Digital Commons Network^™

Student Understanding And Engagement In A Class Employing Comps Computer Mediated Problem Solving: A First Look, Jung Hee Kim, Michael Glass, Taehee Kim, Kelvin Bryant, Angelica Willis, Ebonie Mcneil, Zachery Thomas

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

COMPS computer-mediated group discussion exercises are being added to a second-semester computer programming class. The class is a gateway for computer science and computer engineering students, where many students have difficulty succeeding well enough to proceed in their major. This paper reports on first results of surveys on student experience with the exercises. It also reports on the affective states observed in the discussions that are candidates for analysis of group functioning. As a step toward computer monitoring of the discussions, an experiment in using dialogue features to identify the gender of the participants is described.

A Tool For Staging Mixed-Initiative Dialogs, Joshua W. Buck, Saverio Perugini

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

We discuss and demonstrate a tool for prototyping dialog-based systems that, given a high-level specification of a human-computer dialog, stages the dialog for interactive use. The tool enables a dialog designer to evaluate a variety of dialogs without having to program each individual dialog, and serves as a proof-of-concept for our approach to mixed-initiative dialog modeling and implementation from a programming language-based perspective.

Identifying Terrorist Affiliations Through Social Network Analysis Using Data Mining Techniques, Govand A. Ali

Information Technology Master Theses

In a technologically enabled world, local ideologically inspired warfare becomes global all too quickly, specifically terrorist groups like Al Quaeda and ISIS (Daesh) have successfully used modern computing technology and social networking environments to broadcast their message, recruit new members, and plot attacks. This is especially true for such platforms as Twitter and encrypted mobile apps like Telegram or the clandestine Alrawi. As early detection of such activity is crucial to attack prevention data mining techniques have become increasingly important in the fight against the spread of global terrorist activity. This study employs data mining tools to mine Twitter for …

Grounding Robot Motion In Natural Language And Visual Perception, Scott Alan Bronikowski

Open Access Dissertations

The current state of the art in military and first responder ground robots involves heavy physical and cognitive burdens on the human operator while taking little to no advantage of the potential autonomy of robotic technology. The robots currently in use are rugged remote-controlled vehicles. Their interaction modalities, usually utilizing a game controller connected to a computer, require a dedicated operator who has limited capacity for other tasks.

I present research which aims to ease these burdens by incorporating multiple modes of robotic sensing into a system which allows humans to interact with robots through a natural-language interface. I conduct …

Learning In Vision And Robotics, Daniel P. Barrett

Open Access Dissertations

I present my work on learning from video and robotic input. This is an important problem, with numerous potential applications. The use of machine learning makes it possible to obtain models which can handle noise and variation without explicitly programming them. It also raises the possibility of robots which can interact more seamlessly with humans rather than only exhibiting hard-coded behaviors. I will present my work in two areas: video action recognition, and robot navigation. First, I present a video action recognition method which represents actions in video by sequences of retinotopic appearance and motion detectors, learns such models automatically …

Predicting Changes To Source Code, Justin James Roll

Master's Theses

Organizations typically use issue tracking systems (ITS) such as Jira to plan software releases and assign requirements to developers. Organizations typically also use source control management (SCM) repositories such as Git to track historical changes to a code-base. These ITS and SCM repositories contain valuable data that remains largely untapped. As developers churn through an organization, it becomes expensive for developers to spend time determining which software artifact must be modified to implement a requirement. In this work we created, developed, tested and evaluated a tool called Class Change Predictor, otherwise known as CCP, for predicting which class will implement …

Volume 08, Meghan Enzinna, Casey Dawn Gailey, Raven Collins, Chiara Enriquez, Amelia Mcconnell, Alexander Morton, Emma Beckett, Leah G. Parr, Briana Adhikusuma, Taylor Embrey, Rowan Davis, Danielle Sisson, Bianca Cherry, Melissa Cacho, Chloe Woodward, Catherine Rollins, Carson Reeher, Landon Cooper, Haley Vasquez, Marlisha Stewart, Eric Whitehead, Sabrina Walker, James Bates

Incite: The Journal of Undergraduate Scholarship

Introduction from Interim Dean Dr. Jennifer Apperson

Indigenous Peoples and the Modern Era by Meghan Enzinna

"Who Says": How Selena Gomez and the Scene Attempt to Subvert the Popular Standards of Beauty by Casey Dawn Gailey

