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APDPk-means: A new differential privacy clustering algorithm based on arithmetic progression privacy budget allocation 21

APDPk-means: A new differential privacy clustering algorithm...

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21st IEEE International conference on high performance Computing and Communications, 17th IEEE International conference on Smart City and 5th IEEE International conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Fan, Zexuan Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security AND Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728120584

How to protect users' private data during network data mining has become a hot issue in the fields of big data and network information security. Most current researches on differential privacy k-means clustering algorithms focus on optimizing the selection of initial centroids. However, the traditional privacy budget allocation has the problem that the random noise becomes too large as the number of iterations increases, which will reduce the performance of data clustering. To solve the problem, we improved the way of privacy budget allocation in differentially private clustering algorithm DPk-means, and proposed APDPk-means, a new differential privacy clustering algorithm based on arithmetic progression privacy budget allocation. APDPk-means decomposes the total privacy budget into a decreasing arithmetic progression, allocating the privacy budgets from large to small in the iterative process, so as to ensure the rapid convergence in early iteration. The experiment results show that compared with the other differentially private k-means algorithms, APDPk-means has better performance in availability and quality of the clustering result under the same level of privacy protection. © 2019 IEEE.

关键词： Data mining

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INSIDER: Designing In-Storage Computing System for Emerging high-performance Drive

INSIDER: Designing In-Storage Computing System for Emerging ...

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USENIX Annual Technical conference

作者： Ruan, Zhenyuan He, Tong Cong, Jason Univ Calif Los Angeles Los Angeles CA 90024 USA

ISBN: (纸本)9781939133038

We present INSIDER, a full-stack redesigned storage system to help users fully utilize the performance of emerging storage drives with moderate programming efforts. On the hardware side, INSIDER introduces an FPGA-based reconfigurable drive controller as the in-storage computing (ISC) unit;it is able to saturate the high drive performance while retaining enough programmability. On the software side, INSIDER integrates with the existing system stack and provides effective abstractions. For the host programmer, we introduce virtual file abstraction to abstract ISC as file operations;this hides the existence of the drive processing unit and minimizes the host code modification to leverage the drive computing capability. By separating out the drive processing unit to the data plane, we expose a clear drive-side interface so that drive programmers can focus on describing the computation logic;the details of data movement between different system components are hidden. With the software/hardware co-design, INSIDER runtime provides crucial system support. It not only transparently enforces the isolation and scheduling among offloaded programs, but it also protects the drive data from being accessed by unwarranted programs. We build an INSIDER drive prototype and implement its corresponding software stack. The evaluation shows that INSIDER achieves an average 12X performance improvement and 31X accelerator cost efficiency when compared to the existing ARM-based ISC system. Additionally, it requires much less effort when implementing applications. INSIDER is open-sourced [5], and we have adapted it to the AWS F1 instance for public access.

关键词： Digital storage

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FPGA Based Blockchain System for Industrial IoT

FPGA Based Blockchain System for Industrial IoT

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

作者： Lei Xu Lin Chen Zhimin Gao Hanyee Kim Taeweon Suh Weidong Shi Computer Science Department University of Texas Rio Grande Valley Computer Science Department Texas Tech University Computer Science Department Auburn University at Montgomery Computer Science Department Korea University Computer Science Department University of Houston

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

Industrial IoT (IIoT) is critical for industrial infrastructure modernization and digitalization. Therefore, it is of utmost importance to provide adequate protection of the IIoT system. A modern IIoT system usually consists of a large number of devices that are deployed in multiple locations and owned/managed by different entities who do not fully trust each other. These features make it harder to manage the system in a coherent manner and utilize existing security mechanisms to offer adequate protection. The emerging blockchain technology provides a powerful tool for IIoT system management and protection because the IIoT nature of distributed deployment and involvement of multiple stakeholders fits the design philosophy of blockchain well. Most existing blockchain construction mechanisms are not scalable enough and too heavy for an IIoT system. One promising way to overcome these limitations is utilizing hardware based trusted execution environment (TEE) in blockchain construction. However, most of the existing works on this direction do not consider the characteristics of IIoT devices (e.g., fixed functionality and limited supply) and face several limitations when they are applied for IIoT system management and protection, such as high energy consumption, single root-of-trust, and low decentralization level. To mitigate these challenges, we propose a novel field programmable gate array (FPGA) based blockchain system. It leverages the FPGA to build a simple but efficient TEE for IIoT devices, and removes the single root-of-trust by allowing all stakeholders to participate in the management of the devices. The FPGA based blockchain system shifts the computation/storage intensive part of blockchain management to more powerful computers but still involves the IIoT devices in the block construction to achieve a high level of decentralization. We implement the major FPGA components of the design and evaluate the performance of the whole system with a simulation tool to

