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Missouri University of Science and Technology

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Articles 301 - 330 of 1938

Full-Text Articles in Computer Sciences

Demo-Abstract: A Dtn System For Tracking Miners Using Gae-Lstm And Contact Graph Routing In An Underground Mine, Abhay Goyal, Sanjay Kumar Madria, Samuel Frimpong Oct 2023

Demo-Abstract: A Dtn System For Tracking Miners Using Gae-Lstm And Contact Graph Routing In An Underground Mine, Abhay Goyal, Sanjay Kumar Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Localization and prediction of movement of miners in underground mines have been a constant problem more so during a mine disaster. Due to the unavailability of GPS signals, the pillars are used as a method to locate these miners, and thus, location prediction is also carried out with reference to these pillars. In this work, we demon- strate a Delay-tolerant Network (DTN) system called Miner-Finder that leverages Machine Learning (ML) framework (GAE-LSTM) that works on edge devices (e.g., mobile phones, tablets) to predict the location of miners in an underground mine. The information such as speed, angle, time, nearest pillar …

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Hprop: Hierarchical Privacy-Preserving Route Planning For Smart Cities, Francis Tiausas, Keiichi Yasumoto, Jose Paolo Talusan, Hayato Yamana, Hirozumi Yamaguchi, Shameek Bhattacharjee, Abhishek Dubey, Sajal K. Das Oct 2023

Hprop: Hierarchical Privacy-Preserving Route Planning For Smart Cities, Francis Tiausas, Keiichi Yasumoto, Jose Paolo Talusan, Hayato Yamana, Hirozumi Yamaguchi, Shameek Bhattacharjee, Abhishek Dubey, Sajal K. Das

Computer Science Faculty Research & Creative Works

Route Planning Systems (RPS) are a core component of autonomous personal transport systems essential for safe and efficient navigation of dynamic urban environments with the support of edge-based smart city infrastructure, but they also raise concerns about user route privacy in the context of both privately owned and commercial vehicles. Numerous high-profile data breaches in recent years have fortunately motivated research on privacy preserving RPS, but most of them are rendered impractical by greatly increased communication and processing overhead. We address this by proposing an approach called Hierarchical Privacy-Preserving Route Planning (HPRoP), which divides and distributes the route-planning task across …

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Catching Elusive Depression Via Facial Micro-Expression Recognition, Xiaohui Chen, Tony Tie (T.) Luo Oct 2023

Catching Elusive Depression Via Facial Micro-Expression Recognition, Xiaohui Chen, Tony Tie (T.) Luo

Computer Science Faculty Research & Creative Works

Depression is a common mental health disorder that can cause consequential symptoms with continuously depressed mood that leads to emotional distress. One category of depression is Concealed Depression, where patients intentionally or unintentionally hide their genuine emotions through exterior optimism, thereby complicating and delaying diagnosis and treatment and leading to unexpected suicides. In this article, we propose to diagnose concealed depression by using facial micro-expressions (FMEs) to detect and recognize underlying true emotions. However, the extremely low intensity and subtle nature of FMEs make their recognition a tough task. We propose a facial landmark-based Region-of-Interest (ROI) approach to address the …

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Affine Image Registration Of Arterial Spin Labeling Mri Using Deep Learning Networks, Zongpai Zhang, Huiyuan Yang, Yanchen Guo, Nicolas R. Bolo, Matcheri Keshavan, Eve Derosa, Adam K. Anderson, David C. Alsop, Lijun Yin, Weiying Dai Oct 2023

Affine Image Registration Of Arterial Spin Labeling Mri Using Deep Learning Networks, Zongpai Zhang, Huiyuan Yang, Yanchen Guo, Nicolas R. Bolo, Matcheri Keshavan, Eve Derosa, Adam K. Anderson, David C. Alsop, Lijun Yin, Weiying Dai

Computer Science Faculty Research & Creative Works

Convolutional neural networks (CNN) have demonstrated good accuracy and speed in spatially registering high signal-to-noise ratio (SNR) structural magnetic resonance imaging (sMRI) images. However, some functional magnetic resonance imaging (fMRI) images, e.g., those acquired from arterial spin labeling (ASL) perfusion fMRI, are of intrinsically low SNR and therefore the quality of registering ASL images using CNN is not clear. In this work, we aimed to explore the feasibility of a CNN-based affine registration network (ARN) for registration of low-SNR three-dimensional ASL perfusion image time series and compare its performance with that from the state-of-the-art statistical parametric mapping (SPM) algorithm. The …

