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10th IEEE Joint International Information technology and Artificial Intelligence Conference, ITAIC 2022

作者： Jing, Zhang Weijie, Ding Zhejiang Police College Department of Computer and Information Security Hangzhou310053 China Big-data and Network Security Research Institute Zhejiang Police College Hangzhou310053 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Hangzhou310053 China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

Aiming at the high frequency of urban traffic accidents and the relative shortage of police resources, a multi-objective discrete Jaya algorithm is proposed to solve the problem of police resource deployment in emergency traffic accident handling. The purpose is to make timely response and handling of traffic accidents through the rational allocation of police deployment points in the jurisdiction. Taking the coverage of accident points and the average response time of accidents as the optimization objectives, the discrete Jaya algorithm is used as the global search strategy to approach the Pareto front by constantly approaching the optimal solution and far away from the worst solution, and then the neighborhood search strategy is used to improve the local search ability of the algorithm. Finally, a practical example is given to verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Law enforcement

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm 29

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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Poseidon: Practical Homomorphic Encryption Accelerator

Poseidon: Practical Homomorphic Encryption Accelerator

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IEEE Symposium on High-Performance computer architecture

作者： Yinghao Yang Huaizhi Zhang Shengyu Fan Hang Lu Mingzhe Zhang Xiaowei Li State Key Laboratory of Computer Architecture Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China Zhongguancun Laboratory Beijing China

With the development of the important solution for privacy computing, the explosion of data size and computing intensity in Fully Homomorphic Encryption (FHE) has brought enormous challenges to the hardware design. In this paper, we propose a practical FHE accelerator - "Poseidon", which focuses on improving the hardware resource and bandwidth consumption. Poseidon supports complex FHE operations like Bootstrapping, Keyswitch, Rotation and so on, under limited FPGA resources. It refines these operations by abstracting five key operators: Modular Addition (MA), Modular Multiplication (MM), Number Theoretic Transformation (NTT), Automorphsim and Shared Barret Reduction (SBT). These operators are combined and reused to implement higher-level FHE operations. To utilize the FPGA resources more efficiently and improve the parallelism, we adopt the radix-based NTT algorithm and propose HFAuto, an optimized automorphism implementation suitable for FPGA. Then, we design the hardware accelerator based on the optimized key operators and HBM to maximize computational efficiency. We evaluate Poseidon with four domain-specific FHE benchmarks on Xilinx Alveo U280 FPGA. Empirical results show that the efficient reuse of the operator cores and on-chip storage enables superior performance compared with the state-of-the-art GPU, FPGA and accelerator ASICs. We highlight the following results: (1) up to 370× speedup over CPU for the basic operations of FHE; (2) up to 1300×/52× speedup over CPU and the FPGA solution for the key operators; (3) up to 10.6×/8.7× speedup over GPU and the ASIC solution for the FHE benchmark.

关键词： Data privacy Graphics processing units Bandwidth Benchmark testing Parallel processing Explosions System-on-chip

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Energy-efficient NTT Design with One-bank SRAM and 2-D PE Array

Energy-efficient NTT Design with One-bank SRAM and 2-D PE Ar...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Jianan Mu Huajie Tan Jiawen Wu Haotian Lu Chip-Hong Chang Shuai Chen Shengwen Liang Jing Ye Huawei Li Xiaowei Li State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences CASTEST Tianjin University Nanyang Technological University Rock-Solid Security Lab Fiberhome

In Number Theoretic Transform (NTT) operation, more than half of the active energy consumption stems from memory accesses. Here, we propose a generalized design method to improve the energy efficiency of NTT operation by considering the effect of processing element (PE) geometry and memory organization on the data flow between PEs and memory. To decrease the number of data bits that are required to be accessed from the memory, a two-dimensional (2-D) PE array architecture is used. A pair of ping-pong buffers are proposed to transposed swap the coefficients to enable a single bank of memory to be used with the 2-D PE array to reduce the average memory bit access energy without compromising the throughput. Our experimental results show that this design method can produce NTT accelerators with up to 69.8% saving in average energy consumption compared with the existing designs based on multi-bank SRAM and one-bank SRAM with one-dimensional PE array with the same number of PEs and total memory size.

