OpenRaiser/Intern-Atlas huggingface dataset:
Identifying User Sessions in Interactions with Intelligent Digital Assistants
Authors: Rishabh Mehrotra, Ahmed El Kholy, Imed Zitouni, Milad Shokouhi, Ahmed Hassan
Venue: WWW
Year: 2017

Search sessions have traditionally been considered as the focal unit of analysis for seeking behavioral insights from user interactions. While most session identification techniques have focused on the traditional web search setting; in this work, we instead consider user interactions with digital assistants (e.g. Cortana, Siri) and aim at identifying session boundary cut-offs. To our knowledge, this is one of the first studies investigating user interactions with a desktop based digital assistant. Historically, most user session identification strategies based on inactivity thresholds are either inherently arbitrary, or set at about 30 minutes. We postulate that such 30 minute thresholds may not be optimal for segregating user interactions with intelligent assistants into sessions. Instead, we model user-activity times as a Gaussian mixture model and look for evidence of a valley to identify optimal inter-activity thresholds for identifying sessions. Our results suggest a smaller threshold($\sim$2 minutes) for session boundary cut-off in digital assistants than the traditionally used 30 minute threshold for web search engines.
Deals or No Deals: Contract Design for Online Advertising
Authors: Vahab S. Mirrokni, Hamid Nazerzadeh
Venue: WWW
Year: 2017

Billions of dollars worth of display advertising are sold via contracts and deals. This paper presents a formal study of preferred deals, a new generation of contracts for selling online advertisement, that generalize the traditional reservation contracts; these contracts are suitable for advertisers with advanced targeting capabilities. We propose a constant-factor approximation algorithm for maximizing the revenue that can be obtained from these deals. We show, both theoretically and via data analysis, that deals, with appropriately chosen minimum-purchase guarantees, can yield significantly higher revenue than auctions. We evaluate our algorithm using data from Google's ad exchange platform. Our algorithm obtains about 90% of the optimal revenue where the second-price auction, even with personalized reserve, obtains at most 52% of the benchmark.
On Low Overlap among Search Results of Academic Search Engines
Authors: Anasua Mitra, Amit Awekar
Venue: WWW
Year: 2017

Number of published scholarly articles is growing exponentially. To tackle this information overload, researchers are increasingly depending on niche academic search engines. Recent works have shown that two major general web search engines: Google and Bing, have high level of agreement in their top search results. In contrast, we show that various academic search engines have low degree of agreement among themselves. We performed experiments using 2500 queries over four academic search engines. We observe that overlap in search result sets of any combination of academic search engines is significantly low and in most of the cases the search result sets are mutually exclusive. We also discuss implications of this low overlap.
Blotter: Low Latency Transactions for Geo-Replicated Storage
Authors: Henrique Moniz, João Leitão, Ricardo J. Dias, Johannes Gehrke, Nuno M. Preguiça, Rodrigo Rodrigues
Venue: WWW
Year: 2017

Most geo-replicated storage systems use weak consistency to avoid the performance penalty of coordinating replicas in different data centers. This departure from strong semantics poses problems to application programmers, who need to address the anomalies enabled by weak consistency. In this paper we use a recently proposed isolation level, called Non-Monotonic Snapshot Isolation, to achieve ACID transactions with low latency. To this end, we present Blotter, a geo-replicated system that leverages these semantics in the design of a new concurrency control protocol that leaves a small amount of local state during reads to make commits more efficient, which is combined with a configuration of Paxos that is tailored for good performance in wide area settings. Read operations always run on the local data center, and update transactions complete in a small number of message steps to a subset of the replicas. We implemented Blotter as an extension to Cassandra. Our experimental evaluation shows that Blotter has a small overhead at the data center scale, and performs better across data centers when compared with our implementations of the core Spanner protocol and of Snapshot Isolation on the same codebase.
Understanding the Impacts of Open Access: Specialist Scholarly Books and their Communities
Authors: Lucy Montgomery
Venue: WWW
Year: 2017

Specialist scholarly books, the key form of publication in the Humanities and some Social Sciences, are often the forgotten younger-siblings of scholarly big data projects. Humanities disciplines are "peer review" rather than citation-based. Demand for data on how outputs perform from researchers, institutions and funding agencies -- has been lower than in the case of the exactsciences. Like book publishing more generally, large-scaleand timely data about distribution and usage of monographs has been hard to collect (or to access) until recently. No single publisher controls more than 4%. Books have been slower than journals to transition to digital formats[1]. Uniform approaches to DOIs and metadata are taking time to crystallise. Users engage with books in ways that are often difficult to 'see' at scale. Books have a much longer shelf-life than do articles; their impact can be measured in years and sometimes decades[2].
CityDNA: Smart City Dimensions' Correlations for Identifying Urban Profile
Authors: Vaia Moustaka, Athena Vakali, Leonidas G. Anthopoulos
Venue: WWW
Year: 2017

