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Leveraging Transformer-Based Ocr Model With Generative Data Augmentation For Engineering Document Recognition, Wael Khallouli, Mohammad Shahab Uddin, Andres Sousa-Poza, Jiang Li, Samuel Kovacic Jan 2025

Leveraging Transformer-Based Ocr Model With Generative Data Augmentation For Engineering Document Recognition, Wael Khallouli, Mohammad Shahab Uddin, Andres Sousa-Poza, Jiang Li, Samuel Kovacic

Engineering Management & Systems Engineering Faculty Publications

The long-standing practice of document-based engineering has resulted in the accumulation of a large number of engineering documents across various industries. Engineering documents, such as 2D drawings, continue to play a significant role in exchanging information and sharing knowledge across multiple engineering processes. However, these documents are often stored in non-digitized formats, such as paper and portable document format (PDF) files, making automation difficult. As digital engineering transforms processes in many industries, digitizing engineering documents presents a crucial challenge that requires advanced methods. This research addresses the problem of automatically extracting textual content from non-digitized legacy engineering documents. We introduced …

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From Cyclones To Cybersecurity: A Call For Convergence In Risk And Crisis Communications Research, Ann Marie Reinhold, Ross J. Gore, Barry Ezell, Clemente I. Izurieta, Elizabeth A. Shanahan Jan 2025

From Cyclones To Cybersecurity: A Call For Convergence In Risk And Crisis Communications Research, Ann Marie Reinhold, Ross J. Gore, Barry Ezell, Clemente I. Izurieta, Elizabeth A. Shanahan

VMASC Publications

Effective risk and crisis communication can improve health and safety and reduce harmful effects of hazards and disasters. A robust body of literature investigates mechanisms for improving risk and crisis communication. While effective risk and crisis communication strategies are equally desired across different hazard types (e.g., natural hazards, cyber security), the extent to which risk and crisis communication experts utilize the “lessons learned” from scientific domains outside their own is suspect. Therefore, we hypothesized that risk and crisis communication research is siloed according to academic disciplines at the detriment to the advancement of the field of risk communications research writ …

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A Happy Medium?: Using Image Generators To Explore Solution-Focused Art Therapy’S Miracle Question, Daniel A. Hernried Jan 2025

A Happy Medium?: Using Image Generators To Explore Solution-Focused Art Therapy’S Miracle Question, Daniel A. Hernried

Art Therapy | Master's Theses

This mixed methods, randomized, single-session study tested whether integrating text-to-image generations into Solution-Focused Brief Art Therapy alters therapeutic rapport and short-term outcomes relative to traditional artmaking materials. Participants were assigned by coin flip to create using either a text-to-image generator or convention media (23 per group), completing immediate and three-day follow-ups. Alliance was measured using DREAM (Dimensions of Regard, Empathy, and Authenticity Metric), and problems were rated pre/post; groups did not differ significantly on DREAM total or facets, and both modalities produced reliable pre-to-post reductions in problem severity. At the same time, process differences were pronounced: the AI condition showed …

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A Framework For Mixed Reality Within Healthcare Education, Benyamin Ebadinia Jan 2025

A Framework For Mixed Reality Within Healthcare Education, Benyamin Ebadinia

Theses

Understanding complex three-dimensional systems and spatial relationships is a recurring difficulty in healthcare education, where students are often expected to reason about internal structures and multi-system processes from 2D diagrams, textbook figures, and static mannequins. This thesis presents the design, implementation, and mixed-methods evaluation of Systems Simulation, a reusable mixed reality (MR) application intended to help undergraduate nursing students explore human anatomy and pathophysiology using immersive 3D visualization.

