A database for publications published by researchers and students at SimulaMet.
Status
Research area
Journal articles
Energy-efficient online control of a water distribution network based on Deep Reinforcement Learning
IEEE Internet of Things Journal (2023).Status: Submitted
Energy-efficient online control of a water distribution network based on Deep Reinforcement Learning
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | IEEE Internet of Things Journal |
| Publisher | IEEE Internet of Things Journal |
| Notes | This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the IKTPLUSS INDURB grant 270730/O70 from the Research Council of Norway. |
Analyzing postprandial metabolomics data using multiway models: A simulation study
bioRxiv (2023).Status: Submitted
Analyzing postprandial metabolomics data using multiway models: A simulation study
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery , TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | bioRxiv |
| Publisher | bioRxiv |
| URL | https://www.biorxiv.org/content/10.1101/2022.12.19.521154v2 |
| DOI | 10.1101/2022.12.19.521154 |
Backsourcing of Information Technology - A Systematic Literature Review
Submitted to a journal (2023).Status: Submitted
Backsourcing of Information Technology - A Systematic Literature Review
Context: Backsourcing is the process of insourcing previously outsourced activities. When companies experience environmental or strategic changes, or challenges with outsourcing, backsourcing can be a viable alternative. While outsourcing and related processes have been extensively studied in information technology, few studies report experiences with backsourcing.
Objectives: We intend to summarize the results of the research literature on the backsourcing of IT, with a focus on software development. By identifying practical relevance experience, we aim to present findings that may help companies considering backsourcing. In addition, we aim to identify gaps in the current research literature and point out areas for future work.
Method: Our systematic literature review (SLR) started with a search for empirical studies on the backsourcing of IT. From each study we identified the contexts in which backsourcing occurs, the factors leading to the decision to backsource, the backsourcing process itself, and the outcomes of backsourcing. We employed inductive coding to extract textual data from the papers identified and qualitative cross-case analysis to synthesize the evidence from backsourcing experiences.
Results: We identified 17 papers that reported 26 cases of backsourcing, six of which were related to software development. The cases came from a variety of contexts. The most common reasons for backsourcing were improving quality, reducing costs, and regaining control of outsourced activities. The backsourcing process can be described as containing five sub-processes: change management, vendor relationship management, competence building, organizational build-up, and transfer of ownership. Furthermore, we identified 14 positive outcomes and nine negative outcomes of backsourcing. Finally, we aggregated the evidence and detailed three relationships of potential use to companies considering backsourcing.
Conclusion: The backsourcing of IT is a complex process; its implementation depends on the prior outsourcing relationship and other contextual factors. Our systematic literature review may contribute to a better understanding of this process by identifying its components and their relationships based on the peer-reviewed literature. Our results may also serve as a motivation and baseline for further research on backsourcing and may provide guidelines and process fragments from which practitioners can benefit when they engage in backsourcing.
| Afilliation | Software Engineering |
| Project(s) | EDOS: Effective Digitalization of Public Sector, Department of IT Management |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | Submitted to a journal |
| Publisher | x |
| Keywords | backshoring, Backsourcing, information technology, software development, Software Engineering, systematic literature review |
Efficient Interpretable Nonlinear Modeling for Multiple Time Series
IEEE Transactions on Signal Processing (2023).Status: Submitted
Efficient Interpretable Nonlinear Modeling for Multiple Time Series
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | IEEE Transactions on Signal Processing |
| Publisher | IEEE Transactions on Signal Processing |
An Online Multiple Kernel Parallelizable Learning Scheme
IEEE Signal Processing Letters (2023).Status: Submitted
An Online Multiple Kernel Parallelizable Learning Scheme
The performance of reproducing kernel Hilbert space-based methods is known to be sensitive to the choice of the reproducing kernel. Choosing an adequate reproducing kernel can be challenging and computationally demanding, especially in data-rich tasks without prior information about the solution domain. In this paper, we propose a learning scheme that scalably combines several single kernel-based online methods to reduce the kernel-selection bias. The proposed learning scheme applies to any task formulated as a regularized empirical risk minimization convex problem. More specifically, our learning scheme is based on a multi-kernel learning formulation that can be applied to widen any single-kernel solution space, thus increasing the possibility of finding higher-performance solutions. In addition, it is parallelizable, allowing for the distribution of the computational load across different computing units. We show experimentally that the proposed learning scheme outperforms the combined single-kernel online methods separately in terms of the cumulative regularized least squares cost metric.
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | IEEE Signal Processing Letters |
| Publisher | IEEE Signal Processing Letters |
| URL | https://arxiv.org/pdf/2308.10101.pdf |
Characterizing human postprandial metabolic response using multiway data analysis
bioRxiv (2023).Status: Submitted
Characterizing human postprandial metabolic response using multiway data analysis
| Afilliation | Machine Learning |
| Project(s) | Department of Data Science and Knowledge Discovery , TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | bioRxiv |
| Publisher | biorxiv |
| DOI | 10.1101/2023.08.31.555521 |
Online Joint Topology Identification and Signal Estimation from Streams with Missing Data
IEEE Transactions on Signal and Information Processing over Networks (2023).Status: Submitted
Online Joint Topology Identification and Signal Estimation from Streams with Missing Data
| Afilliation | Machine Learning |
| Project(s) | Signal and Information Processing for Intelligent Systems |
| Publication Type | Journal Article |
| Year of Publication | 2023 |
| Journal | IEEE Transactions on Signal and Information Processing over Networks |
| Publisher | IEEE Transactions on Signal and Information Processing over Networks |
| Notes | This work is a joint collaboration between SimulaMet and University of Agder. This work was supported by the SFI Offshore Mechatronics grant 237896/O30 from the Research Council of Norway. |
Journal articles
Experiential Learning Approach for Software Engineering Courses at Higher Education Level
arXiv preprint arXiv:2012.14178 (2021).Status: Submitted
Experiential Learning Approach for Software Engineering Courses at Higher Education Level
Background: Software project management activities help to introducing software process models in Software Engineering courses. However, these activities should be adequately aligned with the learning outcomes and support student's progression.
Objective: Present and evaluate an approach to help students acquire theoretical and practical knowledge and experience real-world software projects' challenges. The approach combines a serious game and a design-implement task in which students develop a controlled-scale software system.
Methods: To evaluate our approach, we analyzed the students' perceptions collected through an online survey, their project plans, and their final reports using thematic analysis.
Results: Results suggest that the approach promotes knowledge acquisition, enables students' progression, reinforces theoretical concepts, and is properly aligned with the course's learning outcomes.
Conclusion: The approach seems to help introducing software process models in Software Engineering courses. Our experience can also be inspiring for educators willing to apply our approach in similar courses.
| Afilliation | Software Engineering |
| Project(s) | Department of IT Management |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | arXiv preprint arXiv:2012.14178 |
| Publisher | Springer |
Journal articles
Revealing the State-of-the-Art in Large-Scale Agile Development: A Systematic Mapping Study
arXiv preprint arXiv:2007.05578 (2020).Status: Submitted
Revealing the State-of-the-Art in Large-Scale Agile Development: A Systematic Mapping Study
| Afilliation | Software Engineering |
| Project(s) | Department of IT Management |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | arXiv preprint arXiv:2007.05578 |
| Publisher | arXiv |
