OpenRaiser/Intern-Atlas huggingface dataset:
The Effect of Aging on Visual Attention Shifting in Collaborative Document Editing
Authors: Patricia Duangcham, Vajirasak Vanijja, Sachi Mizobuchi
Venue: WWW
Year: 2017

Many tasks, including driving, require frequent shifts in visual attention. Some visual shifting of attention is due to distraction from a secondary task, or from non-task relevant stimuli in the environment. Older people (e.g. over 60) are generally slower to react to stimuli and less skillful in learning to use new technologies. In this paper we are interested in studying the effect of age on the ability to shift visual attention in a task that involves computer use. An experiment is reported involving 10 participants, five who were over the age of 60 and five who were in their twenties and thirties. A collaborative editing task was carried out where participants, run one at a time, had to edit a document in real-time based on comments made by three research assistants (who were not participants for purposes of data collection). In one condition participants had to constantly shift their visual attention from a desktop computer they were using to a large shared screen where the research assistants made editing suggestions and used a laser pointer to indicate which part of the document (shown on the large shared screen) they were referring to. As expected, time to perform each editing task increased in the two-screen (vs. one screen) condition. However, the amount of slowing due to the use of two screens for the task was considerably greater for the older group. The results are interpreted in terms of the use of redesigned tools and technologies to assist older workers perform more effectively.
Distributed Ledgers & Linked Data
Authors: Peter Evans-Greenwood
Venue: WWW
Year: 2017

Distributed are increasing being thought of as a platform for decentralised applications -- DApps -- and the the focus for many is shifting from Bitcoin to Smart Contracts. It's thought that encoding contracts and putting them "on the blockchain" will result in a new generation of organisations that are leaner and more efficient than their forebears ("CApps"?"), disrupting these forebears in the process.
Truth Discovery from Conflicting Multi-Valued Objects
Authors: Xiu Susie Fang
Venue: WWW
Year: 2017

Truth discovery is a fundamental research topic, which aims at identifying the true value(s) of objects of interest given the conflicting multi-sourced data. Although considerable research efforts have been conducted on this topic, we can still point out two significant issues unsolved: i) single-valued assumption, i.e., current methods assume only one true value for each object, while in reality objects with multiple true values widely exist; ii) sparse ground truth, i.e., current works evaluate and compare existing truth discovery methods based on datasets with limited ground truth. Therefore, the empirical studies might be biased and cannot legitimately validate the existing methods. In this PhD project, we propose a full-fledged graph-based model, SmartMTD (Smart Multi-valued Truth Discovery), which incorporates four important implications to conduct truth discovery for multi-valued objects. Two graphs are constructed and further used to derive two aspects of source reliability via random walk computations. We also present a general approach, which utilizes Markov chain models with Bayesian inference, for comparing the existing truth discovery methods and validate our approach without ground truth. Initial empirical studies on two real-world datasets show the effectiveness of SmartMTD.
Carina: Interactive Million-Node Graph Visualization using Web Browser Technologies
Authors: Dezhi Fang, Matthew Keezer, Jacob Williams, Kshitij Kulkarni, Robert S. Pienta, Duen Horng Chau
Venue: WWW
Year: 2017

We are working on a scalable, interactive visualization system, called Carina, for people to explore million-node graphs. By using latest web browser technologies, Carina offers fast graph rendering via WebGL, and works across desktop (via Electron) and mobile platforms. Different from most existing graph visualization tools, Carina does not store the full graph in RAM, enabling it to work with graphs with up to 69M edges. We are working to improve and open-source Carina, to offer researchers and practitioners a new, scalable way to explore and visualize large graph datasets.
Value Veracity Estimation for Multi-Truth Objects via a Graph-Based Approach
Authors: Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Anne H. H. Ngu
Venue: WWW
Year: 2017

A fundamental issue with current truth discovery methods is that they generally assume only one true value for each object, while in reality objects may have multiple true values. In this work, we propose a graph-based approach, called SmartMTD, to relax this assumption in truth discovery. SmartMTD models two types of source relations with additional quantification to precisely estimate source reliability and to detect malicious agreement among sources for multi-truth discovery. In particular, two graphs are constructed based on the modeled source relations, which are further used to derive two aspects of source reliability via random walk computation.
On Recognizing Abnormal Human Behaviours by Data Stream Mining with Misclassified Recalls
Authors: Simon Fong, Shimin Hu, Wei Song, Kyungeun Cho, Raymond K. Wong, Sabah Mohammed
Venue: WWW
Year: 2017

