OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Matching of Resource Needs and Availabilities in Microblogs for Post-Disaster Relief
Authors: Moumita Basu, Anurag Shandilya, Kripabandhu Ghosh, Saptarshi Ghosh
Venue: WWW
Year: 2018

During a disaster event, it is essential to know about needs and availabilities of different types of resources, for coordinating relief operations. Microblogging sites are frequently used for aiding post-disaster relief operations, and there have been prior attempts to identify tweets that inform about resource needs and availabilities (termed as need-tweets and availability-tweets respectively). However, there has not been much attempt to effectively utilise such tweets. We introduce the problem of automatically matching need-tweets with appropriate availability-tweets, which is practically important for coordination of post-disaster relief operations. We also experiment with several methodologies for automatically matching need-tweets and availability-tweets.
Satire or Fake News: Social Media Consumers' Socio-Demographics Decide
Authors: Michele Bedard, Chianna Schoenthaler
Venue: WWW
Year: 2018

Ever since the surprising results from the 2016 U.S. presidential race, the subject of Fake News in our worldwide media consumption has grown steadily. On a smaller scale, mainstream media have taken a closer look at the relatively narrow genre of satirical news content. Ed Koltonski of Kent State, defines satirical news as designed specifically to entertain the reader, usually with irony or wit, to critique society or a social figure and invoke change or reform. Using field experiment, survey and focus group methods we sought to determine if media consumers' ability to differentiate between satirical news and fake news is tied to socio-demographic factors. We found that age, education, sex, and political affiliation predict understanding of "fake news" and satire. Furthermore, the ability to identify different types of misinformation when presented with screen shots from social media posts appears to be related to these variables. Focus group comments were also analyzed to gain a richer perspective on how participants interpreted the SMS screen shots. Using our primary research, we seek to determine if there is a correlation between social media consumers understanding of the difference between satirical news versus fake news and their varying socio-demographic factors
MetaExp: Interactive Explanation and Exploration of Large Knowledge Graphs
Authors: Freya Behrens, Sebastian Bischoff, Pius Ladenburger, Julius Rückin, Laurenz Seidel, Fabian Stolp, Michael Vaichenker, Adrian Ziegler, Davide Mottin, Fatemeh Aghaei, Emmanuel Müller, Martin Preusse, Nikola Müller, Michael Hunger
Venue: WWW
Year: 2018

We present MetaExp, a system that assists the user during the exploration of large knowledge graphs, given two sets of initial nodes. At its core, MetaExp presents a small set of meta-paths to the user, which are sequences of relationships among node types. Such meta-paths do not overwhelm the user with complex structures, yet they preserve semantically-rich relationships in a graph. MetaExp engages the user in an interactive procedure, which involves simple meta-paths evaluations to infer a user-specific similarity measure. This similarity measure incorporates the domain knowledge and the preferences of the user, overcoming the fundamental limitations of previous methods based on local node neighborhoods or statically determined similarity scores. Our system provides a user-friendly interface for searching initial nodes and guides the user towards progressive refinements of the meta-paths. The system is demonstrated on three datasets, Freebase, a movie database, and a biological network.
Panel on Cognitive Service Engineering
Authors: Boualem Benatallah, Fabio Casati
Venue: WWW
Year: 2018

Cognitive services and conversational digital assistants are emerging as the engine that powers natural interactions between humans, software services, devices and "things" - supported by advances in AI and human computations. Not surprisingly, many large and small tech companies are rushing to occupy this space by providing platforms for building cognitive services and conversational bots. Digital assistants interact in a natural way (through text or voice) with both software and humans to get information and perform actions, from checking the weather to booking a restaurants and a cab ride, managing cloud resources, answering simple scientific questions, and preparing a decaf latte using IoT enabled coffee machines. User requests or tasks are often expressed in natural language, an interaction ensues to clarify the intent and the details, and the answer is sought - or the appropriate service or device is invoked - based on the cognitive service understanding. While the potential of this new wave of services is exciting, it also brings significant challenges: we are far away from the comfort of developing deterministic software that responds to API calls by invoking other APIs. Now we have to understand, guess, explore options, take decisions based on probabilistic models over a large set of possible intents and services, all while engaging with users. Doing so brings a large set of engineering challenges related to the development, training, tuning and evolution of such services. This panel will discuss such challenges and identify interesting opportunities for research as well as promising trends.
Language-Integrated Queries: a BOLDR Approach
Authors: Véronique Benzaken, Giuseppe Castagna, Laurent Daynès, Julien Lopez, Kim Nguyen, Romain Vernoux
Venue: WWW
Year: 2018

