Computer Sciences Commons^™

Learning Analytics Through Machine Learning And Natural Language Processing, Bokai Yang

Theses and Dissertations

The increase of computing power and the ability to log students’ data with the help of the computer-assisted learning systems has led to an increased interest in developing and applying computer science techniques for analyzing learning data. To understand and investigate how learning-generated data can be used to improve student success, data mining techniques have been applied to several educational tasks. This dissertation investigates three important tasks in various domains of educational data mining: learners’ behavior analysis, essay structure analysis and feedback providing, and learners’ dropout prediction. The first project applied latent semantic analysis and machine learning approaches to investigate …

Sebd: A Stream Evolving Bot Detection Framework With Application Of Pac Learning Approach To Maintain Accuracy And Confidence Levels, Eiman Alothali, Kadhim Hayawi, Hany Alashwal

All Works

A simple supervised learning model can predict a class from trained data based on the previous learning process. Trust in such a model can be gained through evaluation measures that ensure fewer misclassification errors in prediction results for different classes. This can be applied to supervised learning using a well-trained dataset that covers different data points and has no imbalance issues. This task is challenging when it integrates a semi-supervised learning approach with a dynamic data stream, such as social network data. In this paper, we propose a stream-based evolving bot detection (SEBD) framework for Twitter that uses a deep …

Making Music Social: Creating A Spotify-Based Social Media Platform, Dalton J. Craven

Senior Theses

DKMS is a new type of social media platform for music lovers and groups of friends. It integrates tightly with Spotify, one of the largest music streaming services in the world. Users of DKMS can see what their friends are listening to, receive recommendations of new songs to listen to, and analyze their several key numerical metrics (happiness, danceability, loudness, and energy) of their top songs.

DKMS was built as part of the year-long Capstone senior design course at the University of South Carolina. A deployed app is visible at https://dkms.vercel.app, and the open-source code is visible at https://github.com/SCCapstone/DKMS.

Utilizing Deep Learning Methods In The Identification And Synthesis Of Gene Regulations, Jiandong Wang

Theses and Dissertations

Gene expression is the fundamental differentiation and development process of life. Although all cells in an organism have essentially the same DNA, cell types and activities vary due to changes in gene expression. Gene expression can be influenced by many gene regulations. RNA editing contributes to the variety of RNA and proteins by allowing single nucleotide substitution. Reverse transcription can alter the expression status of genes by inducing genetic diversity and polymorphism via novel insertions, deletions, and recombination events. Gene regulation is critical to normal development because it enables cells to respond rapidly to environmental changes. However, identifying gene regulations …

Assessing The Impact Of The Covid-19 Pandemic On Project Management Methodologies, Adrian Leung

LMU Theses and Dissertations

This research paper explores the impact of COVID-19 and the shift to remote work on project management practices across multiple industries. Through interviews with project managers, the study finds that companies with pre-existing remote work policies were better equipped to handle the transition to remote work. In contrast, companies without pre-existing policies faced increased challenges in communication, team morale, and workload management. The study also highlights the struggle to maintain work-life balance, the importance of communication, and the need to address technical difficulties. Project managers emphasized the importance of accountability and maintaining productivity during remote work. Overall, the study finds …

A Package Of Smartphone And Sensor-Based Objective Measurement Tools For Physical And Social Exertional Activities For Patients With Illness-Limiting Capacities, Arafat Mahmood

Dissertations (1934 -)

Patients with several incompletely diagnosed and understood chronic diseases suffer from symptoms that limit their functional capacity. In particular, patients with chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) and long covid syndromes have variable fatigue, malaise, poor and unrefreshing sleep, and delayed post-exertional exacerbations of these symptoms. There are no specific tests for these patients to diagnose their diseases properly. These patients must be aware of their daily activities and energy expenditure. Even a little physical effort or socially extroverted behavior can make them tired and incapable of continuing their daily routine. A comprehensive summary of the measured activities at any particular …

Regulating Artificial Intelligence In International Investment Law, Mark Mclaughlin

