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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Min Tian Huazeng Liu Junjie Wang Qi Liu Wei Du Zanjun Zhang Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan Key Laboratory of High Performance Computing Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shaanxi Key Laboratory of Large Scale Electromagnetic Computing Xidian University Xi’an China

Sparse LU factorization is a critical kernel in scientific computing and engineering applications. A better nonzero pattern of sparse matrixes can accelerate LU factorization by reordering. Traditionally it’s difficult to predict which non-zero pattern is optimal for a sparse matrix. In this paper, we proposed a graph convolutional neural network (GCN) for adaptively selecting the optimal reordering algorithm from five candidate reordering algorithms in the Matrix preprocessing step, referred to as SPM-GCN. After using our SPM-GCN, the average numerical factorization time outperformed the other five algorithms, and the average numerical factorization time was reduced by 17.3% compared with the default method of swSuperLU.

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A Nature-Inspired Energy-Efficient Clustering Protocol Based on Improved Hybrid Grey Wolf Optimization for FANETs 7

A Nature-Inspired Energy-Efficient Clustering Protocol Based...

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7th IEEE International Conference on Electronics and Communication Engineering, ICECE 2024

作者： Xu, Mengying Zu, Yunxiao Zhou, Jie Dai, Jiapeng Chen, Jing Liu, Meiru School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China Shihezi University Department of Information Science and Technology Shihezi China

ISBN: (纸本)9798331517755

Flying ad hoc networks (FANETs) are composed of multiple UA V nodes that are distributed across a wide area and linked via wireless communication to gather and transmit environmental data. However, the battery-powered UAVs in FANETs imposes significant energy limitations, thereby shortening the network's operational lifespan. This paper proposes an energy-efficient clustering protocol based on improved hybrid grey wolf optimization (EEC-IHGWO), which can optimally select cluster heads (CHs), significantly extend network lifetime and maximize energy efficiency. The selection criteria include factors like remaining energy, the distance from cluster members to CHs, and node density. The performance of the EEC-IHGWO method, measured by network lifetime, the number of living nodes, and remaining energy, is assessed against traditional algorithms such as LEACH, as well as the more recent PSO and FCM. The simulation results demonstrate that EEC-IHGWO sustains a longer network lifetime and higher energy efficiency. © 2024 IEEE.

关键词： Ad hoc networks

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Backdoor Detection in Digital Twin-Driven Smart Grid via Contribution Analysis

Backdoor Detection in Digital Twin-Driven Smart Grid via Con...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Peiming Xu Peilin Luo Yixin Jiang Tian Dong Zhihong Liang Fazhong Liu Yan Meng Haojin Zhu Electric Power Research Institute CSG Guangzhou Guangdong China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China School of Electronics Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

With the integration of smart grid and digital twin technologies, federated learning becomes pivotal in ensuring privacy-preserving AI model training. In such scenarios, each user within the smart grid is geographically dispersed, prohibiting the sharing of raw data to safeguard privacy. However, this setup is susceptible to backdoor attacks. When an attacker initiates such an attack, the smart grid model is manipulated to produce the attacker's desired output, while maintaining normal functionality under standard conditions. The inherent distributed nature of federated learning complicates the detection of backdoor attacks. To address this, we model the federated learning process within a digital twin as a cooperative game. By employing the Shapley value to measure the contribution of nodes in each training round, we propose an outlier detection algorithm to identify malicious users. The effectiveness of the proposed algorithm has been evaluated via extensive experiments.

关键词： Training Data privacy Federated learning Games Smart grids Digital twins Game theory Detection algorithms Anomaly detection Standards

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A Nature-Inspired Energy-Efficient Clustering Protocol Based on Improved Hybrid Grey Wolf Optimization for FANETs

A Nature-Inspired Energy-Efficient Clustering Protocol Based...

