OpenRaiser/Intern-Atlas huggingface dataset:
PhD Symposium Chairs' Welcome
Authors: Sylvie Calabretto, Lalana Kagal, Maria Maleshkova
Venue: WWW
Year: 2018

It is our great pleasure to welcome you to the WWW 2018 PhD Symposium. The goal of the PhD Symposium is to provide a supportive atmosphere for PhD students to present and receive feedback on their ongoing work. Students at different stages in their research get an opportunity to present and discuss their problem statements, goals, methods and results. The symposium aims to provide students with useful guidance on various aspects of their research from established researchers and other PhD students, working in areas related to the World Wide Web. Finally, the symposium also aims to enable PhD students to interact with other participants and potential collaborators in order to stimulate an exchange of ideas, suggestions and experiences. We received 25 PhD proposal submissions from all around the world covering a broad range of topics. We evaluated them with respect to methodology, approach, relevance and novelty, selecting 8 papers for long presentation and 4 papers for short presentation. We also took in account the coverage of the different areas related to WWW as well as the potential audience and the possibility to receive feedback from seniors and peers.
Surviving the Web: A Journey into Web Session Security
Authors: Stefano Calzavara, Riccardo Focardi, Marco Squarcina, Mauro Tempesta
Venue: WWW
Year: 2018

We survey the most common attacks against web sessions, i.e., attacks which target honest web browser users establishing an authenticated session with a trusted web application. We then review existing security solutions which prevent or mitigate the different attacks, by evaluating them along four different axes: protection, usability, compatibility and ease of deployment. Based on this survey, we identify five guidelines that, to different extents, have been taken into account by the designers of the different proposals we reviewed. We believe that these guidelines can be helpful for the development of innovative solutions approaching web security in a more systematic and comprehensive way.
The Past, Present, and Future of Learning Analytics
Authors: Elsa Cardoso
Venue: WWW
Year: 2018

Learning Analytics (LA) is a recent research field, in which Business Intelligence and Analytics techniques are applied to learners and their contexts, with the purpose of acquiring a greater insight about the entire learning process (including outcomes). In this talk, we explore the LA landscape, delving into the definitions, techniques, challenges, and lessons learned.
Efficient Context-Aware Sequential Recommender System
Authors: Leonardo Cella
Venue: WWW
Year: 2018

Traditional collaborative filtering, and content-based approaches attempt to learn a static recommendation model in a batch fashion. These approaches are not suitable in highly dynamic recommendation scenarios, like news recommendation and computational advertisement. Due to this well-known limitation, in the last decade a lot of efforts have been spent over the study of online learning techniques. Currently, a lot of attention has been devoted to improvements on the theoretical guarantees, without caring too much about computational cost and memory footprint. However, in the era of big-data content features tend to be high-dimensional, which leads to a direct challenge for traditional on-line learning algorithms (e.g., multi-armed bandits) since these are mostly designed for low-dimensional feature spaces. In this work we face the aforementioned problem, investigating an approximated context-aware bandit learner. Our model takes into account the problem of finding the actual low-dimensional manifold spanned by data content-features. In particular, we propose to store the covariance matrix of the previously seen contexts in a compressed space, without losing too much in terms of recommendation quality. With this work we provide an overview over the main properties, describe the adopted techniques, and report on preliminary experimental results on a synthetic dataset. We also discuss a drawback of the proposed method that may appear in typical scenarios and suggest future research avenues.
Joint Label Inference in Networks
Authors: Deepayan Chakrabarti, Stanislav Funiak, Jonathan Chang, Sofus A. Macskassy
Venue: WWW
Year: 2018

We consider the problem of inferring node labels in a partially labeled graph where each node in the graph has multiple label types and each label type has a large number of possible labels. Existing approaches such as Label Propagation fail to consider interactions between the label types. Our proposed method, called EdgeExplain, explicitly models these interactions, while still allowing scalable inference under a distributed message-passing architecture. On a large subset of the Facebook social network, collected in a previous study, EdgeExplain outperforms label propagation for several label types, with lifts of up to $120%$ for [email protected] and 60% for [email protected]
Editorial Algorithms: Optimizing Recency, Relevance and Diversity for Automated News Curation
Authors: Abhijnan Chakraborty, Mohammad Luqman, Sidhartha Satapathy, Niloy Ganguly
Venue: WWW
Year: 2018

