A machine learning taxonomic classifier for science publications

Dissertação de mestrado integrado em Engineering and Management of Information SystemsThe evolution in scientific production, associated with the growing interdomain collaboration of knowledge and the increasing co-authorship of scientific works remains supported by processes of manual, highly subjective classification, subject to misinterpretation. The very taxonomy on which this same classification process is based is not consensual, with governmental organizations resorting to taxonomies that do not keep up with changes in scientific areas, and indexers / repositories that seek to keep up with those changes. We find a reality distinct from what is expected and that the domains where scientific work is recorded can easily be misrepresentative of the work itself. The taxonomy applied today by governmental bodies, such as the one that regulates scientific production in Portugal, is not enough, is limiting, and promotes classification in areas close to the desired, therefore with great potential for error. An automatic classification process based on machine learning algorithms presents itself as a possible solution to the subjectivity problem in classification, and while it does not solve the issue of taxonomy mismatch this work shows this possibility with proved results. In this work, we propose a classification taxonomy, as well as we develop a process based on machine learning algorithms to solve the classification problem. We also present a set of directions for future work for an increasingly representative classification of evolution in science, which is not intended as airtight, but flexible and perhaps increasingly based on phenomena and not just disciplines.A evolução na produção de ciência, associada à crescente colaboração interdomínios do conhecimento e à também crescente coautoria de trabalhos permanece suportada por processos de classificação manual, subjetiva e sujeita a interpretações erradas. A própria taxonomia na qual assenta esse mesmo processo de classificação não é consensual, com organismos estatais a recorrerem a taxonomias que não acompanham as alterações nas áreas científicas, e indexadores/repositórios que procuram acompanhar essas mesmas alterações. Verificamos uma realidade distinta do espectável e que os domínios onde são registados os trabalhos científicos podem facilmente estar desenquadrados. A taxonomia hoje aplicada pelos organismos governamentais, como o caso do organismo que regulamenta a produção científica em Portugal, não é suficiente, é limitadora, e promove a classificação em domínios aproximados do desejado, logo com grande potencial para erro. Um processo de classificação automática com base em algoritmos de machine learning apresenta-se como uma possível solução para o problema da subjetividade na classificação, e embora não resolva a questão do desenquadramento da taxonomia utilizada, é apresentada neste trabalho como uma possibilidade comprovada. Neste trabalho propomos uma taxonomia de classificação, bem como nós desenvolvemos um processo baseado em machine learning algoritmos para resolver o problema de classificação. Apresentamos ainda um conjunto de direções para trabalhos futuros para uma classificação cada vez mais representativa da evolução nas ciências, que não pretende ser hermética, mas flexível e talvez cada vez mais baseada em fenómenos e não apenas em disciplinas

A Comparative Study of India and Australia Open Access Repositories in OpenDOAR

An open access repository or open archive is a digital platform that holds research output and provides free, immediate, and permanent access to research results for anyone to use, download and distribute. To facilitate open access such repositories must be interoperable according to the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH). Search engines harvest the content of open access repositories, constructing a database of worldwide, free of charge available research. OpenDOAR is the quality-assured, global Directory of Open Access Repositories. Its repositories provide free, open access to academic outputs and resources. This paper deals with Comparative analysis of India and Australia repositories listed in OpenDOAR in terms of their growth, type, operational status, content type, software, subject coverage, language, and policies regarding content, submission, and preservatio

Process Optimization and Design of an Automation Controller for a Multidisciplinary Combat Engineering System

Design of an automation controller for a “Electro-Hydro-Mechanical Object Laying System” is presented in this paper, which is a multidisciplinary equipment consisting of Electromechanical and Hydraulic Actuators and large number of sensors for process feedback. There are complex mechanisms and processes involved in this system, which are required to be operated/executed in sequential and parallel manner in real time. The operation of spatially distributed Electromechanical & Hydraulic actuators with feedbacks from multiple type of sensors are required to be synchronized for multiple activities at a faster rate along with safely handling of the objects. All the activities are automated with minimum human intervention to avoid risk to the crew. This paper mainly focuses on electronic controller hardware design for military environment and process optimization to achieve faster object laying rate