Art by Raven Collins

Meltdown on Social Media: Amy's Baking Company Meets Kitchen Nightmares by Nathena Haddrill

Art by Chiara Enriquez

Design by Amelia Mcconnell

Worth More Than a Thousand Words: A Visual Rhetorical Discussion of Virtual Reality by Examining "Clouds Over Sidra" by Alexander Morton

Design by Emma Beckett

The Sonata: An Analysis of Piano Sonata No. 14 in C Minor, K. …

User-Centric Workload Analytics: Towards Better Cluster Management, Suhas Raveesh Javagal

Open Access Theses

Effective management of computing clusters and providing a high quality customer support is not a trivial task. Due to rise of community clusters there is an increase in the diversity of workloads and the user demographic. Owing to this and privacy concerns of the user, it is difficult to identify performance issues, reduce resource wastage and understand implicit user demands. In this thesis, we perform in-depth analysis of user behavior, performance issues, resource usage patterns and failures in the workloads collected from a university-wide community cluster and two clusters maintained by a government lab. We also introduce a set of …

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.

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 …

A Conceptual Model For Video Games And Interactive Media, Rachel I. Clarke, Jacob Jett, Simone Sacchi, Jin Ha Lee

School of Information Studies - Faculty Scholarship

In this paper, we describe a conceptual model for video games and interactive media. Existing conceptual models such as the Functional Requirements for Bibliographic Records (FRBR) are not adequate to represent the unique descriptive attributes, levels of variance, and relationships among video games. Previous video game-specific models tend to focus on the development of video games and their technical aspects. Our model instead attempts to reflect how users such as game players, collectors, and scholars understand video games and the relationships among them. We specifically consider use cases of gamers, with future intentions of using this conceptual model as a …

Opinion Question Answering By Sentiment Clip Localization, Lei Pang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

This article considers multimedia question answering beyond factoid and how-to questions. We are interested in searching videos for answering opinion-oriented questions that are controversial and hotly debated. Examples of questions include "Should Edward Snowden be pardoned?" and "Obamacare-unconstitutional or not?". These questions often invoke emotional response, either positively or negatively, hence are likely to be better answered by videos than texts, due to the vivid display of emotional signals visible through facial expression and speaking tone. Nevertheless, a potential answer of duration 60s may be embedded in a video of 10min, resulting in degraded user experience compared to reading the …

A More Accurate Model For Finding Tutorial Segments Explaining Apis, He Jiang, Jingxuan Zhang, Xiaochen Li, Zhilei Ren, David Lo

Research Collection School Of Computing and Information Systems

Developers prefer to utilize third-party libraries when they implement some functionalities and Application Programming Interfaces (APIs) are frequently used by them. Facing an unfamiliar API, developers tend to consult tutorials as learning resources. Unfortunately, the segments explaining a specific API scatter across tutorials. Hence, it remains a challenging issue to find the relevant segments. In this study, we propose a more accurate model to find the exact tutorial fragments explaining APIs. This new model consists of a text classifier with domain specific features. More specifically, we discover two important indicators to complement traditional text based features, namely co-occurrence APIs and …

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 …

Incorporating Priors For Medical Image Segmentation Using A Genetic Algorithm, Payel Ghosh, Melanie Mitchell, James A. Tanyi, Arthur Y. Hung

Computer Science Faculty Publications and Presentations

Medical image segmentation is typically performed manually by a physician to delineate gross tumor volumes for treatment planning and diagnosis. Manual segmentation is performed by medical experts using prior knowledge of organ shapes and locations but is prone to reader subjectivity and inconsistency. Automating the process is challenging due to poor tissue contrast and ill-defined organ/tissue boundaries in medical images. This paper presents a genetic algorithm for combining representations of learned information such as known shapes, regional properties and relative position of objects into a single framework to perform automated three-dimensional segmentation. The algorithm has been tested for prostate segmentation …

Semantic Document Clustering Using A Similarity Graph, Lubomir Stanchev

Computer Science and Software Engineering

Document clustering addresses the problem of identifying groups of similar documents without human supervision. Unlike most existing solutions that perform document clustering based on keywords matching, we propose an algorithm that considers the meaning of the terms in the documents. For example, a document that contains the words "dog" and "cat" multiple times may be placed in the same category as a document that contains the word "pet" even if the two documents share only noise words in common. Our semantic clustering algorithm is based on a similarity graph that stores the degree of semantic relationship between terms (extracted from …

