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Articles 91 - 120 of 1938
Full-Text Articles in Computer Sciences
Qder: Query-Specific Document And Entity Representations For Multi-Vector Document Re-Ranking, Shubham Chatterjee, Jeff Dalton
Qder: Query-Specific Document And Entity Representations For Multi-Vector Document Re-Ranking, Shubham Chatterjee, Jeff Dalton
Computer Science Faculty Research & Creative Works
Neural IR has advanced through two distinct paths: entity-oriented approaches leveraging knowledge graphs and multi-vector models capturing fine-grained semantics. We introduce QDER, a neural re-ranking model that unifies these approaches by integrating knowledge graph semantics into a multi-vector model. QDER's key innovation lies in its modeling of query-document relationships: rather than computing similarity scores on aggregated embeddings, we maintain individual token and entity representations throughout the ranking process, performing aggregation only at the final scoring stage-an approach we call "late aggregation." We first transform these fine-grained representations through learned attention patterns, then apply carefully chosen mathematical operations for precise matches. …
Domain-Adaptive Diagnosis Of Lewy Body Disease With Transferability Aware Transformer, Xiaowei Yu, Jing Zhang, Tong Chen, Yan Zhuang, Minheng Chen, Chao Cao, Yanjun Lyu, Lu Zhang, Li Su, Tianming Liu, Dajiang Zhu
Domain-Adaptive Diagnosis Of Lewy Body Disease With Transferability Aware Transformer, Xiaowei Yu, Jing Zhang, Tong Chen, Yan Zhuang, Minheng Chen, Chao Cao, Yanjun Lyu, Lu Zhang, Li Su, Tianming Liu, Dajiang Zhu
Computer Science Faculty Research & Creative Works
Lewy Body Disease (LBD) is a common yet understudied form of dementia that imposes a significant burden on public health. It shares clinical similarities with Alzheimer’s disease (AD), as both progress through stages of normal cognition, mild cognitive impairment, and dementia. A major obstacle in LBD diagnosis is data scarcity, which limits the effectiveness of deep learning. In contrast, AD datasets are more abundant, offering potential for knowledge transfer. However, LBD and AD data are typically collected from different sites using different machines and protocols, resulting in a distinct domain shift. To effectively leverage AD data while mitigating domain shift, …
Coordinating Instruments For Multi-Messenger Astrophysics, Daisy Wang, Ye Htet, Marion Sudvarg, Roger Chamberlain, Jeremy Buhler, James Buckley
Coordinating Instruments For Multi-Messenger Astrophysics, Daisy Wang, Ye Htet, Marion Sudvarg, Roger Chamberlain, Jeremy Buhler, James Buckley
Computer Science Faculty Research & Creative Works
In multi-messenger astrophysics, signals of multiple types (e.g., gravitational waves, neutrinos, electromagnetic waves) are combined in an effort to learn more about the observed phenomena of interest. The Advanced Particle-astrophyics Telescope (APT) is a mission concept for a space-borne instrument that detects gammaray bursts (GRBs) omnidirectionally, facilitating multi-messenger observations by identifying and localizing celestial events of interest. Here, we describe the on-instrument computations for APT and its Antarctic Demonstrator (ADAPT) as well as techniques for follow-up observations of transient events.
