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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Kexue Fu Xiaoyuan Luo Linhao Qu Shuo Wang Ying Xiong Ilias Maglogiannis Longxiang Gao Manning Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Digital Medical Research Center School of Basic Medical Sciences Fudan University and Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Fudan University University of Piraeus

ISBN: (纸本)9798331314385

The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide Images (WSI) classification algorithms in clinical practice. Unlike few-shot learning methods in natural images that can leverage the labels of each image, existing few-shot WSI classification methods only utilize a small number of fine-grained labels or weakly supervised slide labels for training in order to avoid expensive fine-grained annotation. They lack sufficient mining of available WSIs, severely limiting WSI classification performance. To address the above issues, we propose a novel and efficient dual-tier few-shot learning paradigm for WSI classification, named FAST. FAST consists of a dual-level annotation strategy and a dual-branch classification framework. Firstly, to avoid expensive fine-grained annotation, we collect a very small number of WSIs at the slide level, and annotate an extremely small number of patches. Then, to fully mining the available WSIs, we use all the patches and available patch labels to build a cache branch, which utilizes the labeled patches to learn the labels of unlabeled patches and through knowledge retrieval for patch classification. In addition to the cache branch, we also construct a prior branch that includes learnable prompt vectors, using the text encoder of visual-language models for patch classification. Finally, we integrate the results from both branches to achieve WSI classification. Extensive experiments on binary and multi-class datasets demonstrate that our proposed method significantly surpasses existing few-shot classification methods and approaches the accuracy of fully supervised methods with only 0.22% annotation costs. All codes and models will be publicly available on https://***/fukexue/FAST.

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A Survey of Recommender systems Based on Hypergraph Neural Networks 1

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7th International Conference on Smart computing and Communication, SmartCom 2022

作者： Liu, Canwei He, Tingqin Zhu, Hangyu Li, Yanlu Xie, Songyou Hosam, Osama School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Guangdong Financial High-tech Zone "Blockchain +" Fintech Research Institute Guangzhou510623 China Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan411201 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Higher Colleges of Technology Abu Dhabi United Arab Emirates

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

Unlike highly purposeful search, a recommender system tends to uncover the user’s potential interests and is a personalized information filtering system. Recently, the performance of hypergraph neural networks in classification tasks has attracted much attention. Compared with traditional recommender systems, hypergraph neural network-based recommender systems have better mining higher-order associations, accurate modeling of multivariate relationships, handling of multimodal and heterogeneous data, and clustering advantages. This fact drives the development of recommendation algorithms based on hypergraph neural networks. To this end, we 1) define generic links of recommender systems, and systematically analyze the challenges of hypergraph neural network-based recommender systems in different research directions. 2) present some new perspectives on existing weaknesses and future developments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recommender systems

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Joint UAV Deployment and Edge Association for Energy-Efficient Federated Learning

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Wu, Tao Li, Maomao Qu, Yuben Wang, Hongjun Wei, Zhenhua Cao, Jiannong National University of Defense Technology Hefei230009 China Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing211106 China Xi'an Research Institute of High Technology Xi'an710025 China The HongKong Polytechnic University Department of Computing Hong Kong Hong Kong

Recently, federated learning (FL) has become a promising distributed learning paradigm that caters to the recent trend of pushing intelligence from the cloud to the edge. Nevertheless, communication bottlenecks and device dropout can lead to inefficient FL in the large network scale, where massive devices cannot be accessed with severely limited network resources. Inspired by the Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing (MEC), we propose the multi-UAV assisted FL design to provide the intermediate model aggregation in the sky. Specifically, we study the problem of joint UAV dePloyment and edge aSsociation (UPS) to minimize the overall energy consumption, which concerns UAV deployment, edge association, and resource allocation. Unfortunately, solving this problem is non-trivial, due to its infinite search space and the complex coupling among mixed optimization variables. To tackle this difficulty, we exploit the FL bundle generation method to reduce candidate locations of UAVs from infinite to finite. Then, we decompose the initial problem and devise an alternating optimization-based algorithm to achieve the optimal resource allocation in the closed form. On this basis, we design a greedy-based approximation algorithm with logN performance guarantee for UAV deployment and edge association. Extensive simulations are conducted to validate the effectiveness of our proposed solution. Compared with five benchmarks, our proposed algorithm can significantly reduce the overall training energy consumption under the training time constraint, while always maintaining better training performance under different parameter settings. © 2015 IEEE.

关键词： Federated learning

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Self-Supervised Teaching and Learning of Representations on Graphs 23

Self-Supervised Teaching and Learning of Representations on ...

