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Automatic Diagnosis of Schizophrenia and Attention Deficit Hyperactivity Disorder in rs-fMRI Modality using Convolutional Autoencoder Model and Interval Type-2 Fuzzy Regression

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arXiv 2022年

作者： Shoeibi, Afshin Ghassemi, Navid Khodatars, Marjane Moridian, Parisa Khosravi, Abbas Zare, Assef Gorriz, Juan M. Chale-Chale, Amir Hossein Khadem, Ali Rajendra Acharya, U. Faculty of Electrical Engineering FPGA Lab K. N. Toosi University of Technology Tehran Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Dept. of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Deakin University Geelong Australia Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Department of Signal Theory Networking and Communications Universidad de Granada Spain Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy. © 2022, CC BY-NC-ND.

关键词： Support vector machines

Interpretation-Enabled Software Reuse Detection Based on a Multi-level Birthmark Model

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Interpretation-Enabled Software Reuse Detection Based on a M...

International Conference on Software engineering (ICSE)

作者： Xi Xu Qinghua Zheng Zheng Yan Ming Fan Ang Jia Ting Liu Key Laboratory of Intelligent Networks and Network Security Ministry of Education China School of Computer Science and Technology Xi’an Jiaotong University China State Key Lab on Integrated Services Networks School of Cyber Engineering Xidian University China Department of Communications and Networking Aalto University Finland School of Cyber Science and Engineering Xi’an Jiaotong University China

ISBN: (纸本)9781665402965;9781665448314

Software reuse, especially partial reuse, poses legal and security threats to software development. Since its source codes are usually unavailable, software reuse is hard to be detected with interpretation. On the other hand, current approaches suffer from poor detection accuracy and efficiency, far from satisfying practical demands. To tackle these problems, in this paper, we propose ISRD, an interpretation-enabled software reuse detection approach based on a multi-level birthmark model that contains function level, basic block level, and instruction level. To overcome obfuscation caused by cross-compilation, we represent function semantics with Minimum Branch Path (MBP) and perform normalization to extract core semantics of instructions. For efficiently detecting reused functions, a process for "intent search based on anchor recognition" is designed to speed up reuse detection. It uses strict instruction match and identical library call invocation check to find anchor functions (in short anchors) and then traverses neighbors of the anchors to explore potentially matched function pairs. Extensive experiments based on two real-world binary datasets reveal that ISRD is interpretable, effective, and efficient, which achieves 97.2% precision and 94.8% recall. Moreover, it is resilient to cross-compilation, outperforming state-of-the-art approaches.

关键词： Law Semantics Libraries Security Software reusability Software engineering Resilience

Joint User Association and Transmission Scheduling in Integrated mmWave Access and Terahertz Backhaul Networks

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arXiv 2023年

作者： Wang, Lei Ai, Bo Niu, Yong Jiang, Haiyan Mao, Shiwen Zhong, Zhangdui Wang, Ning The State Key Laboratory of Advanced Rail Autonomous Operation The Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China The Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen518055 China The Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The School of Information Engineering Zhengzhou University Zhengzhou450001 China

Terahertz wireless backhaul is expected to meet the high-speed backhaul requirements of future ultra-dense networks using millimeter-wave (mmWave) base stations (BSs). In order to achieve higher network capacity with limited resources and meet the quality of service (QoS) requirements of more users in the integrated mmWave access and terahertz backhaul network, this paper formulates a problem of maximizing the number of users successfully served in both the access and backhaul links. Since the problem is a non-linear integer optimization problem, a minimum rate ratio user association and transmission scheduling algorithm is proposed to obtain a suboptimal solution. The proposed algorithm takes the minimum rate ratio as the user association criterion and schedules first the users with fewer backhaul transmission slots. In addition, the algorithm will update the number of access transmission slots allocated to users and the access scheduling results after the backhaul scheduling phase. Numerical results show that the proposed algorithm outperforms several benchmark algorithms in terms of the number of served users and system throughput, and it can cope with a large number of bursty user requests. Copyright © 2023, The Authors. All rights reserved.

关键词： Millimeter waves

Integrating Over-the-Air Federated Learning and Non-Orthogonal Multiple Access: What Role can RIS Play?

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arXiv 2021年

作者： Ni, Wanli Liu, Yuanwei Yang, Zhaohui Tian, Hui Shen, Xuemin State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

