OpenRaiser/Intern-Atlas huggingface dataset:
Linking ImageNet WordNet Synsets with Wikidata
Authors: Finn Årup Nielsen
Venue: WWW
Year: 2018

The linkage of ImageNet WordNet synsets to Wikidata items will leverage deep learning algorithm with access to a rich multilingual knowledge graph. Here I will describe our on-going efforts in linking the two resources and issues faced in matching the Wikidata and WordNet knowledge graphs. I show an example on how the linkage can be used in a deep learning setting with real-time image classification and labeling in a non-English language and discuss what opportunities lies ahead.
Mining Tours and Paths in Activity Networks
Authors: Sofia Maria Nikolakaki, Charalampos Mavroforakis, Alina Ene, Evimaria Terzi
Venue: WWW
Year: 2018

The proliferation of online social networks and the spread of smart mobile devices enable the collection of information related to a multitude of users' activities. These networks, where every node is associated with a type of action and a frequency, are usually referred to as activity networks. Examples of such networks include road networks, where the nodes are intersections and the edges are road segments. Each node is associated with a number of geolocated actions that users of an online platform took in its vicinity. In these networks, we define a prize-collecting subgraph to be a connected set of nodes, which exhibits high levels of activity, and is compact, i.e., the nodes are close to each other. The k-PCSubgraphs problem we address in this paper is defined as follows: given an activity network and an integer k, identify k non-overlapping and connected subgraphs of the network such that the nodes of each subgraph are close to each other, and the total number of actions they are associated with is high. Here, we define and study two new variants of the k-PCSubgraphs problem, where the subgraphs of interest are tours and paths. Since both these problems are NP-hard, we provide approximate and heuristic algorithms that run in time nearly-linear to the number of edges. In our experiments, we use real activity networks obtained by combining road networks and projecting on them user activity from Twitter and Flickr. Our experimental results demonstrate both the efficiency and the practical utility of our methods.
Combining RDF Graph Data and Embedding Models for an Augmented Knowledge Graph
Authors: Andriy Nikolov, Peter Haase, Daniel M. Herzig, Johannes Trame, Artem Kozlov
Venue: WWW
Year: 2018

Vector embedding models have recently become popular for encoding both structured and unstructured data. In the context of knowledge graphs such models often serve as additional evidence supporting various tasks related to the knowledge base population: e.g., information extraction or link prediction to expand the original dataset. However, the embedding models themselves are often not used directly alongside structured data: they merely serve as additional evidence for structured knowledge extraction. In the metaphactory knowledge graph management platform, we use federated hybrid SPARQL queries for combining explicit information stated in the graph, implicit information from the associated embedding models, and information extracted using vector embeddings in a transparent way for the end user. In this paper we show how we integrated RDF data with vector space models to construct an augmented knowledge graph to be used in customer applications.
Towards Quantifying Sampling Bias in Network Inference
Authors: Lisette Espin Noboa, Claudia Wagner, Fariba Karimi, Kristina Lerman
Venue: WWW
Year: 2018

Relational inference leverages relationships between entities and links in a network to infer information about the network from a small sample. This method is often used when global information about the network is not available or difficult to obtain. However, how reliable is inference from a small labeled sample How should the network be sampled, and what effect does it have on inference error How does the structure of the network impact the sampling strategy We address these questions by systematically examining how network sampling strategy and sample size affect accuracy of relational inference in networks. To this end, we generate a family of synthetic networks where nodes have a binary attribute and a tunable level of homophily. As expected, we find that in heterophilic networks, we can obtain good accuracy when only small samples of the network are initially labeled, regardless of the sampling strategy. Surprisingly, this is not the case for homophilic networks, and sampling strategies that work well in heterophilic networks lead to large inference errors. This finding suggests that the impact of network structure on relational classification is more complex than previously thought.
A Gamified Approach to Naïve Bayes Classification: A Case Study for Newswires and Systematic Medical Reviews
Authors: Giorgio Maria Di Nunzio, Maria Maistro, Federica Vezzani
Venue: WWW
Year: 2018

