Computer Sciences Commons^™

Towards An Improved Understanding Of The Concept Of Style And Its Implications For Textual Style Transfer, Somayeh Jafaritazehjani

Doctoral

The concept of linguistic style denotes that many aspects of text can vary while maintaining a same source core semantic meaning. For example, a message may be written in a formal or informal style. The textual style transfer problem aims at generating a paraphrase of a given text by modifying its style while preserving its content. To the best of our knowledge, within the literature on textual style transfer, there is no standard widely accepted definition of the concept of style. Moreover, very few works have investigated the characteristics of language styles. Therefore, previous research, as far as our knowledge …

More Human Than Human: Llm-Generated Narratives Outperform Human-Llm Interleaved Narratives, Zoie Zhao, Sophie Song, Bridget Duah, Jamie C. Macbeth, Scott Carter, Monica Van, Nayeli Bravo, Matthew Klenk, Katherine Sieck, Alexandre Filipowicz

Computer Science: Faculty Publications

Narrative story generation has gained emerging interest in the field of large language models. The present paper aims to compare stories generated by an LLM only (non-interleaved) with those generated by interleaving human-generated and LLM-generated text (interleaved). The study’s hypothesis is that interleaved stories would perform better than non-interleaved stories. To verify this hypothesis, we conducted two tests with roughly 500 participants each. Participants were asked to rate stories of each type, including an overall score or preference and four facets—logical soundness, plausibility, understandability, and novelty. Our findings indicate that interleaved stories were in fact less preferred than non-interleaved stories. …

Bifurcation Levels Of The Integral Manifolds Of The Newtonian N-Body Problem, Hannah G. Havel

CURE Proceedings

The N-body problem, first proposed by Isaac Newton, is a field of study in mathematics and physics that involves predicting the motion of particles moving under their mutual gravitational attraction. It has significance to many areas of science, including physics and computer science, and is crucial in understanding how the universe works. In fact, it was a primary motivation for Newton's development of calculus. An important application of the N-body problem is within celestial mechanics and involves how planets and other celestial bodies move with mutual gravitational attraction. It is important in developing how satellites behave in space using complicated …

Dynamic Function Learning Through Control Of Ensemble Systems, Wei Zhang, Vignesh Narayanan, Jr-Shin Li

Publications

Learning tasks involving function approximation are preva- lent in numerous domains of science and engineering. The underlying idea is to design a learning algorithm that gener- ates a sequence of functions converging to the desired target function with arbitrary accuracy by using the available data samples. In this paper, we present a novel interpretation of iterative function learning through the lens of ensemble dy- namical systems, with an emphasis on establishing the equiv- alence between convergence of function learning algorithms and asymptotic behavior of ensemble systems. In particular, given a set of observation data in a function learning task, we …

Role-Reversibility, Ai, And Equitable Justice — Or: Why Mercy Cannot Be Automated, Stephen E. Henderson, Kiel Brennan-Marquez

Faculty Articles

A few years ago, we developed the concept of “role-reversibility” in AI governance: the idea that it matters whether a party exercising judgment is reciprocally vulnerable to the effects of judgment. This idea, we argued, supplies a deontic reason to maintain certain spheres of human judgment even if (or when) truly intelligent machines become demonstrably superior in every utilitarian sense. While computer science remains far from that holy grail, generative AI is raging through systems as diverse as healthcare, finance, advertising, law, and academe, making it imperative to further shore up our claim. We do so by situating role-reversibility within …

When Chatgpt Goes Rogue: Exploring The Potential Cybersecurity Threats Of Ai-Powered Conversational Chatbots, Farkhund Iqbal, Faniel Samsom, Faouzi Kamoun, Áine Macdermott

