Computer Sciences Commons^™

Rbac Attack Exposure Auditor. Tracking User Risk Exposure Per Role-Based Access Control Permissions, Adelaide Damrau

Undergraduate Honors Theses

Access control models and implementation guidelines for determining, provisioning, and de-provisioning user permissions are challenging due to the differing approaches, unique for each organization, the lack of information provided by case studies concerning the organization’s security policies, and no standard means of implementation procedures or best practices. Although there are multiple access control models, one stands out, role-based access control (RBAC). RBAC simplifies maintenance by enabling administrators to group users with similar permissions. This approach to managing user permissions supports the principle of least privilege and separation of duties, which are needed to ensure an organization maintains acceptable user access …

Interdisciplinary And Innovative: A Nursing And Computer Science Collaboration To Create A Barcode Medication Administration System, Jodi Lisbeth Berndt, Peter Ohmann, Imad Rahal, Lindsey Hoeschen, Andrew Rothstein

Nursing Faculty Publications

Preventing medication errors remains a priority in nursing education. The implementation of Barcode Medication Administration (BCMA) systems is one strategy that has been used to reduce medication errors. Practice using BCMA in simulated settings may enhance the transfer of these skills to the clinical practice setting. However, the purchase of BCMA educational products available for nursing students can be cost prohibitive for many nursing programs. To overcome the barrier of cost, an interdisciplinary and innovative collaborative approach was used to create a fully functional low-cost BCMA system.

Culture In Computing: The Importance Of Developing Gender-Inclusive Software, Creighton France

Computer Science and Computer Engineering Undergraduate Honors Theses

The field of computing as we know it today exists because of the contributions of numerous female mathematicians, computer scientists, and programmers. While working with hardware was viewed as “a man’s job” during the mid-20th century, computing and programming was viewed as a noble and high-paying field for women to occupy. However, as time has progressed, the U.S. has seen a decrease in the number of women pursuing computer science. The idea that computing is a masculine discipline is common in the U.S. today for reasons such as male-centered marketing of electronics and gadgets, an inaccurate representation of what it …

Source Code Plagiarism Detection Using Jplag & Stack Overflow Data, Sudheer Yetthapu

Masters Theses & Specialist Projects

Advancements in computer technology and internet services have led to the availability of vast amounts of information like videos, articles, research papers, and code samples. Free online information will increase the possibility of plagiarism and collusion among students. People can commit plagiarism in both text and code [1], as tools used to detect plagiarism between texts and between codes are distinct. Traditionally plagiarism in code is detected using manual inspection, which is a tedious process and misses to compare code from previous submissions and external sources. To overcome this issue, systems that can automatically detect plagiarism in code were developed …

Music Mentor, Jacob Webb

Honors College Theses

Extra-curricular learning is on the rise, and many are interested in expanding their current knowledge by utilizing the recent increase in educational technology. While many forms of educational technology exist, there are few interactive and engaging platforms that teach music theory. Apps such as Perfect Ear and MyMusicTheory are great for becoming familiar with reading music and recognizing pitches, however, they often become dry with repetition and repeated tasks. By combining existing technologies that can complete real time conversions from raw audio to MIDI, our goal was to gather information such as harmonies, key and compatible chords from the user’s …

Fast -- Asymptotically Optimal -- Methods For Determining The Optimal Number Of Features, Saied Tizpaz-Niari, Luc Longpré, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In machine learning -- and in data processing in general -- it is very important to select the proper number of features. If we select too few, we miss important information and do not get good results, but if we select too many, this will include many irrelevant ones that only bring noise and thus again worsen the results. The usual method of selecting the proper number of features is to add features one by one until the quality stops improving and starts deteriorating again. This method works, but it often takes too much time. In this paper, we propose …

Context-Aware Gaze-Based Interface For Smart Wheelchair, Tien Pham

Computer Science and Engineering Theses - Archive

Human-Computer Interfaces (HCI) is an essential aspect of modern technology that has revolutionized the way we interact with machines. With the revolution of computers and smart devices and the advent of autonomous vehicles and other machines, there has been a significant advancement in this area that brings convenience to users to interact with technology intuitively and efficiently. However, the importance of HCI goes beyond the convenience of everyday technology. It has become crucial in the development of assistive technologies that empower people with disabilities to live more independently. Person with disabilities, who lack control of one or more parts of …

Enhancing Health Tweet Classification: An Evaluation Of Transformer-Based Models For Comprehensive Analysis, Foram Pankajbhai Patel

