OpenRaiser/Intern-Atlas huggingface dataset:
DREAM in Action: A Distributed and Adaptive RDF System on the Cloud
Authors: Aisha Hasan, Mohammad Hammoud, Reza Nouri, Sherif Sakr
Venue: WWW
Year: 2016

RDF and SPARQL query language are gaining wide popularity and acceptance. This demonstration paper presents DREAM, a hybrid RDF system, which combines the advantages and averts the disadvantages of the centralized and distributed RDF schemes. In particular, DREAM avoids partitioning RDF datasets and reversely partitions SPARQL queries. By not partitioning datasets, DREAM offers a general paradigm for different types of pattern matching queries and entirely precludes intermediate data shuffling (only auxiliary data are shuffled). By partitioning only queries, DREAM suggests an adaptive scheme, which runs queries on different numbers of machines depending on their complexities. DREAM achieves these goals and significantly outperforms related systems via employing a novel graph-based, rule-oriented query planner and a new cost model. This paper proposes demonstrating DREAM live over the cloud using a friendly graphical user interface (GUI). The GUI allows participants to execute and visualize pre-defined and user-defined (which can be written by participants on-the-fly) SPARQL queries over various real-world and synthetic RDF datasets. Furthermore, participants can empirically compare and contrast DREAM against three state-of-the-art RDF systems.
Toward G-OWL: A Graphical, Polymorphic And Typed Syntax For Building Formal OWL2 Ontologies
Authors: Michel Héon, Roger Nkambou, Christian Langheit
Venue: WWW
Year: 2016

The Web Ontology Language (OWL-2) aims at offering a family of syntax such as RDF/XML, Manchester Turtle and others, for building ontologies. Ontology engineering is a complex task that requires skills that are rarely accessible to content experts. On the other hand, to model contents pertaining to a specific domain, graphical modeling is a technique that is often used to offer a knowledge representation tool to content experts that are not well acquainted with the process of formal ontology design. In this paper, we present the way in which the usage of polymorphism and symbol typing of graphical vocabulary have allowed us to design the G-OWL syntax, a graphical syntax that aims to graphically represent domain-specific knowledge using the OWL-2.
PeARS: a Peer-to-peer Agent for Reciprocated Search
Authors: Aurélie Herbelot
Venue: WWW
Year: 2016

This paper presents PeARS (Peer-to-peer Agent for Reciprocated Search), an algorithm for distributed Web search that emulates the offline behaviour of a human with an information need. Specifically, the algorithm models the process of 'calling a friend', i.e. directing one's query to the knowledge holder most likely to answer it. The system allows network users to index and share part of their browsing history in a way that makes them 'experts' on some topics. A layer of distributional semantics agents then performs targeted information retrieval in response to user queries, in a fully automated fashion.
A Graph-Coarsening Approach for Tag Recommendation
Authors: Manel Hmimida, Rushed Kanawati
Venue: WWW
Year: 2016

In this paper we propose a new graph-based tag recommendation approach. The approach is structured into an offline step and an online one. Offline, the hypergraph depicting the history of tags assignment by users to resources is abstracted. On online, for a given target user and a resource, we first compute the set of recommended abstract tags (i.e tag clusters) applying a basic graph-based approach to the abstract graph. A new reduced graph is computed by unfolding the abstract subgraph composed of the set of recommended abstract tags and nodes representing the cluster of users (resp. resources) to which the target user (resp. resource) belongs to. Again the same basic graph-based tag recommendation approach is applied to this new reduced graph in order to compute the final set of tags to recommend. Experiments on real dataset show the effectiveness of the proposed approach.
The Garden of Earthly Delights: Constructing and Revising a Web Development Textbook
Authors: Ricardo Hoar, Randy W. Connolly
Venue: WWW
Year: 2016