Supervised machine learning algorithms require a set of labelled examples to be trained; however, the labelling process is a costly and time consuming task which is carried out by experts of the domain who label the dataset by means of an iterative process to filter out non-relevant objects of the dataset. In this paper, we describe a set of experiments that use gamification techniques to transform this labelling task into an interactive learning process where users can cooperate in order to achieve a common goal. To this end, first we use a geometrical interpretation of Naïve Bayes (NB) classifiers in order to create an intuitive visualization of the current state of the system and let the user change some of the parameters directly as part of a game. We apply this visualization technique to the classification of newswire and we report the results of the experiments conducted with different groups of people: PhD students, Master Degree students and general public. Then, we present a preliminary experiment of query rewriting for systematic reviews in a medical scenario, which makes use of gamification techniques to collect different formulation of the same query. Both the experiments show how the exploitation of gamification approaches help to engage the users in abstract tasks that might be hard to understand and/or boring to perform.
Query Expansion with Neural Question-to-Answer Translation for FAQ-based Question Answering
Authors: Atsushi Otsuka, Kyosuke Nishida, Katsuji Bessho, Hisako Asano, Junji Tomita
Venue: WWW
Year: 2018

We propose a novel Frequently Asked Question (FAQ) retrieval technique with a neural query expansion model. With the growth in Question Answering systems and mobile communications, FAQ retrieval systems have become widely used in site searches and call center support. However, FAQ retrieval often has lexical gaps between queries and answer documents. To bridge these gaps, we design a query expansion model on the basis of an Encoder-Decoder model as a type of deep neural network. The model learns the words that appear in answers for questions using Q&A pair documents and generates the expanded queries from inputted queries to retrieve answer documents. We evaluate our proposed technique in a multi-domain FAQ retrieval task. Experimental results show that our technique retrieves FAQs more accurately than the previous methods.
Ease and Ethics of User Profiling in Black Mirror
Authors: Harshvardhan J. Pandit, Dave Lewis
Venue: WWW
Year: 2018

The use of personal data is a double-edged sword that on one side provides benefits through personalisation and user profiling, while the other raises several ethical and moral implications that impede technological progress. Laws often try to reflect the shifting values of social perception, such as the General Data Protection Regulation (GDPR) catering to explicit consent over use of personal data, though actions may still be legal without being perceived as acceptable. Black Mirror is a TV series that serves to imagine scenarios that test the boundary of such perceptions, and is often described as being futuristic. In this paper, we discuss how existing technologies have already coalesced towards calculating a probability metric or rating as presented by the episode 'Nosedive'. We present real-world instances of such technologies and their applications, and how they can be easily expanded using the interminable web. The dilemma posed by the ethics of such technological applications is discussed using the 'Ethics Canvas', our methodology and tool for encouraging discussions on ethical implications in responsible innovation.
The Cost of Digital Advertisement: Comparing User and Advertiser Views
Authors: Panagiotis Papadopoulos, Nicolas Kourtellis, Evangelos P. Markatos
Venue: WWW
Year: 2018

Digital advertisements are delivered in the form of static images, animations or videos, with the goal to promote a product, a service or an idea to desktop or mobile users. Thus, the advertiser pays a monetary cost to buy ad-space in a content provider»s medium (e.g., website) to place their advertisement in the consumer»s display. However, is it only the advertiser who pays for the ad delivery? Unlike traditional advertisements in mediums such as newspapers, TV or radio, in the digital world, the end-users are also paying a cost for the advertisement delivery. Whilst the cost on the advertiser»s side is clearly monetary, on the end-user, it includes both quantifiable costs, such as network requests and transferred bytes, and qualitative costs such as privacy loss to the ad ecosystem. In this study, we aim to increase user awareness regarding the hidden costs of digital advertisement in mobile devices, and compare the user and advertiser views. Specifically, we built OpenDAMP, a transparency tool that passively analyzes users» web traffic and estimates the costs in both sides. We use a year-long dataset of 1270 real mobile users and by juxtaposing the costs of both sides, we identify a clear imbalance: the advertisers pay several times less to deliver ads, than the cost paid by the users to download them. In addition, the majority of users experience a significant privacy loss, through the personalized ad delivery mechanics.
The Missing Science of Knowledge Curation: Improving Incentives for Large-scale Knowledge Curation
Authors: Praveen K. Paritosh
Venue: WWW
Year: 2018

