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arXiv 2022年

作者： Joloudari, Javad Hassannataj Mojrian, Sanaz Saadatfar, Hamid Nodehi, Issa Fazl, Fatemeh Shirkharkolaie, Sahar Khanjani Alizadehsani, Roohallah Dipu Kabir, H.M. Tan, Ru-San Rajendra Acharya, U. Department of Computer Engineering Faculty of Engineering University of Birjand Birjand*** Iran Department of Information Technology Mazandaran University of Science and Technology Babol Iran Department of Computer Engineering Qom University of Technology Qom Iran Department of Electronic Engineering Faculty of Electrical and Computer Engineering University of Birjand Birjand Iran Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia School of Science and Technology Singapore University of Social Sciences Singapore Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

With the increasing growth of information through smart devices, increasing the quality level of human life requires various computational paradigms presentation including the Internet of Things, fog, and cloud. Between these three paradigms, the cloud computing paradigm as an emerging technology adds cloud layer services to the edge of the network so that resource allocation operations occur close to the end-user to reduce resource processing time and network traffic overhead. Hence, the resource allocation problem for its providers in terms of presenting a suitable platform, by using computational paradigms is considered a challenge. In general, resource allocation approaches are divided into two methods, including auction-based methods (goal, increase profits for service providers-increase user satisfaction and usability) and optimization-based methods (energy, cost, network exploitation, Runtime, reduction of time delay). In this paper, according to the latest scientific achievements, a comprehensive literature study (CLS) on artificial intelligence methods based on resource allocation optimization without considering auction-based methods in various computing environments are provided such as cloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5G networks, vehicular cloud architecture, machine-to-machine communication (M2M), Train-to-Train(T2T) communication network, Peer-to-Peer (P2P) network. Since deep learning methods based on artificial intelligence are used as the most important methods in resource allocation problems;Therefore, in this paper, resource allocation approaches based on deep learning are also used in the mentioned computational environments such as deep reinforcement learning, Q-learning technique, reinforcement learning, online learning, and also Classical learning methods such as Bayesian learning, Cummins clustering, Markov decision process. © 2022, CC BY-SA.

关键词： Reinforcement learning

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Psychoacoustic calibration of loss functions for efficient end-to-end neural audio coding

arXiv

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arXiv 2020年

作者： Zhen, Kai Lee, Mi Suk Sung, Jongmo Beack, Seungkwon Kim, Minje Department of Computer Science and Cognitive Science Program Indiana University BloomingtonIN47408 United States Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Dept. of Intelligent Systems Engineering Indiana University United States

Conventional audio coding technologies commonly leverage human perception of sound, or psychoacoustics, to reduce the bitrate while preserving the perceptual quality of the decoded audio signals. For neural audio codecs, however, the objective nature of the loss function usually leads to suboptimal sound quality as well as high run-time complexity due to the large model size. In this work, we present a psychoacoustic calibration scheme to re-define the loss functions of neural audio coding systems so that it can decode signals more perceptually similar to the reference, yet with a much lower model complexity. The proposed loss function incorporates the global masking threshold, allowing the reconstruction error that corresponds to inaudible artifacts. Experimental results show that the proposed model outperforms the baseline neural codec twice as large and consuming 23.4% more bits per second. With the proposed method, a lightweight neural codec, with only 0.9 million parameters, performs near-transparent audio coding comparable with the commercial MPEG-1 Audio Layer III codec at 112 kbps. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Synthesis of Facial Image using Conditional Generative Adversarial Network 5

Synthesis of Facial Image using Conditional Generative Adver...

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5th International Conference on computer, Communication, Chemical, Materials and Electronic Engineering, IC4ME2 2019

作者： Roy, Shuvendu Akhand, M.A.H. Siddique, N. Khulna University of Engineering Technology Dept. of Computer Science and Engineering Khulna Bangladesh Ulster University School of Computing Engineering and Intelligent Systems United Kingdom

ISBN: (纸本)9781728130606

Face sketch is done by sketch artist for a suspected or missing person from the description of an eyewitness. These methods have been widely used by forensic investigators. It is difficult for the sketch artist to draw perfectly from such verbal descriptions given by eyewitness of scenes and hard for the informer to confirm whether the sketch looks like the real person. In this work, we proposed a conditional generative adversarial network (cGAN) for synthesizing real human face taking a sketch as an input image. The focus of our model is to generate realistic images that preserve the identity the target person verified by face recognition algorithms. The proposed cGAN has been verified on a variety of facial sketches, which confirms the effectiveness and improved facial recognition score. © 2019 IEEE.

