检索结果-内蒙古大学图书馆

Design Automation Conference

作者： Yan Li Xiaoyoung Zeng Zhengqi Gao Liyu Lin Jun Tao Jun Han Xu Cheng Mehdi Tahoori Xiaoyang Zeng State Key Lab. ASIC & System School of Microelectronics Fudan University Shanghai China Chair of Dependable Nano Computing Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9781728110851

ISBN: (纸本)9781728158020

Soft error is a major reliability concern in advanced technology nodes. Although mitigating Soft Error Rate (SER) will inevitably sacrifice area and power, few studies paid attention to optimization methods to explore trade-offs between area, power and SER. This paper proposes an optimization framework based on Bayesian approach for soft-error-tolerant circuit design. It comprises two steps:1) data preprocessing and 2) Bayesian optimization. In the preprocessing step, a strategy incorporating k-means algorithm and a novel sequencing algorithm is used to cluster Flip-Flops (FFs) with similar SER in order to reduce the dimensionality for the subsequent step. Bayesian Neural Network (BNN) is the applied surrogate model for acquiring the posterior distribution of three design metrics, while the Lower confidence bound (LCB) functions are employed as acquisition functions to select the next point based on BNN when optimizing. Finally, the non-dominated sorting genetic algorithm (NSGA-II) is used to search the Pareto Optimal Front (POF) solutions of three LCB functions. Experimental results demonstrate the proposed framework has a 1.4x improvement in accuracy and a 70% reduction in SER with acceptable increases in power and area.

关键词： Optical fibers Sequential analysis Neural networks Clustering algorithms Pareto optimization Bayes methods Circuit synthesis

来源：评论

学校读者我要写书评

暂无评论

Combining Bilstm and Self-Attention Mechanism for Code Search

SSRN

引用

SSRN 2022年

作者： Cao, Ben Liu, Jianxun School of Computer Science and Technology Hunan University of Science and Technology Hunan411100 China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hunan University of Science and Technology Hunan411100 China

With the wide application of deep learning in code search, especially the proposed code search model based on attention mechanism, the accuracy of code search has been greatly improved. However, the attention mechanism only captures the attention weight relationship between two words in the code fragment, without considering the contextual semantic relationship that exists between words in the code fragment, which can help improve the accuracy of code search. To address this problem, this paper proposes a model that combining BiLSTM and self-attention mechanisms for code search(CBLSAM-CS). The model first captures the contextual relationship of each word in the code fragment by a recurrent neural network, and then uses the self-attentive mechanism to extract deep-level features of the sequence. In order to verify the effectiveness of the proposed model, the paper conducts an experimental comparison with three other baseline models, on the basis of a public dataset containing 18 million code *** experimental results show that the proposed model in this paper achieves 92.4% and 93.5% in MRR value and NDCG metrics, respectively, which are better than the baseline ***, it shows that the CBLSAM-CS model proposed in this paper can effectively improve the accuracy and efficiency of code search. © 2022, The Authors. All rights reserved.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Multi-Layer Self-Attention Residual Network for Code Search

SSRN

引用

SSRN 2022年

作者： Hu, Haize Professor, Jianxun Liu School of Computer Science and Technology Hunan University of Science and Technology Hunan411100 China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hunan University of Science and Technology Hunan411100 China

To improve the efficiency of programming, software developers usually search existing code snippets in open source code repositories to modify and reuse them. Therefore, how to get the right code snippet from the open source code repository quickly and accurately is the focus of current software development research. Nowadays, code search is one of the solutions. Traditional information retrieval techniques have been gradually abandoned by researchers due to their low search efficiency and accuracy. In recent years, the code search methods based on machine learning alternatives have been widely used and researched due to the better search results. Various code search models have been proposed, especially the self-attention model, which has greatly improved the accuracy of code search. The self-attentive model for feature extraction mainly relies on the weights of each word in the code sequence unit, while ignoring the structural relationships among the code sequences, which contain rich feature information in these components. To compensate the shortage of self- attention mechanism, a multi-layer self- attention residual network based code search model (MSARN-CS) is proposed in this paper. In the MSARN-CS model, not only the weight of each word in the code sequence unit is considered, but also the effect of embedding between code sequence units is calculated. In addition, an optimization model of residuals is introduced to compensate for the loss of information in the code sequences during the model training. To verify the search effectiveness of the MSARN-CS model, three other baseline models are compared on the basis of extensive source code data. The experimental results show that the MSARN-CS model has better search results compared with the baseline model. Therefore, the MSARN-CS model proposed in the paper can effectively improve the accuracy of code search and enhance the programming efficiency of developers. © 2022, The Authors. All rights reserved.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

Load Balancing a Multi-Block Grids-based Application on Heterogeneous Platform

Load Balancing a Multi-Block Grids-based Application on Hete...