Human activity recognition (HAR) has been a popular research topic, because of its importance in security and healthcare contributing to aging societies. One of the emerging applications of HAR is to monitor needy people such as elders, patients of disabled, or undergoing physical rehabilitation, using sensing technology. In this paper, an improved version of Very Fast Decision Tree (VFDT) is proposed which makes use of misclassified results for post-learning. Specifically, a new technique namely Misclassified Recall (MR) which is a post-processing step for relearning a new concept, is formulated. In HAR, most misclassified instances are those belonging to ambiguous movements. For examples, squatting involves actions in between standing and sitting, falling straight down is a sequence of standing, possibly body tiling or curling, bending legs, squatting and crashing down on the floor; and there may be totally new (unseen) actions beyond the training instances when it comes to classifying "abnormal" human behaviours. Think about the extreme postures of how a person collapses and free falling from height. Experiments using wearable sensing data for multi-class HAR is used, to test the efficacy of the new methodology VFDT+MR, in comparison to a classical data stream mining algorithm VFDT alone.
Longitudinal Ambient Mobile Sensor Monitoring for TCM-oriented Healthcare Assessments: Framework, Challenges and Applications
Authors: Simon Fong, Yan Zhuang, Shimin Hu, Wei Song, Liansheng Liu, Luiz Abel Moutinho
Venue: WWW
Year: 2017

Recently healthcare using information technology progressed with the maturity of tele-health monitoring services through Internet, and the emergence of mobile apps on smartphones that are geared towards recording inputs of intakes (e.g. calories, fat, amount of water etc.), as well as monitoring one's activity levels (e.g. number of steps walked) and emotion states, for inferring one's health condition. In this paper, a novel concept of fusing the current mobile app software technology, big data, sensing technology, and machine learning (in decision rule induction, reasoning, and image recognition) into the principles of TCM healthcare is proposed. TCM has developed a set of practical, universal and thorough, and systematic health care system, which is notably characterized by its relations to our daily lifestyles. Currently it lacks of some ubiquitous tools such as mobile smartphone and its sensing apps to embrace this healthcare concept as a personal assistant, monitoring and advising the user regarding his/her health and forewarning of any health risk. The challenges and potential applications are discussed in this paper. The advantage of this project is tapped from the popularity and convenient use of mobile smartphone and EEG sensors for providing ubiquitous personal TCM healthcare
Web Search as a Linguistic Tool
Authors: Adam Fourney, Meredith Ringel Morris, Ryen W. White
Venue: WWW
Year: 2017

Many people rely on web search engines to check the spelling or grammatical correctness of input phrases. For example, one might search [recurring or reoccurring] to decide between these similar words. While language-related queries are common, they have low click-through rates, lack a strong intent signal, and are generally challenging to study. Perhaps for these reasons, they have yet to be characterized in the literature. In this paper we report the results of two surveys that investigate how, when, and why people use web search to support low-level, language-related tasks. The first survey was distributed by email, and asked participants to reflect on a recent search task. The second survey was embedded directly in search result pages, and captured information about searchers' intents in-situ. Our analysis confirms that language-related search tasks are indeed common, accounting for at least 2.7% of all queries posed by our respondents. Survey responses also reveal: (1) the range of language-related tasks people perform with search, (2) the contexts in which these tasks arise, and (3), the reasons why people elect to use web search rather than relying on traditional proofing tools (e.g., spelling and grammar checkers).
Can You Spot the Fakes?: On the Limitations of User Feedback in Online Social Networks
Authors: David Mandell Freeman
Venue: WWW
Year: 2017

Online social networks (OSNs) are appealing platforms for spammers and fraudsters, who typically use fake or compromised accounts to connect with and defraud real users. To combat such abuse, OSNs allow users to report fraudulent profiles or activity. The OSN can then use reporting data to review and/or limit activity of reported accounts.
How to Use Gamified Dashboards and Learning Analytics for Providing Immediate Student Feedback and Performance Tracking in Higher Education
Authors: Sara de Freitas, David C. Gibson, Victor Alvarez, Leah Irving, Kam Star, Sven Charleer, Katrien Verbert
Venue: WWW
Year: 2017

With the wide use of the Internet and digital data sources, there has been a recent emergence of easy access to student data within learning management systems (LMS), grade data through student information systems (SIS) and broader sector data through benchmarking metrics and standards. Learning analytics on top of this data has introduced greater capabilities for improving student performance through immediate feedback. Current literature considers the role of dashboards for student performance and feedback, but few papers consider the efficacy of fast feedback to students or other ways that information can be fed back to learners. In this paper, we consider the work done by three leading groups addressing the impact of gamification in university education, with a specific focus on how data is presented to the learner, that is using elements such as points, levelling up, narrative and progression to scaffold learning. Results indicate increases in student motivation, engagement, satisfaction, retention and performance enhancements.
Collaborative Optimization for Collective Decision-making in Continuous Spaces
Authors: Nikhil Garg, Vijay Kamble, Ashish Goel, David Marn, Kamesh Munagala
Venue: WWW
Year: 2017