Built in C# with the StereoKit framework for Microsoft HoloLens 2, Systems Simulation organizes nine anatomical body systems within a shared application. Learners can select a system, anchor the model in their …

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J-Necora: A Framework For Optimal Resource Allocation In Cloud-Edge-Things Continuum For Industrial Applications With Mobile Nodes, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi Jan 2025

J-Necora: A Framework For Optimal Resource Allocation In Cloud-Edge-Things Continuum For Industrial Applications With Mobile Nodes, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

In the Industrial Internet of Things (IIoT) landscape, where the Cloud-to-Things Continuum (C2TC) paradigm is now a reality, industrial applications need to cope with highly heterogeneous network and computing resources. Moreover, many industrial applications also involve Mobile Nodes (MNs). Efficient allocation of network and computing resources to meet the stringent requirements of such applications is often a very challenging task. In this paper, we propose J-NECORA (Joint NEtwork and COmputing Resource Allocation), a comprehensive analytical framework to derive the optimal joint allocation of network and computing resources in the C2TC, that guarantees the application requirements, even in the presence of …

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Correction: Yolo-Based Miner Detection Using Thermal Images In Underground Mines (Mining, Metallurgy & Exploration, (2025), 10.1007/S42461-025-01249-6), Cyrus Addy, Venkata Sriram Siddhardh Nadendla, Kwame Awuah-Offei Jan 2025

Correction: Yolo-Based Miner Detection Using Thermal Images In Underground Mines (Mining, Metallurgy & Exploration, (2025), 10.1007/S42461-025-01249-6), Cyrus Addy, Venkata Sriram Siddhardh Nadendla, Kwame Awuah-Offei

Computer Science Faculty Research & Creative Works

In the original published article, Figure 3 appears with the Fig. 1 caption, Figure 1 appears with the Fig. 2 caption, and Figure 2 appears with the Fig. 3 caption. The article has been updated to correct this error.

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Message From The Phd Dissertation Showcase Chairs, Sanjay Kumar Madria, Anita Graser Jan 2025

Message From The Phd Dissertation Showcase Chairs, Sanjay Kumar Madria, Anita Graser

Computer Science Faculty Research & Creative Works

No abstract provided.

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Dynamic Resource Allocation In Cloud-To- Things Continuum For Real-Time Iot Applications, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi Jan 2025

Dynamic Resource Allocation In Cloud-To- Things Continuum For Real-Time Iot Applications, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi

Computer Science Faculty Research & Creative Works

The proliferation of loT devices and the growing demand for real-time applications have driven a shift in the computation paradigm, from Cloud computing to Edge computing, creating the Cloud-to-Things Continuum (C2TC). Many real-time loT applications involve Mobile Nodes (MNs), which may dynamically join or leave. In addition, in future reconfigurable loT systems, applications with different requirements will coexist, and will be dynamically introduced or removed. All this asks for dynamic management mechanisms to ensure the requirements of different real-time applications, even when the system configuration changes over time. In this paper, we propose DJ-NECORA, an online algorithm for the joint …

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Feature Fusion Transferability Aware Transformer For Unsupervised Domain Adaptation, Xiaowei Yu, Zhe Huang, Zao Zhang Jan 2025

Feature Fusion Transferability Aware Transformer For Unsupervised Domain Adaptation, Xiaowei Yu, Zhe Huang, Zao Zhang

Computer Science Faculty Research & Creative Works

Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from labeled source domains to improve performance on the unlabeled target domains. While Convolutional Neural Networks (CNNs) have been dominant in previous UDA methods, recent research has shown promise in applying Vision Transformers (ViTs) to this task. In this study, we propose a novel Feature Fusion Transferability Aware Transformer (FFTAT) to enhance ViT performance in UDA tasks. Our method introduces two key innovations: First, we introduce a patch discriminator to evaluate the transferability of patches, generating a transferability matrix. We integrate this matrix into self-attention, directing the model to focus …

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Ca-Vqvae: Cortical Folding Aware Numerical Representation Of White-Matter Structure, Yanjun Lyu, Jing Zhang, Lu Zhang, Tong Chen, Xiaowei Yu, Minheng Chen, Yan Zhuang, Chao Cao, Tianming Liu, Dajiang Zhu Jan 2025

Ca-Vqvae: Cortical Folding Aware Numerical Representation Of White-Matter Structure, Yanjun Lyu, Jing Zhang, Lu Zhang, Tong Chen, Xiaowei Yu, Minheng Chen, Yan Zhuang, Chao Cao, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

