Digital Commons Network^™

Objective.Gg: Uniting Scholastic Esports, Douglas Beirne

Posters - 2024

Objective.gg is a startup recruitment platform within the scholastic esports scene that seeks to unite esports prospects with collegiate esports programs in an effective manner. Objective.gg seeks to accomplish its mission by operating an online platform that allows prospects and collegiate coaches to create their own profiles and connect with one another, building a community in the process. This platform will begin with a free tier, but additional features will be offered through a subscription-based model with two additional pricing tiers to choose from. The online platform will be supported by free online and paid in-person tournaments for Objective.gg platform …

Shutting Out Noise And Understanding Artificial Intelligence, Lauren J. Yu

Michigan Law Review

A review of Noise: A Flaw in Human Judgment. By Daniel Kahneman, Olivier Sibony and Cass R. Sunstein, and You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It’s Making the World a Weirder Place. By Janelle Shane.

Bidirectional Paper-Repository Tracing In Software Engineering, Daniel Garijo, Miguel Arroyo, Esteban González Guardia, Christoph Treude, Nicola Tarocco

Research Collection School Of Computing and Information Systems

While computer science papers frequently include their associated code repositories, establishing a clear link between papers and their corresponding implementations may be challenging due to the number of code repositories used in research publications. In this paper we describe a lightweight method for effectively identifying bidirectional links between papers and repositories from both LaTeX and PDF sources. We have used our approach to analyze more than 14000 PDF and Latex files in the Software Engineering category of Arxiv, generating a dataset of more than 1400 paper-code implementations and assessing current citation practices on it.

Githubinclusifier: Finding And Fixing Non-Inclusive Language In Github Repositories, Liam Todd, John Grundy, Christoph Treude

Research Collection School Of Computing and Information Systems

Non-inclusive language in software artefacts has been recognised as a serious problem. We describe a tool to find and fix non-inclusive language in a variety of GitHub repository artefacts. These include various README files, PDFs, code comments, and code. A wide variety of non-inclusive language including racist, ageist, ableist, violent and others are located and issues created, tagging the artefacts for checking. Suggested fixes can be generated using third-party LLM APIs, and approved changes made to documents, including code refactorings, and committed to the repository. The tool and evaluation data are available from: https://github. com/LiamTodd/github-inclusifier

Going Viral: Case Studies On The Impact Of Protestware, Youmei Fan, Dong Wang, Supastsara Wattanakriengkrai, Hathaichanok Damrongsiri, Christoph Treude, Hideaki Hata, Raula Gaikovina Kula

Research Collection School Of Computing and Information Systems

Maintainers are now self-sabotaging their work in order to take political or economic stances, a practice referred to as "protestware". In this poster, we present our approach to understand how the discourse about such an attack went viral, how it is received by the community, and whether developers respond to the attack in a timely manner. We study two notable protestware cases, i.e., Colors.js and es5-ext, comparing with discussions of a typical security vulnerability as a baseline, i.e., Ua-parser, and perform a thematic analysis of more than two thousand protest-related posts to extract the different narratives when discussing protestware.

Teaching Software Development For Real-World Problems Using A Microservice-Based Collaborative Problem-Solving Approach, Yi Meng Lau, Christian Michael Koh, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Experienced and skillful software developers are needed in organizations to develop software products effective for their business with shortened time-to-market. Such developers will not only need to code but also be able to work in teams and collaboratively solve real-world problems that organizations arefacing. It is challenging for educators to nurture students to become such developers with strong technical, social, and cognitive skills. Towards addressing the challenge, this study presents a Collaborative Software Development Project Framework for a course that focuses on learning microservices architectures anddeveloping a software application for a real-world business. Students get to work in teams to …

Digital Frontiers In Aesthetics: Applying Dewey's Insights To Generative Ai, Malcolm F. Lathrop-Allen

Student Publications

The last few years have seen the emergence of ‘artificially intelligent’ systems en masse, which perform tasks which had previously only been possible by human intelligence. Arguably, the impact of ‘AI 2.0’ has been felt most prominently in the art world — artists have panicked as DALL-E, Midjourney, and other image generation algorithms manufacture pieces which previously required weeks of painstaking labor to create. This project seeks to develop a more critical framework for this novel mode of artistic creation and propose better ways of thinking about, using, and “becoming with” artificial intelligence in the domain of artistry. The first …

