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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： S Priyadharshini G. Naveen Sundar D. Narmadha Senthil Kumar Thangavel Adeyemi Abel Ajibesin Selvanayaki Kolandapalayam Shanmugam Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa Department of Mathematics and Computer Science Ashland University USA

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future.

关键词： Training Emotion recognition Accuracy Computational modeling Speech recognition Speech enhancement Predictive models Feature extraction Data models Convolutional neural networks

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Joint Automatic Speech Recognition And Structure Learning For Better Speech Understanding

arXiv

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arXiv 2025年

作者： Hu, Jiliang Li, Zuchao Shen, Mengjia Ai, Haojun Li, Sheng Zhang, Jun Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China National Institute of Information and Communications Technology Japan

Spoken language understanding (SLU) is a structure prediction task in the field of speech. Recently, many works on SLU that treat it as a sequence-to-sequence task have achieved great success. However, This method is not suitable for simultaneous speech recognition and understanding. In this paper, we propose a joint speech recognition and structure learning framework (JSRSL), an end-to-end SLU model based on span, which can accurately transcribe speech and extract structured content simultaneously. We conduct experiments on name entity recognition and intent classification using the Chinese dataset AISHELL-NER and the English dataset SLURP. The results show that our proposed method not only outperforms the traditional sequence-to-sequence method in both transcription and extraction capabilities but also achieves state-of-the-art performance on the two datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Speech recognition

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Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks

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IEEE Network 2024年

作者： He, Jiayi Luo, Xiaofeng Kang, Jiawen Du, Hongyang Xiong, Zehui Chen, Ci Niyato, Dusit Shen, Xuemin Guangdong University of Technology School of Automation Guangzhou510006 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore487372 Singapore University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Semantic Communication (SemCom) plays a pivotal role in 6G networks, offering a viable solution for future efficient communication. Through nonlinear mapping of semantic representations, Deep Learning (DL)-based semantic communications further enhance communication efficiency. However, the vulnerability of DL models to security threats, such as adversarial attacks, poses significant challenges for practical applications of SemCom systems. These vulnerabilities allow attackers to tamper with messages and eavesdrop on private information, especially in wireless communication scenarios. Although existing defenses attempt to address specific threats, they often fail to simultaneously handle multiple heterogeneous attacks. To overcome this limitation, we propose a novel Mixture-of-Experts (MoE)-based SemCom system, comprising a gating network and multiple experts, each specializing in different security challenges. The gating network adaptively selects suitable experts to counter heterogeneous attacks based on user-defined security requirements. Multiple experts collaborate to accomplish semantic communication tasks while meeting the security requirements of users. A case study in vehicular networks demonstrates the efficacy of the MoE-based SemCom system. Simulation results show that the proposed MoE-based SemCom system effectively mitigates concurrent heterogeneous attacks, with minimal impact on downstream task accuracy. © 1986-2012 IEEE.

关键词： Vehicular ad hoc networks

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Joint Trajectory Optimization and Power Control for Cognitive UAV-Assisted Secure Communications

Joint Trajectory Optimization and Power Control for Cognitiv...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Can Wang Helin Yang Liang Xiao Huabing Lu Zehui Xiong Department of Information & Communication Engineering Xiamen University Xiamen China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen University Xiamen China School of Information & Communication Engineering Dalian University of Technology Dalian China Pillar of Information System Technology & Design Singapore University of Technology and Design Singapore

Cognitive unmanned aerial vehicle (UAV) communication systems combine benefits of both the cognitive radio and UAV, which improves the spectral efficiency and communication coverage area. However, high-quality air-ground channel links maybe more vulnerable to potential eavesdropping or jamming attacks. Thus, this paper proposes a secure transmission approach assisted by deploying a cooperative UAV to transmit artificial noise to jam an active eavesdropper, in order to maximize the system secrecy rate under the quality of service (QoS) requirement of a primary device. Specifically, we jointly optimize the flight trajectory and transmission power of the cooperative jammer to maximize the system's secrecy rate under strict constraints. To achieve this, we convert the non-convex problem into an approximately convex problem using the block coordinate descent algorithm and successive convex approximation method. Simulation results show that compared to existing algorithms, the proposed algorithm in this study can significantly improve the system's secrecy rate.

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Joint Automatic Speech Recognition And Structure Learning For Better Speech Understanding

Joint Automatic Speech Recognition And Structure Learning Fo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiliang Hu Zuchao Li Mengjia Shen Haojun Ai Sheng Li Jun Zhang Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China National Institute of Information and Communications Technology Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

关键词： Accuracy Intent recognition Speech recognition Signal processing Acoustics Speech processing Automatic speech recognition

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Security Enhancing for IRS-Aided NOMA Networks via Constructive Interference Precoding

Security Enhancing for IRS-Aided NOMA Networks via Construct...