With a large number of stories emerging from the newsrooms, media websites need to curate interesting news for their readers. Although traditionally news was curated solely by human editors, increasing news volume has led media outlets to adopt editorial algorithms. However, such algorithms are often proprietary, and smaller outlets do not have the resources to build them from scratch. In this paper, we present a novel framework 'Samar' to automatically curate news by optimizing recency, relevance and diversity of the selected stories. Evaluations over two real-world news datasets show that Samar outperforms several state-of-the-art baselines in matching the news curation performed by human editors.
Understanding Types of Cyberbullying in an Anonymous Messaging Application
Authors: Arpita Chakraborty, Yue Zhang, Arti Ramesh
Venue: WWW
Year: 2018

The possibility of anonymity and lack of effective ways to identify inappropriate messages have resulted in a significant amount of online interaction data that attempt to harass, bully, or offend the recipient. In this work, we perform a preliminary linguistic study on messages exchanged using one such popular web/smartphone application---Sarahah, that allows friends to exchange messages anonymously. Since messages exchanged via Sarahah are private, we collect them when the recipient shares it on Twitter. We then perform an analysis of the different kinds of messages exchanged through this application. Our linguistic analysis reveals that a significant number of these messages (~20%) include inappropriate, hurtful, or profane language intended to embarrass, offend, or bully the recipient. Our analysis helps in understanding the different ways in which anonymous message exchange platforms are used and the different types of bullying present in such exchanges.
Fully Dynamic k-Center Clustering
Authors: T.-H. Hubert Chan, Arnaud Guerquin, Mauro Sozio
Venue: WWW
Year: 2018

Static and dynamic clustering algorithms are a fundamental tool in any machine learning library. Most of the efforts in developing dynamic machine learning and data mining algorithms have been focusing on the sliding window model (where at any given point in time only the most recent data items are retained) or more simplistic models. However, in many real-world applications one might need to deal with arbitrary deletions and insertions. For example, one might need to remove data items that are not necessarily the oldest ones, because they have been flagged as containing inappropriate content or due to privacy concerns. Clustering trajectory data might also require to deal with more general update operations. We develop a (2+ε)-approximation algorithm for the k-center clustering problem with "small»» amortized cost under the fully dynamic adversarial model. In such a model, points can be added or removed arbitrarily, provided that the adversary does not have access to the random choices of our algorithm. The amortized cost of our algorithm is poly-logarithmic when the ratio between the maximum and minimum distance between any two points in input is bounded by a polynomial, while k and epsilon are constant. Our theoretical results are complemented with an extensive experimental evaluation on dynamic data from Twitter, Flickr, as well as trajectory data, demonstrating the effectiveness of our approach.
JSExplain: A Double Debugger for JavaScript
Authors: Arthur Charguéraud, Alan Schmitt, Thomas Wood
Venue: WWW
Year: 2018

We present JSExplain, a reference interpreter for JavaScript that closely follows the specification and that produces execution traces. These traces may be interactively investigated in a browser, with an interface that displays not only the code and the state of the interpreter, but also the code and the state of the interpreted program. Conditional breakpoints may be expressed with respect to both the interpreter and the interpreted program. In that respect, JSExplain is a double-debugger for the specification of JavaScript.
Presenting and Preserving the Change in Taxonomic Knowledge for Linked Data (Extended Abstract)
Authors: Rathachai Chawuthai, Hideaki Takeda, Vilas Wuwongse, Utsugi Jinbo
Venue: WWW
Year: 2018

Linked Open Data (LOD) technology enables web of data and exchangeable knowledge graphs through the Internet. However, the change in knowledge is happened everywhere and every time, and it becomes a challenging issue of linking data precisely because the misinterpretation and misunderstanding of some terms and concepts may be dissimilar under different context of time and different community knowledge. To solve this issue, we introduce an approach to the preservation of knowledge graph, and we select the biodiversity domain to be our case studies because knowledge of this domain is commonly changed and all changes are clearly documented. Our work produces an ontology, transformation rules, and an application to demonstrate that it is feasible to present and preserve knowledge graphs and provides open and accurate access to linked data. It covers changes in names and their relationships from different time and communities as can be seen in the cases of taxonomic knowledge.
Towards Probabilistic Bitemporal Knowledge Graphs
Authors: Melisachew Wudage Chekol, Heiner Stuckenschmidt
Venue: WWW
Year: 2018

