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Articles 271 - 300 of 919
Full-Text Articles in Computer Sciences
Drone-Truck Cooperated Delivery Under Time Varying Dynamics, Arindam Khanda, Federico Corò, Sajal K. Das
Drone-Truck Cooperated Delivery Under Time Varying Dynamics, Arindam Khanda, Federico Corò, Sajal K. Das
Computer Science Faculty Research & Creative Works
Rapid technological developments in autonomous unmanned aerial vehicles (or drones) could soon lead to their large-scale implementation in the last-mile delivery of products. However, drones have a number of problems such as limited energy budget, limited carrying capacity, etc. On the other hand, trucks have a larger carrying capacity, but they cannot reach all the places easily. Intriguingly, last-mile delivery cooperation between drones and trucks can synergistically improve delivery efficiency. In this paper, we present a drone-truck co-operated delivery framework under time-varying dynamics. Our framework minimizes the total delivery time while considering low energy consumption as the secondary objective. The …
Region-Adaptive, Error-Controlled Scientific Data Compression Using Multilevel Decomposition, Qian Gong, Ben Whitney, Chengzhu Zhang, Xin Liang, Anand Rangarajan, Jieyang Chen, Lipeng Wan, Paul Ullrich, Qing Liu, Robert Jacob, Sanjay Ranka, Scott Klasky
Region-Adaptive, Error-Controlled Scientific Data Compression Using Multilevel Decomposition, Qian Gong, Ben Whitney, Chengzhu Zhang, Xin Liang, Anand Rangarajan, Jieyang Chen, Lipeng Wan, Paul Ullrich, Qing Liu, Robert Jacob, Sanjay Ranka, Scott Klasky
Computer Science Faculty Research & Creative Works
The increase of computer processing speed is significantly outpacing improvements in network and storage bandwidth, leading to the big data challenge in modern science, where scientific applications can quickly generate much more data than that can be transferred and stored. As a result, big scientific data must be reduced by a few orders of magnitude while the accuracy of the reduced data needs to be guaranteed for further scientific explorations. Moreover, scientists are often interested in some specific spatial/temporal regions in their data, where higher accuracy is required. The locations of the regions requiring high accuracy can sometimes be prescribed …
Wikimarks: Harvesting Relevance Benchmarks From Wikipedia, Laura Dietz, Shubham Chatterjee, Connor Lennox, Sumanta Kashyapi, Pooja Oza, Ben Gamari
Wikimarks: Harvesting Relevance Benchmarks From Wikipedia, Laura Dietz, Shubham Chatterjee, Connor Lennox, Sumanta Kashyapi, Pooja Oza, Ben Gamari
Computer Science Faculty Research & Creative Works
We provide a resource for automatically harvesting relevance benchmarks from Wikipedia - which we refer to as "Wikimarks"to differentiate them from manually created benchmarks. Unlike simulated benchmarks, they are based on manual annotations of Wikipedia authors. Studies on the TREC Complex Answer Retrieval track demonstrated that leaderboards under Wikimarks and manually annotated benchmarks are very similar. Because of their availability, Wikimarks can fill an important need for Information Retrieval research. We provide a meta-resource to harvest Wikimarks for several information retrieval tasks across different languages: paragraph retrieval, entity ranking, query-specific clustering, outline prediction, and relevant entity linking and many more. …
Bert-Er: Query-Specific Bert Entity Representations For Entity Ranking, Shubham Chatterjee, Laura Dietz
Bert-Er: Query-Specific Bert Entity Representations For Entity Ranking, Shubham Chatterjee, Laura Dietz
Computer Science Faculty Research & Creative Works
Entity-oriented search systems often learn vector representations of entities via the introductory paragraph from the Wikipedia page of the entity. As such representations are the same for every query, our hypothesis is that the representations are not ideal for IR tasks. In this work, we present BERT Entity Representations (BERT-ER) which are query-specific vector representations of entities obtained from text that describes how an entity is relevant for a query. Using BERT-ER in a downstream entity ranking system, we achieve a performance improvement of 13-42% (Mean Average Precision) over a system that uses the BERT embedding of the introductory paragraph …
Mgard+: Optimizing Multilevel Methods For Error-Bounded Scientific Data Reduction, Xin Liang, Ben Whitney, Jieyang Chen, Lipeng Wan, Qing Liu, Dingwen Tao, James Kress, David Pugmire, Matthew Wolf, Norbert Podhorszki, Scott Klasky
