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IEEE Conference on Aerospace

作者： Noah Perryman Nicholas Franconi Gary Crum Christopher Wilson Alan D. George Department of Electrical and Computer Engineering NSF Center for Space High-Performance and Resilient Computing (SHREC) University of Pittsburgh Pittsburgh PA Science Data Processing Branch Code 587 Software Engineering Division NASA Goddard Space Flight Center Greenbelt MD

There is a direct need for high-visibility NASA missions that provide significant scientific impact or have a high mission class using completely radiation-hardened (rad-hard) electronics solutions, to enable artificial intelligence (AI) applications in harsh environments despite severe size, weight, and power (SWaP) constraints. For these missions, where current state-of-the-art solutions are too power-demanding or are incapable of surviving the intended radiation environment, an alternative rad-hard processing architecture that can leverage the control-flow capabilities of scalar processors while also incorporating the hardware-acceleration capabilities of an FPGA is of significant value. Therefore, in this research, we propose a miniaturized (3.5 in. × 3.5 in. form factor) processor card featuring the GR740 quad-core rad-hard processor and the CertusPro-NX-RT radiation-tolerant FPGA, called the SpaceCube GR740 Host for Onboard Science and Telemetry (GHOST) architecture. The card will be designed to conform to the NASA Goddard Space Flight Center (GSFC) CubeSat Card Specification (CS2), which provides a common template to build new 1U CubeSat-sized cards compatible with a variety of other avionics designs. The GR740 features a fault-tolerant quad- processor LEON4FT SPARC V8 integer unit with a 7-stage pipeline and 4×4 KiB instruction and data caches. To extend the capabilities of the GR740, the CertusPro-NX-RT, a low-power radiation-tolerant FPGA, was combined to create a hybrid system architecture, providing the benefits of a programmable logic fabric. The GHOST architecture supports high-reliability, general-purpose processing and system monitoring via the GR740 while simultaneously increasing the AI-based application performance via a combination of the GR740 and CertusPro-NX-RT.

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Commercial Dishes Can Be My Ladder: Sustainable and Collaborative Data Offloading in LEO Satellite Networks

arXiv

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arXiv 2025年

作者： Chou, Yi Ching Chen, Long Wang, Hengzhi Wang, Feng Fang, Hao Zhao, Haoyuan Zhang, Miao Fan, Xiaoyi Liu, Jiangchuan School of Computing Science Simon Fraser University Canada College of Computer Science and Software Engineering Shenzhen University China Department of Computer and Information Science The University of Mississippi United States Jiangxing Intelligence Inc. Shenzhen China

Low Earth Orbit (LEO) satellite networks, characterized by their high data throughput and low latency, have gained significant interest from both industry and academia. Routing data efficiently within these networks is essential for maintaining a high quality of service. However, current routing strategies, such as bent-pipe and inter-satellite link (ISL) routing, have their unique challenges. The bent-pipe strategy requires a dense deployment of dedicated ground stations, while the ISL-based strategy can negatively impact satellite battery lifespan due to increased traffic load, leading to sustainability issues. In this paper, we propose sustainable collaborative offloading, a framework that orchestrates groups of existing commercial resources like ground stations and 5G base stations for data offloading. This orchestration enhances total capacity, overcoming the limitations of a single resource. We propose the collaborator group set construction algorithm to construct candidate groups and the collaborator selection and total payment algorithm to select offloading targets and determine payments no less than the costs. Extensive real-world-based simulations show that our solution significantly improves energy consumption, satellite service life, and end-to-end latency. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite links

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Layer Decomposition Learning Based on Discriminative Feature Group Split with Bottom-Up Intergroup Feature Fusion for Single Image Deraining

SSRN

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SSRN 2023年

作者： Jang, Yunseon Le, Duc-Tai Son, Chang-Hwan Choo, Hyunseung Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon16419 Korea Republic of Department of Software Science Engineering Kunsan National University Gunsan54150 Korea Republic of