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Edge-Computing-Driven Internet Of Things: A Survey, Linghe Kong, Jinlin Tan, Junqin Huang, Guihai Chen, Shuaitian Wang, Xi Jin, Peng Zeng, Muhammad Khan, Sajal K. Das Sep 2023

Edge-Computing-Driven Internet Of Things: A Survey, Linghe Kong, Jinlin Tan, Junqin Huang, Guihai Chen, Shuaitian Wang, Xi Jin, Peng Zeng, Muhammad Khan, Sajal K. Das

Computer Science Faculty Research & Creative Works

The Internet of Things (IoT) is impacting the world's connectivity landscape. More and more IoT devices are connected, bringing many benefits to our daily lives. However, the influx of IoT devices poses non-trivial challenges for the existing cloud-Based computing paradigm. in the cloud-Based architecture, a large amount of IoT data is transferred to the cloud for data management, analysis, and decision making. It could not only cause a heavy workload on the cloud but also result in unacceptable network latency, ultimately undermining the benefits of cloud-Based computing. to address these challenges, researchers are looking for new computing models for the …

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Is Performance Fairness Achievable In Presence Of Attackers Under Federated Learning?, Ashish Gupta, George Markowsky, Sajal K. Das Sep 2023

Is Performance Fairness Achievable In Presence Of Attackers Under Federated Learning?, Ashish Gupta, George Markowsky, Sajal K. Das

Computer Science Faculty Research & Creative Works

In the last few years, Federated Learning (FL) has received extensive attention from the research community because of its capability for privacy-preserving, collaborative learning from heterogeneous data sources. Most FL studies focus on either average performance improvement or the robustness to attacks, while some attempt to solve both jointly. However, the performance disparities across clients in the presence of attackers have largely been unexplored. In this work, we propose a novel Fair Federated Learning scheme with Attacker Detection capability (abbreviated as FFL+AD) to minimize performance discrepancies across benign participants. FFL+AD enables the server to identify attackers and learn their malign …

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Is Performance Fairness Achievable In Presence Of Attackers Under Federated Learning?, Ashish Gupta, George Markowsky, Sajal K. Das Sep 2023

Is Performance Fairness Achievable In Presence Of Attackers Under Federated Learning?, Ashish Gupta, George Markowsky, Sajal K. Das

Computer Science Faculty Research & Creative Works

In the last few years, Federated Learning (FL) has received extensive attention from the research community because of its capability for privacy-preserving, collaborative learning from heterogeneous data sources. Most FL studies focus on either average performance improvement or the robustness to attacks, while some attempt to solve both jointly. However, the performance disparities across clients in the presence of attackers have largely been unexplored. in this work, we propose a novel Fair Federated Learning scheme with Attacker Detection capability (abbreviated as FFL+AD) to minimize performance discrepancies across benign participants. FFL+AD enables the server to identify attackers and learn their malign …

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Qc-Sane: Robust Control In Drl Using Quantile Critic With Spiking Actor And Normalized Ensemble, Surbhi Gupta, Gaurav Singal, Deepak Garg, Sarangapani Jagannathan Sep 2023

Qc-Sane: Robust Control In Drl Using Quantile Critic With Spiking Actor And Normalized Ensemble, Surbhi Gupta, Gaurav Singal, Deepak Garg, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Recently Introduced Deep Reinforcement Learning (DRL) Techniques in Discrete-Time Have Resulted in Significant Advances in Online Games, Robotics, and So On. Inspired from Recent Developments, We Have Proposed an Approach Referred to as Quantile Critic with Spiking Actor and Normalized Ensemble (QC-SANE) for Continuous Control Problems, Which Uses Quantile Loss to Train Critic and a Spiking Neural Network (NN) to Train an Ensemble of Actors. the NN Does an Internal Normalization using a Scaled Exponential Linear Unit (SELU) Activation Function and Ensures Robustness. the Empirical Study on Multijoint Dynamics with Contact (MuJoCo)-Based Environments Shows Improved Training and Test Results Than …

