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Combining permissioned blockchain and Bayesian best-worst method for transparent supplier selection in supply chain management

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Science China(Technological Sciences) 2024年第8期67卷 2579-2593页

作者： LIU JiaJun ZHANG Jie LENG JieWu Shanghai Engineering Research Center of Industrial Big Data and Intelligent System Institute of Artificial IntelligenceDonghua UniversityShanghai 201620China State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangdong University of TechnologyGuangzhou 510006China

Supplier selection is an important business activity in order to realize the purchasing function in supply chain *** supplier selection process includes four stages,i.e.,bidding inviting,bidding,group decision-making,and results disclosure,involving the participation of manufacturing service demanders(MSDs),manufacturing service suppliers(MSSs),and ***,all the participants have raised concerns about the increased transparency in supplier ***,this study proposes a transparent supplier selection method by considering the engagement of *** this method,the Bayesian best-worst method(Bayesian BWM)is used to aggregate decision-makers'preferences into the overall optimal weights of the alternative MSSs,and the MSS with the largest weight is considered the suitable MSS for ***,blockchain is introduced to record the decision-making process information about supplier selection through a customized smart contract,where MSSs act as supervisors to supervise the decision-making process through the distributed consensus mechanism rather than directly participate in the decision-making ***,a case study of supplier selection in purchasing vibration acceleration sensors is *** result shows that the proposed method can support MSDs in selecting suitable MSS from alternative MSSs by aggregating decision-makers'preferences,and blockchain can provide credible information about the supplier selection process for MSSs,MSDs,and *** this way,the transparency of supplier selection is enhanced.

关键词： blockchain supplier selection supply chain transparency Bayesian BWM group decision-making

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Device-edge collaborative occluded face recognition method based on cross-domain feature fusion

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Digital Communications and Networks 2025年第2期11卷 482-492页

作者： Puning Zhang Lei Tan Zhigang Yang Fengyi Huang Lijun Sun Haiying Peng School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing Innovation Center of Industrial Big-Data Co.Lt

The lack of facial features caused by wearing masks degrades the performance of facial recognition systems. Traditional occluded face recognition methods cannot integrate the computational resources of the edge layer and the device layer. Besides, previous research fails to consider the facial characteristics including occluded and unoccluded parts. To solve the above problems, we put forward a device-edge collaborative occluded face recognition method based on cross-domain feature fusion. Specifically, the device-edge collaborative face recognition architecture gets the utmost out of maximizes device and edge resources for real-time occluded face recognition. Then, a cross-domain facial feature fusion method is presented which combines both the explicit domain and the implicit domain facial. Furthermore, a delay-optimized edge recognition task scheduling method is developed that comprehensively considers the task load, computational power, bandwidth, and delay tolerance constraints of the edge. This method can dynamically schedule face recognition tasks and minimize recognition delay while ensuring recognition accuracy. The experimental results show that the proposed method achieves an average gain of about 21% in recognition latency, while the accuracy of the face recognition task is basically the same compared to the baseline method.

关键词： Occluded face recognition Cross-domain feature fusion Device-edge collaboration

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Towards privacy-preserving and efficient word vector learning for lightweight IoT devices

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Digital Communications and Networks 2024年第4期10卷 895-903页

作者： Nan Jia Shaojing Fu Guangquan Xu Kai Huang Ming Xu National University of Defense Technology ChangshaChina Big Data School Qingdao Huanghai UniversityQingdaoChina Tianjin Key Laboratory of Advanced Networking College of Intelligence and ComputingTianjin UniversityTianjinChina College of Joint Operations National Defense UniversityShijiazhuangChina

Nowadays,Internet of Things(IoT)is widely deployed and brings great opportunities to change people's daily *** realize more effective human-computer interaction in the IoT applications,the Question Answering(QA)systems implanted in the IoT services are supposed to improve the ability to understand natural ***,the distributed representation of words,which contains more semantic or syntactic information,has been playing a more and more important role in the QA ***,learning high-quality distributed word vectors requires lots of storage and computing resources,hence it cannot be deployed on the resource-constrained IoT *** is a good choice to outsource the data and computation to the cloud ***,it could cause privacy risks to directly upload private data to the untrusted ***,realizing the word vector learning process over untrusted cloud servers without privacy leakage is an urgent and challenging *** this paper,we present a novel efficient word vector learning scheme over encrypted *** first design a series of arithmetic computation *** we use two non-colluding cloud servers to implement high-quality word vectors learning over encrypted *** proposed scheme allows us to perform training word vectors on the remote cloud servers while protecting *** analysis and experiments over real data sets demonstrate that our scheme is more secure and efficient than existing privacy-preserving word vector learning schemes.

