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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Deng, Zhiying Li, Jianjun Guo, Zhiqiang Li, Guohui Huazhong University of Science and Technology School of Computer Science and Technology China Huazhong University of Science and Technology School of Software Engineering China

ISBN: (纸本)9781728163277

Next-basket recommendation (NBR) is a type of recommendation task that focuses on mining user interests based on the sequential basket records in which users purchase multiple items at a time. Limited by the sparsity brought by short-term user interaction behavior, existing NBR methods typically fail to mine fine-grained and complete representation of user interests, resulting in unsatisfactory recommendation performance. To address this issue, we propose a novel Multi-aspect Interest Neighbor-augmented Network (MINN) to capture fine-grained and complete representation of user interest for the next basket prediction. Specifically, we first design a multi-aspect interest encoder to learn representations of users and items in the multi-aspect interest space. Semantic neighbors are then selected for user to enhance the user's representation of interest through a semantic neighbor augmentation mechanism. Finally, the user's representation of interest and his/her repeated purchase behavior are jointly considered to implement the next basket prediction. Extensive experimental studies on two benchmark datasets demonstrate that MINN outperforms several representative NBR methods and achieves new state-of-the-art results. © 2023 IEEE.

关键词： multi-aspect interest learning neighbor augmentation next-basket recommendation Recommender systems

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Towards optimized tensor code generation for deep learning on sunway many-core processor

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Frontiers of computer science 2024年第2期18卷 1-15页

作者： Mingzhen LI Changxi LIU Jianjin LIAO Xuegui ZHENG Hailong YANG Rujun SUN Jun XU Lin GAN Guangwen YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China National University of Singapore Singapore 119077Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214000China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing 100854China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing deep learning compilers,TVM is well known for its efficiency in code generation and optimization across diverse hardware *** the meanwhile,the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning *** paper combines the trends in these two ***,we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as *** addition,we leverage the architecture features during the compilation such as core group for massive parallelism,DMA for high bandwidth memory transfer and local device memory for data locality,in order to generate efficient codes for deep learning workloads on *** experiment results show that the codes generated by swTVM achieve 1.79x improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway,across eight representative *** work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in *** believe this work will encourage more people to embrace the power of deep learning and Sunwaymany-coreprocessor.

关键词： sunway processor deep learning compiler code generation performance optimization

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A Secure and Efficient Framework for Multi-Round Data Trading Over the Internet of Artificially Intelligent Things

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IEEE Internet of Things Magazine 2022年第1期5卷 119-124页

作者： Zheng, Xu Zhang, Lizong Hui, Bei Tian, Ling Cai, Zhipeng School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Information and Software Engineering University of Electronic Science and Technology of China China Georgia State University Department of Computer Science United States

IoT data trading has greatly benefited the popularization of both the Internet of Things (IoT) and Artificial Intelligence of Things (AIoT). Current solutions mainly treat the dataset owned by each device as a commodity and focus on maintaining a data market. Meanwhile, inspired by recent achievements in distributed machine learning and federated learning, AIoT can actually realize its services by retaining the data locally on devices and only exchanging model parameters. However, existing IoT data trading methods fail to meet this secure and efficient process. Therefore, this article proposes a novel framework for data trading over AIoT. It allows the service provider to trade on deep learning model training instead of purchasing the full datasets. Key factors for the security issues and data pricing are addressed for the framework, including the design of the learning parameters and the comprehensive privacy concerns. Then a novel data trading strategy is proposed for the service provider to jointly maintain the model performance and the budget consumption. Evaluation results reveal the improvements on the model performance. Finally, some open problems in the design of data trading in AIoTs are discussed. © 2018 IEEE.

关键词： Internet of things

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Asymptotic behavior of least squares estimators for nonlinear autoregressive models

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science China(Information sciences) 2023年第6期66卷 299-300页

作者： Zhaobo LIU Chanying LI College of Computer Science and Software Engineering Shenzhen University Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences

Dear editor,Estimations of nonlinear autoregressive(AR) models in the literature typically involve ergodic series. Based on this assumption,the asymptotic theory has been established accordingly(see [1–3]). However,t...

