OpenRaiser/Intern-Atlas huggingface dataset:
I'm Listening to your Location! Inferring User Location with Acoustic Side Channels
Authors: Youngbae Jeon, Minchul Kim, Hyunsoo Kim, Hyoungshick Kim, Jun Ho Huh, Ji Won Yoon
Venue: WWW
Year: 2018

Electrical network frequency (ENF) signals have common patterns that can be used as signatures for identifying recorded time and location of videos and sound. To enable cost-efficient, reliable and scalable location inference, we created a reference map of ENF signals representing hundreds of locations world wide -- extracting real-world ENF signals from online multimedia streaming services (e.g., YouTube and Explore). Based on this reference map of ENF signals, we propose a novel side-channel attack that can identify the physical location of where a target video or sound was recorded or streamed from. Our attack does not require any expensive ENF signal receiver nor any software to be installed on a victim»s device -- all we need is the recorded video or sound files to perform the attack and they are collected from world wide web. The evaluation results show that our attack can infer the intra-grid location of the recorded audio files with an accuracy of $76$% when those files are $5$ minutes or longer. We also showed that our proposed attack works well even when video and audio data are processed within a certain distortion range with audio codecs used in real VoIP applications.
EMOFIEL: Mapping Emotions of Relationships in a Story
Authors: Harshita Jhavar, Paramita Mirza
Venue: WWW
Year: 2018

We present EMOFIEL, a system that identifies characters and scenes in a story from a fictional narrative summary, generates appropriate scene descriptions, identifies the emotion flow between a given directed pair of story characters in each interaction, and organizes them along the story timeline. These emotions are identified using two emotion modelling approaches: categorical and dimensional emotion models. The generated plots show that in a particular scene, two characters can share multiple emotions together with different intensity. Furthermore, the directionality of the emotion can be captured as well, depending on which character is more dominant in each interaction. EMOFIEL provides a web-based GUI that allows users to query the annotated stories to explore the emotion mapping of a given character pair throughout a given story, and to explore scenes for which a certain emotion peaks.
When Online Dating Meets Nash Social Welfare: Achieving Efficiency and Fairness
Authors: Yongzheng Jia, Xue Liu, Wei Xu
Venue: WWW
Year: 2018

Mobile dating applications such as Coffee Meets Bagel, Tantan, and Tinder, have become significant for young adults to meet new friends and discover romantic relationships. From a system designer's perspective, in order to achieve better user experience in these applications, we should take both the efficiency and fairness of a dating market into consideration, so as to increase the overall satisfaction for all users. Towards this goal, we investigate the nature of diminishing marginal returns for online dating markets (i.e., captured by the submodularity), and trade-off between the efficiency and fairness of the market with Nash social welfare. We further design effective online algorithms to the apps. We verify our models and algorithms through sound theoretical analysis and empirical studies by using real data and show that our algorithms can significantly improve the ecosystems of the online dating applications.
Multi-task Learning for Author Profiling with Hierarchical Features
Authors: Zhile Jiang, Shuai Yu, Qiang Qu, Min Yang, Junyu Luo, Juncheng Liu
Venue: WWW
Year: 2018

Author profiling is an important but challenging task. In this paper, we propose a novel Multi-Task learning framework for Author Profiling (MTAP), in which a document modeling module is shared across three different author profiling tasks (i.e., age, gender and job classification tasks). To further boost author profiling, we integrate hierarchical features learned by different models. Concretely, we employ CNN, LSTM and topic model to learn the character-level, word-level and topic-level features, respectively. MTAP thus leverages the benefits of supervised deep neural neural networks as well as an unsupervised probabilistic generative model to enhance the document representation learning. Experimental results on a real-life blog dataset show that MTAP has robust superiority over competitors and sets state-of-the-art for all the three author profiling tasks
Outliers Detection vs. Control Questions to Ensure Reliable Results in Crowdsourcing.: A Speech Quality Assessment Case Study
Authors: Rafael Zequeira Jiménez, Laura Fernández Gallardo, Sebastian Möller
Venue: WWW
Year: 2018