Computer Science and Engineering Theses - Archive

The task of health tweet classification entails identifying whether a given tweet is health-related or not. While existing research in this area has made significant progress in classifying tweets into specific sub-domains of health, such as mental health, COVID-19, or specific diseases, there is a need for a more comprehensive approach that considers a broader range of health-related topics. This thesis addresses this need by proposing a diverse and comprehensive dataset that includes various existing health-related datasets, data collected through a keyword-based approach, and manually annotated data. However, the use of health-related keywords in a figurative or non-health context poses …

Emauth: A Usable Continuous Authentication Scheme, Vighnesh Sivaraman

Computer Science and Engineering Theses - Archive

ABSTRACT: In recent years, wearables have become an integral part of daily life for most users. Wearables provide a seamless supplement to their main counterparts such as smartphones, and tablets. With the increasing prevalence of smart devices in a ubiquitous manner, protecting data has become a crucial aspect of security in all areas of life, as individuals can be vulnerable to such threats regardless of their location. As a security measure, authentication measures have been adopted. Traditional/ Legacy Authentication mechanisms such as TouchID, FaceID, and Password/PIN are incorporated into the everyday life of users. There also exists another mode of …

Quantum Multi-Solution Bernoulli Search With Applications To Bitcoin’S Post-Quantum Security, Alexandru Cojocaru, Juan Garay, Fang Song, Petros Wallden

Computer Science Faculty Publications and Presentations

A proof of work (PoW) is an important cryptographic construct which enables a party to convince other parties that they have invested some effort in solving a computational task. Arguably, its main impact has been in the setting of cryptocurrencies such as Bitcoin and its underlying blockchain protocol, which have received significant attention in recent years due to its potential for various applications as well as for solving fundamental distributed computing questions in novel threat models. PoWs enable the linking of blocks in the blockchain data structure, and thus the problem of interest is the feasibility of obtaining a sequence …

Caspi: Collaborative Photon Processing For Active Single-Photon Imaging, Jongho Lee, Atul Ingle, Jenu V. Chacko, Kevin W. Eliceiri, Mohit Gupta

Computer Science Faculty Publications and Presentations

Image sensors capable of capturing individual photons have made tremendous progress in recent years. However, this technology faces a major limitation. Because they capture scene information at the individual photon level, the raw data is sparse and noisy. Here we propose CASPI: Collaborative Photon Processing for Active Single-Photon Imaging, a technology-agnostic, application-agnostic, and training-free photon processing pipeline for emerging high-resolution single-photon cameras. By collaboratively exploiting both local and non-local correlations in the spatio-temporal photon data cubes, CASPI estimates scene properties reliably even under very challenging lighting conditions. We demonstrate the versatility of CASPI with two applications: LiDAR imaging over a …

Toward A Deeper Integration Of Low-Fidelity Sketches Into Mobile Application Development, Soumik Mohian

Computer Science and Engineering Dissertations - Archive

Mobile application development often starts with creating low-fidelity sketches of user interfaces. Integrating these sketches into the software development process can reduce repetition, narrow the gap between user perception and final implementation, and improve app resilience. In this study, we introduce the DoodleUINet dataset, which comprises over 10K sketches of UI elements. Our Doodle2App tool converts low-fidelity sketches into a single-page, compilable Android app. At the same time, our PSDoodle provides an interactive, partial sketch-based search engine with a top-10 screen retrieval accuracy comparable to the state-of-the-art SWIRE line of work but with a 50% reduction in the average required …

Bluetooth Low Energy Indoor Positioning System, Jackson T. Diamond, Jordan Hanson

Whittier Scholars Program

Robust indoor positioning systems based on low energy bluetooth signals will service a wide range of applications. We present an example of a low energy bluetooth positioning system. First, the steps taken to locate the target with the bluetooth data will be reviewed. Next, we describe the algorithms of the set of android apps developed to utilize the bluetooth data for positioning. Similar to GPS, the algorithms use trilateration to approximate the target location by utilizing the corner devices running one of the apps. Due to the fluctuating nature of the bluetooth signal strength indicator (RSSI), we used an averaging …

Connecting The Dots For Contextual Information Retrieval, Pei-Chi Lo

Dissertations and Theses Collection (Open Access)