The emergence of open information extraction as a tool for constructing and expanding knowledge graphs has aided the growth of temporal data, for instance, YAGO, NELL and Wikidata. While YAGO and Wikidata maintain the valid time of facts, NELL records the time point at which a fact is retrieved from some Web corpora. Collectively, these knowledge graphs (KGs) store facts extracted from Wikipedia and other sources. Due to the imprecise nature of the extraction tools that are used to build and expand KGs, such as NELL, the facts in the KGs are weighted (a confidence value representing the correctness of a fact). Additionally, NELL can be considered as a transaction time KG because every fact is associated with extraction date. On the other hand, YAGO and Wikidata use the valid time model because they only maintain facts together with their validity time (temporal scope). In this paper, we propose a bitemporal model (that combines transaction and valid time models) for maintaining and querying probabilistic temporal knowledge graphs. We report our evaluation results of the proposed approach.
When E-commerce Meets Social Media: Identifying Business on WeChat Moment Using Bilateral-Attention LSTM
Authors: Tianlang Chen, Yuxiao Chen, Han Guo, Jiebo Luo
Venue: WWW
Year: 2018

WeChat Business, developed on WeChat, the most extensively used instant messaging platform in China, is a new business model that bursts into people's lives in the e-commerce era. As one of the most typical WeChat Business behaviors, WeChat users can advertise products, advocate companies and share customer feedback to their WeChat friends by posting a WeChat Moment--a public status that contains images and a text. Given its popularity and significance, in this paper, we propose a novel Bilateral-Attention LSTM network (BiATT-LSTM) to identify WeChat Business Moments based on their texts and images. In particular, different from previous schemes that equally consider visual and textual modalities for a joint visual-textual classification task, we start our work with a text classification task based on an LSTM network, then we incorporate a bilateral-attention mechanism that can automatically learn two kinds of explicit attention weights for each word, namely 1) a global weight that is insensitive to the images in the same Moment with the word, and 2) a local weight that is sensitive to the images in the same Moment. In this process, we utilize visual information as a guidance to figure out the local weight of a word in a specific Moment. Two-level experiments demonstrate the effectiveness of our framework. It outperforms other schemes that jointly model visual and textual modalities. We also visualize the bilateral-attention mechanism to illustrate how this mechanism helps joint visual-textual classification.
Fine-grained Video Attractiveness Prediction Using Multimodal Deep Learning on a Large Real-world Dataset
Authors: Xinpeng Chen, Jingyuan Chen, Lin Ma, Jian Yao, Wei Liu, Jiebo Luo, Tong Zhang
Venue: WWW
Year: 2018

Nowadays, billions of videos are online ready to be viewed and shared. Among an enormous volume of videos, some popular ones are widely viewed by online users while the majority attract little attention. Furthermore, within each video, different segments may attract significantly different numbers of views. This phenomenon leads to a challenging yet important problem, namely fine-grained video attractiveness prediction, which only relies on video contents to forecast video attractiveness at fine-grained levels, specifically video segments of several second length in this paper. However, one major obstacle for such a challenging problem is that no suitable benchmark dataset currently exists. To this end, we construct the first fine-grained video attractiveness dataset (FVAD), which is collected from one of the most popular video websites in the world. In total, the constructed FVAD consists of 1,019 drama episodes with 780.6 hours covering different categories and a wide variety of video contents. Apart from the large amount of videos, hundreds of millions of user behaviors during watching videos are also included, such as view counts, "fast-forward, "fast-rewind, and so on, where "view counts" reflects the video attractiveness while other engagements capture the interactions between the viewers and videos. First, we demonstrate that video attractiveness and different engagements present different relationships. Second, FVAD provides us an opportunity to study the fine-grained video attractiveness prediction problem. We design different sequential models to perform video attractiveness prediction by relying solely on video contents. The sequential models exploit the multimodal relationships between visual and audio components of the video contents at different levels. Experimental results demonstrate the effectiveness of our proposed sequential models with different visual and audio representations, the necessity of incorporating the two modalities, and the complementary behaviors of the sequential prediction models at different levels.
Fine-Grained Analysis of Financial Tweets
Authors: Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen
Venue: WWW
Year: 2018

This paper decribes our experimental methods and results in FiQA 2018 Task 1. There are two subtasks : (1) to predict continuous sentiment score between -1 to 1, and (2) to determine which aspect(s) are related to the content of financial tweets. First, we propose a preprocessing procedure for decomposing financial tweets. Second, we collect over 334K labeled financial tweets to enlarge the scale of the experiments. Third, the sentiment prediction task is separated into two steps in this paper, i.e., (1) bullish/bearish and (2) sentiment degree. We compare the results of the CNN, CRNN and Bi-LSTM models. Besides, we further combine the results of the best models in both steps as the model of subtask 1. Finally, we make an investigation of aspects in depth, and propose some clues for dealing with the 14 aspects.
Learning the Chinese Sentence Representation with LSTM Autoencoder
Authors: Mu-Yen Chen, Tien-Chi Huang, Vera Yu Shu, Chia-Chen Chen, Tsung-Che Hsieh, Neil Y. Yen
Venue: WWW
Year: 2018

