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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2457-2470页

作者： Kai Wang Cuicui Zhu Lili Yin Sheng Li Madina Mansurova Hao Huang School of Computer Science and Technology Xinjiang University Urumqi China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Urumqi China Xinjiang Key Laboratory of Multilingual Information Technology Urumqi China School of Engineering Institute of Science Tokyo Kanagawa Japan Department of Artificial Intelligence and Big Data Al-Farabi Kazakh National University Almaty Kazakhstan

Text-to-speech (TTS) synthetic data augmentation has been widely used in various speech processing tasks, but its effectiveness in speech separation remains understudied. In this paper, we present SpeakerAugment+ (SA+), a neural TTS-based dynamic data augmentation framework for speech separation. The SA+ framework consists of two modules: speaker module, which learns a Gaussian Mixture Model (GMM) to characterize the distribution over speaker embeddings in training data and samples unseen speaker embeddings during inference; speech module, which conditions speech synthesis using speaker embeddings with controllable speaker parameters. SA+ incorporates three augmentation techniques: speaker generation, parameter manipulation and utterance generation, enhancing speaker and utterance diversity from different perspectives. Following the SA+ framework, we design FS2-SA+ and Matcha-SA+, which are based on FastSpeech 2 and Matcha-TTS, respectively. We evaluate SA+ across multiple separation models and datasets, and the results demonstrate a substantial improvement in speech separation performance. Matcha-SA+ generates higher-quality speech and achieves better separation performance in intra-corpus tests. Conversely, FS2-SA+ supports a broad range of speaker parameter adjustments, leading to better generalization. Besides, the relationship between harmonicity and speech separation is a widely researched topic, and our findings indicate that speech lacking an explicit harmonic structure, when generated by neural TTS, can function as augmented data to improve speech separation. This research underscores the effectiveness of neural TTS-based data augmentation in speech separation tasks. We hope that our work can offer insights for future studies investigating data augmentation strategies within speech separation.

关键词： data augmentation Training Speech synthesis Speech recognition Speech processing Training data Vocoders Speech enhancement Harmonic analysis Optimization

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Digital Twin-Driven MADRL Approaches for Communication-Computing-Control Co-Optimization

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IEEE Journal on Selected Areas in Communications 2025年

作者： Yuan, Xiaoming Tian, Hansen Zhang, Xinling Du, Hongyang Zhang, Ning Huang, Kaibin Cai, Lin Northeastern University Hebei Key Laboratory of Marine Perception Network and Data Processing The Qinhuangdao Branch Campus Qinhuangdao066004 China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong999077 Hong Kong University of Windsor Department of Electrical and Computer Engineering WindsorONN9B 3P4 Canada University of Victoria Department of Electrical and Computer Engineering VictoriaBCV8W 3P6 Canada

The unpredictability of network environments, limited edge resources, and the high complexity of collaborative policies are significantly hindering the development of the Industrial Internet of Things (IIoT). These challenges are particularly pronounced in healthcare, where high-priority, delay-sensitive medical tasks and large-scale personalized services face substantial obstacles. To address these challenges, this paper proposes the Self-Attention Enhanced QMIX with Multi-Pass Multi-Task Execution (SAE-MT-QMIX) algorithm, aimed at optimizing communication and computing resource allocation as well as task offloading strategies. By leveraging Digital Twin (DT) support, the algorithm achieves collaborative optimization of communication, computing, and control within the Internet of Medical Things (IoMT), significantly enhancing the quality of service for massive personalized applications. The algorithm adopts a distributed execution and centralized training framework: the distributed execution component uses the Multi-Pass Multi-Task Deep Q-Network (MPMT-DQN) algorithm to handle the complexity of parameterized action spaces in multi-task scenarios, while the centralized training component employs the Self-Attention Enhanced QMIX (SAE-QMIX) algorithm to dynamically optimize credit assignment across multiple users. Simulation results demonstrate that SAE-MT-QMIX significantly reduces delay and energy consumption compared to baseline methods. It ensures effective optimization of communication, computing, and control in dynamic IoMT, efficiently addressing diverse demands and tasks while enhancing service quality and system adaptability. © 1983-2012 IEEE.

关键词： Industrial internet of things (IIoT)

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Spatial-Temporal Analysis of Collective Emotional Resonance in China During Global Health Crisis 25

Spatial-Temporal Analysis of Collective Emotional Resonance ...

