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Sequence-Aware Online Container Scheduling with Reinforcement Learning in Parked Vehicle Edge Computing

IEEE Transactions on Vehicular Technology 2025年

作者： Wu, Jianqiu Guo, Jianxiong Tang, Zhiqing Luo, Chuanwen Wang, Tian Jia, Weijia Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Department of Computer Science Zhuhai519087 China Beijing Normal University Advanced Institute of Natural Sciences Zhuhai519087 China Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Zhuhai519087 China Beijing Forestry University School of Information Science and Technology Beijing100083 China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083 China

Intelligent vehicles, often parked for long periods, are ideally suited to serve as computational nodes to expand the Mobile Edge Computing (MEC) infrastructure, with containerization significantly enhancing the system's load balancing, self-healing, resource isolation, and security. However, fluctuations in task demand and frequent container image downloads during peak hours create high loads on containerized nodes, as multiple mobile devices offload tasks simultaneously, leading to significant processing delays. Many existing studies make the simplified assumption of predefined patterns of task arrivals, which overlooks this issue and makes suboptimal decisions. In this paper, we consider a Parked Vehicles (PVs)-extended MEC scenario, where multiple devices request services on PVs functioning as edge servers, all controlled by a central base station. Task arrivals follow observed patterns based on long-term trends, such as peak and off-peak periods, resembling realistic arrival patterns rather than predefined ones. To optimize task offloading by identifying these patterns, we propose the Sequence-Aware Task Scheduling (SATS) algorithm, which is a policy gradient-based deep reinforcement learning approach that integrates Transformer and LSTM architectures to capture patterns in time-series task arrivals and relationships between nodes in a collaborative and containerized environment, thereby enhancing the efficiency of online task scheduling. The primary objective of SATS is to optimize the task offloading policy and minimize delay and energy consumption for all devices and PVs. Extensive numerical comparisons against baselines demonstrate the effectiveness and advantages of our algorithm. © 1967-2012 IEEE.

关键词： Mobile edge computing

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A thread-based electrochemical sensor for spatial monitoring of wound oxygenation 24

A thread-based electrochemical sensor for spatial monitoring...

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24th International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2020

作者： Xia, Junfei Zeng, Wenxin Wang, Wei Owyeung, Rachel Arsenescu, Victor Sonkusale, Sameer Department of Electrical and Computer Engineering Tufts University United States Nano Lab Advanced Technology Laboratory Tufts University United States Department of Chemical and Biological Engineering Tufts University United States Department of Biomedical Engineering Tufts University United States

ISBN: (纸本)9781733419017

Continuous monitoring of wound oxygenation plays a pivotal role in acute and chronic wound management. Current oxygen sensors are not able to measure oxygen levels deep inside the wound. In this study we develop a flexible thread-based oxygen sensor that combines the unique advantages of minimal invasiveness, superior flexibility and amenable for tissue integration. The sensor is featured by a simple and low-cost fabrication approach which allows for 'sensing at the tip' to probe geometrically complex wound bed in three dimensions with high spatial accuracy and high sensitivity. © 2020 CBMS-0001

关键词： Oxygenation

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SDVADC: Secure Deduplication and Virtual Auditing of Data in Cloud 3

SDVADC: Secure Deduplication and Virtual Auditing of Data in...

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3rd International Conference on Computing and Network Communications, CoCoNet 2019

作者： Geeta, C.M. Shreyas Raju, R.G. Raghavendra, S. Buyya, Rajkumar Venugopal, K.R. Iyengar, S.S. Patnaik, L.M. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru India Lab University of Melbourne Australia Bangalore University Bengaluru India Department of Computer Science and Engineering Florida International University United States INSA National Institute of Advanced Studies Indian Institute of Science Campus Bangalore India

Over the last few years, deploying data to cloud service for repository is an appealing passion that avoids efforts on significant information sustenance and administration. In distributed repository utilities, deduplication technique is often exploited to minimize the capacity and bandwidth necesseties of amenities by erasing repetitive data and caching only a solitary duplicate of them. Proof-of-Ownership mechanisms authorize any possessor of the identical information to approve to the distributed repository server that he possess the information in a dynamic way. In repository utilities with enormous information, the repository servers may intend to minimize the capacity of cached information, and the customers may want to examine the integrity of their information with a reasonable cost. We propose Secure Deduplication and Virtual Auditing of Data in Cloud (SDVADC) mechanism that realizes integrity auditing and deduplication of information in cloud. The mechanism supports secure deduplication of information and effective virtual auditing of the documents during the download process. In addition, the proposed mechanism lowers the burden of dataowner to audit documents by himself and there is no need to delegate auditing to the Third Party Auditor (TPA). Experimental results demonstrate that the virtual auditing has low auditing time cost relative to the existing public auditing schemes. © 2020 The Authors. Published by Elsevier B.V.