We present BOLDR, a modular framework that enables the evaluation in databases of queries containing application logic and, in particular, user-defined functions. BOLDR also allows the nesting of queries for different databases of possibly different data models. The framework detects the boundaries of queries present in an application, translates them into an intermediate representation together with the relevant language environment, rewrites them in order to avoid query avalanches and to make the most out of database optimizations, and converts the results back to the application. Our experiments show that the techniques we implemented are applicable to real-world database applications, successfully handling a variety of language-integrated queries with good performances.
Reflections Regarding ICT and a Citizen-centric Future Path of Smart Sustainable Cities: AW4City 2018 Keynote
Authors: Pernilla Bergmark
Venue: WWW
Year: 2018

This paper looks into the use of Information and Communication Technology (ICT) for Smart Sustainable Cities (SSC). It specifically points towards ICT's potential to help cities mitigate climate change and to support a 2ºC or lower trajectory and to involve citizens in city planning and when implementing solutions. The paper also focuses on the modelling, assessment methodologies and indicators for ICT and sustainability aspects of cities, not least with regards to climate change mitigation. Especially, it highlights the city GG emissions assessment standard by ITU, and the indicator sets from ITU and ISO and their enhancement. The paper emphasizes a sustainability and citizen-centric perspective, seeing ICT as instrumental in this respect. For assessments, the above mentioned ITU-T standard is considered reusable for impacts beyond global warming. For indicators, further research on outcome and impact indicators is suggested, and also strengthening of the socio-economic and cultural aspects.
Spatio-Temporal Analysis for Smart City Data
Authors: María Bermúdez-Edo, Payam M. Barnaghi
Venue: WWW
Year: 2018

The data gathered from smart cities can help citizens and city manager planners know where and when they should be aware of the repercussions regarding events happening in different parts of the city. Most of the smart city data analysis solutions are focused on the events and occurrences of the city as a whole, making it difficult to discern the exact place and time of the consequences of a particular event. We propose a novel method to model the events in a city in space and time. We apply our methodology for vehicular traffic data basing our models in (convolutional) neuronal networks.
BABELTOWER: How Language Affects Criminal Activity in Stolen Webmail Accounts
Authors: Emeric Bernard-Jones, Jeremiah Onaolapo, Gianluca Stringhini
Venue: WWW
Year: 2018

We set out to understand the effects of differing language on the ability of cybercriminals to navigate webmail accounts and locate sensitive information in them. To this end, we configured thirty Gmail honeypot accounts with English, Romanian, and Greek language settings. We populated the accounts with email messages in those languages by subscribing them to selected online newsletters. We also hid email messages about fake bank accounts in fifteen of the accounts to mimic real-world webmail users that sometimes store sensitive information in their accounts. We then leaked credentials to the honey accounts via paste sites on the Surface Web and the Dark Web, and collected data for fifteen days. Our statistical analyses on the data show that cybercriminals are more likely to discover sensitive information (bank account information) in the Greek accounts than the remaining accounts, contrary to the expectation that Greek ought to constitute a barrier to the understanding of non-Greek visitors to the Greek accounts. We also extracted the important words among the emails that cybercriminals accessed (as an approximation of the keywords that they possibly searched for within the honey accounts), and found that financial terms featured among the top words. In summary, we show that language plays a significant role in the ability of cybercriminals to access sensitive information hidden in compromised webmail accounts.
Twitter and the Press: an Ego-Centred Analysis
Authors: Chiara Boldrini, Mustafa Toprak, Marco Conti, Andrea Passarella
Venue: WWW
Year: 2018

Ego networks have proved to be a valuable tool for understanding the relationships that individuals establish with their peers, both in offline and online social networks. Particularly interesting are the cognitive constraints associated with the interactions between the ego and the members of their ego network, whereby individuals cannot maintain meaningful interactions with more than 150 people, on average. In this work, we focus on the ego networks of journalists on Twitter, and we investigate whether they feature the same characteristics observed for other relevant classes of Twitter users, like politicians and generic users. Our findings are that journalists are generally more active and interact with more people than generic users. Their ego network structure is very aligned with reference models derived from the social brain hypothesis and observed in general human ego networks. Remarkably, the similarity is even higher than the one of politicians and generic users ego networks. This may imply a greater cognitive involvement with Twitter than with other social interaction means. Moreover, the ego networks of journalists are much stabler than those of politicians and generic users, and the ego-alter ties are often information-driven.
DARQL: Deep Analysis of SPARQL Queries
Authors: Angela Bonifati, Wim Martens, Thomas Timm
Venue: WWW
Year: 2018

