OpenRaiser/Intern-Atlas huggingface dataset:
Smarter Markets: Bringing Intelligence into the Exchange
Authors: David M. Pennock
Venue: WWW
Year: 2019

Billions of dollars in financial securities exchange hands every day in independent continuous double auctions. Although the auctions are automated, fast, open 24-7, and have worldwide scope and massive scale, the underlying auction rules have not changed much for over 100 years. Advertisement auctions, on the other hand, have rapidly evolved, incorporating optimization and machine learning directly into their allocation rules. The downside is a less-transparent auction, but the upsides for efficiency and expressiveness are tremendous. The trend toward smarter markets will expand into finance and well beyond, pervading how markets are designed. I will discuss markets that optimize and learn, using prediction markets and advertising markets as key examples.
From Research Articles to Knowledge Graphs
Authors: Vayianos Pertsas, Panos Constantopoulos
Venue: WWW
Year: 2019

Understanding and extracting knowledge contained in text and encoding it as linked data for the WEB is a highly complex task that poses several challenges, requiring expertise from different fields such as conceptual modeling, natural language processing and web technologies including web mining, linked data generation and publishing, etc. When it comes to the scholarly domain, the transformation of human readable research articles into machine comprehensible knowledge bases is considered of high importance and necessity today due to the explosion of scientific publications in every major discipline, that makes it increasingly difficult for experts to maintain an overview of their domain or relate ideas from different domains. This situation could be significantly alleviated by knowledge bases capable of supporting queries such as: find all papers that address a given problem; how was the problem solved; which methods are employed by whom in addressing particular tasks; etc. that currently cannot be addressed by commonly used search engines such as Google Scholar or Semantic Scholar. This tutorial addresses the above challenge by introducing the participants to methods required in order to model knowledge regarding a given domain, extract information from available texts using advanced machine learning techniques, associate it with other information mined from the web in order to infer new knowledge and republish everything as linked open data on the Web. To this end, we will use a specific use case – that of the scholarly domain, and will show how to model research processes, extract them from research articles, associate them with contextual information from article metadata and other linked repositories and create knowledge bases available as linked data. Our aim is to show how methodologies from different computer science fields, namely natural language processing, machine learning and conceptual modeling, can be combined with Web technologies in a single meaningful workflow.
An Anatomy of a Lie
Authors: Dina Pisarevskaya, Boris Galitsky, Jay Taylor, Andrey Ozerov
Venue: WWW
Year: 2019

Automated detection of text with misrepresentations such as fake reviews is an important task for online reputation management. The dataset of customer complaints - emotionally charged texts which are very similar to reviews and include descriptions of problems customers experienced with certain businesses – is presented. It contains 2746 complaints about banks and provides clear ground truth, based on available factual knowledge about the financial domain. Among them, 400 texts were manually tagged. Initial experiments were performed in order to explore the links between implicit cues of the rhetoric structure of texts and the validity of arguments, and also how truthful/deceptive are these texts.
Supervised Lexicon Extraction for Emotion Classification
Authors: Alberto Purpura, Chiara Masiero, Gianmaria Silvello, Gian Antonio Susto
Venue: WWW
Year: 2019

Emotion Classification (EC) aims at assigning an emotion label to a textual document with two inputs – a set of emotion labels (e.g. anger, joy, sadness) and a document collection. The best performing approaches for EC are dictionary-based and suffer from two main limitations: (i) the out-of-vocabulary (OOV) keywords problem and (ii) they cannot be used across heterogeneous domains. In this work, we propose a way to overcome these limitations with a supervised approach based on TF-IDF indexing and Multinomial Linear Regression with Elastic-Net regularization to extract an emotion lexicon and classify short documents from diversified domains. We compare the proposed approach to state-of-the-art methods for document representation and classification by running an extensive experimental study on two shared and heterogeneous data sets.
Crowd-Mapping Urban Objects from Street-Level Imagery
Authors: Sihang Qiu, Achilleas Psyllidis, Alessandro Bozzon, Geert-Jan Houben
Venue: WWW
Year: 2019

Knowledge about the organization of the main physical elements (e.g. streets) and objects (e.g. trees) that structure cities is important in the maintenance of city infrastructure and the planning of future urban interventions. In this paper, a novel approach to crowd-mapping urban objects is proposed. Our method capitalizes on strategies for generating crowdsourced object annotations from street-level imagery, in combination with object density and geo-location estimation techniques to enable the enumeration and geo-tagging of urban objects. To address both the coverage and precision of the mapped objects within budget constraints, we design a scheduling strategy for micro-task prioritization, aggregation, and assignment to crowd workers. We experimentally demonstrate the feasibility of our approach through a use case pertaining to the mapping of street trees in New York City and Amsterdam. We show that anonymous crowds can achieve high recall (up to 80%) and precision (up to 68%), with geo-location precision of approximately 3m. We also show that similar performance could be achieved at city scale, possibly with stringent budget constraints.
Tutorial: Sequence-Aware Recommender Systems
Authors: Massimo Quadrana, Dietmar Jannach, Paolo Cremonesi
Venue: WWW
Year: 2019

