi, Alex Deng, Jeff Omhover, Pawel Janowski
Venue: WWW
Year: 2019

The Internet and the general digitalization of products and operations provides an unprecedented opportunity to accelerate innovation while applying a rigorous and trustworthy methodology for supporting key product decisions. Developers of connected software, including web sites, applications, and devices, can now evaluate ideas quickly and accurately using controlled experiments, also known as A/B tests. From front-end user-interface changes to backend algorithms, from search engines (e.g., Google, Bing, Yahoo!) to retailers (e.g., Amazon, eBay, Etsy) to social networking services (e.g., Facebook, LinkedIn, Twitter) to travel services (e.g., Expedia, Airbnb, Booking.com) to many startups, online controlled experiments are now utilized to make data-driven decisions at a wide range of companies. The theory of a controlled experiment is simple, but for the practitioner the deployment and evaluation of online controlled experiments at scale (100’s of concurrently running experiments) across a variety of web sites, mobile apps, and desktop applications presents many pitfalls and new research challenges. In this tutorial, we will introduce the overall A/B testing methodology, walkthrough use cases using real examples, and then focus on practical and research challenges in scaling experimentation. We will share key lessons learned from scaling experimentation at Microsoft to thousands of experiments per year and outline promising directions for future work.
Personalized Online Spell Correction for Personal Search
Authors: Jai Prakash Gupta, Zhen Qin, Michael Bendersky, Donald Metzler
Venue: WWW
Year: 2019

Spell correction is a must-have feature for any modern search engine in applications such as web or e-commerce search. Typical spell correction solutions used in production systems consist of large indexed lookup tables based on a global model trained across many users over a large scale web corpus or a query log. For search over personal corpora, such as email, this global solution is not sufficient, as it ignores the user's personal lexicon. Without personalization, global spelling fails to correct tail queries drawn from a user's own, often idiosyncratic, lexicon. Personalization using existing algorithms is difficult due to resource constraints and unavailability of sufficient data to build per-user models. In this work, we propose a simple and effective personalized spell correction solution that augments existing global solutions for search over private corpora. Our event driven spell correction candidate generation method is specifically designed with personalization as the key construct. Our novel spell correction and query completion algorithms do not require complex model training and is highly efficient. The proposed solution has shown over 30% click-through rate gain on affected queries when evaluated against a range of strong commercial personal search baselines - Google's Gmail, Drive, and Calendar search production systems.
Predictive Crawling for Commercial Web Content
Authors: Shuguang Han, Bernhard Brodowsky, Przemek Gajda, Sergey Novikov, Mike Bendersky, Marc Najork, Robin Dua, Alexandrin Popescul
Venue: WWW
Year: 2019

Web crawlers spend significant resources to maintain freshness of their crawled data. This paper describes the optimization of resources to ensure that product prices shown in ads in a context of a shopping sponsored search service are synchronized with current merchant prices. We are able to use the predictability of price changes to build a machine learned system leading to considerable resource savings for both the merchants and the crawler. We describe our solution to technical challenges due to partial observability of price history, feedback loops arising from applying machine learned models, and offers in cold start state. Empirical evaluation over large-scale product crawl data demonstrates the effectiveness of our model and confirms its robustness towards unseen data. We argue that our approach can be applicable in more general data pull settings.
Inferring Search Queries from Web Documents via a Graph-Augmented Sequence to Attention Network
Authors: Fred X. Han, Di Niu, Kunfeng Lai, Weidong Guo, Yancheng He, Yu Xu
Venue: WWW
Year: 2019