Can Organizational Focus On Responsible Ai Lead To Improved Ai Adoption By Employees?, Seema Chokshi

Dissertations and Theses Collection (Open Access)

The duality inherent in Artificial Intelligence technology entails that while AI has the potential to bring about transformative benefits to organizations, unintended consequences of AI applications could lead to biased and discriminatory outcomes, which could have negative consequences for the organization and society in general. Concerns about such unintended consequences are an impediment to AI adoption where unwilling employees and practitioners often fear ethical breaches, thereby, negatively impacting their engagement with AI driven applications. In response to these concerns various organizations and regulatory bodies have developed governing frameworks broadly known as Responsible AI standards, that set guidelines to design, …

Enhancing Cybersecurity Awareness In The United Arab Emirates: An Assessment Of Current Practices And The Development Of An Ai-Enhanced Mobile Application, Meera Humaid Alalawi

Theses

In today's interconnected world, individuals, private corporations, public institutions, and governments face increasingly sophisticated cyber threats and attacks, highlighting the critical need for individuals and organizations to understand cybersecurity comprehensively. Cyberattacks have affected many countries and infrastructures in different sectors worldwide, including the United Arab Emirates (UAE), which has become a main target for cybercrime due to its booming economy and tourism. The UAE considers cybersecurity an increasingly critical issue in our digital world, and increasing cybersecurity awareness among residents is essential to protect themselves and their organizations from cyberattacks. The primary objectives of this study are to identify key …

A Trustworthy Self-Sovereign Data And Identity Management Framework, Efat Fathalla

Electrical & Computer Engineering Theses & Dissertations

Data is a fundamental building block in the digital world, providing a basis for decision making and growth across numerous applications. In our modern world, we have become accustomed to collecting data on everything, including devices, machines, and people. The increased value of such data has led to aggressive harvesting mechanisms that prioritize data collection, storage, and pervasiveness while often disregarding security, privacy concerns, and compliance with regulations and standards. Such a pervasive attitude towards data has resulted in a loss of control, prompting concerns among individuals and mobilizing the scientific community towards advocating for data self-sovereignty.

Self-Sovereign Identity (SSI) …

Performing Information Extraction For Mission Engineering Applications, Samuel R. Koski

Engineering Management & Systems Engineering Theses & Dissertations

The process of extracting structured data from unstructured and semi-structured text is manual, time consuming and error prone. Current natural language processing approaches for automating this process are difficult to verify for non-trivial and context-sensitive corpora. Large Language Models (LLMs) like ChatGPT have become a subject of considerable interest, opening a promising avenue of exploration. However, there is limited evidence on the performance of LLMs for information extraction.

In this dissertation, an approach is proposed to evaluate the accuracy of Stanford OpenIE and OpenAI's ChatGPT for this purpose. This includes comparing Resource Description Framework (RDF) triples extracted by each of …

Sentiment Analysis Of Online Healthy Community Based On Semantic Enhancement, Pu Han, Ye Dongyu

Journal of Scientific Information Research

[Purpose/significance]In order to make full use of the value of text dependent syntactic information and prior emotion knowledge in emotion analysis， a semantic enhanced online healthy community emotion analysis model was proposed. [Method/process]Firstly， feature vectors for pre-processed online health community data are generated by Word2Vec and BERT; then local and global information of online review text are extracted using TextCNN and BiLSTM respectively based on dual-channel idea; then sentiment knowledge and dependency grammar information are merged in graph attention networks for semantic enhancement; finally， dual-channel features are fused and perform online health community sentiment classification in fully connected layer. [Result/conclusion]The …

Filter-Based Stance Network For Rumor Verification, Jun Li, Yi Bin, Yunshan Ma, Yang Yang, Zi Huang, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Rumor verification on social media aims to identify the truth value of a rumor, which is important to decreasethe detrimental public effects. A rumor might arouse heated discussions and replies, conveying differentstances of users that could be helpful in identifying the rumor. Thus, several works have been proposedto verify a rumor by modelling its entire stance sequence in the time domain. However, these works ignorethat such a stance sequence could be decomposed into controversies with different intensities, which could beused to cluster the stance sequences with the same consensus. In addition, the existing stance extractors fail toconsider both the impact …

