Computer Engineering Commons^™

การอัตโนมัติกระแสงานของระบบการจัดการเนื้อหาด้วยพาวเวอร์ออโตเมต, อภิญญา แขนสูงเนิน

Chulalongkorn University Theses and Dissertations (Chula ETD)

งานวิจัยนี้นำเสนอการพัฒนาระบบอัตโนมัติกระแสงานของระบบจัดการเนื้อหา หรือ ซีเอ็มเอส โดยใช้ ไมโครซอฟต์ พาวเวอร์ ออโตเมต และ เอไอ บิวเดอร์ บูรณาการกับ สตราพี ซีเอ็มเอส และ แชร์พอยต์ เพื่อเพิ่มประสิทธิภาพ ลดความผิดพลาดจากการดำเนินงานด้วยมือ และยกระดับความคล่องตัวในองค์กร กระแสงานที่พัฒนาแชร์พอยต์ประกอบด้วยการบูรณาการเว็บฮุก ของ สตราพี ซีเอ็มเอส เพื่อเริ่มต้นกระบวนการอัตโนมัติ การตรวจสอบความถูกต้องของเนื้อหาด้วยเอไอ บิวเดอร์ การบันทึกข้อมูลเหตุการณ์และข้อผิดพลาดในแชร์พอยต์ อีกทั้งการแจ้งเตือนผ่านไมโครซอฟท์ ทีม นอกจากนี้ ยังมีการใช้ จีพีที 4.0 สำหรับการวิเคราะห์บันทึกข้อผิดพลาด เพื่อเสนอแนวทางแก้ไขและปรับปรุงกระบวนการ ผลการทดลองในธนาคารพาณิชย์ไทยแห่งหนึ่งแสดงให้เห็นว่าสามารถลดระยะเวลาในการจัดการเนื้อหาได้ร้อยละ 87.5 และลดข้อผิดพลาดจากการป้อนข้อมูลได้ร้อยละ 80 เมื่อเทียบกับการทำงานแบบเดิมด้วยมนุษย์ งานวิจัยนี้ได้เน้นให้เห็นถึงศักยภาพของการใช้เทคโนโลยีดิจิทัลที่นำไปสู่โซลูชันการอัตโนมัติระบบการจัดการเนื้อหาที่เป็นรูปธรรม

Causal Neuro-Symbolic Ai: A Synergy Between Causality And Neuro-Symbolic Methods, Utkarshani Jaimini, Cory Henson, Amit Sheth

Publications

Causal Neuro-Symbolic AI combines the benefits of causality with Neuro-Symbolic Artificial Intelligence (NeSyAI). More specifically, it (1) enriches NeSyAI systems with explicit representations of causality, (2) integrates causal knowledge with domain knowledge, and (3) enables the use of NeSyAI techniques for causal AI tasks. The explicit causal representation yields insights that predictive models may fail to analyze from observational data. It can also assist people in decision-making scenarios where discerning the cause of an outcome is necessary to choose among various interventions.

Ontolog Summit 2024 Talk Report: Healthcare Assistance Challenges-Driven Neurosymbolic Ai, Kaushik Roy

Publications

Although Artificial Intelligence technology has proven effective in providing healthcare assistance by analyzing health data, it still falls short in supporting decision-making. This deficiency largely stems from the predominance of opaque neural networks, particularly in mental health care AI applications, which raise concerns about their unpredictable and unverifiable nature. This skepticism hinders the transition from information support to decision support. This presentation will explore neurosymbolic approaches that combine neural networks with symbolic control and verification mechanisms. These approaches aim to unlock AI’s full potential by enhancing information analysis and decision-making support for healthcare assistance1.