Dictionaries, encyclopedias, knowledge graphs, annotated corpora, library classification systems and world maps are all examples of human-curated knowledge resources that have been highly valuable to science as well as amortized across multiple large-scale systems in practice. Many of these were started and built even before a crowdsourcing research community existed. While the last decade has seen unprecedented growth in research and practice in building crowdsourcing systems to do increasingly complex tasks at scale, many of these resources are still woefully incompletelacking coverage in languages and subject matter domains. Moreover, many knowledge resources needed to fill other semantic gaps for artificial intelligence systems simply don't exist or arent being built. Why I argue that we don't have the right incentives, and that in order to improve the incentives, we have some fundamental scientific questions to answer. While building a large knowledge resource, we have little more than intuitions when it comes to estimating the reusability, maintainability, and long-term value of the effort. These make it difficult to fund or manage such projects, often requiring herculean personalities or fortunate businesses. Building or expanding a resource is often not seen as "sexy," which results in lack of resources to answer those questions in any principled manner. These problems begin to outline a new science of curation, making progress on which could help improve the discussion around and funding for building sorely needed knowledge resources.
Learning Procedures from Text: Codifying How-to Procedures in Deep Neural Networks
Authors: Hogun Park, Hamid Reza Motahari-Nezhad
Venue: WWW
Year: 2018

A lot of knowledge about procedures and how-tos are described in text. Recently, extracting semantic relations from the procedural text has been actively explored. Prior work mostly has focused on finding relationships among verb-noun pairs or clustering of extracted pairs. In this paper, we investigate the problem of learning individual procedure-specific relationships (e.g. is method of, is alternative of, or is subtask of) among sentences. To identify the relationships, we propose an end-to-end neural network architecture, which can selectively learn important procedure-specific relationships. Using this approach, we could construct a how-to knowledge base from the largest procedure sharing-community, wiki-how.com. The evaluation of our approach shows that it outperforms the existing entity relationship extraction algorithms.
An Empirical Study of the Framework Impact on the Security of JavaScript Web Applications
Authors: Ksenia Peguero, Nan Zhang, Xiuzhen Cheng
Venue: WWW
Year: 2018

\textitBackground: JavaScript frameworks are widely used to create client-side and server-side parts of contemporary web applications. Vulnerabilities like cross-site scripting introduce significant risks in web applications.\\ \textitAim: The goal of our study is to understand how the security features of a framework impact the security of the applications written using that framework.\\ \textitMethod: In this paper, we present four locations in an application, relative to the framework being used, where a mitigation can be applied. We perform an empirical study of JavaScript applications that use the three most common template engines: Jade/Pug, EJS, and Angular. Using automated and manual analysis of each group of applications, we identify the number of projects vulnerable to cross-site scripting, and the number of vulnerabilities in each project, based on the framework used.\\ \textitResults: We analyze the results to compare the number of vulnerable projects to the mitigation locations used in each framework and perform statistical analysis of confounding variables.\\ \textitConclusions: The location of the mitigation impacts the application's security posture, with mitigations placed within the framework resulting in more secure applications.
The Role of Digital Technologies in Promoting Smart City Governance
Authors: Gabriela Viale Pereira, Gregor Eibl, Peter Parycek
Venue: WWW
Year: 2018

