OpenRaiser/Intern-Atlas huggingface dataset:
PROFILES & DATA: SEARCH International Workshop on Profiling and Searching Data on the Web Chairs' Welcome & Organization
Authors: Laura Koesten, Elena Demidova, Vadim Savenkov, John G. Breslin, Óscar Corcho, Stefan Dietze, Elena Simperl
Venue: WWW
Year: 2018

The web of data has seen tremendous growth recently. New forms of structured data have emerged in the form of web markup, such as schema.org and web tables. Exploiting these rich, heterogeneous and evolving data sources has become increasingly important for many different types of applications, including (federated) search, question answering and fact verification. The objective of the PROFILES & DATA:SEARCH Workshop is to bring together researchers and practitioners interested in the development of data search techniques, data profiling, and dataset retrieval on the web. This includes looking at the specifics of data-centric information seeking behaviours, understanding interaction challenges in data search on the web, and analysing the cognitive processes involved in the consumption of structured data by users. At the same time, we aim to discuss technologies addressing data search including semantics, information retrieval for web data (ranking algorithms and indexing), in particular in the context of decentralised and distributed systems, such as the web. We are interested in approaches to analyse, characterise and discover data sources. We want to facilitate a discussion around data search across formats and domain-specific applications. The PROFILES & DATA:SEARCH Workshop includes papers on a variety of topics such as profiling and data search, including querying and searching for structured data, profiling applications for cultural heritage, as well as data quality improvements through schema inference, content analysis and communities.
Towards Trust­-based Decentralized Ad-Hoc Social Networks
Authors: Kevin Koidl
Venue: WWW
Year: 2018

Social Media has transformed modern day society. It can be argued that one of the main drivers behind this transformation are novel ways to effectively distribute content in a highly targeted fashion and at scale. Recently, this effectiveness has come under attack based on new phenomena known as Fake News, Filter Bubble and Echo Chambers. The public debate about the impact of these phenomena on modern day society ranges from demanding a complete social media shutdown to government intervention and censorship. Furthermore, it appears that Social Media Platform providers are not sure what countermeasures are needed to address these new challenges. The main concern is that Black Mirror like scenarios will emerge simply by allowing privately held companies decide what content is conforming to public norms leading to a distortion of values. This paper presents an alternative solution by focusing on empowering meaningful relationships and not content engagement. The main motivation behind the proposed solution is to create social networks that follow a Trust by Design paradigm. This paper introduces and discusses the above-­mentioned challenges and presents a novel new social media concept seeking to overcome current challenges.
A Combinational Optimization Approach for Advertising Budget Allocation
Authors: Deguang Kong, Xiannian Fan, Konstantin Shmakov, Jian Yang
Venue: WWW
Year: 2018

Bid optimization, which aims to find the competitive bid to achieve the best performance for the advertiser, is an important problem in online advertising. The optimal bid recommendation enables the advertisers to make informed decisions without actually spending the budget. In this paper, we consider a bid optimization scenario that the advertiser's budget can be split across multiple campaigns. To achieve the optimal performance, we formalize the bid optimization problem as a constraint combinational optimization problem, and derive an effective method to solve it. Experiment studies on real- world ad campaigns demonstrate the effectiveness of our method.
Demystifying Advertising Campaign for CPA Goal Optimization
Authors: Deguang Kong, Konstantin Shmakov, Jian Yang
Venue: WWW
Year: 2018

In cost-per-click (CPC) or cost-per-impression (CPM) advertising campaigns, advertisers always run the risk of spending the bud- get without getting enough conversions. Moreover, the bidding on advertising inventory has few connections with propensity that can reach to cost-per-acquisition (CPA) goals. To address this problem, this paper presents a bid optimization scenario to achieve the desired CPA goals for advertisers. In particular, we build the optimization engine to make a decision by solving the constrained optimization problem. The proposed model can naturally recommend the bid that meets the advertisers' expectations by making inference over history auction behaviors. The bid optimization model outperforms the baseline methods on real-world campaigns, and can be applied into a wide range of scenarios for performance improvement and revenue liftup.
An Inflection Point Approach for Advertising Bid Optimization
Authors: Deguang Kong, Konstantin Shmakov, Jian Yang
Venue: WWW
Year: 2018