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High-speed and long-reach PON employing PM-DQPSK signal and coherent receiver

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Journal of Optical Communications 2023年第s1期44卷 S97-S105页

作者： Qin, Panke Meng, Hui Zhang, Lei Chen, Guangxuan College of Computer Science and Technology Henan Polytechnic University Henan Jiaozuo454000 China Joint Logistics College National Defence University Beijing100858 China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public Security Zhejiang Police College Zhejiang Hangzhou310053 China

The development of communication technology requires a new type of passive optical network (PON) architecture. The architecture needs to be based on time division multiplexing (TDM) of 40 Gbit/s downstream and 10 Gbit/s upstream, and it can realize TDM PON prototype. The PON system achieved the system distance of 120 km supporting 128 ONU with power budget of 53.5 dB via polarization multiplexing differential quadrature phase shift keying format signal which employs the coherent receiver and the digital signal processing. © 2023 Walter de Gruyter GmbH. All rights reserved.

关键词： Passive optical networks

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Long Text Classification Based on BERT 5

Long Text Classification Based on BERT

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5th IEEE Information technology, Networking, Electronic and Automation Control Conference, ITNEC 2021

作者： Weijie, DIng Yunyi, Li Jing, Zhang Xuchen, Shen Big-data and Network Security Research Institute Zhejiang Police College Hangzhou310053 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Hangzhou310053 China Zhejiang Police College Department of Investigation Hangzhou310053 China Zhejiang Police College Department of Computer and Information Security Hangzhou310053 China Science and Technology Management Section Hangzhou311203 China

ISBN: (纸本)9781665415989

Existing text classification algorithms generally have limitations in terms of text length and yield poor classification results for long texts. To address this problem, we propose a BERT-based long text classification method. First, we slice the long text and use BERT to encode the sliced clauses to obtain the local semantic information. Second, we use BiLSTM to fuse the local semantic information and adopt the attention mechanism to increase the weight of important clauses in the long text, so as to obtain the global semantic information. Finally, the global semantic information is input to the softmax layer for classification. Experimental results show that the proposed method achieves higher accuracy than commonly used models. © 2021 IEEE.

关键词： Classification (of information)

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yan Zeng ChengChuang Huang YiJie Ni ChunBao Zhou JiLin Zhang Jue Wang MingYao Zhou MeiTing Xue YunQuan Zhang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou China Data Security Governance Zhejiang Engineering Research Center Hangzhou China School of ITMO Joint Institute Hangzhou Dianzi University Hangzhou China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing China HuaWei State Key Laboratory of Computer Architecture Institute of Computing Technology of the Chinese Academy of Sciences Beijing China

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network’s dataflow graph and device cluster’s topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method.

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Software Defect Prediction via Deep Belief Network

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Chinese Journal of Electronics 2019年第5期28卷 925-932页

作者： WEI Hua SHAN Chun HU Changzhen ZHANG Yu YU Xiao School of Computer Science and Technology Beijing Institute of Technology China Information Technology Security Evaluation Center Beijing Key Laboratory of Software Security Engineering Technology Beijing Institute of Technology School of Electrical and Information Engineering and Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture State Key Laboratory in China for Geomechanics and Deep Underground Engineering (Beijing) China University of Mining and Technology School of Computer Science and Technology Shandong University of Technology

Defect distribution prediction is a meaningful topic because software defects are the fundamental cause of many attacks and data loss. Building accurate prediction models can help developers find bugs and prioritize their testing efforts. Previous researches focus on exploring different machine learning algorithms based on the features that encode the characteristics of *** problem of data redundancy exists in software defect data set, which has great influence on prediction *** propose a defect distribution prediction model(Deep belief network prediction model, DBNPM), a system for detecting whether a program module contains defects. The key insight of DBNPM is Deep belief network(DBN)technology, which is an effective deep learning technique in image processing and natural language processing,whose features are similar to defects in source *** results show that DBNPM can efficiently extract and process the data characteristics of source program and the performance is better than Support vector machine(SVM), Locally linear embedding SVM(LLE-SVM), and Neighborhood preserving embedding SVM(NPE-SVM).

