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arXiv 2025年

作者： Shek, Chak Lam Bedi, Amrit Singh Basak, Anjon Novoseller, Ellen Waytowich, Nick Narayanan, Priya Manocha, Dinesh Tokekar, Pratap Department of Electrical and Computer Engineering University of Maryland College ParkMD United States Department of Computer Science University of Central Florida FL United States DEVCOM US Army Research Laboratory AdelphiMD United States Department of Computer Science University of Maryland College ParkMD United States

In this work, we present a novel cooperative multiagent reinforcement learning method called Locality based Factorized Multi-Agent Actor-Critic (Loc-FACMAC). Existing state-of-the-art algorithms, such as FACMAC, rely on global reward information, which may not accurately reflect the quality of individual robots’ actions in decentralized systems. We integrate the concept of locality into critic learning, where strongly related robots form partitions during training. Robots within the same partition have a greater impact on each other, leading to more precise policy evaluation. Additionally, we construct a dependency graph to capture the relationships between robots, facilitating the partitioning process. This approach mitigates the curse of dimensionality and prevents robots from using irrelevant information. Our method improves existing algorithms by focusing on local rewards and leveraging partition-based learning to enhance training efficiency and performance. We evaluate the performance of Loc-FACMAC in three environments: Hallway, Multi-cartpole, and Bounded-Cooperative-Navigation. We explore the impact of partition sizes on the performance and compare the result with baseline MARL algorithms such as LOMAQ, FACMAC, and QMIX. The experiments reveal that, if the locality structure is defined properly, Loc-FACMAC outperforms these baseline algorithms up to 108%, indicating that exploiting the locality structure in the actor-critic framework improves the MARL performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Multipurpose robots

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QUADFormer: Learning-based Detection of Cyber Attacks in Quadrotor UAVs

arXiv

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arXiv 2024年

作者： Wang, Pengyu Yang, Zhaohua Yang, Nachuan Wang, Zikai Li, Jialu Zhang, Fan Wang, Chaoqun Wang, Jiankun Meng, Max Q.-H. Shi, Ling Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The School of Control Science and Engineering Shandong University Shandong China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Department of Electrical and Computer Engineering The University of Alberta Canada

Safety-critical intelligent cyber-physical systems, such as quadrotor unmanned aerial vehicles (UAVs), are vulnerable to different types of cyber attacks, and the absence of timely and accurate attack detection can lead to severe consequences. When UAVs are engaged in large outdoor maneuvering flights, their system constitutes highly nonlinear dynamics that include non-Gaussian noises. Therefore, the commonly employed traditional statistics-based and emerging learning-based attack detection methods do not yield satisfactory results. In response to the above challenges, we propose QUADFormer, a novel Quadrotor UAV Attack Detection framework with transFormer-based architecture. This framework includes a residue generator designed to generate a residue sequence sensitive to anomalies. Subsequently, this sequence is fed into a transformer structure with disparity in correlation to specifically learn its statistical characteristics for the purpose of classification and attack detection. Finally, we design an alert module to ensure the safe execution of tasks by UAVs under attack conditions. We conduct extensive simulations and real-world experiments, and the results show that our method has achieved superior detection performance compared with many state-of-the-art methods. (Video1) Copyright © 2024, The Authors. All rights reserved.

关键词： Antennas

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Empirical Privacy Variance

arXiv

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arXiv 2025年

作者： Hu, Yuzheng Wu, Fan Xian, Ruicheng Liu, Yuhang Zakynthinou, Lydia Kamath, Pritish Zhang, Chiyuan Forsyth, David Siebel School of Computing and Data Science University of Illinois Urbana-Champaign United States Institute of Automation Chinese Academy of Sciences China Department of Electrical Engineering and Computer Science University of California Berkeley United States Google Research United States