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Libra: Harvesting Idle Resources Safely And Timely In Serverless Clusters, Hanfei Yu, Christian Fontenot, Hao Wang, Jian Li, Xu Yuan, Seung Jong Park Aug 2023

Libra: Harvesting Idle Resources Safely And Timely In Serverless Clusters, Hanfei Yu, Christian Fontenot, Hao Wang, Jian Li, Xu Yuan, Seung Jong Park

Computer Science Faculty Research & Creative Works

Serverless computing has been favored by users and infrastructure providers from various industries, including online services and scientific computing. Users enjoy its auto-scaling and ease-of-management, and providers own more control to optimize their service. However, existing serverless platforms still require users to pre-define resource allocations for their functions, leading to frequent misconfiguration by inexperienced users in practice. Besides, functions' varying input data further escalate the gap between their dynamic resource demands and static allocations, leaving functions either over-provisioned or under-provisioned. This paper presents Libra, a safe and timely resource harvesting framework for multi-node serverless clusters. Libra makes precise harvesting decisions …

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An Integrated Finite Element Method And Machine Learning Algorithm For Brain Morphology Prediction, Poorya Chavoshnejad, Liangjun Chen, Xiaowei Yu, Jixin Hou, Nicholas Filla, Dajiang Zhu, Tianming Liu, Gang Li, Mir Jalil Razavi, Xianqiao Wang Aug 2023

An Integrated Finite Element Method And Machine Learning Algorithm For Brain Morphology Prediction, Poorya Chavoshnejad, Liangjun Chen, Xiaowei Yu, Jixin Hou, Nicholas Filla, Dajiang Zhu, Tianming Liu, Gang Li, Mir Jalil Razavi, Xianqiao Wang

Computer Science Faculty Research & Creative Works

The human brain development experiences a complex evolving cortical folding from a smooth surface to a convoluted ensemble of folds. Computational modeling of brain development has played an essential role in better understanding the process of cortical folding but still leaves many questions to be answered. A major challenge faced by computational models is how to create massive brain developmental simulations with affordable computational sources to complement neuroimaging data and provide reliable predictions for brain folding. In this study, we leveraged the power of machine learning in data augmentation and prediction to develop a machine-learning-based finite element surrogate model to …

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Neuro-Symbolic Representations For Information Retrieval, Laura Dietz, Hannah Bast, Shubham Chatterjee, Jeff Dalton, Jian Yun Nie, Rodrigo Nogueira Jul 2023

Neuro-Symbolic Representations For Information Retrieval, Laura Dietz, Hannah Bast, Shubham Chatterjee, Jeff Dalton, Jian Yun Nie, Rodrigo Nogueira

Computer Science Faculty Research & Creative Works

This tutorial will provide an overview of recent advances on neuro-symbolic approaches for information retrieval. A decade ago, knowledge graphs and semantic annotations technology led to active research on how to best leverage symbolic knowledge. At the same time, neural methods have demonstrated to be versatile and highly effective. From a neural network perspective, the same representation approach can service document ranking or knowledge graph reasoning. End-to-end training allows to optimize complex methods for downstream tasks. We are at the point where both the symbolic and the neural research advances are coalescing into neuro-symbolic approaches. The underlying research questions are …

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Generative Relevance Feedback With Large Language Models, Iain Mackie, Shubham Chatterjee, Jeffrey Dalton Jul 2023

Generative Relevance Feedback With Large Language Models, Iain Mackie, Shubham Chatterjee, Jeffrey Dalton

Computer Science Faculty Research & Creative Works

Current query expansion models use pseudo-relevance feedback to improve first-pass retrieval effectiveness; however, this fails when the initial results are not relevant. Instead of building a language model from retrieved results, we propose Generative Relevance Feedback (GRF) that builds probabilistic feedback models from long-form text generated from Large Language Models. We study the effective methods for generating text by varying the zero-shot generation subtasks: queries, entities, facts, news articles, documents, and essays. We evaluate GRF on document retrieval benchmarks covering a diverse set of queries and document collections, and the results show that GRF methods significantly outperform previous PRF methods. …

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Detecting Mental Distresses Using Social Behavior Analysis In The Context Of Covid-19: A Survey, Sahraoui Dhelim, Liming Chen, Sajal K. Das, Huansheng Ning, Chris Nugent, Gerard Leavey, Dirk Pesch, Eleanor Bantry-White, Devin Michael Burns Jul 2023