Neurosymbolic Customized And Compact Copilots, Kaushik Roy, Megha Chakraborty, Yuxin Zi, Manas Gaur, Amit Sheth

Publications

Large Language Models (LLMs) are credible with open-domain interactions such as question answering, summarization, and explanation generation [1]. LLM reasoning is based on parametrized knowledge, and as a consequence, the models often produce absurdities and inconsistencies in outputs (e.g., hallucinations and confirmation biases) [2]. In essence, they are fundamentally hard to control to prevent off-the-rails behaviors, are hard to fine-tune, customize for tailored needs, prompt effectively (due to the “tug-of-war” between external and parametric memory), and extremely resource-hungry due to the enormous size of their extensive parametric configurations [3,4]. Thus, significant challenges arise when these models are required to perform …

A Comprehensive Survey On Rare Event Prediction, Chathurangi Shyalika Jayakody Kankanamalage, Ruwan Wickramarachchi, Amit Sheth

Publications

Rare event prediction involves identifying and forecasting events with a low probability using machine learning (ML) and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of rare events, it requires using specialized methods within each step of the ML pipeline, i.e., from data processing to algorithms to evaluation protocols. Predicting the occurrences of rare events is important for real-world applications, such as Industry 4.0, and is an active research area in statistics and ML. This paper comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, …

Comparing The Sensitivity And Degree Of Boolean Functions Via The Hypercube, Annie Rupp

Maseeh Summer Undergraduate Research Experience

This research explores the relationships between various complexity measures of Boolean functions—specifically Query Complexity, Sensitivity, Spectral Sensitivity, and Degree—modeled via hypercube sensitivity graphs and adjacency matrices. While the theoretical bounds for total functions are generalized, it remains unknown whether a function exists where these measures are perfectly equal. Focusing on the tight bounds demonstrated by the OR and PARITY functions, this work outlines a future research framework utilizing a contradiction proof style. By assuming and reverse-engineering established proofs, the study aims to uncover mathematical contradictions that could definitively determine the existence of such a total function.

A Fully Automated Global Post-Hoc Method Based On Abstract Argumentation For Explainable Artificial Intelligence And Its Application On Fully Connected Dense Deep Neural Networks, Giulia Vilone

Dissertations

Explainable Artificial Intelligence (XAI) has rapidly grown in the past decade due to the prevalence of machine learning, especially deep learning, in fields like healthcare and finance. While these models excel in accuracy, their complexity hampers transparency and interpretability. Ensuring understandable explanations for AI predictions fosters trust, prevents errors, complies with regulations, and enhances model refinement. The research project outlined in this thesis unfolds in phases. It commences with a comprehensive review of existing XAI studies, contributing to the field’s knowledge by proposing a taxonomy that organises theories and notions related to explainability, the evaluation approaches for XAI methods, and …

Information Access For Infrastructurally-Challenged Environments And Beyond Through Mutually Aware Spectrum Sharing Technologies, Karyn Doke

Electronic Theses & Dissertations (2024 - present)

The Radio Frequency (RF) spectrum is scarce and to make it available for new mobile wireless services, regulators are forced to re-allocate spectrum from existing services or develop mechanisms to share spectrum with new entries. Television White Space (TVWS) and Citizen Broadband Radio Service (CBRS) are two examples of recently commercialized spectrum sharing technologies. TVWS enables sharing among fixed wireless broadband technologies (secondary users) and terrestrial TV broadcast services (primary users). CBRS enables spectrum sharing among 5G/LTE (secondary users) and naval radar (primary users). With both technologies, a central database determines when it is safe for secondary users to operate …

Ensemble Learning With Sleep Mode Management To Enhance Anomaly Detection In Iot Environment, Khawlah Harahsheh, Rami Al-Naimat, Malek Alzaqebah, Salam Shreem, Esraa Aldreabi, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The rapid proliferation of Internet of Things (IoT) devices has underscored the critical need for energy-efficient cybersecurity measures. This presents the dual challenge of maintaining robust security while minimizing power consumption. Thus, this paper proposes enhancing the machine learning performance through Ensemble Techniques with Sleep Mode Management (ELSM) approach for IoT Intrusion Detection Systems (IDS). The main challenge lies in the high-power consumption attributed to continuous monitoring in traditional IDS setups. ELSM addresses this challenge by introducing a sophisticated sleep-awake mechanism, activating the IDS system only during anomaly detection events, effectively minimizing energy expenditure during periods of normal network operation. …

Personalized Bayesian Inference For Explainable Healthcare Management And Intervention, Utkarshani Jaimini, Krishnaprasad Thirunaravan, Maninder Kalra, Robin Dawson, Amit Sheth

