OpenRaiser/Intern-Atlas huggingface dataset:
Measuring the Impact of Topic Drift in Scholarly Networks
Authors: Tehmina Amjad, Ali Daud, Min Song
Venue: WWW
Year: 2018

With the increase in collaboration among researchers of various disciplines, changing the research topic or working on multiple topics is not an unusual behavior. Several comprehensive efforts have been made for predicting, quantifying, and studying the researcher's impact. The question, that how the change in the field of interest over time or working in more than one topics can influence the scientific impact, remains unanswered. In this research, we study the effect of topic drift on the scientific impact of an author. We apply Author Conference Topic (ACT) model to extract topic distribution of individual authors who are working on multiple topics to compare and analyze with authors who work on a single topic. We analyze the productivity of the authors on the basis of publication count, citation count and h-index. We find that authors who stick to one topic, produce a higher impact and gain more attention. To further strengthen our results we gather the h-index of top-ranked authors working on one topic and top-ranked authors working on multiple topics and examine whether there are similar trends in their progress. The results show an evidence of significant impact of topic drift on career choices of researchers.
Smart Transportation Applications' Business Models: A Comparison
Authors: Leonidas G. Anthopoulos, Amel Attour
Venue: WWW
Year: 2018

An increasing amount of applications can be located in several cities that attempt to deal with mobility issues like traffic management, transportation safety, congestion control, taxi booking, car sharing, carpooling etc. The aim of this work in progress article is to collect information with regard to carpooling applications and attempt to recognize the underlying business models.
AW4City 2018 Chairs' Welcome & Organization
Authors: Leonidas G. Anthopoulos, Marijn Janssen, Vishanth Weerakkody
Venue: WWW
Year: 2018

Following up the success of the past events at WWW2015, WWW2016 and WWW2017, the 4th AW4City 2018 aims to keep on attracting a significant international attention with regard to web applications for smart cities. More specifically, the aim of this workshop is to focus on the applications smart city component and more specifically on the design and implementation of web-based applications and Apps that deliver smart services or address smart city challenges. This year, the proposed workshop will emphasize on the contribution of web applications and Apps to citizen centricity. In the era of cities, municipal leaders, service and utility providers are making an important shift regarding thinking of people as customers and of customer experience. This shift is not a simple task since it demands a continuous service monitoring, assessment and improvement [1;2;3], which normally is based on accurate data analysis and appears as a thinking that makes government and providers more personal and responsive.
Shut Up and Run: the Never-ending Quest for Social Fitness
Authors: Linda Anticoli, Marco Basaldella
Venue: WWW
Year: 2018

In this paper we explore possible negative drawbacks in the use of wearable sensors, i.e., wearable devices used to detect different kinds of activity, e.g., from step and calories counting to heart rate and sleep monitoring. These technologies, which in the latter years witnessed a rapid development in terms of accuracy and diffusion, are now available on different platforms at reasonable prices and can lead to an healthier behavior in people using them. Nevertheless, we will try to investigate possibly harming behaviors related to these devices. We will provide different scenarios in which wearable sensors, in connection with social media, data mining, or other technologies, could prove harmful for their users.
Incremental Matrix Co-factorization for Recommender Systems with Implicit Feedback
Authors: Susan C. Anyosa, João Vinagre, Alípio M. Jorge
Venue: WWW
Year: 2018

Recommender systems try to predict which items a user will prefer. Traditional models for recommendation only take into account the user-item interaction, usually expressed by explicit ratings. However, in these days, web services continuously generate auxiliary data from users and items that can be incorporated into the recommendation model to improve recommendations. In this work, we propose an incremental Matrix Co-factorization model with implicit user feedback, considering a real-world data-stream scenario. This model can be seen as an extension of the conventional Matrix Factorization that includes additional dimensions to be decomposed in the common latent factor space. We test our proposal against a baseline algorithm that relies exclusively on interaction data, using prequential evaluation. Our experimental results show a significant improvement in the accuracy of recommendations, after incorporating an additional dimension in three music domain datasets.
Characterizing Efficient Referrals in Social Networks
Authors: Reut Apel, Elad Yom-Tov, Moshe Tennenholtz
Venue: WWW
Year: 2018

Users of social networks often focus on specific areas of that network, leading to the well-known "filter bubble" effect. Connecting people to a new area of the network in a way that will cause them to become active in that area could help alleviate this effect and improve social welfare. Here we present preliminary analysis of network referrals, that is, attempts by users to connect peers to other areas of the network. We classify these referrals by their efficiency, i.e., the likelihood that a referral will result in a user becoming active in the new area of the network. We show that by using features describing past experience of the referring author and the content of their messages we are able to predict whether referral will be effective, reaching an AUC of 0.87 for those users most experienced in writing efficient referrals. Our results represent a first step towards algorithmically constructing efficient referrals with the goal of mitigating the "filter bubble" effect pervasive in on line social networks.
GeoSensor: On-line Scalable Change and Event Detection over Big Data
Authors: Giorgos Argyriou, George Papadakis, George Stamoulis, Efi Karra Taniskidou, Nikiforos Pittaras, George Giannakopoulos, Sergio Albani, Michele Lazzarini, Emanuele Angiuli, Anca Popescu, Argyros Argyridis, Manolis Koubarakis
Venue: WWW
Year: 2018