Mgard+: Optimizing Multilevel Methods For Error-Bounded Scientific Data Reduction, Xin Liang, Ben Whitney, Jieyang Chen, Lipeng Wan, Qing Liu, Dingwen Tao, James Kress, David Pugmire, Matthew Wolf, Norbert Podhorszki, Scott Klasky
Computer Science Faculty Research & Creative Works
Nowadays, data reduction is becoming increasingly important in dealing with the large amounts of scientific data. Existing multilevel compression algorithms offer a promising way to manage scientific data at scale but may suffer from relatively low performance and reduction quality. In this paper, we propose MGARD+, a multilevel data reduction and refactoring framework drawing on previous multilevel methods, to achieve high-performance data decomposition and high-quality error-bounded lossy compression. Our contributions are four-fold: 1) We propose to leverage a level-wise coefficient quantization method, which uses different error tolerances to quantize the multilevel coefficients. 2) We propose an adaptive decomposition method which …
Classification Of Alzheimer's Disease Via Vision Transformer: Classification Of Alzheimer's Disease Via Vision Transformer, Yanjun Lyu, Xiaowei Yu, Dajiang Zhu, Lu Zhang
Classification Of Alzheimer's Disease Via Vision Transformer: Classification Of Alzheimer's Disease Via Vision Transformer, Yanjun Lyu, Xiaowei Yu, Dajiang Zhu, Lu Zhang
Computer Science Faculty Research & Creative Works
Deep models are powerful in capturing the complex and non-linear relationship buried in brain imaging data. However, the huge number of parameters in deep models can easily overfit given limited imaging data samples. In this work, we proposed a cross-domain transfer learning method to solve the insufficient data problem in brain imaging domain by leveraging the knowledge learned in natural image domain. Specifically, we employed ViT as the backbone and firstly pretrained it using ImageNet-21K dataset and then transferred to the brain imaging dataset. A slice-wise convolution embedding method was developed to improve the standard patch operation in vanilla ViT. …
Ultrafast Error-Bounded Lossy Compression For Scientific Datasets, Xiaodong Yu, Sheng Di, Kai Zhao, Jiannan Tian, Dingwen Tao, Xin Liang, Franck Cappello
Ultrafast Error-Bounded Lossy Compression For Scientific Datasets, Xiaodong Yu, Sheng Di, Kai Zhao, Jiannan Tian, Dingwen Tao, Xin Liang, Franck Cappello
Computer Science Faculty Research & Creative Works
Today's scientific high-performance computing applications and advanced instruments are producing vast volumes of data across a wide range of domains, which impose a serious burden on data transfer and storage. Error-bounded lossy compression has been developed and widely used in the scientific community because it not only can significantly reduce the data volumes but also can strictly control the data distortion based on the user-specified error bound. Existing lossy compressors, however, cannot offer ultrafast compression speed, which is highly demanded by numerous applications or use cases (such as in-memory compression and online instrument data compression). In this paper, we propose …
Analysis Of Federated Scheduling For Integer-Valued Workloads, Marion Sudvarg, Chris Gill
Analysis Of Federated Scheduling For Integer-Valued Workloads, Marion Sudvarg, Chris Gill
Computer Science Faculty Research & Creative Works
In federated scheduling of parallel real-time tasks on multiprocessor systems, high-utilization tasks are allocated dedicated processors on which they execute exclusively. Several methods exist for allocating a sufficient number of processors to guarantee that each task meets its deadline. In this paper, we propose two new strategies for allocating unit-speed cores to tasks with integer workload and deadline values. The first method can be performed in constant time for each high-utilization task, given the task's total workload, critical-path length, and deadline. The second method exploits the DAG structure of high-utilization tasks, providing a potentially better schedule in pseudo-polynomial time. We …
Accelerating Serverless Computing By Harvesting Idle Resources, Hanfei Yu, Hao Wang, Jian Li, Xu Yuan, Seung Jong Park
Accelerating Serverless Computing By Harvesting Idle Resources, Hanfei Yu, Hao Wang, Jian Li, Xu Yuan, Seung Jong Park
Computer Science Faculty Research & Creative Works