In this demonstration, we showcase DARQL, the first tool for deep, large-scale analysis of SPARQL queries. We have harvested a large corpus of query logs with different lineage and sizes, from DBPedia to BioPortal and Wikidata, whose total number of queries amounts to 180M. We ran a wide range of analyses on the corpus, spanning from simple tasks (keyword counts, triple counts, operator distributions), moderately deep tasks (projection test, query classification), and deep analysis (shape analysis, well-designedness, weakly well-designedness, hypertreewidth, and fractional edge cover). The key goal of our demonstration is to let the users dive into the SPARQL query logs of our corpus and let them discover the inherent characteristics of the queries. The entire corpus of SPARQL queries is stored in a DBMS. The tool has a GUI that allows users to ask sophisticated analytical queries on the SPARQL logs. These analytical queries can both be directly written in SQL or composed by a visual query builder tool. The results of the analytical queries are represented both textually (as SPARQL queries) and visually. The DBMS performs the searches within the corpus quite efficiently. To the best of our knowledge, this is the first demonstration of this kind on such a large corpus and with such a number of varied tests.
Panels Track Chairs' Welcome
Authors: Angela Bonifati, Romain Vuillemot
Venue: WWW
Year: 2018

It is our great pleasure to welcome you to the WWW 2018 Panels Track. We have selected three panel proposals among the ones received. We also took in account the coverage of the different areas related to WWW, inputs from the community with a shared document, as well as the suggestions of the authors of the research tracks in deciding the subjects of the three panels.
Towards Component-based, Domain-specific, Efficient Question Answering Systems
Authors: Andreas Both
Venue: WWW
Year: 2018

In the past, the research, as well as industry, brought Question Answering (QA) into daily use. However, there is still an obvious gap between the claim of providing access to any--structured or unstructured--knowledge stored in the world using an interface fitting the demands of regular users. On the one hand side, implementing Question Answering Systems is still hard and time-consuming, on the other hand side, the QA community is still struggling on defining a common ground for collaboration across research fields regarding, for example, realistic benchmarks, maintainability, and broad coverage of knowledge sources. In the talk, challenges of Question Answering will be highlight w.r.t. hybrid QA, domain-specific QA, cross knowledge base QA, etc. Particularly the industry perspective is also presented while aiming at a Question Answering platform which can be assembled from industry components as well as components of the research community. First steps towards this long-term vision are provided by the Qanary framework and similar frameworks aiming at a collaborative approach for the development QA systems which should lead to effective implementations, improved research results as well as a platform economy for QA. Such a platform would industry lead to a tighter collaboration while academics would have the opportunity of accessing precious data for further improvements.
Practical Privacy-Preserving Friend Recommendations on Social Networks
Authors: William Brendel, Fangqiu Han, Luís Marujo, Luo Jie, Aleksandra Korolova
Venue: WWW
Year: 2018

Making friend recommendations is an important task for social networks, as having more friends typically leads to a better user experience. Most current friend recommendations systems grow the existing network at the cost of privacy. In particular, any given user's friend graph may be directly or indirectly leaked as a result of such recommendations. In many situations this is not desirable, as the friend list may reveal much about the user--from their identity to their sexual orientation and interests. In this work, we focus on the "cold start" problem of making friend recommendations for new users while raising the bar on protecting the privacy of the friend list of all users. We propose a practical friend recommendation framework, tested on the Snapchat social network, that preserves the privacy of users' friends lists with respect to brute-force attacks and scales to millions of users.
Personalized Real-Time Monitoring of Amateur Cyclists on Low-End Devices: Proof-of-Concept & Performance Evaluation
Authors: Mathias De Brouwer, Femke Ongenae, Glenn Daneels, Esteban Municio, Jeroen Famaey, Steven Latré, Filip De Turck
Venue: WWW
Year: 2018

Enabling real-time collection and analysis of cyclist sensor data could allow amateur cyclists to continuously monitor themselves, receive personalized feedback on their performance, and communicate with each other during cycling events. Semantic Web technologies enable intelligent consolidation of all available context and sensor data. Stream reasoning techniques allow to perform advanced processing tasks by correlating the consolidated data to enable personalized and context-aware real-time feedback. In this paper, these technologies are leveraged and evaluated to design a Proof-of-Concept application of a personalized real-time feedback platform for amateur cyclists. Real-time feedback about the user's heart rate and heart rate training zones is given through a web application. The performance and scalability of the platform is evaluated on a Raspberry Pi. This shows the potential of the framework to be used in real-life cycling by small groups of amateur cyclists, who can only access low-end devices during events and training.
A Formal Semantics of the Core DOM in Isabelle/HOL
Authors: Achim D. Brucker, Michael Herzberg
Venue: WWW
Year: 2018