As rain streaks hinder feature extraction based on image gradients, the performance of computer vision algorithms such as pedestrian detection and lane detection can be negatively affected. Image deraining is an essential pre-processing step for the algorithms to work reliably in adverse weather conditions. However, detail and texture information in the object and background areas can be lost during deraining due to the structural similarity between rain streaks and object details. To address this, we present a novel layer decomposition learning network (LDLNet) to separate rain streak and object detail layers of the image and remove rain streaks effectively. The proposed LDLNet consists of two parts: discriminative group feature split and group feature merging. The former arranges sparse residual attention modules in series to extract spatial contextual features of rain images following a novel bottom-up inter-group feature fusion approach. The group feature merging part aggregates all the group features with different image appearances into a residual image. Experimental results reveal that the proposed approach makes group features more discriminative, represented by different image appearances including rain streaks and object details. LDLNet achieves superior rain removal and detail preservation in both synthetic datasets and real-world rainy images compared to the state-of-the-art rain removal models. © 2023, The Authors. All rights reserved.

关键词： Rain

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Debiasing Graph Representation Learning based on Information Bottleneck

arXiv

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arXiv 2024年

作者： Zhang, Ziyi Ouyang, Mingxuan Lin, Wanyu Lan, Hao Yang, Lei The School of Software Engineering South China University of Technology Guangzhou China The Hong Kong Polytechnic University Hong Kong The Department of Computing The Hong Kong Polytechnic University Hong Kong The Department of Computer Science and Technology Tsinghua University Beijing China

—Graph representation learning has shown superior performance in numerous real-world applications, such as finance and social networks. Nevertheless, most existing works might make discriminatory predictions due to insufficient attention to fairness in their decision-making processes. This oversight has prompted a growing focus on fair representation learning. Among recent explorations on fair representation learning, prior works based on adversarial learning usually induce unstable or counterproductive performance. To achieve fairness in a stable manner, we present the design and implementation of GRAFair, a new framework based on a variational graph auto-encoder. The crux of GRAFair is the Conditional Fairness Bottleneck, where the objective is to capture the trade-off between the utility of representations and sensitive information of interest. By applying variational approximation, we can make the optimization objective tractable. Particularly, GRAFair can be trained to produce informative representations of tasks while containing little sensitive information without adversarial training. Experiments on various real-world datasets demonstrate the effectiveness of our proposed method in terms of fairness, utility, robustness, and stability. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Resource Allocation-Aware Efficient Interference Management Technique for Ultra-Dense Femto Environment

SSRN

引用

SSRN 2024年

作者： Guo, Wanying Wang, Bojun Zhao, Lian Siddiqui, Isma Farah School of Information and Communication Engineering Nanjing Institute of Technology Nanjing China College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Cyberspace Institute Institute of Dataspace Hefei Comprehensive NationalScience Center Hefei China Department of Software Engineering Mehran University of Engineering & Technology Jamshoro Pakistan

Addressing the challenge of severe inter-layer and intra-layer interference in densely deployed Femtocells, we introduce an efficient resource allocation strategy centered on clustering. Initially, we employ the Fractional Frequency Reuse (FFR) technique to mitigate interference by segmenting cells into distinct ***, leveraging graph theory and convex optimization, we cluster the Femto Base Stations (FBS) to enhance efficiency. An algorithm prioritizing user fairness governs sub-channel allocation among FBSs. Additionally, a distributed power control algorithm dynamically adjusts FBS power levels, further enhancing system throughput. Comparative analysis reveals that our approach surpasses traditional non-clustered methods and three other comparison methods, exhibiting superior system throughput and Signal-to-Interference Ratio (SINR). Notably, our method significantly enhances user fairness, promoting greater satisfaction among Femto User Equipment (FUE) users. © 2024, The Authors. All rights reserved.