引用

IEEE International Conference on Computational Science and Engineering, CSE

作者： Yonggang Che Chuanfu Xu Zhenghua Wang Institute for Quantum Information & State Key Lab. of High Performance Computing College of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Lab National University of Defense Technology Changsha China

ISBN: (数字)9781665403986

ISBN: (纸本)9781665403993

This paper presents a load balancing method for a multi-block grids-based CFD (Computational Fluid Dynamics) application on heterogeneous platform. This method includes an asymmetric task scheduling scheme and a load balancing model. The idea is to balance the computing speed between the CPU and the coprocessor by adjusting the workload and the numbers of threads on both sides. Optimal load balance parameters are empirically selected, guided by a performance model. Performance evaluation is conducted on a computer server consists of two Intel Xeon E5-2670 v3 CPUs and two MIC coprocessors (Xeon Phi 5110P and Xeon Phi 7120P) for the simulation of turbulent combustion in a supersonic combustor. The results show that the performance is highly sensitive to the load balance parameters. With the optimal parameters, the heterogeneous computing achieves a maximum speedup of 2.30 × for a 6-block mesh, and a maximum speedup of 2.66 × for a 8-block mesh, over the CPU-only computing.

关键词： Computational modeling Computational fluid dynamics Load management Servers Task analysis Load modeling Coprocessors

来源：评论

学校读者我要写书评

暂无评论

Highly Fluent Sign Language Synthesis Based on Variable Motion Frame Interpolation

Highly Fluent Sign Language Synthesis Based on Variable Moti...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Ni Zeng Yiqiang Chen Yang Gu Dongdong Liu Yunbing Xing Beijing Key Lab. of Mobile Computing and Pervasive Device Inst. of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Wuhan National Laboratory of Optoelectronics Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Sign Language Synthesis (SLS) is a domain-specific problem where multiple sign language words are stitched to generate a whole sentence in video, which serves to facilitate communications between the hearing-impaired people and healthy population. This paper presents a Variable Motion Frame Interpolation (VMFI) method for highly fluent SLS in scattered videos. Existing approaches for SLS mainly focus on mechanical virtual human technology, lacking high flexibility and natural effect. Also, the representative solutions to interpolate frames usually assume that the motion object moves at a constant speed which is not suitable for predicting the complex hand motion in frames of scattered sign language videos. To address the above issues, the proposed VMFI adopts acceleration to predict more accurate interpolated frames based on an end-to-end convolutional neural network. The framework of VMFI consists of variable optical flow estimation network and high-quality frame synthesis network that can approximate and fuse the intermediate optical flow to generate interpolated frames for synthesis. Experimental results on our realistic collected Chinese sign language dataset demonstrate that the proposed VMFI model achieves efficiency by performing better in PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity) and MA (Motion Activity) and gets higher score in MOS (Mean Opinion Score) than other two representative methods.

关键词： Interpolation PSNR Assistive technology Estimation Gesture recognition Feature extraction Optical flow

来源：评论

学校读者我要写书评

暂无评论

Robust unsupervised feature selection by nonnegative sparse subspace learning

引用

Neurocomputing 2019年 334卷 156-171页

作者： Wei Zheng Hui Yan Jian Yang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China School of Computer Engineering Jinling Institute of Technology Nanjing PR China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing PR China

Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l 2,1 -norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l 1 -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.

关键词： Subspace learning Non-negative matrix factorization Unsupervised feature selection

来源：评论

学校读者我要写书评

暂无评论

Leaf image recognition using Fourier transform based on ordered sequence

Leaf image recognition using Fourier transform based on orde...

引用

8th International Conference on Intelligent computing technology, ICIC 2012

作者： Yang, Li-Wei Wang, Xiao-Feng Department of Automation University of Science and Technology of China Hefei Anhui 230027 China Intelligent Computing Laboratory Hefei Institute of Intelligent Machines Chinese Academy of Sciences P.O. Box 1130 Hefei Anhui 230031 China Key Lab. of Network and Intelligent Information Processing Hefei University Hefei Anhui 230601 China

ISBN: (纸本)9783642315879

There are a number of leaf recognition methods, but most of them are based on Euclidean space. In this paper, we will introduce a new description of feature for the leaf image recognition, which represents the leaf contour with the ordered sequence. For a leaf image, points on the contour represent the most important information of the leaf. Thus, by extracting serial points of the leaf contour, the unique corresponding ordered sequence can be obtained for a contour. Then, we can compute the amplitude-frequency feature by performing the Discrete Fourier transform on the ordered sequence. Since the low-frequency part of the Fourier transform represents the global information and the high-frequency part the local details, we can adopt the amplitude-frequency feature for leaf image recognition. Experimental results on the famous Swedish library and ICL library show that the proposed feature is effective for leaf image recognition. © 2012 Springer-Verlag.

