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Swallow swarm fuzzy C-Means algorithm (SSFCM) and distributed processing framework for big image data

AIP conference Proceedings 2022年第1期2393卷

作者： Vigneshwari Kalaiselvi Vels Institute of Science Technology and Advanced Studies Chennai India

The recent period of image gathering process cannot be manipulated effectively on the system because of huge set of images and due to the high expenditure costs for the algorithms used to process on the recent images. Therefore, the processing of an image needs the help of distributed computing concepts. The subject named distributed computing is considered to be a complex area which requires sound technical learning and cannot be utilized by the research conducted so as to develop on the algorithms used for image processing; all the existing works are more concentrated on the optimization process used for processing the images with the aid of algorithms like-wise completely ignoring the inherent deficiency of an individual node-based processing method, where the quality of the image looks poor. In order to rectify this problem, static big image is processed by image Cloud processing (ICP) is a technique, which is proposed on modern days, which contains 2 different parts of processing methods i.e. SICP and DICP, so as to obtain the dynamic input of the static big image. The techniques used in the processing of small images are SICP and DICP that limits the capabilities of processing and the even images are broken down, at the time of cluster failure, when it needs to process the clusters on the timely basis. In order to rectify this problem, this paper proposes the design of Swallow Swarm Fuzzy C-Means Algorithm (SSFCM) based distributed processing framework for the clustering of images because of their poor requirements for computations. This work includes pre-processing of images with the help of image enhancement using Histogram Equalization (HE) and Adaptive Median Filtering (AMF). So as to accomplish SICP, 2 novel representations named Pure-image and Big-image are modeled to combine and execute by means of the algorithm named SSFCM-Map Reduce (SSFCM-MR) to attain large number of optimized configurations. In the formulated work, SSFCM algorithm is implemented th

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Forgery Detection For High-Resolution Digital images Using FCM And PBFOAAlgorithm

Forgery Detection For High-Resolution Digital Images Using F...

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International conference on parallel distributed and Grid Computing (PDGC)

作者： Satinder Jeet Kaur Nidhi Bhatla Sachdeva Engineering College for Girls Gharuan(Mohali) India

ISBN: (数字)9781728171326

ISBN: (纸本)9781728171333

image forgery detection is the area of research in the field of biometric and forensics. Digital pictures are the resource of data. In the present world of technology, image processing software tools have developed to generate and modify digital images from one location to another. With the current technology, it is simple to establish image forgery by addition and subtraction of the components from the pictures that lead to image interfering. Copy-move image forgery is created by copying and pasting the element in a similar image. Hence, copy-move forgery has become an area of research in the image forensic unit. Various methods have been implemented to detect digital image forgery. Some issues still required to resolve like time complexity, fake, and blurred image. In existing research, the block and feature-based approach used to remove a forged area from the image using SIFT and RANSAC algorithm. The forgery dataset of the 80 pictures collected to achieve accuracy of up to 95%. In the research work, the PBFOA method has been implemented to optimize and extract the features using the component analysis method. FCM is used for image segmentation in the input image. PBFOA is based on an optimization process to select valuable features based on the calculation of the fitness function. In this method, two steps are used to re-verify the instance, features (i) Slower and faster condition. BFOA steps are described in detail in this research paper. Initial steps, Spread the feature set in the whole system. In the rapid condition selected and to eliminate the valuable features one at a time, then reproduction phase is implemented with the help of the fitness function to recover the feature values and detect the forgery information in the uploaded image. The simulation setup using MATLAB 2016a version and improve the accuracy rate and image quality parameter. Performance analysis depends on the proposed metrics FAR, FRR, ACC, Precision, Recall, and compared with the exist

关键词： Forgery Feature extraction Digital images Databases image recognition Handheld computers Grid computing

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Speculative distributed CSV Data Parsing for Big Data Analytics 19

Speculative Distributed CSV Data Parsing for Big Data Analyt...

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ACM SIGMOD International conference on Management of Data (SIGMOD)

作者： Ge, Chang Li, Yinan Eilebrecht, Eric Chandramouli, Badrish Kossmann, Donald Microsoft Res Redmond WA USA Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9781450356435

There has been a recent flurry of interest in providing query capability on raw data in today's big data systems. These raw data must be parsed before processing or use in analytics. Thus, a fundamental challenge in distributed big data systems is that of efficient parallel parsing of raw data. The difficulties come from the inherent ambiguity while independently parsing chunks of raw data without knowing the context of these chunks. Specifically, it can be difficult to find the beginnings and ends of fields and records in these chunks of raw data. To parallelize parsing, this paper proposes a speculation-based approach for the CSV format, arguably the most commonly used raw data format. Due to the syntactic and statistical properties of the format, speculative parsing rarely fails and therefore parsing is efficiently parallelized in a distributed setting. Our speculative approach is also robust, meaning that it can reliably detect syntax errors in CSV data. We experimentally evaluate the speculative, distributed parsing approach in Apache Spark using more than 11,000 real-world datasets, and show that our parser produces significant performance benefits over existing methods.

