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Airport Detection from Remote Sensing Images Using Transferable Convolutional Neural Networks

Airport Detection from Remote Sensing Images Using Transfera...

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International Joint Conference on Neural Networks

作者： Peng Zhang Xin Niu Yong Dou Fei Xia National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Electronic Engineering College Navy Engineering University Wuhan China

ISBN: (纸本)9781509006212

This paper presents a method for airport detection from optical satellite images using deep convolutional neural networks (CNN). To achieve fast detection with high accuracy, region proposal by searching adjacent parallel line segments has been applied to select candidate fields with potential runways. These proposals were further classified by a CNN model transfer learned from AlexNet to identify the final airport regions from other confusing classes. The proposed method has been tested on a remote sensing dataset consisting of 120 airports. Experiments showed that the proposed method could recognize airports from a large complex area in seconds with an accuracy of 84.1%.

关键词： Airports Proposals Remote sensing Image segmentation Feature extraction Atmospheric modeling Joining processes

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Transductive Convolutive Nonnegative Matrix Factorization for Speech Separation

Transductive Convolutive Nonnegative Matrix Factorization fo...

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International Conference on Computer science and Network technology

作者： Yaodan Mai Long Lan Naiyang Guan Xiang Zhang Zhigang Luo College of Computer National University of Defense Technology Changsha 410073 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9781467381741

Nonnegative matrix factorization (NMF) is an effective speech separation approach of extracting discriminative components of different speaker. However, traditional NMF focuses only on the additive combination of the components and ignores the dependencies of speeches. Convolutive NMF (CNMF) captures the dependencies of speeches by overlapping components and achieves better separation performance. NMF and CNMF learn dictionaries for speakers in the absence of mixture, and thus they are unable to get enough information to learn dictionaries accurately when testing speeches are available. To handle this problem, transductive NMF (TNMF) is proposed which simultaneously utilizes speech of each speaker and mixture to learn more meaningful features of speakers, and significantly boost speech separation. CNMF addresses the dependencies of speech signals while it ignores the positive effect of mixtures in learning dictionaries. TNMF emphasizes the transductive learning of dictionaries while it fails to consider dependencies of speeches. This paper proposes transductive convolutive NMF (TCNMF) to overcome the deficiencies of both CNMF and TNMF. Experimental results show that our method makes significant improvement compared to aforementioned NMFbased methods.

关键词： Nonnegative matrix factorization single-channel speech separation transductive convolutive dictionary learning speech separation Speech matrix decomposition Loudspeakers Dictionaries as Topic NMF protocol Nonnegative mixtures DEPENDENCE

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FedNAT: Byzantine-robust Federated Learning through Activation-based Attention Transfer

FedNAT: Byzantine-robust Federated Learning through Activati...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Mengxin Wang Liming Fang Kuiqi Chen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Nanjing University of Aeronautics and Astronautics Shenzhen Research Institute Shenzhen China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Changsha China

Federated learning (FL) is a decentralized machine learning framework that prioritizes privacy by allowing clients to train statistical models without sharing their private data, thus eliminating the impact of data fortresses. However, the presence of Byzantine attacks, such as data poisoning and backdoor attack, threatens the robustness of FL schemes. Currently, existing mainstream defense methods are susceptible to multiple adaptive attacks, some of which even violate the privacy principle of FL. Furthermore, these defense schemes become less robust when subjected to targeted poisoning attacks with highly non-IID data distributions. In this work, we propose FedNAT, a novel Byzantine-robust FL framework for whittling away these limitations mentioned above. Specifically, FedNAT first performs a privacy-respecting attention refinement on the activation layer outputs of the local uploads. Then, the server scores the local attentions by calculating their Wasserstein distances and clusters them through the k-median algorithm for global attention aggregation, thus rejecting poisoned local attentions for untargeted attacks. After this process, the global attention is transferred to local attention through the FedNAT loss function, which erases backdoors through the distillation concept. We conduct a comprehensive experimental evaluation to demonstrate that FedNAT significantly outperforms existing robust FL schemes in defending against Byzantine poisoning attacks under both IID and highly non-IID data proportions.

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Identifying faults in large-scale distributed systems by filtering noisy error logs

Identifying faults in large-scale distributed systems by fil...