W4-Groups: Modeling The Who, What, When And Where Of Group Behavior Via Mobility Sensing, Akansha Atrey, Camellia Zakaria, Rajesh Krishna Balan, Prashant Shenoy

Research Collection School Of Computing and Information Systems

Human social interactions occur in group settings of varying sizes and locations, depending on the type of social activity. The ability to distinguish group formations based on their purposes transforms how group detection mechanisms function. Not only should such tools support the effective detection of serendipitous encounters, but they can derive categories of relation types among users. Determining who is involved, what activity is performed, and when and where the activity occurs are critical to understanding group processes in greater depth, including supporting goal-oriented applications (e.g., performance, productivity, and mental health) that require sensing social factors. In this work, we …

The Social Pot: A Social Media Application, Reid Long

ASPIRE 2024

The Social Pot is a web application that allows a user to post to Instagram and X simultaneously from one place. The user creates a Social Pot Account and from there can set their Instagram username and password within the home page. Once the user attempts to post, it will redirect them to login to X which once successful will make the tweet. Used the API 'instagram-private-api'. User needed to give access to my X Project which in turn gave an Auth token (via X redirect URL). The auth token was then sent to my endpoint in order to get …

Preserving Linguistic Diversity In The Digital Age: A Scalable Model For Cultural Heritage Continuity, James Hutson, Pace Ellsworth, Matt Ellsworth

Faculty Scholarship

In the face of the rapid erosion of both tangible and intangible cultural heritage globally, the urgency for effective, wide-ranging preservation methods has never been greater. Traditional approaches in cultural preservation often focus narrowly on specific niches, overlooking the broader cultural tapestry, particularly the preservation of everyday cultural elements. This article addresses this critical gap by advocating for a comprehensive, scalable model for cultural preservation that leverages machine learning and big data analytics. This model aims to document and archive a diverse range of cultural artifacts, encompassing both extraordinary and mundane aspects of heritage. A central issue highlighted in the …

Improving Medical Image Classification Accuracy Through Unsupervised Segmentation And Confounder Mitigation With Limited Data, Nikolai Fetisov

USF Tampa Graduate Theses and Dissertations

Medical images are indispensable for assisting health care professionals to make more accurate cancer diagnosis and prognosis decisions. Several image modalities exist including, but not limited to, histopathology or whole slide images (WSI), computed tomography (CT), positron emission tomography (PET) and radiography (i.e., X-Ray), each having their own application in clinical practice.

Today, machine learning and deep learning methods have evolved to the point of being practically usable. These approaches learn and extract knowledge from data to make possible automating certain tasks. At the point of writing this dissertation, they have reached human-level performance in general image recognition tasks, became …

An Automated Approach For Improving The Inference Latency And Energy Efficiency Of Pretrained Cnns By Removing Irrelevant Pixels With Focused Convolutions, Caleb Tung, Nick Eliopoulos, Purvish Jajal, Gowri Ramshankar, Chen-Yun Yang, Nicholas Synovic, Xuecen Zhang, Vipin Chaudhary, George K. Thiruvathukal, Yung-Hsiang Lu

Computer Science: Faculty Publications and Other Works

Computer vision often uses highly accurate Convolutional Neural Networks (CNNs), but these deep learning models are associated with ever-increasing energy and computation requirements. Producing more energy-efficient CNNs often requires model training which can be cost-prohibitive. We propose a novel, automated method to make a pretrained CNN more energy-efficient without re-training. Given a pretrained CNN, we insert a threshold layer that filters activations from the preceding layers to identify regions of the image that are irrelevant, i.e. can be ignored by the following layers while maintaining accuracy. Our modified focused convolution operation saves inference latency (by up to 25%) and energy …

The Impact Of Data Preparation And Model Complexity On The Natural Language Classification Of Chinese News Headlines, Torrey J. Wagner, Dennis Guhl, Brent T. Langhals