Research Collection Yong Pung How School Of Law

The interaction between artificial intelligence (AI) and international investment treaties is an uncharted territory of international law. Concerns over the national security, safety, and privacy implications of AI are spurring regulators into action around the world. States have imposed restrictions on data transfer, utilised automated decision-making, mandated algorithmic transparency, and limited market access. This article explores the interaction between AI regulation and standards of investment protection. It is argued that the current framework provides an unpredictable legal environment in which to adjudicate the contested norms and ethics of AI. Treaties should be recalibrated to reinforce their anti-protectionist origins, embed human-centric …

Geometry And Coding: Introducing An Interactive And Integrated Mathematics-Computer Science Unit, Kimberly Beck, Jessica F. Shumway

Publications

As part of a collaborative project between Utah State University, the Cache County School District, and Stanford, instructional units were designed for fifth-grade students. These units integrated math concepts of geometrical shapes and computer science concepts of sequences, conditionals, and loops. One component of the unit was implemented in math classrooms by math teachers, and the other component was implemented in computer labs. This presentation will focus on the math unit as presented at the National Council of Teachers of Mathematics (NCTM-V).

C-Wall: Conflict-Resistance In Privacy-Preserving Cloud Storage, Xiaoguo Li, Tao Xiang, Yi Mu, Fuchun Guo, Zhongyuan Yao

Research Collection School Of Computing and Information Systems

Following the success of cloud computing, it has been shown its importance to realize various access control models in the cloud storage setting. Chinese Wall is a traditional access control model in business for solving the conflict of interest (CoI) problem, and it would be very interesting to achieve conflict-resistant in cloud storage system. However, the access control model does not ensure the privacy of users, and it may reveal the user's interest, investment tendency, etc. Therefore, it raises a big challenge to implement the Chinese Wall without compromising the user's privacy. In this paper, we focus on the Chinese …

Supporting Novices Author Audio Descriptions Via Automatic Feedback, Rosiana Natalie, Joshua Shi-Hao Tseng, Hernisa Kacorri, Kotaro Hara

Research Collection School Of Computing and Information Systems

Audio descriptions (AD) make videos accessible to those who cannot see them. But many videos lack AD and remain inaccessible as traditional approaches involve expensive professional production. We aim to lower production costs by involving novices in this process. We present an AD authoring system that supports novices to write scene descriptions (SD)—textual descriptions of video scenes—and convert them into AD via text-to-speech. The system combines video scene recognition and natural language processing to review novice-written SD and feeds back what to mention automatically. To assess the effectiveness of this automatic feedback in supporting novices, we recruited 60 participants to …

Parsing-Conditioned Anime Translation: A New Dataset And Method, Zhansheng Li, Yangyang Xu, Nanxuan Zhao, Yang Zhou, Yongtuo Liu, Dahua Lin, Shengfeng He

Research Collection School Of Computing and Information Systems

Anime is an abstract art form that is substantially different from the human portrait, leading to a challenging misaligned image translation problem that is beyond the capability of existing methods. This can be boiled down to a highly ambiguous unconstrained translation between two domains. To this end, we design a new anime translation framework by deriving the prior knowledge of a pre-Trained StyleGAN model. We introduce disentangled encoders to separately embed structure and appearance information into the same latent code, governed by four tailored losses. Moreover, we develop a FaceBank aggregation method that leverages the generated data of the StyleGAN, …

Asdf: A Differential Testing Framework For Automatic Speech Recognition Systems, Daniel Hao Xian Yuen, Andrew Yong Chen Pang, Zhou Yang, Chun Yong Chong, Mei Kuan Lim, David Lo

Research Collection School Of Computing and Information Systems

Recent years have witnessed wider adoption of Automated Speech Recognition (ASR) techniques in various domains. Consequently, evaluating and enhancing the quality of ASR systems is of great importance. This paper proposes Asdf, an Automated Speech Recognition Differential Testing Framework to test ASR systems. Asdf extends an existing ASR testing tool, the CrossASR++, which synthesizes test cases from a text corpus. However, CrossASR++ fails to make use of the text corpus efficiently and provides limited information on how the failed test cases can improve ASR systems. To address these limitations, our tool incorporates two novel features: (1) a text transformation module …

Code Will Tell: Visual Identification Of Ponzi Schemes On Ethereum, Xiaolin Wen, Kim Siang Yeo, Yong Wang, Ling Cheng, Feida Zhu, Min Zhu