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International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (MAPE)

作者： Jingying Bao Yang Cao Xiaoqi Qin Lexi Xu Nan Zhao Dusit Niyato School of Information and Communication Engineering Dalian University of Technology Dalian China Engineering Systems and Design Pillar Singapore University of Technology and Design Singapore State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Research Institute China United Network Communications Corporation Beijing China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331533922

ISBN: (纸本)9798331533939

In this paper, we propose a constructive interference precoding (CIP) enabled secure intelligent reflecting surface (IRS)-non-orthogonal multiple access (NOMA) scheme, lever-aging both the inter-user interference and artificial noise to boost the legitimate transmission of far user, while inducing the eavesdropper to decode the deceptive information. Based on this CIP-NOMA scheme, we minimize the transmit power under the perfect channel state information (CSI), subject to the CIP constraint for the far user and eavesdropper, while guaranteeing the quality of service and successive interference cancellation for the near user and the IRS unit modulus constraint. To handle this non-convex problem, an alternating optimization algorithm is proposed. Specifically, by alternately optimizing the precoding vectors at the base station and the IRS reflecting matrix via the successive convex approximation and the penalty-based algorithm, respectively, a reliable solution can be obtained. Simulation results demonstrate that the proposed scheme can achieve better security performance with less energy consumption compared to the conventional NOMA.

关键词： Microwave antennas Wireless communication NOMA Precoding Simulation Noise Approximation algorithms Vectors Security Reliability

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Building ResilientWeb 3.0 with Quantum Information Technologies and Blockchain: An Ambilateral View

arXiv

引用

arXiv 2023年

作者： Ren, Xiaoxu Xu, Minrui Niyato, Dusit Kang, Jiawen Xiong, Zehui Qiu, Chao Wang, Xiaofei College of Intelligence and Computing Tianjin University Tianjin300072 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore

Web 3.0 pursues the establishment of decentralized ecosystems based on blockchain technologies to drive the digital transformation of physical commerce and governance. Through consensus algorithms and smart contracts in blockchain, which are based on cryptography technologies, digital identity, digital asset management, decentralized autonomous organization, and decentralized finance are realized for secure and transparent digital economy services in Web 3.0 for promoting the integration of digital and physical economies. With the rapid realization of quantum devices, Web 3.0 is being developed in parallel with the deployment of quantum cloud computing and quantum Internet. In this regard, quantum computing first disrupts the original cryptographic systems that protect data security while reshaping modern cryptography with the advantages of quantum computing and communication. Therefore, this survey provides a comprehensive overview of blockchain-based Web 3.0 and its quantum and post-quantum enhancement from the ambilateral perspective. On the one hand, some post-quantum migration methods, and anti-quantum signatures offer potential ways to achieve unforgeable security under quantum attack for the internal technologies of blockchain. On the other hand, some quantum/post-quantum encryption and verification algorithms improve the external performance of the blockchain, enabling a decentralized, valuable, secure blockchain system. Finally, we discuss the future directions toward developing a provable secure decentralized digital ecosystem. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Digitalisation and Green Strategies: A Systematic Review of the Textile, Apparel and Fashion Industries

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Circular Economy and Sustainability 2025年 1-53页

作者： Orisadare, Emmanuel Ayo Achukwu, Okechukwu Emmanuel Ogunyemi, Abiodun Afolayan Adedeji, Deborah Oluwayemisi Diyaolu, Idowu Jamiu Ugwu, Eunice Ifenyinwa Oluwatope, Ayodeji Oludola Bakare, Kudirat Oyebisi Awoyelu, Iyabo Olukemi Department of Information and Communication Technology University of Ilesa Osun State Ilesa Nigeria School of Computing Engineering and Technology Sir Ian Wood Building Robert Gordon University Garthdee Road Aberdeen AB10 7GJ United Kingdom School of Digital Technologies Tallinn University Narva mnt 29 Tallinn 10120 Estonia Department of Clothing and Textile Design Faculty of Sciences University of Ilesa Ilesa Nigeria Department of Family Nutrition and Consumer Sciences Obafemi Awolowo University Osun State Ile-Ife Nigeria Department of Home Economics and Hospitality Management Education Faculty of Vocational and Technical Education University of Nigeria Enugu State Nsukka Nigeria Department of Computer Science and Engineering Obafemi Awolowo University Osun State Ile-Ife Nigeria