关键词： Generative adversarial networks

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An Improved N-gram Distance for Names Matching 1

An Improved N-gram Distance for Names Matching

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1st International Conference of intelligent Computing and Engineering, ICOICE 2019

作者： Al-Hagree, Salah Al-Sanabani, Maher Hadwan, Mohammed Al-Hagery, Mohammed A. Ibb University Dept. of Computer Sciences and Information Technology Ibb Yemen Faculty of Computer Science and Information Systems Thamar University Yemen Intelligent and Analytics Group Dept. of Information Technology College of Computer Qassim University Buriydah Saudi Arabia Faculty of Applied Sciences Taiz University Dept. of Computer Science Taiz6803 Yemen College of Computer Qassim University Computer Science Department Saudi Arabia

ISBN: (数字)9781728144870

ISBN: (纸本)9781728144870

N-gram distance (N-DIST) was developed by Kondrak"s lately to measure the distance between two strings. It was found that, this distance could be computed by a smart dynamic programming procedure. The N-DIST has played important roles in a wide array of applications due to its representational and computational efficiency. To effect a more sensible, distance measure, the normalized edit distance was proposed. Many algorithms and studies have been dedicated along this line with impressive performances in last years. There is, however, a fundamental problem with the original definition of N-DIST that has remained without improved: its context-free nature. In determining the possible actions, i.e., deletion, insertion, transposition and substitution, consider work was given to the local behaviors of the string question that indeed encompass great amount of useful information concerning its content. In this proposed framework, two operations are developed. The original N-DIST algorithm does not consider the transposition operations and the algorithm has fixed the cost of insertion and deletion operations. In addition, the proposed E-N-DIST algorithm computes the costs of substitution and transposition operations is dependent on 2n+1-1 states while the original N-DIST algorithm has been only dependent on 2n states. In this paper, the experiments carried out show the E-N-DIST algorithm, which gives a sort of results that are more accurate than the algorithms under discussion. © 2019 IEEE.

关键词： Dynamic programming

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EEG Based Autism Detection Using CNN Through Correlation Based Transformation of Channels' Data

EEG Based Autism Detection Using CNN Through Correlation Bas...

引用

IEEE Region 10 Symposium

作者： Zahrul Jannat Peya M.A.H. Akhand Jannatul Ferdous Srabonee N. Siddique Dept. of Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh School of Computing Engineering and Intelligent Systems Ulster University Northern Ireland UK

ISBN: (数字)9781728173665

ISBN: (纸本)9781728173672

Early diagnosis of autism or autism spectrum disorder (ASD) can help improving behavioral, language development and communication skill. As ASD is a neurodevelopmental disorder, brain signals are used to early diagnosis. Among different brain signals, electroencephalography (EEG) is the effective one. Electrical brain activity is measured through EEG signal from the scalp of human over a period of time and is used to analyze complex neuropsychiatric problems of brain. This study investigates an EEG based ASD detection using CNN, the well-known deep learning method for image analysis and classification. At first, the individual EEG channel data are transformed into 2D form using Pearson's Correlation Coefficient and then classification is carried out using the well-known CNN model residual neural network. Experiments performed on clinical EEG data show that the proposed approach achieved a classification accuracy of 100%.

关键词： Electroencephalography Autism Two dimensional displays Correlation Brain Training Support vector machines

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Robust Face Detection Using Hybrid Skin Color Matching under Different Illuminations 2

Robust Face Detection Using Hybrid Skin Color Matching under...

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2nd International Conference on Electrical, computer and Communication Engineering, ECCE 2019

作者： Akash, Md. Asif Anjum Akhand, M.A.H. Siddique, N. Dept. of Computer Science and Engineering Khulna University of Engineering Technology Khulna Bangladesh School of Computing Engineering and Intelligent Systems Ulster University Northern United Kingdom

ISBN: (纸本)9781538691113

Recently, human skin color property incorporation is considered a way to improve face detection. There are different skin color matching techniques and each individual one is found suitable for a specific illumination condition. In this study, a robust face detection is developed considering a hybrid skin color detection method which combines complementary strength of different color detection methods. The proposed hybrid skin color detection method is tested with the most popular Haar Feature Based Face Detection (HFFD) method and identified performance on a large number of images from a benchmark face data set. From the experimental results and analysis, it is revealed that proposed face detection system improves the performance of HFFD significantly. © 2019 IEEE.

关键词： Face recognition

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On the Philosophical, Cognitive and Mathematical Foundations of Symbiotic Autonomous systems (SAS)

arXiv

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arXiv 2021年

作者： Wang, Yingxu Karray, Fakhri Kwong, Sam Plataniotis, Konstantinos N. Leung, Henry Hou, Ming Tunstel, Edward Rudas, Imre J. Trajkovic, Ljiljana Kaynak, Okyay Kacprzyk, Janusz Zhou, Mengchu Smith, Michael H. Chen, Philip Patel, Shushma University of Calgary Canada FIEEE Dept. of Electrical & Computer Engineering University of Waterloo ON Canada FIEEE Department of Computer Science City University of Hong Kong Hong Kong FIEEE Dept. of Electrical & Computer Engineering University of Toronto ON Canada SMIEEE Toronto Research Centre DRDC Canada FIEEE Autonomous & Intelligent Systems Dept. Raytheon Technologies Research Center United States Óbuda University Budapest Hungary FIEEE School of Engineering Science Simon Fraser University BurnabyBC Canada FIEEE Bogazici University Bebek Istanbul Turkey FIEEE Systems Research Institute Polish Academy of Sciences Warsaw Poland FIEEE Dept. of Computer Science New Jersey Institute of Technology NJ United States SMIEEE Furaxa Inc. OrindaCA United States FIEEE School of Computer Science and Engineering South China University of Technology Guangzhou China FBCS Faculty of Computing Engineering and Media De Montfort University LeicesterLE1 9BH United Kingdom