Maximizing Edge Connectivity In Graph Partitioning Using Hotspots, Isam A. Alobaidi, Hiba G. Fareed, Jennifer L. Leopold, Andrea E. Smith
Maximizing Edge Connectivity In Graph Partitioning Using Hotspots, Isam A. Alobaidi, Hiba G. Fareed, Jennifer L. Leopold, Andrea E. Smith
Computer Science Faculty Research & Creative Works
Graphs have long been used to model relationships between entities. For some applications, a single graph is sufficient; for other problems, a collection of graphs may be more appropriate to represent the underlying data. Many contemporary problem domains, for which graphs are an ideal data model, contain an enormous amount of data (e.g., social networks). Hence, researchers frequently employ parallelized or distributed processing. The graph data must first be partitioned and assigned to the multiple processors in a way that the workload is balanced and inter-processor communication is minimized. The latter problem may be complicated by the existence of edges …
Predicting Battery Levels Of Sensor Nodes Using Reinforcement Learning In Harsh Underground Mining Environments, Manish Anand Yadav, Mohamed Elmahallawy, Sanjay Madria, Samuel Frimpong
Predicting Battery Levels Of Sensor Nodes Using Reinforcement Learning In Harsh Underground Mining Environments, Manish Anand Yadav, Mohamed Elmahallawy, Sanjay Madria, Samuel Frimpong
Computer Science Faculty Research & Creative Works
Underground mining is a hazardous environment, with frequent accidents leading to significant loss of life each year. To enhance safety, sensor nodes monitor key environmental factors such as temperature, toxic gases, and miners' locations, as well as transmit critical messages. Miners interact with these sensors, which track their movements, enabling their location to be determined even without GPS signals. Therefore, predicting the battery life of these sensors is essential for: (i) rerouting miners during emergencies, (ii) ensuring timely maintenance, and most importantly (iii) identifying sensors that need energy harvesting to maintain vital communication within the mine. In this work, we …
Beyond Boundaries: A Comprehensive Survey Of Transferable Attacks On Ai Systems, Guangjing Wang, Ce Zhou, Yuanda Wang, Bocheng Chen, Hanqing Guo, Qiben Yan
Beyond Boundaries: A Comprehensive Survey Of Transferable Attacks On Ai Systems, Guangjing Wang, Ce Zhou, Yuanda Wang, Bocheng Chen, Hanqing Guo, Qiben Yan
Computer Science Faculty Research & Creative Works
As Artificial Intelligence (AI) systems increasingly underpin critical applications, from autonomous vehicles to biometric authentication, their vulnerability to transferable attacks presents a growing concern. These attacks, designed to generalize across instances, domains, models, tasks, modalities, or even hardware platforms, pose severe risks to security, privacy, and system integrity. This survey delivers the first comprehensive review of transferable attacks across seven major categories, including evasion, backdoor, data poisoning, model stealing, model inversion, membership inference, and side-channel attacks. We introduce a unified six-dimensional taxonomy: cross-instance, cross-domain, cross-modality, cross-model, cross-task, and cross-hardware, which systematically captures the diverse transfer pathways of adversarial strategies. Through …
Fact-Based Counter Narrative Generation To Combat Hate Speech, Brian Wilk, Homaira Huda Shomee, Suman Kalyan Maity, Sourav Medya
Fact-Based Counter Narrative Generation To Combat Hate Speech, Brian Wilk, Homaira Huda Shomee, Suman Kalyan Maity, Sourav Medya
Computer Science Faculty Research & Creative Works
Online hatred has become an increasingly pervasive issue, affecting individuals and communities across various digital platforms. To combat hate speech in such platforms, counter narratives (CNs) are regarded as an effective method. In recent years, there has been growing interest in using generative AI tools to construct CNs. However, most of the generative models produce generic responses to hate speech and can hallucinate, reducing their effectiveness. To address the above limitations, we propose a counter narrative generation method that enhances CNs by providing non-aggressive, fact-based narratives with relevant background knowledge from two distinct sources, including a web search module. Furthermore, …
Introducing Gridtrees For File Browsing And Hierarchical Data Visualization, Nathan Tibbetts, Satish Puri
Introducing Gridtrees For File Browsing And Hierarchical Data Visualization, Nathan Tibbetts, Satish Puri
Miners Solving for Tomorrow Research Conference
No abstract provided.
Unsupervised Contrastive Learning Based Clustering For Robust Emotion Classification, Nozaer Omar, Sanjay Kumar Madria
Unsupervised Contrastive Learning Based Clustering For Robust Emotion Classification, Nozaer Omar, Sanjay Kumar Madria
Miners Solving for Tomorrow Research Conference
No abstract provided.