Recommender systems are widely used in online applications to help users find items of interest and help them deal with information overload. In this tutorial, we discuss the class of sequence-aware recommender systems. Differently from the traditional problem formulation based on a user-item rating matrix, the input to such systems is a sequence of logged user interactions. Likewise, sequence-aware recommender systems implement alternative computational tasks, such as predicting the next items a user will be interested in an ongoing session or creating entire sequences of items to present to the user. We propose a problem formulation, sketch a number of computational tasks, review existing algorithmic approaches, and finally discuss evaluation aspects of sequence-aware recommender systems.
SGX-PySpark: Secure Distributed Data Analytics
Authors: Do Le Quoc, Franz Gregor, Jatinder Singh, Christof Fetzer
Venue: WWW
Year: 2019

Data analytics is central to modern online services, particularly those data-driven. Often this entails the processing of large-scale datasets which may contain private, personal and sensitive information relating to individuals and organisations. Particular challenges arise where cloud is used to store and process the sensitive data. In such settings, security and privacy concerns become paramount, as the cloud provider is trusted to guarantee the security of the services they offer, including data confidentiality. Therefore, the issue this work tackles is “How to securely perform data analytics in a public cloud?” To assist this question, we design and implement SGX-PySpark- a secure distributed data analytics system which relies on a trusted execution environment (TEE) such as Intel SGX to provide strong security guarantees. To build SGX-PySpark, we integrate PySpark - a widely used framework for data analytics in industry to support a wide range of queries, with SCONE - a shielded execution framework using Intel SGX.
Constructing Test Collections using Multi-armed Bandits and Active Learning
Authors: Md. Mustafizur Rahman, Mücahid Kutlu, Matthew Lease
Venue: WWW
Year: 2019

While test collections provide the cornerstone of system-based evaluation in information retrieval, human relevance judging has become prohibitively expensive as collections have grown ever larger. Consequently, intelligently deciding which documents to judge has become increasingly important. We propose a two-phase approach to intelligent judging across topics which does not require document rankings from a shared task. In the first phase, we dynamically select the next topic to judge via a multi-armed bandit method. In the second phase, we employ active learning to select which document to judge next for that topic. Experiments on three TREC collections (varying scarcity of relevant documents) achieve t 0.90 correlation for P@10 ranking and find 90% of the relevant documents at 48% of the original budget. To support reproducibility and follow-on work, we have shared our code online1.
Auction Theory from the Bidder Standpoint
Authors: Suju Rajan, Noureddine El Karoui
Venue: WWW
Year: 2019

Much of classical auction theory has been developed from the standpoint of the seller, trying to understand how to optimize auctions to maximize seller revenue for instance. This is still a source of very active current research. Billions of auctions are now run on the Internet everyday between the same sellers and bidders and this creates a need to better understand auctions from the bidders’ perspective. In this talk we will present some recent results on this question, showing for instance that auctions that are reputed to be truthful are not truthful anymore when the seller optimizes the auction format based on bidders’ past bids, provide explicit and simple to implement shading strategies that improve bidders’ utility (on and off equilibrium) and are robust to various forms of estimation error and mechanism changes. We will also discuss various equilibrium questions. We take a mostly functional analytic point of view on these problems. If time permits, we will discuss ongoing work on a machine-learning-based perspective. Joint work with Thomas Nedelec, Marc Abeille, Clément Calauzènes, Benjamin Heymann and Vianney Perchet while doing research at Criteo.
A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps
Authors: Jathushan Rajasegaran, Naveen Karunanayake, Ashanie Gunathillake, Suranga Seneviratne, Guillaume Jourjon
Venue: WWW
Year: 2019

Counterfeit apps impersonate existing popular apps in attempts to misguide users. Many counterfeits can be identified once installed, however even a tech-savvy user may struggle to detect them before installation. In this paper, we propose a novel approach of combining content embeddings and style embeddings generated from pre-trained convolutional neural networks to detect counterfeit apps. We present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits for top-10,000 apps. Under conservative assumptions, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps in Google Play Store. We also find 1,565 potential counterfeits asking for at least five additional dangerous permissions than the original app and 1,407 potential counterfeits having at least five extra third party advertisement libraries.
Web Experience in Mobile Networks: Lessons from Two Million Page Visits
Authors: Mohammad Rajiullah, Andra Lutu, Ali Safari Khatouni, Mah-Rukh Fida, Marco Mellia, Anna Brunström, Özgü Alay, Stefan Alfredsson, Vincenzo Mancuso
Venue: WWW
Year: 2019