This study retains the meanings of the original text using Autoencoder (AE) in this regard. This study uses the different loss (includes three types) to train the neural network model, hopes that after compressing sentence features, it can still decompress the original input sentences and classify the correct targets, such as positive or negative sentiment. In this way, it supposed to get the more relative features (compressing sentence features) in the sentences to classify the targets, rather than using the classification loss that may classify by the meaningless features (words). In the result, this study discovers that adding additional features for correction of errors does not interfere with the learning. Also, not all words are needed to be restored without distortion after applying the AE method.
Generating Schema Labels through Dataset Content Analysis
Authors: Zhiyu Chen, Haiyan Jia, Jeff Heflin, Brian D. Davison
Venue: WWW
Year: 2018

Impoverished descriptions and convoluted schema labels are common challenges in data-centric tasks such as schema matching and data linking, especially when datasets can span domains. To address these issues, we consider the task of schema label generation. Typically, schema labels are created by dataset providers and are useful for users to understand a dataset. The motivation behind the task is that a lot of data linking systems require overlapping information between two datasets and rely on unique identifiers of schema labels. Moreover, it is common for schema labels in different datasets to have different identifiers even when they refer to the same concept. With no naming standard for schema labels, unintelligible labels are widely found in real-world datasets. For example, many schema labels contain abbreviations and compound nouns that hinder automated matching of attributes in corresponding datasets. Through schema label generation, more common (and thus understandable) schema labels can be provided to allow for broader schema matches in contexts such as dataset search and data linking. We develop a variety of features based on analysis of dataset content to enable machine learning methods to recommend useful labels. We test our approach on two real-world data collections and demonstrate that our method is able to outperform the alternative approach.
Information Recall Support for Elderly People in Hyper Aged Societies
Authors: Hsin-Hsi Chen, Manabu Okumura
Venue: WWW
Year: 2018

Memory loss, common seen in elderly people, affects their social interaction in the daily life very much. This 3-year international project jointly funded by Taiwan Ministry of Science and Technology (MOST) and Japan Science and Technology Agency (JST) investigates together the crucial issues behind the hyper aged societies. We aim at developing technologies and systems to provide information recall support for elderly people at the right time and at the right place.
Hierarchical Variational Memory Network for Dialogue Generation
Authors: Hongshen Chen, Zhaochun Ren, Jiliang Tang, Yihong Eric Zhao, Dawei Yin
Venue: WWW
Year: 2018

Dialogue systems help various real applications interact with humans in an intelligent natural way. In dialogue systems, the task of dialogue generation aims to generate utterances given previous utterances as contexts. Among various spectrums of dialogue generation approaches, end-to-end neural generation models have received an increase of attention. These end-to-end neural generation models are capable of generating natural-sounding sentences with a unified neural encoder-decoder network structure. The end-to-end structure sequentially encodes each word in an input context and generates the response word-by-word deterministically during decoding. However, lack of variation and limited ability in capturing long-term dependencies between utterances still challenge existing approaches. In this paper, we propose a novel hierarchical variational memory network (HVMN), by adding the hierarchical structure and the variational memory network into a neural encoder-decoder network. By emulating human-to-human dialogues, our proposed method can capture both the high-level abstract variations and long-term memories during dialogue tracking, which enables the random access of relevant dialogue histories. Extensive experiments conducted on three large real-world datasets verify a significant improvement of our proposed model against state-of-the-art baselines for dialogue generation.
Optimal Vehicle Dispatching for Ride-sharing Platforms via Dynamic Pricing
Authors: Mengjing Chen, Weiran Shen, Pingzhong Tang, Song Zuo
Venue: WWW
Year: 2018

Over the past few years, ride-sharing has been proven to be an effective way to relieve urban traffic congestion, as evidenced by several emerging ride-sharing platforms such as Uber and Didi. A key economic problem for these platforms is to design a revenue-optimal (or welfare-optimal) pricing scheme and a corresponding vehicle dispatching policy that incorporates geographic information, and more importantly, dynamic supply and demand. In this paper, we aim to solve this problem by introducing a unified model that takes into account both travel time and driver redirection. We tackle the non-convexity problem using the "ironing" technique and formulate the optimization problem as a Markov decision process (MDP), where the states are the driver distributions and the decision variables are the prices. Our main finding is to give an efficient algorithm that computes the exact revenue (or welfare) optimal randomized pricing schemes. We characterize the optimal solutions of the MDP by primal-dual analysis of a convex program. We also conduct empirical analysis of our solution with real data of a major ride-sharing platform and show its significant advantages over fixed pricing schemes as well as those prevalent surge-based pricing schemes.