OpenRaiser/Intern-Atlas huggingface dataset:
Innovative IoT-aware Services for a Smart Museum
Authors: Vincenzo Mighali, Giuseppe Del Fiore, Luigi Patrono, Luca Mainetti, Stefano Alletto, Giuseppe Serra, Rita Cucchiara
Venue: WWW
Year: 2015

Smart cities are a trading topic in both the academic literature and industrial world. The capability to provide the users with added-value services through low-power and low-cost smart objects is very attractive in many fields. Among these, art and culture represent very interesting examples, as the tourism is one of the main driving engines of modern society. In this paper, we propose an IoT-aware architecture to improve the cultural experience of the user, by involving the most important recent innovations in the ICT field. The main components of the proposed architecture are: (i) an indoor localization service based on the Bluetooth Low Energy technology, (ii) a wearable device able to capture and process images related to the user's point of view, (iii) the user's mobile device useful to display customized cultural contents and to share multimedia data in the Cloud, and (iv) a processing center that manage the core of the whole business logic. In particular, it interacts with both wearable and mobile devices, and communicates with the outside world to retrieve contents from the Cloud and to provide services also to external users. The proposal is currently under development and it will be validated in the MUST museum in Lecce.
From "Selena Gomez" to "Marlon Brando": Understanding Explorative Entity Search
Authors: Iris Miliaraki, Roi Blanco, Mounia Lalmas
Venue: WWW
Year: 2015

Consider a user who submits a search query "Shakira" having a specific search goal in mind (such as her age) but at the same time willing to explore information for other entities related to her, such as comparable singers. In previous work, a system called Spark, was developed to provide such search experience. Given a query submitted to the Yahoo search engine, Spark provides related entity suggestions for the query, exploiting, among else, public knowledge bases from the Semantic Web. We refer to this search scenario as explorative entity search. The effectiveness and efficiency of the approach has been demonstrated in previous work. The way users interact with these related entity suggestions and whether this interaction can be predicted have however not been studied. In this paper, we perform a large-scale analysis into how users interact with the entity results returned by Spark. We characterize the users, queries and sessions that appear to promote an explorative behavior. Based on this analysis, we develop a set of query and user-based features that reflect the click behavior of users and explore their effectiveness in the context of a prediction task.
Children Seen But Not Heard: When Parents Compromise Children's Online Privacy
Authors: Tehila Minkus, Kelvin Liu, Keith W. Ross
Venue: WWW
Year: 2015

Children's online privacy has garnered much attention in media, legislation, and industry. Adults are concerned that children may not adequately protect themselves online. However, relatively little discussion has focused on the privacy breaches that may occur to children at the hands of others, namely, their parents and relatives. When adults post information online, they may reveal personal information about their children to other people, online services, data brokers, or surveillant authorities. This information can be gathered in an automated fashion and then linked with other online and offline sources, creating detailed profiles which can be continually enhanced throughout the children's lives.
TrueView: Harnessing the Power of Multiple Review Sites
Authors: Amanda J. Minnich, Nikan Chavoshi, Abdullah Mueen, Shuang Luan, Michalis Faloutsos
Venue: WWW
Year: 2015

Online reviews on products and services can be very useful for customers, but they need to be protected from manipulation. So far, most studies have focused on analyzing online reviews from a single hosting site. How could one leverage information from multiple review hosting sites? This is the key question in our work. In response, we develop a systematic methodology to merge, compare, and evaluate reviews from multiple hosting sites. We focus on hotel reviews and use more than 15 million reviews from more than 3.5 million users spanning three prominent travel sites. Our work consists of three thrusts: (a) we develop novel features capable of identifying cross-site discrepancies effectively, (b) we conduct arguably the first extensive study of cross-site variations using real data, and develop a hotel identity-matching method with 93% accuracy, (c) we introduce the TrueView score, as a proof of concept that cross-site analysis can better inform the end user. Our results show that: (1) we detect 7 times more suspicious hotels by using multiple sites compared to using the three sites in isolation, and (2) we find that 20% of all hotels appearing in all three sites seem to have low trustworthiness score. Our work is an early effort that explores the advantages and the challenges in using multiple reviewing sites towards more informed decision making.
Finding Relevant Indian Judgments using Dispersion of Citation Network
Authors: Akshay Minocha, Navjyoti Singh, Arjit Srivastava
Venue: WWW
Year: 2015

We construct a complex citation network of a subset of Indian Constitutional Articles and the legal judgments that invoke them. We describe, how this dataset is constructed and also introduce the term of dispersion from network science related to social networks, in the context of legal relevance. Our research shows that dispersion is a decisive structural feature to show the importance of relevant legal judgments and landmark decisions. Our method provides similarity information about the document in question, which otherwise remains undetected by standard citation metrics.
EXPOSÉ: EXploring Past news fOr Seminal Events
Authors: Arunav Mishra, Klaus Berberich
Venue: WWW
Year: 2015