关键词： Blockchain Tools Hardware Stakeholders Security Field programmable gate arrays Industrial Internet of Things

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Communication-aware and energy saving virtual machine allocation algorithm in data center 21

Communication-aware and energy saving virtual machine alloca...

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作者： Luo, Juan Fan, Xin Yin, Luxiu College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781728120584

Data centers can provide users with reliable and on-demand cloud services. Efficient virtual machine allocation strategies are important for improving data center performance and increasing allocation efficiency. However, the existing allocation algorithms have limited consideration for the communication delay between virtual machines caused by the data center topology. Therefore, this study formalizes the network communication problem of virtual machines in data centers by studying these centers network topology. Accordingly, this paper proposes an energy saving allocation algorithm based on communication-aware, called ESBCA, which optimizes the data center traffic from the two stages of virtual machine migration and placement. In the migration phase, virtual machines with high traffic volumes are preferred to be migrated. In the placement stage, different targets are set and the destination physical machine is systematically determined. Experimental results show that the proposed algorithm evidently reduces the data traffic, improves the efficiency of the data center migration and decreases the energy consumption of data centers. © 2019 IEEE.

关键词： Virtual machine

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Super-resolution-based traffic prohibitory sign recognition 21

Super-resolution-based traffic prohibitory sign recognition

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作者： Xiao, Degui Liu, Liang College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781728120584

Traffic signs occupy a small proportion in the image, which makes feature extraction difficult. This study focuses on small prohibitory signs. First, we introduce super resolution (SR), which can improve the quality of the image, to solve the recognition problem of small prohibitory signs. Second, we propose a new model that can detect and classify various prohibitory signs compared with a traditional joint model. The proposed model utilizes the color and shape of prohibitory signs in generating proposals and filters the negative samples. The retained proposals are divided into small, medium, and large objects based on the size of prohibitory signs, and the small and medium objects are reconstructed through SR. Finally, the proposals are classified by using support vector machine (SVM) algorithm. Experiments on Tsinghua-Tencent 100K and German Traffic Sign Recognition Benchmark (GTSRB) datasets demonstrate that the proposed method is feasible. The proposed method achieves recognition accuracies of 78% and 67% on the small objects of Tsinghua- Tencent 100K and GTSRB datasets, respectively, which exceed the recognition accuracy without SR. © 2019 IEEE.

关键词： Traffic signs

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Optimization of a network retrograde analysis system implemented on HDFS 21

Optimization of a network retrograde analysis system impleme...

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作者： Yuan, Xiaofang Zhang, Wubin College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781728120584

The Apache Hadoop is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. In addition, it provides a distributed file system (HDFS) that stores data on the compute nodes, providing very high aggregate bandwidth across the cluster. However, the problem of small-file storage in Big data is restricted by the ability of HDFS to work effectively, as it is originally designed to deal with large files. In order to construct a network retrograde analysis system (RAS), we propose a method to optimize the performance of HDFS for small file storage. In this system, a large number of network packet (pcap) files are stored in HDFS, each 40 to 50 MB in size. To reduce the number of files, pcap files are merged into larger files, meanwhile indexes for each larger file are also generated. A distributed caching index strategy is introduced to improve the read speed of pcap files in HDFS. The experimental results show that our system can improve the access efficiencies of small files in HDFS, while reducing the NameNode memory consumption by about 90 compared to the original HDFS, and 70% to Hadoop Archive program (HAR), respectively. The access time per MB file is reduced remarkly, by about 50% lower than the original HDFS, and 70% than that of HAR. © 2019 IEEE.