All Works

ChatGPT has garnered significant interest since its release in November 2022 and it has showcased a strong versatility in terms of potential applications across various industries and domains. Defensive cybersecurity is a particular area where ChatGPT has demonstrated considerable potential thanks to its ability to provide customized cybersecurity awareness training and its capability to assess security vulnerabilities and provide concrete recommendations to remediate them. However, the offensive use of ChatGPT (and AI-powered conversational agents, in general) remains an underexplored research topic. This preliminary study aims to shed light on the potential weaponization of ChatGPT to facilitate and initiate cyberattacks. We …

Lstda: Link Stability And Transmission Delay Aware Routing Mechanism For Flying Ad-Hoc Network (Fanet), Farman Ali, Khalid Zaman, Babar Shah, Tariq Hussain, Habib Ullah, Altaf Hussain, Daehan Kwak

All Works

The paper presents a new protocol called Link Stability and Transmission Delay Aware (LSTDA) for Flying Ad-hoc Network (FANET) with a focus on network corridors (NC). FANET consists of Unmanned Aerial Vehicles (UAVs) that face challenges in avoiding transmission loss and delay while ensuring stable communication. The proposed protocol introduces a novel link stability with network corridors priority node selection to check and ensure fair communication in the entire network. The protocol uses a Red-Black (R-B) tree to achieve maximum channel utilization and an advanced relay approach. The paper evaluates LSTDA in terms of End-to-End Delay (E2ED), Packet Delivery Ratio …

Explainable Machine Learning For Evapotranspiration Prediction, Bamory Koné, Rima Grati, Bassem Bouaziz, Khouloud Boukadi

All Works

No abstract provided.

Machine Learning Predictions Of Electricity Capacity, Marcus Harris, Elizabeth Kirby, Ameeta Agrawal, Rhitabrat Pokharel, Francis Puyleart, Martin Zwick

Complex Systems Faculty Publications and Presentations

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This …

Multivariate Regression And Variance In Concrete Curing Methods: Strength Prediction With Experiments, Haiyan Sally Xie, Sai Ram Gandla, Owen Shi, Pranshoo Solanki

Faculty Publications – Technology

Because concrete strengths and quality are affected by various factors, multivariate regression models are often used to analyze the differences between predicted and target outputs. However, the variableness of a predicted output and how individual input parameters affect prediction reliabilities are still uncertain in practical applications, especially for the prediction of compressive strengths of concrete. This study aims to develop multivariate models for predicting concrete strengths and providing the variance analysis of prediction results by comparisons with experiment outcomes. First, this paper provides an in-depth examination of established variance analysis methods in the context of commonly used multivariate regression models. …

Peer-To-Peer Energy Trading In Smart Residential Environment With User Behavioral Modeling, Ashutosh Timilsina

Theses and Dissertations--Computer Science

Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid.

Trading energy among users in a decentralized fashion has been referred …

General-Purpose Planning Algorithms In Partially-Observable Stochastic Games, Bryan Mckenney

Honors Theses and Capstones

Partially observable stochastic games (POSGs) are difficult domains to plan in because they feature multiple agents with potentially opposing goals, parts of the world are hidden from the agents, and some actions have random outcomes. It is infeasible to solve a large POSG optimally. While it may be tempting to design a specialized algorithm for finding suboptimal solutions to a particular POSG, general-purpose planning algorithms can work just as well, but with less complexity and domain knowledge required. I explore this idea in two different POSGs: Navy Defense and Duelyst.

In Navy Defense, I show that a specialized algorithm framework, …

Architectural Design Of A Blockchain-Enabled, Federated Learning Platform For Algorithmic Fairness In Predictive Health Care: Design Science Study, Xueping Liang, Juan Zhao, Yan Chen, Eranga Bandara, Sachin Shetty

VMASC Publications

Background: Developing effective and generalizable predictive models is critical for disease prediction and clinical decision-making, often requiring diverse samples to mitigate population bias and address algorithmic fairness. However, a major challenge is to retrieve learning models across multiple institutions without bringing in local biases and inequity, while preserving individual patients' privacy at each site.

