Computer Sciences Commons^™

Engagingness And Responsiveness Behavior Models On The Enron Email Network And Its Application To Email Reply Order Prediction, Byung-Won On, Ee Peng Lim, Jing Jiang, Loo Nin Teow

Research Collection School Of Computing and Information Systems

In email networks, user behaviors affect the way emails are sent and replied. While knowing these user behaviors can help to create more intelligent email services, there has not been much research into mining these behaviors. In this paper, we investigate user engagingness and responsiveness as two interaction behaviors that give us useful insights into how users email one another. Engaging users are those who can effectively solicit responses from other users. Responsive users are those who are willing to respond to other users. By modeling such behaviors, we are able to mine them and to identify engaging or responsive …

Warcreate And Wail: Warc, Wayback, And Heritrix Made Easy, Mat Kelly, Michael L. Nelson, Michele C. Weigle

Computer Science Faculty Publications

[First slide]

The Problem

Institutional Tools, Personal Archivists

• ON YOUR MACHINE

-Complex to Operate

-Require Infrastructure

• DELEGATED TO INSTITUTIONS

-$$$

-Lose original perspective

• Locale content tailoring (DC vs. San Francisco)
• Observation Medium (PC web browser vs. Crawler)

The Impact Of Smartphone Adoption On Consumers’ Switching Behavior In Broadband And Cable Tv Services, Gwangjae Jung

Research Collection School Of Computing and Information Systems

The emergence of smartphones has brought a technology disruption to the telecom business. Due to the various services offered in association with smartphones, people can surf the web or watch TV. This research is related to telecom services overall, and has the goal of finding the impact of smartphone adoption to consumers’ switching behavior in broadband and cable TV services. This research adopts a quasi-experimental design and investigates the causal effect of smartphone service adoption on broadband and cable TV service choices. The data collection involves five years of consumer service subscriptions in a Singaporean telecommunications company. I tested for …

Do Household Cable Tv Viewing Patterns Demonstrate Efficiency And Concentration?, Rae Chang, Pulak Ghosh, Gwangjae Jung, Robert J. Kauffman, Peiran Zhang

Research Collection School Of Computing and Information Systems

No abstract provided.

Hypergraph Index: An Index For Context-Aware Nearest Neighbor Query On Social Networks, Yazhe Wang, Baihua Zheng

Research Collection School Of Computing and Information Systems

Social network has been touted as the No. 2 innovation in a recent IEEE Spectrum Special Report on “Top 11 Technologies of the Decade”, and it has cemented its status as a bona fide Internet phenomenon. With more and more people starting using social networks to share ideas, activities, events, and interests with other members within the network, social networks contain a huge amount of content. However, it might not be easy to navigate social networks to find specific information. In this paper, we define a new type of queries, namely context-aware nearest neighbor (CANN) search over social network to …

Visualizing Digital Collections At Archive-It, Michele C. Weigle, Michael L. Nelson

Computer Science Presentations

PDF of a powerpoint presentation from a Archive-It Partners Meeting in Annapolis, Maryland, December 3, 2012. Also available on Slideshare.

User Taglines: Alternative Presentations Of Expertise And Interest In Social Media, Hemant Purohit, Alex Dow, Omar Alonso, Lei Duan, Kevin Haas

Kno.e.sis Publications

Web applications are increasingly showing recommended users from social media along with some descriptions, an attempt to show relevancy - why they are being shown. For example, Twitter search for a topical keyword shows expert twitterers on the side for 'whom to follow'. Google+ and Facebook also recommend users to follow or add to friend circle. Popular Internet newspaper- The Huffington Post shows Twitter influencers/ experts on the side of an article for authoritative relevant tweets. The state of the art shows user profile bios as summary for Twitter experts, but it has issues with length constraint imposed by user …

Are Twitter Users Equal In Predicting Elections? A Study Of User Groups In Predicting 2012 U.S. Republican Primaries, Lu Chen, Wenbo Wang, Amit P. Sheth

Kno.e.sis Publications

Existing studies on predicting election results are under the assumption that all the users should be treated equally. However, recent work [14] shows that social media users from different groups (e.g., “silent majority” vs. “vocal minority”) have significant differences in the generated content and tweeting behavior. The effect of these differences on predicting election results has not been exploited yet. In this paper, we study the spectrum of Twitter users who participate in the on-line discussion of 2012 U.S. Republican Presidential Primaries, and examine the predictive power of different user groups (e.g., highly engaged users vs. lowly engaged users, right-leaning …

The Ssn Ontology Of The W3c Semantic Sensor Network Incubator Group, Michael Compton, Payam Barnaghi, Luis Bermudez, Raul Garcia-Castro, Oscar Corcho, Simon Cox, John Graybeal, Manfred Hauswirth, Cory Andrew Henson, Arthur Herzog, Vincent Huang, Krzysztof Janowicz, W. David Kelsey, Danh Le Phuoc, Laurent Lefort, Myriam Leggieri, Holger Neuhaus, Andriy Nikolov, Kevin Page, Alexandre Passant, Amit P. Sheth, Kerry Taylor

Kno.e.sis Publications

The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations — the SSN ontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSN ontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSN ontology. It further gives an example and describes the use of the ontology in recent research projects.