Research Collection School Of Computing and Information Systems

Ethereum has become a popular blockchain with smart contracts for investors nowadays. Due to the decentralization and anonymity of Ethereum, Ponzi schemes have been easily deployed and caused significant losses to investors. However, there are still no explainable and effective methods to help investors easily identify Ponzi schemes and validate whether a smart contract is actually a Ponzi scheme. To fill the research gap, we propose PonziLens, a novel visualization approach to help investors achieve early identification of Ponzi schemes by investigating the operation codes of smart contracts. Specifically, we conduct symbolic execution of opcode and extract the control flow …

Morphologically-Aware Vocabulary Reduction Of Word Embeddings, Chong Cher Chia, Maksim Tkachenko, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

We propose SubText, a compression mechanism via vocabulary reduction. The crux is to judiciously select a subset of word embeddings which support the reconstruction of the remaining word embeddings based on their form alone. The proposed algorithm considers the preservation of the original embeddings, as well as a word’s relationship to other words that are morphologically or semantically similar. Comprehensive evaluation of the compressed vocabulary reveals SubText’s efficacy on diverse tasks over traditional vocabulary reduction techniques, as validated on English, as well as a collection of inflected languages.

Graphsearchnet: Enhancing Gnns Via Capturing Global Dependencies For Semantic Code Search, Shangqing Liu, Xiaofei Xie, Jjingkai Siow, Lei Ma, Guozhu Meng, Yang Liu

Research Collection School Of Computing and Information Systems

Code search aims to retrieve accurate code snippets based on a natural language query to improve software productivity and quality. With the massive amount of available programs such as (on GitHub or Stack Overflow), identifying and localizing the precise code is critical for the software developers. In addition, Deep learning has recently been widely applied to different code-related scenarios, ., vulnerability detection, source code summarization. However, automated deep code search is still challenging since it requires a high-level semantic mapping between code and natural language queries. Most existing deep learning-based approaches for code search rely on the sequential text ., …

How To Find Actionable Static Analysis Warnings: A Case Study With Findbugs, Rahul Yedida, Hong Jin Kang, Huy Tu, Xueqi Yang, David Lo, Tim Menzies

Research Collection School Of Computing and Information Systems

Automatically generated static code warnings suffer from a large number of false alarms. Hence, developers only take action on a small percent of those warnings. To better predict which static code warnings should ot be ignored, we suggest that analysts need to look deeper into their algorithms to find choices that better improve the particulars of their specific problem. Specifically, we show here that effective predictors of such warnings can be created by methods that ocally adjust the decision boundary (between actionable warnings and others). These methods yield a new high water-mark for recognizing actionable static code warnings. For eight …

Bubbleu: Exploring Augmented Reality Game Design With Uncertain Ai-Based Interaction, Minji Kim, Kyungjin Lee, Rajesh Krishna Balan, Youngki Lee

Research Collection School Of Computing and Information Systems

Object detection, while being an attractive interaction method for Augmented Reality (AR), is fundamentally error-prone due to the probabilistic nature of the underlying AI models, resulting in sub-optimal user experiences. In this paper, we explore the effect of three game design concepts, Ambiguity, Transparency, and Controllability, to provide better gameplay experiences in AR games that use error-prone object detection-based interaction modalities. First, we developed a base AR pet breeding game, called Bubbleu that uses object detection as a key interaction method. We then implemented three different variants, each according to the three concepts, to investigate the impact of each design …

A Learner-Verifier Framework For Neural Network Controllers And Certificates Of Stochastic Systems, Krishnendu Chatterjee, Thomas A. Henzinger, Dorde Zikelic, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Reinforcement learning has received much attention for learning controllers of deterministic systems. We consider a learner-verifer framework for stochastic control systems and survey recent methods that formally guarantee a conjunction of reachability and safety properties. Given a property and a lower bound on the probability of the property being satisfied, our framework jointly learns a control policy and a formal certificate to ensure the satisfaction of the property with a desired probability threshold. Both the control policy and the formal certificate are continuous functions from states to reals, which are learned as parameterized neural networks. While in the deterministic case, …

Socially Aware Natural Language Processing With Commonsense Reasoning And Fairness In Intelligent Systems, Sirwe Saeedi

