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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Haiyang Lin Mingyu Yan Duo Wang Wenming Li Xiaochun Ye Zhimin Tang Dongrui Fan State Key Laboratory of Computer Architecture ICT CAS Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

Deep neural networks achieve huge success in computer vision, natural language process and other fields. As larger networks and larger datasets bring better performance, the training time grows rapidly. Distributed training uses parallelization strategy to accelerate the training of deep neural network. In the existing distributed training framework of deep neural network, the parameter server architecture has poor scalability and is not suitable for large-scale distributed training. Ring/HD All Reduce algorithm perform well in scalability under ideal circumstances. However, in many realistic scenarios, imbalance will occur frequently in the distributed training system, which is mainly the imbalance of compute ability and network. Due to the structure characteristics of synchronous distributed training and All Reduce algorithm, imbalance will bring serious impact on performance. At present, there is a lack of research on both compute ability imbalance and network imbalance, especially dynamic imbalance. To alleviate this problem, we analyze All Reduce algorithm first, and propose RingDL, which optimizes the performance of distributed training system by alleviating imbalance through customizable dynamic workloads strategy and link strategy. The experimental results show that RingDL significantly reduces idle time in computing phase and synchronous phase, improving the overall performance.

关键词： Training Deep learning Degradation Computer vision Heuristic algorithms Scalability Neural networks

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Streamline Ring ORAM Accesses through Spatial and Temporal Optimization

Streamline Ring ORAM Accesses through Spatial and Temporal O...

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IEEE Symposium on High-Performance Computer Architecture

作者： Dingyuan Cao Mingzhe Zhang Hang Lu Xiaochun Ye Dongrui Fan Yuezhi Che Rujia Wang Tsinghua University Beijing China State Key Laboratory of Computer Architecture ICT CAS Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing China Illinois Institute of Technology

Memory access patterns could leak temporal and spatial information in a sensitive program; therefore, obfuscated memory access patterns are desired from the security perspective. Oblivious RAM (ORAM) has been the favored candidate to eliminate the access pattern leakage through randomly remapping data blocks around the physical memory space. Meanwhile, accessing memory with ORAM protocols results in significant memory bandwidth overhead. For each memory request, after going through the ORAM obfuscation, the main memory needs to service tens of actual memory accesses, and only one real access out of them is useful for the program execution. Besides, to ensure the memory bus access patterns are indistinguishable, extra dummy blocks need to be stored and transmitted, which cause memory space waste and poor performance. In this work, we introduce a new framework, String ORAM, that accelerates the Ring ORAM accesses with Spatial and Temporal optimization schemes. First, we identify that dummy blocks could significantly waste memory space and propose a compact ORAM organization that leverages the real blocks in memory to obfuscate the memory access pattern. Then, we identify the inefficiency of current transaction-based Ring ORAM scheduling on DRAM devices and propose an effective scheduling technique that can overlap the time spent on row buffer misses while ensuring correctness and security. With a minimal modification on the hardware and software, and negligible impact on security, the framework reduces 30.05% execution time and up to 40% memory space overhead compared to the state-of-the-art bandwidth-efficient Ring ORAM.

关键词： Protocols Space debris Memory management Random access memory Performance gain Scheduling Software

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The Ordered Lattice Host Framework Induced Guest Li+ Disordering in High Performance Cobalt-Free Ni-Rich Cathode Materials

SSRN

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SSRN 2024年

作者： Xu, Jia Zhang, Xiaoyu Sun, Shixiong Fu, Rong Cheng, Fangyuan Wei, Peng Li, Qing Fang, Chun Lin, He Han, Jiantao State Key Laboratory of Material Processing and Die & Mould Technology School of Materials Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China School of Materials Science and Engineering State Key Laboratory of Advanced Special Steel Shanghai University Shanghai200444 China School of Mathematical and Physical Sciences University of Technology SydneyNSW2007 Australia Shanghai Synchrotron Radiation Facility Zhangjiang Lab Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201204 China

Recently, due to high price, resource shortage and unstable supply of cobalt, the development of low-cost cobalt-free Ni-rich cathodes has attracted extensive attention with the ever-increasing lithium-ion batteries (LIBs) industry. Selecting cost-effective elements to replace cobalt in Ni-rich cathodes is urgent. However, the principle of structural design of Ni-rich cathode remains unclear, hampering the selection of alternative elements. Herein, the cobalt-free cathodes of LiNi0.95Mg0.05O2 (NiMg) and LiNi0.95Mn0.05O2 (NiMn) are designed as alternatives to LiNi0.96Co0.04O2 (NiCo). NiMg has comparable cycle stability with NiCo, while NiMn has inferior cycle performance. Reverse Monte Carlo modelling was used to generate structural model and uncover local structure by fitting pair distribution function. It reveals Mn causes more severe Jahn-Teller distortions and disordered lattice host framework (Ni0.95M0.05O2, M = Co/Mn/Mg) than Co and Mg due to the strong size effect and coulomb interactions of Mn in Ni0.95Mn0.05O2 layer. The outstanding cycle stability of NiMg and NiCo originates from the ordered lattice host frameworks, which relieve stress and inhibit particle breakage during cycle. Meanwhile, the ordered lattice host framework induced guest Li+ disordering reduces Li+ diffusion energy barrier, improving the rate capability. This study provides a new perspective for the structural design of cobalt-free Ni-rich cathodes. © 2024, The Authors. All rights reserved.