关键词： computers

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Augmented Reality Navigation in Robot-Assisted Surgery with a Teleoperated Robotic Endoscope

Augmented Reality Navigation in Robot-Assisted Surgery with ...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Veronica Penza Alberto Neri Maria Koskinopoulou Enrico Turco Domenico Soriero Stefano Scabini Domenico Prattichizzo Leonardo S. Mattos Biomedical Robotics Lab Advanced Robotics Istituto Italiano di Tecnologia Genova Italy Department of Computer Science Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genoa Genova Italy Istituto Italiano di Tecnologia Genova Italy Department of Information Engineering and Mathematics University of Siena Siena Italy Unit of Surgical Oncology IRCCS Policlinico San Martino Genoa Italy IRCCS Policlinico San Martino Genoa Italy

Augmented reality (AR) is considered one of the most promising solutions for safer procedures in several surgical specialities. Fusing patient-specific pre-operative information, typically 3D models extracted from CT scans or MRI, with real-time surgical images allows the surgeon to have detailed information on the anatomical structure of the surgical target intra-operatively. The coupling of AR and Robotics represents the next step towards introducing awareness into the surgical room, thus enhancing the surgeon's perceptual, cognitive and manipulative capabilities. This paper presents a novel integrated system for real-time AR navigation in robotic minimally invasive surgery (RMIS), composed of a robotic endoscopic camera, a robotic teleoperation implementing a software-based Remote Center of Motion (RCM), and an AR navigation software based on an initial manual registration of virtual 3D models with the real anatomy. The integrated system, as well as the individual modules, were evaluated in simulated surgical-like setups for accuracy and repeatability. The proposed system can perform high-precision tasks (position accuracy around $1 mm$ and AR error lower than 7%), showing potential for application in different surgical procedures and setting the basis for autonomous robotic surgery operations.

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Diff5T: Benchmarking Human Brain Diffusion MRI with an Extensive 5.0 Tesla K-Space and Spatial Dataset

arXiv

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arXiv 2024年

作者： Wang, Shanshan Yu, Shoujun Cheng, Jian Jia, Sen Tie, Changjun Zhu, Jiayu Peng, Haohao Dong, Yijing He, Jianzhong Zhang, Fan Xing, Yaowen Jia, Xiuqin Yang, Qi Tian, Qiyuan Guo, Hua Li, Guobin Zheng, Hairong Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Beihang University Beijing China United-Imaging Healthcare Shanghai China School of Biomedical Engineering Tsinghua University Beijing China Department of Radiology Beijing Chaoyang Hospital Capital Medical University Key Lab of Medical Engineering for Cardiovascular Disease Ministry of Education Beijing China Institute of Information Processing and Automation College of Information Engineering Zhejiang University of Technology China University of Electronic Science and Technology of China Chengdu China

Diffusion magnetic resonance imaging (dMRI) provides critical insights into the microstructural and connectional organization of the human brain. However, the availability of high-field, open-access datasets that include raw k-space data for advanced research remains limited. To address this gap, we introduce Diff5T, a first comprehensive 5.0 Tesla diffusion MRI dataset focusing on the human brain. This dataset includes raw k-space data and reconstructed diffusion images, acquired using a variety of imaging protocols. Diff5T is designed to support the development and benchmarking of innovative methods in artifact correction, image reconstruction, image preprocessing, diffusion modelling and tractography. The dataset features a wide range of diffusion parameters, including multiple b-values and gradient directions, allowing extensive research applications in studying human brain microstructure and connectivity. With its emphasis on open accessibility and detailed benchmarks, Diff5T serves as a valuable resource for advancing human brain mapping research using diffusion MRI, fostering reproducibility, and enabling collaboration across the neuroscience and medical imaging communities. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

arXiv

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arXiv 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China University of Kent CanterburyCT2 7NP United Kingdom Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Intelligent Transportation Systems Lab Innopolis University Innopolis Russia School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi’an710071 China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Self-sanitizing reusable glove via 3D-printing and common mold making method

引用

Materials Today: Proceedings 2023年

作者： Sadasivuni, Kishor Kumar Maurya, Muni Raj Houkan, Mohammad Talal Cabibihan, John-John Geetha, Mithra Al-Maadeed, Somaya Omar, Hafsa Asli, Noor Asnida Center for Advanced Materials Qatar University PO Box 2713 Doha Qatar Department of Mechanical and Industrial Engineering Qatar University PO Box 2713 Doha Qatar Department of Computer Science and Engineering Qatar University PO Box 2713 Doha Qatar School of Physics and Material Studies Faculty of Applied Sciences Universiti Teknologi MARA Shah Alam 40450 Selangor Malaysia NANO-Scitech Lab Centre for Functional Materials and Nanotechnology Institute of Science Universiti Teknologi MARA Malaysia

