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6th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2021

作者： Wu, Xiaojing Xue, Xingsi Hu, Wenyu Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology 33 Xuefu South Road Fuzhou Fujian350118 China Intelligent Information Processing Research Center Fujian University of Technology Fuzhou Fujian350118 China Institute of Artificial Intelligence Fujian University of Technology Fuzhou Fujian350118 China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou Fujian350118 China School of Information Science and Engineering Fujian University of Technology Fuzhou Fujian350118 China

ISBN: (纸本)9783030697167

The ontology matching technique aims at establishing the correspondence between two ontologies’ entities to solve their heterogeneity problem. In the matching process, the quality of the single matcher’s alignment can not be ensured. Usually, multiple matches are required to work together to increase the evidence of potential matches or mismatches. Selecting, combining, and adjusting these ontology matches to obtain high-quality ontology comparisons is one of the main challenges in ontology matching. This paper proposes an Argumentation Framework (A.F.) based on the ontology extracting technique to address this challenge. First, the proposed approach obtains the alignments obtained by different ontology matches. It extracts the final alignment by debating the disputed entity correspondences with A.F. The experiment is conducted on the Bibliographic track provided by Ontology Alignment Evaluation Initiative (OAEI), and the statistical comparison with the state-of-the-art ontology matching techniques shows the effectiveness of the proposed approach. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Extraction

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Adaptive Fine-grained Access Control Method in Social Internet of Things

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International Journal of Network Security 2021年第1期23卷 42-48页

作者： Zhang, Hongbin Ma, Pengcheng Liu, Bin School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang China Hebei Key Laboratory of Network and Information Security Hebei Normal China School of Economics and Management Hebei University of Science China Technology Research Center of Big Data and Social Computing Hebei University of Science China

Social Internet of Things (SloT), as a new carrier of integration of social and Internet of Things, applies the research results of social networks from different aspects of the Internet of Things. Different types of connected intelligent objects interact socially, compared with random data access between them, access control technology is more stringent. This paper integrates social attributes into attributebased access control of Internet of Things, initializes relational attribute tags, and labels social interest attributes for different objects, then quantifes tag similarity and implements initial access control authorization, integrates social attributes into game theory to dynamically adjust access control policies, so the adaptive finegrained division of access control under the Social Internet of Things is effectively realized. The experimental results show that our method can not only effectively carry out initial authorization according to tag similarity, but also further adaptively adjust the permission policy according to social attributes, and further meet the finegrained partition requirements of access control, which is ensure the effective implementation of access control under the Social Internet of things. © 2021. All Rights Reserved.

关键词： Game theory

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Dual-path transformer network: Direct context-aware modeling for end-to-end monaural speech separation

arXiv

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arXiv 2020年

作者： Chen, Jingjing Mao, Qirong Liu, Dong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of big data ubiquitous perception and intelligent agriculture applications Zhenjiang China

The dominant speech separation models are based on complex recurrent or convolution neural network that model speech sequences indirectly conditioning on context, such as passing information through many intermediate states in recurrent neural network, leading to suboptimal separation performance. In this paper, we propose a dual-path transformer network (DPTNet) for end-to-end speech separation, which introduces direct context-awareness in the modeling for speech sequences. By introduces a improved transformer, elements in speech sequences can interact directly, which enables DPTNet can model for the speech sequences with direct context-awareness. The improved transformer in our approach learns the order information of the speech sequences without positional encodings by incorporating a recurrent neural network into the original transformer. In addition, the structure of dual paths makes our model efficient for extremely long speech sequence modeling. Extensive experiments on benchmark datasets show that our approach outperforms the current state-of-the-arts (20.6 dB SDR on the public WSj0-2mix data corpus). Copyright © 2020, The Authors. All rights reserved.

关键词： Separation

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Synchronization of Multi-Weighted Complex Networks with Mixed Variable Delays and Uncertainties Via Impulsive Pinning Control

SSRN

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SSRN 2023年

作者： Fan, Hongguang Shi, Kaibo Wen, Hui Zhao, Yi College of Computer Chengdu University Chengdu610106 China Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province Chengdu University Chengdu610106 China School of Electronic Information and Electrical Engineering Chengdu University Chengdu610106 China New Engineering Industry College Putian University Fujian Putian351100 China Engineering Research Center of Big Data Application in Private Health Medicine Fujian Province University Fujian Putian351100 China College of Mathematical and Statistical Shenzhen University Shenzhen518060 China

In this paper, we examine the synchronization problem in nonlinearly-coupled multi-weighted complex networks that contain uncertainties and varying delays. The complexities of these networks arise from the presence of both internal time-varying delays and multiple coupling time-varying delays. To address this challenge, we employ impulsive pinning control schemes and incorporate the impulsive pinning control ratio in the construction of the Lyapunov function, which allows us to determine the optimal number of pinned nodes at each pulse instant. Our analysis, based on stability theory, provides synchronization criteria that are strongly dependent on the multi-weights and time delays present in the network. Finally, we present numerical simulations of a delayed complex network to demonstrate the validity and efficacy of our derived theoretical results. © 2023, The Authors. All rights reserved.

