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Study on stocks based on big data methods and computer programs

Study on stocks based on big data methods and computer progr...

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2020 Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2020

作者： Wang, Cheng Zou, Hailei Yin, Juncheng China Jiliang Univ. Applied Math Dept Hang Zhou City China

ISBN: (纸本)9781728160665

Recently big data is a hot topic in many fields. Here we use distributed algorithms to treat big data in Chinese stock market. First the two stock indexes (Shanghai Composite and Shenzhen Composite indexes) from 1990-2019 are collected. Then the data are divided to several subsets and are analyzed separately via computer programs. Finally the results for every subset are combined due to distributed algorithms theory. In addition to big data scheme, we adopt stochastic processes theory and moment equations methods. © 2020 IEEE.

关键词： Big data

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distributed Analysis Dictionary Learning Based on Consensus Constraints

Distributed Analysis Dictionary Learning Based on Consensus ...

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IEEE International conference on Signal and image processing (ICSIP)

作者： Liu Yang Jing Dong Jie Zhang Xiaoqing Luo Jian Guan College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China College of Electrical and Information Engineering Zhengzhou University of Light Industry Zhengzhou China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (纸本)9781665446006

distributed dictionary learning is to learn a global dictionary so that all data distributed in a network has sparse representation in the domain of the dictionary. Existing works are based on sparse synthesis model. We consider this problem based on the sparse analysis model, and propose a distributed analysis dictionary learning (ADL) algorithm using consensus constraints. In particular, local dictionaries corresponding to local data at each node are introduced, and the distributed ADL problem is formulated as a minimization problem with consensus constraints on local dictionaries and the global dictionary. An optimization method consisting of a sparse coding stage and a dictionary update stage is then developed. Experimental results have shown that the performance of the proposed algorithm is comparable to centralized ADL algorithms.

关键词： Analytical models Dictionaries image coding conferences Optimization methods distributed databases Machine learning

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PIM: A visualization-oriented web application for monitoring and debugging of large-scale image processing studies

PIM: A visualization-oriented web application for monitoring...

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SPIE conference on Medical Imaging - Imaging Informatics for Healthcare, Research, and Applications

作者： Kroes, Thomas Achterberg, Hakim Koek, Marcel Versteeg, Adriaan Niessen, Wiro van der Lugt, Aad van het Hof, Peter van Lew, Baldur Lelieveldt, Boudewijn Leiden Univ Div Image Proc Med Ctr NL-2333 ZA Leiden Netherlands Erasmus MC Biomed Imaging Grp Rotterdam Dept Radiol & Nucl Med NL-3015 GD Rotterdam Netherlands Erasmus MC Dept Med Informat NL-3015 GD Rotterdam Netherlands Univ Med Ctr Dept Biomed Data Sci Sequencing Anal Support Core NL-2333 ZA Leiden Netherlands

ISBN: (数字)9781510634046

ISBN: (纸本)9781510634039;9781510634046

Advances in computer hard- and software have enabled the automated extraction of biomarkers from large scale imaging studies by means of image processing pipelines. For large cohort studies, ample storage- and computing resources are required: pipelines are typically executed in parallel on one or more High Performance Computing Clusters (HPC). As processing is distributed, it becomes more cumbersome to obtain detailed progress and status information of large-scale experiments. Especially in a research-oriented environment, where image processing pipelines are often in an experimental stage, debugging is a crucial part of the development process that relies heavily on a tight collaboration between pipeline developers and clinical researchers. Debugging a running pipeline is a challenging and time-consuming process for seasoned pipeline developers, and nearly impossible for clinical researchers, often involving parsing of complex logging systems and text files, and requires special knowledge of the HPC environment. In this paper, we present the Pipeline Inspection and Monitoring web application (PIM). The goal of PIM is to make it more straightforward and less time-consuming to inspect complex, long running image processing pipelines, irrespective of the level of technical expertise and the workflow engine. PIM provides an interactive, visualization-based web application to intuitively track progress, view pipeline structure and debug running image processing pipelines. The level of detail is fully customizable, supporting a wide variety of tasks (e.g. quick inspection and thorough debugging) and thereby facilitating both clinical researchers and pipeline developers in monitoring and debugging.

关键词： Population Imaging Graph Visualization Monitoring Debugging Workflow Engine Level of detail distributed Computing

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Apply image Identification to Improve the Localization of the Self-Driving Vehicles

Apply Image Identification to Improve the Localization of th...