In health care and public health practice, it is critical to settings control practices that are critical to reducing the transmission of infections through cross-contamination. To provide protection from cross-contamination, use and throw gloves are routinely used. However, single-time use and inconsistent sanitization of used gloves remain a large problem and elevate the risk of catching viruses, germs, pathogens, and contaminants. The study reports reusable self-sanitizing gloves via 3D-printing and common hand molding methods. The major contribution is frequent self-sanitization of gloves without any manual intervention. The elastomeric material is used for fabricating gloves and continuous channels are embedded within the elastomeric material that runs through the entire glove surface, covering the front, back, and fingers. Elastomeric material allows the engagement of fingers for gripping objects. While the embedded channel is provided with uniformly spaced openings to eject the sanitizing solution. The glove surface is textured with a porous morphology that acts as mini and micro reservoirs for sterilizing solution ejected through embedded channel opening. The embedded channel is connected to a sanitizing solution storage tank. The incorporation of sanitizing solution storage tank enables its usage over a longer period. This uniquely constructed design of the gloves even assists in the effective sterilization of infected surface that comes in contact with the gloves. The gloves can be customized to improve comfortability by fabricating them from the 3D-printed mound developed based on the palm size of the user. The developed technology can be used by individuals working in hospitals, the transport sector, delivery units, schools, offices, industries, etc. We strongly believe that this technology will be highly useful in minimizing the risk of getting infected through cross-contamination and will help in maintaining hygienic as well as safe surroundings. © 202

关键词： Viruses

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Physicochemical Characterization Core Satellite Self-Assembled Magneto Hybrid Nanostructure Based on IONP/GNP/antiHER-2

SSRN

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SSRN 2025年

作者： Tavakolikia, Fatemeh Mirzapoor, Aboulfazl Abdolahad, Mohammad Department of Nanobiotechnology Faculty of Biological Sciences Tarbiat Modares University P.O. Box 14115-175 Tehran Iran Advanced and smart Nanobiosystems lab Faculty of Biological Sciences Tarbiat Modares University P.O. Box 14115-175 Tehran Iran Nano Electronic Center of Excellence Nano-bioelectronic Devices Lab. Cancer Electronics Research Group School of Electrical and Computer Eng. College of Engineering University of Tehran P.O. Box: 14395-515 Tehran Iran

With the growth of breast cancer statistics in women, studying and finding an effective treatment for this cancer has pulled into the consideration of researchers. Recently, drug delivery systems noticed in the diagnosis and treatment of cancer. The premise of these systems is the presence of a Nano carrier on which the drugs are set. One of the common Nano carriers is magnetic iron oxide nanoparticles, which is a suitable option as a Nano carrier due to its biocompatibility and easy disposal. Among the common antibodies in breast cancer cells, we can highlight the antiHER-2 to form smart drug delivery systems. Tetracycline is a group of antibiotics that proven to have anti-cancer properties, so this antibiotic can cause cancer cell apoptosis. In general, this research revolves around the design and fabrication of tetracycline of core satellite self-assembled magneto hybrid nanostructure based on magnetic Nano particles IONP/GNP/ targeted with antiHER-2 for targeted Breast Cancer treatment. The fabricated nanostructures showed good performance in drug delivery and reduction of cancer. © 2025, The Authors. All rights reserved.

关键词： Nanoclay

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Design and Fabrication of Core Satellite Self-Assembled Magneto Hybrid Nanostructure Based on Ionp/Gnp/Antiher-2 for Targeted Breast Cancer Treatment

SSRN

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SSRN 2025年

关键词： Nanoclay

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MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition

arXiv

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arXiv 2023年

作者： Xing, Jiazheng Xu, Chao Wang, Mengmeng Dai, Guang Sun, Baigui Liu, Yong Wang, Jingdong Zhao, Jian Laboratory of Advanced Perception on Robotics and Intelligent Learning College of Control Science and Engineering Zhejiang University Zhejiang Hangzhou310027 China Alibaba Group China College of Computer Science and Technology Zhejiang University of Technology China SGIT AI Lab State Grid Corporation of China China Baidu China China Northwestern Polytechnical University Shanxi Xi’an China

Applying large-scale vision-language pre-trained models like CLIP to few-shot action recognition (FSAR) can significantly enhance both performance and efficiency. While several studies have recognized this advantage, most of them resort to full-parameter fine-tuning to make CLIP’s visual encoder adapt to the FSAR data, which not only costs high computations but also overlooks the potential of the visual encoder to engage in temporal modeling and focus on targeted semantics directly. To tackle these issues, we introduce MA-FSAR, a framework that employs the Parameter-Efficient Fine-Tuning (PEFT) technique to enhance the CLIP visual encoder in terms of action-related temporal and semantic representations. Our solution involves a Fine-grained Multimodal Adaptation, which is different from the previous attempts of PEFT in regular action recognition. Specifically, we first insert a Global Temporal Adaptation that only receives the class token to capture global motion cues efficiently. Then these outputs integrate with visual tokens to enhance local temporal dynamics by a Local Multimodal Adaptation, which incorporates text features unique to the FSAR support set branch to highlight fine-grained semantics related to actions. In addition to these token-level designs, we propose a prototype-level text-guided construction module to further enrich the temporal and semantic characteristics of video prototypes. Extensive experiments demonstrate our superior performance in various tasks using minor trainable parameters. © 2023, CC BY-SA.

关键词： Semantics

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