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33rd International Conference on Software Engineering and Knowledge Engineering, SEKE 2021

作者： Luo, Jiwen Wang, Tao Chang, Junsheng Guo, Xiaoting National Key Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)1891706527

Effectively assessing the results of users' online learning and enhancing social recognition has become a major development direction for online education platforms. For computer education, this article constructs a technical capability assessment model. This model integrates professional concepts in the field of computer science and extracts knowledge concepts from educational resources. The model first extracts candidate concepts, then uses a graph propagation algorithm to quantify candidate concepts and obtains concepts from them, and finally uses prerequisite relationships to further quantify the concepts mastered by students. The model combines the prerequisite relationship among concepts to quantify the skills that students have mastered. It can not only effectively evaluate the user's skill mastery but also lays a foundation for subsequent course recommendations and career recommendations for users. The model is tested in the real learning environment of 250 students. This model has been proved to own certain practicability and reliability by Kendall rank correlation coefficient, which is used as an evaluation index. © 2021 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Students

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A Deeply Pipelined 64-bit Multiplier for High-Performance RISC-V Processors

A Deeply Pipelined 64-bit Multiplier for High-Performance RI...

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Frontiers technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Wenyi Liu Feng Hu Guilan Li Bangjian Xu Xin Niu College of Computer Science and Electronic Hunan University Changsha China Science and Technology on Parallel and Distributed Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

The multiplier is an important component of the processor's computing unit. Multiplication, multiplication, addition, and multiplication and subtraction operations are widely used in various signal processing algorithms. Based on this, this article extends the multiplication instructions of RISC-V, designs a 64-bit multiplier that combines the booth2 algorithm and wallace compression technology, and designs a deep pipeline mechanism for the multiplier to improve performance. Finally, Through logical simulation and emulation, the result output is correct. Comprehensive results show that under the 28nm cmos process, the MAC unit can reach 2.22Ghz.

关键词： Program processors Pipelines Emulation Signal processing algorithms Computer architecture CMOS process Encoding Circuit synthesis Testing

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A data-assisted physics-informed neural network(DA-PINN)for fretting fatigue lifetime prediction

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International Journal of Mechanical System Dynamics 2024年第3期4卷 361-373页

作者： Can Wang Qiqi Xiao Zhikun Zhou Yongyu Yang Gregor Kosec Lihua Wang Magd Abdel Wahab Soete Laboratory Department of Electrical EnergyMetalsMechanical Constructions and SystemsFaculty of Engineering and ArchitectureGhent UniversityGentBelgium Department of Engineering Mechanics School of Aerospace EngineeringHuazhong University of Science and TechnologyWuhanChina Key Laboratory for Mechanics Anhui Polytechnic UniversityWuhuChina Parallel and Distributed Systems Laboratory Jožef Stefan InstituteLjubljanaSlovenia School of Aerospace Engineering and Applied Mechanics Tongji UniversityShanghaiChina College of Engineering Yuan Ze UniversityTaiwanChina

In this study,we present for the first time the application of physics-informed neural network(PINN)to fretting fatigue *** PINN has recently been applied to pure fatigue lifetime prediction,it has not yet been explored in the case of fretting *** propose a data-assisted PINN(DA-PINN)for predicting fretting fatigue crack initiation *** traditional PINN that solves partial differential equations for specific problems,DA-PINN combines experimental or numerical data with physics equations as part of the loss function to enhance prediction *** DA-PINN method,employed in this study,consists of two main ***,damage parameters are obtained from the finite element method by using critical plane method,which generates a data set used to train an artificial neural network(ANN)for predicting damage parameters in other ***,the predicted damage parameters are combined with the experimental parameters to form the input data set for the DA-PINN models,which predict fretting fatigue *** results demonstrate that DA-PINN outperforms ANN in terms of prediction accuracy and eliminates the need for high computational costs once the damage parameter data set is ***,the choice of loss-function methods in DA-PINN models plays a crucial role in determining its performance.

关键词： crack initiation data-assisted fretting fatigue finite element method physics-informed neural network

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GNNRL-Smoothing: A Prior-Free Reinforcement Learning Model for Mesh Smoothing

arXiv

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arXiv 2024年

作者： Wang, Zhichao C. Chen, Xinhai C. Gong, Chunye Y. Yang, Bo Deng, Liang Sun, Yufei F. Pang, Yufei F. Liu, Jie National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory China National University of Defense Technology Laboratory of Digitizing Software for Frontier Equipment China China Aerodynamics Research and Development Center China Nankai University College of Software China