This paper presents the analysis of the SmartGov project as a case of smart technologies application, such as expert-based Fuzzy Cognitive Maps, social media applications and open data, to promote citizen engagement and support decision-making. The objective of this paper is to analyze the role of digital technologies as inputs to achieve smart city governance. The main results are illustrated in a framework that combines the smart city governance elements in a real case description.
ProxyTorrent: Untangling the Free HTTP(S) Proxy Ecosystem
Authors: Diego Perino, Matteo Varvello, Claudio Soriente
Venue: WWW
Year: 2018

Free web proxies promise anonymity and censorship circumvention at no cost. Several websites publish lists of free proxies organized by country, anonymity level, and performance. These lists index hundreds of thousand of hosts discovered via automated tools and crowd-sourcing. A complex free proxy ecosystem has been forming over the years, of which very little is known. In this paper we shed light on this ecosystem via ProxyTorrent, a distributed measurement platform that leverages both active and passive measurements. Active measurements discover free proxies, assess their performance, and detect potential malicious activities. Passive measurements relate to proxy performance and usage in the wild, and are collected by free proxies users via a Chrome plugin we developed. ProxyTorrent has been running since January 2017, monitoring up to 180,000 free proxies and totaling more than 1,500 users over a 10 months period. Our analysis shows that less than 2% of the proxies announced on the Web indeed proxy traffic on behalf of users; further, only half of these proxies have decent performance and can be used reliably. Around 10% of the working proxies exhibit malicious behaviors, e.g., ads injection and TLS interception, and these proxies are also the ones providing the best performance. Through the analysis of more than 2 Terabytes of proxied traffic, we show that web browsing is the primary user activity. Geo-blocking avoidance is not a prominent use-case, with the exception of proxies located in countries hosting popular geo-blocked content.
Adaptive Optimisation For Continuous Multi-Way Joins Over RDF Streams
Authors: Danh Le Phuoc
Venue: WWW
Year: 2018

The join operator is a core component of an RDF Stream Processing engine. The join operations usually dominate the processing load of a query execution plan. Due to the constantly updating nature of continuous queries, the query optimiser has to frequently change the optimal execution plan for a query. However, optimising the join executing plan for every execution step might be prohibitively expensive, hence, dynamic optimisation of continuous join operations is still a challenging problem so far. Therefore, this paper proposes the first adaptive optimisation approach towards this problem in the context of RDF Stream Processing. The approach comes with two dynamic cost-based optimisation algorithms which use a light-weight process to search for the best execution plan for every execution step. The experiments show the encouraging results towards this direction.
Detection of Strength and Causal Agents of Stress and Relaxation for Tweets
Authors: Reshmi Gopalakrishna Pillai
Venue: WWW
Year: 2018

The ability to detect human stress and relaxation is central for timely diagnosing stress-related diseases, ensuring customer satisfaction in services and managing human-centric applications such as traffic management. Traditional methods employ stress measuring scales or physiological monitoring which may be intrusive and inconvenient. Instead, the ubiquitous nature of social media can be leveraged to identify stress and relaxation. In this PhD research, we introduce an improved method to detect expressions of stress and relaxation in social media content. It uses word sense vectors for word sense disambiguation to improve the performance of the first ever lexicon-based stress/relaxation detection algorithm TensiStrength. Experimental results show that TensiStrength with word sense disambiguation performs better than the original TensiStrength and state-of-the-art machine learning methods in terms of Pearson's correlation and accuracy. We also suggest a novel, word-vector based approach for detecting causes of stress and relaxation in social media content.
Detection of Stress and Relaxation Magnitudes for Tweets
Authors: Reshmi Gopalakrishna Pillai, Mike Thelwall, Constantin Orasan
Venue: WWW
Year: 2018