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34th ACM Web Conference, WWW 2025

作者： Zhang, Limiao Qi, Xinyang Ma, Haiping Gao, Jie Zhang, Xingyi Hu, Yanqing Jin, Yaochu Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei China Institutes of Physical Science and Information Technology Anhui University Hefei China School of Innovation and Entrepreneurship Shandong University Qingdao China School of Computer Science and Technology Anhui University Hefei China Department of Statistics and Data Science College of Science Southern University of Science and Technology Shenzhen China Trustworthy and General AI Lab School of Engineering Westlake University Hangzhou China

ISBN: (纸本)9798400712746

The 21st century has already witnessed so many outbreaks with pandemic potential, including SARS (2002), H1N1 (2009), MERS (2012), Ebola (2014), Zika virus (2015), and the COVID-19 pandemic (2019). Using 60 million geotagged Sina Weibo tweets covering over 20 million active accounts, we investigate the collective emotional dynamics on social media in the most recent global pandemic, i.e., COVID-19. This research features two highlights: (1) It focuses on the Chinese population located in the initial epicenter of the pandemic. (2) It examines the initial year after the pandemic outbreak, a critical period where emotions were most intense due to the uncertainty and rapid developments related to the crisis. Using cross-disciplinary methods, we reveal a positive connection between online emotional resonance and geographic proximity, demonstrating a direct mapping between virtual network distances and physical spatial embedding. We propose a percolation-based index to measure the nationwide emotional resonance level with which we illustrate the significant economic impact of the global health issue. Finally, we identify a leader-follower pattern in emotional resonance fluctuations based on time-lag emotion correlations, revealing that less active regions play a crucial role in leading and responding to emotional changes. In the face of long COVID and emerging global health crises, our analysis elucidates how collective emotional resonance evolves, providing potential directions for online opinion interventions during global shocks. © 2025 Copyright held by the owner/author(s).

关键词： Economic and social effects

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Rore: robust and efficient antioxidant protein classification via a novel dimensionality reduction strategy based on learning of fewer features

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Genomics and Informatics 2024年第1期22卷 1-10页

作者： Meng, Chaolu Hou, Yongqi Zou, Quan Shi, Lei Su, Xi Ju, Ying College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Hohhot China School of Computer Science Inner Mongolia University Hohhot China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China Department of Spine Surgery Changzheng Hospital Naval Medical University Huangpu District No. 415 Fengyang Road Shanghai China Foshan Women and Children Hospital Foshan China School of Informatics Xiamen University Xiamen China

In protein identification, researchers increasingly aim to achieve efficient classification using fewer features. While many feature selection methods effectively reduce the number of model features, they often cause information loss caused by merely selecting or discarding features, which limits classifier performance. To address this issue, we present Rore, an algorithm based on a feature-dimensionality reduction strategy. By mapping the original features to a latent space, Rore retains all relevant feature information while using fewer representations of the latent features. This approach significantly preserves the original information and overcomes the information loss problem associated with previous feature selection. Through extensive experimental validation and analysis, Rore demonstrated excellent performance on an antioxidant protein dataset, achieving an accuracy of 95.88% and MCC of 91.78%, using vectors including only 15 features. The Rore algorithm is available online at http://112.124.26.17:8021/Rore. © The Author(s) 2024.

关键词： Dimensionality reduction Protein classifier Protein sequence Robust feature

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Federated reasoning LLMs: a survey

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Frontiers of computer Science 2025年第12期19卷 1912613-1912613页

作者： Shuyue WEI Yongxin TONG Zimu ZHOU Yi XU Jingkai GAO Tongyu WEI Tianran HE Weifeng LV 1State Key Laboratory of Complex & Critical Software Environment Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing School of Computer Science and Engineering Beihang University Beijing 100191 China 2Department of Data Science City University of Hong Kong Hong Kong 999077 China

Reasoning has long been regarded as a distinctive hallmark of human cognition, and recent advances in the artificial intelligence community have increasingly focused on the reasoning large language models (rLLMs). How... 详细信息

r L L M s

). However, due to strict privacy regulations, the domain-specific reasoning knowledge is often distributed across multiple data owners, limiting the

r L L M

’s ability to fully leverage such valuable resources. In this context, federated learning (FL) has gained increasing attention in both the academia and industry as a promising privacy-preserving paradigm for addressing the challenges in the data-efficient training of

r L L M s

. In this paper, we conduct a comprehensive survey on fe

关键词： federated learning reasoning LLMs fine tuning retrieval-augmented generation

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Evolutionary Multiobjective Feature Selection Assisted by Unselected Features

Evolutionary Multiobjective Feature Selection Assisted by Un...