With the aim of integrating over-the-air federated learning (AirFL) and non-orthogonal multiple access (NOMA) into an on-demand universal framework, this paper proposes a reconfigurable intelligent surface (RIS)-aided hybrid network by leveraging the RIS to flexibly adjust the decoding order of heterogeneous data. A new metric of computation rate is defined to measure the performance of AirFL users. Upon this, the objective of this work is to maximize the achievable hybrid rate by jointly optimizing the transmit power, controlling the receive scalar, and designing the reflection coefficients. Since the concurrent transmissions of all computation and communication signals are aided by the discrete phase-shifting elements at the RIS, the formulated problem (P0) is a challenging mixed-integer programming problem. To tackle this intractable issue, we decompose the original problem (P0) into a non-convex problem (P1) and a combinatorial problem (P2), which are characterized by the continuous and discrete variables, respectively. For the transceiver design problem (P1), the power allocation subproblem is first solved by difference-of-convex programming, and then the receive control subproblem is addressed by successive convex approximation, where the closed-form expressions of simplified cases are derived to obtain deep insights. For the reflection design problem (P2), a relaxation-then-quantization method is adopted to find a suboptimal solution for striking a trade-off between complexity and performance. Afterwards, an alternating optimization algorithm is developed to solve the non-linear non-convex problem (P0) iteratively. Finally, simulation results reveal that i) the proposed RIS-aided hybrid network can support on-demand communication and computation efficiently, ii) the system performance can be improved by properly selecting the location of the RIS, and iii) the designed algorithms are also applicable to conventional networks with only AirFL or NOMA users. Copyr

关键词： Iterative methods

Joint channel estimation and data detection in cell-free massive MU-MIMO systems

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arXiv 2021年

作者： Song, Haochuan Goldstein, Tom You, Xiaohu Zhang, Chuan Tirkkonen, Olav Studer, Christoph the LEADS Southeast University Nanjing China the National Mobile Communications Research Laboratory Southeast University Nanjing China the Purple Mountain Laboratories Nanjing China the Department of Computer Science University of Maryland College ParkMD United States the Department of Communications and Networking Aalto University Espoo Finland the Department of Information Technology and Electrical Engineering ETH Zurich Zurich Switzerland

We propose a joint channel estimation and data detection (JED) algorithm for densely-populated cell-free massive multiuser (MU) multiple-input multiple-output (MIMO) systems, which reduces the channel training overhead caused by the presence of hundreds of simultaneously transmitting user equipments (UEs). Our algorithm iteratively solves a relaxed version of a maximum a-posteriori JED problem and simultaneously exploits the sparsity of cell-free massive MU-MIMO channels as well as the boundedness of QAM constellations. In order to improve the performance and convergence of the algorithm, we propose methods that permute the access point and UE indices to form so-called virtual cells, which leads to better initial solutions. We assess the performance of our algorithm in terms of root-mean-squared-symbol error, bit error rate, and mutual information, and we demonstrate that JED significantly reduces the pilot overhead compared to orthogonal training, which enables reliable communication with short packets to a large number of UEs. Copyright © 2021, The Authors. All rights reserved.

关键词： MIMO systems

Enabling dynamic and intelligent workflows for HPC, data analytics, and AI convergence

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FUTURE GENERATION computer SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 134卷 414-429页

作者： Ejarque, Jorge Badia, Rosa M. Albertin, Loic Aloisio, Giovanni Baglione, Enrico Becerra, Yolanda Boschert, Stefan Berlin, Julian R. D'Anca, Alessandro Elia, Donatello Exertier, Francois Fiore, Sandro Flich, Jose Folch, Arnau Gibbons, Steven J. Koldunov, Nikolay Lordan, Francesc Lorito, Stefano Lovholt, Finn Macias, Jorge Marozzo, Fabrizio Michelini, Alberto Monterrubio-Velasco, Marisol Pienkowska, Marta de la Puente, Josep Queralt, Anna Quintana-Orti, Enrique S. Rodriguez, Juan E. Romano, Fabrizio Rossi, Riccardo Rybicki, Jedrzej Kupczyk, Miroslaw Selva, Jacopo Talia, Domenico Tonini, Roberto Trunfio, Paolo Volpe, Manuela [a]Barcelona Supercomputing Center (BSC) Spain [b]Centre Internacional de Mètodes Numèrics a l’Enginyeria (CIMNE) Spain [c]Universitat Politècnica de Catalunya (UPC) Spain [d]Jülich Supercomputing Centre (JSC) Germany [e]Universitat Politècnica de València (UPV) Spain [f]Atos BDS R&D HPC & AI Software France [g]DtoK Lab Srl Italy [h]Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) Italy [i]Department of Information Engineering and Computer Science University of Trento Italy [j]Universidad de Málaga (UMA) Spain [k]Istituto Nazionale di Geofisica e Vulcanologia (INGV) Italy [l]Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung Germany [m]Consejo Superior Investigaciones Cientificas (CSIC) Spain [n]Eidgenössische Technische Hochschule (ETH) Zürich Switzerland [o]Siemens AG Germany [p]Norwegian Geotechnical Institute (NGI) Norway [q]Poznan Supercomputing and Networking Center (PSNC) Poland