We propose the notion of empirical privacy variance and study it in the context of differentially private fine-tuning of language models. Specifically, we show that models calibrated to the same (Ε, δ)-DP guarantee using DP-SGD with different hyperparameter configurations can exhibit significant variations in empirical privacy, which we quantify through the lens of memorization. We investigate the generality of this phenomenon across multiple dimensions and discuss why it is surprising and relevant. Through regression analysis, we examine how individual and composite hyperparameters influence empirical privacy. The results reveal a no-free-lunch trade-off: existing practices of hyperparameter tuning in DP-SGD, which focus on optimizing utility under a fixed privacy budget, often come at the expense of empirical privacy. To address this, we propose refined heuristics for hyperparameter selection that explicitly account for empirical privacy, showing that they are both precise and practically useful. Finally, we take preliminary steps to understand empirical privacy variance. We propose two hypotheses, identify limitations in existing techniques like privacy auditing, and outline open questions for future research. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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Analyzing the function of smart security in the context of the internet of things

Analyzing the function of smart security in the context of t...

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Advance computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： K. Venkatagurunatham Naidu Anchu Krishnan Pillai C. E. Rajaprabha Rajesh Sharma R Atowar Ul Islam Shaik Khasimbee Dept. of Computer Science and Engineering Dr M.G.R Educational and Research Institute Chennai Applied Science and Humanities Xavier Institute of Engineering Mumbai Maharashtra Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore Department of Computer Science and Engineering School of Electrical Engineering and Computing Adama Science and Technology University Nazareth Ethiopia Department of Computer Science and Electronics University of Science and Technology Meghalaya Baridua Meghalaya Department of Computer Science and Engineering Aditya College of Engineering Surampalem Andhra Pradesh

Technology is getting better day by day and human beings are using the latest technology possible for their needs and safety. Many devices have been invented that use the internet and with this human safety issues are also taken care of up to a great extent. GPS tracking devices, SHE (Society Harnessing Equipment), AESHS (Advanced Electronic System for Human Safety), ILA Systems are one of the most useful applications and devices that are used for human safety. Human safety and especially the safety of girls have become an emerging and sensitive issue in the present world scenario. This article deals with the role of the Internet of Things in advancing smart security. The issues are taken care of with the help of these upgraded devices that are easily accessible from a remote location with an internet connection.

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Autonomous Multiple-Trolley Collection System with Nonholonomic Robots: Design, Control, and Implementation

arXiv

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arXiv 2024年

作者： Xie, Peijia Xia, Bingyi Hu, Anjun Zhao, Ziqi Meng, Lingxiao Sun, Zhirui Gao, Xuheng Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Jiaxing Research Institute Southern University of Science and Technology Jiaxing China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada

The intricate and multi-stage task in dynamic public spaces like luggage trolley collection in airports presents both a promising opportunity and an ongoing challenge for automated service robots. Previous research has primarily focused on handling a single trolley or individual functional components, creating a gap in providing cost-effective and efficient solutions for practical scenarios. In this paper, we propose a mobile manipulation robot incorporated with an autonomy framework for the collection and transportation of multiple trolleys that can significantly enhance operational efficiency. We address the key challenges in the trolley collection problem through the novel design of the mechanical system and the vision-based control strategy. We design a lightweight manipulator and docking mechanism, optimized for the sequential stacking and transportation of multiple trolleys. Additionally, based on the Control Lyapunov Function and Control Barrier Function, we propose a novel vision-based control with the online Quadratic Programming which significantly improves the accuracy and efficiency of the collection process. The practical application of our system is demonstrated in real-world scenarios, where it successfully executes multiple-trolley collection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Quadratic programming

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Single-and Multiuser Virtual Reality Training for Forklift Driving 2

Single-and Multiuser Virtual Reality Training for Forklift D...