Serverless computing automates fine-grained resource scaling and simplifies the development and deployment of online services with stateless functions. However, it is still non-trivial for users to allocate appropriate resources due to various function types, dependencies, and input sizes. Misconfiguration of resource allocations leaves functions either under-provisioned or over-provisioned and leads to continuous low resource utilization. This paper presents Freyr, a new resource manager (RM) for serverless platforms that maximizes resource efficiency by dynamically harvesting idle resources from over-provisioned functions to under-provisioned functions. Freyr monitors each function's resource utilization in real-time, detects over-provisioning and under-provisioning, and learns to harvest idle resources …
A Parallel Algorithm Template For Updating Single-Source Shortest Paths In Large-Scale Dynamic Networks, Arindam Khanda, Sriram Srinivasan, Sanjukta Bhowmick, Boyana Norris, Sajal K. Das
A Parallel Algorithm Template For Updating Single-Source Shortest Paths In Large-Scale Dynamic Networks, Arindam Khanda, Sriram Srinivasan, Sanjukta Bhowmick, Boyana Norris, Sajal K. Das
Computer Science Faculty Research & Creative Works
The Single Source Shortest Path (SSSP) problem is a classic graph theory problem that arises frequently in various practical scenarios; hence, many parallel algorithms have been developed to solve it. However, these algorithms operate on static graphs, whereas many real-world problems are best modeled as dynamic networks, where the structure of the network changes with time. This gap between the dynamic graph modeling and the assumed static graph model in the conventional SSSP algorithms motivates this work. We present a novel parallel algorithmic framework for updating the SSSP in large-scale dynamic networks and implement it on the shared-memory and GPU …
Improving I/O Performance For Exascale Applications Through Online Data Layout Reorganization, Lipeng Wan, Axel Huebl, Junmin Gu, Franz Poeschel, Ana Gainaru, Ruonan Wang, Jieyang Chen, Xin Liang, Dmitry Ganyushin, Todd Munson, Ian Foster, Jean Luc Vay, Norbert Podhorszki, Kesheng Wu
Improving I/O Performance For Exascale Applications Through Online Data Layout Reorganization, Lipeng Wan, Axel Huebl, Junmin Gu, Franz Poeschel, Ana Gainaru, Ruonan Wang, Jieyang Chen, Xin Liang, Dmitry Ganyushin, Todd Munson, Ian Foster, Jean Luc Vay, Norbert Podhorszki, Kesheng Wu
Computer Science Faculty Research & Creative Works
The applications being developed within the U.S. Exascale Computing Project (ECP) to run on imminent Exascale computers will generate scientific results with unprecedented fidelity and record turn-around time. Many of these codes are based on particle-mesh methods and use advanced algorithms, especially dynamic load-balancing and mesh-refinement, to achieve high performance on Exascale machines. Yet, as such algorithms improve parallel application efficiency, they raise new challenges for I/O logic due to their irregular and dynamic data distributions. Thus, while the enormous data rates of Exascale simulations already challenge existing file system write strategies, the need for efficient read and processing of …
Measurement Errors In Range-Based Localization Algorithms For Uavs: Analysis And Experimentation, Francesco Betti Sorbelli, Cristina M. Pinotti, Simone Silvestri, Sajal K. Das
Measurement Errors In Range-Based Localization Algorithms For Uavs: Analysis And Experimentation, Francesco Betti Sorbelli, Cristina M. Pinotti, Simone Silvestri, Sajal K. Das
Computer Science Faculty Research & Creative Works
Localizing Ground Devices (GDs) is an Important Requirement for a Wide Variety of Applications, Such as Infrastructure Monitoring, Precision Agriculture, Search and Rescue Operations, to Name a Few. to This End, Unmanned Aerial Vehicles (UAVs) or Drones Offer a Promising Technology Due to their Flexibility. However, the Distance Measurements Performed using a Drone, an Integral Part of a Localization Procedure, Incur Several Errors that Affect the Localization Accuracy. in This Paper, We Provide Analytical Expressions for the Impact of Different Kinds of Measurement Errors on the Ground Distance between the UAV and GDs. We Review Three Range-Based and Three Range-Free …
Minimizing The Deployment Cost Of Uavs For Delay-Sensitive Data Collection In Iot Networks, Wenzheng Xu, Tao Xiao, Junqi Zhang, Weifa Liang, Zichuan Xu, Xuxun Liu, Xiaohua Jia, Sajal K. Das
Minimizing The Deployment Cost Of Uavs For Delay-Sensitive Data Collection In Iot Networks, Wenzheng Xu, Tao Xiao, Junqi Zhang, Weifa Liang, Zichuan Xu, Xuxun Liu, Xiaohua Jia, Sajal K. Das
Computer Science Faculty Research & Creative Works