Smart cities evolve over multiple themes and areas with the development of cyber-physical systems and smart services that address several urban issues regarding economy, mobility, environment, people, living and governance. This evolution has obliged the definition of several conceptualization and evaluation models, which respect alternative smart city perspectives. This work proposes smart city profiling with the introduction of the "CityDNA" model, according which, smart city's dimensions' relevance can be captured and visualized. Based on this model, a smart city's profile can be defined and characterized, under a simple comprehensive view of local needs and challenges. A particular smart city scenario is highlighted as a proof of concept for CityDNA and future design and implementation ideas are identified and justified.
Neural Underpinnings of Website Legitimacy and Familiarity Detection: An fNIRS Study
Authors: Ajaya Neupane, Nitesh Saxena, Leanne M. Hirshfield
Venue: WWW
Year: 2017

In this paper, we study the neural underpinnings relevant to user-centered web security through the lens of functional near-infrared spectroscopy (fNIRS). Specifically, we design and conduct an fNIRS study to pursue a thorough investigation of users' processing of legitimate vs. illegitimate and familiar vs. unfamiliar websites. We pinpoint the neural activity in these tasks as well as the brain areas that control such activity. We show that, at the neurological level, users process the legitimate websites differently from the illegitimate websites when subject to phishing attacks. Similarly, we show that users exhibit marked differences in the way their brains process the previously familiar websites from unfamiliar websites. These findings have several defensive and offensive implications. In particular, we discuss how these differences may be used by the system designers in the future to differentiate between legitimate and illegitimate websites automatically based on neural signals. Similarly, we discuss the potential for future malicious attackers, with access to neural signals, in compromising the privacy of users by detecting whether a website is previously familiar or unfamiliar to the user.
Using Pairwise Comparisons in the Online Social Moderation of Performance Assessment
Authors: C. Paul Newhouse, Pina Tarricone
Venue: WWW
Year: 2017

Assessing the performance of a student involves some form of judgement, and where more than one assessor is involved this usually requires some form of moderation to ensure consistent and fair results. Often this involves meetings or communication between assessors, which is referred to as social moderation. This paper reports on a study that investigated the use of online technologies to support a form of social moderation of artworks submitted for assessment in a senior secondary school course in Western Australia. Online systems were used to facilitate communications and provide access to digital representations of the submissions along with assessment tools. In particular a pairwise comparison judging online tool was used. This approach to social moderation was tested in a realistic context involving a sample of 12 teachers from rural schools for whom face-to-face meetings would be difficult. The aim was to investigate whether the use of these online systems would support good moderation outcomes and valuable professional learning for those involved. The study found that this approach to online social moderation was feasible, and participants perceived that it had improved the consistency of their judgements because they had developed an improved understanding of the assessment criteria and standard of work. However, analysis of scores and reliability data suggested some were not adequately consistent, and it was likely that this was due to their inexperience in assessing such work. Therefore some changes to the processes of this form of online social moderation were recommended.
eAssistant: Cognitive Assistance for Identification and Auto-Triage of Actionable Conversations
Authors: Hamid R. Motahari Nezhad, Kalpa Gunaratna, Juan M. Cappi
Venue: WWW
Year: 2017

The browser and screen have been the main user interfaces of the Web and mobile apps. The notification mechanism is an evolution in the user interaction paradigm by keeping users updated without checking applications. Conversational agents are posed to be the next revolution in user interaction paradigms. However, without intelligence on the triage of content served by the interaction and content differentiation in applications, interaction paradigms may still place the burden of information overload on users. In this paper, we focus on the problem of intelligent identification of actionable information in the content served by applications, and in particular in productivity applications (such as email, chat, messaging, social collaboration tools, etc.). We present eAssistant, which offers a novel fine-grained action identification method in an adaptive, personalizable, and online-trainable manner, and a cognitive agent/API that uses action information and user-centric conversation characteristics to auto-triage user conversations. The introduced method identifies individual actions and associated metadata; it is extensible in terms of the number of action classes; it learns in an online and continuous manner via user interactions and feedback, and it is personalizable to different users. We have evaluated the proposed method using real-world datasets. The results show that the method achieves higher accuracy compared to traditional ways of formulating the problem, while exhibiting additional desired properties of online, personalized, and adaptive learning. In eAssistant, we introduce a multi-dimensional learning model of conversations auto-triage, defined based on a user study and NLP-based information extraction techniques, to auto-triage user conversations on social collaboration and productivity tools.
Understanding International Migration using Tensor Factorization
Authors: Hieu Nguyen, Kiran Garimella
Venue: WWW
Year: 2017

