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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

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Privacy-Preserving Federated Compressed Learning Against Data Reconstruction Attacks Based on Secure Data 30th

Privacy-Preserving Federated Compressed Learning Against Dat...

引用

30th International conference on Neural Information Processing, ICONIP 2023

作者： Xiao, Di Li, Jinkun Li, Min College of Computer Science Chongqing University Chongqing400044 China

ISBN: (纸本)9789819981830

Federated learning is a new distributed learning framework with data privacy preserving in which multiple users collaboratively train models without sharing data. However, recent studies highlight potential privacy leakage through shared gradient information. Several defense strategies, including gradient information encryption and perturbation, have been suggested. But these strategies either involve high complexity or are susceptible to attacks. To counter these challenges, we propose to train on secure compressive measurements by compressed learning, thereby achieving local data privacy protection with slight performance degradation. A feasible method to boost performance in compressed learning is the joint optimization of the sampling matrix and the inference network during the training phase, but this may suffer from data reconstruction attacks again. Thus, we further incorporate a traditional lightweight encryption scheme to protect data privacy. Experiments conducted on MNIST and FMNIST datasets substantiate that our schemes achieve a satisfactory balance between privacy protection and model performance. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Privacy-preserving techniques

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Heterogeneous Graph Neural Network Focused on Structural Features for Link Prediction 5

Heterogeneous Graph Neural Network Focused on Structural Fea...

引用

5th International conference on Artificial Intelligence and computer Engineering, ICAICE 2024

作者： Bai, Longjie Wang, Yongli Liu, Dongmei School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331528911

Heterogeneous Graph Neural Networks (HGNNs) inherit some of the mechanisms of traditional graph neural networks, and are able to focus on graph structures that contain different types of nodes and edges, effectively embedding their structural and semantic features into node representations. Little work has investigated whether these capabilities play a critical utility in the problem of link prediction for heterogeneous graphs. In this paper, we target these issues by proposing Heterogeneous Graph Neural Network Focused on Structural Features (SFH4LP). Mean value aggregation is used to simplify the computation of neighbor aggregation, while centrality coding is added to complement node representation. In order to exploit the structural information of the graph more comprehensively, the model pays deep attention to the edges and spatial relationships in the metapaths. An improved self-attention mechanism based on Transformer is proposed to explore the information implied on the metapaths and achieve high-quality fusion of features from different metapaths to generate the final node representations. Experimental results on three heterogeneous graph datasets show that the model proposed in this paper outperforms existing baseline models in terms of the effectiveness of heterogeneous graph embedding, and exhibits good performance for the link prediction task. © 2024 IEEE.

关键词： Prediction models

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VNet: A GAN-Based Multi-Tier Discriminator Network for Speech Synthesis Vocoders

VNet: A GAN-Based Multi-Tier Discriminator Network for Speec...

引用

2024 IEEE International conference on Systems, Man, and Cybernetics, SMC 2024

作者： Cao, Yubing Li, Yongming Wang, Liejun Yu, Yinfeng School of Computer Science and Technology Xinjiang University Urumqi830049 China

ISBN: (纸本)9781665410205

Since the introduction of Generative Adversarial Networks (GANs) in speech synthesis, remarkable achievements have been attained. In a thorough exploration of vocoders, it has been discovered that audio waveforms can be generated at speeds exceeding real-time while maintaining high fidelity, achieved through the utilization of GAN-based models. Typically, the inputs to the vocoder consist of band-limited spectral information, which inevitably sacrifices high-frequency details. To address this, we adopt the full-band Mel spectrogram information as input, aiming to provide the vocoder with the most comprehensive information possible. However, previous studies have revealed that the use of full-band spectral information as input can result in the issue of over-smoothing, compromising the naturalness of the synthesized speech. To tackle this challenge, we propose VNet, a GAN-based neural vocoder network that incorporates full-band spectral information and introduces a Multi-Tier Discriminator (MTD) comprising multiple sub-discriminators to generate high-resolution signals. Additionally, we introduce an asymptotically constrained method that modifies the adversarial loss of the generator and discriminator, enhancing the stability of the training process. Through rigorous experiments, we demonstrate that the VNet model is capable of generating high-fidelity speech and significantly improving the performance of the vocoder. © 2024 IEEE.

关键词： Gallium nitride

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Decoupling Learning and Remembering: a Bilevel Memory Framework with Knowledge Projection for Task-Incremental Learning

Decoupling Learning and Remembering: a Bilevel Memory Framew...