White matter (WM) serves as a fundamental component of the brain providing essential structural support and facilitating the brain cognitive processes. Thus, an accurate and efficient description of the brain's white matter structure is essential for understanding brain function connectivity and development. In this work we used the deep model to combine the information of the WM fiber bundle shape and its related cortical folding patterns together representing the WM fiber bundle from diffusion MRI tractography into a pre-defined low-dimensional space and generate the numerical representation vector. This cortical-aware vector-quantized variational encoder (CA-VQVAE) framework leverages cortical locations and folding patterns …

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Using Structural Similarity And Kolmogorov-Arnold Networks For Anatomical Embedding Of Cortical Folding Patterns, Minheng Chen, Chao Cao, Tong Chen, Yan Zhuang, Jing Zhang, Yanjun Lyu, Xiaowei Yu, Lu Zhang, Tianming Liu, Dajiang Zhu Jan 2025

Using Structural Similarity And Kolmogorov-Arnold Networks For Anatomical Embedding Of Cortical Folding Patterns, Minheng Chen, Chao Cao, Tong Chen, Yan Zhuang, Jing Zhang, Yanjun Lyu, Xiaowei Yu, Lu Zhang, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

The 3-hinge gyrus (3HG) is a newly defined folding pattern, which is the conjunction of gyri coming from three directions in cortical folding. Many studies demonstrated that 3HGs can be reliable nodes when constructing brain networks or connectome since they simultaneously possess commonality and individuality across different individual brains and populations. However, 3HGs are identified and validated within individual spaces, making it difficult to directly serve as the brain network nodes due to the absence of cross-subject correspondence. The 3HG correspondences represent the intrinsic regulation of brain organizational architecture, traditional image-based registration methods tend to fail because individual anatomical properties …

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Secure Data Relay In Federated Digital Twins Of Iot-Enabled Smart Interconnected Factories, Anusha Vangala, Jack Wyeth, Ashok Kumar Das, Sajal K. Das Jan 2025

Secure Data Relay In Federated Digital Twins Of Iot-Enabled Smart Interconnected Factories, Anusha Vangala, Jack Wyeth, Ashok Kumar Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

Smart interconnected factories allow manufacturing units from physically distanced factory sites to communicate classified information needed for additive manufacturing. Each factory has interconnected digital twins of their equipment autonomously managed by a Point-of-Contact digital twin creating a hierarchical system with federated digital twins. The data sharing between the factories must be directed through an edge node responsible for managing multiple factories. We proposed a novel lightweight protocol to prevent the leakage of classified information at any nodes other than the origin and destination digital twins. It leverages elliptic curve cryptography to design a proxy re-encryption scheme with the edge node …

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Alertble: Alert Workzone Hazards Using Hybrid Filtering And Machine-Learning-Enabled Ble, Samuel Akinyede, Sejun Song Jan 2025

Alertble: Alert Workzone Hazards Using Hybrid Filtering And Machine-Learning-Enabled Ble, Samuel Akinyede, Sejun Song

Computer Science Faculty Research & Creative Works

Collision hazard detection in industrial work zones faces challenges from signal instability, mobility-induced fluctuations, and nonline-of-sight (NLOS) conditions. While Bluetooth low energy (BLE) offers cost-effective proximity sensing, its received signal strength indicator (RSSI) variability - fluctuating by ±10 dBm even at fixed distances - limits reliability in safety-critical applications. This article presents AlertBLE, a hybrid BLE-based hazard detection system that combines extended Kalman filter (EKF) and adaptive moving average (AMA) algorithms to achieve up to 94% RSSI variance reduction in static NLOS conditions. The system introduces speed-aware safety thresholds based on reaction time and braking distance models, dynamically expanding hazard …

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Governing Intelligence: Singapore’S Evolving Ai Governance Framework, Jason G. Allen, Jane Loo, Jose Luna Jan 2025

Governing Intelligence: Singapore’S Evolving Ai Governance Framework, Jason G. Allen, Jane Loo, Jose Luna