关键词： Privacy-preserving Word vector learning Secret sharing Internet of things

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Fine-grained Personalized Hierarchical Federated Learning towards Heterogeneous Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 16349-16362页

作者： Zhang, Puning Shao, Shuman Wu, Chao Yang, Zhigang Zhang, Ruoying Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing400065 China Chongqing Innovation Center of Industrial Big-Data Co. Ltd National Engineering Laboratory for Industrial Big-data Application Technology Chongqing400707 China China Merchants Testing Vehicle Technology Research Institute Co. Ltd Chongqing404100 China Chongqing University of Posts and Telecommunications School of Cyber Security and Information Law Chongqing400065 China Chongqing Key Laboratory of Ubiquitous Sensing and Networking Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China China China Academy of Information and Communications Beijing100083 China

High mobility and non-independent and identically distributed (non-iid) data in the Internet of Vehicles (IoV) pose challenges for the convergence speed and accuracy of federated learning model training. Traditional federated learning approaches fail to account for the unique characteristics of local vehicle data, limiting the model performances. To address this, we propose a personalized hierarchical federated learning method tailored for heterogeneous IoV. Our approach customizes models for each vehicle, enhancing their performance on local data. We introduce a network-aware client selection method that dynamically chooses vehicle clients based on their spatiotemporal distribution and changing channel conditions. Additionally, our fine-grained personalized learning method leverages frequency domain analysis to extract and retain both fundamental and personalized knowledge from local model parameters. This allows us to build highly accurate personalized models suited to each vehicle's data characteristics. Experimental results show that our method improves model accuracy by up to 10.31% and reduces training time by up to 24.93% compared to the latest personalized federated learning algorithms. © 2025 IEEE.

关键词： Federated learning

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Adaptive Successive POI Recommendation via Trajectory Sequences Processing and Long Short-Term Preference Learning

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Computers, Materials & Continua 2024年第10期81卷 685-706页

作者： Yali Si Feng Li Shan Zhong Chenghang Huo Jing Chen Jinglian Liu School of Computer Science and Engineering Changshu Institute of TechnologyChangshu215500China Suzhou Industrial Intelligence and Big Data Laboratory Suzhou215000China School of Information Science and Engineering Yanshan UniversityQinhuangdao066004China College of Electronic and Information Engineering Guangdong Ocean UniversityZhanjiang524088China

Point-of-interest(POI)recommendations in location-based social networks(LBSNs)have developed rapidly by incorporating feature information and deep learning ***,most studies have failed to accurately reflect different users’preferences,in particular,the short-term preferences of inactive *** better learn user preferences,in this study,we propose a long-short-term-preference-based adaptive successive POI recommendation(LSTP-ASR)method by combining trajectory sequence processing,long short-term preference learning,and spatiotemporal ***,the check-in trajectory sequences are adaptively divided into recent and historical sequences according to a dynamic time ***,an adaptive filling strategy is used to expand the recent check-in sequences of users with inactive check-in behavior using those of similar active *** further propose an adaptive learning model to accurately extract long short-term preferences of users to establish an efficient successive POI recommendation system.A spatiotemporal-context-based recurrent neural network and temporal-context-based long short-term memory network are used to model the users’recent and historical checkin trajectory sequences,*** experiments on the Foursquare and Gowalla datasets reveal that the proposed method outperforms several other baseline methods in terms of three evaluation *** specifically,LSTP-ASR outperforms the previously best baseline method(RTPM)with a 17.15%and 20.62%average improvement on the Foursquare and Gowalla datasets in terms of the Fβmetric,respectively.

关键词： Location-based social networks adaptive successive point-of-interest recommendation long short-term preference trajectory sequences

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IDOS: A Unified Debiasing Method via Word Shuffling 12th

IDOS: A Unified Debiasing Method via Word Shuffling

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Tang, Yuanhang Ouyang, Yawen Wu, Zhen Zhang, Baohua Zhang, Jiaying Dai, Xinyu National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Big Data and Artificial Intelligence Laboratory Industrial and Commercial Bank of China Shanghai China

ISBN: (纸本)9783031446955

Recent studies show that advanced natural language understanding (NLU) models may exploit dataset biases to achieve superior performance on in-distribution datasets but fail to generalize to out-of-distribution datasets that do not contain such biases. Previous works have made promising progress in mitigating dataset biases with an extra model to estimate them. However, these methods rely on prior bias knowledge or tedious model-tuning tricks which may be hard to apply widely. To tackle the above problem, we propose to model biases by shuffling the words of the input sample, as word shuffling can break the semantics that relies on correct word order while keeping the biases that are unaffected. Thanks to word shuffling, we further propose IDOS, a unified debiasing method that enables bias estimation and debiased prediction by one single NLU model. Experimental results on three NLU benchmarks show that despite its simplicity, our method improves the generalization ability of NLU models and achieves a comparable performance to previous debiasing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Semantics

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A Method for Reconstructing Three-Dimensional Structure of Nasal Cavity Based on a Short-Source Acoustic Tube 4th

A Method for Reconstructing Three-Dimensional Structure of N...

