| 1. |
- Karlsudd, Peter, 1958-
(författare)
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Swedish Student-Teachers in Digital Activities : Digital Competence Through Development-Oriented Thesis Projects
- 2019
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Ingår i: International Journal of Computer Science & Information Technology (IJCSIT). - 0975-4660 .- 0975-3826. ; 11:3
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Tidskriftsartikel (refereegranskat)abstract
- This article defines attempts using development-oriented thesis projects to increase teachers’ and pupils’ digital capability. To offer a more practice-oriented focus in the teacher education, the elementary school student-teachers were stimulated to participate in thesis projects with the purpose of developing the school events.Thirteen of the development-oriented thesis projects carried out during 2015-2018 involved testing the student-teacher’s ability to study and formulate the competence needs regarding digital learning at the practicum-school, as well as the results of carrying out activities for increasing the digital competence. The investigation is based on a review of completed thesis projects, process journals, and presentations and discussions in subsequent reports. An initial analysis of the thirteen development projects reveals two clear goal directions. One focus is on traditional knowledge goals and the other on more social goals. The outcomes clearly show that development-oriented thesis projects can be an effective way to increase the digital skills of teachers and pupils. Projects with distinct goals for collaboration and shared learning have reached further goal attainment than the projects focused more on discrete instruction and learning.When digital tools were used as a means to work with another area, for example, physical activity or democracy issues, the developed competence in digital skills became more pronounced and lasting. Digital competence is an important development area for school activities, and this study shows that development-oriented thesis projects can be an effective means toward a successful project.
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| 2. |
- Olsson, Tomas, et al.
(författare)
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Case-Based Reasoning for Explaining Probabilistic Machine Learning
- 2014
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Ingår i: International Journal of Computer Science & Information Technology (IJCSIT). - : Academy and Industry Research Collaboration Center (AIRCC). - 0975-4660 .- 0975-3826. ; 6:2, s. 87-101
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Tidskriftsartikel (refereegranskat)abstract
- This paper describes a generic framework for explaining the prediction of probabilistic machine learning algorithms using cases. The framework consists of two components: a similarity metric between cases that is defined relative to a probability model and an novel case-based approach to justifying the probabilistic prediction by estimating the prediction error using case-based reasoning. As basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Lastly, we show the applicability of the proposed approach by deriving a metric for linear regression, and apply the proposed approach for explaining predictions of the energy performance of households.
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| 3. |
- Olsson, Tomas, et al.
(författare)
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Case-Based Reasoning for Explaining Probabilistic Machine Learning
- 2014. - 7
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Ingår i: International Journal of Computer Science and Information Technology. - : Academy and Industry Research Collaboration Center (AIRCC). - 0975-4660 .- 0975-3826. ; 6, s. 87-101
-
Tidskriftsartikel (refereegranskat)abstract
- This paper describes a generic framework for explaining the prediction of probabilistic machine learning algorithms using cases. The framework consists of two components: a similarity metric between cases that is defined relative to a probability model and an novel case-based approach to justifying the probabilistic prediction by estimating the prediction error using case-based reasoning. As basis for deriving similarity metrics, we define similarity in terms of the principle of interchangeability that two cases are considered similar or identical if two probability distributions, derived from excluding either one or the other case in the case base, are identical. Lastly, we show the applicability of the proposed approach by deriving a metric for linear regression, and apply the proposed approach for explaining predictions of the energy performance of households.
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| 4. |
- Syberfeldt, Anna, et al.
(författare)
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A comparative evaluation of the GPU vs. the CPU for parallelization of evolutionary algorithms through multiple independent runs
- 2017
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Ingår i: International Journal of Computer Science & Information Technology (IJCSIT). - : A I R C C Publishing Corporation. - 0975-4660 .- 0975-3826. ; 9:3, s. 1-14
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Tidskriftsartikel (refereegranskat)abstract
- Multiple independent runs of an evolutionary algorithm in parallel are often used to increase the efficiency of parameter tuning or to speed up optimizations involving inexpensive fitness functions. A GPU platform is commonly adopted in the research community to implement parallelization, and this platform has been shown to be superior to the traditional CPU platform in many previous studies. However, it is not clear how efficient the GPU is in comparison with the CPU for the parallelizing multiple independent runs, as the vast majority of the previous studies focus on parallelization approaches in which the parallel runs are dependent on each other (such as master-slave, coarse-grained or fine-grained approaches). This study therefore aims to investigate the performance of the GPU in comparison with the CPU in the context of multiple independent runs in order to provide insights into which platform is most efficient. This is done through a number of experiments that evaluate the efficiency of the GPU versus the CPU in various scenarios. An analysis of the results shows that the GPU is powerful, but that there are scenarios where the CPU outperforms the GPU. This means that a GPU is not the universally best option for parallelizing multiple independent runs and that the choice of computation platform therefore should be an informed decision. To facilitate this decision and improve the efficiency of optimizations involving multiple independent runs, the paper provides a number of recommendations for when and how to use the GPU.
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