Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved withthe conference event and publication of the proceedings record.
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved withthe conference event and publication of the proceedings record.
the proceedings contain 52 papers. the special focus in this conference is on Advanced Concepts for Intelligent Vision systems. the topics include: Two-Camera Synchronization and Trajectory Reconstruction for a Touch ...
ISBN:
(纸本)9783030014483
the proceedings contain 52 papers. the special focus in this conference is on Advanced Concepts for Intelligent Vision systems. the topics include: Two-Camera Synchronization and Trajectory Reconstruction for a Touch Screen Usability Experiment;comparison of Co-segmentation Methods for Wildlife Photo-identification;an Efficient Agglomerative Algorithm Cooperating with Louvain Method for Implementing Image Segmentation;robust Feature Descriptors for Object Segmentation Using Active Shape Models;foreground Background Segmentation in Front of Changing Footage on a Video Screen;Multi-organ Segmentation of Chest CT Images in Radiation Oncology: Comparison of Standard and Dilated UNet;Diffuse Low Grade Glioma NMR Assessment for Better Intra-operative Targeting Using Fuzzy Logic;identification of Saimaa Ringed Seal Individuals Using Transfer Learning;enhanced Codebook Model and Fusion for Object Detection with Multispectral Images;Matrix Descriptor of Changes (MDC): Activity Recognition Based on Skeleton;Unsupervised Perception Model for UAVs Landing Target Detection and Recognition;parallel and distributed Local Fisher Discriminant Analysis to Reduce Hyperspectral Images on Cloud computing Architectures;bayesian Vehicle Detection Using Optical Remote Sensing Images;Integrating UAV in IoT for RoI Classification in Remote Images;Enhanced Line Local Binary Patterns (EL-LBP): An Efficient Image Representation for Face Recognition;Single Sample Face Recognition by Sparse Recovery of Deep-Learned LDA Features;recursive Chaining of Reversible Image-to-Image Translators for Face Aging;automatically Selecting the Best Pictures for an Individualized Child Photo Album;face Detection in Painting Using Deep Convolutional Neural Networks;robust Geodesic Skeleton Estimation from Body Single Depth;person Re-Identification with a Body Orientation-Specific Convolutional Neural Network;person Re-identification Using Group Context.
In recent years, the amount of data increases continuously. With newly emerging paradigms, such as the Internet of things, this trend will even intensify in the future. Extracting information and, consequently, knowle...
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Android operating system always occupies the highest market share in mobile operating systems. Security analysis on Android operating systems often focuses on analyzing applications (APK files) when installed on the p...
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In the emerging digital age, massive production of data is occurred actively or passively by collecting data from users and environment via applications, sensor devices and so on. that makes it important and crucial t...
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ISBN:
(纸本)9781538659304
In the emerging digital age, massive production of data is occurred actively or passively by collecting data from users and environment via applications, sensor devices and so on. that makes it important and crucial to have the ability to process big data efficiently and effectively utilize it. the challenge to process big data is that it has high volume, velocity, variety, as well as veracity and value. In this paper, we present a survey of related work and prescribe our recommendations towards building Bayesian classification for big data environments. It is based on MapReduce and is distributed, parallel, single pass and incremental which makes it feasible to be deployed and executed on cloud computing platform We also carry out scalability analysis of the proposed solution that it can train Bayesian classifier to perform predictive analytics by processing big data with large volume, velocity and variety.
Online searching is one of the most frequently performed actions and search engines need to provide relevant results, while maintaining scalability. In this paper we introduce a novel approach grounded in Cohesion Net...
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ISBN:
(纸本)9783319993447;9783319993430
Online searching is one of the most frequently performed actions and search engines need to provide relevant results, while maintaining scalability. In this paper we introduce a novel approach grounded in Cohesion Network Analysis in the form of a semantic search engine incorporated in our Hub-Tech platform. Our aim is to help researchers and people unfamiliar with a domain find meaningful articles online, relevant for their project scope. In addition, we integrate state-of-the-art technologies to ensure scalability and low response time, namely SOLR - for data storage and full-text search functionalities - and Akka - for parallel and distributed processing. Preliminary validations denote promising search results, the software being capable to suggest articles in approximately the same way as humans consider them most appropriate - 75% are close results and top 20% are identical to user recommendations. Moreover, Hub-Tech recommended more suitable articles than Google Scholar for our specific task of searching for articles related to a detailed description given as input query (50 + words).
Today we have an established stack of tools to develop applications, systems and services for scientific purposes. However, not so long ago a new technology, called .NET Core appeared. It's supervised by Microsoft...
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As an effective method in dealing withthe massive data, the serial processing, aiming to obtain the useful information quickly, cannot satisfy our calculation requirements with high-performance. However, both distrib...
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ISBN:
(纸本)9781509067046
As an effective method in dealing withthe massive data, the serial processing, aiming to obtain the useful information quickly, cannot satisfy our calculation requirements with high-performance. However, bothdistributedcomputing and parallelcomputing are good choices in calculating high-volume data with high-performance. As a parallelcomputing framework based on memory computing large data, Spark was of great concern as soon as it was proposed. this paper, based on the limitations of k-medoids algorithm which is sensitive to the center point and requires a large number of iterations in the process of calculating the new center point, puts forward a parallel algorithm named Canopy-Kmedoids within the platform of Spark. the algorithm obtains the K center points by the Canopy algorithm. By analyzing the performance of parallel operators and the advantage of using Spark to have good performance for iterative computation. In addition, this method reduces the frequency of reading or writing the shuffle and disk, which effectively overcomes the shortcomings of k-medoids. the experimental results show that the parallel algorithm achieves a relative ideal speedup ratio and can handle the massive data efficiently as well.
Withthe vigorous development of network applications, typical SDN (Software Defined Networks) such as data centers are gradually carrying more and more complex network traffic. this poses a great challenge for networ...
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ISBN:
(纸本)9781538625880
Withthe vigorous development of network applications, typical SDN (Software Defined Networks) such as data centers are gradually carrying more and more complex network traffic. this poses a great challenge for network monitoring - how to realize real-time, high-accuracy capture of traffic changes at low cost. In this paper, we propose a trigger-based monitoring approach called EffiView. this approach provides three ways to monitor flow statistics, including flow-stat triggering, FlowRemoved parsing and active polling. the flow-stat triggering can occur on all multiples of the presupposed flow-stat threshold for each flow entry. the latter two ways are complementary to the flow-stat triggering. FlowRemoved parsing is used to acquire flow statistics from FlowRemoved messages and active polling is conditionally carried out by the controller at the expiration of monitoring period. EffiView achieves low-cost monitoring by combining the three ways efficiently, while ensuring high accuracy and fine granularity. Based on the NetMagic platform, We implement EffiView and evaluate its monitoring performance. the experimental results show that EffiView can reach great advantages over traditional monitoring approaches.
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