On The Robustness Of Adaptive Resonance Theory Neural Networks, Shane Cairns, Leonardo Enzo Brito Da Silva, Sasha Petrenko, Donald C. Wunsch
On The Robustness Of Adaptive Resonance Theory Neural Networks, Shane Cairns, Leonardo Enzo Brito Da Silva, Sasha Petrenko, Donald C. Wunsch
Miners Solving for Tomorrow Research Conference
No abstract provided.
Replicating And Testing The First Point-Contact Transistor, Lucas Ethington, Lana Herkenhoff, Braden Stillmaker, Punit Turlapati
Replicating And Testing The First Point-Contact Transistor, Lucas Ethington, Lana Herkenhoff, Braden Stillmaker, Punit Turlapati
Miners Solving for Tomorrow Research Conference
No abstract provided.
Analytical Dispatch Strategies For Pumped Storage Hydro: A Conditional Dynamic Programming Approach To Discontinuous Multi-Period Optimization Problems, Jian Liu, Jianwen Zhang, Zaiwu Gong, Donald C. Wunsch, Rui Bo
Analytical Dispatch Strategies For Pumped Storage Hydro: A Conditional Dynamic Programming Approach To Discontinuous Multi-Period Optimization Problems, Jian Liu, Jianwen Zhang, Zaiwu Gong, Donald C. Wunsch, Rui Bo
Electrical and Computer Engineering Faculty Research & Creative Works
The increasing integration of renewable energy sources like wind and solar poses significant challenges to secure and stable grid operation. Energy storage systems, particularly pumped storage hydro (PSH), play a crucial role in balancing power supply and demand. Traditional analytical studies of PSH economic dispatch problems often assume zero lower bounds for generating and pumping rates to simplify analysis and derive analytical solutions for multi-period optimization problems. However, the inherent mechanical design constraints of PSH require non-zero minimum flow rates for efficient operation. We analyze two scenarios, merchants having PSH only and merchants having both PSH and wind farms. In …
A Comprehensive Survey Of Data-Driven Solutions For Lorawan: Challenges And Future Directions, Poonam Maurya, Abhishek Hazra, Preti Kumari, Troels Bundgaard Sørensen, Sajal K. Das
A Comprehensive Survey Of Data-Driven Solutions For Lorawan: Challenges And Future Directions, Poonam Maurya, Abhishek Hazra, Preti Kumari, Troels Bundgaard Sørensen, Sajal K. Das
Computer Science Faculty Research & Creative Works
Long-range Wide-area Network (LoRaWAN) is an innovative and prominent communication protocol in the domain of Low-power Wide-area Networks (LPWAN), known for its ability to provide long-range communication with low energy consumption. However, the practical implementation of the LoRaWAN protocol, operating at the Medium Access Control layer and specially built to work upon the LoRa physical layer, presents numerous research challenges, including network congestion, interference, optimal resource allocation, collisions, scalability, and security. To mitigate these challenges effectively, the adoption of cutting-edge data-driven technologies such as Deep Learning (DL) and Machine Learning (ML) emerges as a promising approach. Interestingly, very few existing …
Few-Shot Transfer Learning For Individualized Braking Intent Detection On Neuromorphic Hardware, Nathan A. Lutes, V. Sriram Siddhardth Nedendla, K. Krishnamurthy
Few-Shot Transfer Learning For Individualized Braking Intent Detection On Neuromorphic Hardware, Nathan A. Lutes, V. Sriram Siddhardth Nedendla, K. Krishnamurthy
Mechanical and Aerospace Engineering Faculty Research & Creative Works
This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a Brain Chip Akida AKD1000 neuromorphic system-on-chip for developing individual-level, instead of traditionally used group-level, models using electroencephalographic data. The efficacy of the method is studied on an advanced driver assist system related task of predicting braking intention. Approach. Data are collected from participants operating an NVIDIA JetBot on a testbed simulating urban streets for three different scenarios. Participants receive a braking indicator in the form of: (1) an audio countdown in a nominal baseline, stress-free environment; (2) an …
Electronic Component Authenticity Identification System And Related Methods, Yunghsiao Chung, Feng Yu, Stephen Edward Saddow, Junjie Xiong
Electronic Component Authenticity Identification System And Related Methods, Yunghsiao Chung, Feng Yu, Stephen Edward Saddow, Junjie Xiong
Computer Science Faculty Research & Creative Works
A method and a system for identifying authenticity of an electronic component is disclosed. The method may include obtaining chip data of an electronic component; extracting feature information of the chip data for reducing noise of the chip data; providing the feature information of the chip data to a trained deep learning model; and providing a user with an authenticity indication for the electronic component based on an output of the deep learning model. Other aspects, embodiments, and features are also claimed and described.
