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Improving Space and Time Efficiency in Hadoop Architecture Using Machine Learning Algorithms

Improving Space and Time Efficiency in Hadoop Architecture U...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International conference on

作者： Murali Vivekanandam Kiran Karasala Aptimized

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

This research study explores the application of Hadoop distributed File System (HDFS), MapReduce, and Machine Learning algorithms, along with Social Spider Optimization (SSO), to effectively analyze and utilize large volumes of unstructured data generated by social media platforms. The study focuses on analyzing user behavior and preferences within the context of online shopping, considering individual and business perspectives. The research proposes novel approaches, including the derby model, virtual local file system, a refined recommender system, and parallelized SSO, to efficiently handle various data formats and improve the performance of data processing tasks. The proposed methods demonstrate improved efficiency in terms of space and time requirements compared to existing approaches. This research contributes to the advancement of big data analytics techniques and has the potential to revolutionize how businesses understand and leverage user behavior in the digital age.

关键词： Training Machine learning algorithms File systems Computational modeling Predictive models parallel processing Boosting Data models Load modeling Business

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Accelerating Adversarial Attack using Process-in-Memory Architecture 18

Accelerating Adversarial Attack using Process-in-Memory Arch...

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18th IEEE International conference on Mobility, Sensing and Networking (MSN)

作者： Liu, Shiyi Bavikadi, Sathwika Sen, Tanmoy Shen, Haiying Sutradhar, Purab Ranjan Ganguly, Amlan Dinakarrao, Sai Manoj Pudukotai Smith, Brian L. Univ Virginia Dept Engn Syst & Environm Charlottesville VA 22903 USA George Mason Univ Dept Elect & Comp Engn Fairfax VA USA Univ Virginia Dept Comp Sci Charlottesville VA USA Rochester Inst Technol Rochester NY USA

ISBN: (纸本)9781665464574

Recent research has demonstrated that machine learning algorithms are vulnerable to adversarial attacks, in which small but carefully crafted input perturbations can lead to algorithm failure. It has been demonstrated that certain adversarial attack algorithms are capable of producing these types of perturbations. These attack methods are inapplicable when the attack must be generated in near real time. The use of a hardware accelerator, such as a Process-in-Memory (PIM) architecture, is a potential method for addressing this issue. The PIM architecture is regarded as a superior option for data-intensive applications such as solving optimization problems and Deep Neural Networks (DNN) due to its capacity for ultra-low-latency parallel processing. However, implementing an adversarial attack algorithm directly on the PIM platform is inefficient due to the PIM architecture's complexity and overhead costs. To address this issue, we utilize a novel adversarial attack scheme based on the PIM that leverages Look-up-Table (LUT)-based processing. The proposed LUT-based PIM architecture is capable of being dynamically programmed to execute the operations necessary for an adversarial attack algorithm. Our simulations reveal that the proposed method is capable of achieving an ultra-low operating delay and energy-efficiency performance.

关键词： Black-box adversarial attack Deep neural network processing in memory (PIM)

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Implementation and Optimization of Target Detection based on Multi-core DSP 10

Implementation and Optimization of Target Detection based on...

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10th IEEE Joint International Information Technology and Artificial Intelligence conference, ITAIC 2022

作者： Yang, Tao Jia, Qingzhong Tao, Ximing Huang, Hong Beijing Institute of Technology Key Laboratory of Dynamic and Control of Flight Vehicle Ministry of Education Beijing China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

As an important branch in the field of image processing, target detection requires more and more real-time algorithm with its wide application in industry, agriculture, medical and military industries, which also puts forward higher requirement for the optimization and application of embedded platform. There are two kinds of target detection algorithms: traditional detection algorithm and deep-learning-based detection algorithm. In the embedded platform, due to the limitation of hardware performance, the detection algorithm based on deep learning is difficult to realize because of high complexity. Therefore, the improvement, optimization and transplantation of traditional algorithms have become hot spots. In this paper, a detection algorithm based on Canny edge feature matching is proposed, and multi-core parallel development of TMS320C6678 platform is carried out. parallelism of the algorithm is improved by using methods such as image chunking, vectorized data packaging, optimization of data flow, etc. By connecting the video board based on FPGA for physical verification, the speed of the algorithm after multi-core optimization is increased by 7.6 times, which has certain engineering practical significance. © 2022 IEEE.

关键词： Digital signal processing

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Middleware 2021 Doctoral Symposium - Proceedings of the 22nd International Middleware conference: Doctoral Symposium

Middleware 2021 Doctoral Symposium - Proceedings of the 22nd...