关键词： distributed parsing parallel csv

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A Novel Deep Neural Network Model for Credit Risk Prediction of Chinese Farmers

A Novel Deep Neural Network Model for Credit Risk Prediction...

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IEEE International conference on Data Science in Cyberspace (DSC)

作者： Yalong Xie Aiping Li Ziniu Liu Kai Chen Hongkui Tu College of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665418164

China is a big agricultural county with more than 500 million rural population. In China, farmers usually loan from rural commercial banks or rural credit cooperatives. It is crucial for the national economic development and the improvement of people's standard of living that how to reasonably use funds to subsidize the agricultural population and reduce the risk of rural loans. At present, credit risk prediction of farmers mainly depends on the experience of experts in the business field, and there is little published research on using artificial intelligence methods to solve this problem. This paper presents a complete set of methods, including data collection, feature selection, etc. We propose a novel deep neural network model named DNN-CRP for credit risk prediction of Chinese framers. Experiments on an actual credit loan dataset of Chinese farmers are presented, and experimental results show that the comprehensive performance of the DNN-CRP model is better than current state-of-the-art models. It is believed that the DNN-CRP model proposed in this paper can help banks improve the efficiency of the credit loan business of farmers and reduce credit risks.

关键词： Deep learning Sensitivity Neural networks Sociology Predictive models Data collection Data models

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parallel tracking and reconstruction of states in heuristic optimization systems on GPUs 20

Parallel tracking and reconstruction of states in heuristic ...

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20th International conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2019

作者： Koster, Marcel Grob, Julian Kruger, Antonio Saarland Informatics Campus Saarbrücken Germany

ISBN: (纸本)9781728126166

Modern heuristic optimization systems leverage the parallel processing power of Graphics processing Units (GPUs). Many states are maintained and evaluated in parallel to improve runtime by orders of magnitudes in comparison to purely CPUbased approaches. A well known example is the parallel Monte Carlo tree search, which is often used in combination with more advanced machine-learning methods these days. However, all approaches require different optimization states in memory to update or manipulate variables and observe their behavior over time. Large real-world problems often require a large number of states that are typically limited by the amount of available memory. This is particularly challenging in cases in which older states (that are not currently being evaluated) are still required for backtracking purposes. In this paper, we propose a new general high-level approach to track and reconstruct states in the scope of heuristic optimization systems on GPUs. Our method has a considerably lower memory consumption compared to traditional approaches and scales well with the complexity of the optimization problem. © 2019 IEEE.

关键词： Graphics processing unit

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Data acquisition and analysis methods in UAV-based applications for Precision Agriculture 15

Data acquisition and analysis methods in UAV-based applicati...

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15th Annual International conference on distributed Computing in Sensor Systems (DCOSS)

作者： Tsouros, Dimosthenis C. Triantafyllou, Anna Bibi, Stamatia Sarigannidis, Panagiotis G. Univ Western Macedonia Dept Informat & Telecommun Engn Kozani Greece

ISBN: (纸本)9781728105703

Emerging technologies such as Internet of Things (IoT) can provide significant potential in Precision Agriculture enabling the acquisition of real-time environmental data. IoT devices like Unmanned Aerial Vehicles (UAVs) equipped with cameras, sensors, and GPS receivers can deliver a variety of IoT services and applications related to fields management, by capturing images from great heights. However, there are many issues to be resolved before the effective use of UAVs in the agriculture domain, including the data collection and processing methods. There is still no standardized workflow and processes for most UAV-based applications for Precision Agriculture. In this paper we summarize the data acquisition methods and technologies to acquire images in UAV-based Precision Agriculture and appoint the benefits and drawbacks of each one. We also review popular data analysis methods of remotely sensed imagery and discuss the outcomes of each method and its potential application in the farming operations.

关键词： Remote Sensing IoT UAV UAS Unmanned Aerial Vehicle Unmanned Aerial System image processing Precision Agriculture Smart Farming Review

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Accelerating DNN Inference by Edge-Cloud Collaboration

Accelerating DNN Inference by Edge-Cloud Collaboration

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IEEE International conference on Performance, Computing and Communications (IPCCC)

作者： Jianan Chen Qi Qi Jingyu Wang Haifeng Sun Jianxin Liao The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665443326

Deep neural networks (DNN) have become indispensable tools for intelligent applications today. The demand for deploying DNN on the edge devices increases dramatically. Unfortunately, it is challenging because the DNN inference is computation-intensive, but edge devices are always resource-constraint. Prior solutions attempted to address these challenges with collaboration between cloud and edge devices, but they do not take the inference request rate into account. However, the inference delay will increase dramatically while the request rate becomes *** this paper, we propose a scheme to dynamic partition DNN into two or three parts and distribute them at the edge and cloud, achieving the lowest delay with the change of request rate. The scheme selects the optimal partition points of DNN with a layer evaluation model (LEM) and a total delay prediction model (DPM) under different request rates. The experiments of distributed deploying AlexNet, VGG, NiN and ResNet DNN models on image classification dataset imageNet show that the proposed scheme significantly reduces the total end-to-end latency by fully using both the edge and cloud resources. It reduces the inference delay by 1.3 to 1.6 times and improves the throughput 1.2 to 1.7 times compared to the state of art partition approach.