In online advertising, a common objective for advertisers is to get the maximum returns on investment given the budget. On one hand, if the bid is too high, the advertiser pays more money than he should pay for the same number of clicks. On the other hand, it the bid is too low, the advertiser cannot win in auctions and therefore it loses the opportunity. A challenging problem is how to recommend the bid to achieve the maximum values for advertisers. In this paper, we present an inflection point approach for bid recommendation from discovering the bid price of click(bid)1 function at which the function changes from significant increase (i.e. concave downward) to slow increase (convex upward). We derive the optimal solution using history sparse and noisy observations given the budget limit. In real word advertising campaign evaluations, the proposed bid recommendation scenario brings in 15.37% bid increase and 30.24% click increase over the baselines.
The Exploitation of OpenAPI Documentation for the Generation of Web Frontends
Authors: István Koren, Ralf Klamma
Venue: WWW
Year: 2018

New Internet-enabled devices and Web services are introduced on a daily basis. Documentation formats are available that describe their functionalities in terms of API endpoints and parameters. In particular, the OpenAPI specification has gained considerable influence over the last years. Web-based solutions exist that generate interactive OpenAPI documentation with HTML5 & JavaScript. They allow developers to quickly get an understanding what the services and devices do and how they work. However, the generated user interfaces are far from real-world practices of designers and end users. We present an approach to overcome this gap, by using a model-driven methodology resulting in state-of-the-art responsive Web user interfaces. To this end, we use the Interaction Flow Modeling Language (IFML) as intermediary model specification to bring together APIs and frontends. Our implementation is based on open standards like Web Components and SVG. A screencast of our tool is available at https://youtu.be/KFOPmPShak4
Copernicus App Lab: A Platform for Easy Data Access Connecting the Scientific Earth Observation Community with Mobile Developers
Authors: Manolis Koubarakis, Hervé Caumont, Ulrike Daniels, Erwin Goor, Lara König, Valentijn Venus
Venue: WWW
Year: 2018

Copernicus App Lab is a two year project (November 2016 to October 2018) funded by the European Commission under the H2020 program. The consortium consists of AZO (project coordinator), National and Kapodistrian University of Athens, Terradue, RAMANI and VITO. The main objective of Copernicus App Lab is to make Earth observation data produced by the Copernicus program of the European Union available on the Web as linked data to aid their use by mobile developers.
The Impact of Semantic Context Cues on the User Acceptance of Tag Recommendations: An Online Study
Authors: Dominik Kowald, Paul Seitlinger, Tobias Ley, Elisabeth Lex
Venue: WWW
Year: 2018

In this paper, we present the results of an online study with the aim to shed light on the impact that semantic context cues have on the user acceptance of tag recommendations. Therefore, we conducted a work-integrated social bookmarking scenario with 17 university employees in order to compare the user acceptance of a context-aware tag recommendation algorithm called 3Layers with the user acceptance of a simple popularity-based baseline. In this scenario, we validated and verified the hypothesis that semantic context cues have a higher impact on the user acceptance of tag recommendations in a collaborative tagging setting than in an individual tagging setting. With this paper, we contribute to the sparse line of research presenting online recommendation studies.
SafeKeeper: Protecting Web Passwords using Trusted Execution Environments
Authors: Klaudia Krawiecka, Arseny Kurnikov, Andrew Paverd, Mohammad Mannan, N. Asokan
Venue: WWW
Year: 2018