关键词： Cathodes

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High-performance optimization of SM4-GCM based on FPGA

High-performance optimization of SM4-GCM based on FPGA

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advanced computing and Endogenous Security, International Conference on

作者： Shun Sen Lv Bin Li Xiaojie Chen Qinglei Zhou School of Information Engineering Zhengzhou University Zhengzhou China State Key Laboratory of Mathmatical Engineering and Advanced Computing Zhengzhou China Schoo of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665424509

SM4-GCM is an encryption algorithm with authentication function. The algorithm achieves the purpose of data security and information integrity. The SM4-GCM algorithm, implemented using traditional software methods, has low throughput and high resource consumption. In order to further improve the algorithm performance, this paper uses FPGA to optimize the SM4-GCM algorithm to achieve full-pipeline parallel acceleration. Firstly, the SM4 module is optimized using pipelining techniques. Then, the GHASH module is optimized using the Karatsuba algorithm and fast reduction. Finally, a loosely coupled architecture is used to connect various modules with asynchronous FIFOs, which improves the resource utilization and throughput of the FPGA circuit. The experimental results show that the throughput of the optimized SM4-GCM algorithm reaches 28.8 Gbps. It is better than other schemes, has a higher throughput, and meets the actual application requirements.

关键词： Power demand Software algorithms Computer architecture Throughput Software Encryption Resource management

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AttCorr: A Novel Deep Learning Model for Flow Correlation Attacks on Tor

AttCorr: A Novel Deep Learning Model for Flow Correlation At...

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Consumer Electronics and Computer engineering (ICCECE), 2021 IEEE International Conference on

作者： Ji Li Chunxiang Gu Xieli Zhang Xi Chen Wenfen Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China School of Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728183190

ISBN: (纸本)9781728183206

The proliferation of illegal information and criminal behavior on anonymous networks arouses the demand for deanonymization attacks on anonymous communication systems. Flow correlation is a common technique for deanonymization attacks, but most related works currently are based on statistical learning models, thus suffering from burdensome artificial feature engineering. In this paper, we design a novel deep learning model AttCorr for flow correlation attacks on Tor. AttCorr takes the raw traffic features of flow pair as input, including packet sizes, flow directions and inter-packet delays, and uses the multi-head attention mechanism to capture the flow information involved in features and the complex nature of noise in Tor. Experiment results show that AttCorr achieves the same level of accuracy as the stateof-the art method of DeepCorr with lower complexity, simpler feature processing and better interpretability.

关键词： Deep learning Training Correlation Statistical learning Telecommunication traffic Feature extraction Delays

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Large contributions from optical phonons to thermal transport in hexagonal carbon-boron-nitrogen monolayers

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Physical Review B 2023年第23期108卷 235420-235420页

作者： Xue-Kun Chen (陈学坤) Yue Zhang (张越) Qing-Qing Luo (罗青清) Xue Chen (陈雪) Pin-Zhen Jia (贾聘真) Wu-Xing Zhou (周五星) School of Mathematics and Physics and Hunan Key Laboratory of Mathematical Modeling and Scientific Computing University of South China Hengyang 421001 China Department of Mathematics and Physics Hunan Institute of Technology Hengyang 421002 China School of Materials Science and Engineering and Hunan Provincial Key Laboratory of Advanced Materials for New Energy Storage and Conversion Hunan University of Science and Technology Xiangtan 411201 People's Republic of China

As representative semiconducting hexagonal carbon-boron-nitride lattices, C6BN and C2BN are experimentally realized two-dimensional (2D) plane materials and have recently become the focus of research. Herein, combining first-principles calculations with the Boltzmann transport equation, we performed a comprehensive study on the phonon interaction and thermal conductivity in C6BN and C2BN monolayers. It is found that the thermal conductivities of C6BN and C2BN monolayers at room temperature are reduced by 79% and 73%, respectively, due to four-phonon scattering, compared with the results including three-phonon scattering only. We can attribute this phenomenon to giant four-phonon scattering exclusive for the heat-carrying out-of-plane acoustic (ZA) phonons, because the reflection symmetry allows four-ZA processes much higher than three-ZA processes, and the quasiparallel behavior between the ZA and low-lying out-of-plane optical (ZO) branches contribute to a broad phase space for four-phonon scattering as well. Moreover, C6BN monolayer exhibits unusual behavior that optical phonons contribute about ∼60% to the overall thermal conductivity under the four-phonon picture, which differs from the traditional case that acoustic phonons dominate thermal conductivity. Unexpectedly, two low-lying ZO modes have as high as 38% contributions to the thermal transport at 300 K under the four-phonon picture, causing 60% contribution of optical phonon modes, apparently larger than that of the three-phonon case (15%) and many other 2D materials, also indicating the four-phonon scattering has a more significant effect on acoustic phonons than on optical phonons. This finding not only highlights insight into the nature of phonon transport, but also provides a promising strategy for manipulation of heat transport based on optical phonon modes.

