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Against Mobile Collusive Eavesdroppers: Cooperative Secure Transmission and Computation in UAV-Assisted MEC Networks

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IEEE Transactions on Mobile computing 2025年第6期24卷 5280-5297页

作者： Zhao, Mingxiong Wang, Zirui Guo, Kun Zhang, Rongqian Quek, Tony Q. S. Ministry of Education Engineering Research Center of Integration and Application of Digital Learning Technology Beijing100039 China Ministry of Education Engineering Research Center of Cyberspace Kunming650504 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650221 China Yunnan University Yunnan Key Laboratory of Software Engineering Kunming650091 China East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing School of Communications and Electronics Engineering Shanghai200241 China Yunnan University School of Information Science and Engineering Kunming650500 China Singapore University of Technology and Design Information Systems Technology and Design 487372 Singapore National Pilot School of Software Yunnan University Kunming China

In Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge computing (MEC) networks, the security of transmission faces significant challenges due to the vulnerabilities of line-of-sight links and potential eavesdropping on two-hop links. This paper addresses these challenges with an innovative Cooperative Secure Transmission and Computation strategy (CSTC), specifically engineered for time-slotted UAV-assisted MEC networks plagued by mobile collusive eavesdroppers. These eavesdroppers significantly bolster their interception capabilities through coordinated and optimized movements, escalating the security threats. To neutralize these risks, the proposed CSTC employs the UAV and remote devices as helper nodes to emit jamming signals, thereby thwarting eavesdropping activities, while simultaneously facilitating the efficient relay of users' tasks to the base station for advanced processing. The CSTC aims to maximize the sum Secrecy Transmission Rate (STR) satisfying task latency constraints. It involves a joint optimization of UAV trajectory, jamming beamformers, transmit power, and data offloading strategy to expedite task transmission. Additionally, a real-time computation scheduling approach is developed based on a newly defined metric, the Urgency Degree of Users (UDoU), to enhance task processing efficiency. Our extensive simulations validate that the CSTC not only elevates the sum STR but also consistently meets latency constraints, demonstrating its robustness against advanced mobile eavesdropping techniques. © 2002-2012 IEEE.

关键词： Jamming

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Using Fitts' Law to Benchmark Assisted Human-Robot Performance 25

Using Fitts' Law to Benchmark Assisted Human-Robot Performan...

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Jiahe Pan Jonathan Eden Denny Oetomo Wafa Johal Department of Mechanical Engineering The University of Melbourne Melbourne Australia School of Computing and Information Systems The University of Melbourne Melbourne Australia

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

Shared control systems aim to combine human and robot abilities to improve task performance. However, achieving optimal performance requires that the robot's level of assistance adjusts the operator's cognitive workload in response to the task difficulty. Understanding and dynamically adjusting this balance is crucial to maximizing efficiency and user satisfaction. In this paper, we propose a novel benchmarking method for shared control systems based on Fitts' Law to formally parameterize the difficulty level of a target-reaching task. With this we systematically quantify and model the effect of task difficulty (i.e. size and distance of target) and robot autonomy on task performance and operators' cognitive load and trust levels. Our empirical results (N=24) not only show that both task difficulty and robot autonomy influence task performance, but also that the performance can be modelled using these parameters, which may allow for the generalization of this relationship across more diverse setups. We also found that the users' perceived cognitive load and trust were influenced by these factors. Given the challenges in directly measuring cognitive load in real-time, our adapted Fitts' model presents a potential alternative approach to estimate cognitive load through determining the difficulty level of the task, with the assumption that greater task difficulty results in higher cognitive load levels. We hope that these insights and our proposed framework inspire future works to further investigate the generalizability of the method, ultimately enabling the benchmarking and systematic assessment of shared control quality and user impact, which will aid in the development of more effective and adaptable systems.

关键词： Adaptation models Teleoperators Systematics Human-robot interaction Benchmark testing Cognitive load Control systems Real-time systems Robots Load modeling

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Dual Distillation: A Lightweight and Effective Intrusion Detection Model for IoT Security

Dual Distillation: A Lightweight and Effective Intrusion Det...

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International Global Information Infrastructure Symposium (GIIS)

作者： Chonghao Pei Lotfi Mhamdi School of Electronic and Electrical Engineering University of Leeds UK College of Computing & Systems Abdullah Al Salem University Kuwait

ISBN: (数字)9798331520113

ISBN: (纸本)9798331520120

With the exponential growth of IoT devices, ensuring efficient and accurate network anomaly detection in resource-constrained environments has become a pressing challenge. A lightweight dual distillation approach is proposed, combining data distillation and model distillation. Data distillation is achieved through KMeans clustering and refinement, while model distillation leverages a random forest (RF) teacher model to guide a simplified multi-layer perceptron (MLP) student model. The approach is validated on CICIDS2017 and TON-IoT datasets, where dataset sizes are reduced to 20% and 40%, respectively. Experimental results demonstrate that the proposed method enhances MLP accuracy on CICIDS2017 from 93.17% to 99.66% and on TON-IoT from 89.00% to 93.14%. Moreover, the method significantly reduces model parameters compared to the RF teacher model while maintaining competitive performance, making it highly suitable for resource-constrained IoT environments.