Dissertations

Although Artificial Intelligence (AI) promises to deliver ever more user-friendly consumer applications, recent mishaps involving fake information and biased treatment serve as vivid reminders of the pitfalls of AI. AI can harbor latent biases and flaws that can cause harm in diverse and unexpected ways. It is crucial to understand the reasons for, mechanisms behind, and circumstances under which AI can fail. For instance, a lack of commonsense reasoning can lead to biased or unfair decisions made by Machine Learning (ML) systems. For example, if an ML system is trained on data that is biased or unrepresentative of the real …

Developing Resilient Cyber-Physical Systems: A Review Of State-Of-The-Art Malware Detection Approaches, Gaps, And Future Directions, M. Imran Malik, Ahmed Ibrahim, Peter Hannay, Leslie F. Sikos

Research outputs 2022 to 2026

Cyber-physical systems (CPSes) are rapidly evolving in critical infrastructure (CI) domains such as smart grid, healthcare, the military, and telecommunication. These systems are continually threatened by malicious software (malware) attacks by adversaries due to their improvised tactics and attack methods. A minor configuration change in a CPS through malware has devastating effects, which the world has seen in Stuxnet, BlackEnergy, Industroyer, and Triton. This paper is a comprehensive review of malware analysis practices currently being used and their limitations and efficacy in securing CPSes. Using well-known real-world incidents, we have covered the significant impacts when a CPS is compromised. In …

How Technology May Be Used For Future Disease Prediction: A Systematic Literature Review, Rich P. Manprisio, Mohammed Salam

Research Days

Exasperated by the current pandemic, our healthcare system continues to struggle with the accuracy and effectiveness of disease treatments. However, despite these growing challenges, technological advancements have aided potential disease prediction. There has been a positive correlation between utilizing technologies and leveraging them for disease predictions. Thanks to our continued reliance and technological advancement, current research shows that it has many viable options to aid the healthcare field. This systematic review looks at the current state of how technologies have been and can be used to improve healthcare.

Research Trends In Cybercrime And Cybersecurity: A Review Based On Web Of Science Core Collection Database, Ling Wu, Qiong Peng, Michael Lembke

International Journal of Cybersecurity Intelligence & Cybercrime

Studies on cybercrime and cybersecurity have expanded in both scope and breadth in recent years. This study offers a bibliometric review of research trends in cybercrime and cybersecurity over the past 26 years (1995-2021) based on Web of Science core collection database. Specifically, we examine the growth of scholarship and the expanded scope of subject categories and relevant journals. We also analyze the research collaboration network based on authors’ affiliated institutions and countries. Finally, we identify major topics within the fields, how each topic relates to – and diverges from – one another, and their evolution over time. Overall, we …

Perceptions Of Political Violence, Trisha Patel, Andrew Wachtel, Cameron N. Smith

[Archive] Belmont University Research Symposium (BURS)

Recently, political violence in the United States has been an increasingly salient public issue. Previous statistical connections have been made between low yearly income, low education, and right-wing political affiliation to a higher acceptance of political violence (Jasko et al., 2022). Using a dataset published through Harvard, this study aims to address the white population and their beliefs regarding current issues. The dataset was previously assembled by researchers studying the connection of people’s faith to political tendencies. Data is currently being analyzed and results will be presented at BURS.

Snowmass 2021 Computational Frontier Compf4 Topical Group Report Storage And Processing Resource Access, W. Bhimji, D. Carder, E. Dart, J. Duarte, I. Fisk, R. Gardner, C. Guok, B. Jayatilaka, T. Lehman, M. Lin, C. Maltzahn, S. Mckee, M. S. Neubauer, O. Rind, O. Shadura, N. V. Tran, P. Van Gemmeren, G. Watts, B. A. Weaver, F. Würthwein

Holland Computing Center: Faculty Publications

The Snowmass 2021 CompF4 topical group’s scope is facilities R&D, where we consider “facilities” as the hardware and software infrastructure inside the data centers plus the networking between data centers, irrespective of who owns them, and what policies are applied for using them. In other words, it includes commercial clouds, federally funded High Performance Computing (HPC) systems for all of science, and systems funded explicitly for a given experimental or theoretical program. However, we explicitly consider any data centers that are integrated into data acquisition systems or trigger of the experiments out of scope here. Those systems tend to have …

Robert X House Collection, Dan Bonenberger, Brooke Boyst, Franklin Haywood, Carrie Malas, Jaclyn Panter, Ian Tomashik, Laura Waskiwicz, Taylor Williams, Austin Martin

Digital Heritage Preservation Collection

This collection examines the Robert X House at 18827 Keystone Street in Detroit, Michigan, a significant site in the history of the Nation of Islam and the broader narrative of Black empowerment and civil rights in mid-20th century America. Originally built in 1951 as a modest Federal Housing Administration (FHA) minimum ranch-style home, the property was purchased by Robert Davenport (later known as Robert X), an influential leader in the Nation of Islam. The house became a hub for religious instruction, community-building, and activism.

