OpenRaiser/Intern-Atlas huggingface dataset:
OSTRICH: Versioned Random-Access Triple Store
Authors: Ruben Taelman, Miel Vander Sande, Ruben Verborgh
Venue: WWW
Year: 2018

The Linked Open Data cloud is evergrowing and many datasets are frequently being updated. In order to fully exploit the potential of the information that is available in and over historical dataset versions, such as discovering evolution of taxonomies or diseases in biomedical datasets, we need to be able to store and query the different versions of Linked Datasets efficiently. In this demonstration, we introduce OSTRICH, which is an efficient triple store with supported for versioned query evaluation. We demonstrate the capabilities of OSTRICH using a Web-based graphical user interface in which a store can be opened or created. Using this interface, the user is able to query in, between, and over different versions, ingest new versions, and retrieve summarizing statistics.
POSTER: Predicting Website Abuse Using Update Histories
Authors: Yuta Takata, Mitsuaki Akiyama, Takeshi Yagi, Kunio Hato, Shigeki Goto
Venue: WWW
Year: 2018

Threats of abusing websites that webmasters have stopped updating have increased. In this poster, we propose a method of predicting potentially abusable websites by retrospectively analyzing updates of software that composes websites. The method captures webmaster behaviors from archived snapshots of a website and analyzes the changes of web servers and web applications used in the past as update histories. A classifier that predicts website abuses is finally built by using update histories from snapshots of known malicious websites before the detections. Evaluation results showed that the classifier could predict various website abuses, such as drive-by downloads, phishes, and defacements, with accuracy: a 76% true positive rate and a 26% false positive rate.
"You are no Jack Kennedy": On Media Selection of Highlights from Presidential Debates
Authors: Chenhao Tan, Hao Peng, Noah A. Smith
Venue: WWW
Year: 2018

Political speeches and debates play an important role in shaping the images of politicians, and the public often relies on media outlets to select bits of political communication from a large pool of utterances. It is an important research question to understand what factors impact this selection process. To quantitatively explore the selection process, we build a three- decade dataset of presidential debate transcripts and post-debate coverage. We first examine the effect of wording and propose a binary classification framework that controls for both the speaker and the debate situation. We find that crowdworkers can only achieve an accuracy of 60% in this task, indicating that media choices are not entirely obvious. Our classifiers outperform crowdworkers on average, mainly in primary debates. We also compare important factors from crowdworkers» free-form explanations with those from data-driven methods and find interesting differences. Few crowdworkers mentioned that "context matters", whereas our data show that well-quoted sentences are more distinct from the previous utterance by the same speaker than less-quoted sentences. Finally, we examine the aggregate effect of media preferences towards different wordings to understand the extent of fragmentation among media outlets. By analyzing a bipartite graph built from quoting behavior in our data, we observe a decreasing trend in bipartisan coverage.
Learning Large Scale Ordinal Ranking Model via Divide-and-Conquer Technique
Authors: Lu Tang, Sougata Chaudhuri, Abraham Bagherjeiran, Ling Zhou
Venue: WWW
Year: 2018

Structured prediction, where outcomes have a precedence order, lies at the heart of machine learning for information retrieval, movie recommendation, product review prediction, and digital advertising. Ordinal ranking, in particular, assumes that the structured response has a linear ranked order. Due to the extensive applicability of these models, substantial research has been devoted to understanding them, as well as developing efficient training techniques. One popular and widely cited technique of training ordinal ranking models is to exploit the linear precedence order and systematically reduce it to a binary classification problem. This facilitates the usage of readily available, powerful binary classifiers, but necessitates an expansion of the original training data, where the training data increases by $K-1$ times of its original size, with K being the number of ordinal classes. Due to prevalent nature of problems with large number of ordered classes, the reduction leads to datasets which are too large to train on single machines. While approximation methods like stochastic gradient descent are typically applied here, we investigate exact optimization solutions that can scale. In this paper, we present a divide-and-conquer (DC) algorithm, which divides large scale binary classification data into a cluster of machines and trains logistic models in parallel, and combines them at the end of the training phase to create a single binary classifier, which can then be used as an ordinal ranker. It requires no synchronization between the parallel learning algorithms during the training period, which makes training on large datasets feasible and efficient. We prove consistency and asymptotic normality property of the learned models using our proposed algorithm. We provide empirical evidence, on various ordinal datasets, of improved estimation and prediction performance of the model learnt using our algorithm, over several standard divide-and-conquer algorithms.
Siamese Cookie Embedding Networks for Cross-Device User Matching
Authors: Ugo Tanielian, Anne-Marie Tousch, Flavian Vasile
Venue: WWW
Year: 2018