GeoSensor is a novel system that enriches change detection over satellite images with event detection over news items and social media content. GeoSensor faces the major challenges of Big Data: volume (a single satellite image may be a few GBs), variety (its data sources include two different types of satellite images and various types of user-generated content) and veracity, as the accuracy of the end result is crucial for the usefulness of our system. To overcome these three challenges, while offering on-line functionality, GeoSensor comprises a complex architecture that is based on the open-source platform developed in the H2020 project Big Data Europe. Through the presented demonstration, both the effectiveness and the efficiency of GeoSensor's functionalities are highlighted.
Augmenting Intelligence with Humans-in-the-Loop (HumL@WWW2018) Chairs' Welcome & Organization
Authors: Lora Aroyo, Gianluca Demartini, Anna Lisa Gentile, Chris Welty
Venue: WWW
Year: 2018

It is our great pleasure to welcome you to the WWW 2018 Augmenting Intelligence with Humans-in-the-loop ([email protected]), http://w3id.org/huml/HumL-WWW2018/ The workshop program includes two invited talks. Praveen Paritosh (Google Research) explores the right incentives to motivate human contribution to create knowledge resources. Elena Simperl (University of Southampton) surveys how humans and bots contribute together to the development of the Wikidata knowledge graph. Seven full papers and one short paper were accepted, covering a wide range of topics related to the efficient and effective combination of the strong sides of both machine and crowd computation. Empirical results were provided and discussed with respect to (1) methods for data quality ensurance and labeling task efficiency, (2) the role of gamification elements for improving crowd performance as well as the role of quantum mathematics to simulate human behavior.
The Quantum Collective
Authors: Lora Aroyo, Chris Welty
Venue: WWW
Year: 2018

AI and collective intelligence systems universally suffer from a deficiency of context. There are innumerable possible contexts that may possibly change the interpretation of some signal, that may change the proper response to some stimulus. For example, an image understanding system that does not recognize an arrest event in a zoomed image of a person's face. How is it possible to know there is more information, outside of what the system can access, that affects the interpretation of data The solution to the context problem in practice today is a pragmatic, engineering one: analyze errors (in recommendations, question answers, image recognition, etc.), classify the kinds of contextual information that caused the wrong behavior, find the most common type of context that causes errors, and add information about that kind of context to the system. Clearly this approach is neither general nor scalable, and ignores the infamous long tail of possible contextual information that may affect a system's understanding and its behavior. In this paper we outline a new, more general, approach to recognizing context. The approach is grounded in a fairly simple intuition: the mathematics underlying quantum mechanics is far more appropriate for modeling, and therefore simulating, human cognitive behavior than the standard toolset from classical statistics. Notions such as Heisenberg's uncertainty principle, superpositions of states, and entanglement have direct and measurable analogs in collective intelligence.
Enhancing Community Interactions with Data-Driven Chatbots-The DBpedia Chatbot
Authors: Ram G. Athreya, Axel-Cyrille Ngonga Ngomo, Ricardo Usbeck
Venue: WWW
Year: 2018

In this demo, we introduce the DBpedia chatbot, a knowledge-graph-driven chatbot designed to optimize community interaction. The bot was designed for integration into community software to facilitate the answering of recurrent questions. Four main challenges were addressed when building the chatbot, namely (1) understanding user queries, (2) fetching relevant information based on the queries, (3) tailoring the responses based on the standards of each output platform (i.e. Web, Slack, Facebook) as well as (4) developing subsequent user interactions with the DBpedia chatbot. With this demo, we will showcase our solutions to these four challenges.
International Workshop on Modeling Social Media (MSM 2018) Chairs' Welcome & Organization
Authors: Martin Atzmueller, Alvin Chin, Christoph Trattner
Venue: WWW
Year: 2018

Bienvenue! It is our great pleasure to welcome you to the WWW 2018 International Workshop on Modeling Social Media (MSM'2018) - Applying Machine Learning and AI for Modeling Social Media. This is our 9th edition of our workshop. Social networks such as Facebook, Twitter, and LinkedIn have paved the way for generating huge amount of diverse, streaming bit data in a short period of time. Such social media data require the application of big data analytics to produce meaningful information to both information consumers and data generators. Machine learning and AI techniques are particularly effective in situations where deep and predictive insights need to be uncovered from such social media data sets that are large, diverse and fast changing. The workshop aims to address machine learning and AI methods, frameworks, algorithms, and the applications and evaluation of these approaches on social media, big data and the web. We received 11 papers from all around the world covering a broad range of topics, and we accepted 8 papers resulting in a 72% acceptance rate. We evaluated them regarding relevance, quality, and novelty, selecting 4 full papers and 4 short papers. Each paper was reviewed by 3 reviewers and then decisions were made from the reviews and the workshop chairs.
Homophily at Academic Conferences
Authors: Martin Atzmueller, Florian Lemmerich
Venue: WWW
Year: 2018