There are many information retrieval tasks that depend on knowledge graphs to return contextually relevant result of the query. We call them Knowledgeenriched Contextual Information Retrieval (KCIR) tasks and these tasks come in many different forms including query-based document retrieval, query answering and others. These KCIR tasks often require the input query to contextualized by additional facts from a knowledge graph, and using the context representation to perform document or knowledge graph retrieval and prediction. In this dissertation, we present a meta-framework that identifies Contextual Representation Learning (CRL) and Contextual Information Retrieval (CIR) to be the two key components in …

Uibee: An Improved Deep Instance Segmentation And Classification Of Ui Elements In Wireframes, Cahi̇t Berkay Kazangi̇rler, Caner Özcan, Buse Yaren Teki̇n

Turkish Journal of Electrical Engineering and Computer Sciences

User Interface (UI) is a basic concept in which individuals interact with any computer program or technological device to create a graphical design. In the initial stages of app development, UI prototype is a must. An automatic analysis system for the basic execution of UI designs will considerably speed up the development of designs according to old-fashioned methods. In this approach, it is aimed at saving cost and time by automating the process. For the aforesaid objective, we present a new approach rather than the traditional methods. For this reason, a high amount of elements in wireframes are detected and …

Toward Digital Phenotyping: Human Activity Representation For Embodied Cognition Assessment, Mohammad Zakizadehghariehali

Computer Science and Engineering Dissertations - Archive

Cognition is the mental process of acquiring knowledge and understanding through thought, experience and senses. Based on Embodied Cognition theory, physical activities are an important manifestation of cognitive functions. As a result, they can be employed to both assess and train cognitive skills. In order to assess various cognitive measures, the ATEC system has been proposed. It consists of physical exercises with different variations and difficulty levels, designed to provide assessment of executive and motor functions. This thesis focuses on obtaining human activity representation from recorded videos of ATEC tasks in order to automatically assess embodied cognition performance. Representation learning …

Optimizing Resource Utilization, Efficiency And Scalability In Deep Learning Systems, Xiaofeng Wu

Computer Science and Engineering Dissertations - Archive

This thesis addresses the challenges of utilization, efficiency, and scalability faced by deep learning systems, which are essential for high-performance training and serving of deep learning models. Deep learning systems play a critical role in developing accurate and complex models for various applications, including image recognition, natural language understanding, and speech recognition. This research focuses on understanding and developing deep learning systems that encompass data preprocessing, resource management, multi-tenancy, and distributed model training. The thesis proposes several solutions to improve the performance, scalability, and efficiency of deep learning applications. Firstly, we introduce SwitchFlow, a scheduling framework that addresses the limitations …

Repytah: An Open-Source Python Package For Building Aligned Hierarchies For Sequential Data, Chenhui Jia, Lizette Carpenter, Thu Tran, Amanda Y. Liu, Sasha Yeutseyva, Mariun Tapal, Yingke Wang, Zoie Kexin Zhou, Jordan Moody, Denise Nava, Eleanor Donaher, Lillian Yusha Jiang, Ben Bruncati, Katherine M. Kinnaird

Computer Science: Faculty Publications

We introduce repytah, a Python package that constructs the aligned hierarchies representation that contains all possible structure-based hierarchical decompositions for a finite length piece of sequential data aligned on a common time axis. In particular, this representation–introduced by Kinnaird (2016) with music-based data (like musical recordings or scores) as the primary motivation–is intended for sequential data where repetitions have particular meaning (such as a verse, chorus, motif, or theme). Although the original motivation for the aligned hierarchies representation was finding structure for music-based data streams, there is nothing inherent in the construction of these representations that limits repytah to only …

Identifying And Analyzing Multi-Star Systems Among Tess Planetary Candidates Using Gaia, Katie E. Bailey

Electronic Theses and Dissertations

Exoplanets represent a young, rapidly advancing subfield of astrophysics where much is still unknown. It is therefore important to analyze trends among their parameters to learn more about these systems. More complexity is added to these systems with the presence of additional stellar companions. To study these complex systems, one can employ programming languages such as Python to parse databases such as those constructed by TESS and Gaia to bridge the gap between exoplanets and stellar companions. Data can then be analyzed for trends in these multi-star exoplanet systems and in juxtaposition to their single-star counterparts. This research was able …

The Six Emotional Dimension (6de) Model: A Multidimensional Approach To Analyzing Human Emotions And Unlocking The Potential Of Emotionally Intelligent Artificial Intelligence (Ai) Via Large Language Models (Llm), Jay Ratican, James Hutson