关键词： Femtocell

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Towards Multi-Objective High-Dimensional Feature Selection via Evolutionary Multitasking

arXiv

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arXiv 2024年

作者： Feng, Yinglan Feng, Liang Liu, Songbai Kwong, Sam Tan, Kay Chen Department of Computer Science City University of Hong Kong Hong Kong College of Computer Science Chongqing University China College of Computer Science and Software Engineering Shenzhen University China Department of Computing The Hong Kong Polytechnic University Hong Kong

Evolutionary Multitasking (EMT) paradigm, an emerging research topic in evolutionary computation, has been successfully applied in solving high-dimensional feature selection (FS) problems recently. However, existing EMT-based FS methods suffer from several limitations, such as a single mode of multitask generation, conducting the same generic evolutionary search for all tasks, relying on implicit transfer mechanisms through sole solution encodings, and employing single-objective transformation, which result in inadequate knowledge acquisition, exploitation, and transfer. To this end, this paper develops a novel EMT framework for multiobjective high-dimensional feature selection problems, namely MO-FSEMT. In particular, multiple auxiliary tasks are constructed by distinct formulation methods to provide diverse search spaces and information representations and then simultaneously addressed with the original task through a multi-slover-based multitask optimization scheme. Each task has an independent population with task-specific representations and is solved using separate evolutionary solvers with different biases and search preferences. A task-specific knowledge transfer mechanism is designed to leverage the advantage information of each task, enabling the discovery and effective transmission of high-quality solutions during the search process. Comprehensive experimental results demonstrate that our MO-FSEMT framework can achieve overall superior performance compared to the state-of-the-art FS methods on 26 datasets. Moreover, the ablation studies verify the contributions of different components of the proposed MO-FSEMT. Copyright © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Stakeholder Identification Overview and Challenges in Requirements engineering Prospective 2

Stakeholder Identification Overview and Challenges in Requir...

引用

2nd International Conference on computing and Information Technology, ICCIT 2022

作者： Elneel, Duha Awad Fakharudin, Abdul Sahli Ahmed, Elsadig Musa Kahtan, Hasan Abdullateef, Mansoor Universiti Malaysia Pahang Faculty of Computing Kuantan26300 Malaysia Multimedia University Faculty of Business Melaka Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur50603 Malaysia Knowbis Solutions Consultancy Selangor Malaysia

ISBN: (纸本)9781665436052

Stakeholder identification (SI) illustrates a critical part of the requirements elicitation activity. It helps software analysts gather accurate system requirements to ensure high quality and avoid system failure. Stakeholder identification considered one of the earliest stages of project development. The main problem related to project success and failure is identifying the real stakeholders and selecting the right one. The lack of stakeholder identification methodologies and the shortage of previous research in identifying the stakeholders for different systems, encourage the research to propose a guideline framework, assist analysts in building the convenient methodology for identifying real stakeholders. Stakeholders are defined based on different projects and the various services they provide. This difference leads to the fact that a particular and systematic technique must be used to identify stakeholders based on the project's types. The exploration of stakeholder identification matters and the guideline framework is a baseline for future research in requirements engineering. © 2022 IEEE.

关键词： Requirements engineering

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Conflict-Free Genetic Algorithm with Nash Equilibrium Seeking for Game-Based Battery Swapping Station Recommendation

Conflict-Free Genetic Algorithm with Nash Equilibrium Seekin...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Sun, Chang-Long Xu, Xin-Xin Chen, Chun-Hua Hong, Jun He, Zhenan Yu, Dengxiu Kwong, Sam Zhan, Zhi-Hui School of Computer Science and Engineering South China University of Technology Guangzhou510006 China School of Computer Science and Technology Ocean University of China Qingdao266100 China School of Software Engineering South China University of Technology Guangzhou510006 China College of Computer Science Sichuan University Chengdu China School of Artificial Intelligence Northwestern Polytechnical University Xi'an China Lingnan University Department of Computing and Decision Science Hong Kong College of Artificial Intelligence Nankai University Tianjin300350 China