Academic conferences are a backbone for the exchange of ideas in scientific communities. However, so far little is known about the communication networks emerging at those venues. Besides personal knowledge, network homophily has been identified as a driving factor for establishing contacts and followerships in social networks, i.e., people are more likely to engage with others if they are similar with respect to certain attributes. In this paper, we describe work in progress on investigating homophily at four academic conferences based on face-to-face (F2F) contact data collected using wearable sensors between conference participants. In particular, we study which personal attributes are predictive for face-to-face contacts. For that purpose, we obtained diverse personal attributes from online sources in order to elicit a variety of hypotheses, which can then be compared using descriptive statistics and a Bayesian method for comparing hypotheses in networks. Our results suggest that personal knowledge (as derived from DBLP and ResearchGate networks) and homophilic behavior with respect to several attributes, e.g., gender or country of origin, are important factors for contacts at academic conferences.
Preferential Attachment as a Unique Equilibrium
Authors: Chen Avin, Avi Cohen, Pierre Fraigniaud, Zvi Lotker, David Peleg
Venue: WWW
Year: 2018

This paper demonstrates that the Preferential Attachment rule naturally emerges in the context of evolutionary network formation, as the unique Nash equilibrium of a simple social network game. In this game, each node aims at maximizing its degree in the future, representing its social capital in the "society" formed by the nodes and their connections. This result provides additional formal support to the commonly used Preferential Attachment model, initially designed to capture the "rich get richer" aphorism. In the process of establishing our result, we expose new connections between Preferential Attachment, random walks, and Young»s Lattice.
Truth or Lie: Automatically Fact Checking News
Authors: Lucas Azevedo
Venue: WWW
Year: 2018

In the actual scenario of ever-growing data consumption speed and quantity, factors like news source decentralization, citizen journalism and democratization of media, make the task of manually checking and correcting disinformation across the internet impractical or infeasible . Here, there is an imperative need for a fast and reliable way to account for the veracity of what is produced and spread as information: Automatic fact-checking. In this work we present the problem of fact-checking in the era of big data and post-truth. Some existing approaches for this task are presented and their main features discussed and compared. Concluding, a new approach inspired on the best components of the existing ones is presented.
Patient Selection for Clinical Trials Using Temporalized Ontology-Mediated Query Answering
Authors: Franz Baader, Stefan Borgwardt, Walter Forkel
Venue: WWW
Year: 2018

Finding suitable candidates for clinical trials is a labor-intensive task that requires expert medical knowledge. Our goal is to design (semi-)automated techniques that can support clinical researchers in this task. We investigate the issues involved in designing formal query languages for selecting patients that are eligible for a given clinical trial, leveraging existing ontology-based query answering techniques. In particular, we propose to use a temporal extension of existing approaches for accessing data through ontologies written in Description Logics. We sketch how such a query answering system could work and show that eligibility criteria and patient data can be adequately modeled in our formalism.
The Data Complexity of Answering Instance Queries in FL0
Authors: Franz Baader, Pavlos Marantidis, Maximilian Pensel
Venue: WWW
Year: 2018

Ontology-mediated query answering can be used to access large data sets through a mediating ontology. It has drawn considerable attention in the Description Logic (DL) community where both the complexity of query answering and practical query answering approaches based on rewriting were investigated in detail. Surprisingly, there is still a gap in what is known about the data complexity of query answering w.r.t. ontologies formulated in the inexpressive DL FL0. While it is known that the data complexity of answering conjunctive queries w.r.t. FL0 ontologies is coNP-complete, the exact complexity of answering instance queries was open until now. In the present paper, we show that answering instance queries w.r.t. FL0 ontologies is in P for data complexity. Together with the known lower bound of P-completeness for a fragment of FL0, this closes the gap mentioned above.
Virtual Representations for an Iterative IoT Deployment
Authors: Sebastian R. Bader, Maria Maleshkova
Venue: WWW
Year: 2018