Crowdsourcing provides an exceptional opportunity for the rapid collection of human input for data acquisition and labelling. This approach have been adopted in multiple domains and researchers are now able to reach a demographically diverse audience at low cost. However, it remains the question of whether the results are still valid and reliable. Previous work have introduced different mechanisms to ensure data reliability in crowdsourcing. This work examines to which extend, "trapping question" or "outliers detection" assure reliable results to the detriment of, overloading task content with stimuli that are not of interest for the researcher, or by discarding data points that might be the true opinion of a worker. To this end, a speech quality assessment study have been conducted in a web crowdsourcing platform, following the ITU-T Rec. P.800. Workers assessed the speech stimuli of the database 501 from the ITU-T Rec. P.863. We examine results' validity in terms of correlations to previous ratings collected in laboratory. Our outcomes shows that neither of the techniques under investigation improve results accuracy by itself, but a combination of both. Our goal is to provide empirical guidance for designing experiments in crowdsourcing while ensuring data reliability.
A High-Performance Graph Engine for Efficient Social Network Analysis
Authors: Yong-Yeon Jo, Myung-Hwan Jang, Hyungsoo Jung, Sang-Wook Kim
Venue: WWW
Year: 2018

Existing single-machine based graph engines do not leverage the characteristic of social networks following the power-law degree distribution. We propose a new graph engine tailored for processing and analyzing large-scale social networks efficiently by exploiting the power-law degree property
ORSUM Chairs' Welcome & Organization
Authors: Alípio Jorge, João Vinagre, Pawel Matuszyk, Myra Spiliopoulou
Venue: WWW
Year: 2018

It is our great pleasure to welcome you to the WWW 2018 Workshop on Online Recommender Systems and User Modeling (ORSUM). We have received eleven submissions covering highly relevant topics in the research related to recommender systems and user modeling. During this workshop its participants will have the opportunity to see eight presentations corresponding to the accepted papers and to discuss the recent advances on these topics both with authors and other researchers in the audience. The topics of the talks include, among others, location and news recommendation, local models for online recommendations, recommendations' diversity, page optimization, context-aware recommender systems, crowdsourcing and incremental matrix factorization methods.
A Framework for Human-in-the-loop Monitoring of Concept-drift Detection in Event Log Stream
Authors: Sylvio Barbon Junior, Gabriel Marques Tavares, Victor G. Turrisi da Costa, Paolo Ceravolo, Ernesto Damiani
Venue: WWW
Year: 2018

One of the main challenges of Cognitive Computing (CC) is reacting to evolving environments in near-real time. Therefore, it is expected that CC models provide solutions by examining a summary of past history, rather than using full historical data. This strategy has significant benefits in terms of response time and space complexity but poses new challenges in term of concept-drift detection, where both long term and short terms dynamics should be taken into account. In this paper, we introduce the Concept-Drift in Event Stream Framework (CDESF) that addresses some of these challenges for data streams recording the execution of a Web-based business process. Thanks to CDESF support for feature transformation, we perform density clustering in the transformed feature space of the process event stream, observe track concept-drift over time and identify anomalous cases in the form of outliers. We validate our approach using logs of an e-healthcare process.
Verification of the Expected Answer Type for Biomedical Question Answering
Authors: Sanjay Kamath, Brigitte Grau, Yue Ma
Venue: WWW
Year: 2018

Extractive Question Answering (QA) focuses on extracting precise answers from a given paragraph to questions posed in natural language. Deep learning models are widely used to address this problem and can fetch good results, provided there exists enough data for learning. Such large datasets have been released in open domain, but not in specific domains, such as the medical domain. However, the medical domain has a great amount of resources such as UMLS thesaurus, ontologies such as SNOMED CT, and tools such as Metamap etc that could be useful. In this paper, we apply transfer learning for getting a DNN baseline system on biomedical questions and we study if structured resources can help in selecting the answers based on the recognition of the Expected Answer Type (EAT), which has been proved useful in open domain QA systems. This study relies on different representations for LAT and we study if gold standard answers and answers of our model have some positive impact from the LAT.
Mining the Web of Life Sciences Linked Open Data for Mechanism-Based Pharmacovigilance
Authors: Maulik R. Kamdar
Venue: WWW
Year: 2018

The vision of the Semantic Web has stimulated the development of Web-scale architectures for discovering implicit associations from multiple heterogeneous data and knowledge sources. In biomedicine, using W3C-established standards and Linked Data principles, data publishers have transformed and linked several datasets to create a huge web of Life Sciences Linked Open Data (LSLOD). However, mining the LSLOD cloud is still very difficult and often impossible for biomedical researchers due to several challenges: structural heterogeneity, lack of vocabulary reuse, inconsistencies, and incompleteness. To discover drug-adverse reaction associations and their mechanistic explanations, I have developed a novel architecture that combines information retrieval and association discovery. The architecture demonstrates favorable AUROC statistics against baseline methods in pharmacovigilance, and provides confidence values on underlying biological mechanisms. I quantify the several challenges associated with mining the LSLOD cloud for biomedical applications through an empirical analysis of more than 40 different sources. Ideally, the architecture can be extended in other domains to realize the goal of implicit association discovery.
A Feasibility Study on Crowdsourcing to Monitor Municipal Resources in Smart Cities
Authors: Thivya Kandappu, Archan Misra, Desmond Koh, Randy Daratan Tandriansyah, Nikita Jaiman
Venue: WWW
Year: 2018

