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Generative Adversarial Networks Based Cognitive Feedback Analytics System for Integrated Cyber-Physical System and Industrial Iot Networks

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Research Square

Research Square 2021年

作者： Upreti, Kamal Syed, Mohammad Haider Alam, Mohammad Shabbir Alhudhaif, Adi Shuaib, Mohammed Sharma, Arvind K. Department of Information & Technology Inderprastha Engineering College Ghaziabad India College of Computing and Informatics Saudi Electronic University Saudi Arabia Department of Computer Science College of computer science and Information Technology Jazan University Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences in Al-kharj Prince Sattam bin Abdulaziz University P.O. Box 151 al-Kharj11942 Saudi Arabia College of Computer Science & IT Jazan University Saudi Arabia Dept. of CSI University of Kota Rajasthan India

In the modern era of technologies, the internet grows in the advancement of our day-to-day life like automation devices. The devices to set up industries with integrated cyber-physical systems and industrial IoT applications. Generative adversarial networks (GAN) can generate Cognitive feedback analysis with various data for both generator and discriminator in a supervised model. Neural networks are used for artificial intelligence algorithms, but in adversarial networks, feedback analytics is analyzed with the significance of data. The modern age of intelligent manufacturing will indeed be ushered in by Cyber-Physical Production Systems (CPPS). However, because of the connections between the virtual and physical worlds, CPPS would be subject to cross-domain assaults. Against Denial-of-Service (DoS) threats, this paper concentrates on complex performance feedback management of Cyber-Physical Systems (CPS). To begin, a swapping system modelling approach for the complex response feedback CPS is provided by analyzing the distinct effects of DoS assaults on the sensor-controller (SC) and controller-to-actuator (C-A) channels, accordingly. Given the difference in bandwidth between the dual channels and the accused's energy cap, it is reasonable to conclude that an offender can only jam a single communication stream at a point and also that the possible number of successive DoS attacks is limited. Second, using a packet-based transfer scheme, a nested switching paradigm is built on the foundation of the switching mechanism, considering both the spatial heterogeneity and the temporal durability of DoS attacks. The probability of discriminator gets analyzed feedback data to check whether actual data or fake data is sampled, and it is generated. Cognitive feedback supports genetic algorithms to sample the feedback data in a system for advanced technologies. © 2021, CC BY.

关键词： Cyber Physical System

Expression of Concern for: Wireless Sensor Network to Connect Isolated Nodes using Link Assessment Technique

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Expression of Concern for: Wireless Sensor Network to Connec...

Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： B.Barani Sundaram Amit Pandey Aschalew Tirulo Abiko Janga Vijaykumar Adola Haile Genale P. Karthika College of Informatics Bule Hora University Bule hora Ethiopia College of Informatics CSI Executive member CSRSCD BHU Ethiopia School of Computing and Informatics Wachemo University Hosana Ethioipa Dept. Of Information Technology College of Informatics Bule Hora University Bule Hora Ethiopia Department of Information Science Bule Hora University Bule hora Ethiopia Department of Computer Applications Kalasalingam Academy of Research and Education Krishnankoil India

ISBN: (数字)9781665419604

ISBN: (纸本)9781665429986

A simultaneous geography control effort is implemented for full-reachability systems and a genetic algorithm with dynamic transformation to find the breakthrough in a blaze-scene. The proposed scheme efficiently takes care of the issue of separate node for transmission run. The suggested technique makes it possible to improve the display of the genetic algorithm in order to maintain a strategic distance from the possible arrangements which fall into nearby ideal. Different experimental simulations have to be confirmed the performance quality, reliability and timeliness of designed approaches. In the Wireless Sensor established network, can connect at least two sensors in a network sensors and deliver jointly via the obtained transmission extend to form the network of total accessibility. Accordingly, excellent blaze exhaust system with complete network accessibility the proposed approaches in this paper established topology and real-time routing methods. This device supports the protection and security of individuals in a blaze scene.

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Defining Network Switch Preference by Network Traffic Using Stable Marriage Problem 21

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Defining Network Switch Preference by Network Traffic Using ...

21st International Electronics Symposium, IES 2019

作者： Alimudin, Akhmad Winarno, Idris Ishida, Yoshiteru Politeknik Elektronika Negeri Surabaya Dept. of Multimedia Creative Technology Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Dept. of Computer and Informatics Surabaya Indonesia Toyohashi University of Technology Dept. of Computer Science and Engineering Toyohashi Japan

ISBN: (纸本)9781728144498

A network switch is an important network device on a computer network, the role of a switch can determine the good and bad of a network service. In a data center, sometimes it has several switch devices with different capabilities (diversity). For this reason, we need a strategy to carry out the effectiveness of using switch devices on computer networks. One way that can be done to realize this is by choosing the right partner between switches and hosts. The matching theory makes it possible to overcome this problem. In this study, it was simulated using the Stable Marriage Problem (SMP) method. The initial state to determine the ranking of pairs is done by using several attributes that are owned by each switch and host such as interfaces, services on the host, and data traffic on the device. Simulations are performed on mininet using Software-defined networking (SDN). © 2019 IEEE.