At its core, the Document Object Model (DOM) defines a tree-like data structure for representing documents in general and HTML documents in particular. It is the heart of any modern web browser. Formalizing the key concepts of the DOM is a prerequisite for the formal reasoning over client-side JavaScript programs and for the analysis of security concepts in modern web browsers. We present a formalization of the core DOM, with focus on the node-tree and the operations defined on node-trees, in Isabelle/HOL. We use the formalization to verify the functional correctness of the most important functions defined in the DOM standard. Moreover, our formalization is extensible, i.e., can be extended without the need of re-proving already proven properties and executable, i.e., we can generate executable code from our specification.
Network Model Selection Using Task-Focused Minimum Description Length
Authors: Ivan Brugere, Tanya Y. Berger-Wolf
Venue: WWW
Year: 2018

Networks are fundamental models for data used in practically every application domain. In most instances, several implicit or explicit choices about the network definition impact the translation of underlying data to a network representation, and the subsequent question(s) about the underlying system being represented. Users of downstream network data may not even be aware of these choices or their impacts. We propose a task-focused network model selection methodology which addresses several key challenges. Our approach constructs network models from underlying data and uses minimum description length (MDL) criteria for selection. Our methodology measures efficiency, a general and comparable measure of the network's performance of a local (i.e. node-level) predictive task of interest. Selection on efficiency favors parsimonious (e.g. sparse) models to avoid overfitting and can be applied across arbitrary tasks and representations. We show stability, sensitivity, and significance testing in our methodology.
Real-Time Emulation of a Marshall JCM 800 Guitar Tube Amplifier, Audio FX Pedals, in a Virtual Pedal Board
Authors: Michel Buffa, Jérôme Lebrun
Venue: WWW
Year: 2018

The ANR project WASABI will last 42 months and consists in developing a 2 million songs database with interactive WebAudio enhanced client applications. Client applications target composers, music schools, sound engineering schools, musicologists, music streaming services and journalists. In this paper, we present a virtual pedal board (a set of chainable audio effects on the form of "pedals"), and a guitar tube amplifier simulation for guitarists, that will be associated with songs from the WASABI database. Music schools and music engineering schools are interested in such tools that can be run in a Web page, without the need to install any further software. Take a classic rock song: isolate the guitar solo, study it, then mute it and play guitar real-time along the other tracks using an online guitar amplifier that reproduces the real guitar amp model used in the song, with its signature sound, proper dynamic and frequency response. Add some audio effects such as a reverberation, a delay, a flanger, etc. in order to reproduce Pink Floyd's guitar sound or Eddie Van Halen famous "Brown Sound". Learn interactively, guitar in hands, how to fine tune a compressor effect, or how to shape the sound of a tube guitar amp, how to get a "modern metal" or a "Jimi Hendrix" sound, using only your Web browser.
Towards an open Web Audio plugin standard
Authors: Michel Buffa, Jérôme Lebrun, Jari Kleimola, Oliver Larkin, Stéphane Letz
Venue: WWW
Year: 2018

Web Audio is a recent W3C API that brings the world of computer music applications to the browser. Although developers have been actively using it since the first beta implementations in 2012, the number of web apps built using Web Audio API cannot yet compare to the number of commercial and open source audio software tools available on native platforms. Many of the sites using this new technology are of an experimental nature or are very limited in their scope. While JavaScript and Web standards are increasingly flexible and powerful, C and C++ are the languages most often used for real-time audio applications and domain specific languages such as FAUST facilitate rapid development with high performance. Our work aims to create a continuum between native and browser based audio app development and to appeal to programmers from both worlds. This paper presents our proposal including guidelines and proof of concept implementations for an open Web Audio plug-in standard - essentially the infrastructure to support high level audio plug-ins for the browser.
Reinforcement Mechanism Design for e-commerce
Authors: Qingpeng Cai, Aris Filos-Ratsikas, Pingzhong Tang, Yiwei Zhang
Venue: WWW
Year: 2018

We study the problem of allocating impressions to sellers in e-commerce websites, such as Amazon, eBay or Taobao, aiming to maximize the total revenue generated by the platform. We employ a general framework of reinforcement mechanism design, which uses deep reinforcement learning to design efficient algorithms, taking the strategic behaviour of the sellers into account. Specifically, we model the impression allocation problem as a Markov decision process, where the states encode the history of impressions, prices, transactions and generated revenue and the actions are the possible impression allocations in each round. To tackle the problem of continuity and high-dimensionality of states and actions, we adopt the ideas of the DDPG algorithm to design an actor-critic policy gradient algorithm which takes advantage of the problem domain in order to achieve convergence and stability. We evaluate our proposed algorithm, coined IA(GRU), by comparing it against DDPG, as well as several natural heuristics, under different rationality models for the sellers - we assume that sellers follow well-known no-regret type strategies which may vary in their degree of sophistication. We find that IA(GRU) outperforms all algorithms in terms of the total revenue.