Web development is widely considered to be a difficult topic to teach successfully within post-secondary computing programs. One reason for this difficulty is the large number of shifting technologies that need to be taught along with the conceptual complexity that needs to be mastered by both student and professor. Another challenge is helping students see the scope of web development, and their role in an era where the web is a part of everyday human affairs. This paper describes our 2014 textbook [2], its reception, and our plans for a second edition revision (which will be published in early 2017). Our hope is that a discussion of current teaching materials will provide opportunities to spark a dialogue not only about current technological topics, but perspectives on web development from other disciplines.
D-ForenRIA: Distributed Reconstruction of User-Interactions for Rich Internet Applications
Authors: Salman Hooshmand, Akib Mahmud, Gregor von Bochmann, Muhammad Faheem, Guy-Vincent Jourdan, Russ Couturier, Iosif-Viorel Onut
Venue: WWW
Year: 2016

We present D-ForenRIA, a distributed forensic tool to automatically reconstruct user-sessions in Rich Internet Applications (RIAs), using solely the full HTTP traces of the sessions as input. D-ForenRIA recovers automatically each browser state, reconstructs the DOMs and re-creates screenshots of what was displayed to the user. The tool also recovers every action taken by the user on each state, including the user-input data. Our application domain is security forensics, where sometimes months-old sessions must be quickly reconstructed for immediate inspection. We will demonstrate our tool on a series of RIAs, including a vulnerable banking application created by IBM Security for testing purposes. In that case study, the attacker visits the vulnerable web site, and exploits several vulnerabilities (SQL-injections, XSS...) to gain access to private information and to perform unauthorized transactions. D-ForenRIA can reconstruct the session, including screenshots of all pages seen by the hacker, DOM of each page and the steps taken for unauthorized login and the inputs hacker exploited for the SQL-injection attack. D-ForenRIA is made efficient by applying advanced reconstruction techniques and by using several browsers concurrently to speed up the reconstruction process. Although we developed D-ForenRIA in the context of security forensics, the tool can also be useful in other contexts such as aided RIAs debugging and automated RIAs scanning.
Interledger: Creating a Standard for Payments
Authors: Adrian Hope-Bailie, Stefan Thomas
Venue: WWW
Year: 2016

The web has enabled free and open information exchange for a vast number of users around the world. However, it has so far failed to do the same for payments. Instead of finding the cheapest route for each payment from a competitive network of providers, we rely on a small number of proprietary operators with global reach.
It ROCS!: The RASH Online Conversion Service
Authors: Angelo Di Iorio, Alejandra N. González-Beltrán, Francesco Osborne, Silvio Peroni, Francesco Poggi, Fabio Vitali
Venue: WWW
Year: 2016

In this poster paper we introduce the RASH Online Conversion Service, i.e., a Web application that allows the conversion of ODT documents into RASH, a HTML-based markup language for writing scholarly articles, and from RASH into LaTeX according to Springer LNCS and ACM ICPS.
Author2Vec: Learning Author Representations by Combining Content and Link Information
Authors: Ganesh J, Soumyajit Ganguly, Manish Gupta, Vasudeva Varma, Vikram Pudi
Venue: WWW
Year: 2016

In this paper, we consider the problem of learning representations for authors from bibliographic co-authorship networks. Existing methods for deep learning on graphs, such as DeepWalk, suffer from link sparsity problem as they focus on modeling the link information only. We hypothesize that capturing both the content and link information in a unified way will help mitigate the sparsity problem. To this end, we present a novel model 'Author2Vec', which learns low-dimensional author representations such that authors who write similar content and share similar network structure are closer in vector space. Such embeddings are useful in a variety of applications such as link prediction, node classification, recommendation and visualization. The author embeddings we learn are empirically shown to outperform DeepWalk by 2.35% and 0.83% for link prediction and clustering task respectively.
Experiences with Curricula for a BSc and MSc in Web Development: 2008-2016
Authors: Brigitte Jellinek
Venue: WWW
Year: 2016

In this position paper, we describe our experience in designing and delivering a bachelor and a master program in web development at Salzburg University of Applied Sciences, Austria. While aiming to archive similar learning objectives as other programs in web development or web engineering, our historical roots in an arts program and our decision to focus on dynamic programming languages have led us to a unique program.
Web-scale Multimedia Search for Internet Video Content
Authors: Lu Jiang
Venue: WWW
Year: 2016

