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Davidsson, Paul, ProfessorORCID iD iconorcid.org/0000-0003-0998-6585
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Publications (10 of 118) Show all publications
Boiko, O., Shepeliev, D., Shendryk, V., Malekian, R. & Davidsson, P. (2023). A Comparison of Machine Learning Prediction Models to Estimate the Future Heat Demand. In: 2023 IEEE 13th International Conference on Consumer Electronics - Berlin (ICCE-Berlin): . Paper presented at 2023 IEEE 13th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), Berlin, Germany, 03-05 September 2023. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>A Comparison of Machine Learning Prediction Models to Estimate the Future Heat Demand
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2023 (English)In: 2023 IEEE 13th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper, Published paper (Refereed)
Abstract [en]

This paper compares machine learning models for short-term heat demand forecasting in residential and multi-family buildings, evaluating model suitability, data impact on accuracy, computation time, and accuracy improvement methods. The findings are relevant for energy suppliers, researchers, and decision-makers in optimizing energy management and improving heat demand forecasting. The included models in the study are k-NN, Polynomial Regression, and LSTM with weather data, building type, and time index as input variables. Single-dimensional models (Autoregression, SARIMA, and Prophet) based on historical consumption are also studied. LSTM consistently outperforms other models in accuracy across different input variable combinations, measured using mean absolute percentage error (MAPE). The incorporation of historical consumption data improved the performance of k-NN and Polynomial Regression models. The paper also explores dataset volume impact on accuracy and compares training and prediction times. k-NN has the least prediction times, Polynomial Regression takes longer, and LSTM requires more time. All models exhibit acceptable prediction times for heat consumption. LSTM outperforms single-dimensional models in accuracy and has lower prediction times compared to AR, SARIMA, and Prophet models.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Series
IEEE International Conference on Consumer Electronics-Berlin, ISSN 2166-6814, E-ISSN 2166-6822
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:mau:diva-64889 (URN)10.1109/icce-berlin58801.2023.10375622 (DOI)979-8-3503-2415-0 (ISBN)979-8-3503-2416-7 (ISBN)
Conference
2023 IEEE 13th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), Berlin, Germany, 03-05 September 2023
Available from: 2024-01-09 Created: 2024-01-09 Last updated: 2024-01-09Bibliographically approved
Alkhabbas, F., Alawadi, S., Ayyad, M., Spalazzese, R. & Davidsson, P. (2023). ART4FL: An Agent-Based Architectural Approach for Trustworthy Federated Learning in the IoT. In: 2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC): . Paper presented at 2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC), Tartu, Estonia, 18-20 September 2023. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>ART4FL: An Agent-Based Architectural Approach for Trustworthy Federated Learning in the IoT
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2023 (English)In: 2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC), Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper, Published paper (Refereed)
Abstract [en]

The integration of the Internet of Things (IoT) and Machine Learning (ML) technologies has opened up for the development of novel types of systems and services. Federated Learning (FL) has enabled the systems to collaboratively train their ML models while preserving the privacy of the data collected by their IoT devices and objects. Several FL frameworks have been developed, however, they do not enable FL in open, distributed, and heterogeneous IoT environments. Specifically, they do not support systems that collect similar data to dynamically discover each other, communicate, and negotiate about the training terms (e.g., accuracy, communication latency, and cost). Towards bridging this gap, we propose ART4FL, an end-to-end framework that enables FL in open IoT settings. The framework enables systems' users to configure agents that participate in FL on their behalf. Those agents negotiate and make commitments (i.e., contractual agreements) to dynamically form federations. To perform FL, the framework deploys the needed services dynamically, monitors the training rounds, and calculates agents' trust scores based on the established commitments. ART4FL exploits a blockchain network to maintain the trust scores, and it provides those scores to negotiating agents' during the federations' formation phase.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
National Category
Computer Systems
Identifiers
urn:nbn:se:mau:diva-63749 (URN)10.1109/fmec59375.2023.10306036 (DOI)001103180200036 ()2-s2.0-85179515213 (Scopus ID)979-8-3503-1697-1 (ISBN)979-8-3503-1698-8 (ISBN)
Conference
2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC), Tartu, Estonia, 18-20 September 2023
Available from: 2023-11-20 Created: 2023-11-20 Last updated: 2023-12-28Bibliographically approved
Jevinger, Å., Zhao, C., Persson, J. A. & Davidsson, P. (2023). Artificial intelligence for improving public transport: a mapping study. Public Transport, 1-60
Open this publication in new window or tab >>Artificial intelligence for improving public transport: a mapping study
2023 (English)In: Public Transport, ISSN 1866-749X, E-ISSN 1613-7159, p. 1-60Article in journal (Refereed) Epub ahead of print
Abstract [en]