Robert X, a former boxer and musician, became a prominent figure in Detroit’s Nation of Islam …

V2v And V2i Based Safety And Platooning Algorithms For Connected And Autonomous Vehicles, Omkar Dokur

USF Tampa Graduate Theses and Dissertations

Connected Vehicles (CVs) make transportation safe by communicating with vehicles and the infrastructure in their neighborhood. CVs are embedded with onboard units (OBUs) which transmit basic safety messages (BSMs) containing the location, heading, and velocity information of the vehicle using either Dedicated Short-Range Communications (DSRC) or Cellular Vehicle-to-Everything (C-V2X) technology. These BSMs can be used to warn drivers using various vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) applications. Along with these applications, CV technology also gives rise to cooperative vehicular driving applications such as platooning. A group of vehicles can negotiate and drive jointly close to each other in a cooperative manner …

Simulation Of Mathematical Model Of Network Interference On Global System For Mobile Communication, Yidiat O. Aderinto, Yusuf Musa

Tanzania Journal of Science

Network interference is the incorporation of undesirable signals to desirable ones in an interconnected system. In this paper, the simulation of mathematical model of network interference with respect to global system of mobile communication is presented. The model was formulated and analyzed mathematically. Numerical simulation was carried out using real life data from Communication Towers Nigeria Limited, Northwest regional office Kaduna, Nigeria. The results obtained were very close to that of laboratory investigation. Keywords: Mathematical Model, Network, Interference, Congestion, Mobile communication, Stability

A Cloud Based Model Symbiotic Organism Search Algorithm For Placement Of Distributed Energy Resources In The Electrical Secondary Distribution Networks, Shamte Kawambwa, Daudi Mnyanghwalo

Tanzania Journal of Science

Abstract The increased penetration of distributed energy resources (DERs) technologies to residential users has fostered the need for DERs integration and control methods in the secondary distribution networks (SDN). In order to reap the potential advantages of DERs and achieve their inclusion in the electrical power system while avoiding their negative impacts, the DERs should be optimally placed and sized. Considering the nature of electrical networks and DER operations, the DERs placement is a nondeterministic polynomial hard (NP-hard) optimization problem. Metaheuristic algorithms are efficient for solving DER placement problems. Metaheuristic algorithms for DER placement in SDN involve high computational effort, …

Maintenance Scheduling Algorithm For Transformers In Tanzania Electrical Secondary Distribution Networks, Hadija Mbembati, Kwame Ibwe, Baraka Maiseli

Tanzania Journal of Science

The drive by the government of Tanzania to electrify every village has resulted into expansion of the electrical secondary distribution networks (ESDNs). Therefore, maintenance management is of the highest priority for the smooth operation of the ESDNs to reduce unscheduled downtime and unexpected mechanical failures. Studies show that condition-based predictive maintenance (CBPdM) method allows the utility company to monitor, analyze and process the information obtained from ESDNs transformers. Thus, this study adopts the CBPdM method to develop a maintenance scheduling algorithm that can predict the transformer state, forecast maintenance time based on transformer load profile and schedule its maintenance using …

Evaluation Of Image Enhancement Techniques For Electrical Capacitance Tomography Applications, Alfred J Mwambela

Tanzania Journal of Science

The fast generation of images in Electrical Capacitance Tomography (ECT) systems is a desirable feature for many industrial applications. Non-iterative reconstruction algorithms which qualify for this requirement generate poor-quality images. The Linear Back Projection (LBP) is the fastest non-iterative reconstruction algorithm. The challenge is to find a technique to improve the quality of images from LBP at a low computational cost. Image enhancement techniques have been investigated for improving the quality of images reconstructed from the LBP algorithm. Simulated and measured static and dynamic flow data were used in the evaluation. The performance results were benchmarked with results from the …