The World Wide Web has been witnessing an explosion of video content. Video data are becoming one of the most valuable sources to assess insights and information. However, existing video search methods are still based on text matching (text-to-text search), and could fail for the huge volumes of videos that have little relevant metadata or no metadata at all. In this paper, we propose an accurate, efficient and scalable semantic search method for Internet videos that allows for intelligent and flexible search schemes over the video content (text-to-video search and text&video-to-video search). To achieve this ambitious goal, we propose several novel methods to improve accuracy and efficiency. The extensive experiments demonstrate that the proposed methods are able to surpass state-of-the-art accuracy and efficiency on multiple datasets. Based on the proposed methods, we implement E-Lamp Lite, the first of its kind large-scale semantic search engine for Internet videos. According to National Institute of Standards and Technology (NIST), it achieved the best accuracy in the TRECVID Multimedia Event Detection (MED) 2013, 2014 and 2015, one of the most representative task for content-based video search. To the best of our knowledge, E-Lamp Lite is the first content-based semantic search system that is capable of indexing and searching a collection of 100 million videos.
A Test Suite for Evaluating POS Taggers across Varieties of English
Authors: Anna Jørgensen, Anders Søgaard
Venue: WWW
Year: 2016

We present a suite of 12 datasets for evaluating POS taggers across varieties of English to enable researchers to evaluate the robustness of their models. The suite includes three new datasets, sampled from lyrics from black American hip-hop artists, southeastern American Twitter, and the subtitles from the TV series The Wire. We present an example eval- uation of an off-the-shelf POS tagger across these datasets.
Cryptographic Currencies Crash Course (C4): Tutorial
Authors: Aljosha Judmayer, Edgar R. Weippl
Venue: WWW
Year: 2016

"Bitcoin is a rare case where practice seems to be ahead of theory." Joseph Bonneau et al.[15]
Visual Event Mining from the Twitter Stream
Authors: Takamu Kaneko, Keiji Yanai
Venue: WWW
Year: 2016

Twitter is a unique microblog, which is different from conventional social media in terms of its quickness and on-the-spot-ness. Many Twitter's users send messages, which is commonly called "tweets", to Twitter on the spot with mobile phones or smart phones, and some of them send photos and geotags as well as tweets. Most of the photos are sent to Twitter soon after taken. In case of photos related to some events, most of them are taken during the events. We think that Twitter event photo mining is more useful to under- stand what happens currently over the world than only text-based Twitter event mining. In this paper, we propose a system to mine events visually from the Twitter stream. To do that, we use not only tweets having both geotags and photos but also tweets having geotags or photos for textual analysis or visual analysis. Although there exist many works related to Twitter mining using only text analysis such as typhoon and earthquake detection by Sakaki et al. [1], only a limited number of works exist on Twitter mining using image analysis. Nakaji et al. [2] proposed a system to mine representative photos related to the given keyword or term from a large number of geo-tweet photos. They extracted representative photos related to events such as "typhoon" and "New Year's Day". They used only geotagged photo tweets the number of which are limited compared to all the photo tweets. Gao et al. [3] proposed a method to mine brand product photos fromWeibo which employs supervised image recognition, which is different from event detection. They integrated visual features and social factors (users, relations, and locations) as well as textual features for brand product photo mining. In this paper, we detect visual events using geotagged non-photo tweets and non-geotagged photo tweets as well as geotagged photo tweets. In the experiments, we show some examples of detected events and their photos such as "rainbow", "fireworks" and "festival".
Time Series Analysis Using NOC
Authors: Noriaki Kawamae
Venue: WWW
Year: 2016

We present a time series analysis employing natural language processing (NLP) techniques, and show the effect of N-gram over Context (NOC), that is a one of topic models that enjoy success in NLP, in this analysis.
Privacy Behaviour and Profile Configuration in Twitter
Authors: Taraneh Khazaei, Lu Xiao, Robert E. Mercer, Atif Khan
Venue: WWW
Year: 2016