Passwords are by far the most widely-used mechanism for authenticating users on the web, out-performing all competing solutions in terms of deployability (e.g. cost and compatibility). However, two critical security concerns are phishing and theft of password databases. These are exacerbated by users» tendency to reuse passwords across different services. Current solutions typically address only one of the two concerns, and do not protect passwords against rogue servers. Furthermore, they do not provide any verifiable evidence of their (server-side) adoption to users, and they face deployability challenges in terms of ease-of-use for end users, and/or costs for service providers. We present SafeKeeper, a novel and comprehensive solution to ensure secrecy of passwords in web authentication systems. Unlike previous approaches, SafeKeeper protects users» passwords against very strong adversaries, including external phishers as well as corrupted (rogue) servers. It is relatively inexpensive to deploy as it (i) uses widely available hardware-based trusted execution environments like Intel SGX, (ii) requires only minimal changes for integration into popular web platforms like WordPress, and (iii) imposes negligible performance overhead. We discuss several challenges in designing and implementing such a system, and how we overcome them. Via an 86-participant user study, systematic analysis and experiments, we show the usability, security and deployability of SafeKeeper, which is available as open-source.
Crowd-based Multi-Predicate Screening of Papers in Literature Reviews
Authors: Evgeny Krivosheev, Fabio Casati, Boualem Benatallah
Venue: WWW
Year: 2018

Systematic literature reviews (SLRs) are one of the most common and useful form of scientific research and publication. Tens of thousands of SLRs are published each year, and this rate is growing across all fields of science. Performing an accurate, complete and unbiased SLR is however a difficult and expensive endeavor. This is true in general for all phases of a literature review, and in particular for the paper screening phase, where authors filter a set of potentially in-scope papers based on a number of exclusion criteria. To address the problem, in recent years the research community has began to explore the use of the crowd to allow for a faster, accurate, cheaper and unbiased screening of papers. Initial results show that crowdsourcing can be effective, even for relatively complex reviews. In this paper we derive and analyze a set of strategies for crowd-based screening, and show that an adaptive strategy, that continuously re-assesses the statistical properties of the problem to minimize the number of votes needed to take decisions for each paper, significantly outperforms a number of non-adaptive approaches in terms of cost and accuracy. We validate both applicability and results of the approach through a set of crowdsourcing experiments, and discuss properties of the problem and algorithms that we believe to be generally of interest for classification problems where items are classified via a series of successive tests (as it often happens in medicine).
Crowd-Machine Collaboration for Item Screening
Authors: Evgeny Krivosheev, Bahareh Harandizadeh, Fabio Casati, Boualem Benatallah
Venue: WWW
Year: 2018

In this paper we describe how crowd and machine classifier can be efficiently combined to screen items that satisfy a set of predicates. We show that this is a recurring problem in many domains, present machine-human (hybrid) algorithms that screen items efficiently and estimate the gain over human-only or machine-only screening in terms of performance and cost.
Privacy Preserving Distributed Analysis of Social Networks
Authors: Varsha Bhat Kukkala
Venue: WWW
Year: 2018

Social networks have been a popular choice of study, given the surge of online data on friendship networks, communication networks, collaboration networks etc. This popularity, however, is not true for all types of social networks. In the current work, we draw the reader's attention to a class of social networks which are investigated to a limited extent, classified as distributed sensitive social networks. It constitutes of networks where the presence or absence of edges in the network is distributedly known to a set of parties, who regard this information as their private data. Supply chain networks, informal networks such as trust network, advice network, enmity network, etc. are a few examples of the same. A major reason for the lack of any substantial study on these networks has been the unavailability of data. As a solution, we propose a privacy preserving approach to investigating these networks. We show the feasibility of using secure multiparty computation techniques to perform the required analysis, while preserving the privacy of every individual's data. The possible approaches that can be considered to ensure the design of efficient secure protocols are discussed such as efficient circuit design, ORAM based secure computation, use of oblivious data structures, etc. The results obtained in the direction of secure network analysis algorithms are also presented.
Utilizing Geo-tagged Tweets to Understand Evacuation Dynamics during Emergencies: A case study of Hurricane Sandy
Authors: Dheeraj Kumar, Satish V. Ukkusuri
Venue: WWW
Year: 2018