Mesh smoothing methods can enhance mesh quality by eliminating distorted elements, leading to improved convergence in simulations. To balance the efficiency and robustness of traditional mesh smoothing process, previous approaches have employed supervised learning and reinforcement learning to train intelligent smoothing models. However, these methods heavily rely on labeled dataset or prior knowledge to guide the models’ learning. Furthermore, their limited capacity to enhance mesh connectivity often restricts the effectiveness of smoothing. In this paper, we first systematically analyze the learning mechanisms of recent intelligent smoothing methods and propose a prior-free reinforcement learning model for intelligent mesh smoothing. Our proposed model integrates graph neural networks with reinforcement learning to implement an intelligent node smoothing agent and introduces, for the first time, a mesh connectivity improvement agent. We formalize mesh optimization as a Markov Decision Process and successfully train both agents using Twin Delayed Deep Deterministic Policy Gradient and Double Dueling Deep Q-Network in the absence of any prior data or knowledge. We verified the proposed model on both 2D and 3D meshes. Experimental results demonstrate that our model achieves feature-preserving smoothing on complex 3D surface meshes. It also achieves state-of-the-art results among intelligent smoothing methods on 2D meshes and is 7.16 times faster than traditional optimization-based smoothing methods. Moreover, the connectivity improvement agent can effectively enhance the quality distribution of the mesh. © 2024, CC BY.

关键词： Deep reinforcement learning

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Evaluating a New Attention Framework Based on Matrix Blocking for Attention Models on FPGAs

Evaluating a New Attention Framework Based on Matrix Blockin...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Xiaohang Liu Jingfei Jiang Jinwei Xu Lei Gao National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

The attention mechanism has recently shown superior performance in natural language processing and computer vision tasks. But its complex dataflow and large-scale matrix calculation with huge computing and memory overhead pose a great challenge for the design of hardware accelerators. And previous solutions that benefited from matrix partitioning are bounded by the softmax function. In this paper, we propose a new attention framework that can dramatically improve the performance of attention model inference for long sequence tasks on FPGAs. We design a novel accelerator architecture that employs two systolic arrays and a ping-pong structure to accelerate attention calculation. Meanwhile, we propose an analytical model to predict resource usage and performance, which guides a fast design space exploration. Experiments using the state-of-the-art BERT demonstrate the design achieves 4.61 and 1.24× improvement in speed and energy efficiency compared to CPU and GPU on the Xilinx XCZU11EG platform.

关键词： Performance evaluation Graphics processing units Accelerator architectures Systolic arrays Natural language processing Mobile handsets Space exploration

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WBA: A Warping-based Approach to Generating Imperceptible Adversarial Examples

WBA: A Warping-based Approach to Generating Imperceptible Ad...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Chengyao Hua Shigeng Zhang Weiping Wang Zhankai Li Jian Zhang School of Computer Science and Engineering Central South University Changsha China Science and Technology on Parallel and Distributed Processing Laboratory (PDL)

The human can easily recognize the incongruous parts of an image, for example, perturbations unrelated to the image itself, but are poor at spotting the small geometric transformations. However, in terms of the robustness of deep neural networks (DNNs), the ability to properly recognize objects with small geometric transformations is still a challenge. In this work, we investigate the problem from the perspective of adversarial attacks: does the performance of DNNs degrade even when small geometric transformations are applied to images? To this end, we propose a novel adversarial attack method, called WBA, a Warping-Based Adversarial attack method, which does not introduce information independent of the original images but manipulates the existing pixels of the images by elastic warping transformations to generate adversarial examples that are imperceptible to the human eye. At the same time, existing adversarial attacks typically generate adversarial examples by modifying pixels in the spatial domain of the image, the addition of such perturbations introduces extra information unrelated to the image itself and is easily detected by the naked eyes. We demonstrate the effectiveness of WBA by extensive experiments on commonly used datasets, including MNIST, CIFAR10, and ImageNet. The results show that WBA can quickly generate adversarial examples with the highest adversarial strength, consumes less time, and can be comparable to optimization-based adversarial attack methods in image perception evaluation metrics such as LPIPS, SSIM, and far more than gradient direction-based iterative methods.

关键词： Measurement Deep learning Privacy Image recognition Perturbation methods Neural networks Robustness

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An empirical study on the structure evolution of deep learning models: taking SAR image processing a case study

An empirical study on the structure evolution of deep learni...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Huanxi Liu Xiang He Dawei Feng Han Bao National Laboratory for Parallel and Distributed Processing (PDL) National University of Defense Technology China School of Information and Communication National University of Defense Technology China

With the continuous improvement on model performance, deep learning models have been widely deployed and achieved promising outcomes in various fields in recent years. However, due to the escalating volumes of training data and the complexity of application problems, it becomes more and more challenging to design a neural network with better performance by hand. Analysing the evolution of typical neural network structures has important reference significance for designing a network structure. In this paper, we select the open source models in SAR image processing for an empirical analysis on the evolution of neural network structures. We analyse the evolution of 239 open source deep learning models from the aspects of framework, computing unit, model computation amount and the combined use of various computing units. Results reveal that preference and co-occurrence exist in computing units, while the average number of convolution, activation and normalization layer increases significantly over time. Model complexity shows an overall upward trend, and the characteristics of SAR image are more and more taken into consideration during the model structure design.