关键词： Defect prediction Software security Deep belief network(DBN)

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OpenAPMax: Abnormal Patterns-based Model for Real-World Alzheimer’s Disease Diagnosis

arXiv

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arXiv 2023年

作者： Huang, Yunyou Guan, Xianglong Lu, Xiangjiang Liang, Xiaoshuang Miao, Xiuxia Xie, Jiyue Liu, Wenjing Ma, Li Tang, Suqin Zhang, Zhifei Zhan, Jianfeng The Key Lab of Education Blockchain and Intelligent Technology Ministry of Education The Guangxi Key Laboratory of Multi-Source Information Mining and Security Guangxi Normal University China The School of Electronic and Information Engineering School of Integrated Circuits Guangxi Normal University Guilin530015 China The Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China The State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100086 China The Department of Physiology and Pathophysiology Capital Medical University Beijing100069 China The Guilin Medical University Guilin541001 China The Guangxi Key Lab of Multi-Source Information Mining & Security China

Alzheimer’s disease (AD) cannot be reversed, but early diagnosis will significantly benefit patients’ medical treatment and care. In recent works, AD diagnosis has the primary assumption that all categories are known a prior—a closed-set classification problem, which contrasts with the open-set recognition problem. This assumption hinders the application of the model in natural clinical settings. Although many open-set recognition technologies have been proposed in other fields, they are challenging to use for AD diagnosis directly since 1) AD is a degenerative disease of the nervous system with similar symptoms at each stage, and it is difficult to distinguish from its pre-state, and 2) diversified strategies for AD diagnosis are challenging to model uniformly. In this work, inspired by the concerns of clinicians during diagnosis, we propose an open-set recognition model, OpenAPMax, based on the anomaly pattern to address AD diagnosis in real-world settings. OpenAPMax first obtains the abnormal pattern of each patient relative to each known category through statistics or a literature search, clusters the patients’ abnormal pattern, and finally, uses extreme value theory (EVT) to model the distance between each patient’s abnormal pattern and the center of their category and modify the classification probability. We evaluate the performance of the proposed method with recent open-set recognition, where we obtain state-of-the-art results. Copyright © 2023, The Authors. All rights reserved.

关键词： Patient treatment

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Training, testing and benchmarking medical AI models using Clinical AIBench

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BenchCouncil Transactions on Benchmarks, Standards and Evaluations 2022年第1期2卷 100037-100037页

作者： Huang, Yunyou Miao, Xiuxia Zhang, Ruchang Ma, Li Liu, Wenjing Zhang, Fan Guan, Xianglong Liang, Xiaoshuang Lu, Xiangjiang Tang, Suqing Zhang, Zhifei Guangxi Key Lab of Multi-Source Information Mining & Security School of Computer Science and Engineering & School of Software Guangxi Normal University Guilin China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Guilin Medical University Guilin China Department of Physiology and Pathophysiology Capital Medical University Beijing China Faculty of Education Guangxi Normal University Guilin China International Open Benchmark Council Beijing China

AI technology has been used in many clinical research fields, but most AI technologies are difficult to land in real-world clinical settings. In most current clinical AI research settings, the diagnosis task is to identify different types of diseases among the given ones. However, the diagnosis in real-world settings needs dynamically developing inspection strategies based on the existing resources of medical institutions and identifying different kinds of diseases out of many possibilities. To promote the development of different clinical AI technologies and the implementation of clinical applications, we propose a benchmark named Clinical AIBench for developing, verifying, and evaluating clinical AI technologies in real-world clinical settings. Specifically, Clinical AIBench can be used for: (1) Model training and testing: Researchers can use the data to train and test their models. (2)Model evaluation: Researchers can use Clinical AIBench to objectively, fairly, and comparably evaluate various models of different researchers. (3) Clinical value evaluation: Researchers can use the clinical indicators provided by Clinical AIBench to evaluate the clinical value of models, which will be applied in real-world clinical settings. For convenience, Clinical AIBench provides three different levels of clinical settings: restricted clinical setting, which is named closed clinical setting, data island clinical setting, and real-world clinical setting, which is called open clinical setting. In addition, Clinical AIBench covers three diseases: Alzheimer's disease, COVID-19, and dental. Clinical AIBench provides python APIs to researchers. The data and source code are publicly available from the project website https://***/clinical_aibench/. © 2022 The Authors

关键词： COVID-19

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