Many societal decision problems lie in high-dimensional continuous spaces not amenable to the voting techniques common for their discrete or single-dimensional counterparts. These problems are typically discretized before running an election or decided upon through negotiation by representatives. We propose a meta-algorithm called Iterative Local Voting for collective decision-making in this setting, in which voters are sequentially sampled and asked to modify a candidate solution within some local neighborhood of its current value, as defined by a ball in some chosen norm. In general, such schemes do not converge, or, when they do, the resulting solution does not have a natural description. We first prove the convergence of this algorithm under appropriate choices of neighborhoods to plausible solutions in certain natural settings: when the voters' utilities can be expressed in terms of some form of distance from their ideal solution, and when these utilities are additively decomposable across dimensions. In many of these cases, we obtain convergence to the societal welfare maximizing solution. We then describe an experiment in which we test our algorithm for the decision of the U.S. Federal Budget on Mechanical Turk with over 4,000 workers, employing neighborhoods defined by §L1, §L2 and §L∞ balls. We make several observations that inform future implementations of such a procedure.
Media Attention to Science
Authors: Kiran Garimella, Han Xiao
Venue: WWW
Year: 2017

How has media attention to science changed over the past decade? Does media attention bring scientific attention too? To answer these questions, we collected media attention statistics from Altmetric.com for over 40,000 papers published in PNAS journal in the last 13 years. Our analysis reveals that (i) Media attention to science has slowly, but steadily increased over time, (ii) Media attention doesn't necessarily translate into scientific attention (citations) and, (iii) It is non-trivial to predict the amount of media attention a paper gets from attributes such as authors, author affiliations, abstract, and title.
Modeling of Human Movement Behavioral Knowledge from GPS Traces for Categorizing Mobile Users
Authors: Shreya Ghosh, Soumya K. Ghosh
Venue: WWW
Year: 2017

Human movement analysis and categorization of mobile users based on their movement semantics are challenging tasks. Further, due to security and privacy issues, insufficient labeled or user-annotated data (or, ground-truth data) makes the user-classification from GPS traces more complex. In this work, we present a framework which models user movement patterns containing both spatio-temporal and semantic information, generates semantic stay-point taxonomy by analysing GPS traces of all users, summarizes individuals' GPS traces and clusters users based on the semantics of their movement patterns. To alleviate labelled data scarcity problem while user categorization in a particular region of interest (ROI), we propose a method to transfer knowledge derived from a set of GPS traces of a geographically distanced but similar type of ROI. An extensive set of experiments using real GPS trace dataset of Kharagpur, India and Dartmouth, Hanover, USA have been carried out to demonstrate the effectiveness of our proposed framework.
Drawing Sound Conclusions from Noisy Judgments
Authors: David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan
Venue: WWW
Year: 2017

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as [email protected] and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.
Task Routing and Assignment in Crowdsourcing based on Cognitive Abilities
Authors: Jorge Gonçalves, Michael Feldman, Subingqian Hu, Vassilis Kostakos, Abraham Bernstein
Venue: WWW
Year: 2017

Appropriate task routing and assignment is an important, but often overlooked, element in crowdsourcing research and practice. In this paper, we explore and evaluate a mechanism that can enable matching crowdsourcing tasks to suitable crowd-workers based on their cognitive abilities. We measure participants' visual and fluency cognitive abilities with the well-established Kit of Factor-Referenced Cognitive Test, and measure crowdsourcing performance with our own set of developed tasks. Our results indicate that participants' cognitive abilities correlate well with their crowdsourcing performance. We also built two predictive models (beta and linear regression) for crowdsourcing task performance based on the performance on cognitive tests as explanatory variables. The model results suggest that it is feasible to predict crowdsourcing performance based on cognitive abilities. Finally, we discuss the benefits and challenges of leveraging workers' cognitive abilities to improve task routing and assignment in crowdsourcing environments.
Green Virtual Network Embedding for Collaborative Edge Computing in Environment-Friendly Optical-Wireless Networks
Authors: Xiaoxue Gong, Lei Guo, Zhaolong Ning
Venue: WWW
Year: 2017

To reduce the cost and delay caused by transferring data to the remote cloud, the trend is to design an intelligent Edge Device (ED) for preliminary data processing, i.e., edge computing. Some EDs usually form a group where they wirelessly communicate with each other. Different ED groups are interconnected by optical fiber cables. Through coordinating the use of ED groups, we can perform a collaborative edge computing in a hybrid network where a cost-efficient optical-wireless convergence is achieved by virtualization. In this paper, we use the virtual network to describe one computing-application's requirement for the substrate resource, and we investigate how embed multiple virtual networks onto the common network infrastructure. In our approach, a graph-cutting algorithm is firstly utilized to embed as many virtual networks as possible onto the specified EDs within the same group. However, a single ED group cannot handle all computing applications competing for limited wireless and computing resources. To solve this challenging problem, we transform the virtual networks-impossibly embedded onto the same ED group into new ones processed by ED groups. Simulations results demonstrate the green feature of our solution: 1) the total transmitting power assigned for EDs is effectively reduced using the graph cutting algorithm provided that all of computing applications can be solved by a single ED group; 2) our method accepts more virtual networks with the improvement ratio of 77%, through the coordination of ED groups. In addition, there is a good match between the algorithm result and the optimal number of consumed wavelengths per optical fiber cable.
Clustered Model Adaption for Personalized Sentiment Analysis
Authors: Lin Gong, Benjamin Haines, Hongning Wang
Venue: WWW
Year: 2017