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International Conference on Dependable Systems and Networks Workshops (DSN-W)

作者： Xiang Rao Huaimin Wang Dianxi Shi Zhenbang Chen Hua Cai Qi Zhou Tingtao Sun National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Computing Platform Alibaba Cloud Computing Corporation Hangzhou China

Extracting fault features with the error logs of fault injection tests has been widely studied in the area of large scale distributed systems for decades. However, the process of extracting features is severely affected by a large amount of noisy logs. While the existing work tries to solve the problem by compressing logs in temporal and spatial views or removing the semantic redundancy between logs, they fail to consider the co-existence of other noisy faults that generate error logs instead of injected faults, for example, random hardware faults, unexpected bugs of softwares, system configuration faults or the error rank of a log severity. During a fault feature extraction process, those noisy faults generate error logs that are not related to a target fault, and will strongly mislead the resulted fault features. We call an error log that is not related to a target fault a noisy error log. To filter out noisy error logs, we present a similarity-based error log filtering method SBF, which consists of three integrated steps: (1) model error logs into time series and use haar wavelet transform to get the approximate time series; (2) divide the approximate time series into sub time series by valleys; (3) identify noisy error logs by comparing the similarity between the sub time series of target error logs and the template of noisy error logs. We apply our log filtering method in an enterprise cloud system and show its effectiveness. Compared with the existing work, we successfully filter out noisy error logs and increase the precision and the recall rate of fault feature extraction.

关键词： Time series analysis Noise measurement Feature extraction Wavelet transforms Computer crashes Complexity theory Approximation methods

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Bi-Objective Scheduling Algorithm for Hybrid Workflow in JointCloud

Bi-Objective Scheduling Algorithm for Hybrid Workflow in Joi...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Rui Li Huaimin Wang Peichang Shi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China State Key Laboratory of Complex & Critica Software Environment College of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

Big data workflows are widely used in IoT, recommended systems, and real-time vision applications, and they continue to grow in complexity. These hybrid workflows consist of both resource-intensive batch jobs and latency-sensitive stream jobs. Examples include the data analytics workflow, which incorporates batch data transformations and low-latency querying, and the machine learning workflow, which processes stream data feature extraction before performing batch training and low-latency inference. However, existing research on workflow scheduling primarily focuses on either stream or batch workflows, neglecting the efficient scheduling of hybrid workflows that respect their diverse resource requirements and the costly data transfers between *** this article, we propose a hybrid workflow model that defines the optimal placement of hybrid workflows (OHWP) as a bi-objective optimization problem. Our proposed model takes into account parameters related to inter-communication between stream and batch jobs, as well as the heterogeneous resources in JointCloud environment. Additionally, we present OHWP-PS (OHWP on a Pruned Space), a scheduling algorithm for hybrid workflows that minimizes both cost and latency by improving the initial population and dynamically updating the search space. The results demonstrate that the proposed OHWP-PS algorithm is effective and competitive across all experiments.

关键词： Training Scheduling algorithms Machine vision Heuristic algorithms Machine learning Feature extraction Remote working

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Design and Implementation of a Highly Efficient DGEMM for 64-Bit ARMv8 Multi-core Processors

Design and Implementation of a Highly Efficient DGEMM for 64...

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International Conference on parallel processing (ICPP)

作者： Feng Wang Hao Jiang Ke Zuo Xing Su Jingling Xue Canqun Yang School of Computer Science National University of Defense Technology Changsha China School of Computer Science and Engineering University of New South Wales NSW Australia Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

This paper presents the design and implementation of a highly efficient Double-precision General Matrix Multiplication (DGEMM) based on Open BLAS for 64-bit ARMv8 eight-core processors. We adopt a theory-guided approach by first developing a performance model for this architecture and then using it to guide our exploration. The key enabler for a highly efficient DGEMM is a highly-optimized inner kernel GEBP developed in assembly language. We have obtained GEBP by (1) maximizing its compute-to-memory access ratios across all levels of the memory hierarchy in the ARMv8 architecture with its performance-critical block sizes being determined analytically, and (2) optimizing its computations through exploiting loop unrolling, instruction scheduling and software-implemented register rotation and taking advantage of A64 instructions to support efficient FMA operations, data transfers and prefetching. We have compared our DGEMM implemented in Open BLAS with another implemented in ATLAS (also in terms of a highly-optimized GEBP in assembly). Our implementation outperforms the one in ALTAS by improving the peak performance (efficiency) of DGEMM from 3.88 Gflops (80.9%) to 4.19 Gflops (87.2%) on one core and from 30.4 Gflops (79.2%) to 32.7 Gflops (85.3%) on eight cores. These results translate into substantial performance (efficiency) improvements by 7.79% on one core and 7.70% on eight cores. In addition, the efficiency of our implementation on one core is very close to the theoretical upper bound 91.5% obtained from micro-benchmarking. Our parallel implementation achieves good performance and scalability under varying thread counts across a range of matrix sizes evaluated.

关键词： Registers Kernel Computational modeling Program processors Assembly Memory management

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Using Redundant Threads for Fault Tolerance of OpenMP Programs

Using Redundant Threads for Fault Tolerance of OpenMP Progra...