关键词： Phonons Thermal conductivity Boron nitride Graphene First-principles calculations Lattice-Boltzmann methods

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Progress in research on ultrasound radiomics for predicting the prognosis of breast cancer

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Cancer Innovation 2023年第4期2卷 283-289页

作者： Xuantong Gong Xuefeng Liu Xiaozheng Xie Yong Wang Department of Ultrasound National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBijingChina State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBejjing Advanced Innovation Center for Big Data and Brain Computing(BDBC)Beihang UniversityBeijingChina School of Computer and Communication Engi neering.University of Science and Technology Beijing BeijingChina

Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women *** means of predicting the prognosis of breast cancer are very helpful in guiding treatment and improving patients'*** extracted by radiomics reflect the genetic and molecular characteristics of a tumor and are related to its biological behavior and the patient's ***,radiomics provides a new approach to noninvasive assessment of breast cancer *** is one of the commonest clinical means of examining breast *** recent years,some results of research into ultrasound radiomics for diagnosing breast cancer,predicting lymph node status,treatment response,recurrence and survival times,and other aspects,have been *** this article,we review the current research status and technical challenges of ultrasound radiomics for predicting breast cancer *** aim to provide a reference for radiomics researchers,promote the development of ultrasound radiomics,and advance its clinical application.

关键词： breast cancer deeplearning prognosisprediction radiomics ultrasound

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Distributed Deep Learning System for Efficient Face Recognition in Surveillance System

Distributed Deep Learning System for Efficient Face Recognit...

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作者： Jinjin Liu Zhifeng Chen Xiaonan Li Tongxin Wei School of Computer Science Zhongyuan University of Technology State Key Laboratory of Mathematical Engineering and Advanced Computing School of Cyberspace Security Zhengzhou University

In view of the bandwidth consumption caused by data stream transmission in video analysis system and the demand for accurate online real-time analysis of massive data, this paper proposes a deep learning model framework for face recognition employed in the embedded system. Through data collaboration, the cloud could build a more complex data set with a small amount of uploaded data gathered by the end devices. And the framework collaboration makes sure that the fully-trained cloud model directly download or distillate knowledge to the end devices. Experiments show that the deep model not only realizes the real-time response and the accurate response of the cloud system, but also greatly reduces the bandwidth consumption caused by sample data transmission in the model training process.

关键词： Deep learning distributed model data transfer model distillation

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Metaheuristic Optimization Methods and Techniques for Multiple Sclerosis Detection 4th

Metaheuristic Optimization Methods and Techniques for Multip...

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4th International Conference on IoT and Big Data Technologies for Health Care, IotCare 2023

作者： Wang, Jiaji Wu, Meng Chen, Shuwen Zhu, Huisheng School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Physics and Information Engineering Jiangsu Second Normal University Jiangsu Nanjing210016 China State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China

ISBN: (纸本)9783031900839

This paper explores the application of metaheuristic optimization methods in the context of Multiple Sclerosis (MS) detection, addressing the challenges of early and accurate diagnosis. It introduces Genetic Algorithms, Simulated Annealing, and Particle Swarm Optimization as effective tools for feature selection and classification in MS detection. Additionally, the paper discusses emerging metaheuristic optimization methods and their potential contributions. Through critical evaluation, it emphasizes the need for method selection based on task-specific requirements. In conclusion, this research highlights the promise of metaheuristic optimization in revolutionizing MS detection, urging further development and validation on larger datasets. Future directions include hybrid algorithms and advanced machine learning integration for improved diagnostic precision. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Particle swarm optimization (PSO)

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Use Tabu Search Particle Swarm Optimization Algorithm to Detect COVID-19 4th

Use Tabu Search Particle Swarm Optimization Algorithm to Det...

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4th International Conference on IoT and Big Data Technologies for Health Care, IotCare 2023

作者： Chen, Shuwen Wang, Jiaji Zhu, Huisheng School of Physics and Information Engineering Jiangsu Second Normal University Jiangsu Nanjing210016 China State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom

ISBN: (纸本)9783031900839

Efficient and accurate COVID-19 detection methods are paramount in the ongoing battle against the global pandemic. In this study, we present a novel approach that leverages the Tabu Search Particle Swarm Optimization (TS-PSO) algorithm for COVID-19 detection. Unlike conventional methods, our approach combines the strengths of both global and local search strategies, enhancing the accuracy of diagnosis. We conducted extensive experiments, benchmarking our TS-PSO-based approach against established methods. The results reveal that our TS-PSO method achieves competitive performance, with improvements in sensitivity, specificity, precision, accuracy, F1 score, MCC, and FMI. This innovative approach not only advances the field of COVID-19 detection but also demonstrates its potential as a valuable tool for healthcare professionals in the ongoing fight against the pandemic. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Tabu search

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