Use Of Rss Feeds To Push The Online Content To Users, Dan Ma

Research Collection School Of Computing and Information Systems

Many websites use Really Simple Syndication (RSS) feeds to actively push their online content to users rather than waiting for users to pull the content passively. In this paper, I construct a theoretical game model to study the profitability of an RSS-PUSH delivery mechanism. The model assumes a general profit structure for websites and heterogeneous users. To access valuable online content, users incur a variety of costs. They choose either to visit the website in the conventional way (the PULL model) or, if it is supported by the website, to use RSS (the PUSH model). Interestingly, I show that although …

Community-Based Classification Of Noun Phrases In Twitter, Freddy Chong Tat Chua, William W. Cohen, Justin Betterridge, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Many event monitoring systems rely on counting known keywords in streaming text data to detect sudden spikes in frequency. But the dynamic and conversational nature of Twitter makes it hard to select known keywords for monitoring. Here we consider a method of automatically finding noun phrases (NPs) as keywords for event monitoring in Twitter. Finding NPs has two aspects, identifying the boundaries for the subsequence of words which represent the NP, and classifying the NP to a specific broad category such as politics, sports, etc. To classify an NP, we define the feature vector for the NP using not just …

Finding Thoughtful Comments From Social Media, Gottipati Swapna, Jing Jiang

Research Collection School Of Computing and Information Systems

Online user comments contain valuable user opinions. Comments vary greatly in quality and detecting high quality comments is a subtask of opinion mining and summarization research. Finding attentive comments that provide some reasoning is highly valuable in understanding the user’s opinion particularly in sociopolitical opinion mining and aids policy makers, social organizations or government sectors in decision making. In this paper we study the problem of detecting thoughtful comments. We empirically study various textual features, discourse relations and relevance features to predict thoughtful comments. We use logistic regression model and test on the datasets related to sociopolitical content. We found …

On Recommending Hashtags In Twitter Networks, Su Mon Kywe, Tuan-Anh Hoang, Ee Peng Lim, Feida Zhu

Research Collection School Of Computing and Information Systems

Twitter network is currently overwhelmed by massive amount of tweets generated by its users. To effectively organize and search tweets, users have to depend on appropriate hashtags inserted into tweets. We begin our research on hashtags by first analyzing a Twitter dataset generated by more than 150,000 Singapore users over a three-month period. Among several interesting findings about hashtag usage by this user community, we have found a consistent and significant use of new hashtags on a daily basis. This suggests that most hashtags have very short life span. We further propose a novel hashtag recommendation method based on collaborative …

A Survey Of Recommender Systems In Twitter, Su Mon Kywe, Ee Peng Lim, Feida Zhu

Research Collection School Of Computing and Information Systems

Twitter is a social information network where short messages or tweets are shared among a large number of users through a very simple messaging mechanism. With a population of more than 100M users generating more than 300M tweets each day, Twitter users can be easily overwhelmed by the massive amount of information available and the huge number of people they can interact with. To overcome the above information overload problem, recommender systems can be introduced to help users make the appropriate selection. Researchers have began to study recommendation problems in Twitter but their works usually address individual recommendation tasks. There …

Extracting And Normalizing Entity-Actions From Users' Comments, Swapna Gottipati, Jing Jiang

Research Collection School Of Computing and Information Systems

With the growing popularity of opinion-rich resources on the Web, new opportunities and challenges arise and aid people in actively using such information to understand the opinions of others. Opinion mining process currently focuses on extracting the sentiments of the users on products, social, political and economical issues. In many instances, users not only express their sentiments but also contribute their ideas, requests and suggestions through comments. Such comments are useful for domain experts and are referred to as actionable content. Extracting actionable knowledge from online social media has attracted a growing interest from both academia and the industry. We …

A Mechanism For Organizing Last-Mile Service Using Non-Dedicated Fleet, Shih-Fen Cheng, Duc Thien Nguyen, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Unprecedented pace of urbanization and rising income levels have fueled the growth of car ownership in almost all newly formed megacities. Such growth has congested the limited road space and significantly affected the quality of life in these megacities. Convincing residents to give up their cars and use public transport is the most effective way in reducing congestion; however, even with sufficient public transport capacity, the lack of last-mile (from the transport hub to the destination) travel services is the major deterrent for the adoption of public transport. Due to the dynamic nature of such travel demands, fixed-size fleets will …