Over the last decade, the number of devices per person has increased substantially. This poses a challenge for cookie-based personalization applications, such as online search and advertising, as it narrows the personalization signal to a single device environment. A key task is to find which cookies belong to the same person to recover a complete cross-device user journey. Recent work on the topic has shown the benefits of using unsupervised embeddings learned on user event sequences. In this paper, we extend this approach to a supervised setting and introduce the Siamese Cookie Embedding Network (SCEmNet), a siamese convolutional architecture that leverages the multi-modal aspect of sequences, and show significant improvement over the state-of-the-art.
Property Label Stability in Wikidata: Evolution and Convergence of Schemas in Collaborative Knowledge Bases
Authors: Thomas Pellissier Tanon, Lucie-Aimée Kaffee
Venue: WWW
Year: 2018

Stability in Wikidata's schema is essential for the reuse of its data. In this paper, we analyze the stability of the data based on the changes in labels of properties in six languages. We find that the schema is overall stable, making it a reliable resource for external usage.
Travel Itinerary Recommendations with Must-see Points-of-Interest
Authors: Kendall Taylor, Kwan Hui Lim, Jeffrey Chan
Venue: WWW
Year: 2018

Travelling and touring are popular leisure activities enjoyed by millions of tourists around the world. However, the task of travel itinerary recommendation and planning is tedious and challenging for tourists, who are often unfamiliar with the various Points-of-Interest (POIs) in a city. Apart from identifying popular POIs, the tourist needs to construct a travel itinerary comprising a subset of these POIs, and to order these POIs as a sequence of visits that can be completed within his/her available touring time. For a more realistic itinerary, the tourist also has to account for travelling time between POIs and visiting times at individual POIs. Furthermore, this itinerary should incorporate tourist preferences such as desired starting and ending POIs (e.g., POIs that are near the tourist's hotel) and a subset of must-see POIs (e.g., popular POIs that a tourist must visit). We term this the TourMustSee problem, which is based on a variant of the Orienteering problem. Following which, we propose the LP+M algorithm for solving the TourMustSee problem as an Integer Linear Program (ILP). Using a Flickr dataset of POI visits in seven touristic cities, we compare LP+M against various ILP-based baselines, and the results show that LP+M recommends better travel itineraries in terms of POI popularity, total POIs visited, total touring time utilized and must-visit POI(s) inclusion.
Web Reasoning Using Fact Tagging
Authors: Mehdi Terdjimi, Lionel Médini, Michael Mrissa
Venue: WWW
Year: 2018

Today's Web applications tend to reason about cyclic data (i.e. facts that re-occur periodically) on the client side. Although they can benefit from efficient incremental maintenance algorithms capable of handling frequent data updates, existing rule-based algorithms cause successive re-derivations of previously inferred information. In this paper, we propose an incremental maintenance approach for rule-based reasoning that prevents successive re-computations of fact derivations. We tag (i.e. annotate) facts to keep trace of their provenance and validity. We compare our solution with the DRed-based incremental reasoning algorithm and show that it significantly outperforms this algorithm for fact updates in re-occurring situations, to the cost of tagging facts at their first insertion. Our experiments show that this cost can be recovered within a small number of cycles of deletions and reinsertions of explicit facts. We discuss the utility and limitations of our approach on Web clients and provide implementation packages of this reasoner that can be directly integrated in Web applications, on both server and client sides.
I Read but Don't Agree: Privacy Policy Benchmarking using Machine Learning and the EU GDPR
Authors: Welderufael B. Tesfay, Peter Hofmann, Toru Nakamura, Shinsaku Kiyomoto, Jetzabel M. Serna
Venue: WWW
Year: 2018

With the continuing growth of the Internet landscape, users share large amount of personal, sometimes, privacy sensitive data. When doing so, often, users have little or no clear knowledge about what service providers do with the trails of personal data they leave on the Internet. While regulations impose rather strict requirements that service providers should abide by, the defacto approach seems to be communicating data processing practices through privacy policies. However, privacy policies are long and complex for users to read and understand, thus failing their mere objective of informing users about the promised data processing behaviors of service providers. To address this pertinent issue, we propose a machine learning based approach to summarize the rather long privacy policy into short and condensed notes following a risk-based approach and using the European Union (EU) General Data Protection Regulation (GDPR) aspects as assessment criteria. The results are promising and indicate that our tool can summarize lengthy privacy policies in a short period of time, thus supporting users to take informed decisions regarding their information disclosure behaviors.
Can Common Crawl Reliably Track Persistent Identifier (PID) Use Over Time
Authors: Henry S. Thompson, Jian Tong
Venue: WWW
Year: 2018

