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Energy, QoS and Security Aware Edge Services 1

2nd International Symposium on Security in computer and Information Sciences, EuroCybersec 2021

作者： Gelenbe, Erol Nowak, Mateusz P. Frohlich, Piotr Fiolka, Jerzy Checinski, Jacek Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland LabI3S Universite Cote d’Azur Grand Chateau Nice06103 France Faculty of Automatic Control Electronics and Computer Science The Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (数字)9783031093579

ISBN: (纸本)9783031093562

With the development of communication technologies and the increasing bandwidth of optical fibres and transmission speeds in current 5G and future 6G wireless networks, there is a growing demand for solutions organising traffic in such networks, taking into account both end-to-end transmissions and the possibility of data processing by edge services. The most pressing problems of today’s computer networks are not only bandwidth and transmission delays, but also security and energy consumption, which is becoming increasingly important in today’s climate. This paper presents a solution based on neural networks that organises network traffic taking into account the above criteria - quality of service (QoS), energy consumption and security. © 2022, The Author(s).

关键词： Quality of service

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Asymmetric Feedback Learning in Online Convex Games

arXiv

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arXiv 2023年

作者： Wang, Zifan Yi, Xinlei Shen, Yi Zavlanos, Michael M. Johansson, Karl H. Division of Decision and Control Systems School of Electrical Enginnering and Computer Science Kth Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden The Lab for Information & Decision Systems Massachusetts Institute of Technology United States The Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States

This paper considers online convex games involving multiple agents that aim to minimize their own cost functions using locally available feedback. A common assumption in the study of such games is that the agents are symmetric, meaning that they have access to the same type of information or feedback. Here we lift this assumption, which is often violated in practice, and instead consider asymmetric agents;specifically, we assume some agents have access to first-order gradient feedback and others have access to the zeroth-order oracles (cost function evaluations). We propose an asymmetric feedback learning algorithm that combines the agent feedback mechanisms. We analyze the regret and Nash equilibrium convergence of this algorithm for convex games and strongly monotone games, respectively. Specifically, we show that our algorithm always performs between pure first-order and zeroth-order methods, and can match the performance of these two extremes by adjusting the number of agents with access to zeroth-order oracles. Therefore, our algorithm incorporates the pure first-order and zeroth-order methods as special cases. We provide numerical experiments on an online market problem for both deterministic and risk-averse games to demonstrate the performance of the proposed algorithm. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

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Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy

arXiv

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arXiv 2023年

作者： Xiao, Jiaren Dai, Quanyu Shen, Xiao Xie, Xiaochen Dai, Jing Lam, James Kwok, Ka-Wai Department of Mechanical Engineering The University of Hong Kong Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computer Science and Technology Hainan University Haikou China Department of Automation Harbin Institute of Technology Shenzhen China Institute for Automatic Control and Complex Systems University of Duisburg-Essen Duisburg Germany

Label scarcity in a graph is frequently encountered in real-world applications due to the high cost of data labeling. To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a labeled source graph to aid in node classification on a target graph with limited labels. SSDA tasks need to overcome the domain gap between the source and target graphs. However, to date, this challenging research problem has yet to be formally considered by the existing approaches designed for cross-graph node classification. This paper proposes a novel method called SemiGCL to tackle the graph Semi-supervised domain adaptation with Graph Contrastive Learning and minimax entropy training. SemiGCL generates informative node representations by contrasting the representations learned from a graph’s local and global views. Additionally, SemiGCL is adversarially optimized with the entropy loss of unlabeled target nodes to reduce domain divergence. Experimental results on benchmark datasets demonstrate that SemiGCL outperforms the state-of-the-art baselines on the SSDA tasks. © 2023, CC BY-NC-ND.