ISBN: (纸本)9781665410205

The rapid growth of electric vehicles (EVs) has led to significant challenges in providing efficient and sustainable charging solutions. This paper addresses the battery swapping station (BSS) recommendation problem by proposing a novel conflict-free genetic algorithm (CFGA) integrated with a Nash equilibrium seeking (NES) approach to identify optimal Nash equilibrium (ONE) solutions to such a non-cooperative optimization problem. The CFGA employs specialized crossover and mutation operators to generate offspring that satisfy the constraints of the problem, ensuring that each EV decides a unique battery swap strategy without conflict. Firstly, an order crossover operator is proposed to preserve the order of genes in the chromosomes. Secondly, a replacement and exchange mutation operator is proposed to enhance mutation diversity. The resulting optimal solution is then used as the initial strategy for the NES, which iteratively converges to the ONE. The proposed CFGA with NES algorithm is evaluated under both small-scale and large-scale cases, demonstrating its effectiveness in achieving a balance between costs for EVs and utilization for BSSs. The study's findings have practical implications for the smart grid and EV integration, offering a robust method for optimizing EV infrastructure and operations. © 2024 IEEE.

关键词： Charging stations

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Detection of ARP Spoofing Attack in Healthcare Networks Using a LSTM Model

Detection of ARP Spoofing Attack in Healthcare Networks Usin...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Salam Al-E'mari Yousef Sanjalawe Ghader Kurdi Budoor Allehyani Information Security Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Information Technology Department King Abdullah II School for Information Technology The University of Jordan (UJ) Amman Jordan Department of Data Science College of Computing Umm Al-Qura University (UQU) Makkah Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University (UQU) Makkah Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Healthcare networks face significant cybersecurity threats due to their extensive interconnectivity, with Address Resolution Protocol (ARP) spoofing posing a major risk to the confidentiality and integrity of medical data. ARP spoofing is a Man-in-the-Middle (MITM) attack where an attacker sends falsified ARP messages to associate their MAC address with the IP address of a legitimate device, enabling unauthorized interception or manipulation of network traffic. In this paper, we propose an LSTM-based detection model designed to effectively identify ARP spoofing attacks in healthcare environments. Unlike traditional methods that rely on static markers at fixed time intervals, the LSTM model leverages temporal dependencies in network traffic, enhancing detection accuracy and adaptability to evolving attack patterns. Experimental results based on the CICIoMT Dataset 2024 demonstrate that the proposed model achieves a detection accuracy of 98.5%, significantly outperforming conventional approaches. These findings highlight the effectiveness of LSTM networks in mitigating ARP spoofing threats, offering a scalable solution for securing healthcare networks.

关键词： Adaptation models Protocols Accuracy Computational modeling Intrusion detection Telecommunication traffic Internet of Medical Things IP networks Long short term memory Faces

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Improved Traffic Filtering for IoT Security

Improved Traffic Filtering for IoT Security

引用

IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Henry Peter Ovili Margaret Okpor Nwanze N. E Tosin Olayinka Emmanuel Ojei Wilson Nwankwo Department of Information Systems & Technology Delta State University of Science and Technology Ozoro Nigeria Department of Cyber Security Delta State University of Science and Technology Ozoro Nigeria Marine Engineering Department Faculty of Engineering Delta State University of Science and Technology Ozoro Department of Computing Wellspring University Benin City Nigeria Department of Software Engineering Delta State University of Science and Technology Ozoro Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

This research presents an advanced traffic filtering technique using a Support Vector Machine (SVM) model to enhance IoT network security, achieving 85% accuracy, with high precision (87%) and recall (96%) in identifying legitimate traffic. However, the model struggles with neutral traffic, highlighting the need for further refinement, and offers a scalable solution for real-time dynamic analysis, with future work focusing on improving neutral traffic classification and integrating additional features to enhance performance.

关键词： Support vector machines Analytical models Humanities Electric potential Accuracy Filtering Focusing Machine learning Network security Real-time systems

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