关键词： Radio frequency Knowledge engineering Accuracy Intrusion detection Pressing Network security Data models Internet of Things Anomaly detection

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Effect of polarization on the point spread function in optical coherence tomography

Effect of polarization on the point spread function in optic...

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Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXIX 2025

作者： Tankam, Patrice Liu, Xiao School of Optometry Indiana University BloomingtonIN United States Intelligent Systems Engineering Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47405 United States

ISBN: (纸本)9781510683587

The use of the polarization property of light to evaluate the birefringence property of tissues as well as changes due to pathological conditions has been gaining interest over the past years with the introduction of different variants of optical coherence tomography (OCT) including polarization-sensitive OCT and cross-polarization OCT (CP-OCT). While partially polarized light sources have been commonly used in OCT, the contribution of different states of polarization of the light source to the system's point spread function (PSF) has not been fully studied. This effect can have major implications on OCT images, leading to data misinterpretation. This study evaluated the impact of polarization and phase distribution of the source on the PSF of OCT. A mathematical framework with different considerations of the phase distribution of the light source was developed, followed by simulations and experimental evaluations. Three partially polarized light sources with different degrees of polarization were considered for this evaluation. Overall, the simulated and experimental PSFs were in good agreement, validating the effect of source polarization and phase statistics on the PSFs of OCT systems. The effect of polarization and phase distribution of the source was highlighted in cross-polarization configuration, in which the reduction in the amplitude of the PSF to almost zero was associated with the emergence of two adjacent peaks associated with the mean phase difference ∆, as well as the statistics of the phase distribution between two orthogonal x and y axes. © 2025 SPIE.

关键词： Optical transfer function

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Can you pass that tool?: Implications of Indirect Speech in Physical Human-Robot Collaboration

arXiv

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arXiv 2025年

作者： Zhang, Yan Ratnayake, Tharaka Sachintha Sew, Cherie Knibbe, Jarrod Goncalves, Jorge Johal, Wafa School of Computing and Information Systems University of Melbourne MelbourneVIC Australia University of Melbourne MelbourneVIC Australia School of Electrical Engineering and Computer Science The University of Queensland BrisbaneQLD Australia School of Computing and Informatio Systems University of Melbourne MelbourneVIC Australia

Indirect speech acts (ISAs) are a natural pragmatic feature of human communication, allowing requests to be conveyed implicitly while maintaining subtlety and flexibility. Although advancements in speech recognition have enabled natural language interactions with robots through direct, explicit commands—providing clarity in communication—the rise of large language models presents the potential for robots to interpret ISAs. However, empirical evidence on the effects of ISAs on human-robot collaboration (HRC) remains limited. To address this, we conducted a Wizard-of-Oz study (N=36), engaging a participant and a robot in collaborative physical tasks. Our findings indicate that robots capable of understanding ISAs significantly improve human’s perceived robot anthropomorphism, team performance, and trust. However, the effectiveness of ISAs is task- and context-dependent, thus requiring careful use. These results highlight the importance of appropriately integrating direct and indirect requests in HRC to enhance collaborative experiences and task performance. © 2025, CC BY-NC-SA.

关键词： Collaborative robots

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Person Tracking and Modelling for a Human Interaction Robot 25

Person Tracking and Modelling for a Human Interaction Robot

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Adam Golding Wafa Johal Claude Sammut Computer Science and Engineering UNSW Sydney Sydney Australia School of Computing & Information Systems The University of Melbourne Melbourne Australia

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

For a robot to perform personalised human support in a home, it is essential to uniquely identify the people it is working for. Each individual has their own attributes, knowledge of the environment, and experiences which the robot should learn and keep in its memory. One of the current challenges is for a robot to identify and re-identify an individual while dealing with visual occlusion, changes in appearance, and identity switching. This paper presents a novel approach to address these challenges in robot vision by extending current state-of-the-art solutions to enhance person memorisation by taking multiple viewpoints to extract features for matching and maintaining this information in a world model of the robot.

关键词： Visualization Robot vision systems Human-robot interaction Switches Feature extraction Planning Robots Faces Testing Identification of persons

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Cardiac MRI Segmentation of Ventricular Structures and Myocardium Using U-Net Variants

Cardiac MRI Segmentation of Ventricular Structures and Myoca...