Recent increases in digitization and archiving efforts on news data have led to overwhelming amounts of online information for general users, thus making it difficult for them to retrospect on past events. One dimension along which past events can be effectively organized is time. Motivated by this idea, we introduce EXPOSÉ, an exploratory search system that explicitly uses temporal information associated with events to link different kinds of information sources for effective exploration of past events. In this demonstration, we use Wikipedia and news articles as two orthogonal sources. Wikipedia is viewed as an event directory that systematically lists seminal events in a year; news articles are viewed as a source of detailed information on each of these events. To this end, our demo includes several time-aware retrieval approaches that a user can employ for retrieving relevant news articles, as well as a timeline tool for temporal analysis and entity-based facets for filtering results.
Rapido: A Sketching Tool for Web API Designers
Authors: Ronnie Mitra
Venue: WWW
Year: 2015

Well-designed Web APIs must provide high levels of usability and must "get it right" on the first release. One strategy for accomplishing this feat is to identify usability issues early in the design process before a public release. Sketching is a useful way of improving the user experience early in the design phase. Designers can create many sketches and learn from them. The Rapido tool is designed to automate the Web API sketching process and help designers improve usability in an iterative fashion.
On Skewed Distributions and Straight Lines: A Case Study on the Wiki Collaboration Network
Authors: Osnat Mokryn, Alexey Reznik
Venue: WWW
Year: 2015

In this paper, we present a hypothesis that power laws are found only in datasets sampled from a static data, in which each and every item has gained its maximal importance and is not in the process of changing it during the sampling period. We motivate our hypothesis by examining languages, and word-ranking distribution as it appears in books, and in the Bible. To demonstrate the validity of our hypothesis, we experiment with the Wikipedia edit collaboration network. We find that the dataset fits a skewed distribution. Next, we identify its dynamic part. We then show that when the modified part is removed from the obtained dataset, the remaining static part exhibits a good fit to a power law distribution.
Navigation Leads Selection Considering Navigational Value of Keywords
Authors: Róbert Móro, Mária Bieliková
Venue: WWW
Year: 2015

Searching a vast information space such as the Web presents a challenging task and even more so, if the domain is unknown and the character of the task is thus exploratory in its nature. We have proposed a method of exploratory navigation based on navigation leads, i.e. terms that help users to filter the information space of a digital library. In this paper, we focus on the selection of the leads considering their navigational value. We employ clustering based on topic modeling using LDA (Latent Dirichlet Allocation). We present results of a preliminary evaluation on the Annota dataset containing more than 50,000 research papers.
ECOLE: Student Knowledge Assessment in the Education Process
Authors: Dmitry Mouromtsev, Fedor Kozlov, Liubov Kovriguina, Olga Parkhimovich
Venue: WWW
Year: 2015

The paper concerns estimation of students' knowledge based on their learning results in the ECOLE system. ECOLE is the online eLearning system which functionality is based on several ontologies. This system allows to interlink terms from different courses and domains and calculates several educational rates: term knowledge rate, total knowledge rate, domain knowledge rate and term significance rate. All of these rates are used to give the student recommendations about the activities he has to undertake to pass a course successfully.
Developing Smart Cities Services through Semantic Analysis of Social Streams
Authors: Cataldo Musto, Giovanni Semeraro, Marco de Gemmis, Pasquale Lops
Venue: WWW
Year: 2015

This paper presents a domain-agnostic framework for intelligent processing of textual streams coming from social networks. The framework implements a pipeline of techniques for semantic representation, sentiment analysis, automatic content classification, and provides an analytics console to get some findings from the extracted data. The effectiveness of the platform has already been proved by deploying it in two smart cities-related scenarios: in the first it was exploited to monitor the recovering state of the social capital of L'Aquila's city after the dreadful earthquake of April 2009, while in the latter a semantic analysis of the content posted on social networks was performed to build a map of the most at-risk areas of the Italian territory.
A Recommender System for Connecting Patients to the Right Doctors in the HealthNet Social Network
Authors: Fedelucio Narducci, Cataldo Musto, Marco Polignano, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro
Venue: WWW
Year: 2015