Faculty Scholarship

The rapid advancements in artificial intelligence (AI) research, particularly in training large language models (LLMs) such as OpenAI's ChatGPT 3.5 and 4, hold significant potential for future applications in education, healthcare, and assisted living. Emotionally intelligent AI systems can provide personalized and adaptive educational experiences, enhancing engagement and educational outcomes. In healthcare, they can offer empathetic mental health support, augmenting existing resources. In assisted living, AI companions can provide emotional support, cognitive stimulation, and monitoring services, promoting independence and safety. However, ethical considerations and privacy safeguards are crucial to ensure responsible deployment. Integrating emotionally intelligent AI in these domains has …

Differences In Intracellular Protein Levels In Monocytes And Cd4+ Lymphocytes Between Bipolar Depressed Patients And Healthy Controls: A Pilot Study With Tyramine-Based Signal-Amplified Flow Cytometry, Keming Gao, Marzieh Ayati, Nicholas M. Kaye, Mehmet Koyutürk, Joseph R. Calabrese, Stephen J. Ganocy, Hillard M. Lazarus, Eric Christian, David Kaplan

Computer Science Faculty Publications

Highlights

• To measure 18 intracellular proteins in blood cells of bipolar depressed patients and healthy controls;

• TFour proteins in monocytes and 2 proteins in CD4+ T Cells were significantly lower in patients than in healthy controls;

• The studied proteins are involved in prolactin, leptin, BDNF, and interleukin-3 signal pathways;

• Studying intracellular proteins with enhanced flow cytometry may find biomarkers differentiating bipolar disorder from healthy controls.

Abstract

Background

Molecular biomarkers for bipolar disorder (BD) that distinguish it from other manifestations of depressive symptoms remain unknown. The aim of this study was to determine if a very sensitive tyramine-based signal-amplification technology for …

Compositional Prompt Tuning With Motion Cues For Open-Vocabulary Video Relation Detection, Kaifeng Gao, Long Chen, Hanwang Zhang, Jun Xiao, Qianru Sun

Research Collection School Of Computing and Information Systems

Prompt tuning with large-scale pretrained vision-language models empowers open-vocabulary prediction trained on limited base categories, e.g., object classification and detection. In this paper, we propose compositional prompt tuning with motion cues: an extended prompt tuning paradigm for compositional predictions of video data. In particular, we present Relation Prompt (RePro) for Open-vocabulary Video Visual Relation Detection (Open-VidVRD), where conventional prompt tuning is easily biased to certain subject-object combinations and motion patterns. To this end, RePro addresses the two technical challenges of Open-VidVRD: 1) the prompt tokens should respect the two different semantic roles of subject and object, and 2) the tuning …

Link Prediction On Latent Heterogeneous Graphs, Trung Kien Nguyen, Zemin Liu, Yuan Fang

Research Collection School Of Computing and Information Systems

On graph data, the multitude of node or edge types gives rise to heterogeneous information networks (HINs). To preserve the heterogeneous semantics on HINs, the rich node/edge types become a cornerstone of HIN representation learning. However, in real-world scenarios, type information is often noisy, missing or inaccessible. Assuming no type information is given, we define a so-called latent heterogeneous graph (LHG), which carries latent heterogeneous semantics as the node/edge types cannot be observed. In this paper, we study the challenging and unexplored problem of link prediction on an LHG. As existing approaches depend heavily on type-based information, they are suboptimal …

Neural Episodic Control With State Abstraction, Zhuo Li, Derui Zhu, Yujing Hu, Xiaofei Xie, Lei Ma, Yan Zheng, Yan Song, Yingfeng Chen, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Existing Deep Reinforcement Learning (DRL) algorithms suffer from sample inefficiency.Generally, episodic control-based approaches are solutions that leveragehighly-rewarded past experiences to improve sample efficiency of DRL algorithms.However, previous episodic control-based approaches fail to utilize the latentinformation from the historical behaviors (e.g., state transitions, topological similarities,etc.) and lack scalability during DRL training. This work introducesNeural Episodic Control with State Abstraction (NECSA), a simple but effectivestate abstraction-based episodic control containing a more comprehensive episodicmemory, a novel state evaluation, and a multi-step state analysis. We evaluate ourapproach to the MuJoCo and Atari tasks in OpenAI gym domains. The experimentalresults indicate that NECSA achieves higher …

