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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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Versatile Stack Management for Multitasking Sensor Networks

Versatile Stack Management for Multitasking Sensor Networks

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IEEE International Conference on Distributed Computing Systems

作者： Rui Chu Lin Gu Yunhao Liu Mo Li Xicheng Lu National Laboratory for Parallel and Distributed Processing National University of Defense Technology Department of Computer Science and Engineering Hong Kong University of Science and Technology

ISBN: (纸本)9781424472611;9780769540597

The networked application environment has motivated the development of multitasking operating systems for sensor networks and other low-power electronic devices, but their multitasking capability is severely limited because traditional stack management techniques perform poorly on small-memory systems. In this paper, we show that combining binary translation and a new kernel runtime can lead to efficient OS designs on resource-constrained platforms. We introduce SenSmart, a multitasking OS for sensor networks, and present new OS design techniques for supporting preemptive multi-task scheduling, memory isolation, and versatile stack management. We have implemented SenSmart on MICA2/MICAz motes. Evaluation shows that SenSmart performs efficient binary translation and demonstrates a significantly better capability in managing concurrent tasks than other sensornet operating systems.

关键词： parallel processing (COMPUTERS) Sensor networks Electron devices Furnace conveyors Cache Memory System Operating systems osmium

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Locality-sensitive sketching for resilient network flow monitoring

arXiv

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arXiv 2019年

作者： Fu, Yongquan Li, Dongsheng Shen, Siqi Zhang, Yiming Chen, Kai Science and Technology Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology SING Lab Hong Kong University of Science and Technology

Network monitoring is vital in modern clouds and data center networks for traffic engineering, network diagnosis, network intrusion detection, which need diverse traffic statistics ranging fromflow size distributions to heavy hitters. To cope with increasing network rates and massive traffic volumes, sketch based approximate measurement has been extensively studied to trade the accuracy for memory and computation cost, which unfortunately, is sensitive to hash collisions. In addition, deploying the sketch involves fine-grained performance control and instrumentation. This paper presents a locality-sensitive sketch (LSS) to be resilient to hash collisions. LSS proactively minimizes the estimation error due to hash collisions with an autoencoder based optimization model, and reduces the estimation variance by keeping similar network flows to the same bucket array. To illustrate the feasibility of the sketch, we develop a disaggregatedmonitoring application that supports non-intrusive sketching deployment and native network-wide analysis. Testbed shows that the framework adapts to line rates and provides accurate query results. Real-world trace-driven simulations show that LSS remains stable performance underwide ranges of parameters and dramatically outperforms state-of-the-art sketching structures, with over 103 to 105 times reduction in relative errors for per-flow queries as the ratio of the number of buckets to the number of network flows reduces from 10% to 0.1%. Copyright © 2019, The Authors. All rights reserved.

关键词： Intrusion detection

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Efficient Distributed Data Clustering on Spark

Efficient Distributed Data Clustering on Spark

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IEEE International Conference on Cluster Computing

作者： Jia Li Dongsheng Li Yiming Zhang National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9781467365994

Data clustering is usually time-consuming since it by default needs to iteratively aggregate and process large volume of data. Approximate aggregation based on sample provides fast and quality ensured results. In this paper, we propose to leverage approximation techniques to data clustering to obtain the trade-off between clustering efficiency and result quality, along with online accuracy estimation. The proposed method is based on the bootstrap trials. We implemented this method as an Intelligent Bootstrap Library (IBL) on Spark to support efficient data clustering. Intensive evaluations show that IBL can provide a 2x speed-up over the state of art solution with the same error bound.

关键词： Sparks Accuracy Data mining Estimation error Distributed databases Approximation methods

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Optimization techniques of on-chip memory system based on UltraSPARC architecture

Optimization techniques of on-chip memory system based on Ul...

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Asia Pacific Conference on Postgraduate Research in Microelectronics & Electronics (PrimeAsia)

作者： Anwen Huang Jun Gao Chaochao Feng Minxuan Zhang Key Laboratory of Science and Technology of National Defense of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China

It is of great significance to optimize the design of on-chip memory system for improving the performance of multi-core processor. This paper describes the organization of on-chip memory hierarchy in UltraSPARC T2, analyzes the procedure of dealing with the memory access request from multi-core processor in detail. To get the basic parameters of timing characteristic and area consumption, the primary element of on-chip memory system is simulated by ModelSim and synthesized by Synopsys Design Complier with 90 nm standard cell library. Aimed at the potential limiting factor of improving the performance of memory hierarchy, corresponding optimization techniques of critical elements in memory system are explored finally.

关键词： System-on-a-chip Elliptic curve cryptography Hardware Costs Algorithm design and analysis Random access memory Clocks Registers Elliptic curves Protection

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Implicit Neural Alignment Network for Arbitrary-scale Space-Time Video Super-Resolution

Implicit Neural Alignment Network for Arbitrary-scale Space-...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qin Jiang Qinglin Wang Lihua Chi Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Digitizing Software for Frontier Equipment Hunan GuoKe Computility Technology Co. Ltd

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Space-time video super-resolution (STVSR) is a comprehensive task comprising two subtasks: video super resolution in space dimension and video frame interpolation in time dimension. Conventional decoupled two-stage approaches tend to overlook the intrinsic correlation between the two tasks. Overcoming this challenge requires the development of a unified model capable of simultaneously implementing space-time super-resolution across arbitrary scales. Most existing models are confined to training on fixed space upsampling scales or specific frame-rate videos, resulting in limited generalization capabilities for flexible space-time super-resolution scenarios. In response to this limitation, our approach draws inspiration from continuous implicit neural representation. We propose an enhanced Implicit Neural Alignment Network (INAN) based on the VideoINR framework, encompassing feature refinement, precise motion flow estimation, and multi-scale feature fusion to optimize the final implicit neural decoding. Our extensive experimental evaluations on multiple benchmarks underscore the efficacy of the INAN model, indicate its superior performance compared to prior STVSR methods.