引用

International Conference on Information science and Applications (ICISA)

作者： Hongyi Fu Yan Ding Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing National University of Defense Technology Changsha Hunan China Section #620 School of Computer National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781424459421

As the wide application of multi-core processor architecture in the domain of high performance computing, fault tolerance for shared memory parallel programs becomes a hot spot of research. For years, checkpointing has been the dominant fault tolerance technology in this field, and recently, many research works have been engaged with it. However, to those programs which deal with large amount of data, checkpointing may induce massive I/O transfer, which will adversely affect scalability. To deal with such a problem, this paper proposes a fault tolerance approach, making use of redundancy, for shared memory parallel programs. Our scheme avoids saving and restoring computational state during the program's execution, hence does not involve I/O operations, so presents explicit advantage over checkpointing in scalability. In this paper, we introduce our approach and the related compiler tool in detail, and give the experimental evaluation result.

关键词： Fault tolerance Checkpointing Scalability Hardware Application software Multicore processing High performance computing Laboratories distributed processing

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Applying linux high-availability and load balancing servers for video-on-demand (Vod) systems

Applying linux high-availability and load balancing servers ...

引用

5th Pacific Rim Conference on Multimedia, PCM 2004

作者： Yang, Chao-Tung Wang, Ko-Tzu Li, Kuan-Ching Lee, Liang-Teh High Performance Computing Laboratory Dept. of Computer Science and Information Engineering Tunghai University Taichung407 Taiwan Parallel and Distributed Processing Center Dept. of Computer Science and Information Management Providence University Taichung433 Taiwan Dept. of Computer Science and Engineering Tatung University Taipei104 Taiwan

ISBN: (纸本)9783540239772

In this research paper, we integrate and implement High-Availability (HA) and Load-Balancing technologies to clusters of workstations, increasing both the availability and scalability of services and resources in these systems. The cluster of workstations is commonly built and used as web-based VOD servers. Thus, this paper presents the hardware and software configurations of cluster systems working as VOD servers. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Cluster computing

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Orthogonal Nonnegative Locally Linear Embedding

Orthogonal Nonnegative Locally Linear Embedding

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Lei Wei Naiyang Guan Xiang Zhang Zhigang Luo Dacheng Tao National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 P.R. China These authors contributed equally to this paper National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 P.R. China Centre for Quantum Computation and Intelligence Systems and Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia

ISBN: (纸本)9781479906505

Nonnegative matrix factorization (NMF) decomposes a nonnegative dataset X into two low-rank nonnegative factor matrices, i.e., W and H, by minimizing either Kullback-Leibler (KL) divergence or Euclidean distance between X and WH. NMF has been widely used in pattern recognition, data mining and computer vision because the non-negativity constraints on both W and H usually yield intuitive parts-based representation. However, NMF suffers from two problems: 1) it ignores geometric structure of dataset, and 2) it does not explicitly guarantee partsbased representation on any datasets. In this paper, we propose an orthogonal nonnegative locally linear embedding (ONLLE) method to overcome aforementioned problems. ONLLE assumes that each example embeds in its nearest neighbors and keeps such relationship in the learned subspace to preserve geometric structure of a dataset. For the purpose of learning parts-based representation, ONLLE explicitly incorporates an orthogonality constraint on the learned basis to keep its spatial locality. To optimize ONLLE, we applied an efficient fast gradient descent (FGD) method on Stiefel manifold which accelerates the popular multiplicative update rule (MUR). The experimental results on real-world datasets show that FGD converges much faster than MUR. To evaluate the effectiveness of ONLLE, we conduct both face recognition and image clustering on real-world datasets by comparing with the representative NMF methods.

关键词： Nonnegative matrix factorization Locally linear embedding Fast gradient descent Stiefel manifold Dataset Nonnegative matrix decomposition geometrical construction NMF protocol images embedding Flue gas desulfurization Nearest neighbor Orthogonal

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Personalized Reputation Model in Cooperative distributed Systems

Personalized Reputation Model in Cooperative Distributed Sys...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Wei Liu Yang-Bin Tang Huai-Min Wang School of Computer National University of Defense Technology Changsha Hunan China National Laboratory for Parallel and Distributed Processing Changsha Hunan China Institute of Science National University of Defense Technology Changsha Hunan China

Reputation systems provide a promising way to build trust relationships between users in distributed cooperation systems, such as file sharing, streaming, distributed computing and social network, through which a user can distinguish good services or users from malicious ones and cooperate with them. However, most reputation models mainly focus on evaluating the qualities of different services in one dimension, but care less about the preferences of different users. This paper proposes a personalized reputation model which provides each user a personalized trust view on others according to his preference. In our approach, we aggregate the users' preferences with collaborative filtering method and qualify it with user similarity which is integrated into the computing of reputation values. The experimental results suggest that our model can resist possible kinds of malicious behaviors efficiently.

关键词： Computational modeling Measurement Indexes Aggregates Resists Peer to peer computing Electronic mail

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