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Congress on Evolutionary Computation

作者： Xuan Duan Songbai Liu Junkai Ji Lingjie Li Qiuzhen Lin Kay Chen Tan College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

To enhance the generalization of multi-objective feature selection (MOFS) in classification, this paper proposes an evolutionary multitasking algorithm, diverging from previous approaches that exclusively target selected features. The algorithm integrates information from both selected and unselected features, introducing a novel objective to minimize the accuracy of unselected features. This objective, combined with the goal of minimizing classification errors for selected features, forms an auxiliary MOFS task. The paper presents a dual-population evolutionary multitasking framework that synergizes the main MOFS task with the auxiliary task. A knowledge transfer mechanism, based on accuracy preferences, seamlessly shares insights from the auxiliary to the main task, aiming to identify improved Pareto feature subsets. Empirical results demonstrate the superior performance of several state-of-the-art multi-objective algorithms within this framework, highlighting significant improvements across diverse datasets.

关键词： Accuracy Evolutionary computation Multitasking Feature extraction Classification algorithms Task analysis Knowledge transfer

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EvoSampling: A Granular Ball-based Evolutionary Hybrid Sampling with Knowledge Transfer for Imbalanced Learning

arXiv

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arXiv 2024年

作者： Pei, Wenbin Dai, Ruohao Xue, Bing Zhang, Mengjie Zhang, Qiang Cheung, Yiu-Ming Xia, Shuyin The School of Computer Science and Technology Dalian University of Technology Dalian116024 China Ministry of Education Dalian116024 China The School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington6140 New Zealand The Department of Computer Science Hong Kong Baptist University Hong Kong The Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China

Class imbalance would lead to biased classifiers that favor the majority class and disadvantage the minority class. Unfortunately, from a practical perspective, the minority class is of importance in many real-life applications. Hybrid sampling methods address this by oversampling the minority class to increase the number of its instances, followed by undersampling to remove low-quality instances. However, most existing sampling methods face difficulties in generating diverse high-quality instances and often fail to remove noise or low-quality instances on a larger scale effectively. This paper therefore proposes an evolutionary multi-granularity hybrid sampling method, called EvoSampling. During the oversampling process, genetic programming (GP) is used with multi-task learning to effectively and efficiently generate diverse high-quality instances. During the undersampling process, we develop a granular ball-based undersampling method that removes noise in a multi-granular fashion, thereby enhancing data quality. Experiments on 20 imbalanced datasets demonstrate that EvoSampling effectively enhances the performance of various classification algorithms by providing better datasets than existing sampling methods. Besides, ablation studies further indicate that allowing knowledge transfer accelerates the GP's evolutionary learning process. Copyright © 2024, The Authors. All rights reserved.

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A Deletable and Modifiable Blockchain Scheme Based on Record Verification Trees and the Multisignature Mechanism

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computer Modeling in engineering & Sciences 2021年第7期128卷 223-245页

作者： Daojun Han Jinyu Chen Lei Zhang Yatian Shen Yihua Gao Xueheng Wang Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng47500China Institute of Data and Knowledge Engineering Henan UniversityKaifeng47500China School of Computer and Information Engineering Henan UniversityKaifeng47500China Department of Network and Finance Agricultural Bank of China Limited Xuchang BranchXuchang461000China School of Software Pingdingshan UniversityPingdingshan467000China Department of Network and Finance Agricultural Bank of China Limited Nanyang BranchNanyang473000China

As one of the most valuable technologies,blockchains have received extensive attention from researchers and industry circles and are widely applied in various ***,data on a blockchain cannot be *** a result,it is impossible to clean invalid and sensitive data and correct erroneous ***,to a certain extent,hinders the application of blockchains in supply chains and Internet of *** address this problem,this study presents a deletable and modifiable blockchain scheme(DMBlockChain)based on record verification trees(RVTrees)and the multisignature scheme.(1)In this scheme,an RVTree structure is designed and added to the block *** RVTree can not only ensure that a record is true and valid but,owing to its unique binary structure,also verify whether modification and deletion requests are valid.(2)In DMBlockChain,the multisignature mechanism is also *** mechanism requires the stakeholders’signatures for each modification or deletion request and thus ensures that a record will not be modified arbitrarily.A user’s request is deemed valid only if it is dually verified by the RVTree and the multisignature *** analysis finds that DMBlockChain can provide a secure and valid means for modifying and deleting records in a block while ensuring the integrity of the block and that DMBlockChain can effectively save space in some scenarios that require frequent records modification.