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22nd International Middleware conference, Middleware 2021

ISBN: (纸本)9781450391559

The proceedings contain 11 papers. The topics discussed include: Config 2.0: towards reinforcement learning based configuration of stream processing systems;AutoML: towards automation of machine learning systems maintainability;multi-objective evolutionary based feature selection supported by distributed multi-label classification and deep learning on image/video data;efficient parallel execution of block transactions in blockchain;OneOS: a distributed operating system for the internet of things;non-relational multi-level caching for mitigation of staleness & stragglers in distributed deep learning;using blockchain to provide trusted interoperability to system-of-systems in smart cities context;autonomous resource management in distributed stream processing systems;and using blockchain technology for software identity maintenance.

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Multi-View Rotation Double-Layer Fusion CNN-LSTM for SAR Target Recognition

Multi-View Rotation Double-Layer Fusion CNN-LSTM for SAR Tar...

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Algorithm, image processing and Machine Vision (AIPMV), International conference on

作者： Zijun Wang Gong Zhang Daiyin Zhu Qijun Dai College of Electronic and Information engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

Synthetic Aperture Radar (SAR) has a wide range of applications in the military and civilian fields. Due to the angle sensitivity of targets in SAR images and the neglect of correlation information between images from different viewpoints in single-view recognition, multi-view recognition methods have received widespread attention. We propose a multi-view rotation double-layer fusion (MVRDF) CNN-LSTM network to fuse the rotation features of multi-view images. It uses three parallel CNNs to extract features from randomly extracted multi-view images, rotates the feature sequence twice to obtain three sets of features. We add a layer of rotation angle dimension LSTM after a layer of angle dimension LSTM for double-layer fusion. The experimental results based on MSTAR and a measured dataset demonstrate the effectiveness and generalization of the proposed method.

关键词： image recognition Correlation Sensitivity Target recognition Fuses Feature extraction Radar polarimetry Convolutional neural networks Synthetic aperture radar Long short term memory

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Assembly code translation from ARM64 to RISC-V

Assembly code translation from ARM64 to RISC-V

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Summer Virtual conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD-Summer), ACIS International

作者： Tatsuya Tsuchiya Kanemitsu Ootsu Takashi Yokota Shun Kojima Graduate School of Regional Development and Creativity Utsunomiya University Utsunomiya Tochigi Japan

ISBN: (纸本)9798350396386

SIMD instruction-based processing is effective in speeding up highly data-parallel processing such as image recognition. However, processing using the SIMD instruction set requires reconstructed the machine code when changing the number of computing units. Therefore, we have developed RISC-V with vector extensions, a scalable vector extension that can change the number of concurrent parallel operations without changing the machine code, referring to the ARM SVE. There is a problem that RISC-V vectorized machine code cannot be generated due to the lack of a compiler that supports vector extensions. The solution to this problem is to use the assembly code generated by the vectorization compiler for ARM SVE to generate assembly code for RISC-V with vector extensions. By assembling it, we generate RISC-V vectorization machine code. As the first step of implementation, we implement an assembly code translator that performs assembly code translation between the basic instruction set from ARM64 to RISC-V. Since the translated RISC-V program could be executed, it was confirmed that the translation proposed in this study was performed properly.

关键词： Codes Reduced instruction set computing image recognition Artificial intelligence image reconstruction Software engineering

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ParaSurRe: parallel Surface Reconstruction with No Pose Prior

ParaSurRe: Parallel Surface Reconstruction with No Pose Prio...

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IEEE International conference on Multimedia and Expo (ICME)

作者： Wenyu Li Zongxin Ye Sidun Liu Ziteng Zhang Xi Wang Peng Qiao Yong Dou School of Computer National University of Defense Technology

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Surface reconstruction from multi-view images without pose prior is challenging. Recent advances integrate incremental Structure from Motion (SfM) pipeline into surface optimization, enabling simultaneous surface reconstruction and pose estimation. However, due to the inherent incremental registration scheme of SfM, the efficiency of these methods is far from satisfactory, e.g., reconstruction of an object captured by 49 images costs over 9 hours using a high-end GPU. Inspired by divide-and-conquer strategy, we present a parallel Surface Reconstruction method, coined as ParaSurRe, where image collections are divided into non-overlapped clusters and the incremental reconstructions are performed individually in each cluster. Owing to image partitioning, each cluster only accurately reconstructs a part of the surface. The major challenge is to merge multiple partial neural implicit surfaces into a complete one. We propose a confidence-aware surface fusion strategy and a geometry-guided refinement to tackle this issue. Experiments on real-world datasets demonstrate that ParaSurRe reconstructs delicate surfaces from unposed images, and achieves competitive pose estimation performance compared with state-of-the-art methods, with up to 6.5× speedup on a scene captured by 81 images.