关键词： Complex networks

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A Tutorial on Movable Antennas for Wireless Networks

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IEEE Communications Surveys and Tutorials 2025年

作者： Zhu, Lipeng Ma, Wenyan Mei, Weidong Zeng, Yong Wu, Qingqing Ning, Boyu Xiao, Zhenyu Shao, Xiaodan Zhang, Jun Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore National Key Laboratory of Wireless Communications Chengdu611731 China Southeast University National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Beihang University School of Electronic and Information Engineering Beijing100191 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada Beijing Institute of Technology State Key Laboratory of CNS/ATM Miit Key Laboratory of Complex-field Intelligent Sensing Beijing100081 China The Chinese University of HongKong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

Movable antenna (MA) has been recognized as a promising technology to enhance the performance of wireless communication and sensing by enabling antenna movement. Such a significant paradigm shift from conventional fixed antennas (FAs) to MAs offers tremendous new opportunities towards realizing more versatile, adaptive and efficient next-generation wireless networks such as 6G. In this paper, we provide a comprehensive tutorial on the fundamentals and advancements in the area of MA-empowered wireless networks. First, we overview the historical development and contemporary applications of MA technologies. Next, to characterize the continuous variation in wireless channels with respect to antenna position and/or orientation, we present new field-response channel models tailored for MAs, which are applicable to narrowband and wideband systems as well as far-field and near-field propagation conditions. Subsequently, we review the state-of-the-art architectures for implementing MAs and discuss their practical constraints. A general optimization framework is then formulated to fully exploit the spatial degrees of freedom (DoFs) in antenna movement for performance enhancement in wireless systems. In particular, we delve into two major design issues for MA systems. First, we address the intricate antenna movement optimization problem for various communication and/or sensing systems to maximize the performance gains achievable by MAs. Second, we deal with the challenging channel acquisition issue in MA systems for reconstructing the channel mapping between arbitrary antenna positions inside the transmitter and receiver regions. Moreover, we show existing prototypes developed for MA-aided communication/sensing and the experimental results based on them. Finally, the extension of MA design to other wireless systems and its synergy with other emerging wireless technologies are discussed. We also highlight promising research directions in this area to inspire future investigatio

关键词： Antenna arrays

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Radiomics based on dual-energy CT for noninvasive prediction of cervical lymph node metastases in patients with nasopharyngeal carcinoma

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Radiography 2025年第4期31卷 102989-102989页

作者： Li, L. Yang, D. Wu, Y. Sun, R. Qin, Y. Kang, M. Deng, X. Bu, M. Li, Z. Zeng, Z. Zeng, X. Jiang, M. Chen, B.T. Department of Radiology First Affiliated Hospital of Guangxi Medical University Guangxi Nanning 530021 China Department of Radiology Guizhou Provincial People Hospital No.83 East Zhongshan Road Nanming District Guizhou Province Guiyang 550000 China Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University No. 2870 Huaxi Avenue South Guizhou Guiyang 550025 China Department of Nuclear Medicine First Affiliated Hospital of Guangxi Medical University Guangxi Nanning 530021 China Department of Radiation Oncology First Affiliated Hospital of Guangxi Medical University Guangxi Nanning 530021 China Department of Diagnostic Radiology City of Hope National Medical Center Duarte CA United States

Introduction: To develop and validate a machine learning model based on dual-energy computed tomography (DECT) for predicting cervical lymph node metastases (CLNM) in patients diagnosed with nasopharyngeal carcinoma (NPC). Methods: This prospective single-center study enrolled patients with NPC and the study assessment included both DECT and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). Radiomics features were extracted from each region of interest (ROI) for cervical lymph nodes using arterial and venous phase images at 100 keV and 150 keV, either individually as non-fusion models or combined as fusion models on the DECT images. The performance of the random forest (RF) models, combined with radiomics features, was evaluated by area under the receiver operating characteristic curve (AUC) analysis. DeLong's test was employed to compare model performances, while decision curve analysis (DCA) assessed the clinical utility of the predictive models. Results: Sixty-six patients with NPC were included for analysis, which was divided into a training set (n = 42) and a validation set (n = 22). A total of 13 radiomic models were constructed (4 non-fusion models and 9 fusion models). In the non-fusion models, when the threshold value exceeded 0.4, the venous phase at 100 keV (V100) (AUC, 0.9667;95 % confidence interval [95 % CI], 0.9363–0.9901) model exhibited a higher net benefit than other non-fusion models. The V100 + V150 fusion model achieved the best performance, with an AUC of 0.9697 (95 % CI, 0.9393–0.9907). Conclusion: DECT-based radiomics effectively diagnosed CLNM in patients with NPC and may potentially be a valuable tool for clinical decision-making. Implications for practice: This study improved pre-operative evaluation, treatment strategy selection, and prognostic evaluation for patients with nasopharyngeal carcinoma by combining DECT and radiomics to predict cervical lymph node status prior to treatment. © 2025 The Co