Hurricane evacuation is a complex process and a better understanding of the evacuation behavior of the coastal residents could be helpful in planning better evacuation policy. Traditionally, various aspects of the household evacuation decisions have been determined by post-evacuation questionnaire surveys, which are usually time-consuming and expensive. Increased activity of users on social media, especially during emergencies, along with the geo-tagging of the posts, provides an opportunity to gain insights into user's decision-making process, as well as to gauge public opinion and activities using the social media data as a supplement to the traditional survey data. This paper leverages the geo-tagged Tweets posted in the New York City (NYC) in wake of Hurricane Sandy to understand the evacuation behavior of the residents. Based on the geo-tagged Tweet locations, we classify the NYC Twitter users into one of the three categories: outside evacuation zone, evacuees, and non-evacuees and examine the types of Tweets posted by each group during different phases of the hurricane. We establish a strong link between the social connectivity with the decision of the users to evacuate or stay. We analyze the geo-tagged Tweets to understand evacuation and return time and evacuation location patterns of evacuees. The analysis presented in this paper could be useful for authorities to plan a better evacuation campaign to minimize the risk to the life of the residents of the emergency hit areas.
Using SafeKeeper to Protect Web Passwords
Authors: Arseny Kurnikov, Klaudia Krawiecka, Andrew Paverd, Mohammad Mannan, N. Asokan
Venue: WWW
Year: 2018

Although passwords are by far the most widely-used user authentication mechanism on the web, their security is threatened by password phishing and password database breaches. SafeKeeper is a system for protecting web passwords against very strong adversaries, including sophisticated phishers and compromised servers. Compared to other approaches, one of the key differentiating aspects of SafeKeeper is that it provides web users with verifiable assurance that their passwords are being protected. In this paper, we demonstrate precisely how SafeKeeper can be used to protect web passwords in real-world systems. We first explain two important deployability aspects: i) how SafeKeeper can be integrated into the popular WordPress platform, and ii) how ordinary web users can use Intel SGX remote attestation to verify that SafeKeeper is running on a particular server. We then describe three demonstrations to illustrate the use of SafeKeeper: i) showing the user experience when visiting a legitimate website; ii) showing the encryption of the password in transit via live packet-capture; and iii) showing how SafeKeeper performs in the presence of phishing.
Finding Subcube Heavy Hitters in Analytics Data Streams
Authors: Branislav Kveton, S. Muthukrishnan, Hoa T. Vu, Yikun Xian
Venue: WWW
Year: 2018

Modern data streams typically have high dimensionality. For example, digital analytics streams consist of user online activities (e.g., web browsing activity, commercial site activity, apps and social behavior, and response to ads). An important problem is to find frequent joint values (heavy hitters) of subsets of dimensions.
What We Read, What We Search: Media Attention and Public Attention Among 193 Countries
Authors: Haewoon Kwak, Jisun An, Joni Salminen, Soon-Gyo Jung, Bernard J. Jansen
Venue: WWW
Year: 2018

We investigate the alignment of international attention of news media organizations within 193 countries with the expressed international interests of the public within those same countries from March 7, 2016 to April 14, 2017. We collect fourteen months of longitudinal data of online news from Unfiltered News and web search volume data from Google Trends and build a multiplex network of media attention and public attention in order to study its structural and dynamic properties. Structurally, the media attention and the public attention are both similar and different depending on the resolution of the analysis. For example, we find that 63.2% of the country-specific media and the public pay attention to different countries, but local attention flow patterns, which are measured by network motifs, are very similar. We also show that there are strong regional similarities with both media and public attention that is only disrupted by significantly major worldwide incidents (e.g., Brexit). Using Granger causality, we show that there are a substantial number of countries where media attention and public attention are dissimilar by topical interest. Our findings show that the media and public attention toward specific countries are often at odds, indicating that the public within these countries may be ignoring their country-specific news outlets and seeking other online sources to address their media needs and desires.
A Factoid Question Answering System for Vietnamese
Authors: Phuong Le-Hong, Duc-Thien Bui
Venue: WWW
Year: 2018