Publications

Chronic healthcare conditions such as Asthma re- quires constant monitoring and managing of symptoms and their triggers for better quality of life. Each asthma patient reacts very differently to potential triggers. Hence, there is a need to develop a explainable personalized framework for each patient to capture susceptibility to asthma triggers. We developed a personalized knowledge-based probabilistic model to predict asthma exacerbation for different environmental factors utilizing patient generated health data from pediatric asthma patients. Further, the personalized model provides a metric, called Health Coefficient, to quantify the health of a patient for varying environmental factors. We demonstrate the predictive …

Methods That Support The Validation Of Agent-Based Models: An Overview And Discussion, Andrew Collins, Matthew Koehler, Christopher Lynch

Engineering Management & Systems Engineering Faculty Publications

Validation is the process of determining if a model adequately represents the system under study for the model’s intended purpose. Validation is a critical component in building the credibility of a simulation model with its end-users. Effectively conducting validation can be a daunting task for both novice and experienced simulation developers. Further compounding the difficult task of conducting validation is that there is no universally accepted approach for assessing a simulation. These challenges are particularly relevant to the paradigm of Agent-Based Modeling and Simulation (ABMS) because of the complexity found in these models’ mechanisms and in the real-world situations they …

Undergraduate Research On 5g Propagation Analysis In Naval Ship Environment, Otilia Popescu, Dimitrie Popescu, Murat Kuzlu

Engineering Management & Systems Engineering Faculty Publications

Wireless communication systems have experienced rapid advancements over the last decade, with 5G systems becoming the regular standards and 6G systems being under development. However, the naval ship environment still requires more study due to the special indoor characteristics of the spaces below the deck, where the metal structures strongly impact the propagation characteristics, being prone to signal degradation, dead spots, and unreliable connectivity. This paper presents the research work conducted by a team of undergraduate students from Electrical and Computer Engineering (ECE) and Electrical Engineering Technology (EET) majors. The work was a partnership between Old Dominion University engineering programs …

External Runtime Execution Monitoring Of A Cyber Physical System Via Trace Interfaces, Peter Vaughan Truslow

Theses and Dissertations

In the past two decades, Unmanned Aerial Systems have progressed from expensive military hardware or one-off custom builds, to include off-the-shelf drones that can be purchased for a rather affordable price and flown by nearly anyone. As the technology and performance have improved, the door is opened to applications that require operation in environments where the consequences for failure are high, such as operating in the navigable airspace or in urban environments, or with human passengers. This requires a great deal of trust in the reliability and integrity of the control systems of the aircraft. A method of monitoring the …

Securing Internet Of Things (Iot) Data Storage, Savannah Malo

Honors Theses and Capstones

Internet of Things (IoT) devices are commonly known to be susceptible to security attacks, which can lead to the leakage, theft, or erasure of data. Despite similar attack methods used on conventional technologies, IoT devices differ in how they consist of a small amount of hardware, limited networking capability, and utilize NoSQL databases. IoT solutions prefer NoSQL databases since they are compatible for larger datasets, unstructured and time-series data. However, these implementations are less likely to employ critical security features, like authentication, authorization, and encryption. The purpose of this project is to understand why those security measures are not strictly …

Effectiveness Of Generative Ai On The Development Of Graphic Software Artifacts, Gary Clynch, Ellen Mezera

Academic Poster Collection

Effectiveness of Generative AI on the Development of Graphic Software Artifacts

Privacy-Preserving Pedestrian Movement Analysis In Complex Public Spaces, Kunchala Anil

Doctoral

Every journey starts and ends with a walking phase. As a fundamental mode of transportation, it plays a crucial role in the functioning of urban societies. Cities should strive to create public spaces that promote walking by designing pedestrian-friendly infrastructure that is accessible, safe, and convenient for all. An essential step in designing pedestrian-friendly infrastructure is understanding pedestrian usage patterns and behaviour in existing public spaces and infrastructure.

How Can Personalised Feedback In Assignments Help Address Gender Balance In Computing Education?, Alina Berry

Academic Posters Collection

Personalised feedback is frequently used in computing assessments in higher education. Research has shown that personalised feedback positively influences persistence in computer science. Computing and related disciplines are known to show relatively low retention rates. This includes female students, who are strongly underrepresented in computing disciplines, so they can be considered as a particularly important group for retention-driven initiatives. Female science students are more likely to act upon feedback, and personalised feedback has increased intentions to persist among female top performing students in computing. Hence, providing personalised feedback can be considered as a promising gender initiative that has a potential …

Human Centred Xai Taxonomy, Helen Sheridan, Emma Murphy, Dympna O'Sullivan

Academic Posters Collection

No abstract provided.

