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35th Conference on Computational Linguistics and Speech Processing, ROCLING 2023

作者： Zhang, Qiu-Xia Chi, Te-Yu Yang, Te-Lun Tang, Yu-Meng Chen, Ta-Lin Jang, Jyh-Shing Roger Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering Tongji University China Department of Electrical and Computer Engineering University of Texas at Austin United States

ISBN: (纸本)9789869576963

The existing method of using large pre-trained models with prompts for zero-shot text classification possesses powerful representation ability and scalability. However, its commercial availability is relatively limited. The approach of employing class labels and existing datasets to fine-tune smaller models for zero-shot classification is comparatively straightforward, yet it might lead to weaker model generalization ability. This paper introduces three methods to enhance the accuracy and generalization capability of pre-trained models in zeroshot text classification tasks: 1) utilizing pretrained language models and structuring inputs into a standardized multiple-choice format;2) creating a text classification training dataset using Wikipedia text data and refining the pre-trained model through fine-tuning;and 3) suggesting a zero-shot category mapping technique based on GloVe text similarity, wherein Wikipedia categories replace textual categories. Remarkably, without employing labeled samples for fine-tuning, the proposed method achieves results comparable to the best models fine-tuned with labeled samples. © 2023 ROCLING 2023 - Proceedings of the 35th Conference on Computational Linguistics and Speech Processing. All rights reserved.

关键词： Classification (of information)

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Emerging Trends in Massive Access Techniques for B5G and 6G Networks: A Concise Review

Emerging Trends in Massive Access Techniques for B5G and 6G ...

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2024 IEEE Middle East Conference on Communications and Networking, MECOM 2024

作者： Kahsay, Leake Z. Alkhattib, Abdullah Muhaidat, Sami Sofotasios, Paschalis C. Khalifa University Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Khalifa University KU 6G Center Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Carleton University Department of Systems and Computer Engineering Ottawa Canada Khalifa University KU C2PS and 6G Center Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Tampere University Department of Electrical Engineering Tampere Finland

ISBN: (纸本)9798350376715

This review paper provides an overview of various multiple access schemes proposed to meet the massive access (MA) demands of Beyond 5G (B5G) and 6G networks. Initially, it elucidates the challenges associated with MA compared to traditional access methods and discusses several promising MA schemes, detailing their mechanisms. This encompasses an exploration of various Non-Orthogonal Multiple Access (NOMA) schemes such as Sparse Code Multiple Access (SCMA) and MultiUser Shared Access (MUSA), as well as energy-efficient schemes like tensor-based (TB) methods, coded compressed sensing, and uncoupled compressed sensing. The paper also reviews and compares some of the results obtained in existing literature. Finally, it summarizes the latest advancements in MA, presenting them in a comprehensive table. © 2024 IEEE.

关键词： 5G mobile communication systems

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STAR-RIS Assisted Covert Wireless Communications with Randomly Distributed Blockages

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4690-4705页

作者： Li, Xingwang Zhao, Jingwei Chen, Gaojie Hao, Wanming Da Costa, Daniel Benevides Nallanathan, Arumugam Shin, Hyundong Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454099 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Shenzhen518107 China 5GIC 6GIC University of Surrey United Kingdom Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Communication Systems and Sensing Department of Electrical Engineering Dhahran31261 Saudi Arabia Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom Kyung Hee University Department of Electronic Engineering Yongin-si17104 Korea Republic of Kyung Hee University Department of Electronics and Information Convergence Engineering Korea Republic of Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore

As one of the promising technologies, reconfigurable intelligent surface (RIS) and simultaneous transmitting and reflecting RIS (STAR-RIS) have attracted great interest. However, the existing RISs offer broadband tuning capability without filtering function due to the absence of radio frequency (RF) units, which easily leads to the unexpected tuning of the RIS undesired signals, especially in large-scale deployments. For the target network, it is difficult to obtain the parameter settings of RISs to serve other networks, which causes the unpredictability of the wireless environment. In this paper, we consider the covert communication in a STAR-RIS assisted random wireless network with randomly distributed blockages. We investigate the impact of STAR-RIS large-scale deployment on covert communication and leverage its inherent unpredictability for improving the covertness. We derive the average detection error probability for warden within the random wireless networks. Furthermore, we optimize the passive beamforming of STAR-RIS to maximize the covert communication rate, considering both direct and indirect line-of-sight (LoS) links. To address this, we employ an alternating optimization (AO) algorithm based on the semi-definite programming (SDP) method. Finally, numerical results demonstrate significant enhancements and increase covert capability achieved through the large-scale deployment of STAR-RIS. 2002-2012 IEEE.

关键词： Numerical methods

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Challenges and Opportunities in Healthcare and Industrial IoT: A Comparative Analysis 1

Challenges and Opportunities in Healthcare and Industrial Io...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Berjis, Zaid Ameen, Siddeeq Y. Al-Jammas, Mohammed H. Electrical and Computer Engineering Department College of Engineering University of Duhok Duhok Iraq Energy Engineering Department College of Technical Engineering Duhok Polytechnic University Duhok Iraq Computer and Information Engineering Department College of Electronics Ninevah University Mosul Iraq

ISBN: (纸本)9798331533557

The Internet of Things (IoT) has changed many industries by enabling smart devices to transmit data, operate autonomously, and interact in real-time. Among its most prominent applications are in healthcare and industrial sectors, where IoT is classified into two classes: massive Machine-Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC). While mMTC devices prioritize low energy consumption and can handle large-scale deployments with minimal data rates, URLLC devices are focused on mission-critical applications requiring ultra-low latency and high reliability. This paper examines the challenges and opportunities of IoT implementation in healthcare and industrial sectors, focusing on interoperability, data security, and scalability issues. The objective is to provide actionable solutions for the effective integration of IoT technologies. The investigation shows that in healthcare, mMTC is utilized for non-critical monitoring, while URLLC supports critical tasks like remote surgery. In industrial settings, mMTC improves processes like supply chain management, while URLLC enables automation and real-time control of machinery. Despite these opportunities, challenges such as data privacy, security, interoperability, and reliability remain. Addressing these issues is crucial for unlocking the full potential of IoT in healthcare and industrial sectors. © 2024 IEEE.

关键词： Data privacy

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Optimal Resource Allocation Method for Device-to-Device Communication in 5G Networks

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computers, Materials & Continua 2022年第4期71卷 1-15页

作者： Fahd N.Al-Wesabi Imran Khan Saleem Latteef Mohammed Huda Farooq Jameel Mohammad Alamgeer Ali M.Al-Sharafi Byung Seo Kim Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversityYemen Department of Electrical Engineering University of Engineering and TechnologyPeshawarPakistan Department of Medical Instrumentation Techniques Engineering Electrical Engineering Technical CollegeMiddle Technical UniversityBaghdad10013Iraq Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA Department of Software and Communications Engineering Hongik UniversityKorea

With the rapid development of the next-generation mobile network,the number of terminal devices and applications is growing ***,how to obtain a higher data rate,wider network coverage and higher resource utilization in the limited spectrum resources has become the common research goal of ***-to-Device(D2D)communication technology and other frontier communication technologies have ***-to-Device communication technology is the technology that devices in proximity can communicate directly in cellular *** has become one of the key technologies of the fifth-generation mobile communications system(5G).D2D communication technology which is introduced into cellular networks can effectively improve spectrum utilization,enhance network coverage,reduce transmission delay and improve system throughput,but it would also bring complicated and various interferences due to reusing cellular resources at the same *** resource management is one of the most challenging and importing issues to give full play to the advantages of D2D *** resource allocation is an important factor that needs to be addressed in D2D ***,this paper proposes an optimization method based on the game-matching *** main idea is to model the optimization problem of the quality-of-experience based on user fairness and solve it through game-matching *** results show that the proposed algorithm effectively improved the resource allocation and utilization as compared with existing algorithms.