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IEEE International Conference on Electronics and Communication Engineering (ICECE)

作者： Mengying Xu Yunxiao Zu Jie Zhou Jiapeng Dai Jing Chen Meiru Liu School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China Department of Information Science and Technology Shihezi University Shihezi China

ISBN: (数字)9798331517755

ISBN: (纸本)9798331517762

关键词： Wireless communication Protocols Machine learning algorithms Simulation Clustering algorithms Energy efficiency Ad hoc networks Magnetic heads Particle swarm optimization Optimization

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hcaPCG: A Heterogeneous and communication-avoid PCG with Jacobi preconditioner on SW26010-Pro architecture

hcaPCG: A Heterogeneous and communication-avoid PCG with Jac...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Min Tian Yue Liu Li Wang Yanyan Chen Qi Liu Jingshan Pan 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) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Key Laboratory of High Performance Computing Jinan Institute of Supercomputing Technology

Due to its efficiency and versatility, the preconditioned conjugate gradient algorithm has long been a staple in the realm of iterative linear system solvers. In this paper, we proposed an optimized preconditioned conjugate gradient algorithm tailored for the SW26010-Pro manycore processor, The main work includes: Optimizing data block sizes based on the processor’s storage structure; combining thread-level and data-level parallelism, utilizing manual SIMD to improve efficiency; optimizing the memory access pattern by employing direct memory access and overlapping of computation and communication; employing a shared-memory approach to store long vectors across all cores. Furthermore, we design an accelerated algorithm for reduction operations, to avoid data communication. Experimental results show that the hcaPCG yields up to 28.1× speedups on average compared to the original implementation.

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PIOD: An Efficient Parallel Iterative Algorithm for Solving Over-determined Equations

PIOD: An Efficient Parallel Iterative Algorithm for Solving ...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Min Tian Zhenguo Wei Yanlong Li Wei Du Lei Xiao Chaoshuai Xu 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) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Key Laboratory of High Performance Computing Jinan Institute of Supercomputing Technology

The solution of over-determined equations plays a very important role in fields such as data fitting, signal processing, and machine learning. It is of great significance in predicting natural phenomena, optimizing engineering design, and other fields. However, there is currently no efficient method to solve over-determined equations, either being not accurate enough or consuming a lot of time. In this article, we propose a parallel iterative method for solving over-determined equations, called the PIOD algorithm. By using a sub-convergence condition to terminate the iterative calculation, we have developed a task partitioning strategy for the algorithm and implemented parallelization of the solution of over-determined equations on a distributed memory system. Our proposed algorithm achieves an average speedup of 152 ×times compared to the open-source Eigen solver. Additionally, it also achieves a parallel efficiency of over 30%.

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Incremental Context-free Grammar Inference in Black Box Settings

arXiv

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arXiv 2024年

作者： Li, Feifei Chen, Xiao Xiao, Xi Sun, Xiaoyu Chen, Chuan Wang, Shaohua Han, Jitao Tsinghua Shenzhen International Graduate School China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China The University of Newcastle Australia The Australian National University Australia Central University of Finance and Economics China

Black-box context-free grammar inference presents a significant challenge in many practical settings due to limited access to example programs. The state-of-the-art methods, Arvada and Treevada, employ heuristic approaches to generalize grammar rules, initiating from flat parse trees and exploring diverse generalization sequences. We have observed that these approaches suffer from low quality and readability, primarily because they process entire example strings, adding to the complexity and substantially slowing down computations. To overcome these limitations, we propose a novel method that segments example strings into smaller units and incrementally infers the grammar. Our approach, named Kedavra, has demonstrated superior grammar quality (enhanced precision and recall), faster runtime, and improved readability through empirical comparison. © 2024, CC BY.

关键词： Context free grammars

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AH-TDMA: An Adaptive Heterogeneous Tridiagonal Matrix Algorithm on the New Sunway Supercomputer

AH-TDMA: An Adaptive Heterogeneous Tridiagonal Matrix Algori...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Min Tian Qi Liu Yanlong Li Wei Du Zenghui Ren Yue Liu 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) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Key Laboratory of High Performance Computing Jinan Institute of Supercomputing Technology

