Greenstone: open-source DL software

Greenstone is a comprehensive system for constructing and presenting collections of thousands or millions of documents, including text, images, audio, and video. Greenstone libraries contain many collections, individually organized, though they bear a strong family resemblance. Easily maintained, collections can be augmented and rebuilt automatically

Usability and open source software.

Open source communities have successfully developed many pieces of software although most computer users only use proprietary applications. The usability of open source software is often regarded as one reason for this limited distribution. In this paper we review the existing evidence of the usability of open source software and discuss how the characteristics of open-source development influence usability. We describe how existing human-computer interaction techniques can be used to leverage distributed networked communities, of developers and users, to address issues of usability

Increased security through open source

In this paper we discuss the impact of open source on both the security and transparency of a software system. We focus on the more technical aspects of this issue, combining and extending arguments developed over the years. We stress that our discussion of the problem only applies to software for general purpose computing systems. For embedded systems, where the software usually cannot easily be patched or upgraded, different considerations may apply

Open Source Business Solutions

This analyses the Open source movement. Open source development process and management is seen different from the classical point of view. This focuses on characteristics and software market tendencies for the main Open source initiatives. It also points out the labor market future evolution for the software developers.Open source, UNIX, eXtreme Programming, GNU, tokens, lexemes.

BEAT: An Open-Source Web-Based Open-Science Platform

With the increased interest in computational sciences, machine learning (ML), pattern recognition (PR) and big data, governmental agencies, academia and manufacturers are overwhelmed by the constant influx of new algorithms and techniques promising improved performance, generalization and robustness. Sadly, result reproducibility is often an overlooked feature accompanying original research publications, competitions and benchmark evaluations. The main reasons behind such a gap arise from natural complications in research and development in this area: the distribution of data may be a sensitive issue; software frameworks are difficult to install and maintain; Test protocols may involve a potentially large set of intricate steps which are difficult to handle. Given the raising complexity of research challenges and the constant increase in data volume, the conditions for achieving reproducible research in the domain are also increasingly difficult to meet. To bridge this gap, we built an open platform for research in computational sciences related to pattern recognition and machine learning, to help on the development, reproducibility and certification of results obtained in the field. By making use of such a system, academic, governmental or industrial organizations enable users to easily and socially develop processing toolchains, re-use data, algorithms, workflows and compare results from distinct algorithms and/or parameterizations with minimal effort. This article presents such a platform and discusses some of its key features, uses and limitations. We overview a currently operational prototype and provide design insights.Comment: References to papers published on the platform incorporate

Why Modern Open Source Projects Fail

Open source is experiencing a renaissance period, due to the appearance of modern platforms and workflows for developing and maintaining public code. As a result, developers are creating open source software at speeds never seen before. Consequently, these projects are also facing unprecedented mortality rates. To better understand the reasons for the failure of modern open source projects, this paper describes the results of a survey with the maintainers of 104 popular GitHub systems that have been deprecated. We provide a set of nine reasons for the failure of these open source projects. We also show that some maintenance practices -- specifically the adoption of contributing guidelines and continuous integration -- have an important association with a project failure or success. Finally, we discuss and reveal the principal strategies developers have tried to overcome the failure of the studied projects.Comment: Paper accepted at 25th International Symposium on the Foundations of Software Engineering (FSE), pages 1-11, 201