The objective of this study is to provide a better understanding of the potential of using Artificial Intelligence (AI) to improve Public Transport (PT), by reviewing research literature. The selection process resulted in 87 scientific publications constituting a sample of how AI has been applied to improve PT. The review shows that the primary aims of using AI are to improve the service quality or to better understand traveller behaviour. Train and bus are the dominant modes of transport investigated. Furthermore, AI is mainly used for three tasks; the most frequent one is prediction, followed by an estimation of the current state, and resource allocation, including planning and scheduling. Only two studies concern automation; all the others provide different kinds of decision support for travellers, PT operators, PT planners, or municipalities. Most of the reviewed AI solutions require significant amounts of data related to the travellers and the PT system. Machine learning is the most frequently used AI technology, with some studies applying reasoning or heuristic search techniques. We conclude that there still remains a great potential of using AI to improve PT waiting to be explored, but that there are also some challenges that need to be considered. They are often related to data, e.g., that large datasets of high quality are needed, that substantial resources and time are needed to pre-process the data, or that the data compromise personal privacy. Further research is needed about how to handle these issues efficiently.

Place, publisher, year, edition, pages
Springer, 2023
Keywords
Artifcial intelligence · Machine learning · Public transit · Mass transit · Public transport · Literature review
National Category
Computer Sciences Transport Systems and Logistics
Research subject
Transportation studies
Identifiers
urn:nbn:se:mau:diva-64419 (URN)10.1007/s12469-023-00334-7 (DOI)001104065400001 ()2-s2.0-85177171423 (Scopus ID)
Projects
AI and public transport: potential and hindrances
Funder
Vinnova, VINNOVA
Note

Ytterligare finansiär: K2 - The Swedish Knowledge Centre for Public Transport

Available from: 2023-12-14 Created: 2023-12-14 Last updated: 2023-12-22Bibliographically approved
Tegen, A., Davidsson, P. & Persson, J. A. (2023). Human Factors in Interactive Online Machine Learning. In: Paul Lukowicz; Sven Mayer; Janin Koch; John Shawe-Taylor; Ilaria Tiddi (Ed.), HHAI 2023: Augmenting Human Intellect: . Paper presented at HHAI 2023, the 2nd International Conference on Hybrid Human-Artificial Intelligence, 26-30 June 2023, Munich, Germany (pp. 33-45). IOS Press
Open this publication in new window or tab >>Human Factors in Interactive Online Machine Learning
2023 (English)In: HHAI 2023: Augmenting Human Intellect / [ed] Paul Lukowicz; Sven Mayer; Janin Koch; John Shawe-Taylor; Ilaria Tiddi, IOS Press, 2023, p. 33-45Conference paper, Published paper (Refereed)
Abstract [en]

Interactive machine learning (ML) adds a human-in-the-loop aspect to a ML system. Even though the input from human users to the system is a central part of the concept, the uncertainty caused by the human feedback is often not considered in interactive ML. The assumption that the human user is expected to always provide correct feedback, typically does not hold in real-world scenarios. This is especially important for when the cognitive workload of the human is high, for instance in online learning from streaming data where there are time constraints for providing the feedback. We present experiments of interactive online ML with human participants, and compare the results to simulated experiments where humans are always correct. We found combining the two interactive learning paradigms, active learning and machine teaching, resulted in better performance compared to machine teaching alone. The results also showed an increased discrepancy between the experiments with human participants and the simulated experiments when the cognitive workload was increased. The findings suggest the importance of taking uncertainty caused by human factors into consideration in interactive ML, especially in situations which requires a high cognitive workload for the human.