Detecting Mental Distresses Using Social Behavior Analysis In The Context Of Covid-19: A Survey, Sahraoui Dhelim, Liming Chen, Sajal K. Das, Huansheng Ning, Chris Nugent, Gerard Leavey, Dirk Pesch, Eleanor Bantry-White, Devin Michael Burns

Computer Science Faculty Research & Creative Works

Online social media provides a channel for monitoring people's social behaviors from which to infer and detect their mental distresses. During the COVID-19 pandemic, online social networks were increasingly used to express opinions, views, and moods due to the restrictions on physical activities and in-person meetings, leading to a significant amount of diverse user-generated social media content. This offers a unique opportunity to examine how COVID-19 changed global behaviors regarding its ramifications on mental well-being. In this article, we surveyed the literature on social media analysis for the detection of mental distress, with a special emphasis on the studies published …

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Use Only What You Need: Judicious Parallelism For File Transfers In High Performance Networks, Md Arifuzzaman, Engin Arslan Jun 2023

Use Only What You Need: Judicious Parallelism For File Transfers In High Performance Networks, Md Arifuzzaman, Engin Arslan

Computer Science Faculty Research & Creative Works

Parallelism is key to efficiently utilizing high-speed research networks when transferring large volumes of data. However, the monolithic design of existing transfer applications requires the same level of parallelism to be used for reading, write, and network operations for file transfers. This, in turn, overburdens system resources since setting the parallelism level for the slowest component results in unnecessarily high parallelism for other components. Using more than necessary parallelism led to increased overhead on system resources and unfair resource allocation among competing transfers. In this paper, we introduce modular file transfer architecture, Marlin, to separate I/O and network operations for …

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Grm: Generative Relevance Modeling Using Relevance-Aware Sample Estimation For Document Retrieval, Iain Mackie, Ivan Sekulic, Shubham Chatterjee, Jeffrey Dalton, Fabio Crestani Jun 2023

Grm: Generative Relevance Modeling Using Relevance-Aware Sample Estimation For Document Retrieval, Iain Mackie, Ivan Sekulic, Shubham Chatterjee, Jeffrey Dalton, Fabio Crestani

Computer Science Faculty Research & Creative Works

Recent studies show that Generative Relevance Feedback (GRF), using text generated by Large Language Models (LLMs), can enhance the effectiveness of query expansion. However, LLMs can generate irrelevant information that harms retrieval effectiveness. To address this, we propose Generative Relevance Modeling (GRM) that uses Relevance-Aware Sample Estimation (RASE) for more accurate weighting of expansion terms. Specifically, we identify similar real documents for each generated document and use a neural re-ranker to estimate their relevance. Experiments on three standard document ranking benchmarks show that GRM improves MAP by 6-9% and R@1k by 2-4%, surpassing previous methods.

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When Brain-Inspired Ai Meets Agi, Lin Zhao, Lu Zhang, Zihao Wu, Yuzhong Chen, Haixing Dai, Xiaowei Yu, Zhengliang Liu, Tuo Zhang, Xintao Hu, Xi Jiang, Xiang Li, Dajiang Zhu, Dinggang Shen, Tianming Liu Jun 2023

When Brain-Inspired Ai Meets Agi, Lin Zhao, Lu Zhang, Zihao Wu, Yuzhong Chen, Haixing Dai, Xiaowei Yu, Zhengliang Liu, Tuo Zhang, Xintao Hu, Xi Jiang, Xiang Li, Dajiang Zhu, Dinggang Shen, Tianming Liu

Computer Science Faculty Research & Creative Works

Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do. To achieve this, AGI researchers draw inspiration from the human brain and seek to replicate its principles in intelligent machines. Brain-inspired artificial intelligence is a field that has emerged from this endeavor, combining insights from neuroscience, psychology, and computer science to develop more efficient and powerful AI systems. In this article, we provide a comprehensive overview of brain-inspired AI from the perspective of AGI. We begin with the current progress in brain-inspired AI …

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Chargex: Exploring State Switching Attack On Electric Vehicle Charging Systems, Ce Zhou, Qiben Yan, Zhiyuan Yu, Eshan Dixit, Ning Zhang, Huacheng Zeng, Alireza Safdari Ghanhdari May 2023