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4th International Conference on Mechanical Design and Simulation, MDS 2024

作者： Lian, Xiaoqin Wang, Yuqiao Gao, Chao Li, Jin Ma, Guochun Wang, Zitong Bao, Kexin School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing100048 China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing100048 China

ISBN: (纸本)9789819778867

The reconstruction of the three-dimensional structure of the nasal cavity is significant for the diagnosis and treatment of nasal and nasopharynx diseases. This study proposes a method for reconstructing the three-dimensional structure of the nasal cavity based on the short-source acoustic tube, which addresses the inconvenient portability and high cost of the long-source acoustic tube. Firstly, we separate incident and reflected waves based on the propagation characteristics of pulsed acoustic waves in a short-source acoustic tube. Secondly, to reduce the error in calculating the input impulse response sequence, the methods of correcting the direct current (DC) offset component and suppressing the high-frequency noise are used to pre-process the nasal acoustic signals. Finally, we adopt the layered reconstruction method of the three-dimensional structure of the nasal cavity to reconstruct the depth cross-sectional area two-dimensional curve and take a three-dimensional visualization. Through comparative analysis, this method can meet practical application requirements. © The Author(s) 2025.

关键词： Impulse response

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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

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Science China(Information Sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU Shuguang CUI Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

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AdaBoost-SVM Face Recognition Algorithm Based on LBP and Gabor Feature Fusion 5

AdaBoost-SVM Face Recognition Algorithm Based on LBP and Gab...

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5th International Conference on big data and Artificial intelligence and Software Engineering, ICBASE 2024

作者： Lian, Xiaoqin Liu, Chunquan Gao, Chao Ma, Guochun Gong, Yonggang Li, Jin Beijing Technology and Business University School of Computer and Artificial Intelligence Beijing100048 China China National Light Industry Beijing Technology and Business University Key Laboratory of Industrial Internet and Big Data Beijing100048 China

ISBN: (纸本)9798331506612

In recent years, facial recognition technology has seen rapid advancements and is now extensively utilized in security surveillance and financial transactions. The face recognition process usually includes image preprocessing, feature extraction, and classification. However, it is often difficult for a single feature to fully capture the complex features of a human face, thus affecting the accuracy of recognition. To address this issue, this study presented an AdaBoost-SVM face recognition algorithm that fuses local binary pattern and Gabor features, which can enable comprehensive extraction of facial features from images and enhance recognition accuracy. This method first performs bilateral filtering on the face image to reduce noise and retain edge information. Then, it extracts the local binary pattern and Gabor features and weights the fusion. Finally, it uses the AdaBoost-SVM classifier for identification. Moreover, the algorithm performance was comprehensively evaluated using the ten-fold cross-validation method. The experimental results demonstrated that this method achieves recognition accuracy of 98.75% and 96.25% on ORL and Yale A face databases respectively, and still performs well in complex lighting and expression-changing environments, significantly improving the accuracy of face recognition and its robustness. © 2024 IEEE.

关键词： Local binary pattern

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Research on EEG Emotion Recognition Based on Multi-scale Residual Convolutional Neural Network 5

Research on EEG Emotion Recognition Based on Multi-scale Res...

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5th International Conference on big data and Artificial intelligence and Software Engineering, ICBASE 2024

作者： Lian, Xiaoqin Shi, Ziqi Gao, Chao Ma, Guochun Wu, Yelan Guan, Wenyang Beijing Technology and Business University School of Computer and Artificial Intelligence Beijing100048 China China National Light Industry Beijing Technology and Business University Key Laboratory of Industrial Internet and Big Data Beijing100048 China

ISBN: (纸本)9798331506612

Emotion recognition based on EEG signals has become an essential method in fields such as human-computer interaction and disease assessment. To further improve the recognition effect of human emotions, this paper fused the spatial and frequency information in EEG signals. It proposed a multi-scale residual convolutional neural network (ECA-Resnet-CNN) with an ECA attention mechanism to perform multi-scale information fusion on the constructed original features. The method first divides the EEG signals into six frequency bands;then, the differential entropy features are extracted from each frequency band and converted into a spatial-band three-dimensional feature matrix;then, the deep information of the three-dimensional feature matrix is extracted through a multi-scale residual convolutional neural network;finally, the weights of different channels of the feature map are adjusted by introducing the ECA attention mechanism, and the emotion classification is completed. The results show that on the Deap dataset, the arousal dimension and valence dimension binary classification accuracy of this method can reach 97.16% and 96.30% respectively, which is a significant improvement compared with the existing mainstream emotion recognition models, proving the effectiveness of this method. © 2024 IEEE.

关键词： Convolutional neural networks

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