Place, publisher, year, edition, pages
IOS Press, 2023
Series
Frontiers in Artificial Intelligence and Application, ISSN 0922-6389, E-ISSN 1879-8314 ; 368
Keywords
interactive machine learning, online learning, human factors
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-61687 (URN)10.3233/faia230073 (DOI)001150361600003 ()2-s2.0-85171485242 (Scopus ID)978-1-64368-394-2 (ISBN)978-1-64368-395-9 (ISBN)
Conference
HHAI 2023, the 2nd International Conference on Hybrid Human-Artificial Intelligence, 26-30 June 2023, Munich, Germany
Available from: 2023-07-06 Created: 2023-07-06 Last updated: 2024-02-26Bibliographically approved
Dytckov, S., Davidsson, P. & Persson, J. A. (2023). Integrate, not compete! On Potential Integration of Demand Responsive Transport Into Public Transport Network. In: : . Paper presented at 26th IEEE International Conference on Intelligent Transportation Systems ITSC 2023. Bilbao, Bizkaia, Spain: Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Integrate, not compete! On Potential Integration of Demand Responsive Transport Into Public Transport Network
2023 (English)Conference paper, Published paper (Refereed)
Abstract [en]

On-demand transport services are often envisioned as stand-alone modes or as a replacement for conventional public transport modes. This leads to a comparison of service efficiencies, or direct competition for passengers between them. The results of this work point to the positive effects of the inclusion of DRT into the public transport network. We simulate a day of operation of a DRT service in a rural area and demonstrate that a DRT system that focuses on increasing accessibility for travellers with poor public transport access can be quite efficient, especially for reducing environmental impact. We show that DRT, while it produces more vehicle kilometres than private cars would inside the DRT operating zone, can help to reduce the vehicle kilometres travelled for long-distance trips. The results of this study indicate the need for a more systemic evaluation of the impact of the new mobility modes.

Place, publisher, year, edition, pages
Bilbao, Bizkaia, Spain: Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Simulation, Demand-Responsive Transport, Public transport
National Category
Transport Systems and Logistics Computer Sciences
Research subject
Transportation studies
Identifiers
urn:nbn:se:mau:diva-62399 (URN)
Conference
26th IEEE International Conference on Intelligent Transportation Systems ITSC 2023
Available from: 2023-09-08 Created: 2023-09-08 Last updated: 2023-09-15Bibliographically approved
Shendryk, V., Malekian, R. & Davidsson, P. (2023). Interoperability, Scalability, and Availability of Energy Types in Hybrid Heating Systems. In: New Technologies, Development and Application VI: Volume 2. Paper presented at New Technologies and Applications(NT-2023), Sarajevo, 22-24 June 2023 (pp. 3-13). Springer
Open this publication in new window or tab >>Interoperability, Scalability, and Availability of Energy Types in Hybrid Heating Systems
2023 (English)In: New Technologies, Development and Application VI: Volume 2, Springer, 2023, p. 3-13Conference paper, Published paper (Refereed)
Abstract [en]

A promising approach to improve sustainability within the energy sector is to incorporate renewable energy sources into existing energy systems. However, such hybrid energy systems have several characteristics that make developing and coordinating the challenging, e.g. due to the need to manage large amounts of heterogeneous data in a distributed and dynamic manner. This paper analyses important characteristics of hybrid heating systems, such as interoperability, scalability, and availability of energy sources. The purpose is to determine how the availability of different energy sources within a hybrid heating system affects sustainability and environmental impact, as well as how interoperability and scalability can affect the overall performance of the hybrid heating system. All these quality characteristic parameters were considered in the aspect of heterogeneous data management.

Place, publisher, year, edition, pages
Springer, 2023
Series
Lecture Notes in Networks and Systems, ISSN 2367-3370, E-ISSN 2367-3389 ; 707
National Category
Energy Engineering
Identifiers
urn:nbn:se:mau:diva-64309 (URN)10.1007/978-3-031-34721-4_1 (DOI)2-s2.0-85163358597 (Scopus ID)978-3-031-34720-7 (ISBN)978-3-031-34721-4 (ISBN)
Conference
New Technologies and Applications(NT-2023), Sarajevo, 22-24 June 2023
Available from: 2023-12-12 Created: 2023-12-12 Last updated: 2023-12-12Bibliographically approved
Spalazzese, R., De Sanctis, M., Alkhabbas, F. & Davidsson, P. (2023). Shaping IoT Systems Together: The User-System Mixed-Initiative Paradigm and Its Challenges. In: Bedir Tekinerdogan, Catia Trubiani, Chouki Tibermacine, Patrizia Scandurra, Carlos E. Cuesta (Ed.), Software Architecture: 17th European Conference, ECSA 2023, Istanbul, Turkey, September 18–22, 2023, Proceedings. Paper presented at 17th European Conference, ECSA 2023, Istanbul, Turkey, September 18–22, 2023 (pp. 221-229). Springer
Open this publication in new window or tab >>Shaping IoT Systems Together: The User-System Mixed-Initiative Paradigm and Its Challenges
2023 (English)In: Software Architecture: 17th European Conference, ECSA 2023, Istanbul, Turkey, September 18–22, 2023, Proceedings / [ed] Bedir Tekinerdogan, Catia Trubiani, Chouki Tibermacine, Patrizia Scandurra, Carlos E. Cuesta, Springer, 2023, p. 221-229Conference paper, Published paper (Refereed)
Abstract [en]