We report here on the results of two studies using two and four monthly web crawls respectively from the Common Crawl (CC) initiative between 2014 and 2017, whose initial goal was to provide empirical evidence for the changing patterns of use of so-called persistent identifiers. This paper focusses on the tooling needed for dealing with CC data, and the problems we found with it. The first study is based on over 10^12 URIs from over 5 x 10^9 pages crawled in April 2014 and April 2017, the second study adds a further 3 x 10^9 pages from the April 2015 and April 2016 crawls. We conclude with suggestions on specific actions needed to enable studies based on CC to give reliable longitudinal information.
Digital Zombies - the Reanimation of our Digital Selves
Authors: Tabea Tietz, Francesca Pichierri, Maria Koutraki, Dara Hallinan, Franziska Boehm, Harald Sack
Venue: WWW
Year: 2018

What happens to our social media profiles when we die The episode "Be Right Back" as part of Netflix's series "Black Mirror" provides a possible scenario. A digital avatar is created to communicate with close relatives which learns from past social media activities of the deceased user. While the users entrust their social media content to one or more companies, even after their death, it may be reasonable to ask: What will the company really do with a deceased user's data: sell it to manipulate users or create advertisements In this paper we tackle the issues of ownership, ethics, and transparency of post mortem user data.
On the Use of "Deep" Features for Online Image Sharing
Authors: Ashwini Tonge, Cornelia Caragea
Venue: WWW
Year: 2018

Online image sharing in social networking sites such as Facebook, Flickr, and Instagram can lead to unwanted disclosure and privacy violations, when privacy settings are used inappropriately. Despite that social networking sites allow users to set their privacy preferences, this can be cumbersome for the vast majority of users. In this paper, we explore privacy prediction models for social media that can automatically identify private (or sensitive) content from images, before they are shared online, in order to help protect users' privacy in social media. More precisely, we study "deep" visual features that are extracted from various layers of a pre-trained deep Convolutional Neural Network (CNN) as well as "deep" image tags generated from the CNN. Experimental results on a Flickr dataset of thousands of images show that the deep visual features and deep image tags can successfully identify images' private content and substantially outperform previous models for this task.
A Neural Network-based Framework for Non-factoid Question Answering
Authors: Nam Khanh Tran, Claudia Niederée
Venue: WWW
Year: 2018

In this paper, we present a neural network based framework for answering non-factoid questions. The framework consists of two main components: Answer Retriever and Answer Ranker. In the first component, we leverage off-the-shelf retrieval models (e.g. bm25) to retrieve a pool of candidate answers regarding to the input question. Answer Ranker is then used to select the most suitable answer. In this work, we adopt two typical deep learning based frameworks for our Answer Ranker component. One is based on Siamese architecture and the other is the Compare-Aggregate framework. The Answer Ranker component is evaluated separately based on popular answer selection datasets. Our overall system is evaluated using FiQA dataset, a newly released dataset for financial domain and shows promising results.
Stochastic Multi-path Routing Problem with Non-stationary Rewards: Building PayU's Dynamic Routing
Authors: Pankaj Trivedi, Arvind Singh
Venue: WWW
Year: 2018

Payment transaction engine at PayU processes multimillion trans- actions every day through multiple payment gateways. Routing a transaction through an appropriate payment gateway is crucial to the engine for optimizing the availability and cost. The problem is that every transaction needs to choose one of K available payment gateways characterized by an unknown probability reward distri- bution. The reward for a gateway is a combination of its health and cost factors. The reward for a gateway is only realized when transaction is processed by the gateway i.e. by its success or failure. The objective of dynamic routing is to maximize the cumulative expected rewards over some given horizon of transactions' life. To do this, the dynamic switching system needs to acquire informa- tion about gateways (exploration) while simultaneously optimizing immediate rewards by selecting the best gateway at the moment (exploitation); the price paid due to this trade o is referred to as the regret. The main objective is to minimize the regret and maximize the rewards. The basic idea is to choose a gateway according to its probability of being the best gateway. The routing problem is a direct formulation of reinforcement learning (RL) problem. In an RL problem, an agent interacts with a dynamic, stochastic, and incompletely known environment, with the goal of finding an action-selection strategy, or policy, that optimizes some long-term performance measure. Thompson Sampling algorithm has experimentally been shown to be close to optimal.
Re-coding Black Mirror Chairs' Welcome & Organization
Authors: Pinelopi Troullinou, Mathieu d&apos, Aquin, Ilaria Tiddi
Venue: WWW
Year: 2018

This volume of proceedings presents the papers from the 2nd edition of the interdisciplinary workshop Re-coding Black Mirror, held on April 24, 2018 in Lyon, France and co-located with The WEB Conference (WWW2018). Participating to the topical debate of data ethics and algorithmic governance, Re-coding Black Mirror offers the research community tools to reflect on its role in the construction of the technological future and the potential societal implications. The workshop becomes a venue for computer scientists, data scientists and social scientists to create bridges of knowledge. The complexity of the societal phenomena emerging from the development in web technologies urge for interdisciplinary collaboration. Following the slightly futuristic approach to technology of the British-made sci-fi series Black Mirror, we called scientists to create their dystopic scenarios developed from their own existing technologies. Through this thought experiment, researchers considered potential ethical and social risks of technological advancements offering in some cases possible solutions.
VERSE: Versatile Graph Embeddings from Similarity Measures
Authors: Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Emmanuel Müller
Venue: WWW
Year: 2018

