Computer Sciences Commons^™

A Surface-Based Approach For 3d Approximate Convex Decomposition, Onat Zeybek Kuşkonmaz, Yusuf Sahi̇lli̇oğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Approximate convex decomposition enables the simplification of complex shapes into manageable convex components. In this work, we propose a novel surface-based method to achieve this which leads to efficient computation times and sufficiently convex results while avoiding over-approximating the input model. We start approximation using mesh simplification. Then we iterate over the surface polygons of the mesh and divide them into convex groups. We utilize planar and angular equations to determine suitable neighboring polygons for inclusion in forming convex groups. To ensure our method outputs a sufficient result for a wide range of input shapes, we run multiple iterations of …

Developing Linguistic Patterns To Mitigate Inherent Human Bias In Offensive Language Detection, Toygar Tanyel, Besher Alkurdi, Serkan Ayvaz

Turkish Journal of Electrical Engineering and Computer Sciences

With the proliferation of social media, there has been a sharp increase in offensive content, particularly targeting vulnerable groups, exacerbating social problems such as hatred, racism, and sexism. Detecting offensive language use is crucial to prevent offensive language from being widely shared on social media. However, the accurate detection of irony, implication, and various forms of hate speech on social media remains a challenge. Natural language-based deep learning models require extensive training with large, comprehensive, and labeled datasets. Unfortunately, manually creating such datasets is both costly and error-prone. Additionally, the presence of human-bias in offensive language datasets is a major …

A Cascade Genetic Algorithm Based Adaptive Backstepping Impedance Control For Upper Limb Rehabilitation Robot, Mawloud Aichaoui, Ameur Ikhlef

Turkish Journal of Electrical Engineering and Computer Sciences

This paper proposes a novel cascade impedance control architecture designed for the upper limb exoskeleton rehabilitation robot. The proposed architecture comprises two parts: Firstly, the impedance reference trajectory is shaped from the desired trajectory utilizing the desired impedance model and feedback contact torques. The second part of the proposed controller is an adaptive backstepping control, responsible for tracking the generated impedance reference trajectory. Notably, the proposed adaptive backstepping impedance controller is non-model-based control approach, eliminating the need for the robot's model. Furthermore, a genetic algorithm is employed as an offline tuning method for the inner position loop controller, namely the …

Lgformer: Informer-Based Personalized Modeling For Blood Glucose Prediction, Xue Yuewei, Shaopeng Guan, Jia Wanhai

Turkish Journal of Electrical Engineering and Computer Sciences

Effective diabetes management relies on precise prediction of blood glucose levels to minimize complications. However, the patterns and fluctuations in blood glucose vary significantly among patients, posing a challenge for existing prediction methods. Many current approaches fail to accommodate these individual differences, leading to less reliable predictions. In response to this challenge, we present LGformer, a novel prediction model based on the Informer architecture, designed to enhance both flexibility and accuracy. LGformer improves upon Informer by integrating LSTM and GRU layers into its probSparse Self-attention mechanism, allowing for personalized processing of blood glucose data tailored to each patient's unique profile. …

Study On The Seasonal Endobacterial Profile Of Avicennia Officinalis, Focusing On Core Bacterial Consortia Within Mangrove Pneumatophores, Devi R R, Vinod Prabhakar

Karbala International Journal of Modern Science

Microhabitats are smaller pockets of ecosystems with unique conditions and they serve as major contributors of species richness. Mangrove plants offer a diverse array of such microhabitats (rhizosphere, phyllosphere, endosphereetc), and the inhabitant bacteria benefit the host by providing better salinity tolerance, faster and better nutrient mobilization, protection from phytopathogens and assistance in seed germination. Identification of such beneficial bacteria,their growth requirementsandmetabolism, willhave direct application in the field of agriculture and ecosystem restoration. Hence in this study, effort has been made to elucidate the seasonal numerical and compositional profile of pneumatophore bacterial consortia of Avicennia officinalis, from the mangrove …

A Systematic Review Of The Effects Of Ai-Assisted Moderation On Individuals And Groups, Zehui Yu, Lukas Otto, Dennis Assenmacher, Claudia Wagner