关键词： Digital storage

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Image super-resolution model based on bypass convolution and feature weighting 21

Image super-resolution model based on bypass convolution and...

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作者： Pan, Zhenzhong Zhang, Yu Guo, Xing School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781728120584

Image super-resolution(SR) reconstruction aims at reconstructing corresponding high-resolution images from low-resolution images. Methods based on deep CNN have achieved distinguished performance recently. Among these methods, those based on the single-layer features cannot get top reconstruction results since shallow features are ignored, and those based on multi-layer features need to discriminate good and poor features. To address this issue, we propose a new model, BySEDenseSR (Bypass Convolution Squeeze Excitation Dense). Specifically, We use a squeeze excitation block that learns to assign appropriate weights based on the quality of features. This provides an effective way to suppress the effects of poor features. Furthermore, using bypass convolution, BySEDenseSR reduces the forwarding noise, which improves the stability of middle-layer features. Experiments show that compared to LapSRN, BySEDenseSR improves its peak signal-to-noise ratio (PSNR) by 0.16dB, 0.08dB and 0.03dB respectively on Set5, Set14 and BSD100, at a magnification of 4×. © 2019 IEEE.

关键词： Convolution

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Deep Learning performance In Sentiment Analysis

Deep Learning Performance In Sentiment Analysis

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International conference on Electrical, Telecommunication and computer Engineering (ELTICOM)

作者： Boy Utomo Manalu Tulus Syahril Efendi Graduate Program of Computer Science Universitas Sumatera Utara Medan Indonesia Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9781728188706

ISBN: (纸本)9781728188713

A problem of sentiment analysis is the text representation, that encodes text into a continuous vector by arranging projections from semantics to the points in high dimensional space. Deep learning methods have been widely used to solve various sentiment analysis problems. To improve the performance of deep learning in sentiment analysis requires a good method of text representation to be used as an embedding layer. In this study, we analyzed deep learning with the Recurrent Neural Network (RNN) method with Long Short-Term Memory (LSTM) variants in sentiment classification. We compare the performance of the LSTM network with Word2Vec to the network without Word2Vec as word embedding. Sentiment data used is derived from user-provided reviews of the applications in Google Play. The training process involves the Dropout layer and Early Stopping points to prevent overfitting. The results showed that the LSTM network using a word embedding Word2Vec is better than without Word2Vec. LSTM with Word2Vec 300 words dimension got a low error value of 0.3287 with an accuracy of 86.76%. While the LSTM testing results without Word2Vec get the lowest error of 0.3751 with an accuracy of 84.14%.

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The measurements of the cosmic ray energy spectrum and the depth of maximum shower development of Telescope Array Hybrid trigger events 37

The measurements of the cosmic ray energy spectrum and the d...

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37th International Cosmic Ray conference, ICRC 2021

