Databases and Information Systems Commons^™

Robustness Of Multiple Indicators In Automated Screening Systems For Deception Detection, Nathan Twyman, Jeffrey Gainer Proudfoot, Ryan M. Schuetzler, Aaron Elkins, Douglas C. Derrick

Information Systems and Quantitative Analysis Faculty Publications

This study investigates the effectiveness of an automatic system for detection of deception by individuals with the use of multiple indicators of such potential deception. Deception detection research in the information systems discipline has postulated increased accuracy through a new class of screening systems that automatically conduct interviews and track multiple indicators of deception simultaneously. Understanding the robustness of this new class of systems and the limitations of its theoretical improved performance is important for refinement of the conceptual design. The design science proof-of-concept study presented here implemented and evaluated the robustness of these systems for automated screening for deception …

Capstone Database Management, Sharanya Amuduri, Anitha Avula, Ahmed Hussaini Syed

All Capstone Projects

The main aim of this project is to provide a well-structured database for storing information about Grad projects Apart from this, CCDS displays the existing grad projects to the students through web by query implementation. And also they can access information about any particular Grad Project .The main objective of this project is to design an application which is user-friendly and should be able to get the details about student Grad Project. Grad Project is a particular course and administrator for maintaining the data of students, List of Grad projects and modules this system facilitates the administrator to manage Project …

Past Meets Future: Combining Gis, 3d Technologies, And Legacy Data To Reanalyze Ceramics At Copan, Honduras, Stephanie Sterling, Heather Richards-Rissetto, René Viel

UCARE: Research Products

The archaeological site of Copán—a UNESCO World Heritage Site in Honduras—was a primary center for cultural and economic exchange in the Maya world from the fifth to ninth centuries. Our research investigates the sociopolitical climate of the city immediately preceding this collapse. This poster presents the results of a pilot study intended to evaluate the potential of using a combination of digital technologies and legacy data to reanalyze a subset of diagnostic ceramics from select sites outside of Copan’s urban core. Our methods involved:

(1) Applying photogrammetry to generate 3D models for approximately 30 potentially temporally-diagnostic ceramic types

(2) Digitizing, …

Developing A Framework For Creating Mhealth Surveys, Veli Melih Bilen

Master's Theses (2009 -)

Various issues in the design of surveys for mobile health (mHealth) research projects yet exist. As mHealth solutions become more popular, new issues are brought into consideration. Researchers need to collect some critical information from participants in these mHealth studies. These mHealth studies require a specialized framework to create surveys, track progress and analyze user data. In these procedures, mHealth’s needs differ from other studies. Therefore, there has to be a new framework that satisfies needs of mHealth research studies. Although there are studies for creating efficient, robust and user-friendly surveys, there is no solution or study, which is specialized …

What Makes A Music Track Popular In Online Social Networks?, Jing Ren, Jialie Shen, Robert John Kauffman

Research Collection School Of Computing and Information Systems

Tens of thousands of music tracks are uploaded to the Internet every day through social networks that focus on music and videos, as well as portal websites. While some of the content has been popular for decades, some tracks that have just been released have been completely ignored. So what makes a music track popular? Can we predict the popularity of a music track before it is released? In this research, we will focus on an online music social network, Last.fm, and investigate three key factors of a music track that may have impact on its popularity. They include: the …

Persentiment: A Personalized Sentiment Classification System For Microblog Users, Kaisong Song, Ling Chen, Wei Gao, Shi Feng, Daling Wang, Chengqi Zhang

Research Collection School Of Computing and Information Systems

Microblogging services are playing increasingly important roles in our daily life today. It is useful for microblog users to instantly understand the sentiment of a large number of microblogs posted by their friends and make appropriate response. Despite considerable progress on microblog sentiment classification, most of the existing works ignore the influence of personal distinctions of different microblog users on the sentiments they convey, and none of them has provided real-world personalized sentiment classification systems. Considering personal distinctions in sentiment analysis is natural and necessary as different people have different language habits, personal characters, opinion bias and so on. In …