Human-Machine Communication

This review paper provides a conceptualization of AI-assisted content moderation with various degrees of autonomy and summarizes experimental evidence for how different levels of automation in content moderation and related losses of autonomy affect individuals and groups. Our results show that current research predominantly focuses on individuallevel effects, necessitating a shift toward understanding the impact on groups. The study highlights gaps in exploring different levels of AI-assisted moderation interventions and misalignments of different conceptualizations that make comparing research results difficult. The discussion underscores the prevailing emphasis on harmful content removal and advocates for investigating more constructive moderation techniques, emphasizing the …

Development Of A Web-Based Information System For Student Leave Permission At Dar Al-Raudhah Islamic Boarding School: Iso Quality Standards Analysis, Bonita Destiana, Priyanto Priyanto, Rahmatul Irfan, Muhammad Gus Khamim, Muhammad Yusuf Ridlo, Muhammad Iqbal

Elinvo (Electronics, Informatics, and Vocational Education)

Dar Al-Raudhah Entrepreneur, Islamic Boarding School, has adopted digital technology by upgrading hardware and software also investing in reliable internet infrastructure. However, this school still faces issues with students’ leave permission process due to reliance on manual bookkeeping and Excel, which leads to potential errors. Based on those problems, this research aims to create a web-based student leave permission system called SIPERSAN. The SIPERSAN system was developed with a Waterfall development model, which includes requirements analysis, design, implementation, testing, and deployment. The database is managed with MySQL, and the system is developed using PHP with the Laravel framework. Based on …

Relationship-Influenced Cyber Hygiene (Rich) In Community Banks, Monte L. Ward

USF Tampa Graduate Theses and Dissertations

This dissertation is a research study that introduces the theoretical model of Relationship-Influenced Cyber Hygiene (RICH) through its investigation of the phenomenon the researcher experienced: how the interpersonal relationship between top management and cybersecurity personnel within smaller community banks influences the cyber hygiene of top management. Due to the value proposition of community banks to their clients and the limited budget of smaller institutions for investment in cybersecurity controls and initiates, community banks need additional strategies to achieve stronger cyber hygiene, especially as it relates the greatest weakness in most cybersecurity infrastructures—the human element. This study identifies and addresses a …

Designing Customized Loss Functions For Training Deep Neural Networks, Ali Pourramezan Fard

Electronic Theses and Dissertations

This dissertation explores the critical role of loss functions in enhancing the predictive performance of deep machine learning models. Loss functions are an integral element of all the ongoing advances we witness daily in this domain. I design custom loss functions and their impacts on various machine learning tasks, particularly in computer vision.

In the first stage of my research, I aim to improve the prediction performance of deep learning models by providing them with more precise feedback associated with task requirements. This led me to create the concept of assistive loss functions. My first proposed loss function, inspired by …

An Overview Of Generative Ai Initiatives At Minnesota State University, Mankato (So Far), Evan Rusch, Nat Gustafson-Sundell

Library Services Publications

At Minnesota State University, Mankato, we’ve undertaken several experiments and initiatives focused on Generative Artificial Intelligence. We provided several examples at the Generative AI in Libraries (GAIL) conference. For this presentation, we provided a revised and expanded overview of our initiatives for the Northern Ohio Technical Services Librarians (NOTSL) Fall General Meeting. We explained license-related restrictions on uses of AI. We discussed the limitations of the retrieval-augmented generation tools currently available in the library. We summarized how we’ve tested ChatBots to support licensing and we showed how we’ve tried to use AI to improve data visualization for collections outreach. We …

Security Vulnerabilities In Mobile Operating Systems Used In Iot Devices: An Examination Of Current Challenges And Countermeasures, Isain Cortes Jr.

Cybersecurity Undergraduate Research Showcase

No abstract provided.

Autonomous Driving Trajectory Prediction, Carlos Funes

Undergraduate Research Symposium Lightning Talks

Autonomous driving is undoubtedly one of the world's most revolutionary technologies, opening the door to a more secure traffic environment. This innovation has led to vehicles being able to drive by themselves without the necessity of a person behind the wheel, as well as cruise control, lane-keeping assist, and automatic emergency braking. Unfortunately, there is still plenty of work before autonomous driving becomes more popular among drivers. While at UNLV as an undergraduate student/research assistant, one of my goals is to learn how these technologies work to bring ideas into the automotive industry by refining solutions to problems within these …