Adaptive Workload Management For Enhanced Function Performance In Serverless Computing, Priyanka Ashok Birajdar, V. Harsha, Anurag Satpathy, Sourav Kanti Addya
Adaptive Workload Management For Enhanced Function Performance In Serverless Computing, Priyanka Ashok Birajdar, V. Harsha, Anurag Satpathy, Sourav Kanti Addya
Computer Science Faculty Research & Creative Works
Serverless computing streamlines application deployment by removing the need for infrastructure management, but fluctuating workloads make resource allocation challenging. To solve this, we propose an adaptive workload manager that intelligently balances workloads, optimizes resource use, and adapts to changes with auto-scaling, ensuring efficient and reliable serverless performance. Preliminary experiments demonstrate an ≈ 0.6X% and 2X% improvement in execution time and resource utilization compared to the First-Come-First Serve (FCFS) scheduling algorithm.
Collision-Free Exploration By Mobile Agents Using Pebbles, Sajal K. Das, Amit Kumar Dhar, Barun Gorain, Madhuri Mahawar
Collision-Free Exploration By Mobile Agents Using Pebbles, Sajal K. Das, Amit Kumar Dhar, Barun Gorain, Madhuri Mahawar
Computer Science Faculty Research & Creative Works
In this paper, we study collision-free graph exploration in an anonymous network. The network is modeled as a graph G = (V, E) where the nodes of the graph are unlabeled, and each edge incident to a node v has a unique label, called the port number, in {0, 1, ⋯, d - 1}, where d is the degree of the node v. Two identical mobile agents, starting from different nodes in G have to explore the nodes of G in such a way that for every node v in G, at least one mobile agent visits v and no …
Smevca: Stable Matching-Based Ev Charging Assignment In Subscription-Based Models, Arindam Khanda, Anurag Satpathy, Anusha Vangala, Sajal K. Das
Smevca: Stable Matching-Based Ev Charging Assignment In Subscription-Based Models, Arindam Khanda, Anurag Satpathy, Anusha Vangala, Sajal K. Das
Computer Science Faculty Research & Creative Works
The rapid shift from internal combustion engine vehicles to battery-powered electric vehicles (EVs) presents considerable challenges, such as limited charging points (CPs), unpredictable wait times for charging, and difficulty in selecting appropriate CPs for EVs. To address these challenges, we propose a novel end-to-end framework, called Stable Matching based EV Charging Assignment (SMEVCA) that efficiently assigns charge-seeking EVs to CPs with the assistance of roadside units (RSUs). The proposed framework operates within a subscription-based model, ensuring that the subscribed EVs complete their charging within a predefined time limit enforced by a service level agreement (SLA). The framework SMEVCA employs a …
Elastic Scheduling For Graceful Degradation Of Mixed-Criticality Systems, Zhuoran Sun, Marion Sudvarg, Christopher Gill
Elastic Scheduling For Graceful Degradation Of Mixed-Criticality Systems, Zhuoran Sun, Marion Sudvarg, Christopher Gill
Computer Science Faculty Research & Creative Works
Many mixed-criticality system models drop all jobs of low-criticality tasks when a criticality mode switch occurs, ensuring that high-criticality tasks still can meet their deadlines in the new mode. However, this means that even important low-criticality tasks are discarded, which may not be acceptable in some systems in practice. This paper addresses that distinction between criticality and importance through a new Inelastic Graceful Earliest Deadline First with Virtual Deadlines (IG-EDF-VD) scheme that upon a criticality mode switch only discards the least important low-criticality tasks necessary to ensure feasibility. Moreover, we consider elastic scheduling within our mixed-criticality model (EG-EDF-VD), using compression …
Yolo-Based Miner Detection Using Thermal Images In Underground Mines, Cyrus Addy, Venkata Sriram Siddhardh Nadendla, Kwame Awuah-Offei