关键词： D2D communication resource allocation optimization networks

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DeepCNN:Spectro-temporal feature representation for speech emotion recognition

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 401-417页

作者： Nasir Saleem Jiechao Gao Rizwana Irfan Ahmad Almadhor Hafiz Tayyab Rauf Yudong Zhang Seifedine Kadry Department of Electrical Engineering Faculty of Engineering and TechnologyGomal UniversityD.I.KhanPakistan Department of Computer Science University of VirginiaCharlottesvilleVirginiaUSA Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySkaka AljoufSaudi Arabia Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke‐on‐TrentUK School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Speech emotion recognition(SER)is an important research problem in human-computer interaction *** representation and extraction of features are significant challenges in SER *** the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive *** mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based *** parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel *** learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification *** fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature *** Berlin Database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech *** efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset *** proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

关键词： decision making deep learning

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Ordered Clustering-Based Semantic Music Recommender System Using Deep Learning Selection

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computers, Materials & Continua 2025年第5期83卷 3025-3057页

作者： Weitao Ha Sheng Gang Yahya D.Navaei Abubakar S.Gezawa Yaser A.Nanehkaran School of Computer Science and Technology Weinan Normal UniversityWeinan714099China School of Information&Engineering Yancheng Teachers UniversityYancheng224002China Department of Computer and Technology Engineering Qazvin BranchIslamic Azad UniversityQazvin34199-15195Iran School of Information Engineering Sanming UniversitySanming365004China Department ofManagement Information Systems Faculty of Economics and Administrative SciencesCankayaUniversityAnkara06790Türkiye

Music recommendation systems are essential due to the vast amount of music available on streaming platforms,which can overwhelm users trying to find new tracks that match their *** systems analyze users’emotional responses,listening habits,and personal preferences to provide personalized suggestions.A significant challenge they face is the“cold start”problem,where new users have no past interactions to guide *** improve user experience,these systems aimto effectively recommendmusic even to such users by considering their listening behavior and music *** paper introduces a novel music recommendation system that combines order clustering and a convolutional neural network,utilizing user comments and rankings as ***,the system organizes users into clusters based on semantic similarity,followed by the utilization of their rating similarities as input for the convolutional neural *** network then predicts ratings for unreviewed music by ***,the system analyses user music listening behaviour and music *** popularity can help to address cold start users as ***,the proposed method recommends unreviewed music based on predicted high rankings and popularity,taking into account each user’s music listening *** proposed method combines predicted high rankings and popularity by first selecting popular unreviewedmusic that themodel predicts to have the highest ratings for each *** these,the most popular tracks are prioritized,defined by metrics such as frequency of listening across *** number of recommended tracks is aligned with each user’s typical listening *** experimental findings demonstrate that the new method outperformed other classification techniques and prior recommendation systems,yielding a mean absolute error(MAE)rate and rootmean square error(RMSE)rate of approximately 0.0017,a hit rate of 82.45%,an average normalized discounted cumulative gain

关键词： Music recommender system order clustering deep learning

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H2/H∞ Robust Observed-State Feedback Control Based on Slack LMI-LQR for LCL-Filtered Inverters

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IEEE Transactions on Industrial Electronics 2023年第5期70卷 4785-4798页

作者： Tran, Thuy Vi Kim, Kyeong-Hwa Lai, Jih-Sheng Seoul National University of Science and Technology Department of Electrical and Information Engineering Research Center for Electrical and Information Technology Seoul01811 Korea Republic of Virginia Polytechnic Institute and State University Future Energy Electronics Center Department of Electrical and Computer Engineering BlacksburgVA24061 United States