In this paper, we study the deployment of Unmanned Aerial Vehicles (UAVs) to collect data from IoT devices, by finding a data collection tour for each UAV. To ensure the 'freshness' of the collected data, the total time spent in the tour of each UAV that consists of the UAV flying time and data collection time must be no greater than a given delay B, e.g., 20 minutes. In this paper, we consider a problem of deploying the minimum number of UAVs and finding their data collection tours, subject to the constraint that the total time spent in each tour …
Netchain: A Blockchain-Enabled Privacy-Preserving Multi-Domain Network Slice Orchestration Architecture, Guobiao He, Wei Su, Shuai Gao, Ningchun Liu, Sajal K. Das
Netchain: A Blockchain-Enabled Privacy-Preserving Multi-Domain Network Slice Orchestration Architecture, Guobiao He, Wei Su, Shuai Gao, Ningchun Liu, Sajal K. Das
Computer Science Faculty Research & Creative Works
Multi-domain networking slice orchestration is an essential technology for the programmable and cloud-native 5G network. However, existing research solutions are either based on the impractical assumption that operators will reveal all the private network information or time-consuming secure multi-party computation which is only applicable to limited computation scenarios. To provide agile and privacy-preserving end-to-end network slice orchestration services, this paper proposes NetChain, a multi-domain network slice orchestration architecture based on blockchain and trusted execution environment. Correspondingly, we design a novel consensus algorithm CoNet to ensure the strong security, scalability, and information consistency of NetChain. In addition, a bilateral evaluation mechanism …
Speeding Up Routing Schedules On Aisle Graphs With Single Access, Francesco Betti Sorbelli, Stefano Carpin, Federico Coro, Sajal K. Das, Alfredo Navarra, Cristina M. Pinotti
Speeding Up Routing Schedules On Aisle Graphs With Single Access, Francesco Betti Sorbelli, Stefano Carpin, Federico Coro, Sajal K. Das, Alfredo Navarra, Cristina M. Pinotti
Computer Science Faculty Research & Creative Works
In this article, we study the orienteering aisle-graph single-access problem (OASP), a variant of the orienteering problem for a robot moving in a so-called single-access aisle graph, i.e., a graph consisting of a set of rows that can be accessed from one side only. Aisle graphs model, among others, vineyards or warehouses. Each aisle-graph vertex is associated with a reward that a robot obtains when it visits the vertex itself. As the energy of the robot is limited, only a subset of vertices can be visited with a fully charged battery. The objective is to maximize the total reward collected …
Fedvcp: A Federated-Learning-Based Cooperative Positioning Scheme For Social Internet Of Vehicles, Xiangjie Kong, Haoran Gao, Guojiang Shen, Gaohui Duan, Sajal K. Das
Fedvcp: A Federated-Learning-Based Cooperative Positioning Scheme For Social Internet Of Vehicles, Xiangjie Kong, Haoran Gao, Guojiang Shen, Gaohui Duan, Sajal K. Das
Computer Science Faculty Research & Creative Works
Intelligent vehicle applications, such as autonomous driving and collision avoidance, put forward a higher demand for precise positioning of vehicles. The current widely used global navigation satellite systems (GNSS) cannot meet the precision requirements of the submeter level. Due to the development of sensing techniques and vehicle-to-infrastructure (V2I) communications, some vehicles can interact with surrounding landmarks to achieve precise positioning. Existing work aims to realize the positioning correction of common vehicles by sharing the positioning data of sensor-rich vehicles. However, the privacy of trajectory data makes it difficult to collect and train data centrally. Moreover, uploading vehicle location data wastes …
Distributed Matrix Tiling Using A Hypergraph Labeling Formulation, Avah Banerjee, Maxwell Reeser, Guoli Ding
Distributed Matrix Tiling Using A Hypergraph Labeling Formulation, Avah Banerjee, Maxwell Reeser, Guoli Ding
Computer Science Faculty Research & Creative Works
Partitioning large matrices is an important problem in distributed linear algebra computing, used in ML among others. Briefly, our goal is to perform a sequence of matrix algebra operations in a distributed manner on these large matrices. However, not all partitioning schemes work well with different matrix algebra operations and their implementations (algorithms). This is a type of data tiling problem. In this paper we consider a data tiling problem using hypergraphs. We prove some hardness results and give a theoretical characterization of its complexity on random instances. Additionally, we develop a greedy algorithm and experimentally show its efficacy.