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

作者： Shao-Hui Liu Shih-Yen Huang National Chin-Yi University Technology Taichung Taiwan

ISBN: (纸本)9781665447324

Location failure is dangerous for self-driving vehicles. Adaptive Monte Carlo Localization (AMCL)[1] provides wrong coordinates to the self-driving controller in some specific conditions. This paper proposed a scheme to solve this problem. This scheme provides a reference location to AMCL, which could exactly give coordinates to the self-driving controller. The experiment results showed that this reference location could improve the performance of AMCL to provide precise coordinates to the self-driving controller. In addition, to provide reference location to AMCL, this proposed scheme applied Convolutional Neural Network (CNN)[2] to identify the specific scenery front the vehicle. Accordingly, detect particular views will be another challenge for self-driving vehicles.

关键词： Location awareness Monte Carlo methods image recognition Convolutional neural networks Artificial intelligence Software engineering

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Reversible Data Hiding for SMVQ Compressed images Based on De-Clustering Rules

Reversible Data Hiding for SMVQ Compressed Images Based on D...

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

作者： Kun-Sheng Sun Ji-Hwei Horng Chin-Chen Chang National Quemoy University Kinmen Taiwan

ISBN: (纸本)9781665447324

Vector quantization (VQ) is a popular digital image compression technique. Its resulting index table can be further compressed using the side match vector quantization (SMVQ). In this research, we propose a reversible data hiding scheme based on the de-clustering rules to embed secret data during SMVQ compression. Referring to differently assigned codebooks, the de-clustering rules are equally applicable to both compressible and uncompressible VQ indices. The proposed scheme can produce a camouflaged VQ index table with a high payload. Besides, our scheme is free from the indicator bit, which is required in the conventional SMVQ. Experimental results are compared with state-of-the-art methods.

关键词： image coding Vector quantization Digital images Indexes Artificial intelligence Payloads Software engineering

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BLIND image DEBLURRING WITH JOINT EXTREME CHANNELS AND L0-REGULARIZED INTENSITY AND GRADIENT PRIORS

BLIND IMAGE DEBLURRING WITH JOINT EXTREME CHANNELS AND L0-RE...

引用

IEEE International conference on image processing (ICIP)

作者： Zhou, Kai Zhuang, Peixian Xiong, Jiaying Zhao, Jin Du, Muyao Nanjing Univ Informat Sci & Technol Sch Elect & Informat Engn Nanjing Peoples R China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163956

The extreme channels prior (ECP) relies on the bright and dark channels of an image, and the corresponding ECP-based methods perform well in blind image deblurring. However, we experimentally observe that the pixel values of dark and bright channels in some images are not concentratedly distributed on 0 and 1 respectively. Based on this observation, we develop a model with a joint prior which combines the extreme channels prior and the L-0-regularized intensity and gradient prior for blind image deblurring, and previous image deblurring approaches based on dark channel prior, L-0-regularized intensity and gradient, and extreme channels prior can be seen as a particular case of our model. Then we derive an efficient optimization algorithm using the half-quadratic splitting method to address the non-convex L-0-minimization problem. A large number of experiments are finally performed to demonstrate the superiority of the proposed model in details restoration and artifacts removal, and our model outperforms several leading deblurring approaches in terms of subjective results and objective assessments. In addition, our method is more applicable for deblurring natural, text and face images which do not contain many bright or dark pixels.

关键词： Blind image deblurring extreme channels prior L-0-regularized intensity and gradient half-quadratic

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Delay Optimization of Power Internet of Things based on Edge-Cloud Collaboration

Delay Optimization of Power Internet of Things based on Edge...

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International conference on Communication Technology (ICCT)

作者： Julaiti Abulizi Qingsheng Hu Wei Wang State Grid Electronic Power Research Institute Urumqi China School of Information Science and Engineering Southeast University Nanjing China Nanjing Jiruizhidian Technology Co. Ltd. Nanjing China

With the continuous development of the power Internet of Things and the emergence of multi service scenarios, real-time and high-speed process in power data services face more and more challenges. This paper presents an SDN based edge-cloud collaborative method for delay optimization. First, the delay optimization problem is formulated and transformed into a convex optimization problem further. Then a continuously variable coefficient is introduced and its optimal solution is found. Finally a valid task partition strategy that closet to the optimal solution can be found, and the system delay is minimized. Simulation results show that the reasonable loading of edge node tasks to the cloud can effectively reduce the total system delay of power business and alleviate network congestion. Compared with other conventional methods, this edge-cloud collaboration method not only has low system delay, but also makes full use of the control mechanism of SDN to coordinate distributed environment.