Research Collection Yong Pung How School Of Law

This paper provides an outline analysis of the evolving governance framework for Artificial Intelligence (AI) in Singapore. Across the Singapore government, AI solutions are being adopted in line with Singapore’s “Smart Nation Initiative” to leverage technology to make impactful changes to the nation and the economy. In tandem, Singaporean authorities have been assiduous to release a growing number of governance documents, which we analyse together to chart the city-state’s approach to AI governance in international comparison. Characteristics of Singapore’s AI governance approach include an emphasis on consensusbuilding between stakeholders (particularly government and industry but also citizens) andvoluntary or “quasi” regulation, …

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V-Usdt: Vision-Based Uav Swarm Detection And Tracking By Leveraging Swarm Formation Constraints, Md Hasibur Rahman, Sanjay Madria Jan 2025

V-Usdt: Vision-Based Uav Swarm Detection And Tracking By Leveraging Swarm Formation Constraints, Md Hasibur Rahman, Sanjay Madria

Computer Science Faculty Research & Creative Works

The rapid proliferation of Unmanned Aerial Vehicles (UAVs) and UAV swarm technologies has raised critical concerns about security and safety in low-altitude airspace. In response, we propose a vision-based system for detecting and tracking UAV swarms, which combines a novel UAV detection mechanism with a swarm tracking strategy. Our UAV detector incorporates parallel receptive field blocks alongside an attention mechanism to enhance detection performance. This design effectively captures multiscale features of UAVs while prioritizing salient features, ensuring robust detection under diverse conditions. For swarm tracking, we leverage the inherent formation constraints typically maintained by UAV swarms. These constraints allow us …

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Rush: Rule-Based Scheduling For Low-Latency Serverless Computing, Priyanka Ashok Birajdar, Kush Anchalia, Anurag Satpathy, Sourav Kanti Addya Jan 2025

Rush: Rule-Based Scheduling For Low-Latency Serverless Computing, Priyanka Ashok Birajdar, Kush Anchalia, Anurag Satpathy, Sourav Kanti Addya

Computer Science Faculty Research & Creative Works

Serverless computing abstracts server management, enabling developers to focus on application logic while benefiting from automatic scaling and pay-per-use pricing. However, dynamic workloads pose challenges in resource allocation and response time optimization. Response time is a critical performance metric in serverless environments, especially for latency-sensitive applications, where inefficient scheduling can degrade user experience and system efficiency. This paper proposes RUSH (Rule-based Scheduling for Low-Latency Serverless Computing), a lightweight and adaptive scheduling framework designed to reduce cold starts and execution delays. RUSH employs a set of predefined rules that consider system state, resource availability, and timeout thresholds to make proactive, latency-Aware …

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Tintin: A Unified Hardware Performance Profiling Infrastructure To Uncover And Manage Uncertainty, Ao Li, Marion Sudvarg, Zihan Li, Sanjoy Baruah, Chris Gill, Ning Zhang Jan 2025

Tintin: A Unified Hardware Performance Profiling Infrastructure To Uncover And Manage Uncertainty, Ao Li, Marion Sudvarg, Zihan Li, Sanjoy Baruah, Chris Gill, Ning Zhang

Computer Science Faculty Research & Creative Works

Hardware performance counters (HPCs) enable the measurement of microarchitectural events, which are crucial for tracking and predicting program behavior. High-fidelity measurement and precise attribution are essential for accurate profiling. However, existing profiling tools have fundamental challenges in both aspects. In measurement, numerous events compete for limited hardware monitoring resources; while for attribution, applications have diverse requirements, but systems provide limited support. Existing tools mitigate the former limitation through event multiplexing, but this approach introduces non-trivial errors. The latter limitation, however, remains largely unaddressed. This paper introduces Tintin, an HPC profiling infrastructure with a modular three-component design that addresses both challenges. …

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Generative Ai And Finding The Law, Paul D. Callister Jan 2025

