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Evaluating Software System Reliability Using Architecture Based Approach
Sabbineni Srinivas Rao,
Inuganti Nava Sahitha,
Godithi Sireesha,
Palem Manoj
Issue:
Volume 7, Issue 1, February 2018
Pages:
1-4
Received:
20 January 2018
Accepted:
5 February 2018
Published:
23 February 2018
Abstract: Programming dependability is those failure-free programming operations for a specified time clinched alongside a specified earth. On acquire secondary unwavering quality to expansive what's more intricate framework, utilize architecture-based approach. Software reliability is one of the major attributes of the software quality attributes that are availability, interoperability, maintainability, manageability, performance, reliability, reusability. To obtain reliability, used mainly fault tolerance mechanisms in the design process. In this paper there is a comparison between error recovery along with fault tolerance mechanisms versus error propagation in evaluating software system reliability. Here compared two case studies which produce the software reliability.
Abstract: Programming dependability is those failure-free programming operations for a specified time clinched alongside a specified earth. On acquire secondary unwavering quality to expansive what's more intricate framework, utilize architecture-based approach. Software reliability is one of the major attributes of the software quality attributes that are a...
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Customer Focused Collection Services in the Age of Big Data
Issue:
Volume 7, Issue 1, February 2018
Pages:
5-8
Received:
29 September 2017
Accepted:
19 October 2017
Published:
4 April 2018
Abstract: As part of library core functions, collection services had always focused on resources and processes in the print age. With the advent of big data with prevailing digital technologies in the recent decades, academic libraries in the U.S. have increasingly brought customer into the center of collection services. Big data empower these customer-focused services in various formats and scopes. What are some common practices? How effective are they in addressing the customer needs while fulfilling the conventional goals of collection services? This article starts with a historical overview on the evolutions of collection activities from the perspectives of academic libraries in the U.S. It then shares several key trends and common practices enabled by big data to build collection services centering on customers, including demand driven acquisitions models, digital collections development, collection access and discovery enhancements and systematic collection assessments. The article also discusses the multitudes of implications and impacts brought by these new customer-focused collection services on the library and information science (LIS) profession, in technologies, in philosophies, in personnel, in budgets and certainly in user experience.
Abstract: As part of library core functions, collection services had always focused on resources and processes in the print age. With the advent of big data with prevailing digital technologies in the recent decades, academic libraries in the U.S. have increasingly brought customer into the center of collection services. Big data empower these customer-focus...
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Human Activity Recognition Based on Weighted Sum Method and Combination of Feature Extraction Methods
Nang Hung Van Nguyen,
Minh Tuan Pham,
Nho Dai Ung,
Kanta Tachibana
Issue:
Volume 7, Issue 1, February 2018
Pages:
9-14
Received:
26 April 2018
Accepted:
24 May 2018
Published:
13 June 2018
Abstract: Human Activity Recognition (HAR) is one of the most important areas of computer vision research. The biggest difficulty for HAR system is that the camera could only film in one direction, leading to a shortage of data and low recognition results. This paper focuses on researching and building new models of HAR, including Principal Components Analysis (PCA), Linear discriminant Analysis (LDA) is to reduce the dimensionality and size of data, contributing to high recognition accuracy. First, from the 3D motion data, we conducted a pretreatment and feature extraction of objects. Next, we built a recognition model corresponding to each feature extraction method and we used Support Vector Machine (SVM) model to train. Finally, we used weighted methods to combine the results of the model to train and give the final results. The paper experiment on CMU MOCAP database and the percentage receiving proposed method is higher than that from the previous method.
Abstract: Human Activity Recognition (HAR) is one of the most important areas of computer vision research. The biggest difficulty for HAR system is that the camera could only film in one direction, leading to a shortage of data and low recognition results. This paper focuses on researching and building new models of HAR, including Principal Components Analys...
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