Understanding human migration is of great interest to demographers and social scientists. User generated digital data has made it easier to study such patterns at a global scale. Geo coded Twitter data, in particular, has been shown to be a promising source to analyse large scale human migration. But given the scale of these datasets, a lot of manual effort has to be put into processing and getting actionable insights from this data. In this paper, we explore feasibility of using a new tool, tensor decomposition, to understand trends in global human migration. We model human migration as a three mode tensor, consisting of (origin country, destination country, time of migration) and apply CP decomposition to get meaningful low dimensional factors. Our experiments on a large Twitter dataset spanning 5 years and over 100M tweets show that we can extract meaningful migration patterns.
Prediction of Population Health Indices from Social Media using Kernel-based Textual and Temporal Features
Authors: Thin Nguyen, Duc Thanh Nguyen, Mark E. Larsen, Bridianne O&apos, Dea, John Yearwood, Dinh Q. Phung, Svetha Venkatesh, Helen Christensen
Venue: WWW
Year: 2017

From 1984, the US has annually conducted the Behavioral Risk Factor Surveillance System (BRFSS) surveys to capture either health behaviors, such as drinking or smoking, or health outcomes, including mental, physical, and generic health, of the population. Although this kind of information at a population level, such as US counties, is important for local governments to identify local needs, traditional datasets may take years to collate and to become publicly available. Geocoded social media data can provide an alternative reflection of local health trends. In this work, to predict the percentage of adults in a county reporting "insufficient sleep", a health behavior, and, at the same time, their health outcomes, novel textual and temporal features are proposed. The proposed textual features are defined at mid-level and can be applied on top of various low-level textual features. They are computed via kernel functions on underlying features and encode the relationships between individual underlying features over a population. To further enrich the predictive ability of the health indices, the textual features are augmented with temporal information. We evaluated the proposed features and compared them with existing features using a dataset collected from the BRFSS. Experimental results show that the combination of kernel-based textual features and temporal information predict well both the health behavior (with best performance at rho=0.82) and health outcomes (with best performance at rho=0.78), demonstrating the capability of social media data in prediction of population health indices. The results also show that our proposed features gained higher correlation coefficients than did the existing ones, increasing the correlation coefficient by up to 0.16, suggesting the potential of the approach in a wide spectrum of applications on data analytics at population levels.
Knowledge Base Smarter Articulations for the Open Directory Project in a Sustainable Digital Ecosystem
Authors: Shastri L. Nimmagadda, Dengya Zhu, Amit Rudra
Venue: WWW
Year: 2017

We examine the volumes and varieties of data sources of the Open Directory Project (ODP), which can endure, regenerate and flourish with new knowledge. The ODP motivates us in building a knowledge base smarter multidimensional data constructs and models. We articulate the models with new artefacts, addressing the heterogeneity and multidimensionality of the data. The conceptualization and contextualization of various entities and dimensions have emerged with innovation that led us to develop a digital ecosystem-based inventory. The ODP based domain ontologies support the warehouse repository, which accommodates multidimensional data relationships. The concept of a digital ecosystem in the ODP context is to bring the dimensions together and unite with multidimensional schemas. We explore the Big Data, incorporating their characteristics in the ODP constructs and models. The volumes and varieties of the ODP data are logically organized and integrated in the warehouse repositories. The multidimensional data modelling makes the ODP more smart and flexible in an environment, where varieties of business rules and constraints change rapidly. The visualization and interpretation are the other artefacts of the Big Data facilitating us use, reuse, test the interoperability and effectiveness of the data models for sustainable ODP digital ecosystem. We compute the polynomial regressions, based on the data fluctuations of the ODP as observed in the scatter plots, providing new data mining models for knowledge interpretation.
A Video Quality Prediction Model for the Elderly
Authors: Debajyoti Pal, Vajirasak Vanijja
Venue: WWW
Year: 2017