In this work we present a semantic recommender system able to suggest doctors and hospitals that best fit a specific patient profile. The recommender system is the core component of the social network named HealthNet (HN). The recommendation algorithm first computes similarities among patients, and then generates a ranked list of doctors and hospitals suitable for a given patient profile, by exploiting health data shared by the community. Accordingly, the HN user can find her most similar patients, look how they cured their diseases, and receive suggestions for solving her problem. Currently, the alpha version of HN is available only for Italian users, but in the next future we want to extend the platform to other languages. We organized three focus groups with patients, practitioners, and health organizations in order to obtain comments and suggestions. All of them proved to be very enthusiastic by using the HN platform.
Mining Relevant Time for Query Subtopics in Web Archives
Authors: Tu Ngoc Nguyen, Nattiya Kanhabua, Wolfgang Nejdl, Claudia Niederée
Venue: WWW
Year: 2015

With the reflection of nearly all types of social cultural, societal and everyday processes of our lives in the web, web archives from organizations such as the Internet Archive have the potential of becoming huge gold-mines for temporal content analytics of many kinds (e.g., on politics, social issues, economics or media). First hand evidences for such processes are of great benefit for expert users such as journalists, economists, historians, etc. However, searching in this unique longitudinal collection of huge redundancy (pages of near-identical content are crawled all over again) is completely different from searching over the web. In this work, we present our first study of mining the temporal dynamics of subtopics by leveraging the value of anchor text along the time dimension of the enormous web archives. This task is especially useful for one important ranking problem in the web archive context, the time-aware search result diversification. Due to the time uncertainty (the lagging nature and unpredicted behavior of the crawlers), identifying the trending periods for such temporal subtopics relying solely on the timestamp annotations of the web archive (i.e., crawling times) is extremely difficult. We introduce a brute-force approach to detect a time-reliable sub-collection and propose a method to leverage them for relevant time mining of subtopics. This is empirically found effective in solving the problem.
TinCan2PROV: Exposing Interoperable Provenance of Learning Processes through Experience API Logs
Authors: Tom De Nies, Frank Salliau, Ruben Verborgh, Erik Mannens, Rik Van de Walle
Venue: WWW
Year: 2015

A popular way to log learning processes is by using the Experience API (abbreviated as xAPI), also referred to as Tin Can. While Tin Can is great for developers who need to log learning experiences in their applications, it is more challenging for data processors to interconnect and analyze the resulting data. An interoperable data model is missing to raise Tin Can to its full potential. We argue that in essence, these learning process logs are provenance. Therefore, the W3C PROV model can provide the much-needed interoperability. In this paper, we introduce a method to expose PROV using Tin Can statements. To achieve this, we made the following contributions: (1) a formal ontology of the xAPI vocabulary, (2) a context document to interpret xAPI statements as JSON-LD, (3) a mapping to convert xAPI JSON-LD statements into PROV, and (4) a tool implementing this mapping.
Energy and Performance of Smartphone Radio Bundling in Outdoor Environments
Authors: Ana Nika, Yibo Zhu, Ning Ding, Abhilash Jindal, Y. Charlie Hu, Xia Zhou, Ben Y. Zhao, Haitao Zheng
Venue: WWW
Year: 2015

Most of today's mobile devices come equipped with both cellular LTE and WiFi wireless radios, making radio bundling (simultaneous data transfers over multiple interfaces) both appealing and practical. Despite recent studies documenting the benefits of radio bundling with MPTCP, many fundamental questions remain about potential gains from radio bundling, or the relationship between performance and energy consumption in these scenarios. In this study, we seek to answer these questions using extensive measurements to empirically characterize both energy and performance for radio bundling approaches. In doing so, we quantify potential gains of bundling using MPTCP versus an ideal protocol. We study the links between traffic partitioning and bundling performance, and use a novel componentized energy model to quantify the energy consumed by CPUs (and radios) during traffic management. Our results show that MPTCP achieves only a fraction of the total performance gain possible, and that its energy-agnostic design leads to considerable power consumption by the CPU. We conclude that not only there is room for improved bundling performance, but an energy-aware bundling protocol is likely to achieve a much better tradeoff between performance and power consumption.
A Bi-Dimensional User Profile to Discover Unpopular Web Sources
Authors: Romain Noël, Nicolas Malandain, Alexandre Pauchet, Laurent Vercouter, Bruno Grilhères, Stephan Brunessaux
Venue: WWW
Year: 2015

The discovery of new sources of information on a given topic is a prominent problem for Experts in Intelligence Analysis (EIA) who cope with the search of pages on specific and sensitive topics. Their information needs are difficult to express with queries and pages with sensitive content are difficult to find with traditional search engines as they are usually poorly indexed. We propose a double vector to model EIA's information needs, composed of DBpedia resources and keywords, both extracted from Web pages provided by the user. We also introduce a new similarity measure that is used in a Web source discovery system called DOWSER. DOWSER aims at providing users with new sources of information related to their needs without considering the popularity of a page. A series of experiments provides an empirical evaluation of the whole system.
A Serious Game Powered by Semantic Web technologies
Authors: Bernardo Pereira Nunes, Terhi Nurmikko-Fuller, Giseli Rabello Lopes, Chiara Renso
Venue: WWW
Year: 2015