Faculty Publications

Given the emergence of China as a political and economic power in the 21st century, there is increased interest in analyzing Chinese news articles to better understand developing trends in China. Because of the volume of the material, automating the categorization of Chinese-language news articles by headline text or titles can be an effective way to sort the articles into categories for efficient review. A 383,000-headline dataset labeled with 15 categories from the Toutiao website was evaluated via natural language processing to predict topic categories. The influence of six data preparation variations on the predictive accuracy of four algorithms was …

Algorithmic Harms In Child Welfare: Uncertainties In Practice, Organization, And Street-Level Decision-Making, Devansh Saxena, Shion Guha

Health Services and Informatics Research

Algorithms in public services such as child welfare, criminal justice, and education are increasingly being used to make high-stakes decisions about human lives. Drawing upon findings from a two-year ethnography conducted at a child welfare agency, we highlight how algorithmic systems are embedded within a complex decision-making ecosystem at critical points of the child welfare process. Caseworkers interact with algo rithms in their daily lives where they must collect information about families and feed it to algorithms to make critical decisions. We show how the interplay between systemic mechanics and algorithmic decision making can adversely impact the fairness of the …

Challenges On Public Security System In Ai Era—Preface For Special Column “Artificial Intelligence And Public Security”, Juan Cao, Qiang Sheng, Guojie Li

Bulletin of Chinese Academy of Sciences (Chinese Version)

The rapid development of artificial intelligence generated content (AIGC) technology has triggered new public security risks, posing a serious threat to national security and social stability. This study exhibits the recent advances of artificial intelligence (AI) content generation and detection techniques, points out the challenges of detection techniques in real-world scenarios, and advocates that it is necessary to develop AIGC detection technology for public security needs and build a whole-process detection technology system from generative models to online platforms, which supports AIGC to be labeled at the generation phase, identifiable during dissemination and source-traceable after the incident occurs.

Using Chatgpt To Generate Gendered Language, Shweta Soundararajan, Manuela Nayantara Jeyaraj, Sarah Jane Delany

Conference papers

Gendered language is the use of words that denote an individual's gender. This can be explicit where the gender is evident in the actual word used, e.g. mother, she, man, but it can also be implicit where social roles or behaviours can signal an individual's gender - for example, expectations that women display communal traits (e.g., affectionate, caring, gentle) and men display agentic traits (e.g., assertive, competitive, decisive). The use of gendered language in NLP systems can perpetuate gender stereotypes and bias. This paper proposes an approach to generating gendered language datasets using ChatGPT which will provide data for data-driven …

Intelligent Algorithm Safety: Concepts, Scientific Problems And Prospects, Xueqi Cheng, Wei Chen, Huawei Shen, Shiguang Shan, Xilin Chen, Guojie Li

Bulletin of Chinese Academy of Sciences (Chinese Version)

Intelligent algorithms refer to the methods embodied in the computational processes that realize intelligence. These methods are often characterized by being data-driven, involving uncertain computations, and with unexplainable model inferences. These characteristics simultaneously introduce potential safety risks to the application of intelligent algorithms and AI. This study firstly explores the concepts of intelligent algorithm safety. Specifically, intelligent algorithm safety, based on the degree of human-machine integration, extends from the univariate safety of the algorithm itself to the bivariate applicational safety when the algorithm serves humans, and finally evolves into the multivariate systemic safety arises within complex socio-technical systems of human-machine …

On Global Security Governance’S New Trends And China’S Responses From Perspective Of Scientific And Technological Revolution, Haoguang Liang, Linman Wu, Zhujun Liu, Yaojun Zhang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Due to the new round of sci-tech revolution and industrial transformation, the relation between scientific progress and national security has been upgraded to an unprecedented strategic height. Therefore, it is vitally important to explore the current situation and future trajectory of the relation between the two. This study discusses the new trends between global scientific progress and the security of sovereign countries. It believes that scientific progress has catalyzed the new geopolitical contests, development contradiction and hegemonic tools in global security governance, thereby enlightening China’s approach to national security. To maintain holistic national security, China should formulate an overall strategic …

Mind-Iot: Machine Intelligence And Data-Mining For Iot Threats, Kwabena Aboagye-Otchere, Jorge Castillo