关键词： Performance evaluation Art Neural networks Collaboration Predictive models parallel processing Throughput

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GPU Acceleration of Doppler broadening for neutron transport calculations

GPU Acceleration of Doppler broadening for neutron transport...

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2020 International conference on Physics of Reactors: Transition to a Scalable Nuclear Future, PHYSOR 2020

作者： Burke, Paul E. Remley, Kyle E. Griesheimer, David P. Georgia Institute of Technology 770 State St. NW AtlantaGA30313 United States Naval Nuclear Laboratory PO Box 1072 SchenectadyNY12301 United States

ISBN: (纸本)9781713827245

In radiation transport calculations, the effects of material temperature on neutron/nucleus interactions must be taken into account through Doppler broadening adjustments to the microscopic cross section data. Historically, Monte Carlo transport simulations have accounted for this temperature dependence by interpolating among precalculated Doppler broadened cross sections at a variety of temperatures. More recently, there has been much interest in on-the-fly Doppler broadening methods, where reference data is broadened on-demand during particle transport to any temperature. Unfortunately, Doppler broadening operations are expensive on traditional central processing unit (CPU) architectures, making on-the-fly Doppler broadening unaffordable without approximations or complex data preprocessing. This work considers the use of graphics processing unit (GPU)s, which excel at parallel data processing, for on-the-fly Doppler broadening in continuous-energy Monte Carlo simulations. Two methods are considered for the broadening operations - a GPU implementation of the standard SIGMA1 algorithm and a novel vectorized algorithm that leverages the convolution properties of the broadening operation in an attempt to expose additional parallelism. Numerical results demonstrate that similar cross section lookup throughput is obtained for on-the-fly broadening on a GPU as cross section lookup throughput with precomputed data on a CPU, implying that offloading Doppler broadening operations to a GPU may enable on-the-fly temperature treatment of cross sections without a noticeable reduction in cross section processing performance in Monte Carlo transport codes. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://***/licenses/by/4.0/).

关键词： Graphics processing unit

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Multi-agent Supply Planning methods Allowing the Real-Time Performance of the Supply Network Management Systems 1

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9th International conference on Industrial Applications of Holonic and Multi-Agent Systems (HoloMAS)

作者： Tsarev, Alexander ICCS RAS Samara Russia

ISBN: (数字)9783030278786

ISBN: (纸本)9783030278786;9783030278779

The paper deals with the questions of how to develop the automated planning systems that are fast enough to be used in real-time management of supply networks, considering the manual plan corrections by the users. Several practical situations and planning system use cases are considered. The paper proposes several methods that allow the increase of the data processing speed in practical cases. The methods include parallel data processing, dynamic control of the solutions space depth search, self-regulation of the system behavior based of the specifics of the data processed.

关键词： distributed planning Self-regulation parallel processing Coordination Performance Real-time scheduling Real-time management Multi-agent planning Supply network Supply chain

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SA-Net: Shuffle Attention for Deep Convolutional Neural Networks

SA-Net: Shuffle Attention for Deep Convolutional Neural Netw...

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IEEE International conference on Acoustics, Speech and Signal processing

作者： Qing-Long Zhang Yu-Bin Yang State Key Laboratory for Novel Software Technology Nanjing University

Attention mechanisms, which enable a neural network to accurately focus on all the relevant elements of the input, have become an essential component to improve the performance of deep neural networks. There are mainly two attention mechanisms widely used in computer vision studies, spatial attention and channel attention, which aim to capture the pixel-level pairwise relationship and channel dependency, respectively. Although fusing them together may achieve better performance than their individual implementations, it will inevitably increase the computational overhead. In this paper, we propose an efficient Shuffle Attention (SA) module to address this issue, which adopts Shuffle Units to combine two types of attention mechanisms effectively. Specifically, SA first groups channel dimensions into multiple sub-features before processing them in parallel. Then, for each sub-feature, SA utilizes a Shuffle Unit to depict feature dependencies in both spatial and channel dimensions. After that, all sub-features are aggregated and a "channel shuffle" operator is adopted to enable information communication between different sub-features. The proposed SA module is efficient yet effective, e.g., the parameters and computations of SA against the backbone ResNet50 are 300 vs. 25.56M and 2.76e-3 GFLOPs vs. 4.12 GFLOPs, respectively, and the performance boost is more than 1.34% in terms of Top-1 accuracy. Extensive experimental results on common-used benchmarks, including imageNet-1k for classification, MS COCO for object detection, and instance segmentation, demonstrate that the proposed SA outperforms the current SOTA methods significantly by achieving higher accuracy while having lower model complexity.

关键词： image segmentation Computer vision conferences Neural networks Object detection Computer architecture Signal processing

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