It's Not As Bad As You Think: Detecting Ai-Generated Voices, Yong Qin Xu

Undergraduate Research Symposium Lightning Talks

Advances in machine learning have opened up the world to a brand new frontier of fraudulent phone calls which the average person may not be in any way prepared for. From imitations of a loved one's voice to lifelike mimicry of human callers, telephone scams may become harder than ever to anticipate or prevent now that criminals have the help of AI on their side. This is why in my research paper, I aim to analyze and compare two existing methods of detecting the authenticity of human voice recordings in order to demonstrate and explain currently available technology that's capable …

Utilizing A Hybrid Apprach To Link Maternal And Neonatal Records, Vidhani S. Goel, Ana Reyes, Bertille Assoumou, Dodds P. Simangan, Farooq Abdulla, Megumi Akiyama, Deborah A. Kuhls, Kavita Batra

Undergraduate Research Symposium Posters

Linkage of independent datasets allows comprehensive and robust analysis. This study aims to utilize a hybrid strategy to link maternal records with neonatal data with an overarching goal of investigating correlates of adverse birth outcomes.

To link 126,757 records from Nevada Medicaid with 249,181 maternal records from Birth Registry, a hybrid linkage approach was utilized. Data normalization was first performed for the standardization of linkage keys. First, a deterministic approach was used to link these records using a unique identifier followed by a fuzzy or probabilistic algorithm using a set of block variables. These block variables included date of birth, …

Applying Machine‐Learning Methods To Laser Acceleration Of Protons: Lessons Learned From Synthetic Data, Ronak Desai, Thomas Zhang, J. J. Felice, Ricky Oropeza, Joseph R. Smith, Alona Kryshchenko, Chris Orban, Michael L. Dexter, Anil K. Patnaik

Faculty Publications

In this study, we consider three different machine-learning methods—a three-hidden-layer neural network, support vector regression, and Gaussian process regression—and compare how well they can learn from a synthetic data set for proton acceleration in the Target Normal Sheath Acceleration regime. The synthetic data set was generated from a previously published theoretical model by Fuchs et al. 2005 that we modified. Once trained, these machine-learning methods can assist with efforts to maximize the peak proton energy, or with the more general problem of configuring the laser system to produce a proton energy spectrum with desired characteristics. In our study, we focus …

Exploring Customer Behavior Through Emotion Detection, Darlington Omoifo

Master's Theses

This thesis investigates the potential of emotion detection, gaze tracking, and hand movement analysis as tools to enhance understanding of customer behavior in retail environments. In retail, subtle indicators such as facial expressions, gaze direction, and hand gestures provide valuable insights into customer preferences and decision-making processes. The study leverages real-time video processing with a multithreaded approach on a Raspberry Pi-powered system embedded in a gift cart equipped with dual cameras. Using FER for emotion detection and a custom model for gaze and hand tracking, this research identifies customer engagement patterns, focusing on how emotions like happiness correlate with purchasing …

Towards A Comprehensive Metaverse Forensic Framework Based On Technology Task Fit Model, Amna Almutawa, Richard Adeyemi Ikuesan, Huwida Said

All Works

This article introduces a robust metaverse forensic framework designed to facilitate the investigation of cybercrime within the dynamic and complex digital metaverse. In response to the growing potential for nefarious activities in this technological landscape, the framework is meticulously developed and aligned with international standardization, ensuring a comprehensive, reliable, and flexible approach to forensic investigations. Comprising seven distinct phases, including a crucial incident pre-response phase, the framework offers a detailed step-by-step guide that can be readily applied to any virtualized platform. Unlike previous studies that have primarily adapted the existing digital forensic methodologies, this proposed framework fills a critical research …

Holistic Correlation Measure For Enhanced Encapsulation Of Trait Heterogeneity And Discovery Of Co-Expression, Zachary Valleroy

Theses

Large-scale, high-dimensional data analyses can be computationally prohibitive due to combinatorial explosion of the search space for finding complex patterns; a viable alternative is network modeling for abstraction and quantifying intrinsic data associations. Prominent network analysis methods furnish frameworks for model synthesis and validation but rely on standard correlation measures impaired by semi-supervised biases, latent heterogeneity, and uneven discretization techniques. Here we investigate a holistic measure for encapsulating data heterogeneity for enhanced efficacy of revealing complex patterns through network analysis. Our unique correlation metric, K-medoids Utility for Duo Original Similarities (Kudos), exhaustively factors real-valued analyte data to compute …

Vision-Language Integration For Enhanced Locomotion Mode Prediction, Ehsan Ahmadi