Yolo-Based Miner Detection Using Thermal Images In Underground Mines, Cyrus Addy, Venkata Sriram Siddhardh Nadendla, Kwame Awuah-Offei
Computer Science Faculty Research & Creative Works
Well-designed and effective in-mine robots can expedite miner self-rescue during emergencies and reduce fatalities. These in-mine robots for miner self-rescue can carry out diverse tasks such as scouting (including object detection and autonomous navigation), and payload delivery. However, robots that can effectively detect humans in a dark underground mine do not yet exist. This paper investigates challenges in the design of object detection algorithms for in-mine robots using thermal images, especially to detect people in real-time, in low-light conditions. The research team collected 500 thermal images in the Missouri University of Science & Technology Experimental Mine with the help of …
Semi-Supervised Multimodal Multi-Instance Learning For Aortic Stenosis Diagnosis, Zhe Huang, Xiaowei Yu, Benjamin S. Wessler, Michael C. Hughes
Semi-Supervised Multimodal Multi-Instance Learning For Aortic Stenosis Diagnosis, Zhe Huang, Xiaowei Yu, Benjamin S. Wessler, Michael C. Hughes
Computer Science Faculty Research & Creative Works
Automated interpretation of ultrasound imaging of the heart (echocardiograms) could improve the detection and treatment of aortic stenosis (AS), a deadly heart disease. However, existing deep learning pipelines for assessing AS from echocardiograms have two key limitations. First, most methods rely on limited 2D cineloops, thereby ignoring widely available Spectral Doppler imaging that contains important complementary information about pressure gradients and blood flow abnormalities associated with AS. Second, obtaining labeled data is difficult. There are often far more unlabeled echocardiogram recordings available, but these remain underutilized by existing methods. To overcome these limitations, we introduce Semi-supervised Multimodal Multiple-Instance Learning (SMMIL), …
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
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 …
Brain-Adapter: Enhancing Neurological Disorder Analysis With Adapter-Tuning Multimodal Large Language Models, Jing Zhang, Xiaowei Yu, Yanjun Lyu, Lu Zhang, Tong Chen, Chao Cao, Yan Zhuang, Minheng Chen, Tianming Liu, Dajiang Zhu
Brain-Adapter: Enhancing Neurological Disorder Analysis With Adapter-Tuning Multimodal Large Language Models, Jing Zhang, Xiaowei Yu, Yanjun Lyu, Lu Zhang, Tong Chen, Chao Cao, Yan Zhuang, Minheng Chen, Tianming Liu, Dajiang Zhu
Computer Science Faculty Research & Creative Works
Understanding brain disorders is crucial for accurate clinical diagnosis and treatment. Recent advances in Multimodal Large Language Models (MLLMs) offer a promising approach to interpreting medical images with the support of text descriptions. However, previous research has primarily focused on 2D medical images, leaving richer spatial information of 3D images under-explored, and single-modality-based methods are limited by overlooking the critical clinical information contained in other modalities. To address this issue, this paper proposes Brain-Adapter, a novel approach that incorporates an extra bottleneck layer to learn new knowledge and instill it into the original pre-trained knowledge. The major idea is to …
Feature Fusion Transferability Aware Transformer For Unsupervised Domain Adaptation, Xiaowei Yu, Zhe Huang, Zao Zhang
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 …
Echopulse: Ecg Controlled Echocardiograms Video Generation, Yiwei Li, Sekeun Kim, Zihao Wu, Hanqi Jiang, Yi Pan, Pengfei Jin, Sifan Song, Yucheng Shi, Xiaowei Yu, Tianze Yang, Tianming Liu, Quanzheng Li, Xiang Li
