Ml-Based User Identification Through Mouse Dynamics,
2024
San Jose State University
Ml-Based User Identification Through Mouse Dynamics, Rakshit Gupta
Master's Projects
User authentication and identification plays a crucial role in ensuring the security and integrity of digital systems. Traditional authentication methods, such as passwords and biometrics, have inherent limitations that can compromise system security. This research proposes a novel approach to user authentication by leveraging machine learning techniques and behavioral biometrics, specifically mouse dynamics. The primary objective is to develop a sophisticated framework that can accurately identify individuals based on their unique mouse behavior patterns. The study explores and compares multiple deep learning architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Transformer models, to generate embeddings from …
Analysis And Application Of Adaptive Ml Algorithms For Malware Classification,
2024
San Jose State University
Analysis And Application Of Adaptive Ml Algorithms For Malware Classification, Rashmi Boddukuri
Master's Projects
Malware classification is the process of distinguishing malware samples into categories of malware families that it is associated with and remains a critical step in the process of mitigating malware-related threats. In recent years, machine learning techniques have emerged as a powerful tool for such malware classification tasks. In this study, we explore the application of adaptive machine learning models to malware classification in order to analyze and determine how they compare in performance to similar but non-adaptive algorithms. The results achieved in this study share insight into the strengths and limitations of adaptive learning models when applied towards malware …
Adaptive Metric-Driven Load Balancing For Specialized Clusters Using Nginx,
2024
San Jose State University
Adaptive Metric-Driven Load Balancing For Specialized Clusters Using Nginx, Juhi Raju Malkani
Master's Projects
Adaptive Metric-Driven Load Balancer is an innovative two-tier load-balancing system that uses NGINX and Prometheus to optimize resource allocation in specialized cloud clusters. This framework is built to give great performance and flexibility and runs on Google Kubernetes Engine (GKE), but it may also be deployed on local cloud environments for added security. The first tier of our system uses an NGINX-based load balancer to route incoming requests based on content type, sending traffic to hardware-optimized clusters for processing requests through specialized hardware. In our algorithm, the second tier dynamically modifies load distribution throughout each cluster by calculating pod weights …
Learned Modeling And Control Of Continuum Robots For Surgical Applications,
2024
Dartmouth College
Learned Modeling And Control Of Continuum Robots For Surgical Applications, Cameron Jack Wolfe
Dartmouth College Master’s Theses
To reduce morbidity and mortality during surgery, surgeons have increasingly turned to Minimally Invasive Surgery (MIS), which involves passing instruments through small incisions or natural orifices to minimize patient trauma. Although MIS has significantly improved patient outcomes, it hinders a surgeon's dexterity and impairs visual and tactile feedback. These deficiencies have prompted the adoption of Robot-Assisted Surgery (RAS), in which surgeons control robots instead of using handheld instruments. While RAS has improved patient outcomes, robots struggle to navigate constricted spaces due to their rigidity, spurring the development of Continuum Robots (CRs). These flexible infinite degree-of-freedom robots move by bending, allowing …
การอัตโนมัติกระแสงานของระบบการจัดการเนื้อหาด้วยพาวเวอร์ออโตเมต,
2024
คณะวิศวกรรมศาสตร์
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,
2024
University of South Carolina - Columbia
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,
2024
University of South Carolina - Columbia
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,
2024
University of South Carolina - Columbia
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,
2024
University of South Carolina - Columbia
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,
2024
Portland State University
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,
2024
Technological University Dublin
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,
2024
University at Albany, State University of New York
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,
2024
Old Dominion University
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,
2024
University of South Carolina - Columbia
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,
2024
Old Dominion University
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,
2024
Old Dominion University
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,
2024
Virginia Commonwealth University
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,
2024
University of New Hampshire, Durham
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,
2024
Technological University Dublin
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,
2024
Technological University Dublin
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.