Design Strategies For Explainable Ai: 12 Guiding Design Principles For Human-Centred Xai, Helen Sheridan, Dympna O'Sullivan, Emma Murphy

Academic Posters Collection

With the growing demand for transparent Al systems, the EU's Al Act emphasizes the need for accessible explanations to foster user trust and ethical Al use [1]. Our research explores the "gulf of explanation" in XAl, evaluating how well current systems align with users' mental models and engaging experts to improve human-centred XAl design [2]. This work particularly considers the application of a new and novel HCl framework, a set of 12 key design principles for human-centred XAI.

Personalised Feedback On Assessments In Computing Modules - Gender Equality Action In Context, Alina Berry

Academic Posters Collection

Personalised feedback in computing higher education has been known to positively influence retention of women. The issue of gender inequality in computing field is well known and one of the efforts to address it is the development of a gender equality toolkit (TechMate), with one initiative (action) in the toolkit being personalised feedback. While all actions in the toolkit are research-driven, the aim of this work was to evaluate the action on personalised feedback in a local context.

The study comprised of 10 semi-structured interviews with computing lecturers at TU Dublin who provide personalised feedback, a student survey with 68 …

Emotional Intelligence Coaching For Higher Education Lecturers: Their Experience And Its Impact On Trait Emotional Intelligence, Stress, And Teaching Self-Efficacy, Eoghan Guiry

Doctoral

Previous research has established that emotional intelligence (EI) can be developed through targeted intervention; however, this was previously untested with lecturers. Guided by the job demands-resources model, this study explored the impact of an EI coaching intervention on lecturers’ trait EI, perceived stress, work-related stress, and teaching self-efficacy at Technological University Dublin. Additionally, the study explored the lecturers’ experiences of partaking in the intervention. Employing a pragmatic approach, mixed methods were used in a randomised controlled trial.

Applications Of Predictive And Generative Ai Algorithms: Regression Modeling, Customized Large Language Models, And Text-To-Image Generative Diffusion Models, Suhaima Jamal

College of Graduate Studies: Theses & Dissertations

The integration of Machine Learning (ML) and Artificial Intelligence (AI) algorithms has radically changed predictive modeling and classification tasks, enhancing a multitude of domains with unprecedented analytical capabilities. Predictive modeling leverages ML and AI to forecast future trends or behaviors based on historical data, while classification tasks categorize data into distinct classes, from email filtering to medical diagnosis. Concurrently, text-to-image generation has emerged as a transformative potential, allowing visual content creation directly from textual descriptions. These advancements are pivotal in design, art, entertainment, and visual communication, as well as enhancing creativity and productivity. This work explores three significant studies in …

Railroad Condition Monitoring Using Distributed Acoustic Sensing And Deep Learning Techniques, Md Arifur Rahman

College of Graduate Studies: Theses & Dissertations

Proper condition monitoring has been a major issue among railroad administrations since it might cause catastrophic dilemmas that lead to fatalities or damage to the infrastructure. Although various aspects of train safety have been conducted by scholars, in-motion monitoring detection of defect occurrence, cause, and severity is still a big concern. Hence extensive studies are still required to enhance the accuracy of inspection methods for railroad condition monitoring (CM). Distributed acoustic sensing (DAS) has been recognized as a promising method because of its sensing capabilities over long distances and for massive structures. As DAS produces large datasets, algorithms for precise …

The Integration Of Neuromorphic Computing In Autonomous Robotic Systems, Md Abu Bakr Siddique

Dissertations, Master's Theses and Master's Reports

Deep Neural Networks (DNNs) have come a long way in many cognitive tasks by training on large, labeled datasets. However, this method has problems in places with limited data and energy, like when planetary robots are used or when edge computing is used [1]. In contrast to this data-heavy approach, animals demonstrate an innate ability to learn by communicating with their environment and forming associative memories among events and entities, a process known as associative learning [2-4]. For instance, rats in a T-maze learn to associate different stimuli with outcomes through exploration without needing labeled data [5]. This learning paradigm …

Performance Characterization And Optimization Of A Point-Cloud-Based Path Planner In Off-Road Terrains, Casey D. Majhor

Dissertations, Master's Theses and Master's Reports

In this dissertation, I present a multifaceted study on the generation of artificial terrains using a multifractal method, and their use in autonomous ground vehicle (AGV) planning and navigation performance characterization in high-fidelity simulations, and automated parameter optimization.