Greedy Algorithms For Scheduling Package Delivery With Multiple Drones, Francesco Betti Sorbelli, Federico Corò, Sajal K. Das, Lorenzo Palazzetti, Cristina M. Pinotti
Greedy Algorithms For Scheduling Package Delivery With Multiple Drones, Francesco Betti Sorbelli, Federico Corò, Sajal K. Das, Lorenzo Palazzetti, Cristina M. Pinotti
Computer Science Faculty Research & Creative Works
Unmanned Aerial Vehicles (or drones) can be used for a myriad of civil applications, such as search and rescue, precision agriculture, or last-mile package delivery. Interestingly, the cooperation between drones and ground vehicles (trucks) can even enhance the quality of service. In this paper, we investigate the symbiosis among a truck and multiple drones in a last-mile package delivery scenario, introducing the Multiple Drone-Delivery Scheduling Problem (MDSP). From the main depot, a truck takes care of transporting a team of drones that will be used to deliver packages to customers. Each delivery is associated with a drone's energy cost, a …
Cansafe: An Mtd Based Approach For Providing Resiliency Against Dos Attack Within In-Vehicle Networks, Ayan Roy, Sanjay Kumar Madria
Cansafe: An Mtd Based Approach For Providing Resiliency Against Dos Attack Within In-Vehicle Networks, Ayan Roy, Sanjay Kumar Madria
Computer Science Faculty Research & Creative Works
Trending towards autonomous transportation systems, modern vehicles are equipped with hundreds of sensors and actuators that increase the intelligence of the vehicles with a higher level of autonomy, as well as facilitate increased communication with entities outside the in-vehicle network. However, increase in a contact point with the outside world has exposed the controller area network (CAN) of a vehicle to remote security vulnerabilities. In particular, an attacker can inject fake high priority messages within the CAN through the contact points, while preventing legitimate messages from controlling the CAN (Denial-of-Service (DoS) attack). In this paper, we propose a Moving Target …
More To Less (M2l): Enhanced Health Recognition In The Wild With Reduced Modality Of Wearable Sensors, Huiyuan Yang, Han Yu, Kusha Sridhar, Thomas Vaessen, Inez Myin-Germeys, Akane Sano
More To Less (M2l): Enhanced Health Recognition In The Wild With Reduced Modality Of Wearable Sensors, Huiyuan Yang, Han Yu, Kusha Sridhar, Thomas Vaessen, Inez Myin-Germeys, Akane Sano
Computer Science Faculty Research & Creative Works
Accurately recognizing health-related conditions from wearable data is crucial for improved healthcare outcomes. To improve the recognition accuracy, various approaches have focused on how to effectively fuse information from multiple sensors. Fusing multiple sensors is a common choice in many applications but may not always be feasible in real-world scenarios. For example, although combining bio signals from multiple sensors (i.e., a chest pad sensor and a wrist wearable sensor) has been proved effective for improved performance, wearing multiple devices might be impractical in the free-living context. To solve the challenges, we propose an effective more to less (M2L) learning framework …
Targeted Content-Sharing In A Multi-Group Dtn Application Using Attribute-Based Encryption, Xiaofei Cao, Shudip Datta, Ram Charan Bolla, Sanjay Kumar Madria
Targeted Content-Sharing In A Multi-Group Dtn Application Using Attribute-Based Encryption, Xiaofei Cao, Shudip Datta, Ram Charan Bolla, Sanjay Kumar Madria
Computer Science Faculty Research & Creative Works
In a battlefield, multiple groups operate with different missions, but their missions and groups can dynamically change based on the evolving situation. Due to the unavailability of network infrastructure after deployment, group members form a Delay Tolerant Network (DTN) which is prone to security attacks. Hence, based on the mission attributes, group memberships, nodes' interests, and data tags determination, targeted contents need to be distributed in a secure fashion to different users. Though existing Attributes Based Encryption (ABE) can provide security of information, revoking a member from a group is always an issue in DTN as the Attribute Authority (AA) …
Volunteer Selection In Collaborative Crowdsourcing With Adaptive Common Working Time Slots, Riya Samanta, Vaibhav Saxena, Soumya K. Ghosh, Sajal K. Das
Volunteer Selection In Collaborative Crowdsourcing With Adaptive Common Working Time Slots, Riya Samanta, Vaibhav Saxena, Soumya K. Ghosh, Sajal K. Das