The textile, apparel, and fashion (TAF) industries are vital to national economies, providing employment and fostering global economic development. On the other hand, TAF industries contribute to approximately 20% of global pollution. The rise of Industry 4.0 (I4.0) technologies—including the Internet of Things (IoT), Artificial Intelligence (AI), and robotics—aims to enhance production efficiency and resource management. However, this transition brings several challenges, such as potential disruptions from automation, concerns regarding energy efficiency, and escalating sustainability demands. In response, Industry 5.0 (I5.0) emphasises the integration of human-centric and sustainable practices, encouraging collaboration between humans and machines. While some existing studies highlight the potential of these technologies as enablers of the circular economy (CE), they often fall short of comprehensively linking these technologies to specific operational and strategic elements of CE. Furthermore, they do not sufficiently address how these digital tools fit within a broader sustainability framework in the context of CE principles. This review study aims to explore how digital transformation through I4.0 and I5.0 technologies can enhance circular economy practices in the TAF industries while alleviating the environmental and social challenges associated with industrial growth. The study examined 42 peer-reviewed studies from 2013 to 2023, employing a descriptive and theoretical literature review methodology to analyse the strategies, impacts, and challenges of digitalisation and green transition in the TAF industries. Findings reveal a significant disparity between firms in the Global North, which focus on technology advancement, and those in the Global South, facing implementation barriers. The study advocates for an approach that contextualises the integration of I4.0 and I5.0 technologies, ensuring that sustainable production practices align with regional needs. ©

关键词： Circular economy Digital transformation Green strategies Industry 4.0 Industry 5.0 Textile, apparel and fashion industries

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Evolutionary Competitive Multiobjective Multitasking: One-Pass Optimization of Heterogeneous Pareto Solutions

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IEEE Transactions on Evolutionary Computation 2024年

作者： Li, Yanchi Wu, Xinyi Gong, Wenyin Xu, Meng Wang, Yubo Gu, Qiong China University of Geosciences School of Computer Wuhan430074 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Hubei University of Arts and Science School of Computer Engineering Xiangyang441053 China Hubei University of Arts and Science Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle Xiangyang441053 China

Competitive multiobjective multitask optimization (CMO-MTO) problems involve multiple tasks with comparable objectives but heterogeneous decision variables. The final Pareto front in CMO-MTO consists of multiple subsets corresponding to different tasks. Since the Pareto front subset of one task may be dominated by that of another, competition arises among tasks. Additionally, there may be exploitable similarities among tasks that evolutionary multitasking methods can leverage. For a comprehensive study of CMO-MTO, we construct 12 benchmark CMO-MTO problems with varied competitive relationships and inter-task similarities. To effectively solve CMO-MTO problems, we propose a reference vector contribution-based multitask evolutionary algorithm (RVC-MTEA). RVC-MTEA facilitates both global and local knowledge transfer based on vector contributions and integrates global archives to gather non-dominated solutions across multiple competitive tasks. Comparative results with four popular single-task and six state-of-the-art multitask evolutionary algorithms demonstrate the efficacy of RVC-MTEA. Finally, we apply RVC-MTEA to several real-world applications, showcasing the potential of CMO-MTO in practical decision-making scenarios. © 1997-2012 IEEE.

关键词： Competition

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Estimation of Lumbar Spine Loading of Low Back Pain Participant During Lifting Using an Open Source Musculoskeletal Model

Estimation of Lumbar Spine Loading of Low Back Pain Particip...

引用

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Preethi Manoharan Adrian Pranata Kwong Ming Tse Rifai Chai School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn VIC Australia Department of Nursing and Allied Health Swinburne University of Technology Hawthorn VIC Australia Department of Mechanical Engineering and Product Design Engineering Swinburne University of Technology Hawthorn VIC Australia

Biomechanical modeling of spinal load during lifting in OpenSim has the potential for rehabilitation and clinical assessment. In the literature, several spinal models have been developed and validated with movement data from healthy individuals. Although these models are valid for predicting spinal load in healthy individuals, it is unknown whether these models are applicable for people with chronic low back pain (CLBP). This study aims to compare the application of the lifting full body (LFB) model between a healthy participant and a participant with CLBP. The participants performed the lifting activity, and the motion capture data was used to analyze how an open-source model predicts the loading of the lumbar spine. Peak spinal loading at L5/S1 joint was estimated as 3.9 kN for the healthy participant and 3.1 kN for the CLBP participant. The results suggest that a longer duration of lift and lower lumbar range of motion reduces lumbar spinal loading.

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