When Peculiarity Makes A Difference: Object Characterisation In Heterogeneous Information Networks, Wei Chen, Feida Zhu, Lei Zhao, Xiaofang Zhou

Research Collection School Of Computing and Information Systems

A central task in heterogeneous information networks (HIN) is how to characterise an entity, which underlies a wide range of applications such as similarity search, entity profiling and linkage. Most existing work focus on using the main features common to all. While this approach makes sense in settings where commonality is of primary interest, there are many scenarios as important where uncommon and discriminative features are more useful. To address the problem, a novel model COHIN (Characterize Objects in Heterogeneous Information Networks) is proposed, where each object is characterized as a set of feature paths that contain both main and …

Semantic Visualization With Neighborhood Graph Regularization, Tuan Minh Van Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Visualization of high-dimensional data, such as text documents, is useful to map out the similarities among various data points. In the high-dimensional space, documents are commonly represented as bags of words, with dimensionality equal to the vocabulary size. Classical approaches to document visualization directly reduce this into visualizable two or three dimensions. Recent approaches consider an intermediate representation in topic space, between word space and visualization space, which preserves the semantics by topic modeling. While aiming for a good fit between the model parameters and the observed data, previous approaches have not considered the local consistency among data instances. We …

Large Scale Online Kernel Learning, Jing Lu, Hoi, Steven C. H., Jialei Wang, Peilin Zhao, Zhi-Yong Liu

Research Collection School Of Computing and Information Systems

In this paper, we present a new framework for large scale online kernel learning, making kernel methods efficient and scalable for large-scale online learning applications. Unlike the regular budget online kernel learning scheme that usually uses some budget maintenance strategies to bound the number of support vectors, our framework explores a completely different approach of kernel functional approximation techniques to make the subsequent online learning task efficient and scalable. Specifically, we present two different online kernel machine learning algorithms: (i) Fourier Online Gradient Descent (FOGD) algorithm that applies the random Fourier features for approximating kernel functions; and (ii) Nyström Online …

Personal Credit Profiling Via Latent User Behavior Dimensions On Social Media, Guangming Guo, Feida Zhu, Enhong Chen, Le Wu, Qi Liu, Yingling Liu, Minghui Qiu

Research Collection School Of Computing and Information Systems

Consumer credit scoring and credit risk management have been the core research problem in financial industry for decades. In this paper, we target at inferring this particular user attribute called credit, i.e., whether a user is of the good credit class or not, from online social data. However, existing credit scoring methods, mainly relying on financial data, face severe challenges when tackling the heterogeneous social data. Moreover, social data only contains extremely weak signals about users’ credit label. To that end, we put forward a Latent User Behavior Dimension based Credit Model (LUBD-CM) to capture these small signals for personal …

Understanding The Determinants Of Human Computation Game Acceptance: The Effects Of Aesthetic Experience And Output Quality, Xiaohui Wang, Dion Hoe-Lian Goh, Ee-Peng Lim, Wei Liang Adrian Vu

Research Collection School Of Computing and Information Systems

Purpose: Human computation games (HCGs) that blend gaming with utilitarian purposes are a potentially effective channel for content creation. The purpose of this paper is to investigate the driving factors behind players’ adoption of HCGs through a music video tagging game. The effects of perceived aesthetic experience (PAE) and perceived output quality (POQ) on HCG acceptance are empirically examined. Design/methodology/approach: An integrative structural model is developed to explain how hedonic and utilitarian factors, including PAE and POQ, working with another salient factor – perceived usefulness (PU) – affect the acceptance of HCGs. The structural equation modeling method is used to …

Real-Time Netnography: Rejecting The Passive Shift, Leesa Costello, Marie-Louise Mcdermott

Research outputs 2014 to 2021

Although netnography emerged in the 1990s, it is a term unfamiliar to many ethnographers and is still touted as a new methodology. Once explained, ethnographers often understand it in terms of online ethnography. While this is helpful, netnography, however, offers a set of steps and analytic approaches that can be applied across a spectrum of involvement online. Its focus is on gaining entree to an online community, distinguishing between participant observation and nonparticipant observation.