Generative Ai And Finding The Law, Paul D. Callister

Faculty Works

Legal information science requires, among other things, principles and theories. The article states six principles or considerations that any discussion of generative AI large language models and their role in finding the law must include. The article concludes that law librarianship will increasingly become legal information science and require new paradigms. In addition to the six principles, the article applies ecological holistic media theory to understand the relationship of the legal community’s cognitive authority, institutions, techné (technology, medium and method), geopolitical factors, and the past and future to understand the changes in this information milieu. The article also explains generative …

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Evaluation Of User Experience And Usability Of Organization Super App, Ananya Kumhan Jan 2025

Evaluation Of User Experience And Usability Of Organization Super App, Ananya Kumhan

Chulalongkorn University Theses and Dissertations (Chula ETD)

The digital transformation of organizations in Thailand has accelerated the development of internal systems that streamline workflows and improve accessibility to organizational information. Financial institutions, which operate with multiple fragmented systems, have increasingly adopted Enterprise SuperApps as a centralized platform that combines internal services such as corporate news, leave requests, employee directories, resource information, and meeting room reservations into a single application. However, usage statistics in several organizations indicate that continuous adoption remains relatively low. Many employees rely on alternative channels or use only selected features, reflecting challenges in user experience (UX) and usability. This study aims to evaluate the …

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Blocfog : Enhanced Transactional Data Encryption Via Fog Computing And Blockchain, Leon Wirz Jan 2025

Blocfog : Enhanced Transactional Data Encryption Via Fog Computing And Blockchain, Leon Wirz

Chulalongkorn University Theses and Dissertations (Chula ETD)

Protecting Personally Identifiable Information (PII) in financial transactions presents ongoing challenges, particularly in achieving a balance between strong security, fine-grained access control, and system efficiency. Existing approaches often rely on complex cryptographic operations that strain end-user devices or lack the flexibility needed for decentralized environments. Although blockchain provides advantages such as auditability and tamper resistance, its use is often limited by high computational costs and latency. This research proposes an efficient and scalable access control framework that integrates Ciphertext-Policy Attribute-Based Encryption (CP-ABE) with a lightweight fog computing layer. The system is designed to offload heavy cryptographic tasks from end users …

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Improving Chinese Hate Speech Detection With Bert-Fasttext Fusion And Bert-Bilstm Fusion, Methini Ma Jan 2025

Improving Chinese Hate Speech Detection With Bert-Fasttext Fusion And Bert-Bilstm Fusion, Methini Ma

Chulalongkorn University Theses and Dissertations (Chula ETD)

Hate speech detection is an essential technique in the online environment, especially on social media platforms. This technique helps to create a safer space and reduce the risk of real-world harm. In Chinese, this task is particularly challenging because of unique linguistic structures and the frequent use of indirect expressions, sarcasm, homophones, character variants, and abbreviations. This study investigates how to improve Chinese hate speech detection by combining BERT with FastText and BERT with BiLSTM. There are six model variants that are configured: frozen BERT and fine-tuned BERT, each further extended with either FastText sentence embeddings or a clause-level BiLSTM. …

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A Proposed Ehrenfeucht-Fraïssé Game Model For Natural Language Processing Generative Adversarial Networks, Don Li Jan 2025

A Proposed Ehrenfeucht-Fraïssé Game Model For Natural Language Processing Generative Adversarial Networks, Don Li

Anthós

Large Language Models (LLM’s) (e.g., ChatGPT) constitute both a significant research area and commercial application of AI. Current major LLM’s are built on Generative Pre-Trained Transformer (GPT) neural network architecture to perform natural language processing (NLP) tasks. Generative Adversarial Network (GAN) is another popular neural network architecture, which leverages a zero-sum game between constituent neural networks within the architecture to train the GAN, and is widely used for visual data applications. This article proposes a new GAN architecture for NLP: an EF-GAN whose underlying algorithm uses Ehrenfeucht–Fraïssé (EF) games, a game-theoretic approach from model theory to determine elementary equivalence of …

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Exploring Research And Tools In Ai Security: A Systematic Mapping Study, Sidhant Narula, Mohammad Ghasemigol, Javier Carnerero-Cano, Amanda Minnich, Emil Lupu, Daniel Takabi Jan 2025