Objective: This study aims to understand the issues of bias and fairness in the machine learning process used in the predictive health care domain. We proposed a software architecture that integrates federated learning and blockchain to improve fairness, while maintaining acceptable prediction accuracy and minimizing overhead …

Digital Transformation, Applications, And Vulnerabilities In Maritime And Shipbuilding Ecosystems, Rafael Diaz, Katherine Smith

VMASC Publications

The evolution of maritime and shipbuilding supply chains toward digital ecosystems increases operational complexity and needs reliable communication and coordination. As labor and suppliers shift to digital platforms, interconnection, information transparency, and decentralized choices become ubiquitous. In this sense, Industry 4.0 enables "smart digitalization" in these environments. Many applications exist in two distinct but interrelated areas related to shipbuilding design and shipyard operational performance. New digital tools, such as virtual prototypes and augmented reality, begin to be used in the design phases, during the commissioning/quality control activities, and for training workers and crews. An application relates to using Virtual Prototypes …

Efficient Maritime Object Detection And Validation For Enhancing Safety Of Uncrewed Marine Systems, Ahmed Saglam, Yiannis Papelis

VMASC Publications

Safe operation of uncrewed maritime systems is a major concern in the presence of other vehicles or obstacles. Typically, perception algorithms utilize sensor data to identify obstacles that must be avoided, and AI algorithms are used to interpret raw sensor data for use in navigation and object avoidance algorithms. However, perception algorithms are typically computationally expensive. In this paper, we present an efficient method for detecting obstacles using raw lidar data in the form of range or Point Cloud, employing computationally efficient techniques that do not depend on trained models or AI matching. The approach
converts the sensor readings into …

The Implementation Of Augmented Reality And Low Latency Protocols In Musical Instrumental Collaborations, Qixiao Zhu

Honors Theses

Past projects involving musical software have been completely virtual, while these software do well in entertainment and education, there is the question of whether these software are playable to the same extent as physical musical instruments. The software presented in this paper, "AR Jam", utilizes various software and hardware tools to form a networked mixed reality system for the users to play music on. The intention of this project is to seek new ways to explore more playable musical instruments in the digital world. The paper presents the software's implementation, challenges such as optimization problems of the synthesizer, and the …

Object Detection And Image Categorization By Transferring Commonsense Knowledge With Premises And Quantifiers, Irina Chernyavsky

Theses, Dissertations and Culminating Projects

Domestic, or household robots, are autonomous robots designed to make our home-life easier by performing chores and mundane tasks such as cleaning, or cooking. Currently domestic robots are specialized to complete a specific task and, therefore, are confined by factors such as mobility, size, and complexity. With the fast development of computer vision and robotics, the need for more compact, advanced and multi-task robots has emerged. Therefore, the robot needs to be multi-functional, able to discern the environment and the tasks. The aim of this paper is to categorize images in domestic robots as relevant to the culinary, laundry, vacuum …

Crosshair Optimizer, Jason Torrence

All Master's Theses

Metaheuristic optimization algorithms are heuristics that are capable of creating a "good enough'' solution to a computationally complex problem. Algorithms in this area of study are focused on the process of exploration and exploitation: exploration of the solution space and exploitation of the results that have been found during that exploration, with most resources going toward the former half of the process. The novel Crosshair optimizer developed in this thesis seeks to take advantage of the latter, exploiting the best possible result as much as possible by directly searching the area around that best result with a stochastic approach. This …

The Potential And Limitations Of Conversational Agents For Chronic Conditions And Well-Being, Ekaterina Uetova, Lucy Hederman, Robert J. Ross, Dympna O'Sullivan

Articles

Conversational agents are becoming more common in the health and wellness domains in part due to assumptions regarding potential improvements in individuals’ outcomes. This paper presents initial findings from a review of conversational agent use in healthcare for chronic conditions and well-being. A search of the literature was performed on electronic databases PubMed, ACM Digital Library, Scopus and IEEE Xplore. Studies were included if they were focused on chronic disorder management, disease prevention or lifestyle change and if systems were tested on target user groups. This paper investigates the health domains, the user profiles and reasons why conversational agents may …