On Effective Location-Aware Music Recommendation, Zhiyong Cheng, Jialie Shen

Research Collection School Of Computing and Information Systems

Rapid advances in mobile devices and cloud-based music service now allow consumers to enjoy music any-time and anywhere. Consequently, there has been an increasing demand in studying intelligent techniques to facilitate context-aware music recommendation. However, one important context that is generally overlooked is user's venue, which often includes surrounding atmosphere, correlates with activities, and greatly influences the user's music preferences. In this article, we present a novel venue-aware music recommender system called VenueMusic to effectively identify suitable songs for various types of popular venues in our daily lives. Toward this goal, a Location-aware Topic Model (LTM) is proposed to (i) …

From Classification To Quantification In Tweet Sentiment Analysis, Wei Gao, Fabrizio Sebastiani

Research Collection School Of Computing and Information Systems

entiment classification has become a ubiquitous enabling technology in the Twittersphere, since classifying tweets according to the sentiment they convey towards a given entity (be it a product, a person, a political party, or a policy) has many applications in political science, social science, market research, and many others. In this paper, we contend that most previous studies dealing with tweet sentiment classification (TSC) use a suboptimal approach. The reason is that the final goal of most such studies is not estimating the class label (e.g., Positive, Negative, or Neutral) of individual tweets, but estimating the relative frequency (a.k.a. “prevalence”) …

Exploration Of Web Technologies: A Real World Application, Andrew Ballard, James Francis, Sam Jentsch

Undergraduate Research Conference

Our team created a web application for a photography studio. In addition to a portfolio for the studio, the application required the ability to manage photographer schedules, handle and organize orders and provide secure user accounts with different access levels for the site.

Developing Probability Maps For Locating And Scouting Unprotected Areas Of Gravel Hill Prairies On Rodman Soils Along The Wabash River Valley Near Lafayette, Indiana, Ryan W.R. Schroeder

Engagement & Service-Learning Summit

No abstract provided.

Concept Based Search Engine: Concept Creation, Aishwarya Rastogi

Master's Projects

Data on the internet is increasing exponentially every single second. There are billions and billions of documents on the World Wide Web (The Internet). Each document on the internet contains multiple concepts (an abstract or general idea inferred from specific instances).

In this paper, we show how we created and implemented an algorithm for extracting concepts from a set of documents. These concepts can be used by a search engine for generating search results to cater the needs of the user. The search result will then be more targeted than the usual keyword search.

The main problem was to extract …

Mobilaudio – A Multimodal Content Delivery Platform For Geo-Services, James Carswell, Keith Gardiner, Charlie Cullen

Articles

Delivering high-quality context-relevant information in a timely manner is a priority for location-based services (LBS) where applications require an immediate response based on spatial interaction. Previous work in this area typically focused on ever more accurately determining this interaction and informing the user in the customary graphical way using the visual modality. This paper describes the research area of multimodal LBS and focuses on audio as the key delivery mechanism. This new research extends familiar graphical information delivery by introducing a geoservices platform for delivering multimodal content and navigation services. It incorporates a novel auditory user interface (AUI) that enables …

A Personalized People Recommender System Using Global Search Approach, Chun-Hua Tsai, Peter Brusilovsky

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

The goal of people recommender system is to generate meaningful social suggestion to users. The abundant data are the key factor in fulfilling a recommendation task, but the cost of user data in a real-world system is high. In this paper, we propose a novel approach that integrates a global search result with a personalized people recommendation system. Our approach utilizes the user identity as a query keyword and processes the search results through five different customized parsers. This approach solves the cold-start issue in recommendation systems and leverages the crossdomain information in order to provide a better recommendation result. …

Semantic, Cognitive, And Perceptual Computing: Paradigms That Shape Human Experience, Amit P. Sheth, Pramod Anantharam, Cory Henson

Kno.e.sis Publications

Unlike machine-centric computing, in which efficient data processing takes precedence over contextual tailoring, human-centric computation provides a personalized data interpretation that most users find highly relevant to their needs. The authors show how semantic, cognitive, and perceptual computing paradigms work together to produce actionable information.