A robust controller for an LCL-filtered grid-connected inverter (GCI) is presented in this article in the context of the system model uncertainties and nonideal grid environment. Under the occurrence of exogenous inputs from parameter drifts affecting the controller and observer, the H2/H∞ approach of the linear quadratic regulator control is developed to obtain the state-feedback gain and observer gain with the system stability guarantee by means of Lyapunov stability theory. Furthermore, the use of slack-linear matrix inequalities to handle parameter-dependent Lyapunov functions in synthesizing the robust controller can achieve a less conservative condition than the conventional quadratic stability. System robustness and stability are assessed through the closed-loop pole map and the discrete-time frequency analysis approaches. Hardware experiments are conducted for the LCL-filtered GCI to validate the feasibility and effectiveness of the proposed control method. Fair comparisons with the conventional schemes are also performed to highlight the superior performance of the proposed scheme. © 1982-2012 IEEE.

关键词： Stability criteria

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Assessing Biological Responses of Saccharomyces Cerevisiae to Low-Frequency Electromagnetic Fields: Insights and Implications 15

Assessing Biological Responses of Saccharomyces Cerevisiae t...

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15th International Conference Elektro, ELEKTRO 2024

作者： Bajtos, Marek Radil, Roman Janousek, Ladislav Dang, Nhat University of Zilina Faculty of Electrical Engineering and Information Technology Department of Electromagnetic and Biomedical Engineering Zilina Slovakia University of Colorado Boulder Department of Electrical Computer and Energy Engineering BoulderCO United States

ISBN: (纸本)9798350372359

This study investigates the biological effects of Low-Frequency Electromagnetic Fields (LF-EMF) on Saccharomyces cerevisiae yeast cells, an essential model organism in biological research. To achieve controlled exposure, the exposure system ensuring uniform exposition while maintaining identical cultivation conditions for both exposed and control samples is used. To evaluate the impact of LF-EMF, yeast cell growth and proliferation were quantified using a Bürker chamber before and after exposure. Our findings reveal statistically significant differences between the exposed and control samples, suggesting a potential biological response to LF-EMF. The present research offers valuable insights into the effects of LF-EMF on biological cells, contributing to the broader understanding of electromagnetic field's impact on living organisms. Furthermore, the proposed exposure system provides a reliable platform for future bioelectromagnetic research, facilitating health risk assessments and investigations into the therapeutic potential of LF-EMF exposure. © 2024 IEEE.

关键词： Electromagnetic fields

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Intelligent Proximate Analysis of Coal Based on Near-Infrared Spectroscopy and Multioutput Deep Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1398-1410页

作者： Zou, Liang Qiao, Jiahui Yu, Xinhui Chen, Xun Lei, Meng School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China

Proximate analysis of coal indicates the moisture, ash, volatile content, and calorific value, which has been widely utilized as the basis for coal characterization. It involves heating the coal under various conditions until a constant weight is obtained. Although it is a relatively simple process that does not require expensive analytical equipment, determining these characteristics is time consuming. An alternative way for proximate analysis is spectral analysis in combination with various machine learning methods. However, most previous works analyze individual characteristics and fail to explore the relationship among them. In this study, we propose a method for proximate analysis based on near-infrared spectroscopy and a multioutput attention Unet (MOA-Unet), which can predict multiple characteristics simultaneously. First, an attention-based Unet is designed as the shared feature extraction subnetwork, including an encoder, a decoder, convolutional block attention modules, and multiscale feature fusion modules, which can improve the representation power of the U-shape network through aggregating features of shallower layers and concatenating features of deeper layers. Second, four individual subnetworks with fully connected layers, designed for four outputs, are utilized for regressing those four characteristics. We employ the gradient normalization algorithm to alleviate the gradient magnitude masking effect caused by training imbalance among different tasks. The proposedMOA-Unet is compared with classical chemometric methods on 670 coal samples from on-site *** experimental results demonstrate that the proposedmodel achieves state-of-the-art performance with correlation coefficients of 0.9015, 0.9538, 0.8986, and 0.8884, corresponding to moisture, ash, volatile content, and calorific value, respectively. Impact Statement-The proximate analysis of coal has been widely utilized as the basis for determining the rank of coal which is in connection with coa

关键词： Feature extraction

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