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Steps Towards Satisficing Distributed Dynamic Team Trust

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arXiv 2023年

作者： Hunt, Edmund R. Baber, Chris Sobhani, Mehdi Milivojevic, Sanja Yusuf, Sagir Musolesi, Mirco Waterson, Patrick Maynard, Sally School of Engineering Mathematics and Technology University of Bristol United Kingdom School of Computer Science University of Birmingham United Kingdom Bristol Digital Futures Institute University of Bristol United Kingdom Department of Computer Science University College London United Kingdom Human Factors and Complex Systems Group Loughborough University United Kingdom

Defining and measuring trust in dynamic, multiagent teams is important in a range of contexts, particularly in defense and security domains. Team members should be trusted to work towards agreed goals and in accordance with shared values. In this paper, our concern is with the definition of goals and values such that it is possible to define ‘trust’ in a way that is interpretable, and hence usable, by both humans and robots. We argue that the outcome of team activity can be considered in terms of ‘goal’, ‘individual/team values’, and ‘legal principles’. We question whether alignment is possible at the level of ‘individual/team values’, or only at the ‘goal’ and ‘legal principles’ levels. We argue for a set of metrics to define trust in human-robot teams that are interpretable by human or robot team members, and consider an experiment that could demonstrate the notion of ‘satisficing trust’ over the course of a simulated mission. © 2023, CC BY.

关键词： Robots

Clustering With Adaptive Graph Learning and Spectral Rotation 7

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Clustering With Adaptive Graph Learning and Spectral Rotatio...

7th International Conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Yuan, Ge Zhou, Jie Huang, Chucheng Gao, Can Wang, Yangbo Shen, Xinrui National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China College of Mathematics and Statistics Shenzhen University Guangdong Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China

ISBN: (纸本)9781839539848

Prototype-based clustering algorithms have garnered considerable attention in the field of machine learning due to their efficiency and interpretability. Nonetheless, these algorithms often face performance degradation when confronted with high-dimensional or non-ellipsoidal data distributions. To surmount these challenges, this study introduces a novel clustering approach, dubbed Clustering with Adaptive Graph learning and Spectral Rotation (CAGSR). In CAGSR, the imposed spectral rotation operation mitigates the discrepancy between the membership matrix, which adheres to the notion of fuzzy clustering, and the spectral representations derived from an adaptive graph rather than a predefined one. This enables the generation of a comprehensive representation of the data across multiple spaces. Furthermore, the clustering and graph learning tasks are jointly optimized in a projected subspace, which can effectively reduce the adverse impact caused by irrelevant features in the original space. The proposed method seamlessly integrates fuzzy clustering, graph structure learning, and spectral rotation into a unified model, facilitating the detection of intrinsic structures. Experimental evaluations conducted on benchmark data sets substantiate the effectiveness of CAGSR when compared to related clustering approaches. © The Institution of Engineering & Technology 2023.

关键词： Fuzzy clustering

Improved Discretization Analysis for Underdamped Langevin Monte Carlo

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arXiv 2023年

作者： Zhang, Matthew Chewi, Sinho Li, Mufan Bill Balasubramanian, Krishnakumar Erdogdu, Murat A. Department of Computer Science University of Toronto Vector Institute Canada Department of Mathematics Massachusetts Institute of Technology United States Department of Statistical Sciences University of Toronto Vector Institute Canada Department of Statistics University of California Davis United States

Underdamped Langevin Monte Carlo (ULMC) is an algorithm used to sample from unnormalized densities by leveraging the momentum of a particle moving in a potential well. We provide a novel analysis of ULMC, motivated by two central questions: (1) Can we obtain improved sampling guarantees beyond strong log-concavity? (2) Can we achieve acceleration for sampling? For (1), prior results for ULMC only hold under a log-Sobolev inequality together with a restrictive Hessian smoothness condition. Here, we relax these assumptions by removing the Hessian smoothness condition and by considering distributions satisfying a Poincaré inequality. Our analysis achieves the state of art dimension dependence, and is also flexible enough to handle weakly smooth potentials. As a byproduct, we also obtain the first KL divergence guarantees for ULMC without Hessian smoothness under strong log-concavity, which is based on a new result on the log-Sobolev constant along the underdamped Langevin diffusion. For (2), the recent breakthrough of Cao, Lu, and Wang (2020) established the first accelerated result for sampling in continuous time via PDE methods. Our discretization analysis translates their result into an algorithmic guarantee, which indeed enjoys better condition number dependence than prior works on ULMC, although we leave open the question of full acceleration in discrete time. Both (1) and (2) necessitate Rényi discretization bounds, which are more challenging than the typically used Wasserstein coupling arguments. We address this using a flexible discretization analysis based on Girsanov’s theorem that easily extends to more general settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Monte Carlo methods