Echopulse: Ecg Controlled Echocardiograms Video Generation, Yiwei Li, Sekeun Kim, Zihao Wu, Hanqi Jiang, Yi Pan, Pengfei Jin, Sifan Song, Yucheng Shi, Xiaowei Yu, Tianze Yang, Tianming Liu, Quanzheng Li, Xiang Li
Computer Science Faculty Research & Creative Works
Echocardiography (ECHO) is essential for cardiac assessments, but its video quality and interpretation heavily rely on manual expertise, leading to inconsistent results from clinical and portable devices. ECHO video generation offers a solution by improving automated monitoring through synthetic data and generating high-quality videos from routine health data. However, existing models often face high computational costs, slow inference, and rely on complex conditional prompts that require experts' annotations. To address these challenges, we propose ECHOPulse, an ECG-conditioned ECHO video generation model. ECHOPulse introduces two key advancements: (1) it accelerates ECHO video generation by leveraging VQ-VAE tokenization and masked visual token …
Exploring The Trade-Offs: Unified Large Language Models Vs Local Fine-Tuned Models For Highly-Specific Radiology Nli Task, Zihao Wu, Lu Zhang, Chao Cao, Xiaowei Yu, Zhengliang Liu, Lin Zhao, Yiwei Li, Haixing Dai, Chong Ma, Gang Li, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu
Exploring The Trade-Offs: Unified Large Language Models Vs Local Fine-Tuned Models For Highly-Specific Radiology Nli Task, Zihao Wu, Lu Zhang, Chao Cao, Xiaowei Yu, Zhengliang Liu, Lin Zhao, Yiwei Li, Haixing Dai, Chong Ma, Gang Li, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu
Computer Science Faculty Research & Creative Works
Recently, ChatGPT and GPT-4 have emerged and gained immense global attention due to their unparalleled performance in language processing. Despite demonstrating impressive capability in various open-domain tasks, their adequacy in highly specific fields like radiology remains untested. Radiology presents unique linguistic phenomena distinct from open-domain data due to its specificity and complexity. Assessing the performance of large language models (LLMs) in such specific domains is crucial not only for a thorough evaluation of their overall performance but also for providing valuable insights into future model design directions: whether model design should be generic or domain specific. To this end, in …
Sosta: Skill-Oriented Stable Task Assignment With Bidirectional Preferences In Crowdsourcing, Riya Samanta, Soumya K. Ghosh, Sajal K. Das
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 …
Pervasive Sensing To Correlate Vehicle Driving Behavior With City-Scale Traffic Dynamics, Debasree Das, Shameek Bhattacharjee, Sandip Chakraborty, Bivas Mitra, Sajal K. Das
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 …
Smartsla: Enabling Quality Of Service In Blockchain-Enabled Iot Networks, Kyle M. Whitlatch, Asad Waqar Malik, Sanjay Madria
Smartsla: Enabling Quality Of Service In Blockchain-Enabled Iot Networks, Kyle M. Whitlatch, Asad Waqar Malik, Sanjay Madria
Computer Science Faculty Research & Creative Works
The significant advancement in Internet of Things (IoT) adoption has enabled Multi-access Edge Computing (MEC) to mitigate IoT sensors limited computational, transmission power constraints, and data distribution overhead. However, integrating MEC with the IoT ecosystem poses several challenges, resulting in integrity issues with the MECs, impacting their capacity to effectively serve users seeking data generated by IoT sensors. To address this, we propose SmartSLA, a blockchain based solution to ensure Quality of Service (QoS) from third party IoT devices. SmartSLA leverages the decentralized and immutable nature of blockchain to combat the shortcomings of MECs. Using smart contracts, we develop a …
Parallel Multi Objective Shortest Path Update Algorithm In Large Dynamic Networks, S. M. Shovan, Arindam Khanda, Sajal K. Das
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 …