We study the problem of search query inference from web documents, where a short, comprehensive natural language query is inferred from a long article. Search query generation or inference is of great value to search engines and recommenders in terms of locating potential target users and ranking content. Despite being closely related to other NLP tasks like abstract generation and keyword extraction, we point out that search query inference is, in fact, a new problem, in that the generated natural language query, which consists of a few words, is expected to be comprehensive enough to lead to the click-through of the corresponding document. Therefore, query generation requires an accurate inference of query words, as well as a deeper level of understanding on document semantic structures. Toward this end, we propose a novel generative model called the Graph-augmented Sequence to Attention (G-S2A) network. Adopting an Encoder-Decoder architecture, G-S2A incorporates a sentence-level Graph Convolutional Network (GCN), a keyword-level GCN, as well as a hierarchical recurrent neural network (RNN) into the encoder to generate structural document representations. An attentional Transformer decoder is then applied to combine different types of encoded features to generate a target query. On a query-document dataset from a real-world search engine, our model outperforms several neural generative models on a wide range of metrics.
Cybersafety 2019: The 4th Workshop on Computational Methods in Online Misbehavior
Authors: Richard Han, Neil Shah
Venue: WWW
Year: 2019

We are excited to oer CyberSafety 2019, the FourthWorkshop on Computational Methods in Online Misbehavior, aliated witheWeb Conference 2019. is workshop oers a forum for researchers in academia, industry and government to share novel research on topics broadly related to the urgent theme of cybersafety, devoted to making the Web safer for its users. is is an area of exploding interest as abuse vectors such as social bots, online misinformation and propaganda, user deception and scams, harassment, hate speech, cyberthreats, cyberbullying and more have proliferated in recent years. A particular aim of the workshop is to encourage interdisciplinary research that brings together computer scientists and researchers from other disciplines, and provides a forum for these researchers to share their ideas. is year, we received 23 paper submissions, all of which were reviewed by the Technical Program Commiee (TPC). Based on reviews, the TPC Co-Chairs selected 8 papers for full presentation, and 6 additional papers for short presentation at the workshop, spanning topics that include spam and bot detection, misinformation, harassment, cyberbullying and more. In addition, the workshop will include several keynote talks from industrial and academic speakers on modern challenges and solutions in computational misbehavior. A detailed program is available at hps://cybersafety2019.github.io/ for more details. is iteration of the workshop succeeds three prior ones, co-located with • WWW 2018 hps://cybersafety2018.github.io/ • WWW 2017 hps://cybersafety2017.github.io/ • CIKM 2016 hps://cybersafety2016.github.io/
Spatio-Temporal Capsule-based Reinforcement Learning for Mobility-on-Demand Network Coordination
Authors: Suining He, Kang G. Shin
Venue: WWW
Year: 2019

As an alternative means of convenient and smart transportation, mobility-on-demand (MOD), typified by online ride-sharing and connected taxicabs, has been rapidly growing and spreading worldwide. The large volume of complex traffic and the uncertainty of market supplies/demands have made it essential for many MOD service providers to proactively dispatch vehicles towards ride-seekers. To meet this need effectively, we propose STRide, an MOD coordination-learning mechanism reinforced spatio-temporally with capsules. We formalize the adaptive coordination of vehicles into a reinforcement learning framework. STRide incorporates spatial and temporal distributions of supplies (vehicles) and demands (ride requests), customers' preferences and other external factors. A novel spatio-temporal capsule neural network is designed to predict the provider's rewards based on MOD network states, vehicles and their dispatch actions. This way, the MOD platform adapts itself to the supply-demand dynamics with the best potential rewards. We have conducted extensive data analytics and experimental evaluation with three large-scale datasets (~ 21 million rides from Uber, Yellow Taxis and Didi). STRide is shown to outperform state-of-the-arts, substantially reducing request-rejection rate and passenger waiting time, and also increasing the service provider's profits, often making 30% improvement over state-of-the-arts.
LearnerExp: Exploring and Explaining the Time Management of Online Learning Activity
Authors: Huan He, Qinghua Zheng, Bo Dong
Venue: WWW
Year: 2019

How do learners schedule their online learning? This issue is concerned by both course instructors and researchers, especially in the context of self-paced online learning environment. Many indicators and methods have been proposed to understand and improve the time management of learning activities, however, there are few tools of visualizing, comparing and exploring the time management to gain intuitive understanding. In this demo, we introduce the LearnExp, an interactive visual analytic system designed to explore the temporal patterns of learning activities and explain the relationships between academic performance and these patterns. This system will help instructors to comparatively explore the distribution of learner activities from multiple aspects, and to visually explain the time management of different learner groups with the prediction of learning performance.
Integrating Route Attractiveness Attributes into Tourist Trip Recommendations
Authors: Daniel Herzog, Sherjeel Sikander, Wolfgang Wörndl
Venue: WWW
Year: 2019