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2023 World Wide Web Conference, WWW 2023

作者： Wan, Liangtian Fu, Zhenqiang Sun, Lu Wang, Xianpeng Xu, Gang Yan, Xiaoran Xia, Feng Key Laboratory for Ubiquitous Network Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian China State Key Laboratory of Marine Resource Utilization in South China Sea School of Information and Communication Engineering Hainan University Haikou China State Key Laboratory of Millimeter Waves School of Information Science and Engineering Southeast University Nanjing China Research Center of Big Data Intelligence Research Institute of Artificial Intelligence Zhejiang Lab Hangzhou China School of Computing Technologies Rmit University Melbourne Australia

ISBN: (纸本)9781450394161

Recent years have witnessed significant advances in graph contrastive learning (GCL), while most GCL models use graph neural networks as encoders based on supervised learning. In this work, we propose a novel graph learning model called GraphTL, which explores self-supervised teaching and learning of representations on graphs. One critical objective of GCL is to retain original graph information. For this purpose, we design an encoder based on the idea of unsupervised dimensionality reduction of locally linear embedding (LLE). Specifically, we map one iteration of the LLE to one layer of the network. To guide the encoder to better retain the original graph information, we propose an unbalanced contrastive model consisting of two views, which are the learning view and the teaching view, respectively. Furthermore, we consider the nodes that are identical in muti-views as positive node pairs, and design the node similarity scorer so that the model can select positive samples of a target node. Extensive experiments have been conducted over multiple datasets to evaluate the performance of GraphTL in comparison with baseline models. Results demonstrate that GraphTL can reduce distances between similar nodes while preserving network topological and feature information, yielding better performance in node classification. © 2023 ACM.

关键词： Classification (of information)

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Adaptive Hybrid FFT: A Novel Pipeline and Memory-Based Architecture for Radix-2k FFT in Large Size Processing

arXiv

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arXiv 2025年

作者： Zhao, Fangyu Xiao, Chunhua Wang, Zhiguo Du, Xiaohua Dong, Bo College of Computer Science Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education China Sichuan Huacun Zhigu Technology Co. Ltd. Chengdu China

In the field of digital signal processing, the fast Fourier transform (FFT) is a fundamental algorithm, with its processors being implemented using either the pipelined architecture, well-known for high-throughput applications but weak in hardware utilization, or the memory-based architecture, designed for area-constrained scenarios but failing to meet stringent throughput requirements. Therefore, we propose an adaptive hybrid FFT, which leverages the strengths of both pipelined and memory-based architectures. In this paper, we propose an adaptive hybrid FFT processor that combines the advantages of both architectures, and it has the following features. First, a set of radix-2kmulti-path delay commutators (MDC) units are developed to support high-performance large-size processing. Second, a conflict-free memory access scheme is formulated to ensure a continuous data flow without data contention. Third, We demonstrate the existence of a series of bit-dimension permutations for reordering input data, satisfying the generalized constraints of variable-length, high-radix, and any level of parallelism for wide adaptivity. Furthermore, the proposed FFT processor has been implemented on a field-programmable gate array (FPGA). As a result, the proposed work outperforms conventional memory-based FFT processors by requiring fewer computation cycles. It achieves higher hardware utilization than pipelined FFT architectures, making it suitable for highly demanding applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Pipeline processing systems

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T-M Net: A Fusion Convolutional Neural Network Method for Lung Aging Assessment

T-M Net: A Fusion Convolutional Neural Network Method for Lu...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Tangbin Tian Jingshan Pan Jing Ge Na Li Dexin Yu Liping Zuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Department of Radiology Qilu Hospital of Shandong University Jinan China

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

As the population ages, the precise assessment of pulmonary senescence becomes essential for the diagnosis and management of chronic respiratory diseases. However, existing medical imaging analysis techniques encounter significant chal-lenges due to data scarcity, especially in the domain of lung CT imaging. This research proposes an innovative method for assessing lung aging in CT images-T-M Net. This method combines transfer learning and MS-CNN to improve the accuracy and efficiency of lung aging assessment in CT images. We employed a pretrained ResNet18 model, capitalizing on transfer learning to harness the insights from extensive datasets, thereby minimizing the computational demands of model training. Furthermore, a channel attention mechanism is integrated within the transfer learning framework to heighten the model's focus on critical pulmonary regions, thus augmenting its predictive accuracy and interpretability. To accommodate the distinctive characteristics of lung CTs, a MS CNN architecture was developed, capturing multi-dimensional information ranging from microscopic details to macroscopic structures through variously sized convolutional kernels. Testing on a lung CT dataset revealed that our approach surpasses traditional methods in accuracy and generalizability, underscoring its potential utility in the early diagnosis and effective treatment of pulmonary ailments.

关键词： Training Accuracy Attention mechanisms Lungs Computational modeling Computed tomography Transfer learning Aging Convolutional neural networks Load modeling

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Overview of Medical Data Privacy Protection based on Blockchain technology 7

Overview of Medical Data Privacy Protection based on Blockch...

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7th IEEE International Conference on Smart Cloud, SmartCloud 2022

作者： Chen, Lin Yu, Qingchun Liang, Wei Cai, Jiahong Zhu, Hangyu Xie, Songyou Hunan University of Science and Technology Hunan Key Laboratory for Service computing and Novel Software Technology School of Computer Science and Engineering Xiangtan China Shenzhen Research Institute Sun Yat-sen University and Novel Software Technology ShenZhen China Hunan University College of Computer Science and Electronic Engineering Changsha China

ISBN: (数字)9781665451796

ISBN: (纸本)9781665451796

With the increasing growth of electronic medical data, the difficulties of data sharing among medical institutions and the leakage of data privacy have become the focus of the public and medical workers. The blockchain has the characteristics of decentralization, traceability, and immutability, which can provide new ideas for fine-grained secure access to medical research. This article first introduces blockchain and blockchain-based privacy protection technology;then analyzes the advantages and disadvantages of electronic medical records, and introduces the current development status of electronic medical records based on blockchain technology;then from data encryption, access the three aspects of control and transaction anonymity introduce the medical data privacy protection method based on blockchain technology;finally, the full text is summarized and prospected. © 2022 IEEE.