Computer Science Faculty Research & Creative Works
Skill-based volunteering is an expanding branch of crowdsourcing where one may acquire sustainable services, solutions, and ideas from the crowd by connecting with them online. The optimal mapping between volunteers and tasks with collaboration becomes challenging for complex tasks demanding greater skills and cognitive ability. Unlike traditional crowdsourcing, volunteers like to work on their own schedule and locations. To address this problem, we propose a novel two-phase framework consisting of Initial Volunteer-Task Mapping (i-VTM) and Adaptive Common Slot Finding (a-CSF) algorithms. The i-VTM algorithm assigns volunteers to the tasks based on their skills and spatial proximity, whereas the a-CSF algorithm …
Securing Federated Learning Against Overwhelming Collusive Attackers, Priyesh Ranjan, Ashish Gupta, Federico Corò, Sajal K. Das
Securing Federated Learning Against Overwhelming Collusive Attackers, Priyesh Ranjan, Ashish Gupta, Federico Corò, Sajal K. Das
Computer Science Faculty Research & Creative Works
In the era of a data-driven society with the ubiquity of Internet of Things (IoT) devices storing large amounts of data localized at different places, distributed learning has gained a lot of traction, however, assuming independent and identically distributed data (iid) across the devices. While relaxing this assumption that anyway does not hold in reality due to the heterogeneous nature of devices, federated learning (FL) has emerged as a privacy-preserving solution to train a collaborative model over non-iid data distributed across a massive number of devices. However, the appearance of malicious devices (attackers), who intend to corrupt the FL model, …
A Novel Echo State Network Autoencoder For Anomaly Detection In Industrial Iot Systems, Fabrizio De Vita, Giorgio Nocera, Dario Bruneo, Sajal K. Das
A Novel Echo State Network Autoencoder For Anomaly Detection In Industrial Iot Systems, Fabrizio De Vita, Giorgio Nocera, Dario Bruneo, Sajal K. Das
Computer Science Faculty Research & Creative Works
The Industrial Internet of Things (IIoT) technology had a very strong impact on the realization of smart frameworks for detecting anomalous behaviors that could be potentially dangerous to a system. In this regard, most of the existing solutions involve the use of Artificial Intelligence (AI) models running on Edge devices, such as Intelligent Cyber Physical Systems (ICPS) typically equipped with sensing and actuating capabilities. However, the hardware restrictions of these devices make the implementation of an effective anomaly detection algorithm quite challenging. Considering an industrial scenario, where signals in the form of multivariate time-series should be analyzed to perform a …
Active Learning Augmented Folded Gaussian Model For Anomaly Detection In Smart Transportation, Venkata Praveen Kumar Madhavarapu, Prithwiraj Roy, Shameek Bhattacharjee, Sajal K. Das
Active Learning Augmented Folded Gaussian Model For Anomaly Detection In Smart Transportation, Venkata Praveen Kumar Madhavarapu, Prithwiraj Roy, Shameek Bhattacharjee, Sajal K. Das
Computer Science Faculty Research & Creative Works
Smart transportation networks have become instrumental in smart city applications with the potential to enhance road safety, improve the traffic management system and driving experience. A Traffic Message Channel (TMC) is an IoT device that records the data collected from the vehicles and forwards it to the Roadside Units (RSUs). This data is further processed and shared with the vehicles to inquire the fastest route and incidents that can cause significant delays. The failure of the TMC sensors can have adverse effects on the transportation network. In this paper, we propose a Gaussian distribution-based trust scoring model to identify anomalous …
Distributed Decision Making For V2v Charge Sharing In Intelligent Transportation Systems, Punyasha Chatterjee, Pratham Majumder, Arpita Debnath, Sajal K. Das
Distributed Decision Making For V2v Charge Sharing In Intelligent Transportation Systems, Punyasha Chatterjee, Pratham Majumder, Arpita Debnath, Sajal K. Das
Computer Science Faculty Research & Creative Works
