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IEEE Transactions on Sustainable Computing 2025年

作者： Li, Dun Li, Hongzhi Crespi, Noel Minerva, Roberto Li, Ming Liang, Wei Li, Kuan-Ching Tsinghua University Department of Industrial Engineering China Institut Polytechnique de Paris Samovar Telecom SudParis Palaiseau91120 France Chizhou University College of Big Data and Artificial Intelligence Chizhou247100 China The Hong Kong Polytechnic University Research Institute for Advanced Manufacturing Hung Hom Hong Kong Hunan University of Science and Technology School of Computer Science and Engineering Hunan University of Science and Technology Sanya Research Institute Hunan Key Laboratory for Service Computing and Novel Software Technology China Providence University Dept. of Computer Science and Information Engineering Taiwan

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data in Industrial Internet of Things (IIoT) devices have highlighted the need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. Besides, we defined a storage model and data format for private data by designing and deploying smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in real-world simulations, Hyper-IIoT demonstrates excellent contract execution time control, stable system throughput, and rapid consensus processes that are capable of handling high throughput and efficient consensus in distributed systems, even in large-scale request scenarios. © 2016 IEEE.

关键词： Smart contract

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Text Mining for Educational Literature on big data with Hadoop

Text Mining for Educational Literature on Big Data with Hado...

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IEEE International Conference on Smart Cloud (SmartCloud)

作者： Haoge Wang Quanyu Wang Wenming Wang Software Architecture and Big Data dept. of Computer Science Beijing Institute of Technology Beijing China

With the development of the information era, the application of big data technology in education has become more and more extensive. This article adopts the Hadoop platform to conduct parallel mining of educational literature on big data. The paper has analyzed the main function of text mining technology, combined Canopy and K-Means algorithm to analyze and research the educational big data literature, learned about the application of big data technology in the field of education in order to provide its services for the construction of "World-class universities and first-class disciplines" and make a contribution to the development of education.

关键词： big data Education Text mining Clustering algorithms Computational modeling software architecture

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Hybrid Deep Learning Approach for Nonfunctional software Requirements Classifications 3

Hybrid Deep Learning Approach for Nonfunctional Software Req...

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3rd International Conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Rahman, Kiramat Ghani, Anwar Ahmad, Rashid Sajjad, Syed Haseeb International Islamic University Dept. of Software Engineering Islamabad Pakistan Big Data Research Center Jeju National University Jeju Korea Republic of Inernational Islamic University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350332391

This paper explores the concept of algorithmic hybridization, which involves combining various machine learning (ML) algorithms to enhance performance by utilizing the benefits of both simultaneously. This study presents a framework that utilizes a combination of long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) with artificial neural networks (ANN) to classify non-functional requirements (NFR). The task of NFR classification is challenging due to the scarcity of supervised learning data. The effectiveness of the proposed approach was assessed by comparing the performance of our integrated model with that of single LSTM and BiLSTM models. To conduct this evaluation, we combined two datasets consisting of 1000 non-functional requirements (NFR). The experimental findings revealed that the proposed approach is effective as the hybrid models exhibited better precision, recall, and F-1 score compared to its counterparts. © 2023 IEEE.

关键词： Requirements engineering

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Decentralized generation of multiple, uncorrelatable pseudonyms without trusted third parties

Decentralized generation of multiple, uncorrelatable pseudon...

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Lecture Notes in computer science

作者： Lehnhardt, Jan Spalka, Adrian CompuGroup Medical Software GmbH Dept. of Data Security and System Architecture Maria Trost 23 Koblenz 56070 Germany University of Bonn Dept. of Computer Science III Römerstr. 164 53117 Bonn Germany

ISBN: (纸本)9783642228896

Regarding the increasing number of applications provided as external services, the importance of pseudonymous data as a means for privacy protection of user entities is growing. Along with it grows the relevance of secure and accurate generation, use and management of pseudonyms. In particular we consider the involvement of third parties in this process as potentially harmful, and therefore favor a decentralized pseudonym generation approach where the role of central components is reduced to a minimum. In this paper, we propose a pseudonym generation mechanism and focus on its implementation based on elliptic curve cryptography, in which every user entity can generate an arbitrary number of uncorrelatable pseudonyms with minimal effort, initially as well as at any later point in time. Because no sensitive information necessary for pseudonym generation is available on central components, our approach provides security as well as flexibility and usability. © 2011 Springer-Verlag.

关键词： Public key cryptography

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2019 ACM Turing Celebration Conference - China, ACM TURC 2019

作者： Li, Xiuhua Wang, Xiaofei Sheng, Zhengguo Hu, Chunqiang Leung, Victor C.M. School of Big Data and Software Engineering Chongqing University Chongqing China School of Computer Science and Technology Tianjin University Tianjin China Dept. of Engineering and Design University of Sussex Falmer Brighton United Kingdom Dept. Electrical and Computer Engineering University of British Columbia Vancouver Canada

ISBN: (纸本)9781450371582

Content requests from mobile users are explosively increasing due to the growing popularity of mobile social activities. However, current mobile network infrastructure cannot effectively support the generated massive mobile data traffic while maintaining consistent and acceptable network performances. To deal with such a serious challenge, caching popular content at the edges of mobile networks has been recognized as a promising solution. In this paper, we introduce the content caching technique in Fog-Cloud based radio access networks for offering joint Fog-enabled and Cloud-enabled data services. Then we propose a collaborative hierarchical Fog-Cloud caching framework based on predicting content popularity with the Auto Regressive model. Our objective is to explore the maximum capacity of the considered network infrastructure by effectively offloading network traffic. Trace-driven evaluations demonstrate the effectiveness of our proposed caching mechanism in terms of traffic offload. © 2019 Association for Computing Machinery.