In the recent times there has been a lot of effort to use the various ICT technologies available to monitor and improve the lifestyle of the older generation. In most of the countries across the globe, these elderly people have to stay alone especially during the daytime, when other family members go out for work. Thus, monitoring their presence and activities remotely through audio/video communications is a widespread practice. However, this group of elderly people generally suffers from various types of vision impairments. Hence, mere installation of surveillance audio/video systems only will not solve the problem, as they need to interact with the system and hear/watch the audio/video communications for their well-being.
Achievement and Friends: Key Factors of Player Retention Vary Across Player Levels in Online Multiplayer Games
Authors: Kunwoo Park, Meeyoung Cha, Haewoon Kwak, Kuan-Ta Chen
Venue: WWW
Year: 2017

Retaining players over an extended period of time is a long-standing challenge in game industry. Significant effort has been paid to understanding what motivates players enjoy games. While individuals may have varying reasons to play or abandon a game at different stages within the game, previous studies have looked at the retention problem from a snapshot view. This study, by analyzing in-game logs of 51,104 distinct individuals in an online multiplayer game, uniquely offers a multifaceted view of the retention problem over the players' virtual life phases. We find that key indicators of longevity change with the game level. Achievement features are important for players at the initial to the advanced phases, yet social features become the most predictive of longevity once players reach the highest level offered by the game. These findings have theoretical and practical implications for designing online games that are adaptive to meeting the players' needs.
Do "Also-Viewed" Products Help User Rating Prediction?
Authors: Chanyoung Park, Donghyun Kim, Jinoh Oh, Hwanjo Yu
Venue: WWW
Year: 2017

For online product recommendation engines, learning high-quality product embedding that captures various aspects of the product is critical to improving the accuracy of user rating prediction. In recent research, in conjunction with user feedback, the appearance of a product as side information has been shown to be helpful for learning product embedding. However, since a product has a variety of aspects such as functionality and specifications, taking into account only its appearance as side information does not suffice to accurately learn its embedding. In this paper, we propose a matrix co-factorization method that leverages information hidden in the so-called "also-viewed" products, i.e., a list of products that has also been viewed by users who have viewed a target product. "Also-viewed" products reflect various aspects of a given product that have been overlooked by visually-aware recommendation methods proposed in past research. Experiments on multiple real-world datasets demonstrate that our proposed method outperforms state-of-the-art baselines in terms of user rating prediction. We also perform classification on the product embedding learned by our method, and compare it with a state-of-the-art baseline to demonstrate the superiority of our method in generating high-quality product embedding that better represents the product.
SWoTSuite: A Toolkit for Prototyping End-to-End Semantic Web of Things Applications
Authors: Pankesh Patel, Amelie Gyrard, Soumya Kanti Datta, Muhammad Intizar Ali
Venue: WWW
Year: 2017

In this demonstration, we present our Semantic Web of Things~(SWoT) prototyping toolkit called SWoTSuite. It is a set of tools supporting an easy and fast prototyping of end-to-end SWoT applications. SWoTSuite facilitates - (i) automation of application development life-cycle, (ii) reducing the amount of time and effort required for developing WoT applications and (iii) an easy integration of semantic web technologies within WoT applications.
Scalable Deep Document / Sequence Reasoning with Cognitive Toolkit
Authors: Sayan Pathak, Pengcheng He, William Darling
Venue: WWW
Year: 2017

Deep Neural Networks (DNNs) have revolutionized the way that machines understand language and have allowed us to create models that answer textual questions, translate pairs of languages, and intelligently compare document corpora. At the heart of these successes lie core techniques that fall into the area of sequence understanding. While powerful, dealing with variable-size sequences in DNNs requires deep understanding and experience in creating such networks, which can be daunting to many scientists and engineers. This tutorial will focus on introducing core concepts, end-to-end recipes, and key innovations facilitated by the cross-platform fully open-source Cognitive Toolkit (formerly called CNTK) with superior scalability (up to 1000 GPUs) for very large data corpora. Specifically, we will present tutorials on basic sequence understanding, intermediate sequence-to-sequence translation (both with and without attention), and the advanced Reasoning Network (ReasoNet) which has achieved industry-leading results in reading comprehension.
Holistic Approach for Sustainable Adaptable User Interfaces for People with Autism Spectrum Disorder
Authors: Claudia De Los Rios Perez, David A. McMeekin, Marita Falkmer, Tele Tan
Venue: WWW
Year: 2017

Understanding the needs of individuals with Autism Spectrum Disorder (ASD) enables the design of adaptable user interfaces addressing multiple sources of heterogeneity such as personal skills, work environments and context of use given their interaction with system and technology. Applying the human-centered design approach places the needs and limitations at a higher priority, creating a multi-disciplinary analysis to build sustainable adaptable technology solutions, contributing towards a viable world using fewer materials and energy consumption.