关键词： Interface states

Robust robotic pouring using audition and haptics

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arXiv 2020年

作者： Liang, Hongzhuo Zhou, Chuangchuang Li, Shuang Ma, Xiaojian Hendrich, Norman Gerkmann, Timo Sun, Fuchun Stoffel, Marcus Zhang, Jianwei Group TAMS Dept. of Informatics Universität Hamburg Institute of General Mechanics RWTH Aachen University Group VCLA Dept. of Statistics University of California Los Angeles United States Dept. of Informatics Universität Hamburg Dept. of Computer Science and Technology Tsinghua University

Robust and accurate estimation of liquid height lies as an essential part of pouring tasks for service robots. However, vision-based methods often fail in occluded conditions while audio-based methods cannot work well in a noisy environment. We instead propose a multimodal pouring network (MP-Net) that is able to robustly predict liquid height by conditioning on both audition and haptics input. MP-Net is trained on a self-collected multimodal pouring dataset. This dataset contains 300 robot pouring recordings with audio and force/torque measurements for three types of target containers. We also augment the audio data by inserting robot noise. We evaluated MP-Net on our collected dataset and a wide variety of robot experiments. Both network training results and robot experiments demonstrate that MP-Net is robust against noise and changes to the task and environment. Moreover, we further combine the predicted height and force data to estimate the shape of the target container. Copyright © 2020, The Authors. All rights reserved.

关键词： Audition

GRAPH CONVOLUTIONAL LEARNING OF MULTIMODAL BRAIN CONNECTOME DATA FOR SCHIZOPHRENIA CLASSIFICATION

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IBRO Neuroscience Reports 2023年 15卷 S781-S781页

作者： Sanjay Ghosh Eshan Bhargava Srikantan Nagarajan University of California San Francisco Dept. Of Radiology And Biomedical Imaging San Francisco United States of America University of Southern California Computer Science Los Angeles United States of America

Stochastic graph transformation for social network modeling

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arXiv 2021年

作者： Behr, Nicolas Bello, Bello Shehu Ehmes, Sebastian Heckel, Reiko Univ. de Paris Cnrs Irif Paris France Dept. of Computer Science Bayero Univ. Kano Nigeria Real-Time Systems Lab Tu Darmstadt Germany School of Informatics Univ. of Leicester United Kingdom

Adaptive networks model social, physical, technical, or biological systems as attributed graphs evolving at the level of both their topology and data. They are naturally described by graph transformation, but the majority of authors take an approach inspired by the physical sciences, combining an informal description of the operations with programmed simulations, and systems of ODEs as the only abstract mathematical description. We show that we can capture a range of social network models, the so-called voter models, as stochastic attributed graph transformation systems, demonstrate the benefits of this representation and establish its relation to the non-standard probabilistic view adopted in the literature. We use the theory and tools of graph transformation to analyze and simulate the models and propose a new variant of a standard stochastic simulation algorithm to recreate the results observed. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph theory

Spatio-temporal Parking behaviour forecasting and analysis before and during COVID-19

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arXiv 2021年

作者： Gong, Shuhui Mo, Xiaopeng Cao, Rui Liu, Yu Tu, Wei Bai, Ruibin School of Information Engineering China University of Geosciences Beijing Beijing China School of Computer Science University of Nottingham Ningbo China Ningbo China Dept. of LSGI & SCRI The Hong Kong Polytechnic University Hong Kong Institute of Remote Sensing and Geographical Information Systems Peking University Beijing China GD Key Lab. of Urban Informatics Dept. of Urban Informatics Shenzhen University Shenzhen China

Parking demand forecasting and behaviour analysis have received increasing attention in recent years because of their critical role in mitigating traffic congestion and understanding travel behaviours. However, previous studies usually only consider temporal dependence but ignore the spatial correlations among parking lots for parking prediction. This is mainly due to the lack of direct physical connections or observable interactions between them. Thus, how to quantify the spatial correlation remains a significant challenge. To bridge the gap, in this study, we propose a spatial-aware parking prediction framework, which includes two steps, i.e. spatial connection graph construction and spatio-temporal forecasting. A case study in Ningbo, China is conducted using parking data of over one million records before and during COVID-19. The results show that the approach is superior on parking occupancy forecasting than baseline methods, especially for the cases with high temporal irregularity such as during COVID-19. Our work has revealed the impact of the pandemic on parking behaviour and also accentuated the importance of modelling spatial dependence in parking behaviour forecasting, which can benefit future studies on epidemiology and human travel behaviours. © 2021, CC BY-NC-ND.