Visualization For Anomaly Detection And Data Management By Leveraging Network, Sensor And Gis Techniques, Zhaoxia Wang, Chee Seng Chong, Rick S. M. Goh, Wanqing Zhou, Dan Peng, Hoong Chor Chin

Research Collection School Of Computing and Information Systems

This paper studies the importance of visualization for discerning and interpreting patterns of data and its application for solving real problems, such as anomaly detection and data management. There are various ways to realize visualization to cater to the needs of numerous real life applications. Depending on needs, a combination of some of these ways may be required for presenting an effective visualization. The authors present visualization schemes for anomaly detection/condition monitoring and data management by leveraging network techniques and combining them with modern techniques such as sensor, database, mobile communication, GPS and GIS techniques. Two case studies are presented …

Demonstration: Dynamic Sensor Registration And Semantic Processing For Ad-Hoc Mobile Environments (Semmob), Pramod Anantharam, Gary Alan Smith, Josh Pschorr, Krishnaprasad Thirunarayan, Amit P. Sheth

Kno.e.sis Publications

SemMOB enables dynamic registration of sensors via mobile devices, search, and near real-time inference over sensor observations in ad-hoc mobile environments (e.g., fire fighting). We demonstrate SemMOB in the context of an emergency response use case that requires automatic and dynamic registrations of sensor devices and annotation of sensor observations, decoding of latitude-longitude information in terms of human sensible names, fusion and abstraction of sensor values using background knowledge, and their visualization using mash-up.

Using Social Influence To Predict Subscriber Churn, Derek Doran, Veena Mendiratta, Chitra Phadke, Dan Kushnir, Huseyin Uzunalioglu

Kno.e.sis Publications

The saturation of mobile phone markets has resulted in rising costs for operators to obtain new customers. These operators thus focus their energies on identifying users that will churn so they can be targeted for retention campaigns. Typical churn prediction algorithms identify churners based on service usage metrics, network performance indicators, and demographic information. Social and peer-influence to churn, however, is usually not considered. In this paper, we describe a new churn prediction algorithm that incorporates the influence churners spread to their social peers. Using data from a major service provider, we show that social influence improves churn prediction and …

Understanding User Triads On Facebook, Derek Doran, Alberta De La Rosa Algarin, Swapna S. Gokhale

Kno.e.sis Publications

Contemporary approaches that analyze user behavior on online social networks only consider interactions among dyads, which are pairs of directly connected users. A large body of sociological work, however, suggests that mutual connections among users can influence their activities, leading to differences between two- and three-way interactions. This paper explores the dynamics of triads among Facebook users based on the wall posts from the New Orleans regional network. Initially, each connection is categorized as a close friendship or an acquiantance, contingent on the number of wall posts exchanged. Subsequently, the impact of different types of connections comprising triads is examined …

Linked Data Demystified: Practical Efforts To Transform Contentdm Metadata For The Linked Data Cloud, Silvia B. Southwick, Cory K. Lampert

Library Faculty Presentations

The library literature and events like the ALA Annual Conference have been inundated with presentations and articles on linked data. At UNLV Libraries, we understand the importance of linked data in helping to better service our users. We have designed and initiated a pilot project to apply linked data concepts to the practical task of transforming a sample set of our CONTENTdm digital collections data into future-oriented linked data. This presentation will outline rationale for beginning work in linked data and detail the phases we will undertake in the proof of concept project. We hope through this research experiment to …

How I Would Like Semantic Web To Be, For My Children., Raghava Mutharaju

Kno.e.sis Publications

Semantic Web, since its inception, has gone through lot of developments in its relatively nascent existence; right from people's perception, to the standards and to its adoption by the industry and more importantly by the scientific community. This impressive growth only seems to increase. In this paper, we project this growth to the next 10 years and highlight some of the facets on which Semantic Web could have a major impact on. We also present the challenges that Semantic Web and its community has to deal with in order to get there.