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2nd IEEE International Conference on Cognitive Aspects of Virtual Reality, CVR 2023

作者： Froland, Tord Hettervik Ravyse, Werner Luimula, Mika Heldal, Ilona Western Norway University of Applied Sciences Chemistry and Biomedical Laboratory Sciences Department of Safety Bergen Norway School of ICT: Futuristic Interactive Technologies Research Group Turku University of Applied Sciences Turku Finland Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9798350338638

Hands-on skills training is a constant requirement in a multitude of practical work settings and virtual reality (VR) is often found to be a valuable training opportunity to safely prepare students for their future work environments. One of the main benefits of utilizing VR-based training is providing high grades of presence and involvement. Although the user experiences of students are a key factor that impacts the use of a VR simulation, it becomes more challenging to decide whether single-or multi-user experiences should be planned and developed. This paper presents the results from a between-subjects case study comparing the user experiences of single-and multi-user VR training for forklift driving. Findings highlight the importance of considering various aspects of VR technology to design VR training environments for high user experiences, including the impact of age, group dynamics, and freedom of movement in the environment. The benefits and challenges of single-and multi-user VR training for high experiences and implications for utilizing serious games in VR-based training are discussed. © 2023 IEEE.

关键词： Virtual reality

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LLM in the Loop: A Framework for Contextualizing Counterfactual Segment Perturbations in Point Clouds

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IEEE Access 2025年 13卷 85507-85525页

作者： Kočić, Veljka Lukač, Niko Rožajac, Džemail Schweng, Stefan Gollob, Christoph Nothdurft, Arne Stampfer, Karl del Ser, Javier Holzinger, Andreas BOKU University Human-Centered AI Laboratory Institute of Forest Engineering Department of Ecosystem Management Climate and Biodiversity Vienna1190 Austria Institute of Human-Centered Computing TU Graz Faculty of Computer Science and Biomedical Engineering Graz8010 Austria University of Maribor Faculty of Electrical Engineering and Computer Science Maribor2000 Slovenia BOKU University Institute of Forest Growth Department of Ecosystem Management Climate and Biodiversity Vienna1190 Austria Derio48160 Spain Department of Mathematics Leioa48940 Spain

Point Cloud Data analysis has seen a major leap forward with the introduction of PointNet algorithms, revolutionizing how we process 3D environments. Yet, despite these advancements, key challenges remain, particularly in optimizing segment perturbations to influence model outcomes in a controlled and meaningful way. Traditional methods struggle to generate realistic and contextually appropriate perturbations, limiting their effectiveness in critical applications like autonomous systems and urban planning. This paper takes a bold step by integrating Large Language Models into the counterfactual reasoning process, unlocking a new level of automation and intelligence in segment perturbation. Our approach begins with semantic segmentation, after which LLMs intelligently select optimal replacement segments based on features such as class label, color, area, and height. By leveraging the reasoning capabilities of LLMs, we generate perturbations that are not only computationally efficient but also semantically meaningful. The proposed framework undergoes rigorous evaluation, combining human inspection of LLM-generated suggestions with quantitative analysis of semantic classification model performance across different LLM variants. By bridging the gap between geometric transformations and high-level semantic reasoning, this research redefines how we approach perturbation generation in Point Cloud Data analysis. The results pave the way for more interpretable, adaptable, and intelligent AI-driven solutions, bringing us closer to real-world applications where explainability and robustness are paramount. © 2013 IEEE.

关键词： k-nearest neighbors

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Influence of Defects on Enhancing Lithium Diffusivity in Crystalline Silicon Anodes for Fast Charging Lithium-Ion Batteries

SSRN

引用

SSRN 2024年

作者： Zand, Ali Lashani Mani, Azam Jalali, Hossein Kookhaee, Maryam Habibi, Alireza Sanaee, Zeinab Mousavi-Khoshdel, Seyed Morteza School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran14395-515 Iran Department of Engineering Science College of Engineering University of Tehran Iran Industrial Electrochemistry Research Laboratory Faculty of Chemistry Iran University of Science and Technology Tehran*** Iran