引用

IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Sun, Wenju Li, Qingyong Zhang, Jing Wang, Wen Geng, Yangli-ao Beijing Jiaotong Univ Beijing Key Lab Traff Data Anal & Min Beijing Peoples R China Beijing Jiaotong Univ Frontiers Sci Ctr Smart High Speed Railway Syst Beijing Peoples R China Wuyi Univ Fujian Educ Inst Key Lab Cognit Comp & Intelligent Informat Proc Nanping Peoples R China

ISBN: (纸本)9798350301298

The dilemma between plasticity and stability arises as a common challenge for incremental learning. In contrast, the human memory system is able to remedy this dilemma owing to its multi-level memory structure, which motivates us to propose a Bilevel Memory system with Knowledge Projection (BMKP) for incremental learning. BMKP decouples the functions of learning and remembering via a bilevel-memory design: a working memory responsible for adaptively model learning, to ensure plasticity;a long-term memory in charge of enduringly storing the knowledge incorporated within the learned model, to guarantee stability. However, an emerging issue is how to extract the learned knowledge from the working memory and assimilate it into the long-term memory. To approach this issue, we reveal that the parameters learned by the working memory are actually residing in a redundant high-dimensional space, and the knowledge incorporated in the model can have a quite compact representation under a group of pattern basis shared by all incremental learning tasks. Therefore, we propose a knowledge projection process to adaptively maintain the shared basis, with which the loosely organized model knowledge of working memory is projected into the compact representation to be remembered in the long-term memory. We evaluate BMKP on CIFAR-10, CIFAR-100, and Tiny-ImageNet. The experimental results show that BMKP achieves state-of-the-art performance with lower memory usage(1).

关键词： continual low-shot meta or long-tail learning Transfer

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MetaCon: Revitalizing Internet Congestion Control with Meta-Reinforcement Learning

MetaCon: Revitalizing Internet Congestion Control with Meta-...

引用

2025 IEEE International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Bai, He Li, Hui Que, Jianming Zhang, Minglong Chong, Peter Han Joo Sudheera, Kalupahana Liyanage Kushan Pei, Xinyuan School of Electronic and Computer Engineering Peking University Shenzhen China School of Computer Science Fuyao University of Science and Technology Fuzhou China Department of Electrical and Computer Engineering Mississippi State University Starkville United States School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Electrical and Information Engineering Faculty of Engineering University of Ruhuna Sri Lanka

ISBN: (纸本)9798350368741

Effective congestion control algorithms (CCAs) are crucial for the smooth operation of Internet communication infrastructure. CCAs adjust transmission rates based on congestion signals, optimizing resource utilization and user experience. However, existing studies, both rule-based and learning-based CCAs, often struggle with generalization and underperform when deployed in real-world environments. When applied to unseen network conditions, hand-crafted schemes or pre-trained models may experience significant performance degradation. To address this challenge, we propose MetaCon, a novel adaptive Internet congestion control approach based on meta-reinforcement learning. MetaCon leverages knowledge learned from prior scenarios to quickly adapt to new environments. Experimental results show that MetaCon outperforms existing algorithms by exhibiting superior generalization and achieving better transmission performance across a wide variety of network conditions. © 2025 IEEE.

关键词： communication network congestion control meta-learning reinforcement learning transport protocol

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Improvement in the Transferability of Target Adversarial Examples Based on Data Augmentation

Improvement in the Transferability of Target Adversarial Exa...

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2024 IEEE International conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wang, Kun Jin, Xing Wang, Fuqiang Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Despite the high performance of deep neural networks in image classification, they are very sensitive to those adversarial examples with small perturbations, which is likely to cause classification errors. Currently, there are various adversarial attack methods developed, but the adversarial samples generated using most methods are still disadvantaged by the poor target ransferability of cross-model in black-box scenarios. To address this issue, a new method called Adaptive Variation Method (AVM) is proposed in this paper to improve the transferability of targeted adversarial examples in black-box scenarios. AVM relies on Adjustment of diversified inputs(ADI) method to achieve data enhancement on the input image and Maximum absolute difference (MD) method to calculate the loss for adversarial perturbation. Specifically, the Resized-Diverse-Inputs (RDI) method is improved in this paper to propose a new diversified input method, namely the Adjustment of diversified inputs (ADI) method. By means of repeated scaling and random filling performed on the input image, the image becomes relatively rough, with its input diversity and random flexibility improved. In addition, the Maximum absolute difference (MD) method is proposed in this paper to enhance the advantage of adversarial samples by minimizing the maximum absolute difference between the adversarially perturbed global image and local image. According to the experimental results, this method improves the success rate in the presence of transfer-based targeted attacks. © 2024 IEEE.

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Tele-Aloha: A Telepresence System with Low-budget and high-authenticity Using Sparse RGB Cameras 24

Tele-Aloha: A Telepresence System with Low-budget and High-a...