LSU Master's Theses

Wearable exoskeletons offer significant potential in enhancing human mobility in industrial environments. However, their adaptability to dynamic, task-intensive settings presents challenges, especially in accurately predicting locomotion modes such as ladder climbing, stair navigation, low-space movement, and obstacle navigation. This research proposes a multimodal framework that integrates visual data and speech commands to improve locomotion mode prediction in unpredictable environments. Multimodal data was collected using smart glasses, capturing both the user’s perspective (field-of-view, FOV) and voice during locomotion tasks. State-of-the-art models—CLIP, ImageBind, and GPT-4o—process these visual and linguistic inputs to predict locomotion activities. The models were evaluated in zero-shot and fine-tuned …

Smartphone Haptics Can Uncover Differences In Touch Interactions Between Asd And Neurotypicals, Ivonne Monarca, Franceli L. Cibrian, Isabel López Hurtado, Monica Tentori

Engineering Faculty Articles and Research

Utilizing touch interactions from smartphones for gathering data and identifying digital markers for screening and monitoring neurological disorders, such as Autism Spectrum Disorder (ASD), is an emerging area of research. Smartphones provide multiple benefits for this kind of study, including unobtrusive data collection via built-in sensors, integrated haptic feedback systems, and the capability to create specialized applications. Acknowledging the significant yet understudied presence of tactile processing differences in individuals with ASD, we designed and developed Feel and Touch, a mobile game that leverages the haptic capabilities of smartphones. This game provides vibrotactile feedback in response to touch interactions and collects …

Medilink: A Secure Blockchain Framework For Multi-Institutional Healthcare, Jorge Castillo, Qian Chen

Informatics and Engineering Systems Faculty Publications

The use of Electronic Medical Records (EMRs) in the healthcare industry has proven to be critical for storing highly sensitive information. Disseminating and protecting healthcare data poses major challenges for the current healthcare information system. Blockchain technology provides solutions to these challenges with its inherited properties, such as decentralization, immutability, and transparency. This provides a unique opportunity to improve data sharing among stakeholders. We propose MediLink, a blockchain-based framework for secure collaborative medical storage. MediLink is designed to (1) protect EMR data from cyber attacks, (2) share healthcare information of patients with different stakeholders, and (3) enable the Internet of …

Dynamic Knowledge Elicitation: Leveraging Student Feedback For Improved Language Model Distillation, Reuven Muller

Master's Theses

Large Language Models (LLMs) have significantly advanced the field of natural language processing but remain resource-intensive and impractical for many organizations. Specialist models offer a viable alternative, often developed through Knowledge Distillation (KD) techniques. However, traditional KD methods rely on predefined static datasets to elicit knowledge from the teacher model, failing to dynamically address the weaknesses of the student model during training. This research introduces two novel methods for adaptive knowledge elicitation: Feedback-Driven Question Generation and Agent-Based Targeted Question Generation. These methods iteratively expand the training dataset based on the student model’s performance, leveraging a teacher model to generate targeted …

Participatory Ethical Regulations: Risk Challenges Of Artificial Intelligence Era And Construction Of Governance Logic, Chenggang Zhang, Lu Pan

Bulletin of Chinese Academy of Sciences (Chinese Version)

Participatory ethical norms emphasize the involvement of diverse stakeholders, aiming to construct a more comprehensive and balanced ethical governance framework. The rapid development of artificial intelligence (AI) technology is leading society through unprecedented transformations, significantly impacting ethical perspectives, social governance models, and the symbiotic relationship between humans and technology. The participatory ethical norms, characterized by multi-stakeholder participation, interactivity, and openness, represent a crucial pathway for addressing the challenges posed by the rapid development of AI technology. Constructing an AI governance framework based on participatory ethical norms provides solutions for the sustainable, fair, and transparent development of AI from multiple aspects …

Ethical Risks And Challenges Of Chatgpt Applications In Education, Jingbo Fan, Hui Liang

Bulletin of Chinese Academy of Sciences (Chinese Version)

ChatGPT is a typical application in the field of natural language processing, with the potential to empower and revolutionize education. It can serve not only as a digital tutor for students but also as a virtual assistant for teachers, driving the transformation of student learning methods and teaching paradigms. Additionally, ChatGPT shows a wide range of applications in the research field. However, while bringing opportunities for educational development, ChatGPT also poses ethical risks and challenges to educational equity. Firstly, ChatGPT may exacerbate the digital divide, leading to unequal educational opportunities. Secondly, it presents risks such as knowledge alienation, algorithmic black-box …