Tourist trip recommender systems (RSs) support travelers in identifying the most attractive points of interests (POIss) and combine the POIss along a route for single- or multi-day trips. Most RSs consider only the quality of POIss when searching for the best recommendation. In this work, we introduce a novel approach that also considers the attractiveness of the routes between POIss. For this purpose, we identify a list of important attributes of route attractiveness and explain how to implement our approach using three exemplary attributes. We develop a web application for demonstration purposes and apply it in a small preliminary user study with 16 participants. The results show that the integration of route attractiveness attributes makes most people choose the more attractive route over the shortest path between two POIss. This paper highlights how tourist trip RSs can support smart tourism. Our work aims to encourage further discussion on collecting and providing environmental data in cities to enable such applications.
Tutorial on Online User Engagement: Metrics and Optimization
Authors: Liangjie Hong, Mounia Lalmas
Venue: WWW
Year: 2019

User engagement plays a central role in companies operating online services, such as search engines, news portals, e-commerce sites, entertainment services, and social networks. A main challenge is to leverage collected knowledge about the daily online behavior of millions of users to understand what engage them short-term and more importantly long-term. Two critical steps of improving user engagement are metrics and their optimization. The most common way that engagement is measured is through various online metrics, acting as proxy measures of user engagement. This tutorial will review these metrics, their advantages and drawbacks, and their appropriateness to various types of online services. Once metrics are defined, how to optimize them will become the key issue. We will survey methodologies including machine learning models and experimental designs that are utilized to optimize these metrics via direct or indirect ways. As case studies, we will focus on four types of services, news, search, entertainment, and e-commerce. We will end with lessons learned and a discussion on the most promising research directions.
An Investigation of Cyber Autonomy on Government Websites
Authors: Hsu-Chun Hsiao, Tiffany Hyun-Jin Kim, Yu-Ming Ku, Chun-Ming Chang, Hung-Fang Chen, Yu-Jen Chen, Chun-Wen Wang, Wei Jeng
Venue: WWW
Year: 2019

From a national security viewpoint, a higher degree of cyber autonomy is crucial to reduce the reliance on external, oftentimes untrustworthy entities, in order to achieve better resilience against adversaries. To probe into the concept of government cyber autonomy, this study examines the external dependency of public-facing government websites across the world's major industrialized, Group of Seven (G7) countries. Over a two-year period, we measured HTTPS adoption rates, the autonomy status of CAs, and the autonomy status of CPs on G7 government websites. We find that approximately 85% of web resources loaded by G7 government sites originate from the United States. By reviewing policy documents and surveying technicians who maintain government websites, we identify four significant forces that can influence the degree of a government's autonomy, including government mandates on HTTPS adoption, website development outsourcing, the citizens' fear of large-scale surveillance, and user confusion. Because a government websites are considered critical information infrastructures, we expect this study to raise awareness of their complex dependency, thereby reducing the risk of blindly trusting external entities when using critical government services.
Exam Keeper: Detecting Questions with Easy-to-Find Answers
Authors: Ting-Lun Hsu, Shih-Chieh Dai, Lun-Wei Ku
Venue: WWW
Year: 2019

We present Exam Keeper, a tool to measure the availability of answers to exam questions for ESL students. Exam Keeper targets two major sources of answers: the web, and apps. ESL teachers can use it to estimate which questions are easily answered by information on the web or by using automatic question answering systems, which should help teachers avoid such questions on their exams or homework to prevent students from misusing technology. The demo video is available at https://youtu.be/rgq0UXOkb8o 1
Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm
Authors: Ziniu Hu, Yang Wang, Qu Peng, Hang Li
Venue: WWW
Year: 2019

