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5th IEEE advanced information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2022

作者： Jiang, Heng Li, Lichun Zhang, Hailong Pan, Hongyi Li, Xue College of Information System Engineering Information Engineering University Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665479677

The coprime array constructs virtual array to obtain higher degrees of freedom (DOF), but the premise of doing so is that the signals are independent of each other. Once there are coherent signals, the signal model of virtual array is destroyed and the method fails. To solve these problems, we propose the stepwise estimation of the Direction of Arrival (DOA) for a mixture of coherent and uncorrelated signals. Firstly, the symmetry of the two coprime subarrays is used to eliminate the uncorrelated signals, and combined with the properties of prime numbers, the coherent source DOA is jointly estimated. The obtained coherent source DOA was combined with the optimization algorithm to remove the coherent signal in the mixed signal. Finally, the virtual domain signal processing method was used to estimate the independent source DOA. This method can effectively reduce the influence of coherent signals on the accuracy of DOA estimation without loss of degrees of freedom. In this paper, the feasibility of the proposed method is proved by theoretical derivation, and the effectiveness of the method is also verified by experimental simulations. © 2022 IEEE.

关键词： Direction of arrival

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Computational Complexity Analysis of Ant Colony Clustering Algorithms: Application to Student's Grouping Problem

Computational Complexity Analysis of Ant Colony Clustering A...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Chniter, Malak Abid, Abir Kallel, Ilhem Kanoun, Slim Mir@cl Lab: Multimedia InfoRmation Systems and Advanced Computing Laboratory University of Sfax ReGIM-Lab: Research Groups in Intelligent Machines Tunisia

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

The task of assessing, grouping and arranging data into meaningful groups or clusters based on their similarities/dissimilarities measures known as cluster analysis. Thereby, there are numerous clustering algorithms: hierarchical and partitional. In the last decade, clustering using bio-inspired algorithms received more attention, specifically the ant clustering algorithms. Regardless, they have required a lot of processing power due to the massive amount of data that has been generated during the last years. As a consequence, determining the computational cost of these algorithms is one of the most interesting tasks in the quest for optimal clustering solutions in a real-time system. This study presents a research guide for the researchers working in the same field. A series of experiments are elaborated to investigate the computational complexity of the most promising algorithms applied to students grouping problem. The results indicate two challenges that arise when using ant clustering algorithms: the difficulty in adjusting parameters and extended computation time. © 2022 IEEE.

关键词： Computational complexity

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RRCNN: Request Response-Based Convolutional Neural Network for ICS Network Traffic Anomaly Detection

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Computers, Materials & Continua 2023年第6期75卷 5743-5759页

作者： Yan Du Shibin Zhang Guogen Wan Daohua Zhou Jiazhong Lu Yuanyuan Huang Xiaoman Cheng Yi Zhang Peilin He Country School of Cybersecurity Chengdu University of Information TechnologyChengdu610225China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225China DAQSOFT CO. LTDChengdu610213China Communication and Information Technology Center Petro China Southwest Oil and Gas CompanyChengdu610057China School of Computing and Information University of PittsburghPittsburgh152607PAUSA

Nowadays,industrial control system(ICS)has begun to integrate with the *** the Internet has brought convenience to ICS,it has also brought severe security *** ICS network traffic anomaly detection methods rely on statistical features manually extracted using the experience of network security *** are not aimed at the original network data,nor can they capture the potential characteristics of network ***,the following improvements were made in this study:(1)A dataset that can be used to evaluate anomaly detection algorithms is produced,which provides raw network data.(2)A request response-based convolutional neural network named RRCNN is proposed,which can be used for anomaly detection of ICS network *** of using statistical features manually extracted by security experts,this method uses the byte sequences of the original network packets directly,which can extract potential features of the network packets in greater *** regards the request packet and response packet in a session as a Request-Response Pair(RRP).The feature of RRP is extracted using a one-dimensional convolutional neural network,and then the RRP is judged to be normal or abnormal based on the extracted *** results demonstrate that this model is better than several other machine learning and neural network models,with F1,accuracy,precision,and recall above 99%.