Artificial Intelligence Foundation Model Risk Identification And Governance Model From Esg Perspective, Jincheng Shi, Guoyu Wang, Yingchun Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

The application ecology of artificial intelligence foundation model is rapidly expanding. The environment, society, and governance are facing new challenges and opportunities. Exploring the construction of a governance framework for the development risks of foundation model has important theoretical value and practical significance for promoting the healthy and sustainable development of artificial intelligence. Based on the theories of ESG and artificial intelligence governance, this study analyzes the development benefits and typical risks of foundation model from the perspective of ESG and then constructs a risk governance framework and implementation strategies for artificial intelligence foundation models. This study shows that a …

Enlightenment Of Us Nairr To Construction Of Artificial Intelligence Innovation Ecosystem In China, Tian Jiang, Li Qian

Bulletin of Chinese Academy of Sciences (Chinese Version)

The National Artificial Intelligence Research Resource (NAIRR) of the United States is of great significance for addressing the new challenges faced by the innovation of artificial intelligence technology. For China, the experience of NAIRR provides valuable references in resource optimization allocation and efficient utilization, which helps us to overcome resource bottlenecks and promote the rapid development of artificial intelligence technology. This study analyzes the enlightenment of NAIRR to the innovation and development of artificial intelligence in China from two key aspects. The first is the innovation-driven elements, where the integration strategies of NAIRR in computing and storage power, data resource, …

Overview On Autonomous Machine Computing, Shaoshan Liu, Yiming Gan, Yinhe Han

Bulletin of Chinese Academy of Sciences (Chinese Version)

Autonomous machine computing, an innovative blend of algorithms, software, and cutting-edge computing hardware, is poised to be the next major paradigm shift in the global economy, following personal, mobile, and cloud computing. This study delves into the research and commercialization of the robotics industry, underscoring the critical importance of establishing a comprehensive autonomous machine computing ecosystem. This study argues that autonomous machine computing necessitates a complete ecosystem that encompasses applications, programming languages, and the foundational hardware architectures, and presents a comprehensive review of significant research contributions across these areas. Moreover, the study explores the synergy between autonomous machine computing and …

Phr-Nft: Decentralized Blockchain Framework With Hyperledger And Nfts For Secure And Transparent Patient Health Records, Huwida E. Said, Nedaa B. Al Barghuthi, Sulafa M. Badi, Faiza Hashim, Shini Girija

All Works

Blockchain technology holds significant promise for healthcare by enhancing the security and integrity of patient health records (PHRs) through decentralized storage and transparent access. However, it has substantial limitations, including problems with scalability, high transaction costs, privacy concerns, and intricate stakeholder access management. This study presents PHR-NFT, a novel framework that strengthens PHR privacy by utilizing Hyperledger Fabric and non-fungible tokens (NFTs) to address these issues. PHR-NFT improves privacy and communication by letting patients keep control of their medical records while permitting temporary, permission-based access by medical professionals. PHR-NFT offers a transparent solution that increases trust among healthcare stakeholders through …

Gmc-137 Iot Security Vulnerabilities And How To Improve Them, Austin D Klein, Ryeon N Naderi, Tyler J Hood, Keren Bassourou

C-Day Computing Showcase

With the increased usage of IoT devices in homes as well as different industries, vulnerabilities have also increased significantly. The IoT devices are small in size, and it is hard to incorporate security in the software because security has high demand for computation. We have been conducting this research in order to find more suitable security methods that are lightweight as well as efficient. We have decided to move away from key hiding algorithms, which have increased time and space consumption, in favor of smaller and quicker block cipher algorithms.

Gmc-157 Text-To-Digital Person Video Generator: Digitalavatargen, Akansha Kesharwani, Nisha Bagdwal, Md E Hossain, Drashti Patel, Nikhil Adigoppula

C-Day Computing Showcase

The Text-to-digital person video generator: DigitalAvatarGen project uses AI to create lifelike videos of 2D digital avatars from user text input. Users enter text, select a voice and select or upload an avatar, and generate a video using DigitalAvatarGen web application which uses Google TTS and SadTalker, to synchronize voice, expressions, and lip movements. Key contributions include a customizable user interface, personalized voice and avatar options, and an optimized backend for efficient video generation. This tool provides an engaging, realistic solution for applications in education, media, and customer interaction.