Active citizenry, whereby citizens actively participate in reporting and addressing challenges in urban service delivery is a strategic goal of smart cities such as Singapore. In spite of the promise, we believe that the success of such large-scale nation-wide crowdsourcing deployments depend on the real-word user preferences and behavioral characteristics of citizens. In this paper, we first present our findings on behavioral preferences and key concerns of citizens regarding smart-city services via an opinion survey conducted with 1300 participants. We then propose a "citizen-controlled" urban services reporting platform where citizens actively report on the status of various municipal resources. We advocate the importance of matching user mobility patterns against task locations to make the platform more efficient (i.e., higher task completion rate and lower detour overhead).
A Case Study on Smart City Projects in India: An Analysis of Nagpur, Allahabad and Dehradun
Authors: Vinay Kandpal
Venue: WWW
Year: 2018

Nagpur has emerged as the topmost smart city in India. In just five months, Nagpur has beaten other cities chosen before it to get the best implementation of smart city plan. A recent stock-taking exercise conducted by urban development ministry has revealed that Nagpur, though chosen as a smart city in September 2016 much after 33 smart cities in two previous rounds has achieved the best investment conversion ratio. India's smart city program hopes to revolutionize city life and improve the quality of life for India's urban population. In the absence of a zonal plan, many parts of Dehradun have witnessed haphazard development over the years, which has already caused much damage to the vision of a planned smart city. Smart City would require smart economy, bright people, smart organization, smart communication, smart engineering, smart transit, fresh environment and bright living. Nevertheless, with mass migration leading to basic problems, like water shortages and overcrowding, the rate at which these cities will be developed will be the key. Several initiatives are being led by the Government of India to convert 100 Cities into Smart Cities. Government to Actively Use PPP Route and Encourage FDI for Effective Implementation of Smart Cities Project in India.
Recent Advances in Recommender Systems: Sets, Local Models, Coverage, and Errors
Authors: George Karypis
Venue: WWW
Year: 2018

Recommender systems are designed to identify the items that a user will like or find useful based on the user's prior preferences and activities. These systems have become ubiquitous and are an essential tool for information filtering and (e-)commerce. Over the years, collaborative filtering, which derive these recommendations by leveraging past activities of groups of users, has emerged as the most prominent approach for solving this problem. This talk will present some of our recent work towards improving the performance of collaborative filtering-based recommender systems and understanding some of their fundamental limitations and characteristics. It will start by analyzing how the ratings that users provide to a set of items relate to their ratings of the set's individual items and, using these insights, will present rating prediction approaches that utilize distant supervision. It will then discuss extensions to approaches based on sparse linear and latent factor models that postulate that users' preferences are a combination of global and local preferences, which are shown to lead to better user modeling and as such improved prediction performance. Finally, the talk will conclude by discussing what can be accurately predicted by latent factor approaches and by analyzing the estimation error of sparse linear and latent factor models and how its characteristics impacts the performance of top N recommendation algorithms.
THOR: Text-enabled Analytics for Humanitarian Operations
Authors: Mayank Kejriwal, Daniel Gilley, Pedro A. Szekely, Jill Crisman
Venue: WWW
Year: 2018

In this demonstration, we present the Text-enabled Humanitarian Operations in Real-time (THOR) framework, which is being prototyped to provide visual and analytical situational awareness to humanitarian and disaster relief (HADR) planners. THOR is a collaborative effort between industrial and university research laboratories, designed with an intent to support both military and civilian HADR operations. At its core, THOR is powered by a domain-specific knowledge graph, which is derived from natural language outputs and is amenable to real-time analytics. THOR is designed to operate in low-resource linguistic environments, process heterogeneous data, including news and social media, reason about arbitrary disasters not knowable in advance, and provide advanced graphical interaction capabilities. We will demo the latest prototype of THOR using an interactive case study situation.
Identifying Task Boundaries in Digital Assistants
Authors: Madian Khabsa, Ahmed El Kholy, Ahmed Hassan Awadallah, Imed Zitouni, Milad Shokouhi
Venue: WWW
Year: 2018