The dichotomy between users' privacy behaviours and their privacy attitudes is a widely observed phenomenon in online social media. Such a disparity can be mainly attributed to the users' lack of awareness about the default privacy setting in social networking websites, which is often open and permissive. This problem has led to a large number of publicly available accounts that may belong to privacy-concerned users. As an initial step toward addressing this issue, we examined whether profile attributes of Twitter users with varying privacy settings are configured differently. As a result of the analysis, a set of features is identified and used to predict user privacy settings. For our best classifier, we obtained an F-score of 0.71, which outperforms the baselines considerably. Hence, profile attributes proved valuable for our task and suggest the possibility of the automatic detection of public accounts intended to be private based on online social footprints.
Competition on Price and Quality in Cloud Computing
Authors: Cinar Kilcioglu, Justin M. Rao
Venue: WWW
Year: 2016

The public cloud "infrastructure as a service" market possesses unique features that make it difficult to predict long-run economic behavior. On the one hand, major providers buy their hardware from the same manufacturers, operate in similar locations and offer a similar menu of products. On the other hand, the competitors use different proprietary "fabric" to manage virtualization, resource allocation and data transfer. The menus offered by each provider involve a discrete number of choices (virtual machine sizes) and allow providers to locate in different parts of the price-quality space. We document this differentiation empirically by running benchmarking tests. This allows us to calibrate a model of firm technology. Firm technology is an input into our theoretical model of price-quality competition. The monopoly case highlights the importance of competition in blocking "bad equilibrium" where performance is intentionally slowed down or options are unduly limited. In duopoly, price competition is fierce, but prices do not converge to the same level because of price-quality differentiation. The model helps explain market trends, such the healthy operating profit margin recently reported by Amazon Web Services. Our empirically calibrated model helps not only explain price cutting behavior but also how providers can manage a profit despite predictions that the market "should be" totally commoditized.
Purchase Influence Mining: Identifying Top-k Items Attracting Purchase of Target Item
Authors: Sungchul Kim, Jinyoung Yeo, Eunyee Koh, Nedim Lipka
Venue: WWW
Year: 2016

Web logs in e-commerce sites consist of user actions on items such as visiting an item description page, adding an item to a wishlist, and purchasing an item. Those items could be represented as nodes in a graph while viewing their relationships as edges according to the user actions. Based on the item graph, identifying items that attract users to purchase the target item could be practically used for supporting business decisions. To do this, we introduce a new task, called `Purchase Influence Mining', that finds the top-k items (PIM-items) maximizing the estimated purchase influence from them to a target item. We solve this problem by modeling the purchase influence as the shortest path between item pair. According to the result, our approach more consistently finds the k PIM-items than the baseline.
Internet Collaboration on Extremely Difficult Problems: Research versus Olympiad Questions on the Polymath Site
Authors: Isabel Mette Kloumann, Chenhao Tan, Jon M. Kleinberg, Lillian Lee
Venue: WWW
Year: 2016

Despite the existence of highly successful Internet collaborations on complex projects, including open-source software, little is known about how Internet collaborations work for solving "extremely" difficult problems, such as open-ended research questions. We quantitatively investigate a series of efforts known as the Polymath projects, which tackle mathematical research problems through open online discussion. A key analytical insight is that we can contrast the polymath projects with mini-polymaths -- spinoffs that were conducted in the same manner as the polymaths but aimed at addressing math Olympiad questions, which, while quite difficult, are known to be feasible. Our comparative analysis shifts between three elements of the projects: the roles and relationships of the authors, the temporal dynamics of how the projects evolved, and the linguistic properties of the discussions themselves. We find interesting differences between the two domains through each of these analyses, and present these analyses as a template to facilitate comparison between Polymath and other domains for collaboration and communication. We also develop models that have strong performance in distinguishing research-level comments based on any of our groups of features. Finally, we examine whether comments representing research breakthroughs can be recognized more effectively based on their intrinsic features, or by the (re-)actions of others, and find good predictive power in linguistic features.
Incremental, Per-Query Ontology Matching with RepMine
Authors: Thomas Kowark, Keven Richly, Matthias Uflacker, Hasso Plattner
Venue: WWW
Year: 2016