关键词： Blockchain record verification trees multisignature DMBlockChain

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Target-Oriented WiFi Sensing for Respiratory Healthcare: from Indiscriminate Perception to In-Area Sensing

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IEEE Network 2024年

作者： Wang, Meng Huang, Jinyang Zhang, Xiang Liu, Zhi Li, Meng Zhao, Peng Yan, Huan Sun, Xiao Dong, Mianxiong Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligence Machine School of Computer and Information Hefei230601 China University of Science and Technology of China CAS Key Laboratory of Electromagnetic Space Information Hefei230026 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan Guizhou Normal University School of Big Data and Computer Science Guiyang550025 China Muroran Institute of Technology Department of Sciences and Informatics 0508585 Japan

Driven by the vision of integrated sensing and communication (ISAC) toward 6G technology, the WiFi-based respiration sensing approach has emerged as a highly competitive candidate for advanced healthcare services. Nevertheless, the indiscriminate perception of all the moving objects within the sensing area raises challenges for system stability and robustness for real-world deployment, especially for the susceptibility to motion interference from other people. Meanwhile, thanks to the emerging in-area wireless sensing technologies, i.e., beamforming, the dynamic environment tolerance of these technologies gives crucial opportunities for target-oriented WiFi-based healthcare services. This article discusses the compelling intersection of in-area WiFi sensing and the future of target-oriented intelligent respiratory healthcare network services. We first present a novel WiFi-based healthcare-assisting framework tailored for inter-user motion interference-tolerable (IMIT) sensing. The detailed components of low-level signal preprocessing, target-direction signal extraction, and healthcare-related services are discussed. By utilizing CSI beamforming technology, which adjusts a directional beam toward the desired direction and adaptively places beam nulls in noisy directions, we can mitigate motion interference and achieve target-oriented healthcare sensing. Finally, we discuss open challenges and potential solutions for target-oriented WiFi healthcare sensing. © 1986-2012 IEEE.

关键词： Wireless local area networks (WLAN)

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Trim21 depletion alleviates bone loss in osteoporosis via activation of YAP1/β-catenin signaling

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Bone Research 2023年第4期11卷 819-833页

作者： Ri-Xu Liu Rong-He Gu Zhi-Peng Li Zhi-Quan Hao Qin-Xiao Hu Zhen-Yan Li Xiao-Gang Wang Wang Tang Xiao-He Wang Yu-Kai Zeng Zhen-Wei Li Qiu Dong Xiao-Feng Zhu Di Chen Ke-Wei Zhao Rong-Hua Zhang Zhen-Gang Zha Huan-Tian Zhang Department of Bone and Joint Surgery the First Affiliated Hospital of Jinan UniversityKey Laboratory of Regenerative Medicine of Ministry of EducationJinan UniversityGuangzhou 510630 GuangdongChina Department of Orthopedic and Spine Surgery The First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou 510120 GuangdongChina School of Basic Medical Sciences of Guangxi Medical University the Fifth Affiliated Hospital of Guangxi Medical UniversityNanning 530022 GuangxiChina Key Laboratory of Big Data-Based Precision Medicine School of Engineering MedicineBeihang University100191 BeijingChina Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization College of PharmacyJinan UniversityGuangzhou 510630 GuangdongChina Research Center for Computer-aided Drug Discovery Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences518005 ShenzhenChina Guangzhou Key Laboratory of Chinese Medicine Research on Prevention and Treatment of Osteoporosis the Third Affiliated Hospital of Guangzhou University of Chinese Medicine510375 GuangzhouChina

Despite the diverse roles of tripartite motif(Trim)-containing proteins in the regulation of autophagy,the innate immune response,and cell differentiation,their roles in skeletal diseases are largely *** recently demonstrated that Trim21 plays a crucial role in regulating osteoblast(OB)differentiation in ***,how Trim21 contributes to skeletal degenerative disorders,including osteoporosis,remains ***,human and mouse bone specimens were evaluated,and the results showed that Trim21 expression was significantly elevated in bone tissues obtained from osteoporosis ***,we found that global knockout of the Trim21 gene(KO,Trim2^(1-/-))resulted in higher bone mass compared to that of the control *** further demonstrated that loss of Trim21 promoted bone formation by enhancing the osteogenic differentiation of bone marrow mesenchymal stem cells(BMSCs)and elevating the activity of OBs;moreover,Trim21 depletion suppressed osteoclast(OC)formation of RAW264.7 *** addition,the differentiation of OCs from bone marrow-derived macrophages(BMMs)isolated from Trim21^(-/-)and Ctsk-cre;Trim21^(f/f)mice was largely compromised compared to that of the littermate control ***,YAP1/β-catenin signaling was identified and demonstrated to be required for the Trim21-mediated osteogenic differentiation of *** importantly,the loss of Trim21 prevented ovariectomy(OVX)-and lipopolysaccharide(LPS)-induced bone loss in vivo by orchestrating the coupling of OBs and OCs through YAP1 *** current study demonstrated that Trim21 is crucial for regulating OB-mediated bone formation and OC-mediated bone resorption,thereby providing a basis for exploring Trim21 as a novel dual-targeting approach for treating osteoporosis and pathological bone loss.

关键词： YAP1 osteoporosis depletion

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