Recently a number of algorithms under the theme of 'unbiased learning-to-rank' have been proposed, which can reduce position bias, the major type of bias in click data, and train a high-performance ranker with click data. Most of the existing algorithms, based on the inverse propensity weighting (IPW) principle, first estimate the click bias at each position, and then train an unbiased ranker with the estimated biases using a learning-to-rank algorithm. However, there has not been a method for unbiased pairwise learning-to-rank that can simultaneously conduct debiasing of click data and training of a ranker using a pairwise loss function. In this paper, we propose a novel framework to accomplish the goal and apply this framework to the state-of-the-art pairwise learning-to-rank algorithm, LambdaMART. Our algorithm named Unbiased LambdaMART can jointly estimate the biases at click positions and the biases at unclick positions, and learn an unbiased ranker. Experiments on benchmark data show that Unbiased LambdaMART can significantly outperform existing algorithms by large margins. In addition, an online A/B Testing at a commercial search engine shows that Unbiased LambdaMART can effectively conduct debiasing of click data and enhance relevance ranking.
Scalable Eye Tracking for Mobile Devices
Authors: Michael Xuelin Huang
Venue: WWW
Year: 2019

Eye tracking provides an effective solution to users’ attention, interest, and engagement. While gaze estimation based on a standard camera can be versatile, it remains challenging to achieve an accurate, robust, and scalable solution on mobile devices. In this talk, I will describe three studies that aim to address these challenges. Specifically, 1) we found that screen reflection on user's cornea can be leveraged for gaze estimation and it considerably improves the practicability of indoor eye tracking. 2) We exploited gaze-hand coordination and applied interaction data for implicit calibration when a user naturally interacts with the computer. This can prevent users from tedious and intrusive calibration in practice. 3) We also proposed to train a multi-device person-specific gaze estimator to accelerate implicit calibration. It adapts the data from different personal devices to learn the shared mapping from user appearance into eye gaze. Taken together, these studies identify indicative eye gaze features, alleviate user calibration effort, and thus pave the way for scalable eye tracking in daily use.
Domain-aware Neural Model for Sequence Labeling using Joint Learning
Authors: Heng Huang, Xiaozhong Liu, Yuliang Yan
Venue: WWW
Year: 2019

Recently, scholars have demonstrated empirical successes of deep learning in sequence labeling, and most of the prior works focused on the word representation inside the target sentence. Unfortunately, the global information, e.g., domain information of the target document, were ignored in the previous studies. In this paper, we propose an innovative joint learning neural network which can encapsulate the global domain knowledge and the local sentence/token information to enhance the sequence labeling model. Unlike existing studies, the proposed method employs domain labeling output as a latent evidence to facilitate tagging model and such joint embedding information is generated by an enhanced highway network. Meanwhile, a redesigned CRF layer is deployed to bridge the 'local output labels' and 'global domain information'. Various kinds of information can iteratively contribute to each other, and moreover, domain knowledge can be learnt in either supervised or unsupervised environment via the new model. Experiment with multiple data sets shows that the proposed algorithm outperforms classical and most recent state-of-the-art labeling methods.
A Multimodal Text Matching Model for Obfuscated Language Identification in Adversarial Communication?
Authors: Longtao Huang, Ting Ma, Junyu Lin, Jizhong Han, Songlin Hu
Venue: WWW
Year: 2019

Obfuscated language is created to avoid censorship in adversarial communication such as sensitive information conveying, strong sentiment expression, secret actions plan, and illegal trading. The obfuscated sentences are usually generated by replacing one word with another to conceal the textual content. Intelligence and security agencies identify such adversarial messages by scanning with a watch-list of red-flagged terms. Though semantic expansion techniques are adopted, the precision and recall of the identification is limited due to the ambiguity and the unbounded creation way. To this end, this paper frames the obfuscated language identification problem as a text matching task, where each message is checked whether matches a red-flagged term. We propose a multimodal text matching model which combining textual and visual features. The proposed model extends a Bi-directional Long Short Term Memory network with a visual-level representation component to achieve the given task. Comparative experiments on real-world dataset demonstrate that the proposed method could achieve a better performance than the previous methods.