The growing energy storage market requires safe and stable high-capacity cycling, silicon (Si) anodes are ideal for this purpose. Electrochemical reactions between Si anodes and lithium pose challenges due to the significant volumetric expansion during lithiation. Expansion material can cause cracks in the structure and result in disconnection from the current collector. Si anodes exhibit a variety array of phenomena, such as amorphization and crystallization upon full lithiation, with variations depending on crystal orientations. It is evident that Lithium ions (Li+) diffusion varies across different Si crystalline surfaces and arrangements. It is possible to improve ion mobility and reduce diffusion barriers through strategic design and the use of complementary materials. First principles simulations can provide fundamental insights into atomic interactions, offering a realistic atomistic representation of lithiation processes. Our simulations involve the step-by-step insertion of Li+ into Si orientations and Si with N vacancies. Atomic imaging of Si (110) doped with N vacancies reveals a more favorable behavior in ionic diffusion. Our results explain the ternary Si-Li-N electrode materials and the identification of optimal site disorders to maximize the rate of Li+ diffusion in crystalline Si anodes. © 2024, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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Action Recognition via Graph Convolutional Networks for the Assisted Living of the Elderly

Action Recognition via Graph Convolutional Networks for the ...

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IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： Zois Tsakiris Lazaros Tsochatzidis Ioannis Pratikakis Dimitrios Menychtas Nikolaos Aggelousis Department of Electrical and Computer Engineering Visual Computing Group Democritus University of Thrace Xanthi Greece Department of Physical Education and Sports Science Biomechanics Laboratory Democritus University of Thrace Komotini Greece

ISBN: (数字)9798350308921

ISBN: (纸本)9798350308938

Automated monitoring systems can play a crucial role in the effective assisted living of the elderly. Such systems aim to detect specific actions or activities of the individual which may indicate discomfort, pain or danger. Towards this end, we employ a skeleton-based representation for the human body, as a graph of interconnected nodes, that can be used as the means of meaningful spatial-temporal information of an action. In this paper, a novel methodology for human action recognition is proposed in the context of Graph Convolutional Networks that, along with the conventional full-body human graph, utilizes an arm-specific graph representation to better capture the fine-grained motion of the arms. For experimental evaluation, a new dataset is created that comprises a variety of actions, performed by multiple elderly individuals, specifically for the purpose of monitoring everyday life activities. Additionally, experimentation is extended to a well-established large dataset, namely NTU RGB+D 120, focusing on the actions that correspond to medical conditions. Experimental results demonstrate the effectiveness of our combined approach, compared to the single graph approach, in terms of recognition accuracy.

关键词： Medical conditions Graph convolutional networks Pain Conferences Focusing Nanoscale devices Assisted living

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computing Power in the Sky: Digital Twin-assisted Collaborative computing with Multi-UAV Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Chao Han, Yu Zhang, Long Jia, Ziye Zhang, Hongliang Hong, Choong Seon Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Jiangsu Nanjing211111 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Kyung Hee University Department of Computer Science and Engineering Gyeonggi-do Yongin-si17104 Korea Republic of University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The collaboration of computing powers (CPs) among unmanned aerial vehicles (UAVs)-mounted edge servers is essential to handle data-intensive tasks of user equipments (UEs). This paper presents a multi-UAV computing power network (CPN) that orchestrates the CP resources of edge servers to cooperatively process tasks through collaborative computing. Due to the frequent interactions among edge servers and the increased complexities for large-scale task executions, we propose a digital twin (DT)-driven multi-UAV CPN framework, where the aerial DT network is created as a digital replica of the multi-UAV network to assist collaborative computing. A joint optimization problem of the service association, offloading power control, task partitions, CP allocation, and UAV trajectory design is formulated to minimize the long-term average delay of UEs. We use the Lyapunov technique to tackle the long-term energy constraint, forming a tractable optimization problem, which is modeled as a Markov decision process to balance the UAV's deficit queue stability and the UE's task completion delay. A hybrid soft actor-critic based deep reinforcement learning algorithm is developed to optimize the joint design by sampling the hybrid action from the Gumbel and Gaussian distributions. Simulation results verify that the multi-UAV CPN contributes to significant performance gains when supported by DTs. © 2025 IEEE.

关键词： Markov processes

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