Multilevel Monte Carlo for a class of Partially Observed Processes in Neuroscience

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arXiv 2023年

作者： Maama, Mohamed Jasra, Ajay Kamatani, Kengo Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia School of Data Science The Chinese University of Hong Kong CN Shenzhen China Institute of Statistical Mathematics Tokyo190-0014 Japan

In this paper we consider Bayesian parameter inference associated to a class of partially observed stochastic differential equations (SDE) driven by jump processes. Such type of models can be routinely found in applications, of which we focus upon the case of neuroscience. The data are assumed to be observed regularly in time and driven by the SDE model with unknown parameters. In practice the SDE may not have an analytically tractable solution and this leads naturally to a time-discretization. We adapt the multilevel Markov chain Monte Carlo method of [15], which works with a hierarchy of time discretizations and show empirically and theoretically that this is preferable to using one single time discretization. The improvement is in terms of the computational cost needed to obtain a pre-specified numerical error. Our approach is illustrated on models applied to examples of Bayesian inference problems using both simulated and real observed data from neuroscience. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

A Filter-Based Learning Approach for Intrusion Detection using The NSL-KDD Network Dataset 3

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A Filter-Based Learning Approach for Intrusion Detection usi...

3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Madala, Chandu Jagan Sekhar Patil, Anita M Santhosh Srinivasan, P. Kousar, Heena Sultanuddin, Sj Sangeeth Kumar, M. Lendi Institute of Engineering & Technology Department of Computer Science & Engineering Andhra Pradesh Vizianagaram535005 India Bharati Vidyapeeth Yashawantrao Mohite Institute of Management Karad Department of Mca Maharashtra Malkapur415539 India Panimalar Engineering College Department of Mathematics Tamil Nadu Chennai600123 India East Point College of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore560049 India Measi Institute of Information Technology Department of Mca Tamil Nadu Chennai600014 India Sona College of Technology Department of Mechanical Engineering Tamil Nadu Salem636005 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Wireless sensor network (WSN) attacks seek to disrupt or eliminate the network's ability to execute its expected duties. Penetration testing is a wireless sensor network defence that detects unknown threats. Because of the significant advancement in computer based products and widespread Internet utilization, it is critical to guarantee hosting and information systems. A hacking technique has been proposed to breach computer security with infiltration. An intrusion detection system (IDS) identifies network breaches with machine learning (ML) methods. Classic ML methods based feature extraction frequently resulted in low efficiency and misreading incursions. To address these challenges, this research provides a novel architecture for IDS that may be facilitated by the filter-based learning methodL The anticipated model performance is comparable to ML techniques. The appropriate solution was built and evaluated using network security laboratories - discovering dataset information (NSL-ICDD). The experimental data reveal that the suggested model filter-based learning detects assaults with more accuracy (i.e., 99 percent) than LDA, CART, and other current approaches. © 2022 IEEE.

关键词： Wireless sensor networks

Quadratic Ⅎ2Performance of Switched Linear Uncertain Stochastic Systems 34

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Quadratic Ⅎ2Performance of Switched Linear Uncertain Stocha...

34th Chinese Control and Decision Conference, CCDC 2022

作者： Chang, Yufang Zhai, Guisheng Xiong, Lianglin Fu, Bo Hubei University of Technology Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy Wuhan430068 China Shibaura Institute of Technology Department of Mathematical Sciences Saitama337-8570 Japan Yunnan Minzu University School of Mathematics and Computer Science Kunming650500 China

ISBN: (纸本)9781665478960

We study global quadratic Ⅎ2 performance in probability (quadratic stability and Ⅎ2 gain γ in probability: abbreviated as GQℲ2(γ)-P) for switched systems which are composed of a finite set of linear stochastic subsystems with norm bounded uncertainties. Assuming that no single subsystem achieves GQ Ⅎ2(γ)-P, we show that if a convex combination of subsystems achieves GQℲ2(γ)-P, then we can design a state-dependent switching law, based on the convex combination of subsystems, such that the entire switched system achieves GQℲ2(γ)-P. A numerical example is provided to show effectiveness of the proposed approach. © 2022 IEEE.

关键词： Stochastic systems

Evaluating Generative Adversarial Networks Performance in Image Synthesis with Graphical Analysis of Loss Functions and Quality Metrics

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Evaluating Generative Adversarial Networks Performance in Im...

Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Biju J Jeevitha R Titus Richard Rini Chowdhury Prashant Kumar K. Radhika Department of Information Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Technology & Advance Studies Department of English Vels Institute of Science Chennai India Department of Information Technology Project Circle BSNL Lalkuthi Kolkata India Department of Mathematics Kongu Engineering College Erode India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Generative Adversarial Networks (GANs) have revolutionized image synthesis by using two neural networks, a generator and a discriminator, to create realistic images from random noise. In this adversarial process, the generator attempts to fool the discriminator, which distinguishes between real and fake images. Advanced variants like conditional GANs and CycleGANs enable tasks like specified image generation and style transfer. Our paper presents improvements in image quality and training stability for GANs by introducing new training methods and loss function modifications to address issues like mode collapse. Evaluated on CelebA and CIFAR-10, our model outperforms previous GANs with Inception Scores of 9.69 and 10.79 and Frechet Inception Distances of 7.91 and -9.69, respectively. These results demonstrate better convergence, more stable training, and higher-quality image generation, establishing a new benchmark for GAN research and applications.

关键词： Training Measurement Analytical models Three-dimensional displays Image synthesis Statistical analysis Benchmark testing Generative adversarial networks Generators Tuning

Combating Interference for Over-the-Air Federated Learning: A Statistical Approach via RIS

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arXiv 2025年

作者： Shi, Wei Yao, Jiacheng Xu, Wei Xu, Jindan You, Xiaohu Eldar, Yonina C. Zhao, Chunming National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

Over-the-air computation (AirComp) integrates analog communication with task-oriented computation, serving as a key enabling technique for communication-efficient federated learning (FL) over wireless networks. However, owing to its analog characteristics, AirComp-enabled FL (AirFL) is vulnerable to both unintentional and intentional interference. In this paper, we aim to attain robustness in AirComp aggregation against interference via reconfigurable intelligent surface (RIS) technology to artificially reconstruct wireless environments. Concretely, we establish performance objectives tailored for interference suppression in wireless FL systems, aiming to achieve unbiased gradient estimation and reduce its mean square error (MSE). Oriented at these objectives, we introduce the concept of phase-manipulated favorable propagation and channel hardening for AirFL, which relies on the adjustment of RIS phase shifts to realize statistical interference elimination and reduce the error variance of gradient estimation. Building upon this concept, we propose two robust aggregation schemes of power control and RIS phase shifts design, both ensuring unbiased gradient estimation in the presence of interference. Theoretical analysis of the MSE and FL convergence affirms the anti-interference capability of the proposed schemes. It is observed that computation and interference errors diminish by an order of O (N1) where N is the number of RIS elements, and the ideal convergence rate without interference can be asymptotically achieved by increasing N. Numerical results confirm the analytical results and validate the superior performance of the proposed schemes over existing baselines. Copyright © 2025, The Authors. All rights reserved.

关键词： Mean square error

HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models

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HIR-Diff: Unsupervised Hyperspectral Image Restoration Via I...

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Li Pang Xiangyu Rui Long Cui Hongzhong Wang Deyu Meng Xiangyong Cao School of Computer Science and Technology Xi'an Jiaotong University Xi'an China MOEKLINNS Laboratory Xi'an Jiaotong University Xi'an China School of Mathematics and Statistics Xi'an Jiaotong University Xi'an China Macao Institute of Systems Engineering Macau University of Science and Technology Taipa Macao

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Hyperspectral image (HSI) restoration aims at recov-ering clean images from degraded observations and plays a vital role in downstream tasks. Existing model-based methods have limitations in accurately modeling the com-plex image characteristics with handcraft priors, and deep learning-based methods suffer from poor generalization ability. To alleviate these issues, this paper proposes an unsupervised HSI restoration framework with pre-trained diffusion model (HIR-Diff), which restores the clean HSls from the product of two low-rank components, i.e., the re-duced image and the coefficient matrix. Specifically, the re-duced image, which has a low spectral dimension, lies in the image field and can be inferred from our improved diffusion model where a new guidance function with total variation (TV) prior is designed to ensure that the reduced image can be well sampled. The coefficient matrix can be effectively pre-estimated based on singular value decomposition (SVD) and rank-revealing QR (RRQR) factorization. Fur-thermore, a novel exponential noise schedule is proposed to accelerate the restoration process (about 5 x acceleration for denoising) with little performance decrease. Ex-tensive experimental results validate the superiority of our method in both performance and speed on a variety of HSI restoration tasks, including HSI denoising, noisy HSI super-resolution, and noisy HSI inpainting. The code is available at https://***/LiPang/HIRDiff.

关键词： Schedules TV Noise reduction Noise Superresolution Diffusion models Image restoration