The solution of tridiagonal linear systems is used in in various fields and plays a crucial role in numerical simulations. However, there is few efficient solver for tridiagonal linear systems on the new Sunway supercomputer. Based on a three-dimensional heat conduction problem, we propose an adaptive heterogeneous tridiagonal matrix algorithm (AH-TDMA). Our major innovations include: (1) To address computational hotspots within AH-TDMA, a multi-level parallel approach involving MPI+Athread has been adopted. (2) An adaptive data partitioning scheme has been set up to achieve load balance. (3) Employing direct memory access and establishing shared space between registers and main memory, instead of employing discrete memory access, is done to enhance memory access efficiency. (4) The optimization of the loop structure has been made in adjusting the sequencing of the dual-layered loops to reduce communication overhead. The experimental results show that, with one core group, the AH-TDMA achieves a speedup of 99.6 times for hotspot and total time speedup up to 59.4 times compared to the Parallel and Scalable Library for Tridiagonal Matrix Algorithm (PaScal TDMA). The AH-TDMA is scalable up to 2048 core groups, with a parallel efficiency of 69.2%.

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A security Reinforcement Model for Edge computing Facilities in 5G power network 1

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5th International Conference on Wireless Communications and Applications, ICWCA 2021

作者： Li, Nige Zhou, Peng Shao, Zhipeng Chen, Xiaoxiao Power Grid Digitizing Technology Department State Grid Smart Grid Research Institute Co. Ltd. Jiangsu Nanjing210003 China State Grid Key Laboratory of Information and Network Security Nanjing210003 China State Grid Zhejiang Electric Power Corporation Information and Telecommunication Branch Hangzhou China

ISBN: (数字)9789811934865

ISBN: (纸本)9789811934858

5G network has attracted much attention from the vertical industry since its commercial use. Compared with previous generations of mobile communication technologies, 5G bandwidth, delay and other communication KPI indicators have been greatly improved. 5G network has three typical application scenarios of large bandwidth, low delay and wide connection. In the power grid scenario, it can meet the needs of various power grid business access. As the key technology to realize 5G, multi-access edge computing (MEC) plays an important role in high-speed and low delay wireless data transmission network and has made indelible contributions to improving end-to-end service quality. In view of today’s increasingly complex network security environment and changing attack means, security attacks against MEC systems and applications have become endless. In order to deal with large-scale and various attacks, this paper studies the micro application security reinforcement and independent application security reinforcement technology deployed in edge computing facilities in the environment of 5G power network, proposes the security reinforcement model of MEC, and strengthens the source code of edge computing facilities based on the so file confusion technology of OLLVM, so as to ensure the safe and stable operation of MEC. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： 5G mobile communication systems

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Knowledge Distillation Using Global Fusion and Feature Restoration for Industrial Defect Detectors

Knowledge Distillation Using Global Fusion and Feature Resto...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zhengqian Feng Xiyao Yue Wang Li Mingle Zhou Delong Han Gang Li SHANDONG SCICOM Information and Economy Research Institute CO. . Ltd 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 Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Deep learning technology has been widely applied in industrial quality inspection tasks to improve the accuracy of defect detection, object recognition, and classification. However, in the task of object detection, both feature-based and regression-based traditional knowledge distillation methods impose overly strict constraints on the student model. To address the above issues, this article proposes Global Fusion and Feature Restoration Knowledge Distillation(FRD). FRD integrates contextual information into the channel through a Global Fusion Module (GFM), and further utilizes attention mechanisms to adaptively focus on the distillation region after separating the foreground background. FRD also uses a Mask Feature Restoration (MFR) to mask and restore a portion of student features, improving the learnability of the model. At the same time, FRD adopts a comprehensive approach of multiple loss superposition constraints, rather than simply using MSE losses to imitate the features of teachers. Experiments have shown that FRD can effectively improve model performance in industrial defect detection tasks. On the aluminum surface defect dataset, FRD increased the mAP index of RetinaNet-Res50 from 57.7% to 61.7%. We also confirmed the effectiveness of FRD for general object detection on the Coco dataset. On a randomly selected COCO dataset containing 4000 images, FRD increased the mAP metric of RetinaNet-Res50 from 21.5% to 43.6%.

关键词： Measurement Deep learning Adaptation models Object detection Inspection Feature extraction Image restoration Object recognition Cybernetics Defect detection

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