214,692 research outputs found
TWAIN Network Extension
CĂlem tĂ©to bakalářskĂ© práce je vytvoĹ™enĂ softwarovĂ© platformy pro sdĂlenĂ datovĂ˝ch zdrojĹŻ systĂ©mu TWAIN po poÄŤĂtaÄŤovĂ© sĂti typu TCP/IP pro operaÄŤnĂ systĂ©my Microsoft Windows. VĂ˝sledná platforma typu klient-server klade dĹŻraz na sdĂlenĂ scannerĹŻ.This bachelor's thesis describes creation of a software platform for sharing TWAIN data sources over TCP/IP network. The target operating system is Microsoft Windows. The resulting client-server platform is designed especially for sharing scanners.
Effectiveness of Microsoft Kaizala and Google Classroom towards students’ mathematical communication skill and self-efficacy in learning statistics
This study aims to describe the effectiveness and differences in the effectiveness of the learning platform with Google Classroom and Microsoft Kaizala in terms of self-efficacy and students' mathematical communication skills. The population of this research is the XII grade students of SMK Negeri 1 Giritronto Wonogiri which consists of sevent classes. From the existing population, two classes were taken randomly, namely twelfth grade of TKJ-I and twelfth grade of TKJ-II as research samples. Twelfth grade of TKJ-I was given treatment by learning using the Microsoft Kaizala platform, while twelfth grade of TKJ-II was given treatment using the Google Classroom platform. The research data were analyzed by statistical one sample t-test, MANOVA test with Hotelling's T2 at a significant level of 0.05 and univariate test to determine which platform is more effective. The results showed that: (1) statistical learning using the Microsoft Kaizala platform was effective in terms of mathematical communication and self-efficacy, while the Google Classroom platform was effective in terms of mathematical communication but not effective in terms of self-efficacy; (2) there is a difference in effectiveness between the Microsoft Kaizala platform and Google Classroom. The Microsot Kaizala platform is more effective than Google Classroom in terms of the mathematical communication skills of class twelfth grade students of SMK Negeri 1 Giritronto Wonogiri
A Clouded Future: Analysis of Microsoft Windows Azure As a Platform for Hosting E-Science Applications
Microsoft Windows Azure is Microsoft\u27s cloud based platform for hosting .NET applications. Azure provides a simple, cost effective method for outsourcing application hosting. Windows Azure has caught the eye of researchers in e-science who require parallel computing infrastructures to process mountains of data. Windows Azure offers the same benefits to e-science as it does to other industries. This paper examines the technology behind Azure and analyzes two case studies of e-science projects built on the Windows Azure platform
Adoption of database technology: A comparative study
Database now a days, has become an essential part for keeping any records secured and privatized In earlier we had least databases with unique features with their own. But as the era rises on, numerous databases came into existence. Among those Microsoft SQL server versions and Oracle database became the best but on the counterpart they have a war among themselves too. Each has some unique features and drawbacks. Hence, this white paper discusses that which database server Oracle or Microsoft SQL server is better in different aspects. It studies the working and response of both the databases in different realms. This paper is divided into four main parts. The first part discusses the security issues of oracle and Microsoft SQL server. The second discusses the comparative cost study which includes administration cost also. Third discusses the platform dependency of each of the database that which is more platform supportive, next highlights the performance issues in both the databases which includes scalability, reliability and availability of Oracle RAC and Microsoft SQL server. Performance comparison is also represented in tabular form. In each section, comparison between these two databases is done. In last section, it consists conclusion drawn analyzing all the data. Keywords: Security comparison, Cost comparison, Platform dependency, Performance compariso
Developing Microsoft Word 2007 Add-On Applications
In 2007, Microsoft released a new version of MS Office that changed the file platform to a universal data format called Extensible Markup Language or XML. XML is meant to be simple, meaningful, and understood by all computer programs. Since Microsoft has moved its file format to XML, tremendous extendibility can be built by software professionals to link MS Office 2007 documents to data not held within the saved documents. This project tested the levels of interactive data between MS Word 2007 and several other XML data sources
The state of SQL-on-Hadoop in the cloud
Managed Hadoop in the cloud, especially SQL-on-Hadoop, has been gaining attention recently. On Platform-as-a-Service (PaaS), analytical services like Hive and Spark come preconfigured for general-purpose and ready to use. Thus, giving companies a quick entry and on-demand deployment of ready SQL-like solutions for their big data needs. This study evaluates cloud services from an end-user perspective, comparing providers including: Microsoft Azure, Amazon Web Services, Google Cloud,
and Rackspace. The study focuses on performance, readiness, scalability, and cost-effectiveness of the different solutions at entry/test level clusters sizes. Results are based on over 15,000 Hive queries derived from the industry standard TPC-H benchmark.
The study is framed within the ALOJA research project, which features an open source benchmarking and analysis platform that has been recently extended to support SQL-on-Hadoop engines.
The ALOJA Project aims to lower the total cost of ownership (TCO) of big data deployments and study their performance characteristics for optimization.
The study benchmarks cloud providers across a diverse range instance types, and uses input data scales from 1GB to 1TB, in order to survey the popular entry-level PaaS SQL-on-Hadoop solutions, thereby establishing a common results-base upon which subsequent research can be carried out by the project. Initial results already show the main performance trends to both hardware and software configuration, pricing, similarities and architectural differences of the evaluated PaaS solutions. Whereas some
providers focus on decoupling storage and computing resources while offering network-based elastic storage, others choose to keep the local processing model from Hadoop for high performance, but reducing flexibility. Results also show the importance of application-level tuning and how keeping up-to-date hardware and software stacks can influence performance even more than replicating the on-premises model in the cloud.This work is partially supported by the Microsoft Azure for Research program, the European Research Council (ERC) under
the EUs Horizon 2020 programme (GA 639595), the Spanish Ministry of Education (TIN2015-65316-P), and the Generalitat
de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft
Audio Transcription and Summarization System using Cloud Computing and Artificial Intelligence
In the modern era, organizations increasingly rely on virtual meetings to address customer issues promptly and effectively. However, dealing with recorded customer calls can be arduous. This review abstract introduces an innovative methodology to summarize audio data from customer interactions, which can streamline virtual meetings. Leveraging a speech recognizer, like AssemblyAI's API, the methodology converts audio data into text, and then employs a Graph-theoretic approach to generate concise summaries.
This review abstract delves into the growing prominence of cloud-based AI and ML services in the tech industry. It underscores the unique competitive strategies and focuses of major players, namely Amazon, Microsoft, and Google, in the realm of AI and ML platform development. The analysis explores these companies' internal applications and external ecosystem, dissecting their respective AI and ML development strategies. Finally, it predicts future directions for AI and ML platforms, including potential business models and emerging trends, while considering how Amazon, Microsoft, and Google align their platform development strategies with these future prospects
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