Chargex: Exploring State Switching Attack On Electric Vehicle Charging Systems, Ce Zhou, Qiben Yan, Zhiyuan Yu, Eshan Dixit, Ning Zhang, Huacheng Zeng, Alireza Safdari Ghanhdari

Computer Science Faculty Research & Creative Works

Electric Vehicle (EV) has become one of the promising solutions to the ever-evolving environmental and energy crisis. The key to the wide adoption of EVs is a pervasive charging infrastructure, composed of both private/home chargers and public/commercial charging stations. The security of EV charging, however, has not been thoroughly investigated. This paper investigates the communication mechanisms between the chargers and EVs and exposes the lack of protection on the authenticity in the SAE J1772 charging control protocol. To showcase our discoveries, we propose a new class of attacks, ChargeX, which aims to manipulate the charging states or charging rates of …

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Generative And Pseudo-Relevant Feedback For Sparse, Dense And Learned Sparse Retrieval, Iain Mackie, Shubham Chatterjee, Jeffrey Dalton May 2023

Generative And Pseudo-Relevant Feedback For Sparse, Dense And Learned Sparse Retrieval, Iain Mackie, Shubham Chatterjee, Jeffrey Dalton

Computer Science Faculty Research & Creative Works

Pseudo-relevance feedback (PRF) is a classical approach to address lexical mismatch by enriching the query using first-pass retrieval. Moreover, recent work on generative-relevance feedback (GRF) shows that query expansion models using text generated from large language models can improve sparse retrieval without depending on first-pass retrieval effectiveness. This work extends GRF to dense and learned sparse retrieval paradigms with experiments over six standard document ranking benchmarks. We find that GRF improves over comparable PRF techniques by around 10% on both precision and recall-oriented measures. Nonetheless, query analysis shows that GRF and PRF have contrasting benefits, with GRF providing external context …

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Fedar+: A Federated Learning Approach To Appliance Recognition With Mislabeled Data In Residential Environments, Ashish Gupta, Hari Prabhat Gupta, Sajal K. Das May 2023

Fedar+: A Federated Learning Approach To Appliance Recognition With Mislabeled Data In Residential Environments, Ashish Gupta, Hari Prabhat Gupta, Sajal K. Das

Computer Science Faculty Research & Creative Works

With the enhancement of people's living standards and the rapid evolution of cyber-physical systems, residential environments are becoming smart and well-connected, causing a significant raise in overall energy consumption. as household appliances are major energy consumers, their accurate recognition becomes crucial to avoid unattended usage and minimize peak-time load on the smart grids, thereby conserving energy and making smart environments more sustainable. Traditionally, an appliance recognition model is trained at a central server (service provider) by collecting electricity consumption data via smart plugs from the clients (consumers), causing a privacy breach. Besides that, the data are susceptible to noisy labels …

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Fedar+: A Federated Learning Approach To Appliance Recognition With Mislabeled Data In Residential Environments, Ashish Gupta, Hari Prabhat Gupta, Sajal K. Das May 2023

Fedar+: A Federated Learning Approach To Appliance Recognition With Mislabeled Data In Residential Environments, Ashish Gupta, Hari Prabhat Gupta, Sajal K. Das

Computer Science Faculty Research & Creative Works

With the enhancement of people's living standards and the rapid evolution of cyber-physical systems, residential environments are becoming smart and well-connected, causing a significant raise in overall energy consumption. As household appliances are major energy consumers, their accurate recognition becomes crucial to avoid unattended usage and minimize peak-time load on the smart grids, thereby conserving energy and making smart environments more sustainable. Traditionally, an appliance recognition model is trained at a central server (service provider) by collecting electricity consumption data via smart plugs from the clients (consumers), causing a privacy breach. Besides that, the data are susceptible to noisy labels …

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Core-Periphery Principle Guided Redesign Of Self-Attention In Transformers, Xiaowei Yu, Lu Zhang, Haixing Dai, Yanjun Lyu, Lin Zhao, Zihao Wu, David Liu, Tianming Liu, Daijiang Zhu Mar 2023

Core-Periphery Principle Guided Redesign Of Self-Attention In Transformers, Xiaowei Yu, Lu Zhang, Haixing Dai, Yanjun Lyu, Lin Zhao, Zihao Wu, David Liu, Tianming Liu, Daijiang Zhu