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Advanced Research in computing (ICARC), International Conference on

作者： Chinthalanka B. Wijesinghe Dulani Meedeniya Pratheepan Yogarajah Department of Computer Science and Engineering University of Moratuwa Sri Lanka School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

Accurate segmentation of the ventricular structures and myocardium from Cardiac Magnetic Resonance (CMR) images is essential to diagnose and manage cardiovascular diseases. This study systematically evaluates the performance of five U-Net variants in cardiac MRI segmentation using the Automated Cardiac Diagnosis Challenge (ACDC) dataset and a hybrid loss function combining Cross-Entropy and dice losses. Among the variants, the Feature Pyramid U-Net achieved the best performance, with Dice coefficients of 0.9388 (Left Ventricle), 0.8759 (Right Ventricle), and 0.8426 (Myocardium), showcasing its superior ability to capture multi-scale features and segment complex anatomical structures. The comprehensive and standardized evaluation conducted in this study provides valuable insights into the strengths and limitations of these architectures for cardiac segmentation.

关键词： Deep learning Image segmentation Magnetic resonance imaging Computational modeling Magnetic resonance Computer architecture Myocardium Cardiovascular diseases Artificial intelligence Next generation networking

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Colorful Vertex Recoloring of Bipartite Graphs

arXiv

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arXiv 2025年

作者： Patt-Shamir, Boaz Rosén, Adi Umboh, Seeun William School of Electrical Engineering Tel Aviv University CNRS Université Paris Cité France School of Computing and Information Systems The University of Melbourne Australia

We consider the problem of vertex recoloring: we are given n vertices with their initial coloring, and edges arrive in an online fashion. The algorithm is required to maintain a valid coloring by means of vertex recoloring, where recoloring a vertex incurs a cost. The problem abstracts a scenario of job placement in machines (possibly in the cloud), where vertices represent jobs, colors represent machines, and edges represent "anti affinity" (disengagement) constraints. online coloring in this setting is a hard problem, and only a few cases were analyzed. One family of instances which is fairly well-understood is bipartite graphs, i.e., instances in which two colors are sufficient to satisfy all constraints. In this case it is known that the competitive ratio of vertex recoloring is Θ(log n). In this paper we propose a generalization of the problem, which allows using additional colors (possibly at a higher cost), to improve overall performance. Concretely, we analyze the simple case of bipartite graphs of bounded largest bond (a bond of a connected graph is an edge-cut that partitions the graph into two connected components). From the upper bound perspective, we propose two algorithms. One algorithm exhibits a trade-off for the uniform-cost case: given Ω(log β) ≤ c ≤ O(log n) colors, the algorithm guarantees that its cost is at most O(logn/c ) times the optimal offline cost for two colors, where n is the number of vertices and β is the size of the largest bond of the graph. The other algorithm is designed for the case where the additional colors come at a higher cost, D > 1: given ∆ additional colors, where ∆ is the maximum degree in the graph, the algorithm guarantees a competitive ratio of O(log D). From the lower bounds viewpoint, we show that if the cost of the extra colors is D > 1, no algorithm (even randomized) can achieve a competitive ratio of o(log D). We also show that in the case of general bipartite graphs (i.e., of unbounded bond size), any determinis

关键词： Coloring

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How Do HCI Researchers Study Cognitive Biases? A Scoping Review 25

How Do HCI Researchers Study Cognitive Biases? A Scoping Rev...

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Proceedings of the 2025 CHI Conference on Human Factors in computing systems

作者： Nattapat Boonprakong Benjamin Tag Jorge Goncalves Tilman Dingler School of Computing and Information Systems University of Melbourne Parkville Victoria Australia School of Computer Science and Engineering University of New South Wales Sydney New South Wales Australia School of Computing and Information Systems University of Melbourne Melbourne Australia Industrial Design Engineering Delft University of Technology Delft Netherlands

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Runtime Backdoor Detection for Federated Learning via Representational Dissimilarity Analysis

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Zhang, Xiyue Xue, Xiaoyong Du, Xiaoning Xie, Xiaofei Liu, Yang Sun, Meng University of Oxford Department of Computer Science United Kingdom Peking University Department of Information and Computing Science School of Mathematical Science China Monash University Department of Software Systems and Cybersecurity Australia Singapore Management University School of Computing and Information Systems Singapore Nanyang Technological University School of Computer Science and Engineering Singapore

Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerable to backdoor attacks, where a single compromised client can poison the shared model. While recent progress has been made in backdoor detection, existing methods face challenges with detection accuracy and runtime effectiveness, particularly when dealing with complex model architectures. In this work, we propose a novel approach to detecting malicious clients in an accurate, stable, and efficient manner. Our method utilizes a sampling-based network representation method to quantify dissimilarities between clients, identifying model deviations caused by backdoor injections. We also propose an iterative algorithm to progressively detect and exclude malicious clients as outliers based on these dissimilarity measurements. Evaluations across a range of benchmark tasks demonstrate that our approach outperforms state-of-the-art methods in detection accuracy and defense effectiveness. When deployed for runtime protection, our approach effectively eliminates backdoor injections with marginal overheads. © 2025 IEEE.

关键词： Federated learning

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