We propose to capture humans' variable and idiosyncratic sentiment via building personalized sentiment classification models at a group level. Our solution roots in the social comparison theory that humans tend to form groups with others of similar minds and ability, and the cognitive consistency theory that mutual influence inside groups will eventually shape group norms and attitudes, with which group members will all shift to align. We formalize personalized sentiment classification as a multi-task learning problem. In particular, to exploit the clustering property of users' opinions, we impose a non-parametric Dirichlet Process prior over the personalized models, in which group members share the same customized sentiment model adapted from a global classifier. Extensive experimental evaluations on large collections of Amazon and Yelp reviews confirm the effectiveness of the proposed solution: it outperformed user-independent classification solutions, and several state-of-the-art model adaptation and multi-task learning algorithms.
Efficient Maximum Flow Maintenance on Dynamic Networks
Authors: Sergio Greco, Cristian Molinaro, Chiara Pulice, Ximena Quintana
Venue: WWW
Year: 2017

While many efficient algorithms for the maximum flow problem have been proposed over the years, they are designed to work with static networks, and thus they need to recompute a new solution from scratch every time an update occurs. Such approaches are impractical in many current application where updates are frequent. To overcome these limitations, this paper proposes efficient incremental algorithms for maintaining the maximum flow in dynamic networks.
What was Hillary Clinton doing in Katy, Texas?
Authors: Toni Grütze, Ralf Krestel, Konstantina Lazaridou, Felix Naumann
Venue: WWW
Year: 2017

During the last presidential election in the United States, Twitter drew a lot of attention. This is because many leading persons and organizations, such as U.S. president Donald J. Trump, showed a strong affection to this medium. In this work we neglect the political contents and opinions shared on Twitter and focus on the question: Can we determine and track the physical location of presidential candidates based on posts in the Twittersphere?
CognitiveDB: An Intelligent Navigator for Large-scale Dark Structured Data
Authors: Michael N. Gubanov, Manju Priya, Maksim Podkorytov
Venue: WWW
Year: 2017

Volume and Variety of Big data [Stonebraker, 2012] are significant impediments for anyone who wants to quickly understand what information is inside a large-scale dataset. Such data is often informally referred to as 'dark' due to the difficulties of understanding its contents. For example, there are millions of structured tables available on the Web, but finding a specific table or summarizing all Web tables to understand what information is available is infeasible or very difficult because of the scale involved.
A Systematic Framework to Optimize Launch Times of Web Apps
Authors: Suresh Kumar Gudla, Jitendra Kumar Sahoo, Abhishek Singh, Joy Bose, Nazeer Ahamed
Venue: WWW
Year: 2017

Web Applications typically have longer launch times compared to native applications, especially upon device boot up or if the browser is not already running in the background. In this paper, we propose an approach to speed up the launch time for web applications, by considering the user's usage of web applications and pre-launching the predicted web applications. We provide implementation details of our model and perform experiments on various web applications to measure the effectiveness of the framework for fast launch of the applications after the device boots.
Holistic Neural Network for CTR Prediction
Authors: Huifeng Guo, Ruiming Tang, Yunming Ye, Xiuqiang He
Venue: WWW
Year: 2017

This paper proposes HNN, a holistic neural network structure for click-through rate (CTR) prediction in recommender systems. Empirically, equipped with HNN, the performance of deep neural networks for CTR prediction are improved on Criteo and Huawei App Store datasets.
GeoSemOLAP: Geospatial OLAP on the Semantic Web Made Easy
Authors: Nurefsan Gür, Jacob Nielsen, Katja Hose, Torben Bach Pedersen
Venue: WWW
Year: 2017

The proliferation of spatial data and the publication of multidimensional (MD) data on the Semantic Web (SW) has led to new opportunities for On-Line Analytical Processing (SOLAP) over spatial data using SPARQL. However, formulating such queries results in verbose statements and can easily become very difficult for inexperienced users. Hence, we have developed GeoSemOLAP to enable users without detailed knowledge of RDF and SPARQL to query the SW with SOLAP. GeoSemOLAP generates SPARQL queries based on high-level SOLAP operators and allows the user to interactively formulate queries using a graphical interface with interactive maps.