关键词： Forecasting

Neural architecture search under black-box objectives with deep reinforcement learning and increasingly-sparse rewards 2

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Neural architecture search under black-box objectives with d...

2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

作者： Alioscha-Perez, Mitchel Berenguer, Abel Diaz Pei, Ercheng Oveneke, Meshia Cedric Sahli, Hichem Vrije Universiteit Brussel VUB-NPU Joint AVSP Lab Electronics and Informatics Dept. Brussels1050 Belgium Northwestern Polytechnical University School of Computer Science VUB-NPU Joint AVSP Lab Xi'an710072 China Interuniv. MicroElectronics Centre Heverlee Belgium

ISBN: (纸本)9781728149851

In this paper, we address the problem of neural architecture search (NAS) in a context where the optimality policy is driven by a black-box Oracle \mathcal{O} with unknown form and derivatives. In this scenario, \mathcal{O}(A-{C}) typically provides readings from a set of sensors on how a neural network architecture A-{C} fares in a target hardware, including its: power consumption, working temperature, \mathbf{cpu}/\mathbf{gpu} usage, central bus occupancy, and more. Current differentiable NAS approaches fail in this problem context due to lack of access to derivatives, whereas traditional reinforcement learning NAS approaches remain too expensive computationally. As solution, we propose a reinforcement learning NAS strategy based on policy gradient with increasingly sparse rewards. We rely on the fact [1] that one does not need to fully train the weights of two neural networks to compare them. Our solution starts by comparing architecture candidates with almost fixed weights and no training, and progressively shifts toward comparisons under full weights training. Experimental results confirmed both the accuracy and training efficiency of our solution, as well as its compliance with soft/hard constraints imposed on the sensors feedback. Our strategy allows finding near-optimal architectures significantly faster, in approximately 1/3 of the time it would take otherwise. © 2020 IEEE.

关键词： Reinforcement learning

Neurite Exchange Imaging (NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange

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arXiv 2021年

作者： Jelescu, Ileana O. de Skowronski, Alexandre Geffroy, Françoise Palombo, Marco Novikov, Dmitry S. CIBM Center for Biomedical Imaging Animal Imaging and Technology Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Lausanne Switzerland Lausanne Switzerland NeuroSpin CEA Saclay France School of Psychology Cardiff University Cardiff United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Centre for Medical Image Computing Dept. of Computer Science University College London London United Kingdom Center for Biomedical Imaging Department of Radiology New York University School of Medicine New YorkNY United States

Biophysical models of diffusion in white matter have been center-stage over the past two decades and are essentially based on what is now commonly referred to as the "Standard Model" (SM) of non-exchanging anisotropic compartments with Gaussian diffusion. In this work, we focus on diffusion MRI in gray matter, which requires rethinking basic microstructure modeling blocks. In particular, at least three contributions beyond the SM need to be considered for gray matter: water exchange across the cell membrane - between neurites and the extracellular space;non-Gaussian diffusion along neuronal and glial processes - resulting from structural disorder;and signal contribution from soma. For the first contribution, we propose Neurite Exchange Imaging (NEXI) as an extension of the SM of diffusion, which builds on the anisotropic Kärger model of two exchanging compartments. Using datasets acquired at multiple diffusion weightings (b) and diffusion times (t) in the rat brain in vivo, we investigate the suitability of NEXI to describe the diffusion signal in the gray matter, compared to the other two possible contributions. Our results for the investigated diffusion time window (~10-45 ms) show minimal diffusivity time-dependence and more pronounced kurtosis decay with time, which is well fit by the exchange model. Moreover, we observe lower signal for longer diffusion times at high b. In light of these observations, we identify exchange as the mechanism that best explains these signal signatures in both low-b and high-b regime, and thereby propose NEXI as the minimal model for gray matter microstructure mapping. We finally highlight multi-b multi-t acquisitions protocols as being best suited to estimate NEXI model parameters reliably. Using this approach, we estimate the inter-compartment water exchange time to be 15 - 60 ms in the rat cortex and hippocampus in vivo, which is of the same order or shorter than the diffusion time in typical diffusion MRI acquisitions. This sugg

关键词： Magnetic resonance imaging