关键词： Cloud computing Simulation Collaboration Optimization methods parallel processing Real-time systems Delays

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Adaptive Auto-Scaling of Delay-Sensitive Serverless Services with Reinforcement Learning

Adaptive Auto-Scaling of Delay-Sensitive Serverless Services...

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IEEE Annual International Computer Software and Applications conference (COMPSAC)

作者： Zhiyu Zhang Tao Wang An Li Wenbo Zhang University of Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

Serverless services such as image recognition and natural language processing have strict response-time constraints. The incoming workloads and resource requirements of a newly deployed serverless service are always unpredictable due to the lack of available historical tracing data. Therefore, making effective auto-scaling decisions for these services is challenging. Open source serverless platforms often work in a best-effort manner, which cannot guarantee the response delay. Moreover, existing studies usually adopt threshold-based methods by configuring additional resource, which cannot well balance the trade-off between the quality of service and resource efficiency. To address the above issues, we propose an adaptive auto-scaling approach for delay-sensitive serverless services with reinforcement learning. First, we characterize the service's resource profile by exploring the performance improvement of different resource allocations with the reinforcement learning method. Then, we propose an adaptive auto-scaling method combining both horizontal and vertical scaling strategies based on the characterized profile to dynamically adjust the resource allocation. Finally, we select three typical services to validate our approach by comparing with two existing state-of-the-art auto-scaling methods. The experimental results show that our approach can accurately characterize services' resource profile, and effectively ensure the response delay constraints while achieving about 10.50% reduction of cost on average.

关键词： image recognition Graphics processing units Reinforcement learning Quality of service parallel processing Dynamic scheduling Software

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Steps of Pre-processing for English to Mizo SMT System 1

引用

2nd International conference on Machine Learning, image processing, Network Security and Data Sciences, MIND 2020

作者： Devi, Chanambam Sveta Purkayastha, Bipul Syam Department of Computer Science Assam University Silchar788011 India

ISBN: (数字)9789811563188

ISBN: (纸本)9789811563171

The idea behind this present paper is to construct the pre-processing techniques on Bilingual parallel Corpus. The parallel corpus is a common essential resource for the application of NLP. The parallel corpus is a huge number of parallel sentences or text of two different languages. We propose a new framework for the pre-processing steps of the Bilingual Corpus of SMT System. The purpose of this study the virtue of the pre-processing methods which are mainly used for training the translation. The pre-processing technique contributes to a main part of the Statistical Machine Translation. In this paper, we survey the various steps of pre-processing methods used for the translation system and also how the complication of the translation method has succeeded in society so far. Experimental results of the pre-processing steps on Bible Domain (https://***/kjv). ‘English-Mizo’ parallel corpus (on bible domain) are discussed and all the results of pre-processing steps are mentioned. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Computer aided language translation

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15th International conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020

15th International Conference on Soft Computing Models in In...

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15th International conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020

ISBN: (纸本)9783030578015

The proceedings contain 83 papers. The special focus in this conference is on Soft Computing Models in Industrial and Environmental Applications. The topics include: A Smart Crutch Tip for Monitoring the Activities of Daily Living Based on a Novel Neural-Network Intelligent Classifier;Hourly Air Quality Index (AQI) Forecasting Using Machine Learning methods;interpretable Deep Learning with Hybrid Autoencoders to Predict Electric Energy Consumption;on the Performance of Deep Learning Models for Time Series Classification in Streaming;An Approach to Forecasting and Filtering Noise in Dynamic Systems Using LSTM Architectures;novel Approach for Person Detection Based on image Segmentation Neural Network;an Adaptive Cognitive Model to Integrate Machine Learning and Visual Streaming Data;smart Song Equalization Based on the Classification of Musical Genres;machine Learning in Classification of the Wax Structure of Breathing Openings on Leaves Affected by Air Pollution;A Preliminary Study for Automatic Activity Labelling on an Elder People ADL Dataset;software Sensors for the Monitoring of Bioprocesses;RGB images Driven Recognition of Grapevine Varieties;discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering;counting Livestock with image Segmentation Neural Network;smart, Precision or Digital Agriculture and Farming - Current State of Technology;an Automated Platform for Microrobot Manipulation;growth Models of Female Dairy Cattle;a Preliminary Study on Crop Classification with Unsupervised Algorithms for Time Series on images with Olive Trees and Cereal Crops;blocks of Jobs for Solving Two-Machine Flow Shop Problem with Normal distributed processing Times;soft Computing Analysis of Pressure Decay Leak Test Detection.

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