Measuring and characterizing web page performance is a challenging task. When it comes to the mobile world, the highly varying technology characteristics coupled with the opaque network configuration make it even more difficult. Aiming at reproducibility, we present a large scale empirical study of web page performance collected in eleven commercial mobile networks spanning four countries. By digging into measurement from nearly two million web browsing sessions, we shed light on the impact of different web protocols, browsers, and mobile technologies on the web performance. We find that the impact of mobile broadband access is sizeable. For example, the median page load time using mobile broadband increases by a third compared to wired access. Mobility clearly stresses the system, with handover causing the most evident performance penalties. Contrariwise, our measurements show that the adoption of HTTP/2 and QUIC has practically negligible impact. To understand the intertwining of all parameters, we adopt state-of-the-art statistical methods to identify the significance of different factors on the web performance. Our analysis confirms the importance of access technology and mobility context as well as webpage composition and browser. Our work highlights the importance of large-scale measurements. Even with our controlled setup, the complexity of the mobile web ecosystem is challenging to untangle. For this, we are releasing the dataset as open data for validation and further research.
Context-Aware Sequential Recommendations withStacked Recurrent Neural Networks
Authors: Lakshmanan Rakkappan, Vaibhav Rajan
Venue: WWW
Year: 2019

Sequential history of user interactions as well as the context of interactions provide valuable information to recommender systems, for modeling user behavior. Modeling both contexts and sequential information simultaneously, in context-aware sequential recommenders, has been shown to outperform methods that model either one of the two aspects. In long sequential histories, temporal trends are also found within sequences of contexts and temporal gaps that are not modeled by previous methods. In this paper we design new context-aware sequential recommendation methods, based on Stacked Recurrent Neural Networks, that model the dynamics of contexts and temporal gaps. Experiments on two large benchmark datasets demonstrate the advantages of modeling the evolution of contexts and temporal gaps - our models significantly outperform state-of-the-art context-aware sequential recommender systems.
Message Distortion in Information Cascades
Authors: Manoel Horta Ribeiro, Kristina Gligoric, Robert West
Venue: WWW
Year: 2019

Information diffusion is usually modeled as a process in which immutable pieces of information propagate over a network. In reality, however, messages are not immutable, but may be morphed with every step, potentially entailing large cumulative distortions. This process may lead to misinformation even in the absence of malevolent actors, and understanding it is crucial for modeling and improving online information systems. Here, we perform a controlled, crowdsourced experiment in which we simulate the propagation of information from medical research papers. Starting from the original abstracts, crowd workers iteratively shorten previously produced summaries to increasingly smaller lengths. We also collect control summaries where the original abstract is compressed directly to the final target length. Comparing cascades to controls allows us to separate the effect of the length constraint from that of accumulated distortion. Via careful manual coding, we annotate lexical and semantic units in the medical abstracts and track them along cascades. We find that iterative summarization has a negative impact due to the accumulation of error, but that high-quality intermediate summaries result in less distorted messages than in the control case. Different types of information behave differently; in particular, the conclusion of a medical abstract (i.e., its key message) is distorted most. Finally, we compare extractive with abstractive summaries, finding that the latter are less prone to semantic distortion. Overall, this work is a first step in studying information cascades without the assumption that disseminated content is immutable, with implications on our understanding of the role of word-of-mouth effects on the misreporting of science.
SWAT: Seamless Web Authentication Technology
Authors: Florentin Rochet, Kyriakos Efthymiadis, François Koeune, Olivier Pereira
Venue: WWW
Year: 2019

We present a seamless challenge-response authentication protocol which leverages on the variations of html5 canvas rendering made by the software and hardware stacks. After a training phase that leads to feature extraction with deep learning techniques, a server becomes able to authenticate a user based on fresh canvasses, hence avoiding replay attacks. The whole authentication process is natively supported by any mainstream browser, stateless on client side and can be transparent to the user. We argue that those features facilitate deployment and composition with other authentication mechanisms without lowering the user experience. We present the threat model against which our protocol is expected to live and discuss its security. We also present a prototype implementation of our protocol and report on a real-word experimentation that we ran in order to analyze its efficiency and effectiveness.
Data-Driven Edu Chatbots
Authors: Donya Rooein
Venue: WWW
Year: 2019