引用

2024 Special Interest Group on computer Graphics and Interactive Techniques conference - conference Papers, SIGGRAPH 2024

作者： Tu, Hanzhang Shao, Ruizhi Dong, Xue Zheng, Shunyuan Zhang, Hao Chen, Lili Wang, Meili Li, Wenyu Ma, Siyan Zhang, Shengping Zhou, Boyao Liu, Yebin Tsinghua University Beijing China BOE Technology Group Beijing China Harbin Institute of Technology Weihai China

ISBN: (纸本)9798400705250

In this paper, we present a low-budget and high-authenticity bidirectional telepresence system, Tele-Aloha, targeting peer-to-peer communication scenarios. Compared to previous systems, Tele-Aloha utilizes only four sparse RGB cameras, one consumer-grade GPU, and one autostereoscopic screen to achieve high-resolution (2048x2048), real-time (30 fps), low-latency (less than 150ms) and robust distant communication. As the core of Tele-Aloha, we propose an efficient novel view synthesis algorithm for upper-body. Firstly, we design a cascaded disparity estimator for obtaining a robust geometry cue. Additionally a neural rasterizer via Gaussian Splatting is introduced to project latent features onto target view and to decode them into a reduced resolution. Further, given the high-quality captured data, we leverage weighted blending mechanism to refine the decoded image into the final resolution of 2K. Exploiting world-leading autostereoscopic display and low-latency iris tracking, users are able to experience a strong three-dimensional sense even without any wearable head-mounted display device. Altogether, our telepresence system demonstrates the sense of co-presence in real-life experiments, inspiring the next generation of communication. © 2024 Owner/Author.

关键词： human performance rendering

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Enhancing Power Density in EV Motors via Thermal Management and high-Speed Operation 4

Enhancing Power Density in EV Motors via Thermal Management ...

引用

4th International conference on Electrical, Communication and computer Engineering, ICECCE 2023

作者： Sakkas, Georgios K. Pouraimis, Panagiotis G. Beniakar, Minos E. Oreinos, Christos Th. Manolas, Iakovos S. Tesla Engineering Hub Athens Greece

ISBN: (纸本)9798350369694

This paper focuses on enhancing power density in electric vehicle (EV) motors, via high-speed operation and optimal thermal management. For the purposes of the analysis, the weak coupling of electromagnetic, structural, and thermal finite element models is employed, to accommodate the consideration of multi-physics design constraints. The inter-dependencies between electromagnetic performance and structural and thermal robustness are highlighted and quantified. The approach offers valuable insights enabling faster application targeting, and production qualified design optimizations inherently respecting involved physics constraints. The methodology is applied on a reference 100kW synchronous IPM V-shape motor. © 2023 IEEE.

关键词： Temperature control

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A More Scalable Sparse Dynamic Data Exchange 31

A More Scalable Sparse Dynamic Data Exchange

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31st Annual IEEE International conference on high performance Computing, Data, and Analytics, HiPC 2024

作者： Geyko, Andrew Collom, Gerald Schafer, Derek Bridges, Patrick Bienz, Amanda Max Planck Institute Universität des Saarlandes Saarbrücken Germany Department of Computer Science University of New Mexico Albuquerque United States

ISBN: (纸本)9798331509095

Parallel architectures are continually increasing in performance and scale while underlying algorithmic infrastructure often fails to take full advantage of available compute power. Within the context of MPI, irregular communication patterns create bottlenecks in parallel applications. One common bottleneck is the sparse dynamic data exchange, often required when forming communication patterns within applications. There is a large variety of approaches for these dynamic exchanges, with optimizations implemented directly in parallel applications. This paper proposes a novel API within an MPI eXtension library, allowing applications to utilize the variety of provided optimizations for sparse dynamic data exchange methods. Further, the paper presents novel locality-aware sparse dynamic data exchange algorithms. Finally, performance results show locality-aware approaches achieve up to 128x over existing approaches when exchanging only pattern of communication, and up to 54x when exchanging data to be communicated as well. © 2024 IEEE.

关键词： Parallel architectures

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Hybrid Optical Wireless Channel and Fiber Channel based high-Throughput Space Networks 9

Hybrid Optical Wireless Channel and Fiber Channel based High...

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9th International conference on computer and Communications, ICCC 2023

作者： Chang, Xiang He, Jianhua Zuo, Jinghao Lu, Lu University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Technology and Engineering Center for Space Utilization Beijing China

ISBN: (纸本)9798350317251

With the development of space information networks, scientific experiments are becoming larger and more complex, necessitating networks with superior scalability, flexibility, low latency, and high throughput. While the FC-AE-1553 protocol is widely adopted by existing space information networks due to its high reliability and throughput characteristics, wired networks face significant challenges in access node scalability, ease of installation, and maintenance. To address these issues, we propose a high-throughput space network based on a hybrid of optical wireless channels and fiber channels. This network structure can significantly enhance the throughput and flexibility of the network. We also analyze the factors affecting network bandwidth and latency under the new architecture and establish a system simulation model to validate the performance of the new network architecture. we believe that Hybrid Space Networks hold the potential to bolster the development of future space information networks, offering higher bandwidth and more flexible network access capabilities for space science applications. © 2023 IEEE.

关键词： Scalability

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