关键词： Entropy

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Measuring single-cell level gene expression stability and variability in healthy and severe COVID-19 patients using Kullback-Leibler divergence

Measuring single-cell level gene expression stability and va...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Hwang, Inseung Jang, Jaeyeon Kim, Kwangsoo Jo, Hye-Yeong Kim, Sang Cheol Jung, Inuk Kyungpook National University School of Computer Science and Engineering Daegu Korea Republic of Seoul National University Hospital Transdisciplinary Department of Medicine and Advanced Technology Seoul Korea Republic of Korea National Institute of Health Korea Disease Control and Prevention Agency Division of Healthcare and Artificial Intelligence Department of Precision Medicine Cheongju Korea Republic of

ISBN: (纸本)9781665468190

Recent advances in single-cell RNA sequencing (scRNA-seq) technology have enabled the acquisition of RNA at the single-cell level, which showed that the expression level of genes is highly variable across and within the cell types. Even well-known housekeeping genes showed high expression variance in a single condition and within the same cell types. Previous studies made efforts to identify stably expressed genes and use them as a yardstick for robust gene expression normalization. On the other hand, drugs were shown to be less effective on genes with high expression variance. Thus, identifying both stably and variably expressed genes is an important task, especially at the single-cell level. In this study, using the Kullback-Leibler divergence method, we proposed a metric to measure the expression stability of each gene. Using private scRNA-seq data composed of 25 severe COVID-19 patients and 40 healthy individuals, we identified variably expressed genes specific to COVID-19-infected patients and healthy cohorts. © 2022 IEEE.

关键词： COVID-19

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A reachability-decidable petri net modeling method and its application to flexible manufacturing systems

A reachability-decidable petri net modeling method and its a...

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International Conference on Automation in Manufacturing, Transportation and Logistics (iCaMaL 2023)

作者： Yue Su Liang Qi MengChu Zhou Remigiusz Wiśniewski Faculty of Innovation Engineering Macau University of Science and Technology Macau People's Republic of China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao People's Republic of China Institute of Control and Computation Engineering University of Zielona Góra Poland

ISBN: (数字)9781837242054

Petri nets (PNs) are graphical and mathematical tools used to model various discrete event systems and analyze their properties. Reachability is their fundamental property. When we use a state equation to determine a state’s (marking’s) reachability, the existence of legal firing sequences (LFSs) corresponding to nonnegative integer solutions (NISs) of the state equation needs to be determined. Our previous work has proposed an algorithm to determine the LFSs’ existence in polynomial time. However, when a state equation has an infinite number of NISs, we cannot check all NISs to determine a marking’s reachability. Hence, this work studies the relationship between the structure of an ordinary PN and the number of NISs of its state equation. Then, a method is proposed to modify the structure of the PN such that the state equation has no more than one NIS. The modified PN is used as the final model of the system, which maintains the functions of its modeled system. Its reachability can be determined in polynomial time. This work shows the application of the proposed method to a flexible manufacturing system’s modeling and analysis.

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Continual Learning of a Time Series Model Using a Mixture of HMMs with Application to the IoT Fuel Sensor Verification

Continual Learning of a Time Series Model Using a Mixture of...

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Federated Conference on computer Science and Information Systems (FedCSIS)

作者： Przemysław Głomb Michał Cholewa Paweł Foszner Jakub Bularz Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland AIUT Sp. z o.o. ul Gliwice Poland

This paper presents an application of a mixture of Hidden Markov Models (HMMs) as a tool for verification of IoT fuel sensors. The IoT fuel sensors report the level of fuel in tanks of a petrol station, and are a key component for monitoring system reliability (billing), safety (fuel/oil leak detection) and security (theft prevention). We propose an algorithm for learning a mixture of HMMs based on a continual learning principle, i.e. it adapts the model while monitoring a sensor over time, signalling unexpected or anomalous sensor reports. We have tested the proposed approach on a real-life data of 15 fuel tanks being monitored with the FuelPrime system, where it has shown a very good performance (average area under ROC curve of 0.94) of detecting anomalies in the sensor data. Additionally we show that the proposed method can be used for trend monitoring and present qualitative analysis of the short and long term learning performance. The proposed method has promising performance score, the resulting model has a high degree of explainability, limited memory and computation requirements and can be easily generalized to other domains of sensor verification.

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Data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes

Data-driven Force Observer for Human-Robot Interaction with ...