作者： Shin, Heungsu Abbasi, R.U. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cady, R. Cheon, B.G. Chiba, J. Chikawa, M. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Fukushima, R. Furlich, G. Gonzalez, R. Hanlon, W. Hayashi, M. Hayashida, N. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inadomi, T. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwakura, H. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawai, H. Kawakami, S. Kawana, S. Kawata, K. Kharuk, I. Kido, E. Kim, H.B. Kim, J.H. Kim, J.H. Kim, M.H. Kim, S.W. Kimura, Y. Kishigami, S. Kubota, Y. Kurisu, S. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lubsandorzhiev, B. Lundquist, J.P. Machida, K. Matsumiya, H. Matsuyama, T. Matthews, J.N. Mayta, R. Minamino, M. Mukai, K. Myers, I. Nagataki, S. Nakai, K. Nakamura, R. Nakamura, T. Nakamura, T. Nakamura, Y. Nakazawa, A. Nishio, E. Nonaka, T. Oda, H. Ogio, S. Ohnishi, M. Ohoka, H. Oku, Y. Okuda, T. Omura, Y. Ono, M. Onogi, R. Oshima, A. Ozawa, S. Park, I.H. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rubtsov, G.I. Ryu, D. Sagawa, H. Sahara, R. Saito, Y. Sakaki, N. Sako, T. Sakurai, N. Sano, K. Sato, K. Seki, T. Sekino, K. Shah, P.D. Shibasaki, Y. Shibata, F. Shibata, N. Shibata, T. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Sone, N. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, Y. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, H. Tanaka, K. Tanaka, M. Tanoue, Y. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Uchihori, Y. Udo, S. Uehama, T. Urban, F. Wong, T. Yada, K. Yamamoto, M. Yamazaki, K. Yang, J. Yashiro, K. Yoshida, F. Yoshioka, Y. Zhezher, Y. Zundel, Z. The University of Tokyo Graduate School of Science Chiba Kashiwa Japan Department of Physics Loyola University Chicago ChicagoIL United States High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT United States Graduate School of Science Osaka City University Osaka Osaka Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul Korea Republic of Department of Physics Tokyo University of Science Chiba Noda Japan Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa Japan The Hakubi Center for Advanced Research Graduate School of Science Kyoto University Kitashirakawa-Oiwakecho Sakyo-ku Kyoto Japan Information Engineering Graduate School of Science and Technology Shinshu University Nagano Nagano Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu Japan Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo Japan Institute for Nuclear Research of the Russian Academy of Sciences Moscow Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo Japan Department of Engineering Science Faculty of Engineering Osaka Electro-Communication University Neyagawashi Osaka Japan Department of Physics Chiba University Chiba Chiba Japan Service de Physique Théorique Université Libre de Bruxelles Brussels Belgium Department of Physics Yonsei University Seodaemun-gu Seoul Korea Republic of Center for Astrophysics and Cosmolo

The Telescope Array experiment is an ultra-high energy cosmic ray observatory located in Millard County, Utah, USA. The observatory consists of 3 fluorescence detector (FD) stations and 507 surface detectors (SD) that cover an area of 700 km2. Hybrid trigger is an external trigger system for the SD arrays that prompts the SD to perform data acquisition when an FD detects a shower-like event. In comparison with the SD autonomous trigger, hybrid trigger allows the SD to collect the data of an air shower that has primary energy below 1018.5 eV, where the efficiency of SD autonomous trigger decreases rapidly. We present the measurements of the cosmic ray energy spectrum and the depth of maximum shower development of hybrid trigger events observed from October 2010 to September 2014. © Copyright owned by the author(s).

关键词： Observatories

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Assembling a high-Productivity DSL for Computational Fluid Dynamics 6

Assembling a High-Productivity DSL for Computational Fluid D...

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6th Platform for Advanced Scientific Computing conference (PASC)

作者： Macia, Sandra Martinez-Ferrer, Pedro J. Mateo, Sergi Beltran, Vicenc Ayguade, Eduard Barcelona Supercomp Ctr Barcelona Spain

ISBN: (纸本)9781450367707

As we move towards exascale computing, an abstraction for ef- DSL, CFD, HPC, FDM fective parallel computation is increasingly needed to overcome the maintainability and portability of scientific applications while ensuring the efficient and full exploitation of high-performance systems. These circumstances require computer and domain scientists to work jointly toward a productive working environment. Domain specific languages address this challenge by abstracting the high-level application layer from the final, complex parallel low-level code. Saiph is an innovative domain specific language designed to reduce the work of computational fluid dynamics domain experts to an unambiguous and straightforward transcription of their problem equations. The high-level language, domain-specific compiler and underlying library are enhanced to make applications developed by scientists intuitive. Additions and improvements are presented, designed for the significant advantage of running computational fluid dynamics applications on different machines with no porting or maintenance issues. Numerical methods and parallel strategies are independently added at the library level covering the explicit finite differences resolution of a vast range of problems. Depending on the application, a specific parallel resolution is automatically derived and applied within Saiph, freeing the user from decisions related to numerical methods or parallel executions while ensuring suitable computations. Through a list of benchmarks, we demonstrate the utility and productivity of the Saiph high-level language together with the correctness and performance of the underlying parallel numerical algorithms.

关键词： DSL CFD HPC FDM

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