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With the many achievements of Machine Learning in the past years, it is likely that the sub-area of Deep Learning will continue to deliver major technological breakthroughs [1]. In order to achieve best results, it is important to know the various different Deep Learning frameworks and their respective properties. This paper provides a comparative overview of some of the most popular frameworks. First, the comparison methods and criteria are introduced and described with a focus on computer vision applications: Features and Uses are examined by evaluating papers and articles, Adoption and Popularity is determined by analyzing a data science study. Then, the frameworks TensorFlow, Keras, PyTorch and Caffe are compared based on the previously described criteria to highlight properties and differences. Advantages and disadvantages are compared, enabling researchers and developers to choose a framework according to their specific needs.
Textile reinforced concrete. Part I: Process model for collaborative research and development
(2003)
The continuing growth of scientific publications raises the question how research processes can be digitalized and thus realized more productively. Especially in information technology fields, research practice is characterized by a rapidly growing volume of publications. For the search process various information systems exist. However, the analysis of the published content is still a highly manual task. Therefore, we propose a text analytics system that allows a fully digitalized analysis of literature sources. We have realized a prototype by using EBSCO Discovery Service in combination with IBM Watson Explorer and demonstrated the results in real-life research projects. Potential addressees are research institutions, consulting firms, and decision-makers in politics and business practice.
Angesichts des anhaltenden Wachstums wissenschaftlicher Veröffentlichungen werden Instrumente benötigt, um Literaturanalysen durch Digitalisierung produktiver zu gestalten. Dieser Beitrag stellt einen Ansatz vor, der bibliographische Daten aus der Literaturdatenbank EBSCO Discovery Service mithilfe von Text-Analytics-Methoden erschließt. Die Lösung basiert auf dem Textanalysesystem IBM Watson Explorer und eignet sich für explorative Literaturanalysen, um beispielsweise den Status quo emergierender Technologiefelder in der Literatur zu reflektieren. Die generierten Ergebnisse sind in den Kontext der zunehmenden Werkzeugunterstützung des Literaturrechercheprozesses einzuordnen und können für intra- sowie interinstitutionelle Wissenstransferprozesse in Forschungs- und Beratungskontexten genutzt werden.
The initial idea of Robotic Process Automation (RPA) is the automation of business processes through the presentation layer of existing application systems. For this simple emulation of user input and output by software robots, no changes of the systems and architecture is required. However, considering strategic aspects of aligning business and technology on an enterprise level as well as the growing capabilities of RPA driven by artificial intelligence, interrelations between RPA and Enterprise Architecture (EA) become visible and pose new questions. In this paper we discuss the relationship between RPA and EA in terms of perspectives and implications. As workin- progress we focus on identifying new questions and research opportunities related to RPA and EA.