The evolution of High-Performance Computing (HPC) platforms enables the design and execution of progressively larger and more complex workflow applications in these systems. The complexity comes not only from the number of elements that compose the workflows but also from the type of computations they perform. While traditional HPC workflows target simulations and modelling of physical phenomena, current needs require in addition data analytics (DA) and artificial intelligence (AI) tasks. However, the development of these workflows is hampered by the lack of proper programming models and environments that support the integration of HPC, DA, and AI, as well as the lack of tools to easily deploy and execute the workflows in HPC systems. To progress in this direction, this paper presents use cases where complex workflows are required and investigates the main issues to be addressed for the HPC/DA/AI convergence. Based on this study, the paper identifies the challenges of a new workflow platform to manage complex workflows. Finally, it proposes a development approach for such a workflow platform addressing these challenges in two directions: first, by defining a software stack that provides the functionalities to manage these complex workflows;and second, by proposing the HPC Workflow as a Service (HPCWaaS) paradigm, which leverages the software stack to facilitate the reusability of complex workflows in federated HPC infrastructures. Proposals presented in this work are subject to study and development as part of the EuroHPC eFlows4HPC project. (C) 2022 Elsevier B.V. All rights reserved.

关键词： High performance computing Distributed computing Parallel programming HPC-DA-AI convergence Workflow development Workflow orchestration

Spreading code identification of legal drones in IoT environment 8

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Spreading code identification of legal drones in IoT environ...

8th International Conference on Emerging Security Technologies, EST 2019

作者： Ali Alheeti, Khattab M. Al-Ani, Muzhir Shaban McDonald-Maier, Klaus Department of Computer Networking Systems College of Computer Science and Information Technology University of Anbar Iraq University of Human Development College of Science and Technology Department of Information Technology Sulaymaniyah Iraq School of Computer Sciences and Electronic Engineering University of Essex Colchester United Kingdom

ISBN: (纸本)9781728155463

The widespread use of drones has become very common today with large-scale civil and military applications. In the next few coming years, the outlook is expected that the number of drones will reach millions. So, these need to be well organised and managed in order to achieve the benefits of IoT with this accelerated environment. Drones or Unmanned Aerial Vehicles (UAVs) must achieved a level of communications to authenticate a legal working. The proposed approach concentrated on preparing each drone with identification key based on the combination of its international sim number with the date of the first action and the local country code. This approach is called Drone IDentification (DID) that generate a unique code for each drone via spreading technique. In this case any drone not apply this regulation is considered as unauthenticated drone and does not allowed to fly. This approach is very important to establish drone regulation via IoT. © 2019 IEEE.

关键词： Drones

Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies

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arXiv 2021年

作者： Shoeibi, Afshin Ghassemi, Navid Khodatars, Marjane Moridian, Parisa Alizadehsani, Roohallah Zare, Assef Khosravi, Abbas Subasi, Abdulhamit Rajendra Acharya, U. Manuel Gorriz, J. K. N. Toosi University of Technology Tehran Iran Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Deakin University VIC3217 Australia Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Institute of Biomedicine Faculty of Medicine University of Turku Turku20520 Finland Department of Computer Science College of Engineering Effat University Jeddah21478 Saudi Arabia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore599489 Singapore the Department of Biomedical Engineering School of Science and Technology SUSS University Singapore599491 Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Department of Signal Theory Networking and Communications Universidad de Granada Spain Department of Psychiatry University of Cambridge United Kingdom

Epileptic seizures are one of the most crucial neurological disorders, and their early diagnosis will help the clinicians to provide accurate treatment for the patients. The electroencephalogram (EEG) signals are widely used for epileptic seizures detection, which provides specialists with substantial information about the functioning of the brain. In this paper, a novel diagnostic procedure using fuzzy theory and deep learning techniques is introduced. The proposed method is evaluated on the Bonn University dataset with six classification combinations and also on the Freiburg dataset. The tunable-Q wavelet transform (TQWT) is employed to decompose the EEG signals into different sub-bands. In the feature extraction step, 13 different fuzzy entropies are calculated from different sub-bands of TQWT, and their computational complexities are calculated to help researchers choose the best set for various tasks. In the following, an autoencoder (AE) with six layers is employed for dimensionality reduction. Finally, the standard adaptive neuro-fuzzy inference system (ANFIS), and also its variants with grasshopper optimization algorithm (ANFIS-GOA), particle swarm optimization (ANFIS-PSO), and breeding swarm optimization (ANFIS-BS) methods are used for classification. Using our proposed method, ANFIS-BS method has obtained an accuracy of 99.74 © 2021, CC BY-NC-ND.

关键词： Wavelet transforms

An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

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arXiv 2021年

作者： Shoeibi, Afshin Moridian, Parisa Khodatars, Marjane Ghassemi, Navid Jafari, Mahboobeh Alizadehsani, Roohallah Kong, Yinan Gorriz, Juan Manuel Ramírez, Javier Khosravi, Abbas Nahavandi, Saeid Acharya, U. Rajendra Data Science and Computational Intelligence Institute University of Granada Spain Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Computer Engineering department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Deakin University VIC3217 Australia School of Engineering Macquarie University Sydney2109 Australia Department of Signal Theory Networking and Communications Universidad de Granada Spain Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DL-based CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper. © 2021, CC BY-NC-ND.

关键词： Forecasting