Computer Science Faculty Research & Creative Works

Designing more efficient, reliable, and explainable neural network architectures is critical to studies that are based on artificial intelligence (AI) techniques. Numerous efforts have been devoted to exploring the best structures, or structural signatures, of well-performing artificial neural networks (ANN). Previous studies, by post-hoc analysis, have found that the best-performing ANNs surprisingly resemble biological neural networks (BNN), which indicates that ANNs and BNNs may share some common principles to achieve optimal performance in either machine learning or cognitive/behavior tasks. Inspired by this phenomenon, rather than relying on post-hoc schemes, we proactively instill organizational principles of BNNs to guide the redesign …

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Building A Unified Data Falsification Threat Landscape For Internet Of Things/Cyberphysical Systems Applications, Shameek Bhattacharjee, Sajal K. Das Mar 2023

Building A Unified Data Falsification Threat Landscape For Internet Of Things/Cyberphysical Systems Applications, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

We Lay Out a Blueprint of a Complete and Parameterized Threat Landscape for Data Falsification/false Data Injection Attacks on Telemetry Data Collected from Internet of Things/cyberphysical Systems Applications under Zero-Trust Assumptions, Helping to Enable Better Validation of Anomaly-Based Attack Detection Methods.

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Customised Multi-Energy Pricing: Model And Solutions, Qiuyi Hong, Fanlin Meng, Jian Liu Feb 2023

Customised Multi-Energy Pricing: Model And Solutions, Qiuyi Hong, Fanlin Meng, Jian Liu

Electrical and Computer Engineering Faculty Research & Creative Works

With the increasing interdependence among energies (e.g., electricity, natural gas and heat) and the development of a decentralized energy system, a novel retail pricing scheme in the multi-energy market is demanded. Therefore, the problem of designing a customized multi-energy pricing scheme for energy retailers is investigated in this paper. In particular, the proposed pricing scheme is formulated as a bilevel optimization problem. At the upper level, the energy retailer (leader) aims to maximize its profit. Microgrids (followers) equipped with energy converters, storage, renewable energy sources (RES) and demand response (DR) programs are located at the lower level and minimize their …

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A Dtn-Based Spatio-Temporal Routing Using Location Prediction Model In Underground Mines, Abhay Goyal, Sanjay Kumar Madria, Samuel Frimpong Jan 2023

A Dtn-Based Spatio-Temporal Routing Using Location Prediction Model In Underground Mines, Abhay Goyal, Sanjay Kumar Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Situational awareness during any disaster depends on effective communication and location tracking. In the case of underground mines, where the communication methods are mostly central, the whole communication channel would be rendered unusable during a disaster. To this end, we propose the use of Delay Tolerant Networks (DTN) to allow the miners to function in a distributed manner and help in locating the injured miners and routing distress messages. Due to the unavailability of GPS signals, the pillar numbers are used to identify the locations of the miners. For spatio-temporal routing of messages, we formulate a new scheme using Contact …

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Scalable Skill-Oriented Task Allocation In Crowdsourcing Within A Serverless Ecosystem, Biswajeet Sethi, Riya Samanta, Soumya K. Ghosh, Sajal K. Das Jan 2023

Scalable Skill-Oriented Task Allocation In Crowdsourcing Within A Serverless Ecosystem, Biswajeet Sethi, Riya Samanta, Soumya K. Ghosh, Sajal K. Das

Computer Science Faculty Research & Creative Works

Allocating the most competent crowdworkers to each upcoming task is a fundamental challenge in crowdsourcing. The mechanism becomes complicated when the arriving tasks require a high level of expertise within a constrained budget. The validation of skill matching between tasks and crowdworkers adds a new dimension to the traditional problem of task allocation. In addition, in real-world scenarios, the influx of both tasks and workers is dynamic, making it nearly impossible to predict the precise amount of computational resources required for the crowdsourcing platform to operate efficiently. Serverless computing is a new pay-per-use, auto-scalable, Function-as-a-Service based model, that ensures parallel …

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Sptframe: A Framework For Spatio-Temporal Information Aware Message Dissemination In Software Defined Vehicular Networks, Ankur Nahar, Debasis Das, Sajal K. Das Jan 2023