My first contribution is a multifractal artificial terrain generation method leveraging the 3D Weierstrass-Mandelbrot function to control terrain roughness. We generate 60 unique off-road terrains while varying the fractal dimension and test the impact on vehicle traversal difficulty. Results show that increasing the fractal dimension decreases low-roughness areas, increases semi-rough and high-roughness areas, and decreases vehicle success rates while increasing vertical accelerations, …

V2i-Based Adaptive Collision Avoidance For Safety And Traffic Efficiency, Vaishnavi Balambeed

Dissertations, Master's Theses and Master's Reports

To leverage the growing communication and connectivity among modern vehicles, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) systems are increasingly being used to implement active safety applications. However, current research often overlooks the impact of algorithms such as collision avoidance on traffic flow efficiency. This work investigates the adaptation of a collision avoidance algorithm implemented in V2I to incorporate a variable time headway and spacing control strategy. The proposed approach aims to maintain higher average speeds among vehicles, lower individual vehicle’s waiting, and travel times in the vicinity of the infrastructural unit while simultaneously avoiding collisions; thereby enhancing both safety and traffic …

Enhancing Privacy While Revealing Vulnerabilities: Strategies For Adaptation, Optimization, And Model Extraction, Madhureeta Das

Dissertations, Master's Theses and Master's Reports

In the evolving landscape of machine learning and artificial intelligence, this dissertation presents a series of innovative contributions spanning several critical areas: embracing semi-supervised domain adaptation for secure knowledge transfer, enhancing the model performance of tiny models, and executing model stealing attacks via diversified prompts. The overarching goal is to enhance the performance, scalability, and security of AI models across various applications.

The first research focus is on semi-supervised domain adaptation within federated learning frameworks. By leveraging semi-supervised learning techniques, this work addresses the challenge of adapting models trained on a source domain to perform effectively on a target domain …

Specification And Implementation Of Arm Isa Using Adl, Jonathan A. Rabideau

Dissertations, Master's Theses and Master's Reports

In this project, we implement a specification of ARM ISA using the ADL system. Current progress includes 79% of planned instructions, 79% of which are complete for our purposes, and also includes components such as a proxy kernel and a temporary preprocessor. Simple programs compiled directly from gcc can already be properly assembled and simulated using the tools generated by ADL. Once the specification is complete, we can integrate it with other components for testing ideas and theories. This report covers our progress and future plans regarding this project, including some background material and problem solutions.

Implementing Associative Learning Using Neuromorphic Robot, Vinay Kumar Pillalamarri

Dissertations, Master's Theses and Master's Reports

Associative learning, a key cognitive process seen across the animal kingdom, enables organisms to form connections between stimuli and adapt their behaviors based on past experiences. A particularly powerful example is fear conditioning, where animals learn to associate a neutral stimulus with an aversive one, allowing them to predict and avoid potential threats. Inspired by this mechanism, this project implements associative learning on an unmanned ground vehicle (UGV) to develop adaptive behavior through neuromorphic principles. Utilizing Nengo for neural modeling, the UGV learns to associate visual (red color) and tactile (vibration) stimuli through Hebbian learning, a biologically inspired synaptic adaptation …

Leveraging Large Language Models For Enhancing Well-Being In The Digital Age, Xiaobo Guo

Dartmouth College Ph.D Dissertations

The 21st century has seen dramatic shifts in human interactions with information, peers, and the environment, primarily driven by the proliferation of online platforms and social media. These advancements offer more access to information and global connectivity, but also present challenges such as information overload, misinformation, online harms, and biased reporting that can negatively impact user well-being. This thesis examines the role of Large Language Models (LLMs) — advanced forms of artificial intelligence that understand and generate human-like text — in enhancing well-being in the digital age. The study begins by exploring the potential of LLMs to detect early signs …