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A quantum federated learning framework for classical clients

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science China(Physics,Mechanics & Astronomy) 2024年第5期67卷 1-10页

作者： Yanqi Song Yusen Wu Shengyao Wu Dandan Li Qiaoyan Wen Sujuan Qin Fei Gao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 00876China Department of Physics The University of Western AustraliaPerthWA 6009Australia School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing100876China

Quantum federated learning(QFL)enables collaborative training of a quantum machine learning(QML)model among multiple clients possessing quantum computing capabilities,without the need to share their respective local ***,the limited availability of quantum computing resources poses a challenge for each client to acquire quantum computing *** raises a natural question:Can quantum computing capabilities be deployed on the server instead?In this paper,we propose a QFL framework specifically designed for classical clients,referred to as CC-QFL,in response to this *** each iteration,the collaborative training of the QML model is assisted by the shadow tomography technique,eliminating the need for quantum computing capabilities of ***,the server constructs a classical representation of the QML model and transmits it to the *** clients encode their local data onto observables and use this classical representation to calculate local *** local gradients are then utilized to update the parameters of the QML *** evaluate the effectiveness of our framework through extensive numerical simulations using handwritten digit images from the MNIST *** framework provides valuable insights into QFL,particularly in scenarios where quantum computing resources are scarce.

关键词： client server quantum

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Heterogeneous Domain Adaptation via Correlative and Discriminative Feature Learning

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IEEE Transactions on Multimedia 2025年 27卷 3447-3461页

作者： Lu, Yuwu Lin, Dewei Shen, Linlin Zhou, Yicong Pan, Jiahui South China Normal University School of Artificial Intelligence Foshan528225 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518055 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China University of Nottingham Ningbo China Department of Computer Science Ningbo315100 China University of Macau Department of Computer and Information Science China

Heterogeneous domain adaptation seeks to learn an effective classifier or regression model for unlabeled target samples by using the well-labeled source samples but residing in different feature spaces and lying different distributions. Most recent works have concentrated on learning domain-invariant feature representations to minimize the distribution divergence via target pseudo-labels. However, two critical issues need to be further explored: 1) new feature representations should be not only domain-invariant but also category-correlative and discriminative, and 2) alleviating the negative transfer caused by the incorrect pseudo-labeling target samples could boost the adaptation performance during the iterative learning process. To address these issues, in this paper, we put forward a novel heterogeneous domain adaptation method to learn category-correlative and discriminative representations, referred to as correlative and discriminative feature learning (CDFL). Specifically, CDFL aims to learn a feature space where class-specific feature correlations between the source and target domains are maximized, the divergences of marginal and conditional distribution between the source and target domains are minimized, and the distances of inter-class distribution are forced to be maximized to ensure the discriminative ability. Meanwhile, a selective pseudo-labeling procedure based on the correlation coefficient and classifier prediction is introduced to boost class-specific feature correlation and discriminative distribution alignment in an iteration way. Extensive experiments certify that CDFL outperforms the state-of-the-art algorithms on five standard benchmarks. © 1999-2012 IEEE.

关键词： Contrastive Learning

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Federated Social Recommendation with Rényi Differential Privacy 3

Federated Social Recommendation with Rényi Differential Pri...

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3rd International Symposium on computer Technology and Information science, ISCTIS 2023

作者： Wang, Tian Jiao Wu, Jie School of Computer and Software Engineering University of Science and Technology Liaoning Liaoning Anshan China

ISBN: (纸本)9798350325386

A social recommendation system based on graph neural networks is a system that uses social relationships between users to generate personalized recommendations. To improve recommendation accuracy, it is usually necessary to collect a large amount of user behavior data, which may lead to user privacy breaches and data misuse. Existing privacy protection methods often sacrifice recommendation effectiveness or increase computational costs. Therefore, this article proposes a federated social recommendation system (FRSRec) based on Rényi differential privacy. By combining hash encryption technology with Rényi differential privacy, user data privacy can be protected while ensuring recommendation effectiveness and computational efficiency. This article conducted experimental evaluations on three real datasets. The experimental results show that the FRSRec model achieves optimal results in both MAE and RMSE, demonstrating that the FRSRec model can effectively achieve privacy protection while improving recommendation quality. © 2023 IEEE.