关键词： Training Interpolation Correlation Superresolution Neural networks Estimation Benchmark testing

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The prediction model based on RBF network in achieving elastic cloud

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Advances in Information sciences and Service sciences 2011年第11期3卷 67-78页

作者： Shi, Peichang Wang, Huaimin Ding, Bo Liu, Ran Wang, Tianzuo National Laboratory for Parallel and Distributed Processing School of Computer and Science National University of Defense Technology China Department of Electronics and Information Engineering Huazhong University of Science Technology China

Cloud needs to have rapid and elastic resources supply capability, because of the fluctuant resources demand of end-users. Multi-scale resources elastic binding is an important method to provide cloud services with rapid and elastic service capability. The most challenging problem in multi-scale resources elastic binding is how to predict the dynamic resource demand of end-users, and then decide when and to what extent multi-scale resources need elastic binding based on the prediction. In this paper, we present the prediction model based on RBF (Radial Basis Function) Network, which is used to predict end-users resource demand in advance. Compared with current prediction methods, it has faster prediction speed and higher prediction accuracy. Then we use traces data (the bandwidth demand of Web type of cloud services) collected from a real-world cloud provider: ChinaCache, as the training and testing data set to validate the method. Finally, we evaluate the predicted results using general prediction accuracy metrics. The results prove that the prediction model based on RBF network is able to resolve the decision problem in multi-scale resources elastic binding.

关键词： Radial basis function networks

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DRAM failure prediction in AIOps: Empirical evaluation, challenges and opportunities

arXiv

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arXiv 2021年

作者： Wu, Zhiyue Xu, Hongzuo Pang, Guansong Yu, Fengyuan Wang, Yijie Jian, Songlei Wang, Yongjun Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology China University of Adelaide Australia

DRAM failure prediction is a vital task in AIOps, which is crucial to maintain the reliability and sustainable service of large-scale data centers. However, limited work has been done on DRAM failure prediction mainly due to the lack of public available datasets. This paper presents a comprehensive empirical evaluation of diverse machine learning techniques for DRAM failure prediction using a large-scale multi-source dataset, including more than three millions of records of kernel, address, and mcelog data, provided by Alibaba Cloud through PAKDD 2021 competition. Particularly, we first formulate the problem as a multi-class classification task and exhaustively evaluate seven popular/state-of-the-art classifiers on both the individual and multiple data sources. We then formulate the problem as an unsupervised anomaly detection task and evaluate three state-of-the-art anomaly detectors. Further, based on the empirical results and our experience of attending this competition, we discuss major challenges and present future research opportunities in this task. © 2021, CC BY.

关键词： Web services

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Image Annotation by Object Hypotheses-oriented Deep Neural Networks

Image Annotation by Object Hypotheses-oriented Deep Neural N...

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2017 2nd International Conference on Software, Multimedia and Communication Engineering（SMCE 2017)

作者： Fang MA Shao-he LV Ke-xin ZHENG Chi JIN Fei CHEN Ke YANG and Yong DOU National Laboratory for Parallel and Distributed Processing National University of Defense Technology University of South China School of Computer Science and Technology

Image annotation generates a set of semantic labels that describe the contents of an input *** deep learning techniques have achieved significant success in many areas of image *** this paper,we present a multi-label image annotation method that combines unsupervised object hypotheses generation and deep neural *** an image,object hypotheses are generated in an unsupervised *** we extract the image features for each hypothesis with a deep neural network *** combining the features of all hypotheses,we get the features of the entire ***,we calculate for each label the probability of that the label is correlated with the given *** can be trained in an end-to-end way using the standard backward propagation *** results on multiple benchmark datasets show that our method is better than the state-of-the-art ones.

关键词： Deep learning Multi-label annotation Object hypotheses

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Fast query over encrypted data based on B+ tree

Fast query over encrypted data based on B+ tree

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2009 International Conference on Apperceiving Computing and Intelligence Analysis, ICACIA 2009

作者： Wang, Zheng-Fei Tang, Ai-Guo Wang, Wei Department of Computer Hunan Business College Changsha 410205 China National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha 410073 China School of Computer Science Fudan University Shanghai 200433 China

ISBN: (纸本)9781424452040

There are a lot of important and sensitive data in databases, which need to be protected from attacks. To secure the data, Cryptography support is an effective mechanism. However, a tradeoff must be made between the performance and the security because encryption and decryption greatly degrade the query performance. In this paper, a framework is proposed that can quickly implement query over encrypted data based on B+ tree. When data are stored in the form of cipher, it not only stores the encrypted data themselves, but also creates a B+ tree index for the data before encrypting them. When querying the encrypted data, firstly, it locates the encrypted records related to the querying predicate based on the B+ tree index;secondly, it decrypts the encrypted records to accomplish the results. Meanwhile, it must encrypt the B+ tree itself to protect it from leaking confidential information. According to the structure of the B+ tree, it encrypts each node of the B+ tree separately. The results of experiments show that the query performance over the encrypted data decreases about 20 percent compared with the plaintext query performance, and validate the functionality and usability of our approach. ©2009 IEEE.

关键词： Cryptography

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