Electric vehicles (EVs) have emerged in the intelligent transportation system (ITS) to meet the increasing environmental concerns. To facilitate on-demand requirement of EV charging, vehicle-to-vehicle (V2V) charge transfer can be employed. However, most of the existing approaches to V2V charge sharing are centralized or semi-centralized, incurring huge message overhead, long waiting time, and infrastructural cost. In this paper, we propose novel distributed heuristic algorithms for V2V charge sharing based on the multi-criteria decision-making policy. The problem is mapped to an alias classical problem (i.e., optimum matching in weighted bipartite graphs), where the goal is to maximize the matching cardinality while …
Leveraging Spanning Tree To Detect Colluding Attackers In Federated Learning, Priyesh Ranjan, Federico Coro, Ashish Gupta, Sajal K. Das
Leveraging Spanning Tree To Detect Colluding Attackers In Federated Learning, Priyesh Ranjan, Federico Coro, Ashish Gupta, Sajal K. Das
Computer Science Faculty Research & Creative Works
Federated learning distributes model training among multiple clients who, driven by privacy concerns, perform training using their local data and only share model weights for iterative aggregation on the server. In this work, we explore the threat of collusion attacks from multiple malicious clients who pose targeted attacks (e.g., label flipping) in a federated learning configuration. By leveraging client weights and the correlation among them, we develop a graph-based algorithm to detect malicious clients. Finally, we validate the effectiveness of our algorithm in presence of varying number of attackers on a classification task using a well-known Fashion-MNIST dataset.
Mobility Management In Industrial Iot Environments, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi
Mobility Management In Industrial Iot Environments, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi
Computer Science Faculty Research & Creative Works
The Internet Engineering Task Force (IETF) has defined the 6TiSCH architecture to enable the Industrial Inter-net of Things (IIoT). Unfortunately, 6TiSCH does not provide mechanisms to manage node mobility, while many industrial applications involve mobile devices (e.g., mobile robots or wearable devices carried by workers). In this paper, we consider the Synchronized Single-hop Multiple Gateway framework to manage mobility in 6TiSCH networks. For this framework, we address the problem of positioning Border Routers in a deployment area, which is similar to the "Art Gallery"problem, proposing an efficient deployment policy for Border Routers based on geometrical rules. Moreover, we define a …
Delivery With Uavs: A Simulated Dataset Via Ats, Giulio Rigoni, Cristina M. Pinotti, Bhumika, Debasis Das, Sajal K. Das
Delivery With Uavs: A Simulated Dataset Via Ats, Giulio Rigoni, Cristina M. Pinotti, Bhumika, Debasis Das, Sajal K. Das
Computer Science Faculty Research & Creative Works
We consider a delivery food service operated by Unmanned Aerial Vehicles (UAVs). Due to the absence of a dataset on UAVs deliveries in the literature, and since it is not possible to perform real tests, we create a dataset using an open-Air Traffic Simulator (ATS). Precisely, we converted a set of food deliveries operated by wheeled vehicles, proposed in the literature [1], into a set of simulated UAVs deliveries. For each delivery, we ran a UAV flight from the source to the destination. The results showed that, as expected, the UAV's course is shorter than the vehicle trajectory on the …
Mdz: An Efficient Error-Bounded Lossy Compressor For Molecular Dynamics, Kai Zhao, Sheng Di, Danny Perez, Xin Liang, Zizhong Chen, Franck Cappello
Mdz: An Efficient Error-Bounded Lossy Compressor For Molecular Dynamics, Kai Zhao, Sheng Di, Danny Perez, Xin Liang, Zizhong Chen, Franck Cappello
Computer Science Faculty Research & Creative Works
Molecular dynamics (MD) has been widely used in today's scientific research across multiple domains including materials science, biochemistry, biophysics, and structural biology. MD simulations can produce extremely large amounts of data in that each simulation could involve a large number of atoms (up to trillions) for a large number of timesteps (up to hundreds of millions). In this paper, we perform an in-depth analysis of a number of MD simulation datasets and then develop an efficient error-bounded lossy compressor that can significantly improve the compression ratios. The contributions are fourfold. (1) We characterize a number of MD datasets and summarize …