In this paper, we describe the development of an end-to-end factoid question answering system for the Vietnamese language. This system combines both statistical models and ontology-based methods in a chain of processing modules to provide high-quality mappings from natural language text to entities. We present the challenges in the development of such an intelligent user interface for an isolating language like Vietnamese and show that techniques developed for inflectional languages cannot be applied as is. Our question answering system can answer a wide range of general knowledge questions with promising accuracy on a test set.
A3embed: Attribute Association Aware Network Embedding
Authors: Jihwan Lee, Sunil Prabhakar
Venue: WWW
Year: 2018

Network embedding aims to learn low-dimensional vector representations for nodes in a network that preserve structural characteristics. It has been shown that such representations are helpful in several graph mining tasks such as node classification, link prediction, and community detection. Some recent works have attempted to extend the approach to attributed networks in which each node is associated with a set of attribute values. They have focused on homophily relationships by forcing nodes with similar attribute values to obtain similar vector representations. This is unnecessarily restrictive and misses the opportunity to harness other types of relationships revealed by patterns in attribute values of connected nodes for learning insightful relationships. In this paper, we propose a new network attributed embedding framework called A3embed that is aware of attribute associations. A3embed favors significant attribute associations, not merely homophily relationships, which contributes to its robustness to diverse attribute vectors and noisy links. The experimental results on real-world datasets demonstrate that the proposed framework achieves better performance on different graph mining tasks compared to existing models.
No Silk Road for Online Gamers!: Using Social Network Analysis to Unveil Black Markets in Online Games
Authors: Eunjo Lee, Jiyoung Woo, Hyoungshick Kim, Huy Kang Kim
Venue: WWW
Year: 2018

Online game involves a very large number of users who are interconnected and interact with each other via the Internet. We studied the characteristics of exchanging virtual goods with real money through the processes called "real money trading (RMT)". This exchange might influence online game user behaviors and cause damage to the reputation of game companies. We examined in-game transactions to reveal RMT by constructing a social graph of virtual goods exchanges in an online game and identifying network communities of users. We analyzed approximately 6,000,000 transactions in a popular online game and inferred RMT transactions by comparing the RMT transactions crawled from an out-game market. Our findings are summarized as follows: (1) the size of the RMT market could be approximately estimated; (2) professional RMT providers typically form a specific network structure (either star-shape or chain) in the trading network, which can be used as a clue for tracing RMT transactions; and (3) the observed RMT market has evolved over time into a monopolized market with a small number of large-sized virtual goods providers.
LAAN: A Linguistic-Aware Attention Network for Sentiment Analysis
Authors: Zeyang Lei, Yujiu Yang, Yi Liu
Venue: WWW
Year: 2018

Sentiment analysis of social media and comment data is an important issue in opinion monitoring. In this work, we propose a Linguistic-Aware Attention Network (LANN) to enhance the performance of convolution neural network (CNN). LANN adopts a two-stage strategy to model the sentiment-specific sentence representation. First, an interactive attention mechanism is designed to model word-level semantics. Second, to capture phrase-level linguistic structure, a dynamic semantic attention is adopted to select the crucial phrase chunks in the sentence. The experiments demonstrate that LANN has robust superiority over competitors and has reached the state-of-the-art performance.
User Fairness in Recommender Systems
Authors: Jurek Leonhardt, Avishek Anand, Megha Khosla
Venue: WWW
Year: 2018

Recent works in recommendation systems have focused on diversity in recommendations as an important aspect of recommendation quality. In this work we argue that the post-processing algorithms aimed at only improving diversity among recommendations lead to discrimination among the users. We introduce the notion of user fairness which has been overlooked in literature so far and propose measures to quantify it. Our experiments on two diversification algorithms show that an increase in aggregate diversity results in increased disparity among the users.