Symbiotic Autonomous systems (SAS) are advanced intelligent and cognitive systems exhibiting autonomous collective intelligence enabled by coherent symbiosis of human-machine interactions in hybrid societies. Basic research in the emerging field of SAS has triggered advanced general AI technologies functioning without human intervention or hybrid symbiotic systems synergizing humans and intelligent machines into coherent cognitive systems. This work presents a theoretical framework of SAS underpinned by the latest advances in intelligence, cognition, computer, and system sciences. SAS are characterized by the composition of autonomous and symbiotic systems that adopt bio-brain-social-inspired and heterogeneously synergized structures and autonomous behaviors. This paper explores their cognitive and mathematical foundations. The challenge to seamless human-machine interactions in a hybrid environment is addressed. SAS-based collective intelligence is explored in order to augment human capability by autonomous machine intelligence towards the next generation of general AI, autonomous computers, and trustworthy mission-critical intelligent systems. Emerging paradigms and engineering applications of SAS are elaborated via an autonomous knowledge learning system that symbiotically works between humans and cognitive robots. © 2021, CC BY-NC-ND.

关键词： Cognitive systems

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Moment-based spectrum sensing under generalized noise channels

arXiv

引用

arXiv 2020年

作者： Miridakis, Nikolaos I. Tsiftsis, Theodoros A. Yang, Guanghua Institute of Physical Internet School of Intelligent Systems Science & Engineering Jinan University Zhuhai Campus Zhuhai519070 China Dept. of Informatics and Computer Engineering University of West Attica Aegaleo12243 Greece

A new spectrum sensing detector is proposed and analytically studied, when it operates under generalized noise channels. Particularly, the McLeish distribution is used to model the underlying noise, which is suitable for both non-Gaussian (impulsive) as well as classical Gaussian noise modeling. The introduced detector adopts a moment-based approach, whereas it is not required to know the transmit signal and channel fading statistics (i.e., blind detection). Important performance metrics are presented in closed forms, such as the false-alarm probability, detection probability and decision threshold. Analytical and simulation results are cross-compared validating the accuracy of the proposed approach. Finally, it is demonstrated that the proposed approach outperforms the conventional energy detector in the practical case of noise uncertainty, yet introducing a comparable computational complexity. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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Generalized energy detection under generalized noise channels

arXiv

引用

arXiv 2020年

Generalized energy detection (GED) is analytically studied when operates under fast-faded channels and in the presence of generalized noise. For the first time, the McLeish distribution is used to model the underlying noise, which is suitable for both non-Gaussian (impulsive) as well as classical Gaussian noise channels. Important performance metrics are presented in closed forms, such as the false-alarm and detection probabilities as well as the decision threshold. Analytical and simulation results are cross-compared validating the accuracy of the proposed approach in the entire signal-to-noise ratio regime. Finally, useful outcomes are extracted with respect to GED system settings under versatile noise environments and when noise uncertainty is present. Copyright © 2020, The Authors. All rights reserved.

关键词： Cognitive radio

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SAHRTA: A Supervisory-Based Adaptive Human-Robot Teaming Architecture

arXiv

引用

arXiv 2020年

作者： Heard, Jamison Fortune, Julian Adams, Julie A. Dept. of Electrical Engineering Rochester Institute of Technology RochesterNY School of Electrical Engineering and Computer Science Oregon State University CorvallisOR Collaborative Robotics and Intelligent Systems Institute Oregon State University CorvallisOR

Supervisory-based human-robot teams are deployed in various dynamic and extreme environments (e.g., space exploration). Achieving high task performance in such environments is critical, as a mistake may lead to significant monetary loss or human injury. Task performance may be augmented by adapting the supervisory interface’s interactions or autonomy levels based on the human supervisor’s workload level, as workload is related to task performance. Typical adaptive systems rely solely on the human’s overall or cognitive workload state to select what adaptation strategy to implement;however, overall workload encompasses many dimensions (i.e., cognitive, physical, visual, auditory, and speech) called workload components. Selecting an appropriate adaptation strategy based on a complete human workload state (rather than a single workload dimension) may allow for more impactful adaptations that ensure high task performance. A Supervisory-Based Adaptive Human-Robot Teaming Architecture (SAHRTA) that selects an appropriate level of autonomy or system interaction based on a complete real-time multi-dimensional workload estimate and predicted future task performance is introduced. SAHRTA was shown to improve overall task performance in a physically expanded version of the NASA Multi-Attribute Task Battery. Copyright © 2020, The Authors. All rights reserved.

关键词： Adaptive systems

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