关键词： Cervical lymph node metastases (CLNM) Dual-energy computed tomography (DECT) Machine learning model Nasopharyngeal carcinoma (NPC) Radiomics

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Phase transitions in mixed traffic streams comprising HDVsand CAVs through the implementation of delayed control mechanisms to mitigate traffic jerk

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Chaos, Solitons & Fractals 2025年 199卷

作者： Tong Zhou Yudan Tian Meilan Zhu Guanghan Peng Darong Huang School of Big Data and Internet of Things Chongqing Vocational Institute of Engineering Chongqing 402260 China College of Physical Science and Technology Guangxi Normal University Guilin 541004 China Engineering Research Center of Autonomous Unmanned System Technology of Ministry of Education Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology and the School of Artificial Intelligence Anhui University Hefei 230601 China

Due to pedestrian traffic and frequent lane changes, rapid acceleration and deceleration of vehicles often occur, resulting in the traffic jerk phenomenon. This issue is particularly pronounced in mixed fleets of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs), posing significant safety hazards. To address this, we propose a heterogeneous lattice model incorporating delayed control to mitigate traffic jerk in environments involving both CAVs and HDVs. Linear stability analysis reveals that traffic jerk adversely affects the stability of heterogeneous vehicles, whereas delayed control mitigates this negative impact. Additionally, nonlinear analysis is conducted to derive the equation related to delayed control aimed at countering traffic jerk. Finally, through simulations examining density, limit cycles, and spectral analysis in scenarios involving CAVs and HDVs, it is demonstrated that delayed control effectively resists the detrimental effects of traffic jerk on heterogeneous vehicles.

关键词： Health hazards

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Correction to: Core sample consensus method for two-view correspondence matching

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Multimedia Tools and Applications 2024年第8期83卷 24631-24631页

作者： Ding, Xintao Li, Boquan Zhou, Wen Zhao, Cheng School of Computer and Information Anhui Normal University Wuhu China Anhui Province Key Laboratory of Network and Information Security Wuhu China Anhui Engineering Research Center of Medical Big Data Intelligent System Wuhu China School of Mathematics and Statistics Anhui Normal University Wuhu China

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Continual learning via inter-task synaptic mapping

arXiv

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arXiv 2021年

作者： Fubing, Mao Weiwei, Weng Pratama, Mahardhika Kien Yee, Edward Yapp National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab. Cluster and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Singapore Institute of Manufacturing Technology A*Star Singapore Singapore

Learning from streaming tasks leads a model to catastrophically erase unique experiences it absorbs from previous episodes. While regularization techniques such as LWF, SI, EWC have proven themselves as an effective avenue to overcome this issue by constraining important parameters of old tasks from changing when accepting new concepts, these approaches do not exploit common information of each task which can be shared to existing neurons. As a result, they do not scale well to large-scale problems since the parameter importance variables quickly explode. An Inter-Task Synaptic Mapping (ISYANA) is proposed here to underpin knowledge retention for continual learning. ISYANA combines task-to-neuron relationship as well as concept-to-concept relationship such that it prevents a neuron to embrace distinct concepts while merely accepting relevant concept. Numerical study in the benchmark continual learning problems has been carried out followed by comparison against prominent continual learning algorithms. ISYANA exhibits competitive performance compared to state of the arts. Codes of ISYANA is made available in https://***/ContinualAL/ISYANAKBS. © 2021, CC BY-NC-SA.

关键词： Neurons

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TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Xia, Yuanjun Liu, Yining Chen, Jingxue Liang, Yangfan Khan, Fazlullah Alturki, Ryan Wang, Xiaopei Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software School of Computer and Information Security Guilin541004 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325027 China University of Technology School of Mathematical and Physical Sciences SydneyNSW2007 Australia Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China Umm AI-Qura University Makkah Department of Software Engineering College of Computing Saudi Arabia Riverside Department of Computer Science and Engineering University of California CA92521 United States

Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL's privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local models or manipulate the central server (CS) to deliver falsified global models. Current privacy-preserving approaches, primarily based on the FedAvg algorithm, fail to optimize training efficiency for non-independent and identically distributed (non-IID) data. This article proposes training-efficient and verifiable aggregation (TEVA) for FL to resolve these issues. This scheme combines threshold Paillier homomorphic encryption (TPHE), verifiable aggregation, and an optimized double momentum update mechanism (OdMum). TEVA not only leverages TPHE to protect the privacy of local models but also ensures the integrity of the global model through a verifiable aggregation mechanism. Additionally, TEVA integrates the OdMum algorithm to effectively address the challenges posed by non-IID data, promoting rapid model convergence and significantly enhancing overall training efficiency. Security analysis indicates that TEVA meets the requirements for privacy protection. Extensive experimental results demonstrate that TEVA can accelerate model convergence while incurring lower computational and communication overheads. © 1975-2011 IEEE.

关键词： Differential privacy

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