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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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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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Extracting hierarchical relationships of aspects from reviews

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Applied Soft Computing 2025年 178卷

作者： Jiangtao Qiu Ling Lin Siyu Wang Chongqing Engineering Research Center for Intelligent Applications of Financial Big Data Chongqing Institute of Engineering Chongqing China School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu China College of Artificial Intelligence (CUIT Shuangliu Industrial College) Chengdu University of Information Technology Chengdu China Gusu Laboratory of Materials Suzhou China

Aspect Based Sentiment Analysis (ABSA) attracts significant attention in recent years. Three elements of ABSA including aspect term, aspect, and domain (or entity) present a hierarchical relationships in e-commerce reviews. Extracting the hierarchical relationships can significantly enhance various applications, such as creating user profiles, identifying hierarchical topics, and visualizing review data. In this study, we proposed a framework to tackle this task, consisting of two primary components: a text adversarial autoencoder that efficiently encodes review content, and a deep network that extracts the clusters of aspect terms from review dataset and organizes them to a hierarchical structure using the Student-Teacher paradigm. Our framework also addresses the challenge of acquiring labeled training data by utilizing self-supervised learning. We evaluated the proposed framework on three public datasets and observed that it outperforms baseline models, indicating the feasibility and effectiveness of our approach.

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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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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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Ray Tracing-driven System of Virtual Fitting Based on Trustworthy Identification of IIoT 41

Ray Tracing-driven System of Virtual Fitting Based on Trustw...

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第41届中国控制会议

作者： Xiangnan Gong Yanhong Wang Jiping Xu Cheng Chi Zhaoyang Wang School of Artificial Intelligence Beijing Technology and Business University Key Laboratory of Industrial Internet and Big Data(Beijing Technology and Business University) China National Light Industry Institute of Industrial Internet and Internet of Things China Academy of Information and Communications Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Virtual fitting system is a promising direction for artificial intelligence(AI) application in the industrial Internet of things(IIoT).However,existing research is beyond the overall links between IIoT and real-world *** this paper,the Product Lifecycle Management(PLM) data is integrated through the identification of IIoT to realize a digital twin *** entire process involves multiple production companies with various PLM systems through *** system introduces dynamic ray tracing to imitate fitting effect in the way of rendering where the whole process of monitoring makes the traceability of the product simple and *** addition,the system has an AI analysis model of user preference with a selection interface,which can build the virtual fitting model according to the users' *** design help users have a more immersive shopping ***,consumers' desire is stimulated to prove the feasibility and effectiveness of the virtual fitting system.

关键词： Ray tracing PLM trustworthy identification virtual fitting IIoT

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Quantum partial least squares regression algorithm for multiple correlation problem

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Chinese Physics B 2022年第3期31卷 177-186页

作者： Yan-Yan Hou Jian Li Xiu-Bo Chen Yuan Tian School of Artificial Intelligence Beijing University of Post and TelecommunicationsBeijing 100876China College of Information Science and Engineering Zaozhuang UniversityZaozhuang 277160China Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Post and TelecommunicationsBeijing 100876China GuiZhou University Guizhou Provincial Key Laboratory of Public Big DataGuiyang 550025China

Partial least squares(PLS) regression is an important linear regression method that efficiently addresses the multiple correlation problem by combining principal component analysis and multiple regression. In this paper, we present a quantum partial least squares(QPLS) regression algorithm. To solve the high time complexity of the PLS regression, we design a quantum eigenvector search method to speed up principal components and regression parameters construction. Meanwhile, we give a density matrix product method to avoid multiple access to quantum random access memory(QRAM)during building residual matrices. The time and space complexities of the QPLS regression are logarithmic in the independent variable dimension n, the dependent variable dimension w, and the number of variables m. This algorithm achieves exponential speed-ups over the PLS regression on n, m, and w. In addition, the QPLS regression inspires us to explore more potential quantum machine learning applications in future works.

