Databases and Information Systems Commons^™

Real-Time Unsupervised Clustering, Gabriel Ferrer

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

In our research program, we are developing machine learning algorithms to enable a mobile robot to build a compact representation of its environment. This requires the processing of each new input to terminate in constant time. Existing machine learning algorithms are either incapable of meeting this constraint or deliver problematic results. In this paper, we describe a new algorithm for real-time unsupervised clustering, Bounded Self-Organizing Clustering. It executes in constant time for each input, and it produces clusterings that are significantly better than those created by the Self-Organizing Map, its closest competitor, on sensor data acquired from a physically embodied …

Proposal For A Digital Archives Program At The Dr. Joann Rayfield Archives, Eric Willey, April Karlene Anderson, Ross Griffiths, Dallas Long

Faculty and Staff Publications – Milner Library

A report submitted in April 2016 discussing the creation of an infrastructure for the collection and preservation of digital files by the archives of Illinois State University. Includes three possible strategies based on monetary cost and staff time, with hardware and software recommendations appropriate to each.

Timbral Data Sonification From Parallel Attribute Graphs, Dale E. Parson, Danielle Emily Hoch, Hallie Langley

Computer Science and Information Technology Faculty

Parallel coordinate plotting is an established data visualization technique that provides means for graphing and exploring multidimensional relational datasets on a two-dimensional display. Each vertical axis represents the range of values for one attribute, and each data tuple appears as a connected path traveling left-to-right across the plot, connecting attribute values for that tuple on the vertical axes. Parallel coordinate plots look like timedomain audio signal waveforms, and they can be translated into audio signals through straightforward mapping algorithms. This study looks at three data sonification algorithms, sonification being the mapping of data into sounds for perceptual exploration, similar to …

Development Of A Wireless Mobile Computing Platform For Fall Risk Prediction, Akm Jahangir Alam Majumder

Dissertations (1934 -)

Falls are a major health risk with which the elderly and disabled must contend. Scientific research on smartphone-based gait detection systems using the Internet of Things (IoT) has recently become an important component in monitoring injuries due to these falls. Analysis of human gait for detecting falls is the subject of many research projects. Progress in these systems, the capabilities of smartphones, and the IoT are enabling the advancement of sophisticated mobile computing applications that detect falls after they have occurred. This detection has been the focus of most fall-related research; however, ensuring preventive measures that predict a fall is …

Front Matter: Proceedings Of The Maics 2016 Conference, University Of Dayton

Content presented at the MAICS conference

Front matter contains:

• A list of program chairs and committee members
• Foreword to the proceedings by James P. Buckley, conference chair; Saverio Perugini, general chair

Editors: Phu H. Phung, University of Dayton; Ju Shen, University of Dayton; Michael Glass, Valparaiso University

Unsupervised Learning Framework For Large-Scale Flight Data Analysis Of Cockpit Human Machine Interaction Issues, Abhishek B. Vaidya

Open Access Theses

As the level of automation within an aircraft increases, the interactions between the pilot and autopilot play a crucial role in its proper operation. Issues with human machine interactions (HMI) have been cited as one of the main causes behind many aviation accidents. Due to the complexity of such interactions, it is challenging to identify all possible situations and develop the necessary contingencies. In this thesis, we propose a data-driven analysis tool to identify potential HMI issues in large-scale Flight Operational Quality Assurance (FOQA) dataset. The proposed tool is developed using a multi-level clustering framework, where a set of basic …

Business Intelligence, Lei Li, Rebecca Rutherfoord, Svetlana Peltsverger, Jack Zheng, Zhigang Li, Nancy Colyar

Computer Science and Information Technology Grants Collections

This Grants Collection for Business Intelligence was created under a Round Two ALG Textbook Transformation Grant.

Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process.

Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials:

• Linked Syllabus
• Initial Proposal
• Final Report

Database Design And Applications, Lei Li, Rebecca Rutherfoord, Svetana Peltsverger, Jack Zheng, Zhigang Li, Nancy Colyar

Computer Science and Information Technology Grants Collections

This Grants Collection for Database Design and Applications was created under a Round Two ALG Textbook Transformation Grant.

Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process.

Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials:

• Linked Syllabus
• Initial Proposal
• Final Report

Advanced Databases, Lei Li, Rebecca Rutherfoord, Svetana Peltsverger, Jack Zheng, Zhigang Li, Nancy Colyar

Computer Science and Information Technology Grants Collections

This Grants Collection for Advanced Databases was created under a Round Two ALG Textbook Transformation Grant.

Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process.

Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials:

• Linked Syllabus
• Initial Proposal
• Final Report

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 …

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 …

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 …

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 …

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”) …

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) …

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 …

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 …

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, …