Digital assistants are emerging to become more prevalent in our daily lives. In interacting with these assistants, users may engage in multiple tasks within a short period of time. Identifying task boundaries and isolating them within a session is critical for measuring the performance of the system on each individual task. In this paper we aim to automatically identify sequences of interactions that together form a task. To this end, we sample interactions from a real world digital assistant and use crowd judges to segment a session into multiple tasks. After that, we use a machine learned model to identify task boundaries. Our learned model with its features significantly outperform the baselines. To the best of our knowledge, this is the first work that aims to identify tasks within digital assistant sessions.
SIDE: Representation Learning in Signed Directed Networks
Authors: Junghwan Kim, Haekyu Park, Ji-Eun Lee, U Kang
Venue: WWW
Year: 2018

Given a signed directed network, how can we learn node representations which fully encode structural information of the network including sign and direction of edges? Node representation learning or network embedding learns a mapping of each node to a vector. The mapping encodes structural information on network, providing low-dimensional dense node features for general machine learning and data mining frameworks. Since many social networks allow trust (friend) and distrust (enemy) relationships described by signed and directed edges, generalizing network embedding method to learn from sign and direction information in networks is crucial. In addition, social theories are critical tool in signed network analysis. However, none of the existing methods supports all of the desired properties: considering sign, direction, and social theoretical interpretation. In this paper, we propose SIDE, a general network embedding method that represents both sign and direction of edges in the embedding space. SIDE carefully formulates and optimizes likelihood over both direct and indirect signed connections. We provide socio-psychological interpretation for each component of likelihood function. We prove linear scalability of our algorithm and propose additional optimization techniques to reduce the training time and improve accuracy. Through extensive experiments on real-world signed directed networks, we show that SIDE effectively encodes structural information into the learned embedding.
AdBudgetKiller: Online Advertising Budget Draining Attack
Authors: I Luk Kim, Weihang Wang, Yonghwi Kwon, Yunhui Zheng, Yousra Aafer, Weijie Meng, Xiangyu Zhang
Venue: WWW
Year: 2018

In this paper, we present a new ad budget draining attack. By repeatedly pulling ads from targeted advertisers using crafted browsing profiles, we are able to reduce the chance of showing their ads to real-human visitors and trash the ad budget. From the advertiser profiles collected by an automated crawler, we infer advertising strategies, train satisfying browsing profiles and launch large-scale attacks. We evaluate our methods on 291 public advertisers selected from Alexa Top 500, where we successfully reveal the targeting strategies used by 87% of the advertisers we considered. We also executed a series of attacks against a controlled advertiser and 3 real-world advertisers within the ethical and legal boundary. The results show that we are able to fetch 40,958 ads and drain up to $155.89 from the targeted advertisers within an hour.
Transfer Learning of Artist Group Factors to Musical Genre Classification
Authors: Jaehun Kim, Minz Won, Xavier Serra, Cynthia C. S. Liem
Venue: WWW
Year: 2018

The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy. Artist labels are less subjective and less noisy, while certain artists may relate more strongly to certain genres. At the same time, at prediction time, it is not guaranteed that artist labels are available for a given audio segment. Therefore, in this work, we propose to apply the transfer learning framework, learning artist-related information which will be used at inference time for genre classification. We consider different types of artist-related information, expressed through artist group factors, which will allow for more efficient learning and stronger robustness to potential label noise. Furthermore, we investigate how to achieve the highest validation accuracy on the given FMA dataset, by experimenting with various kinds of transfer methods, including single-task transfer, multi-task transfer and finally multi-task learning.
Distributed Optimization of All-in-one SVMs for Extreme Classfication
Authors: Marius Kloft
Venue: WWW
Year: 2018

Training of multi-class or multi-label classification machines are embarrassingly parallelizable via the one-vs.-rest approach. However, training of all-in-one multi-class learning machines such as multinomial logistic regression or all-in-one multi-class SVMs (MC-SVMs) is not parallelizable out of the box. In my talk, I present optimization strategies to distribute the training of some all-in-one multi-class SVMs over the classes, which makes them appealing for the use in extreme classification.
A User Centred Perspective on Structured Data Discovery
Authors: Laura Koesten
Venue: WWW
Year: 2018

Structured data is becoming critical in every domain and its availability on the web is increasing rapidly. Despite its abundance and variety of applications, we know very little about how people find data, understand it, and put it to use. This work aims to inform the design of data discovery tools and technologies from a user centred perspective. We aim to better understand what type of information supports people in finding and selecting data relevant for their respective tasks. We conducted a mixed-methods study looking at the workflow of data practitioners when searching for data. From that we identified textual summaries as a key element that supports the decision making process in information seeking activities for data. Based on these results we performed a mixed-methods study to identify attributes people consider important when summarising a dataset. We found text summaries are laid out according to common structures, contain four main information types, and cover a set of dataset features. We describe follow-up studies that are planned to validate these findings and to evaluate their applicability in a dataset search scenario.