Ontology matching enables applications, such as automated data transformation or query rewriting. As it requires domain knowledge, it needs to be carried out by expert users, whose time is scarce and, therefore, should be used efficiently. To this end, the RepMine system presented in this paper does not treat ontology matching as a task of its own, but integrates it into a semi-automated query translation process. By that, users perform a task with immediate benefit for them and simultaneously contribute to alignments between ontologies. Furthermore, the overall task of matching two ontologies is split on a per-query basis and, thus, can be performed incrementally by all system users
Estimating Gender and Age of Web Page Visitors from the Way They Use Their Mouse
Authors: Peter Krátky, Daniela Chudá
Venue: WWW
Year: 2016

Biometric data are affected by physiological properties of people, including gender or age. The paper describes an experiment of discovering gender and age in computer mouse movement data that might be notably beneficial for profiling anonymous visitors browsing the Web. The proposed method extracts features, such as velocity, path straightness or pauses duration, that are used by a multiclassifier system to make an estimate. Age category estimation shows encouraging results of the early method, especially for statistical analysis of a website audience.
Structure and Dynamics of Signed Citation Networks
Authors: Srijan Kumar
Venue: WWW
Year: 2016

Citations are important to track and understand the evolution of human knowledge. At the same time, it is widely accepted that all the citations made in a paper are not equal. However, there is no thorough understanding of how citations are created that explicitly criticize or endorse others. In this paper, we do a detailed study of such citations made within the NLP community by differentiating citations into endorsement (positive), criticism (negative) and neutral categories. We analyse this signed network created between papers and between authors for the first time from a social networks perspective. We make many observations - we find that the citations follow a heavy-tailed distribution and they are created in a way that follows weak balance theory and status theories. Moreover, we find that authors do not change their opinion towards others over time and rarely reciprocate the opinion that they receive. Overall, the paper builds the understanding of the structure and dynamics of positive, negative and neutral citations.
Temponym Tagging: Temporal Scopes for Textual Phrases
Authors: Erdal Kuzey, Jannik Strötgen, Vinay Setty, Gerhard Weikum
Venue: WWW
Year: 2016

For many NLP and IR applications, anchored temporal information extracted from textual documents is of utmost importance. Thus, temporal tagging -- the extraction and normalization of temporal expressions -- has gained a lot of attention in recent years and several tools such as HeidelTime and SUTime are proposed. However, such tools do not address textual phrases with temporal scopes like "Clinton's time as First Lady". While such phrases (so-called temponyms) are not temporal expressions per se, information about their temporal scopes can be helpful in many scenarios, e.g., in the context of temporal information retrieval. In this paper, we describe the integration of a wide range of temponyms to the publicly available temporal tagger HeidelTime to include temponym tagging.
Modeling Complex Clickstream Data by Stochastic Models: Theory and Methods
Authors: Choudur Lakshminarayan, Ram Kosuru, Meichun Hsu
Venue: WWW
Year: 2016

As the website is a primary customer touch-point, millions are spent to gather web data about customer visits. Sadly, the trove of data and corresponding analytics have not lived up to the promise. Current marketing practice relies on ambiguous summary statistics or small-sample usability studies. Idiosyncratic browsing and low conversion (browser-to-buyer) make modeling hard. In this paper, we model browsing patterns (sequence of clicks) via Markov chain theory to predict users' propensity to buy within a session. We focus on model complexity, imputing missing values, data augmentation, and other attendant issues that impact performance. The paper addresses the following aspects; (1) Determine appropriate order of the Markov chain (assess the influence of prior history in prediction), (2) Impute missing transitions by exploiting the inherent link structure in the page sequences, (3) predict the likelihood of a purchase based on variable-length page sequences, and (4) Augment the training set of buyers (which is typically very small: 2% by viewing the page transitions as a graph and exploiting its link structure to improve performance. The cocktail of solutions address important issues in practical digital marketing. Extensive analysis of data applied to a large commercial web-site shows that Markov chain based classifiers are useful predictors of user intent.