关键词： Image recognition

来源：评论

学校读者我要写书评

暂无评论

Sum of squares: a new metric for NFV service chain placement in edge computing environments and efficient heuristic algorithms

引用

CCF Transactions on Networking 2020年第2期3卷 140-153页

作者： Chen, Zhiqi Zhang, Sheng Qian, Zhuzhong Wang, Can Xiao, Mingjun Wu, Jie Lu, Sanglu State Key Lab. for Novel Software Technology Nanjing University Nanjing P.R. China School of Computer Science and Technology/Suzhou Institute for Advanced Study University of Science and Technology of China Hefei China Center for Networked Computing Temple University Philadelphia USA

Both network function virtualization (NFV) and edge computing (EC), especially the latter, are attracting more and more attention in recent years. A growing number of network service providers are migrating their services from the cloud to the edge for better QoS services, while the recent researches on NFV also concentrate on deploying NFV services in edge computing networks. However, NFV deployment in edge networks is a troublesome challenge and is fairly alien from conventional NFV deployment problems in data centres. Edge network differs from the data center network in the following two aspects: firstly, edge nodes are constrained in computing capacity, and secondly, the network connections between edge nodes are unstable and dynamic, which may show large variance over time. This means edge computing should be designed for high-efficient use of physical edge nodes’ resources. To address the challenges above, we investigate a new NFV Service Chain Placement problem in edge computing environments (NSCP-EC) in this paper. We first prove that the NSCP-EC problem is NP-complete. Then we propose a new metric which can better measure the capacity utilization rate of physical resources, and analyze its advantages with details. Based on the new metric, we propose two heuristic but efficient algorithms called MINI and MINI-tree. To confirm the performance of the two algorithms, we conduct simulations. The result demonstrates that MINI gains an advantage over genetic algorithm (GA) and MINI-tree orevails over MINI in tree topology conditions in the aspects of physical resource utilization, acceptance rate and running time. Both theoretical analysis and simulation results confirm the feasibility of the algorithms.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Bayesian-based Wavelet Shrinkage for SAR Image Despeckling Using Cycle Spinning

引用

Journal of Electronic Science and technology 2006年第2期000卷 127-131页

作者： ZHANG De-xiang GAO Qing-wei CHEN Jun-ning Key Lab. Of Intelligent Computing and Signal Processing Anhui University Hefei 230039 China School of Electronic Science and Technology Anhui University Hefei 230039 China School of Electronic Science and Technology Anhui University Hefei 230039 China

A novel and efficient speckle noise reduction algorithm based on Bayesian wavelet shrinkage using cycle spinning is proposed. First, the sub-band decompositions of non-logarithmically transformed SAR images are shown. Then, a Bayesian wavelet shrinkage factor is applied to the decomposed data to estimate noise-free wavelet coefficients. The method is based on the Mixture Gaussian Distributed (MGD) modeling of sub-band coefficients. Finally, multi-resolution wavelet coefficients are reconstructed by wavelet-threshold using cycle spinning. Experimental results show that the proposed despeckling algorithm is possible to achieve an excellent balance between suppresses speckle effectively and preserves as many image details and sharpness as possible. The new method indicated its higher performance than the other speckle noise reduction techniques and minimizing the effect of pseudo-Gibbs phenomena.

关键词： discrete wavelet transform Synthetic Aperture Radar (SAR) despeckling cycle spinning BayesShrink

来源：评论

学校读者我要写书评

暂无评论

The real implementation of NDN forwarding strategy on Android smartphone

The real implementation of NDN forwarding strategy on Androi...

引用

IEEE Annual Ubiquitous computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Xuewei Piao Lei Huang Kaiqi Yuan Jie Yuan Kai Lei School of Electronic and Computer Engineering (SECE) Peking University Shenzhen P.R. China Shenzhen Key Lab for Cloud Computing Technology & Applications (SPCCTA) Peking University Shenzhen P.R. China

ISBN: (纸本)9781509014972

A prototype system of Named Data Networking (NDN) wireless communication for Android is established based on NDN Forwarding Daemon (NFD). By porting NDN to Raspberry Pi and Android smartphone, the NDN-based Android application can connect to an NDN-supported access point built on Raspberry Pi, and communicate with remote NDN Testbed. As a contribution, this paper make a preliminary exploration on deploying NDN on Android terminals and interfacing Android smartphones with multiple NDN networks, on which various NDN forwarding strategies can be tested.

关键词： Androids Humanoid robots Wireless communication Java Libraries Routing Protocols

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：