ISCOOL is an interactive educational platform that helps users develop their skills for objective text analysis and interpretation. The tools incorporated into ISCOOL bridge various disparate sources, including reference datasets for people, and organizations, as well as gazetteers, dictionaries and collections of historical facts. This data serves as the basis for educating learners about the processes of evaluating the implicit and implied content of written material, whilst also providing a wider context in which this information is accessed, interpreted and understood. In the course of gameplay, the user is prompted to choose images that best capture content of a read passage. The interactive features of the game simultaneously test the user's existing knowledge, and ability to critically analyse the text. Results can be saved and shared, allowing the players to continue to interact with the data through conversations with their peers, friends, and family members, and to disseminate information throughout their communities. Users will be able to draw connections between the information they encounter in ISCOOL, and their daily realities - participants are empowered, informed and educated.
People's Perceptions of Personalized Ads
Authors: Katie O&apos, Donnell, Henriette Cramer
Venue: WWW
Year: 2015

Advertising is key to the business model of many online services. Personalization aims to make ads more relevant for users and more effective for advertisers. However, relatively few studies into user attitudes towards personalized ads are available. We present a San Francisco Bay Area survey (N=296) and in-depth interviews (N=24) with teens and adults. People are divided and often either (strongly) agreed or disagreed about utility or invasiveness of personalized ads and associated data collection. Mobile ads were reported to be less relevant than those on desktop. Participants explained ad personalization based on their personal previous behaviors and guesses about demographic targeting. We describe both metrics improvements as well as opportunities for improving online advertising by focusing on positive ad interactions reported by our participants, such as personalization focused not just on product categories but specific brands and styles, awareness of life events, and situations in which ads were useful or even inspirational.
The Importance of Pronouns to Sentiment Analysis: Online Cancer Survivor Network Case Study
Authors: Nir Ofek, Lior Rokach, Cornelia Caragea, John Yen
Venue: WWW
Year: 2015

Online health communities are a major source for patients and their informal caregivers in the process of gathering information and seeking social support. The Cancer Survivors Network of the American Cancer Society has many users and presents a large number of user interactions with regards to coping with cancer. Sentiment analysis is an important process in understanding members' needs and concerns and the impact of users' responses on other members. It aims to determine the participants' subjective attitude and reflect their emotions. Analyzing the sentiment of posts in online health communities enables the investigation of various factors such as what affects the sentiment change and discovery of sentiment change patterns. Since each writer has his or her own personality, and temporal emotional state, behavioral traits can be reflected in the writer's writing style. Pronouns are function-words which often convey some unique styling patterns into the texts. Drawing on a lexical approach to emotions, we conduct factor analysis on the use of pronouns in self-descriptions texts. Our analysis shows that the usage of pronouns has an effect on sentiment classification. Moreover, we evaluated the use of pronouns in our domain, and found it different than standard English usage.
Social Media as Firm's Network and Its Influence on the Corporate Performance
Authors: Jeongwoo Oh
Venue: WWW
Year: 2015

Social media or social network service has attracted a great amount of interest of applied studies, because we can see how people connect, behave, and interact each other through it even at a glance. Individual usage of social media can be viewed from corporate level in the market. This paper starts from such interest as well, trying to verify the applicability of social media in the corporate finance study. The basic question is whether social media interaction of the firm or firm's executive can affect the performance of the corresponding firm. In the study of economics and finance, firm level network has been studied in different contexts, mainly involved with economic benefit. However, the online network has not been enough studied regarding its effect on the corporate performance. In general, firm's decision making process has been regarded as exclusive and confidential, rather than publicly observable, which resulted in focusing more on the closed network in person or between related firms. But we observe that many top executives are already active or even stars on the social media. Therefore, we take a close look at this to determine if networking on social media is personal activity or corporate behavior. In other words, we are interested in whether the internet-based life style with social media can possibly influence on the corporate performance in the market or the firm-level decision making process. We investigate this question by using both social media and market data with firm information. First of all, we identify the determinants of social network behavior of the firms' executives. And next, we estimate the value of social media network on the corporate performance, calculating abnormal returns and analyzing its dynamics in the long term. Finally, we also verify the value of social media network in the context of executives' personal compensation. We expect that this research will provide a new insight about the social network on the corporate performance by adopting online social media as a network variable. It is also expected to broaden the applicability of the online network data to the academic questions in finance research.