There are many online courses and contents on the web, so each learner can find the best one and choose it. However, sometimes many online courses are poorly accessible due to the limits of the search engines on the web. The advent of intelligent systems, and online Chatbots, in particular, has brought improvement in various fields. Education Chatbots improve communication, increase productivity, and simplify learning interaction. This study aims to provide an intelligent Edu Chatbot with a high level of customization to learners who have different needs. This way, they can find their personalized learning path dynamically and their customized content without too much time and effort. This is precisely what e-learning needs because of the enormous amount of material on the web.
Towards Better Entity Linking Evaluation
Authors: Henry Rosales-Méndez
Venue: WWW
Year: 2019

The Entity Linking (EL) task is concerned with linking entity mentions in a text collection with their corresponding knowledge-base entries. Despite the progress made in the evaluation of EL systems, there is still much work to be done, where this Ph.D. research tackles issues concerning EL evaluation. Among these issues, we stress (a) the lack of consensus about the definition of “entity” and the lack of evaluation metrics that allow for different notions of entities, (b) the lack of datasets that allow for cross-language comparison, and (c) the focus on evaluating high-level systems rather than low-level techniques. By addressing these challenges and better understanding the performance of EL systems, our hypothesis is that we can create a more general, more configurable EL framework that can be better adapted to the needs of a particular application. In the early stages of this PhD work, we have identified these problems and begun to address (a) and (b), publishing initial results that constitute a significant step forward in our investigation. However, there are still further challenges that must be addressed before we reach our goal. Our next steps thus involve proposing a more fluid definition of “entity” adaptable to different applications, the definition of quality measures that allow for comparing EL approaches targeting different types of entities, as well as the creation of a customizable EL framework that allows for composing and evaluating individual techniques as appropriate to a particular task.
How New is the (RDF) News?
Authors: Tomer Sagi, Yael Wolf, Katja Hose
Venue: WWW
Year: 2019

Linked Open Data and the RDF format have become the premier method of publishing structured data representing entities and facts. Specifically, media organizations, such as the New York Times and the BBC, have embraced Linked Open Data as a way of providing structured access to traditional media content, including articles, images, and video. To ground RDF entities and predicates in existing Linked Open Data sources, dataset curators provide links for some entities to existing general purpose repositories, such as YAGO and DBpedia, using entity extraction and linking tools. However, these state-of-the-art tools rely on the entities to exist in the knowledge base. How much of the information is actually new and thus unable to be grounded is unclear. In this work, we empirically investigate the prevalence of new entities in news feeds with respect to both public and commercial knowledge graphs.
A Human-in-the-loop Attribute Design Framework for Classification
Authors: Md. Abdus Salam, Mary E. Koone, Saravanan Thirumuruganathan, Gautam Das, Senjuti Basu Roy
Venue: WWW
Year: 2019

In this paper, we present a semi-automated, “human-in-the-loop” framework for attribute design that assists human analysts to transform raw attributes into effective derived attributes for classification problems. Our proposed framework is optimization guided and fully agnostic to the underlying classification model. We present an algebra with various operators (arithmetic, relational, and logical) to transform raw attributes into derived attributes and solve two technical problems: (a) the top-k buckets design problem aims at presenting human analysts with k buckets, each bucket containing promising choices of raw attributes that she can focus on only without having to look at all raw attributes; and (b) the top-l snippets generation problem, which iteratively aids human analysts with top-l derived attributes involving an attribute. For the former problem, we present an effective exact bottom-up algorithm that is empowered by pruning capability, as well as random walk based heuristic algorithms that are intuitive and work well in practice. For the latter, we present a greedy heuristic algorithm that is scalable and effective. Rigorous evaluations are conducted involving 6 different real world datasets to showcase that our framework generates effective derived attributes compared to fully manual or fully automated methods.
A Large-scale Study of Wikipedia Users' Quality of Experience
Authors: Flavia Salutari, Diego N. da Hora, Gilles Dubuc, Dario Rossi
Venue: WWW
Year: 2019

The Web is one of the most successful Internet application. Yet, the quality of Web users' experience is still largely impenetrable. Whereas Web performances are typically gathered with controlled experiments, in this work we perform a large-scale study of one of the most popular websites,namely Wikipedia, explicitly asking (a small fraction of its) users for feedback on the browsing experience. We leverage user survey responses to build a data-driven model of user satisfaction which, despite including state-of-the art quality of experience metrics, is still far from achieving accurate results, and discuss directions to move forward. Finally, we aim at making our dataset publicly available, which hopefully contributes in enriching and refining the scientific community knowledge on Web users' quality of experience (QoE).