关键词： Blockchain

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Joint Extraction of Entities and Relations by Adversarial Training and Mixup Data Augmentation 7

Joint Extraction of Entities and Relations by Adversarial Tr...

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7th International Conference on Computer and Communications, ICCC 2021

作者： Chen, Hongjing Lu, Gang Wu, Xiaojun Zhang, Yumei SNNU Key Laboratory of Intelligent Computing and Service Technology For Folk Song Ministry of Culture and Tourism Shaanxi Normal University School of Computer Science Xi'an China

ISBN: (纸本)9781665409506

Joint extraction of entities and relations is an important task for building a knowledge graph and information extraction. However, small interference in the text can greatly change the semantics of words and sentences in natural language, thereby affecting the model's prediction results. To solve the sensitivity of text to noise, in this paper, we present a method, called BERT of Adversarial Training and Sentence Mixup Data Augmentation (BERT-AT-SMDA), which use the BERT pre-training model to obtain the embedding vector representation of the text and then to incorporate the FGM adversarial training strategy into the fine-tuning of Bert to construct adversarial samples. Both the adversarial sample and the original sample into the encoder to training. Then, to prevent the over-fitting phenomenon and the additional interference caused by the instability of adversarial training, we introduce the mixup data augmentation method after obtaining the hidden layer of the encoder output, which performs a linear interpolation between the output hidden layer. In addition, we also study the effect of linear interpolation at different positions on the model's joint extraction of entities and relations. We conduct experiments on a public dataset produced by the distant supervision method. The experimental results show that our method is effective for the joint extraction method based on tagging scheme. Not only improves the accuracy of entity and relation extraction, and ensures the robustness and generalization of the model. © 2021 IEEE.

关键词： Interpolation

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The Security and Privacy of Mobile Edge computing: An Artificial Intelligence Perspective

arXiv

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arXiv 2024年

作者： Wang, Cheng Yuan, Zenghui Zhou, Pan Xu, Zichuan Li, Ruixuan Wu, Dapeng Oliver Hubei Engineering Research Center on Big Data Security Key Laboratory of Distributed System Security of Hubei Province School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Software The Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian University of Technology Dalian116024 China Intelligent and Distributed Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong

Mobile Edge computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network’s edge. By shifting the load of cloud computing to individual local servers, MEC helps meet the requirements of ultralow latency, localized data processing, and extends the potential of Internet of Things (IoT) for end-users. However, the crosscutting nature of MEC and the multidisciplinary components necessary for its deployment have presented additional security and privacy concerns. Fortunately, Artificial Intelligence (AI) algorithms can cope with excessively unpredictable and complex data, which offers a distinct advantage in dealing with sophisticated and developing adversaries in the security industry. Hence, in this paper we comprehensively provide a survey of security and privacy in MEC from the perspective of AI. On the one hand, we use European Telecommunications Standards Institute (ETSI) MEC reference architecture as our based framework while merging the Software Defined Network (SDN) and Network Function Virtualization (NFV) to better illustrate a serviceable platform of MEC. On the other hand, we focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI. Finally, we comprehensively discuss the opportunities and challenges associated with applying AI to MEC security and privacy as possible future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Network security

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An Energy-efficient Traffic Scheduling Method based on Slime Mould Algorithm for SDN

An Energy-efficient Traffic Scheduling Method based on Slime...

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International Conference on Information systems and Computer Networks (ISCON)

作者： Zheyuan Wang Junli Wang Chungang Yan Ministry of Education Key Laboratory of Embedded System and Service Computing (Tongji University) Shanghai China National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University Shanghai China

Software defined network (SDN) enables efficient and green traffic management by separating the control and data planes. However, the existing itemized scheduling approach is prone to waste of network resources and energy consumption due to the different sensitivity of traffic to time delay. To address these problems, we design a Slime Mould Algorithm based Energy-efficient Traffic Scheduling Method (SMA-ETSM) for SDN. First, we model the traffic scheduling problem with delay requirements to evaluate the decision. Second, in order to minimize the generation of invalid solutions in traffic scheduling, the encoding of the slime mould algorithm is improved, and a slime mould adaptation and update mechanism suitable for energy-efficient traffic scheduling is designed. The experiments show that SMA-ETSM can effectively reduce the energy consumption and improve the overall bandwidth utilization of the network compared with ECMP and ACO algorithm, and it also has some improvement in the operation efficiency compared with the original slime mould algorithm.

关键词： Energy consumption Sensitivity Processor scheduling Scheduling Energy efficiency Real-time systems Time factors

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