Hpcnmf: A High-Performance Toolbox For Non-Negative Matrix Factorization, Karthik Devarajan, Guoli Wang

COBRA Preprint Series

Non-negative matrix factorization (NMF) is a widely used machine learning algorithm for dimension reduction of large-scale data. It has found successful applications in a variety of fields such as computational biology, neuroscience, natural language processing, information retrieval, image processing and speech recognition. In bioinformatics, for example, it has been used to extract patterns and profiles from genomic and text-mining data as well as in protein sequence and structure analysis. While the scientific performance of NMF is very promising in dealing with high dimensional data sets and complex data structures, its computational cost is high and sometimes could be critical for …

Mobile App Tagging, Ning Chen, Steven C. H. Hoi, Shaohua Li, Xiaokui Xiao

Research Collection School Of Computing and Information Systems

Mobile app tagging aims to assign a list of keywords indicating core functionalities, main contents, key features or concepts of a mobile app. Mobile app tags can be potentially useful for app ecosystem stakeholders or other parties to improve app search, browsing, categorization, and advertising, etc. However, most mainstream app markets, e.g., Google Play, Apple App Store, etc., currently do not explicitly support such tags for apps. To address this problem, we propose a novel auto mobile app tagging framework for annotating a given mobile app automatically, which is based on a search-based annotation paradigm powered by machine learning techniques. …

Epistemic Considerations On Extensive-Form Games, Cagil Tasdemir

Dissertations, Theses, and Capstone Projects

In this thesis, we study several topics in extensive-form games. First, we consider perfect information games with belief revision with players who are tolerant of each other’s hypothetical errors. We bound the number of hypothetical non-rational moves of a player that will be tolerated by other players without revising the belief on that player’s rationality on future moves, and investigate which games yield the backward induction solution.

Second, we consider players who have no way of assigning probabilities to various possible outcomes, and define players as conservative, moderate and aggressive depending on the way they choose, and show that all …

Artificially Intelligent Computer Assisted Language Learning System With Ai Student Component, Denee M. Mcclain

Capstone Research Projects

Intelligent Computer Assisted Language Learning (ICALL) systems follow an accepted format, which utilizes an artificially intelligent tutor. The systems allow the user to input a sentence in the target language and the AI tutor analyzes the sentence and provides error correction. This approach can be expensive, impractical, and inflexible. Inflexibility can result in a lower quality of learning for the users of these systems. Here I present an alternative format for ICALL systems that utilizes an artificially intelligent student. This alternative is cost effective and practical because it does not require extra development time to make the artificial intelligence an …

Comparison Of Formulations Of Applied Tasks With Interval, Fuzzy Set And Probability Approaches, Boris Kovalerchuk, Vladik Kreinovich

Departmental Technical Reports (CS)

The focus of this paper is to clarify the concepts of solutions in linear equations in interval, probabilistic and fuzzy sets setting for real word tasks. There is a fundamental difference between formal definitions of the solutions and physically meaningful concept of solution in applied tasks when equations have uncertain components. For instance, a formal definition of the solution in terms of Moore interval analysis can be completely irrelevant for solving a real world task. We show that formal definitions must follow meaningful concept of the solution in the real world. The paper proposed several formalized definitions of the concept …

Knowledge Driven Search Intent Mining, Ashutosh Jadhav

Browse all Theses and Dissertations

Understanding users' latent intents behind search queries is essential for satisfying a user's search needs. Search intent mining can help search engines to enhance its ranking of search results, enabling new search features like instant answers, personalization, search result diversification, and the recommendation of more relevant ads. Hence, there has been increasing attention on studying how to effectively mine search intents by analyzing search engine query logs. While state-of-the-art techniques can identify the domain of the queries (e.g. sports, movies, health), identifying domain-specific intent is still an open problem. Among all the topics available on the Internet, health is one …

Voting Aggregation Leads To (Interval) Median, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

When we have several results of measuring or estimating the same quantities, it is desirable to aggregate them into a single estimate for the desired quantities. A natural requirement is that if the majority of estimates has some property, then the aggregate estimate should have the same property. It turns out that it is not possible to require this forall possible properties -- but we can require it for bounds, i.e., for properties that the value of the quantity is in between given bounds a and b. In this paper, we prove that if we restrict the …