关键词： Fog

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ShoppingGPT: A GPT-based Product Recommendation Dialogue System 4

ShoppingGPT: A GPT-based Product Recommendation Dialogue Sys...

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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Yu, Renjie Guan, Yujing Zhan, Yuancheng Guangxi University of XingJian College of Science and Liberal Arts Dept. of Computer and Information Technology Nanning China Guangxi University of Finance and Economics Dept. of School of Big Data and Artificial Intelligence Nanning China Technology of NanNing University NanNing University Dept. Global Institute of Software Nanning China

ISBN: (纸本)9798350324303

This article introduces a shopping recommendation dialogue system called ShoppingGPT, which aims to help people get comprehensive product recommendations based on individual needs and preferences. The system can leverage existing large-scale generative pre-trained models with the ability to communicate and understand human language to provide more detailed and comprehensive product recommendations through multiple conversations with users. By constructing the shopping recommendation dataset and using the large-scale language model fine-tuning methods, we successfully fine-tuned on a large-scale language model and transformed the shopping recommendation task into a sequential labeling task to adapt to our shopping recommendation dialogue system. And we design a specific method for unlabeled prompt, which uses the basic language knowledge of large-scale language models to output product information for unsupervised annotation for product recommendation. In the experiment, we evaluate several existing language model fine-tuning techniques and explore the generalization ability of the ShoppingGPT system on unlabeled samples. We also compared models before and after fine-tuning to see what the models learned from the data. In the discussion section, we provide a detailed analysis of the advantages and limitations of ShoppingGPT and its potential applications in the shopping field. In addition, we explore the potential security challenges that ShoppingGPT can pose and discuss how to address them. Finally, we discussed how to improve ShoppingGPT to make it perform better and more secure. © 2023 IEEE.

关键词： Large dataset

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Hybrid Deep Learning Approach for Nonfunctional software Requirements Classifications

Hybrid Deep Learning Approach for Nonfunctional Software Req...

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International Conference on Communication, Computing and Digital Systems (C-CODE)

作者： Kiramat Rahman Anwar Ghani Rashid Ahmad Syed Haseeb Sajjad Dept. of Software Engineering International Islamic University Islamabad Pakistan Big Data Research Center Jeju National University Jeju South Korea Dept. of Computer Science Inernational Islamic University Islamabad Pakistan

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ShoppingGPT: A GPT-based Product Recommendation Dialogue System

ShoppingGPT: A GPT-based Product Recommendation Dialogue Sys...

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Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Renjie Yu Yujing Guan Yuancheng Zhan Dept. of Computer and Information Technology Guangxi University of XingJian College of Science and Liberal Arts Nanning China Dept. of School of Big Data and Artificial Intelligence Guangxi University Of Finance and Economics Nanning China Dept. Global Institute of Software Technology of NanNing University NanNing University Nanning China

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Joint long-term cache allocation and short-term content delivery in green cloud small cell networks

arXiv

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arXiv 2019年

作者： Wu, Xiongwei Li, Qiang Li, Xiuhua Leung, Victor C.M. Ching, P.C. Dept. of Electronic Engineering Chinese University of Hong Kong Shatin Hong Kong School of Info. & Comm. Eng. University of Electronic Science and Technology of China Chengdu China School of Big Data & Software Engineering Chongqing University Chongqing China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Dept. of Electrical & Computer Engineering University of British Columbia Vancouver Canada

Recent years have witnessed an exponential growth of mobile data traffic, which may lead to a serious traffic burn on the wireless networks and considerable power consumption. Network densification and edge caching are effective approaches to addressing these challenges. In this study, we investigate joint long-term cache allocation and short-term content delivery in cloud small cell networks (C-SCNs), where multiple smallcell BSs (SBSs) are connected to the central processor via fronthaul and can store popular contents so as to reduce the duplicated transmissions in networks. Accordingly, a long-term power minimization problem is formulated by jointly optimizing multicast beamforming, BS clustering, and cache allocation under quality of service (QoS) and storage constraints. The resultant mixed timescale design problem is an anticausal problem because the optimal cache allocation depends on the future file requests. To handle it, a two-stage optimization scheme is proposed by utilizing historical knowledge of users' requests and channel state information. Specifically, the online content delivery design is tackled with a penalty-based approach, and the periodic cache updating is optimized with a distributed alternating method. Simulation results indicate that the proposed scheme significantly outperforms conventional schemes and performs extremely close to a genie-aided lower bound in the low caching region. Copyright © 2019, The Authors. All rights reserved.

关键词： Channel state information

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Task Offloading for Automatic Speech Recognition in Edge-Cloud Computing Based Mobile Networks

Task Offloading for Automatic Speech Recognition in Edge-Clo...

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IEEE Symposium on computers and Communications (ISCC)

作者： Shitong Cheng Zhenghui Xu Xiuhua Li Xiongwei Wu Qilin Fan Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong SAR China College of Intelligence and Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Dept. Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9781728180861

ISBN: (纸本)9781728180878

Explosively increasing multimedia services and applications, e.g., automatic speech recognition (ASR), have aggravated the burden on the cloud server in mobile networks. To address the challenge, mobile edge computing has emerged for partially alleviating the workload of the cloud server and enhancing the quality of service of mobile users. In this paper, we aim to employ the technique of edge-cloud computing to accelerate the processing of ASR tasks generated by users in mobile networks. Particularly, we deploy a convolutional neural network based encoder in each edge server to extract features of the audio data. Based on certain network constraints (i.e., user association and edge servers' storage/computing capacity), we propose a low-complexity and distributed iterative greedy method to address the formulated nonlinear mixed-integer nonconvex optimization problem. Simulation results demonstrate the effectiveness of the proposed scheme on reducing the total delay in the network.

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