A central vision of the Internet of Things is the representation of the physical world in a consistent virtual environment. Especially in the context of smart factories the connection of the different, heterogeneous production modules through a digital shop floor promises faster conversion rates, data-driven maintenance or automated machine configurations for use cases which haven't been recognized at design time. Nevertheless, these scenarios demand IoT representations of all participating machines and components, which requires high installation efforts and hardware adjustments. We propose an incremental process for bringing the shop floor closer to the IoT vision. Currently the majority of systems, components or parts are not yet connected with the internet and might not even provide the possibility to be technically equipped with sensors. However, those could be essential parts for a realistic digital shop floor representation. We therefore propose Virtual Representations, which are capable of independently calculating a physical object's condition by dynamically collecting and interpreting already available data through RESTful Web APIs. The internal logic of such Virtual Representations are further adjustable at runtime since changes to its respective physical object, its environment or updates to the resource itself should not cause any downtime.
API Learning: Applying Machine Learning to Manage the Rise of API Economy
Authors: Mehdi Bahrami, Junhee Park, Lei Liu, Wei-Peng Chen
Venue: WWW
Year: 2018

Application Programming Interface (API) exposes data and functions of a software application to third-party users. In digital business, API economy is one of the key component for determining the value of provided services. With the rise in number of publicly available APIs, understanding each API endpoint manually is not only labor intensive but it is also an error prone task for software engineers. Due to the complexity of understanding the sheer number of APIs, it is difficult for software developers to find the best possible API combinations (i.e. API Mashups). In this demonstration, we introduce API Learning platform which employs machine-learning based technologies to efficiently search APIs, validate APIs, and generate API mashups. These technologies enable a machine to automatically generate machine-readable API specification from API documentations, understand variety of APIs, validate extracted information through automated API validation, and finally recommend API mashups for a specific purpose. As of now, API Learning platform collected over 14,000 API documentations and generates a machine readable format for REST APIs with an accuracy of 84%. The proposed demo prototype shows how it enables users to quickly find relevant APIs, automatically verify API availability, and get the best possible API mashup recommendations.
When Trust Saves Energy: A Reference Framework for Proof of Trust (PoT) Blockchains
Authors: Leila Bahri, Sarunas Girdzijauskas
Venue: WWW
Year: 2018

Blockchains are attracting the attention of many technical, financial, and industrial parties, as a promising infrastructure for achieving secure peer-to-peer (P2P) transactional systems. At the heart of blockchains is proof-of-work (PoW), a trustless leader election mechanism based on demonstration of computational power. PoW provides blockchain security in trusless P2P environments, but comes at the expense of wasting huge amounts of energy. In this research work, we question this energy expenditure of PoW under blockchain use cases where some form of trust exists between the peers. We propose a Proof-of-Trust (PoT) blockchain where peer trust is valuated in the network based on a trust graph that emerges in a decentralized fashion and that is encoded in and managed by the blockchain itself. This trust is then used as a waiver for the difficulty of PoW; that is, the more trust you prove in the network, the less work you do.
Identifying Your Representative Work Based on Credit Allocation
Authors: Peng Bao, Jiahui Wang
Venue: WWW
Year: 2018

With the rapid development of scientific impact quantification in the field of science of success, the ability to identify the representative work of a researcher has important implications in a wide range of areas, including hiring, funding, and promotion systems. In this paper, we propose a two-step credit allocation algorithm (TSCA) for identifying the representative work of a researcher. This algorithm explicitly captures the importance of a paper, its relevance to other papers, and the unequally distributed contribution of each citation. We validate TSCA by applying it on the citation data from American Physical Society (APS) in the scenario of identifying the Nobel prize winning papers of the Nobel laureates. Experiments demonstrate that the proposed algorithm can significantly outperform the existing methods.
Web Stream Processing Workshop Chairs' Welcome & Organization
Authors: Payam M. Barnaghi, Jean-Paul Calbimonte, Daniele Dell&apos, Aglio
Venue: WWW
Year: 2018

Applications in different domains require reactive processing of massive, dynamically generated streams of data. This trend is increasingly visible also on the Web, where more and more streaming sources are becoming available. These originate from social networks, sensor networks, the Internet of Things (IoT) and many other technologies that use the Web as a platform for sharing data. This has resulted in new Web-centric efforts such as the Web of Things (WoT), which focuses on exposing and describing the IoT resources on the Web; or the Social Web which provides protocols, vocabularies, and APIs to facilitate access to social communications and interactions on the Web.
From MultiJEDI to MOUSSE: Two ERC Projects for Innovating Multilingual Disambiguation and Semantic Parsing of Text
Authors: Valerio Basile, Roberto Navigli
Venue: WWW
Year: 2018

The exponential growth of the Web is resulting in vast amounts of online content. However, the information expressed therein is not at easy reach: what we typically browse is only an infinitesimal part of the Web. And even if we had time to read all the Web we could not understand it, as most of it is written in languages we do not speak. Rather than time, a key problem for a machine is language comprehension, that is, enabling a machine to transform sentences, i.e., sequences of characters, into machine-readable semantic representations linked to existing meaning inventories such as computational lexicons and knowledge bases.