Bytewise Approximate Matching: The Good, The Bad, And The Unknown, Vikram S. Harichandran, Frank Breitinger, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

Hash functions are established and well-known in digital forensics, where they are commonly used for proving integrity and file identification (i.e., hash all files on a seized device and compare the fingerprints against a reference database). However, with respect to the latter operation, an active adversary can easily overcome this approach because traditional hashes are designed to be sensitive to altering an input; output will significantly change if a single bit is flipped. Therefore, researchers developed approximate matching, which is a rather new, less prominent area but was conceived as a more robust counterpart to traditional hashing. Since the conception …

Quantitative Framework For Social Cultural Interactions, Taranjeet Singh Bhatia

Electronic Theses and Dissertations

For an autonomous robot or software agent to participate in the social life of humans, it must have a way to perform a calculus of social behavior. Such a calculus must have explanatory power (it must provide a coherent theory for why the humans act the way they do), and predictive power (it must provide some plausible events from the predicted future actions of the humans). This dissertation describes a series of contributions that would allow agents observing or interacting with humans to perform a calculus of social behavior taking into account cultural conventions and socially acceptable behavior models. We …

Experiments In Idiom Recognition, Jing Peng, Anna Feldman

Department of Computer Science Faculty Scholarship and Creative Works

Some expressions can be ambiguous between idiomatic and literal interpretations depending on the context they occur in, e.g., sales hit the roof vs. hit the roof of the car. We present a novel method of classifying whether a given instance is literal or idiomatic, focusing on verb-noun constructions. We report state-of-the-art results on this task using an approach based on the hypothesis that the distributions of the contexts of the idiomatic phrases will be different from the contexts of the literal usages. We measure contexts by using projections of the words into vector space. For comparison, we implement Fazly et …

Tango: A Spanish-Based Programming Language, Ashley Zegiestowsky

Undergraduate Honors Thesis Collection

The purpose of this thesis is a two-part project. The first part of the project deals with the creation of my own Spanish-based programming language, Tango, using Spanish key words (instead of English key words). The second part of the project relates to the design and implementation of a compiler that follows the grammar rules outlined in the Tango language in order to successfully lexically analyze, parse, semantically analyze, and generate code for Tango. The structure of this thesis begins with a description of the specific goals achieved in the Tango language, an explanation and brief examples of the Tango …

Open Source Software Adoption Evaluation Through Feature Level Sentiment Analysis Using Twitter Data, Muhammad Touseef Ikram, Naveed Anwer Butt, Muhammad Tanvir Afzal

Turkish Journal of Electrical Engineering and Computer Sciences

Adopting open source software from the Internet, developers often encounter the problem of accessing the quality of candidate software. To efficiently adopt the system they need a sort of quality guarantee regarding software resources. To assist the developer in software adoption evaluation we have proposed a software adoption assessment approach based on user comments. In our proposed approach, we first collected the textual reviews regarding the software resource, assigned the sentiment polarity (positive or negative) to each comment, extracted the adoption aspect which the comment talks about, and then based on the adoption aspects of the software generated an aggregated …

A Hybrid Behaviour Recognition And Intrusion Detection Method For Mobile Devices, Ashley Woodiss-Field

Australian Information Security Management Conference

Behaviour-based authorisation is a technique that assesses the user of a device for authenticity by comparing their activities to previously established behaviour profiles. Passwords and other point of entry authorisation techniques are often inadequate for protecting mobile device security as they only provide an initial barrier to usage and do not operate continuously. Behaviour-based authorisation continuously assesses user authorisation, using the device owner’s profile for authentication. This research improves upon behaviour-based authorisation performance by applying a hybridised intrusion detection method. The constituent intrusion detection methods that were applied include context-awareness and self-correction. Performance of a behaviour-based authorisation method can be …

Five-Factor Model As A Predictor For Spoken Dialog Systems, Teresa G. Carter

CCAC Theses and Dissertations

Human behavior varies widely as does the design of spoken dialog systems (SDS). The search for predictors to match a user’s preference and efficiency for a specific dialog interface type in an SDS was the focus of this research. By using personality as described by the Five-Factor Method (FFM) and the Wizard of Oz technique for delivering three system initiatives of the SDS, participants interacted with each of the SDS initiatives in scheduling an airline flight. The three system initiatives were constructed as strict system, which did not allow the user control of the interaction; mixed system, which allowed the …