Exploring Research And Tools In Ai Security: A Systematic Mapping Study, Sidhant Narula, Mohammad Ghasemigol, Javier Carnerero-Cano, Amanda Minnich, Emil Lupu, Daniel Takabi

School of Cybersecurity Faculty Publications

With the pervasive integration of artificial intelligence (AI) in various facets of modern technology, the importance of AI security has been thrust into the spotlight. The field is rapidly evolving, with new challenges and solutions emerging at a swift pace. However, the breadth and depth of AI security research have not been comprehensively mapped in recent times, presenting a crucial need for an extensive review and synthesis of existing literature. Given the increasing reliance on AI in critical domains such as healthcare, finance, and national security, ensuring the resilience and trustworthiness of these systems is imperative. This survey fulfills the …

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Rapid Inference Of Atmospheric Feature Parameters From Light Curves Using Bayesian Neural Networks, Eugenio A. Diaz Jan 2025

Rapid Inference Of Atmospheric Feature Parameters From Light Curves Using Bayesian Neural Networks, Eugenio A. Diaz

Honors Undergraduate Theses

Mapping atmospheres using rotationally modulated light curves offers insights into cloud structures and dynamics. Current retrieval methods, primarily based on Markov Chain Monte Carlo (MCMC) techniques like Aeolus, can infer atmospheric features but are computationally prohibitive for large datasets. This project proposes a neural network (NN) framework for the rapid, variational inference of atmospheric structure from light curves, particularly those of brown dwarfs. The primary approach focuses on training a Bayesian NN (BNN) to perform regression, predicting the spot parameters that describe the object's surface brightness map. Given the scarcity of suitable observational training data, the BNN is trained on …

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Parallel Multi Objective Shortest Path Update Algorithm In Large Dynamic Networks, S. M. Shovan, Arindam Khanda, Sajal K. Das Jan 2025

Parallel Multi Objective Shortest Path Update Algorithm In Large Dynamic Networks, S. M. Shovan, Arindam Khanda, Sajal K. Das

Computer Science Faculty Research & Creative Works

The multi objective shortest path (MOSP) problem, crucial in various practical domains, seeks paths that optimize multiple objectives. Due to its high computational complexity, numerous parallel heuristics have been developed for static networks. However, real-world networks are often dynamic where the network topology changes with time. Efficiently updating the shortest path in such networks is challenging, and existing algorithms for static graphs are inadequate for these dynamic conditions, necessitating novel approaches. Here, we first develop a parallel algorithm to efficiently update a single objective shortest path (SOSP) in fully dynamic networks, capable of accommodating both edge insertions and deletions. Building …

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V2vdiscs: Vehicle To Vehicle Distributed Charge Sharing In Intelligent Transportation Systems, Punyasha Chatterjee, Pratham Majumder, Sajal K. Das Jan 2025

V2vdiscs: Vehicle To Vehicle Distributed Charge Sharing In Intelligent Transportation Systems, Punyasha Chatterjee, Pratham Majumder, Sajal K. Das

Computer Science Faculty Research & Creative Works

Electric Vehicles (EVs) have become popular in the domain of Intelligent Transportation Systems for their ability to mitigate increasing environmental concerns by reducing carbon footprints and conserving fossil fuels. Due to the scarcity of static charging stations, Vehicle-to-Vehicle (V2V) charge sharing can facilitate the on-demand charging requirement of EVs. However, most of the V2V charge-sharing solutions are either centralized or semi-centralized, causing long waiting times, huge message overhead, and high infrastructural costs. For a large network, assigning a suitable donor EV for an acceptor EV as well as maximizing the matching cardinality in a distributed environment is a challenging problem. …

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Sosta: Skill-Oriented Stable Task Assignment With Bidirectional Preferences In Crowdsourcing, Riya Samanta, Soumya K. Ghosh, Sajal K. Das Jan 2025

Sosta: Skill-Oriented Stable Task Assignment With Bidirectional Preferences In Crowdsourcing, Riya Samanta, Soumya K. Ghosh, Sajal K. Das