Embedding a web-scale information network into a low-dimensional vector space facilitates tasks such as link prediction, classification, and visualization. Past research has addressed the problem of extracting such embeddings by adopting methods from words to graphs, without defining a clearly comprehensible graph-related objective. Yet, as we show, the objectives used in past works implicitly utilize similarity measures among graph nodes. In this paper, we carry the similarity orientation of previous works to its logical conclusion; we propose VERtex Similarity Embeddings (VERSE), a simple, versatile, and memory-efficient method that derives graph embeddings explicitly calibrated to preserve the distributions of a selected vertex-to-vertex similarity measure. VERSE learns such embeddings by training a single-layer neural network. While its default, scalable version does so via sampling similarity information, we also develop a variant using the full information per vertex. Our experimental study on standard benchmarks and real-world datasets demonstrates that VERSE, instantiated with diverse similarity measures, outperforms state-of-the-art methods in terms of precision and recall in major data mining tasks and supersedes them in time and space efficiency, while the scalable sampling-based variant achieves equally good result as the non-scalable full variant.
Construction and Applications of TeKnowbase: A Knowledge Base of Computer Science Concepts
Authors: Prajna Upadhyay, Ashutosh Bindal, Manjeet Kumar, Maya Ramanath
Venue: WWW
Year: 2018

In this paper, we make two main contributions. First, we describe the construction and evaluation of TeKnowbase, a knowledge-base of technical concepts in computer science. And second, we show how to use TeKnowbase in a variety of applications, including, generation of pre-requisite concepts for learning a new topic, classification of technical text and querying and ranking computer science articles.
Creating Crowdsourced Research Talks at Scale
Authors: Rajan Vaish, Shirish Goyal, Amin Saberi, Sharad Goel
Venue: WWW
Year: 2018

There has been a marked shift towards learning and consuming information through video. Most academic research, however, is still distributed only in text form, as researchers often have limited time, resources, and incentives to create video versions of their work. To address this gap, we propose, deploy, and evaluate a scalable, end-to-end system for crowdsourcing the creation of short, 5-minute research videos based on academic papers. Doing so requires solving complex coordination and collaborative video production problems. To assist coordination, we designed a structured workflow that enables efficient delegation of tasks, while also motivating the crowd through a collaborative learning environment. To facilitate video production, we developed an online tool with which groups can make micro-audio recordings that are automatically stitched together to create a complete talk. We tested this approach with a group of volunteers recruited from 52 countries through an open call. This distributed crowd produced over 100 video talks in 12 languages based on papers from top-tier computer science conferences. The produced talks consistently received high ratings from a diverse group of non-experts and experts, including the authors of the original papers. These results indicate that our crowdsourcing approach is a promising method for producing high-quality research talks at scale, increasing the distribution and accessibility of scientific knowledge.
Extreme Classification: Tagging on Wikipedia, Recommendation on Amazon & Advertising on Bing
Authors: Manik Varma
Venue: WWW
Year: 2018

I will introduce extreme classi cation which is a new area of machine learning research focusing on multi-class & multi-label problems involving millions of categories. Extreme classification has opened up a new paradigm for thinking about key applications such as tagging, ranking and recommendation. I will discuss algorithms for some of these applications and present results on tagging on Wikipedia, product recommendation on Amazon and search and advertising on the Bing search engine. More details can be found on The Extreme Classification Repository webpage at http://manikvarma.org/downloads/XC/XMLRepository.html
PixieDust: Declarative Incremental User Interface Rendering Through Static Dependency Tracking
Authors: Nick ten Veen, Daco C. Harkes, Eelco Visser
Venue: WWW
Year: 2018

Modern web applications are interactive. Reactive programming languages and libraries are the state-of-the-art approach for declara- tively specifying these interactive applications. However, programs written with these approaches contain error-prone boilerplate code for e ciency reasons. In this paper we present PixieDust, a declarative user-interface language for browser-based applications. PixieDust uses static de- pendency analysis to incrementally update a browser-DOM at run- time, without boilerplate code. We demonstrate that applications in PixieDust contain less boilerplate code than state-of-the-art ap- proaches, while achieving on-par performance.
Gnirut: The Trouble With Being Born Human In An Autonomous World
Authors: Luca Viganò, Diego Sempreboni
Venue: WWW
Year: 2018

What if we delegated so much to autonomous AI and intelligent machines that They passed a law that forbids humans to carry out a number of professions We conceive the plot of a new episode of Black Mirror to reflect on what might await us and how we can deal with such a future.