关键词： Recommender systems

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Real-World Wireless Network Modeling and Optimization:From Model/Data-Driven Perspective

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Chinese Journal of Electronics 2022年第6期31卷 991-1012页

作者： LI Yang ZHANG Shutao REN Xiaohui ZHU Jianhang HUANG Jiajie HE Pengcheng SHEN Kaiming YAO Zhiqiang GONG Jie CHANG Tsunghui SHI Qingjiang LUO Zhiquan Shenzhen Research Institute of Big Data Pengcheng Lab School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) College of Mathematics and Computational Sciences Xiangtan University School of Computer Science and Engineering Sun Yat-Sen University School of Software Engineering Tongji University

With the rapid development of the fifth-generation wireless communication systems, a profound revolution in terms of transmission capacity, energy efficiency, reliability, latency, and connectivity is highly expected to support a new batch of industries and applications. To achieve this goal, wireless networks are becoming extremely dynamic, heterogeneous, and complex. The modeling and optimization for the performance of realworld wireless networks are extremely challenging due to the difficulty to predict the network performance as a function of network parameters, and the prohibitively huge number of parameters to optimize. The conventional network modeling and optimization approaches rely on drive test, trial-and-error, and engineering experience,which are labor intensive, error-prone, and far from optimal. On the other hand, while the research community has spent significant efforts in understanding the fundamental limits of radio channels and developing physical layer techniques to operate close to it, very little is known about the performance limits of wireless networks, where millions of radio channels interact with one another in complex manners. This paper reviews the very recent mathematical and learning based techniques for modeling and optimizing the performance of real-world wireless networks in five aspects, including channel modeling, user demand and traffic modeling, throughput modeling and prediction, network parameter optimization, and IRS empowered performance optimization, and also presents the corresponding notable performance gains.

关键词： network parameter optimization telecommunication computing optimisation mathematical techniques wireless channels performance optimization learning based techniques telecommunication traffic mathematical analysis physical layer techniques channel modeling wireless network modeling fifth generation wireless communication systems learning (artificial intelligence) model-data-driven perspective IRS empowered performance optimization radio channels traffic modeling radio networks

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Secure Consumer-Centric Demand Response Management in Resilient Smart Grid as Industry 5.0 Application with Blockchain-Based Authentication

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1370-1379页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Omar, Marwan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Illinois Institute of Technology Information Technology and Management ChicagoIL60616 United States

The development of the industrial Internet of Things and smart grid networks has emphasized the importance of secure smart grid communication for the future of electric power transmission. However, the current deployment of smart meters and wireless communication methods presents several security concerns that must be addressed to ensure sustainable and resilient smart grid networks for Industry 5.0, with a focus on consumer-centric technologies. In this context, we propose a privacy-focused authentication solution for secure consumer-centric demand response management in smart grid networks, incorporating consumer electronics. Our proposed protocol utilizes blockchain-based authentication to enhance resistance against potential attacks and ensure the integrity of demand response data. Additionally, we have incorporated a PUF to prevent physical attacks. We have conducted informal and formal security analyses to verify the effectiveness of our proposed protocol against various threats and to confirm the session key security. Our comparison study has demonstrated that our protocol provides superior security and added functionalities, with lower computation and communication costs compared to similar protocols. Furthermore, we have measured the computational time for implementing the blockchain component of our protocol while considering the impact of varying numbers of block additions and transactions per block. © 1975-2011 IEEE.

关键词： Authentication

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Enhancing breast cancer histopathological image classification using attention-based high order covariance pooling

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Neural Computing and Applications 2024年第36期36卷 23275-23293页

作者： Waqas, Muhammad Ahmed, Amr Maul, Tomas Liao, Iman Yi School of Computer Science Faculty of Science and Engineering University of Nottingham Malaysia Campus Selangor Dar ul Ehsan Semenyih43500 Malaysia Department of Software Engineering University of Malakand Khyber Pakhtunkhwa Lower Dir Chakdara18800 Pakistan Department of Computer Science Edge Hill University Lancashire L39 4QP United Kingdom School of Computer Science Faculty of Science University of Nottingham NottinghamNG8 1BB United Kingdom

Breast cancer, the most common cancer affecting female patients, presents serious challenges for proper detection. Although computer-aided diagnostic techniques have progressed, their accuracy and efficacy remain limited. To overcome these challenges, we introduce DHA-Net, a new deep learning system that combines an effective attention module (EAM) and a high-order pooling layer with a ResNet-18 backbone. DHA-Net is tested using three well-known breast cancer histopathology image datasets: BreakHis, BACH2018, and a closely related Kaggle-Breast cancer histopathology dataset. Our experiments show that DHA-Net not only improves on existing state-of-the-art approaches, but significantly outperforms them in classifying breast cancer images. This work emphasizes the novel combination of an EAM with high-order pooling, demonstrating DHA-Net’s potential to improve diagnostic accuracy and serve as a more effective tool for medical imaging applications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Lung cancer

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