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

作者： Samuel Tesfazgi Markus Keßler Emilio Trigili Armin Lederer Sandra Hirche Chair of Information-Oriented Control (ITR) TUM School of Computation Information and Technology Technical University of Munich Munich Germany Scuola Superiore Sant’Anna BioRobotics Institute Pontedera Italy Department of Computer Science Learning & Adaptive Systems Group ETH Zurich Zurich Switzerland

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Ensuring safety and adapting to the user’s behavior are of paramount importance in physical human-robot interaction. Thus, incorporating elastic actuators in the robot’s mechanical design has become popular, since it offers intrinsic compliance and additionally provide a coarse estimate for the interaction force by measuring the deformation of the elastic components. While observer-based methods have been shown to improve these estimates, they rely on accurate models of the system, which are challenging to obtain in complex operating environments. In this work, we overcome this issue by learning the unknown dynamics components using Gaussian process (GP) regression. By employing the learned model in a Bayesian filtering framework, we improve the estimation accuracy and additionally obtain an observer that explicitly considers local model uncertainty in the confidence measure of the state estimate. Furthermore, we derive guaranteed estimation error bounds, thus, facilitating the use in safety-critical applications. We demonstrate the effectiveness of the proposed approach experimentally in a human-exoskeleton interaction scenario.

关键词： Estimation error Actuators Uncertainty Force measurement Accuracy Measurement uncertainty Force Human-robot interaction Gaussian processes Observers

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Cluster Synchronization of Kuramoto Oscillators via Pacemakers and Mean-phase Feedback control

Cluster Synchronization of Kuramoto Oscillators via Pacemake...

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IEEE Conference on Decision and control

作者： Ryota Kokubo Mitsuaki Matsubara Rui Kato Hideaki Ishii Department of Computer Science Tokyo Institute of Technology Yokohama Japan Department of Systems and Control Engineering Tokyo Institute of Technology Yokohama Japan Laboratoire d’Automatique de Génie des Procédés et de Génie Pharmaceutique (LAGEPP) Université Claude Bernard Lyon 1 Villeurbanne France Department of Information Physics and Computing The University of Tokyo Tokyo Japan

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Brain networks typically exhibit characteristic synchronization patterns where several synchronized clusters coexist. On the other hand, neurological disorders are considered to be related to pathological synchronization such as excessive synchronization of large populations of neurons. Motivated by these phenomena, this paper presents two approaches to control the cluster synchronization of Kuramoto oscillators. One is based on the use of pacemakers to the clusters, and the other is based on feeding back the mean phases to the clusters. We first show conditions on the pacemaker weights and the feedback gains for the network to achieve cluster synchronization. Then, we propose a method to find optimal feedback gains through convex optimization. A numerical example demonstrates the effectiveness of the proposed methods.

关键词： Neurological diseases Hands Pathology Design methodology Neurons Pacemakers Convex functions Feedback control Synchronization Oscillators

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A Blockchain Approach to IoT Security and Reliability Analysis 3

A Blockchain Approach to IoT Security and Reliability Analys...

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3rd International Conference on Intelligent Engineering and Management, ICIEM 2022

作者： Jain, Nilesh Wahid, Nazia Al-Farhani, Liwa H. Manideep, A. Sai Bhardwaj, Vikash Sangeeth Kumar, M. Mandsaur University Department of Computer Applications Madhya Pradesh Mandsaur India Vishwakarma University Faculty of Science & Technology Department of Mathematics & Statistics Maharashtra Pune India RDUN University System Analysis Control and Information Processing Academy of Engineering Moscow Russia Vignan's Foundation for Science Technology and Research Department of Management Studies Andhra Pradesh Guntur India Dewan VS Institute of Engineering & Technology Uttar Pradesh Meerut India Sona College of Technology Mechanical Engineering Department Tamilnadu Salem India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

An investigation area of interest including the use of blockchain technology to address Internet of Things security concerns (IoT). Despite the fact that numerous relevant ideas have been offered, there are few published works that provide hypothetical and information backing. The examination of model building and execution assessment is the focus of this research. First, an IoT security paradigm is outlined, using blockchain and the Inter-Planetary-File-System (IPFS) in a view. Many of the security risks associated with traditional IoT designs can be avoided with this paradigm, and framework execution is entirely focused on distributed large limit stockpiling, simultaneity, and query. In addition, the suggested model's display is evaluated using normal idleness and throughput, which are important for further investigation and enhancement of this course. The blockchain-based security model's feasibility has been demonstrated via analysis and experiments. © 2022 IEEE.

关键词： Blockchain

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Distributed Event-Triggered Bandit Convex Optimization with Time-Varying Constraints

arXiv

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Mathematics Southeast University Nanjing210096 China The Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms require that each agent broadcasts its decision to its neighbors at each iteration. To better utilize the limited communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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