关键词：

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Graph Structure Learning via Transfer Entropy for Multivariate Time Series Anomaly Detection

Graph Structure Learning via Transfer Entropy for Multivaria...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Mingyu Liu Yijie Wang Xiaohui Zhou Yongjun Wang National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multivariate time series anomaly detection (MTAD) poses a challenge due to temporal and feature dependencies. The critical aspects of enhancing the detection performance lie in accurately capturing the dependencies between variables within the sliding window and effectively leveraging them. Existing studies rely on domain knowledge to pre-set the window size, and overlook the strength of dependencies while calculating direction based on variable similarity. This paper proposes GSLTE, a graph structure learning method for MTAD. GSLTE employs Fast Fourier Transform to conduct iterative segmentation of the whole series, selecting the dominant Fourier frequency as the window size for each subsequence within the minimum interval. GSLTE quantifies the direction and strength of the dependencies based on variable-lag transfer entropy which is achieved through Dynamic Time Warping method to learn asymmetric links between variables. Extensive experiments show that GNN-based MTAD methods applying GSLTE can further improve anomaly detection performance while outperforming state-of-the-art competitors.

关键词： Learning systems Time-frequency analysis Fast Fourier transforms Time series analysis Signal processing Feature extraction Entropy Iterative methods Speech processing Anomaly detection

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F3A: Fairness-Aware AI-Workloads Allocation Considering Multidimensional User Demands in JointCloud

F3A: Fairness-Aware AI-Workloads Allocation Considering Mult...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Jiacheng Yang Guodong Yi Fei Gao Peichang Shi Huaimin Wang National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

With the rapid growth of large language models, cloud computing has become an indispensable component of the AI industry. Cloud service providers(CSPs) are establishing AI data centers to service AI workloads. In the face of this surging need for AI computing power, building a connected computing environment across various clouds and forming a JointCloud presents an attractive solution. However, scheduling AI tasks across multiple AI data centers within a JointCloud environment presents a significant challenge: how to balance users’ demands while ensuring CSPs’ fairness in scheduling. Existing research primarily focuses on optimizing scheduling quality with limited consideration for fairness. Therefore, this paper proposes a Fairness-Aware AI-Workloads Allocation method (F3A), a fair cross-cloud allocation technique for AI tasks. F3A utilizes Point and Token to reflect both the resource status and historical task allocations of AI data centers, enabling the consideration of users’ multidimensional demands and facilitating fair task allocation across multiple centers. In order to better assess the fairness of scheduling, we also devised a fairness indicator(FI), based on the Gini coefficient to measure the fairness of task allocation. The experimental results demonstrate that F3A consistently maintains FI within 0.1 across various cluster sizes and different task quantities, representing an improvement of 76.45% compared to classical fair scheduling algorithms round-robin. F3A exhibits commendable performance in ensuring fairness in task allocation while also demonstrating effectiveness in cost reduction and enhancing user satisfaction.

关键词： Industries Data centers Cloud computing Job shop scheduling Costs Scheduling algorithms Large language models

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DCSA: The Deployment Mechanism of Chained Serverless Applications in JointCloud Environment

DCSA: The Deployment Mechanism of Chained Serverless Applica...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Yaojie Li Peichang Shi Jianfei Liu Rui Li Fei Gao Penghui Ma Dong Xie Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

Serverless computing, comprised of Function as a Service (FaaS) and Backend as a Service (BaaS), has garnered widespread attention owning to its features such as maintenance-free operations, pay-per-use pricing, and automatic scalability. However, practical usage encounters several challenges: 1) The diversity of user applications makes comprehensive performance evaluation difficult, as benchmark and application tests only reflect performance under specific conditions and cannot fully capture users’ actual experiences across different serverless platforms. 2) Disparities in performance and costs across different serverless platforms make it challenging to achieve optimal performance and cost efficiency through single-cloud deployment, thereby underutilizing the advantages of each platform. 3) Vendor lock-in issues restrict the migration of user applications and exacerbate dependence on a single cloud *** address these challenges, this paper proposes a collaborative mechanism, referred to as DCSA, which integrates FaaS and storage services to achieve automatic cross-cloud deployment of user applications while considering both performance and cost comprehensively. Firstly, we adapt the interfaces of different serverless platforms, effectively reducing the complexity of cross-cloud deployment. Secondly, we develop cost and latency models for the cross-cloud deployment of chained serverless applications and propose a deployment scheduling algorithm that simultaneously considers both latency and cost. Finally, we conduct experiments to evaluate the performance of the proposed algorithm. Results demonstrate that our method can effectively reduce latency (up to 2.3%) and lower costs (up to 9.9%).

关键词： Performance evaluation Adaptation models Costs Scheduling algorithms Scalability Serverless computing Collaboration

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