An Efficient Bit Vector Approach To Semantics-Based Machine Perception In Resource-Constrained Devices, Cory Andrew Henson, Krishnaprasad Thirunarayan, Amit P. Sheth

Kno.e.sis Publications

The primary challenge of machine perception is to define efficient computational methods to derive high-level knowledge from low-level sensor observation data. Emerging solutions are using ontologies for expressive representation of concepts in the domain of sensing and perception, which enable advanced integration and interpretation of heterogeneous sensor data. The computational complexity of OWL, however, seriously limits its applicability and use within resource-constrained environments, such as mobile devices. To overcome this issue, we employ OWL to formally define the inference tasks needed for machine perception – explanation and discrimination – and then provide efficient algorithms for these tasks, using bit-vector encodings …

Smu Launches Livelabs To Enhance Singapore’S Capability In Consumer & Social Analytics, Singapore Management University

SMU Press Releases and News

Officiated by Mr Teo Chee Hean, Deputy Prime Minister, Coordinating Minister for National Security, Minister for Home Affairs and Chairman of the National Research Foundation (NRF), and witnessed by over 300 guests comprising the academia, industry partners and senior representatives from private and public sector organisations, SMU and StarHub jointly launched two Interactive Digital Media (IDM) initiatives - LiveLabs Urban Lifestyle Innovation Platform (LiveLabs) and SmartHub, on 5 November 2012. Both city-scale test-beds aim to strengthen Singapore’s standing as a preferred location for innovation and research, particularly in the area of consumer and social analytics. LiveLabs is SMU’s newest research …

Iexplore: Interactive Browsing And Exploring Biomedical Knowledge, Vinh Nguyen, Olivier Bodenreider, Jagannathan Srinivasan, Todd Minning, Thomas Rindflesch, Bastien Rance, Ramakanth Kavuluru, Himi Yalamanchili, Krishnaprasad Thirunarayan, Satya S. Sahoo, Amit P. Sheth

Kno.e.sis Publications

We present iExplore, a Semantic Web based application that helps biomedical researchers study and explore biomedical knowledge interactively. iExplore uses the Biomedical Knowledge Repository (BKR), which integrates knowledge from various sources ranging from information extracted from biomedical literature (from PubMed) to many structured vocabularies in the Unified Medical Language System (UMLS). The current version of BKR provides a unified provenance representation for 12 million semantic predications (triples with a predicate connecting a subject and an object) derived from 87 vocabulary families in the UMLS and 14 million predications extracted from 21 million PubMed abstracts. To engage the domain experts in …

Apps And Apple Devices: Productive Icloud Uses, Cyrus Ford Zarganj

Library Faculty Presentations

This program introduces useful apps for librarians who have two or more Apple devices such as an iPhone, iPad, or MacBook. You can learn how to use the iCloud, sync devices together, and about other useful apps for librarians to work on their projects from different places with different Apple devices without carrying files.

A Theoretical Framework For Serious Game Design: Exploring Pedagogy, Play And Fidelity And Their Implications For The Design Process, Pauline Rooney

Journal Articles

It is widely acknowledged that digital games can provide an engaging, motivating and “fun” experience for students. However an entertaining game does not necessarily constitute a meaningful, valuable learning experience. For this reason, experts espouse the importance of underpinning serious games with a sound theoretical framework which integrates and balances theories from two fields of practice: pedagogy and game design (Kiili, 2005; Seeney & Routledge, 2009). Additionally, with the advent of sophisticated, immersive technologies, and increasing interest in the opportunities for constructivist learning offered by these technologies, concepts of fidelity and its impact on student learning and engagement, have emerged …

Sense Beauty Via Face, Dressing, And/Or Voice, Tam Nguyen, Si Liu, Bingbing Ni, Jun Tan, Yong Rui, Shuicheng Yan

Computer Science Faculty Publications

Discovering the secret of beauty has been the pursuit of artists and philosophers for centuries. Nowadays, the computational model for beauty estimation has been actively explored in computer science community, yet with the focus mainly on facial features. In this work, we perform a comprehensive study of female attractiveness conveyed by single/multiple modalities of cues, i.e., face, dressing, and/or voice, and aim to uncover how different modalities individually and collectively affect the human sense of beauty. To this end, we collect the first Multi-Modality Beauty (M²B) dataset in the world for female attractiveness study, which is thoroughly annotated …

Computing Perception From Sensor Data, Payam Barnaghi, Frieder Ganz, Cory Andrew Henson, Amit P. Sheth

Kno.e.sis Publications

This paper describes a framework for perception creation from sensor data. We propose using data abstraction techniques, in particular Symbolic Aggregate Approximation (SAX), to analyse and create patterns from sensor data. The created patterns are then linked to semantic descriptions that define thematic, spatial and temporal features, providing highly granular abstract representation of the raw sensor data. This helps to reduce the size of the data that needs to be communicated from the sensor nodes to the gateways or highlevel processing components. We then discuss a method that uses abstract patterns created by SAX method and occurrences of different observations …

Privacy Preserving Boosting In The Cloud With Secure Half-Space Queries, Shumin Guo, Keke Chen

Kno.e.sis Publications

This paper presents a preliminary study on the PerturBoost approach that aims to provide efficient and secure classifier learning in the cloud with both data and model privacy preserved.