Clusters, Curves, And Centroids: Stellar Flare Morphology In The Ultraviolet, Vera Berger

Pomona Senior Theses

With a novel sample of 495 high-cadence light curves for stellar flares in the near-ultraviolet, I explore similarity measures, clustering algorithms, averaging methods, and curve fitting techniques for time series. This work seeks to provide insight into whether stellar flares are similar across stars, if we can identify physically meaningful patterns in their light curves, and how to construct a comprehensive model for flares. I construct the first empirical template for flare light curves in the ultraviolet, and compute ``average elements" of flares displaying complex features such as quasi-periodic oscillations and multipeak structures. Developing accurate models for flares in the …

Strategies Information Technology Leaders Used In Implementing Remote Work During The Covid-19, Jouliana Kamal Barghouth

Walden Dissertations and Doctoral Studies

With the imposed lockdown in many countries due to the spread of COVID-19, many leaders were forced to adopt online technologies in transitioning employees to remote work. Leaders not adopting online technologies or remote work during a pandemic are highly susceptible to business closure. Grounded in the technology acceptance model, the purpose of this qualitative single case study was to explore strategies information technology (IT) leaders in Kuwait used to successfully transition employees to remote work during the pandemic. The participants were six IT leaders from a single multinational IT organization who contributed to strategy development during the pandemic to …

Developing Consensus On The Use Of Emotional Intelligence Training In Small Utility Companies, Nathaniel E. Holloway

Walden Dissertations and Doctoral Studies

Park and Shaw shared the impact on organizations from unmotivated and unsatisfied employees link to higher turnover ratios. The use of emotional intelligence in manager training lowered employee turnover by 13%. The problem address in this Delphi study was that small utility companies do not have an emotional intelligence plan in place for managerial training. Goleman and Mayer’s framework was used as the theorical lens for examining response of participants to the Delphi study. A panel of experts submitted data in the form of responses to three rounds of questions regarding the use of emotional intelligence training in small businesses. …

Distinctions Between Choroidal Neovascularization And Age Macular Degeneration In Ocular Disease Predictions Via Multi-Size Kernels Ξcho-Weighted Median Patterns, Alex Liew, Sos Agaian, Samir Benbelkacem

Publications and Research

Age-related macular degeneration is a visual disorder caused by abnormalities in a part of the eye’s retina and is a leading source of blindness. The correct detection, precise location, classification, and diagnosis of choroidal neovascularization (CNV) may be challenging if the lesion is small or if Optical Coherence Tomography (OCT) images are degraded by projection and motion. This paper aims to develop an automated quantification and classification system for CNV in neovascular age-related macular degeneration using OCT angiography images. OCT angiography is a non-invasive imaging tool that visualizes retinal and choroidal physiological and pathological vascularization. The presented system is based …

Fabrication And Errors In The Bibliographic Citations Generated By Chatgpt, William H. Walters, Esther Isabelle Wilder

Publications and Research

Although chatbots such as ChatGPT can facilitate cost-effective text generation and editing, factually incorrect responses (hallucinations) limit their utility. This study evaluates one particular type of hallucination: fabricated bibliographic citations that do not represent actual scholarly works. We used ChatGPT-3.5 and ChatGPT-4 to produce short literature reviews on 42 multidisciplinary topics, compiling data on the 636 bibliographic citations (references) found in the 84 papers. We then searched multiple databases and websites to determine the prevalence of fabricated citations, to identify errors in the citations to non-fabricated papers, and to evaluate adherence to APA citation format. Within this set of documents, …

Design And Development Of Hybrid Spectrum Access Technique For Cr-Iot Network, Sandeep Singh