The ability to automatically detect human stress and relaxation is crucial for timely diagnosing stress-related diseases, ensuring customer satisfaction in services and managing human-centric applications such as traffic management. Traditional methods employ stress-measuring scales or physiological monitoring which may be intrusive and inconvenient. Instead, the ubiquitous nature of the social media can be leveraged to identify stress and relaxation, since many people habitually share their recent life experiences through social networking sites. This paper introduces an improved method to detect expressions of stress and relaxation in social media content. It uses word sense disambiguation by word sense vectors to improve the performance of the first and only lexicon-based stress/relaxation detection algorithm TensiStrength. Experimental results show that incorporating word sense disambiguation substantially improves the performance of the original TensiStrength. It performs better than state-of-the-art machine learning methods too in terms of Pearson correlation and percentage of exact matches. We also propose a novel framework for identifying the causal agents of stress and relaxation in tweets as future work.
Facet Annotation Using Reference Knowledge Bases
Authors: Riccardo Porrini, Matteo Palmonari, Isabel F. Cruz
Venue: WWW
Year: 2018

Faceted interfaces are omnipresent on the web to support data exploration and filtering. A facet is a triple: a domain (e.g., Book), a property (e.g., author, language), and a set of property values (e.g., Austen, Beauvoir, Coelho, Dostoevsky, Eco, Kerouac, Suskind, ..., French, English, German, Italian, Portuguese, Russian, ... ). Given a property (e.g., language), selecting one or more of its values (English and Italian) returns the domain entities (of type Book) that match the given values (the books that are written in English or Italian). To implement faceted interfaces in a way that is scalable to very large datasets, it is necessary to automate facet extraction. Prior work associates a facet domain with a set of homogeneous values, but does not annotate the facet property. In this paper, we annotate the facet property with a predicate from a reference Knowledge Base (KB) so as to maximize the semantic similarity between the property and the predicate. We define semantic similarity in terms of three new metrics: specificity, coverage, and frequency. Our experimental evaluation uses the DBpedia and YAGO KBs and shows that for the facet annotation problem, we obtain better results than a state-of-the-art approach for the annotation of web tables as modified to annotate a set of values.
Robust Factorization Machines for User Response Prediction
Authors: Surabhi Punjabi, Priyanka Bhatt
Venue: WWW
Year: 2018

Factorization machines (FMs) are a state-of-the-art model class for user response prediction in the computational advertising domain. Rapid growth of internet and mobile device usage has given rise to multiple customer touchpoints. This coupled with factors like high cookie churn rate results in a fragmented view of user activity at the advertiser»s end. Current literature assumes procured user signals as the absolute truth, which is contested by the absence of deterministic identity linkage across a user's multiple avatars. In this work, we characterize the data uncertainty using Robust Optimization (RO) paradigm to design approaches that are immune against perturbations. We propose two novel algorithms: robust factorization machine (RFM) and its field-aware variant (RFFM), under interval uncertainty. These formulations are generic and can find applicability in any classification setting under noise. We provide a distributed and scalable Spark implementation using parallel stochastic gradient descent. In the experiments conducted on three real-world datasets, the robust counterparts outperform the baselines significantly under perturbed settings. Our experimental findings reveal interesting connections between choice of uncertainty set and the noise-proofness of resulting models.
Mapping Ordinances and Tweets using Smart City Characteristics to Aid Opinion Mining
Authors: Manish Puri, Xu Du, Aparna S. Varde, Gerard de Melo
Venue: WWW
Year: 2018

This research focuses on mining ordinances (local laws) and public reactions to them expressed on social media. We place particular emphasis on ordinances and tweets relating to Smart City Characteristics (SCCs), since an important aim of our work is to assess how well a given region heads towards a Smart City. We rely on SCCs as a nexus between a seemingly infinite number of ordinances and tweets to be able to map them, and also to facilitate SCC-based opinion mining later for providing feedback to urban agencies based on public reactions. Common sense knowledge is harnessed in our approach to reflect human judgment in mapping. This paper presents our research in ordinance and tweet mapping with SCCs, including the proposed mapping approach, our initial experiments, related discussion, and future work emerging therein. To the best of our knowledge, ours is among the first works to conduct mining on ordinances and tweets for Smart Cities. This work has a broader impact with a vision to enhance Smart City growth.