Sptframe: A Framework For Spatio-Temporal Information Aware Message Dissemination In Software Defined Vehicular Networks, Ankur Nahar, Debasis Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

The volume of vehicular network traffic is very context (time and geographic location) and technology-dependent. Considering both multi-hop geocast and single-hop broadcast techniques, the route availability can be affected by transient and permanent traffic variations. Therefore, our research tackles one of the most pressing challenges in vehicular ad-hoc networks (VANETs), i.e., accommodating fine-grained spatio-temporal variance in vehicular density over time and space. This article proposes a new framework called SpTFrame to achieve fast message dissemination. The proposed approach uses a software-defined vehicular networks (SDVNs) architecture along with a deep reinforcement learning (DRL) model. SpTFrame employs a convolutional neural network (CNN) …

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A Parallel Framework For Efficiently Updating Graph Properties In Large Dynamic Networks, Arindam Khanda, Sajal K. Das Jan 2023

A Parallel Framework For Efficiently Updating Graph Properties In Large Dynamic Networks, Arindam Khanda, Sajal K. Das

Computer Science Faculty Research & Creative Works

Graph queries on large networks leverage the stored graph properties to provide faster results. Since real-world graphs are mostly dynamic, i.e., the graph topology changes over time, the corresponding graph attributes also change over time. In certain situations, recompiling or updating earlier properties is necessary to maintain the accuracy of a response to a graph query. Here, we first propose a generic framework for developing parallel algorithms to update graph properties on large dynamic networks. We use our framework to develop algorithms for updating Single Source Shortest Path (SSSP) and Vertex Color. Then we propose applications of the developed algorithms …

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A Parallel Framework For Efficiently Updating Graph Properties In Large Dynamic Networks, Arindam Khanda, Sajal K. Das Jan 2023

A Parallel Framework For Efficiently Updating Graph Properties In Large Dynamic Networks, Arindam Khanda, Sajal K. Das

Computer Science Faculty Research & Creative Works

Graph queries on large networks leverage the stored graph properties to provide faster results. Since real-world graphs are mostly dynamic, i.e., the graph topology changes over time, the corresponding graph attributes also change over time. In certain situations, recompiling or updating earlier properties is necessary to maintain the accuracy of a response to a graph query. Here, we first propose a generic framework for developing parallel algorithms to update graph properties on large dynamic networks. We use our framework to develop algorithms for updating Single Source Shortest Path (SSSP) and Vertex Color. Then we propose applications of the developed algorithms …

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Programmable Software-Defined Testbed For Visible Light Uav Networks: Architecture Design And Implementation, Yue Zhang, Nan Cen Jan 2023

Programmable Software-Defined Testbed For Visible Light Uav Networks: Architecture Design And Implementation, Yue Zhang, Nan Cen

Computer Science Faculty Research & Creative Works

As of Today, There Has Been Increasing Research on Designing Optimization Algorithms and Intelligent Network Control Methods for Visible Light Unmanned Aerial Vehicles (UAV) Networks to Provide Pervasive and Broadband Connections. for Those Theoretical Analysis based Algorithms, there is an Urgent Need to Have a Visible Light UAV Network Platform that Can Help Evaluate the Proposed Algorithms in Real-World Scenarios. However, to the Best of Our Knowledge, there is Currently No Dedicated High Data Rate and Flexible Visible Light UAV Networking Prototype. to Bridge This Gap, in This Paper, We First Design a Novel Programmable Software-Defined Architecture for Visible Light …

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Using Geographic Location-Based Public Health Features In Survival Analysis, Navid Seidi, Ardhendu S. Tripathy, Sajal K. Das Jan 2023

Using Geographic Location-Based Public Health Features In Survival Analysis, Navid Seidi, Ardhendu S. Tripathy, Sajal K. Das

Computer Science Faculty Research & Creative Works

Time elapsed till an event of interest is often modeled using the survival analysis methodology, which estimates a survival score based on the input features. There is a resurgence of interest in developing more accurate prediction models for time-to-event prediction in personalized healthcare using modern tools such as neural networks. Higher quality features and more frequent observations improve the predictions for a patient, however, the impact of including a patient's geographic location-Based public health statistics on individual predictions has not been studied. This paper proposes a complementary improvement to survival analysis models by incorporating public health statistics in the input …

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