Informatics and Engineering Systems Faculty Publications

The use of electric motors drive numerous industries via robust mechanical and electromechanical systems. However, their ubiquitous presence across the industry makes them potential targets for cyber-attackers, raising serious privacy concerns. Prior studies have demonstrated the feasibility of accurately fingerprinting various devices using electromagnetic signals. Yet, the specialized equipment required for those methods remain complex and expensive. This study presents MIND-IoT, a novel approach that captures and analyzes the unintended magnetic emissions surrounding electric motors. Our method achieves precise fingerprinting of motors, and determines their operational parameters by leveraging low-cost IoT devices. By focusing solely on magnetic fields, MIND-IoT offers …

Advanced Hyperthermia Treatment: Optimizing Microwave Energy Focus For Breast Cancer Therapy, Burak Acar, Tuba Yilmaz Abdolsaheb, Ali Yapar

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents a fast antenna phase optimization scheme to enable microwave power focusing for breast cancer hyperthermia. The power focusing is achieved through the maximization of the deposited electric field on the target malignant tumor tissue. To do so, a malignant breast tumor, the surrounding breast medium, and the skin of the breast are modeled as a cylindrical structure composed of eccentric cylinders, and electric field distribution is computed analytically in terms of cylindrical harmonics. This approach minimized the computational cost and simplified the breast medium model. To ensure applicability across various breast types, the dielectric properties (DPs) of …

Predicting Gender Of Author Using Large Language Models (Llms), Satya Uday Sanku

USF Tampa Graduate Theses and Dissertations

The advent of text data from social media, blogs, movie reviews, and other textual sources has opened new avenues for research, particularly in the domain of Author Profiling. Author Profiling helps in Capturing the Stylistic features and also useful for analyzing the required elements in the written text. This Study addresses one of the tasks in Author Profiling which is termed as gender detection or Classification of Gender from Text. The main goal of this research is to obtain valuable and relevant gender characteristics that will accurately classify the Author’s gender of a review extracted from an Anime Review website. …

Reclaiming The Symbol: Ethics, Rhetoric, And The Humanistic Integration Of Gai - A Burkean Perspective, Daniel Plate, James Hutson

Faculty Scholarship

This study delves into the intersection of generative artificial intelligence (GAI) and the Humanities, guided by the critical insights of Kenneth Burke, a seminal figure in the study of rhetoric and a vocal critic of scientism and positivism. The skepticism of the American literary theorist towards an uncritical embrace of science and technology, and his concerns over the inclination of the Humanities to adopt scientific methodologies at the expense of traditional forms of inquiry, provide a critical framework for examining the new role played by GAI within the Humanities. By framing these tools in the context of Burkean rhetorical theory, …

“It Is Luring You To Click On The Link With False Advertising” - Mental Models Of Clickbait And Its Impact On User’S Perceptions And Behavior Towards Clickbait Warnings, Ankit Shrestha, Arezou Behfar, Mahdi Nasrullah Al-Ameen

Computer Science Student Research

Clickbait, a social engineering attack performed through social media, tricks users through sensationalized or misleading posts into clicking on links that direct them to malicious websites. With the recent boom in social media, clickbait has become a substantial security concern, necessitating efforts from platforms and academia to control it. Despite these attempts, clickbait is effective due to the lack of users’ knowledge. Therefore, we explore user mental models (thought processes about how something works) about clickbait to analyze their deficiencies and their influences on users’ behavior towards clickbait warnings. To this end, we conducted an online study with 770 participants …

Exploring The Design Of Low-End Technology To Increase Patient Connectivity To Electronic Health Records, Rens Kievit, Abdullahi Abubakar Kawu, Mirjam Van Reisen, Dympna O'Sullivan, Lucy Hederman

Conference papers

The tracking of the vitals of patients with long term health problems is essential for clinicians to determine proper care. Using Patient Generated Health Data (PGHD) communicated remotely allows patients to be monitored without requiring frequent hospital visits. Issues might arise when the communication of data digitally is difficult or impossible due to a lack of access to internet or a low level of digital literacy as is the case in many African countries. The VODAN-Africa project (van Reisen et al., 2021) started in 2020 and has greatly increased the capabilities of clinics in different countries in both Africa and …