关键词： Industrial control system(ICS) dataset network traffic anomaly detection

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24th KES International Conference on Knowledge-Based and Intelligent information and Engineering systems, KES 2020

作者： Ammar, Randa Ben Ayed, Yassine Ben Multimedia Information System and Advanced Computing Laboratory Sfax University Tunisia

Alzheimer's disease (AD) is the most leading symptom of neurodegenerative dementia;AD is defined now as one of the most costly chronic diseases. For that automatic diagnosis and control of Alzheimer's disease may have a significant effect on society along with patient well-being. Language disorder is regarded to be among the most common symptoms of AD, as a direct and natural result of cognitive impairment. Hence, the diagnosis of Alzheimer's disease using speech-based features is gaining growing attention. The aim of this study is to extract linguistic features following a proposed taxonomy of language impairment of AD patients. Obtained results indicate that the proposed taxonomy of the linguistic features extracted from the speech samples can be used to differentiate between Alzheimer's disease patients and the healthy control group. Support Vector Machine (SVM) classifier obtained classification accuracy over 90 percent. © 2020 The Authors. Published by Elsevier B.V.

关键词： Taxonomies

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Hierarchical Transformer for Efficient CT Image Super-Resolution

Hierarchical Transformer for Efficient CT Image Super-Resolu...

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Zhicun Zhang Yu Han Siyu Tan Xiaoqi Xi Zhuo Chen LinLin Zhu Lei Li Bin Yan School of Information System Engineering Information Engineering University Zhengzhou China Information Engineering University Zhengzhou China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

This paper proposes a super-resolution (SR) reconstruction method to improve the poor quality of low-resolution (LR) computed tomography (CT) images. Transformer-based methods have shown promising performance in image SR. However, there are two obstacles and key challenges: (1) The hierarchical information of images is not fully utilized; (2) The potential of the self-attention (SA) mechanism has not been fully exploited. To this end, we propose a Hierarchical Transformer for efficient CT image Super-Resolution(HTSR). Our HTSR can capture image hierarchical information from local, regional, and global. Specifically, to achieve efficient region modelling, we design a Content-Aware Transformer Block (CATB), which is a coarse-to-fine image block selection strategy to model long-range dependencies of semantically consistent information and eliminate interference from bad information. Moreover, we introduce Transposed Transformer Block (TTB), which can capture the long-range dependency and is applicable to large-sized images. Finally, combined with the Spatial window Transformer Block (SWTB), we design a network architecture of encoder-decoder, achieving an effective fusion of hierarchical information. Extensive experimental results have shown that the proposed method achieves good performance.

关键词： Computer vision Computed tomography Computational modeling Superresolution Machine learning Interference Reconstruction algorithms Network architecture Transformers Spatial resolution

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Large Scale Model Enabled Semantic Communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ding, Kuiyuan Liu, Fangfang Yang, Yang Chen, Mingzhe Guo, Caili Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering The Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China

ISBN: (纸本)9798350351255

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC. © 2024 IEEE.

关键词： Channel coding

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Improving Speech Emotion Recognition Using Data Augmentation and Balancing Techniques

Improving Speech Emotion Recognition Using Data Augmentation...

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International Conference on Cyberworlds

作者： Chawki Barhoumi Yassine Ben Ayed National School of Electronics and Telecommunications of Sfax Stax Tunisia Multimedia InfoRmation systems and Advanced Computing Laboratory Sfax Tunisia

Speech Emotion Recognition (SER) is a challenging task due to the complexity and variability of human emotions. In this paper, we propose an innovative approach to improve SER performance on the EMODB dataset. Our approach employs data augmentation techniques, such as noise addition and spectrogram shift, as well as balancing techniques, including random oversampling. We also extract five different features from the dataset samples: MFCC, Chroma, Mel Spectrogram, ZCR, and RMS. We compare the performance of four different classifiers - MLP, SVM, KNN, and CNN - with and without the use of our proposed approach. Our results demonstrate that the proposed approach significantly enhances the accuracy of speech emotion recognition compared to the approach without data augmentation and balancing techniques. Our experiments reveal that the proposed approach achieves higher accuracy and F1-score compared to other approaches, with MLP and CNN models achieving 100% accuracy. These findings highlight the effectiveness of data augmentation and balancing techniques in improving the performance of speech emotion recognition. Moreover, our approach holds great potential for application in various real-life scenarios, including mental health monitoring, human-robot interaction, and speech-based virtual assistants.