Fine-Grained Commit-Level Vulnerability Type Prediction By Cwe Tree Structure, Shengyi Pan, Lingfeng Bao, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Identifying security patches via code commits to allow early warnings and timely fixes for Open Source Software (OSS) has received increasing attention. However, the existing detection methods can only identify the presence of a patch (i.e., a binary classification) but fail to pinpoint the vulnerability type. In this work, we take the first step to categorize the security patches into fine-grained vulnerability types. Specifically, we use the Common Weakness Enumeration (CWE) as the label and perform fine-grained classification using categories at the third level of the CWE tree. We first formulate the task as a Hierarchical Multi-label Classification (HMC) problem, …

Diffseer: Difference-Based Dynamic Weighted Graph Visualization, Xiaolin Wen, Yong Wang, Meixuan Wu, Fengjie Wang, Xuanwu Yue, Qiaomu Shen, Yuxin Ma, Min Zhu

Research Collection School Of Computing and Information Systems

Existing dynamic weighted graph visualization approaches rely on users’ mental comparison to perceive temporal evolution of dynamic weighted graphs, hindering users from effectively analyzing changes across multiple timeslices. We propose DiffSeer, a novel approach for dynamic weighted graph visualization by explicitly visualizing the differences of graph structures (e.g., edge weight differences) between adjacent timeslices. Specifically, we present a novel nested matrix design that overviews the graph structure differences over a time period as well as shows graph structure details in the timeslices of user interest. By collectively considering the overall temporal evolution and structure details in each timeslice, an optimization-based …

Algorithms For Unit-Disk Graphs And Related Problems, Yiming Zhao

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

In this dissertation, we study algorithms for several problems on unit-disk graphs and related problems. The unit-disk graph can be viewed as an intersection graph of a set of congruent disks. Unit-disk graphs have been extensively studied due to many of their applications, e.g., modeling the topology of wireless sensor networks. Lots of problems on unit-disk graphs have been considered in the literature, such as shortest paths, clique, independent set, distance oracle, diameter, etc. Specifically, we study the following problems in this dissertation: L₁ shortest paths in unit-disk graphs, reverse shortest paths in unit-disk graphs, minimum bottleneck moving spanning …

Generative Neural Network Approach To Designing And Optimizing Dynamic Inductive Power Transfer Systems, Andrew Pond Curtis

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Electric vehicles (EVs) offer many improvements over traditional combustion engines including increasing efficiency, while decreasing cost of operation and emissions. There is a need for the development of cheap and efficient charging systems for the future success of EVs. Most EVs currently utilize static plug-in charging systems. An alternative charging method of significant interest is dynamic inductive power transfer systems (DIPT). These systems utilize two coils, one placed in the vehicle and one in the roadway to wirelessly charge the vehicle as it passes over. This method removes the current limitations on EVs where they must stop and statically charge …

A Review On Deep-Learning-Based Cyberbullying Detection, Md Tarek Hasan, Md Al Emran Hossain, Md Saddam Hossain Mukta, Arifa Akter, Mohiuddin Ahmed, Salekul Islam

Research outputs 2022 to 2026

Bullying is described as an undesirable behavior by others that harms an individual physically, mentally, or socially. Cyberbullying is a virtual form (e.g., textual or image) of bullying or harassment, also known as online bullying. Cyberbullying detection is a pressing need in today’s world, as the prevalence of cyberbullying is continually growing, resulting in mental health issues. Conventional machine learning models were previously used to identify cyberbullying. However, current research demonstrates that deep learning surpasses traditional machine learning algorithms in identifying cyberbullying for several reasons, including handling extensive data, efficiently classifying text and images, extracting features automatically through hidden layers, …

Determinants Of Cloud Computing Integration And Its Impact On Sustainable Performance In Smes: An Empirical Investigation Using The Sem-Ann Approach, Mohammed A. Al-Sharafi, Mohammad Iranmanesh, Mostafa Al-Emran, Ahmed I. Alzahrani, Fadi Herzallah, Norziana Jamil

Research outputs 2022 to 2026

Although extant literature has thoroughly investigated the incorporation of cloud computing services, examining their influence on sustainable performance, particularly at the organizational level, is insufficient. Consequently, the present research aims to assess the factors that impact the integration of cloud computing within small and medium-sized enterprises (SMEs) and its subsequent effects on environmental, financial, and social performance. The data were collected from 415 SMEs and were analyzed using a hybrid SEM-ANN approach. PLS-SEM results indicate that relative advantage, complexity, compatibility, top management support, cost reduction, and government support significantly affect cloud computing integration. This study also empirically demonstrated that SMEs …