关键词： Training Surface reconstruction Three-dimensional displays Structure from motion Pose estimation Pipelines Graphics processing units

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Twin Branch Transformers-Based Re-Ranking for image Retrieval

Twin Branch Transformers-Based Re-Ranking for Image Retrieva...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Ziqi Wang Wei Wang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen China

image retrieval as an essential task in computer vision aims to search images to match a given query image, which could be solved with a two-stage model: retrieving a series of candidate images with a global image descriptor; re-ranking the nearest images with local descriptors. Recent study indicates that Transformer technique could reduce the number of local descriptors for the re-ranking process, and enable the model to be in parallel. However, there still exist some problems: different input orders of an image pair will produce different outputs (non-symmetric); the mean aggregation module fails to find out the difference between two images. To this end, we propose a twin branch Transformers-based re-ranking approach (TBT-RR). Specifically, we utilize a cross-attention module with shared weights and segment embeddings to make the model symmetrical. Then, we design a learnable aggregation module to adaptively take the average or difference between two images, so that local descriptors could quickly reach an agreement on whether the two images are matched. To output similarity scores between two images, we design a similarity estimation pipeline with a one-way cross-attention module. Finally, we train TBT-RR on the Google Landmark dataset and test it on the Revisited Oxford and Revisited Paris datasets. The experimental results show that the retrieval results of TBT-RR outperform previous re-ranking approaches. Besides, with less memory usage and parameter scale, TBT-RR has 50% increase in the retrieval speed and 40% reduction in the computational complexity.

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Optimization of Micro-object Identification Based on the Mellin Transform and the Use of parallel Computing

Optimization of Micro-object Identification Based on the Mel...

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International Russian Smart Industry conference (SmartIndustryCon)

作者： Isroil I. Jumanov Sunatillo M. Kholmonov Intelligent Systems and Computer Technologies Samarkand State University Samarkand Uzbekistan

Scientific and methodological foundations for the optimal identification of non-stationary objects based on the use of neural networks have been developed. Models and algorithms for detection, extraction of hidden relationships, useful properties and patterns in data, formation of a database and knowledge bases are proposed. Mechanisms have been developed for using the statistical, dynamic and specific characteristics of images, unique features of three, five-layer neural networks and combined models for setting variables with typical recognition and classification tools. Have been developed computational schemes for determining and adjusting the weights of neurons, choosing a suitable activation function, coefficients of synaptic and interneuronal connections, rational neural network architecture, the number of layers and neurons in the layers of the network, a set of functions of nonlinear dependencies "inputs - outputs". Data pre-processing algorithms are implemented that perform the functions of informative features selection, segmentation, object image contour extraction, search based on methods with annealing, prohibition, and stochastic search. Tested neural networks of Hopfield, Hamming, Hebb, Kohonen, bidirectional associative memory were tested. Schemes for two and three-dimensional image reconstruction based on the synthesis of tools for calculating Mellin transform functions, initial values of centroids, and the formation of a suboptimal set of variables are proposed. The identification software package in C++ was developed and implemented in the CUDA parallel computing environment.

关键词： image segmentation image recognition Heuristic algorithms Neurons Artificial neural networks Transforms parallel processing

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Custom Accessors: Enabling Scalable Data Ingestion, (Re-)Organization, and Analysis on distributed Systems

Custom Accessors: Enabling Scalable Data Ingestion, (Re-)Org...

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IEEE International conference on Big Data

作者： Vito Giovanni Castellana Burcu O. Mutlu Ian Di Dio Lavore Jesun Firoz Katherine Wolf Marco Minutoli John Feo Pacific Northwest National Laboratory Richland WA USA Politecnico di Milano Milano Italy

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The emerging class of high velocity and high volume data analytic workflows comprise interwoven data ingestion, organization, and processing stages, with ingestion and organization steps often contributing comparable or even higher computational costs than actual processing steps. Since complex workflows consist of a variety of phases that view and use data differently, being able to construct efficient, scalable, distributed data structures (arrays, vectors, sets, maps, and multi-maps) is essential and requires custom methods to extend and shrink containers, analyze and position data, and, maintain globally-consistent meta-data. In this paper, we propose a novel data-structure access paradigm based on the concept of Accessors. At a high level, accessors are customizable callable objects that can modify the behavior of insert, read, update, and delete operations for distributed containers while preserving atomicity guarantees. Accessors provide a very clean and natural way to implement a variety of programming patterns, e.g., conditional insertion/deletion and cascading computations, which would be otherwise hard (or even impossible) to express in parallel and distributed settings without using locks. We demonstrate the practicality and usefulness of our approach with two representative use cases and study the performance of these applications on a distributed High-Performance Computing system. Our analysis highlights that our proposed abstraction allows for an effective overlapping and concurrent execution of different workflow steps (e.g., data ingestion and analysis), which in a conventional analytics pipeline would execute sequentially, contributing cumulatively to the overall latency.

关键词： Phased arrays Data ingestion Data analysis High performance computing Pipelines distributed databases Organizations Containers Programming Vectors

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