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Performance Analysis of Parkinson Detection Techniques

Performance Analysis of Parkinson Detection Techniques

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International Conference on advanced Technologies for Signal and Image Processing (ATSIP)

作者： Donia Ammous Hajer Bouzayen Yassine Ben Ayed National School of Engineers of Sfax Laboratory of Electronics and Information Technology (LETI) Higher Institute of Computer Science and Multimedia of Sfax Sfax Tunisia Higher Institute of Computer Science and Multimedia of Sfax Sfax Tunisia Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACLE) Higher Institute of Computer Science and Multimedia of Sfax Sfax Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Parkinson’s Disease (PD) presents significant challenges in timely diagnosis and management, driving the need for advanced automatic recognition systems. This comparative study explores various methodologies for PD detection, leveraging machine learning and signal processing techniques across diverse modalities. Key phases, including data collection, preprocessing, feature extraction, selection, and classification, are scrutinized. Through comprehensive analysis of methodologies, datasets, and performance metrics, the study reveals promising accuracies in PD classification. However, challenges such as dataset availability and class imbalance persist. Future research avenues include multi-modal fusion, real-time monitoring, and telemedicine integration, aiming to enhance diagnostic accuracy and accessibility. Embracing these perspectives can accelerate the translation of research findings into clinical practice, improving outcomes for individuals with Parkinson’s disease.

关键词： Measurement Accuracy Telemedicine Machine learning Signal processing Real-time systems Performance analysis

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Understanding Learner Engagement in a Virtual Learning Environment 18th

Understanding Learner Engagement in a Virtual Learning Envir...

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Joint Conferences on 18th International Conference on Intelligent systems Design and Applications, ISDA 2018 and 10th World Congress on Nature and Biologically Inspired computing , NaBIC 2018

作者： Hlioui, Fedia Aloui, Nadia Gargouri, Faiez Multimedia Information System and Advanced Computing Laboratory University of Sfax Sfax Tunisia

ISBN: (纸本)9783030166595

The past few years has seen the rapid growth of educational data mining approaches for the analysis of data obtained from the virtual learning environments (VLE). However, due to the open and online characteristics of VLEs, vast majority of learners may enroll and drop a course freely, resulting in high dropout rates problem. One of the key elements in reducing dropout rates is the accurate and prompt identification of learners’ engagement level and providing individualized assistance. In this respect, this paper proposes a survival modeling technique to study various factors’ impact on attrition over the Open University in UK. We aim to perceive the learning from a psychological engagement perspective, which is necessary to gain a better understanding of learner motivation and subsequent knowledge and skill acquisition. In this way, we provide an innovative process that may help the tutor to interfere with weak learner at the appropriate time, such as dialog prompts, or learning resources to enhance the learning efficiency. It can help developers to evaluate the VLE effectively and expand system function for future development trend. © 2020, Springer Nature Switzerland AG.

关键词： Data mining

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Open Set Recognition for Encrypted Traffic using Intra-class Partition and Boundary Sample Generation 5

Open Set Recognition for Encrypted Traffic using Intra-class...

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5th IEEE advanced information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2022

作者： Li, Xue Fei, Jinlong Qi, Zan Lv, Zhuo Jiang, Heng PLA Information Engineering University State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China PLA Information Engineering University National Digital Switching System Engineering Technological Research Center Zhengzhou China

ISBN: (纸本)9781665479677

Existing deep learning-based encrypted traffic recognition methods can achieve high precision identification performance while protecting user privacy, but almost all of them focus on closed sets, in which training data and test data come from the same label space. However, the real network environment is open, and it is necessary for traffic identification systems to deal with previously unseen categories. To classify known categories at a fine granularity and identify unknown ones, we propose a novel open-set recognition method for encrypted traffic. The decision boundary sample generation method based on constrained Generative Adversarial Networks is presented to generate samples from low-density regions in the known data space. Furthermore, an end-to-end open-set classifier is built using the accurate and diverse simulative samples of unknown classes through intra-class partition and boundary sample generation. Finally, the effectiveness of the proposed method is validated through numerical experiments. © 2022 IEEE.

关键词： Deep learning

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