Mesopotamian Journal of Computer Science

The radio spectrum is an underutilized natural resource with significant untapped potential. The rapid proliferation of Internet of Things (IoT) devices is driving a dramatic increase in radio spectrum demand. These devices can utilize cognitive radio (CR) technology to access the bandwidth left unused by licensed spectrum users, also known as primary users (PUs), to meet end users spectrum requirements efficiently. However, because PUs are given priority, CR-enabled Internet of Things (CR-IoT) devices, also known as secondary users (SU), must frequently communicate with one another in an opportunistic manner or under stringent power constraints. This can complicate CR-IoT device communication, …

The Ethical Implications Of Dall-E: Opportunities And Challenges, Kai-Qing Zhou, Hatem Nabus

Mesopotamian Journal of Computer Science

Artificial intelligence (AI) images, like those produced by DALL-E, have seen explosive growth in the past several years and have the potential to disrupt numerous markets. While the technology offers exciting opportunities for creativity and innovation, it also raises important ethical considerations that must be addressed. These ethical implications include issues related to bias and discrimination, privacy, job displacement, and unintended consequences. To mitigate these challenges, a multi-disciplinary approach is needed, including the development of effective regulations and governance frameworks, the creation of unbiased algorithms, responsible data management practices, and educational and training programs. Additionally, encouraging ethical discussions and debates …

User Preferences For Chatgpt-Powered Conversational Interfaces Versus Traditional Methods, Tam Sakirin, Rachid Ben Said

Mesopotamian Journal of Computer Science

This study examined user preferences for ChatGPT-powered conversational interfaces vs traditional techniques. The study collected data from 175 selected volunteers utilizing a survey questionnaire. Descriptive and inferential statistics were used to detect user preferences and compare them to the literature review. The study found that 70% of users chose ChatGPT-powered conversational interfaces over traditional techniques, citing convenience, efficiency, and personalization. Demographic data was explored. The participants were evenly distributed between male and female (50%) and aged 18 to 55 (mean = 35 years). This study affects ChatGPT and conversational AI development. The results indicate that users want to use these …

Predicting Carbon Dioxide Emissions With The Orange Application: An Empirical Analysis, Israa Ezzat, Alaa Wagih Abdulqader

Mesopotamian Journal of Computer Science

The effects of climate change, such as droughts, storms, and extreme weather, are increasingly being felt around the world. Greenhouse gases are the primary contributors to climate change, with carbon dioxide (CO2) being the most significant. In fact, CO2 accounts for a significant percentage of all greenhouse gas emissions. As a result, reducing CO2 emissions has become a critical priority for mitigating the impacts of climate change and preserving our planet for future generations. Based on simulation and data mining technologies that use historical data, CO2 is expected to continue to rise. Around the world, 80% of CO2 emissions come …

The Evolving Role Of Artificial Intelligence In The Future Of Distance Learning: Exploring The Next Frontier, Maad M. Mijwil, Guma Ali, Emre Sadıkoğlu

Mesopotamian Journal of Computer Science

In recent years, education has become especially related to the applications provided by artificial intelligence technology through a digital environment that includes a set of tools that assist in processing and storing information. Artificial intelligence techniques contribute to the development of students' skills by providing them with advanced scientific content and building their mental capabilities faster. Moreover, these techniques support analysing student data and suggest suitable educational materials and activities for them. Artificial intelligence is a noteworthy tool for the growth of distance education, especially after the development of expert systems that have become a human advisor in many domains, …

Clustering Algorithms And Comparisons In Vehicular Ad Hoc Networks, Radhakrishna Karne, Sreeja Tk

Mesopotamian Journal of Computer Science

Vehicular Ad hoc Network (VANET) is a new era in the transmission of dynamic information across communities. Intelligent Transportation Systems is only one of the many applications for VANET (ITS). The topology of VANET is extremely dynamic, and connections are irregular. These features cause information transmission in the VANET to be unreliable. Vehicle clustering is a successful strategy to increase the network's scalability and connection dependability. Characteristics of the VANET have an impact on clustering performance as well. An extensive explanation of VANET clustering algorithms is given in this article. A complete evaluation of clustering in VANETs is provided based …