关键词： quantum machine learning partial least squares regression eigenvalue decomposition

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Semi-direct Visual SLAM Algorithm Based on Improved Keyframe Selection

Semi-direct Visual SLAM Algorithm Based on Improved Keyframe...

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2021 China Automation Congress, CAC 2021

作者： Xie, Yuxin Wu, Yelan Liu, Jie Lian, Xiaoqin Zhao, Zhiyao School of Artificial Intelligence Beijing Technology and Business University Beijing China China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University Beijing China

ISBN: (纸本)9781665426473

In order to meet the continuous improvement of real-time performance, positioning accuracy and robustness for visual Simultaneous Localization and Mapping (SLAM), and to address the problem that sparse maps are difficult to directly use for autonomous navigation, a semi-direct visual SLAM algorithm based on improved keyframe selection is proposed. It is based on ORB-SLAM2 framework. The front-end fuses feature-based method and direct method to get the camera initial poses. By minimizing the re-projection error to optimize the pose, it obtains a more accurate pose estimation. The proposed redundant keyframe deletion algorithm is applied in the back-end. It is based on the relative motion distance between keyframes, so as to delete the keyframes with overlapping visual fields and reduce the number of keyframes. The filtered keyframes are used for closed-loop detection and dense point cloud map construction. Then the map is filtered and converted into an octree map with higher storage efficiency. Experiments show that the algorithm can improve the real-time performance while ensuring the positioning accuracy, and has strong robustness to the sparse texture or camera's fast-moving scene, and it can reconstruct the 3D maps of different environment. © 2021 IEEE

关键词： Cameras

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Building Health State Recognition Method Based on Multi-channel Convolution Neural Network Fusion 10

Building Health State Recognition Method Based on Multi-chan...

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10th IEEE data Driven Control and Learning Systems Conference, DDCLS 2021

作者： Su, Tingli Li, Jian Yang, Aiqiang Jin, Xuebo Kong, Jianlei Bai, Yuting School of Artificial Intelligence Beijing Technology and Business University 100048 China China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University 100048 China

ISBN: (纸本)9781665424233

The identification of the health status of buildings has been paid more and more attention by all sectors of the society. The early warning of catastrophes or the assessment of the damage degree and residual life of building structures after catastrophes has become a hot topic for scholars from all over the world. In order to improve the performance of building health state recognition, a novel framework based on multi-channel convolution neural network fusion is proposed in this paper. By combining the output results of different convolution neural networks, temporal information and spatial information are used to achieve the accurate classification of building health status. Eventually, with the data collected by the sensor during the earthquake, the proposed framework is proved to be effective and superior. © 2021 IEEE.

关键词： Buildings

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Design of Intelligent Flow Control System for Extracorporeal Cardiopulmonary Support Based on Blood Oxygen Feedback 4

Design of Intelligent Flow Control System for Extracorporeal...

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4th International Academic Exchange Conference on Science and technology Innovation, IAECST 2022

作者： Lian, Xiaoqin Luo, Zhihong Gao, Chao Mi, Jiachen School of Artificial Intelligence Beijing Technology and Business University Beijing China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing China

ISBN: (纸本)9798350320008

In view of the problem that the current extracorporeal cardiopulmonary support equipment only has the function of pumping blood, and requires full-time personnel to operate frequently, and the degree of intelligence is low, this paper designs an intelligent flow control system for extracorporeal cardiopulmonary support based on blood oxygen feedback, which realizes the system's independent constant flow blood perfusion. The overall design idea and architecture of the system based on STM32 and TX2 are given. The hardware composition based on STM32, software design idea and implementation method are described in detail. Experiments are carried out based on the human simulation platform. The experimental results show that the system can perform constant flow blood perfusion autonomously and adjust the perfusion volume intelligently to maintain the human oxygen saturation between 65% and 75%. At the same time, the system can be connected with the remote control center through the Internet of Things to achieve remote monitoring of equipment status. The degree of automation and intelligence of extracorporeal cardiopulmonary support equipment has been generally improved. © 2022 IEEE.

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