Computer Science Faculty Research & Creative Works

Traditional task assignment approaches in crowdsourcing platforms have focused on optimizing utility for workers or tasks, often neglecting the general utility of the platform and the influence of mutual preference considering skill availability and budget restrictions. This oversight can destabilize task allocation outcomes, diminishing user experience, and, ultimately, the platform's long-term utility and gives rise to the Worker Task Stable Matching (WTSM) problem. To solve WTSM, we propose the Skill-oriented Stable Task Assignment with a Bi-directional Preference (SoSTA) method based on deferred acceptance strategy. SoSTA aims to generate stable allocations between tasks and workers considering mutually their preferences, optimizing overall …

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Pervasive Sensing To Correlate Vehicle Driving Behavior With City-Scale Traffic Dynamics, Debasree Das, Shameek Bhattacharjee, Sandip Chakraborty, Bivas Mitra, Sajal K. Das Jan 2025

Pervasive Sensing To Correlate Vehicle Driving Behavior With City-Scale Traffic Dynamics, Debasree Das, Shameek Bhattacharjee, Sandip Chakraborty, Bivas Mitra, Sajal K. Das

Computer Science Faculty Research & Creative Works

Individual driving behavior is a pivotal element that shapes the overall traffic dynamics in a city. In this work, we study and analyze the complex web of relationships between individual driving behaviors and their impact on the overall traffic dynamics of a smart city with two primary objectives: first, understanding the spatial interaction between individual vehicles and their impact on each other, and second, finding anomalous driving behaviors, which lead to congestion and traffic incidents. Specifically, we introduce an overarching modular framework investigating human factors of driver characteristics, vehicle attributes, geographical terrain surrounding the road infrastructure, and environmental conditions. Analyzing …

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Remenet: A Memory-Enhanced Gan Model For Intrusion Detection In Transportation Cyber-Physical Systems, Xin Wang, Lianbo Ma, Sajal K. Das, Zhonghua Liu Jan 2025

Remenet: A Memory-Enhanced Gan Model For Intrusion Detection In Transportation Cyber-Physical Systems, Xin Wang, Lianbo Ma, Sajal K. Das, Zhonghua Liu

Computer Science Faculty Research & Creative Works

Ensuring the safety and reliability of Transportation Cyber-Physical Systems (T-CPS) is critical. However, the increasing interconnectedness of T-CPS exposes them to sophisticated cyberattacks, necessitating robust intrusion detection systems (IDS) to safeguard against evolving threats. This paper aims to enhance the security of T-CPS by addressing two key challenges: effective anomaly detection and handling imbalanced datasets in intrusion detection tasks. In this paper, we propose ReMeNet (Reconstruction Memory Network), a novel intrusion detection model that combines a memory module with a GAN-based architecture to enhance anomaly detection and data reconstruction. To address the challenge of imbalanced datasets, we incorporate a Vector …

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Safenav: Safe Path Navigation Using Landmark Based Localization In A Gps-Denied Environment, Ganesh Sapkota, Sanjay Madria Jan 2025

Safenav: Safe Path Navigation Using Landmark Based Localization In A Gps-Denied Environment, Ganesh Sapkota, Sanjay Madria

Computer Science Faculty Research & Creative Works

In battlefield environments, adversaries frequently disrupt GPS signals, requiring alternative localization and navigation methods. Traditional vision-based approaches like Simultaneous Localization and Mapping (SLAM) and Visual Odometry (VO) involve complex sensor fusion and high computational demand, whereas range-free methods like DV-HOP face accuracy and stability challenges in sparse, dynamic networks. This paper proposes LanBLoc-BMM, a navigation approach using landmark-based localization (LanBLoc) combined with a battlefield-specific motion model (BMM) and Extended Kalman Filter (EKF). Its performance is benchmarked against three state-of-the-art visual localization algorithms integrated with BMM and Bayesian filters, evaluated on synthetic and real-imitated trajectory datasets using metrics including Average Displacement …

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