Internet of Things (IoT) systems are often complex and have to deal with many challenges at the same time, both from a human and technical perspective. In this vision paper, we (i) describe IoT-Together , the Mixed-initiative Paradigm that we devise for IoT user-system collaboration and (ii) critically analyze related architectural challenges.

Place, publisher, year, edition, pages
Springer, 2023
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 14212
Keywords
Mixed-initiative paradigm, User-System Collaboration, Intelligent IoT Systems, Novel Experiences, Goal-driven IoT Systems
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-64271 (URN)10.1007/978-3-031-42592-9_15 (DOI)2-s2.0-85172136763 (Scopus ID)978-3-031-42591-2 (ISBN)978-3-031-42592-9 (ISBN)
Conference
17th European Conference, ECSA 2023, Istanbul, Turkey, September 18–22, 2023
Available from: 2023-12-12 Created: 2023-12-12 Last updated: 2023-12-12Bibliographically approved
Persson, J. A., Bugeja, J., Davidsson, P., Holmberg, J., Kebande, V. R., Mihailescu, R.-C., . . . Tegen, A. (2023). The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning. Applied Sciences, 13(11), Article ID 6516.
Open this publication in new window or tab >>The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning
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2023 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 13, no 11, article id 6516Article in journal (Refereed) Published
Abstract [en]

This paper concerns the novel concept of an Interactive Dynamic Intelligent Virtual Sensor (IDIVS), which extends virtual/soft sensors towards making use of user input through interactive learning (IML) and transfer learning. In research, many studies can be found on using machine learning in this domain, but not much on using IML. This paper contributes by highlighting how this can be done and the associated positive potential effects and challenges. An IDIVS provides a sensor-like output and achieves the output through the data fusion of sensor values or from the output values of other IDIVSs. We focus on settings where people are present in different roles: from basic service users in the environment being sensed to interactive service users supporting the learning of the IDIVS, as well as configurators of the IDIVS and explicit IDIVS teachers. The IDIVS aims at managing situations where sensors may disappear and reappear and be of heterogeneous types. We refer to and recap the major findings from related experiments and validation in complementing work. Further, we point at several application areas: smart building, smart mobility, smart learning, and smart health. The information properties and capabilities needed in the IDIVS, with extensions towards information security, are introduced and discussed.

Place, publisher, year, edition, pages
MDPI, 2023
National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-60144 (URN)10.3390/app13116516 (DOI)001004726600001 ()2-s2.0-85163091186 (Scopus ID)
Available from: 2023-06-07 Created: 2023-06-07 Last updated: 2023-09-05Bibliographically approved
Holmberg, L., Davidsson, P. & Linde, P. (2022). Mapping Knowledge Representations to Concepts: A Review and New Perspectives. In: Explainable Agency in Artificial Intelligence Workshop Proceedings: . Paper presented at 36th AAAI Conference on Artificial Intelligence, February 28-March 1 2022, Vancouver, Canada (pp. 61-70).
Open this publication in new window or tab >>Mapping Knowledge Representations to Concepts: A Review and New Perspectives
2022 (English)In: Explainable Agency in Artificial Intelligence Workshop Proceedings, 2022, p. 61-70Conference paper, Published paper (Refereed)
Abstract [en]

The success of neural networks builds to a large extent on their ability to create internal knowledge representations from real-world high-dimensional data, such as images, sound, or text. Approaches to extract and present these representations, in order to explain the neural network's decisions, is an active and multifaceted research field. To gain a deeper understanding of a central aspect of this field, we have performed a targeted review focusing on research that aims to associate internal representations with human understandable concepts. In doing this, we added a perspective on the existing research by using primarily deductive nomological explanations as a proposed taxonomy. We find this taxonomy and theories of causality, useful for understanding what can be expected, and not expected, from neural network explanations. The analysis additionally uncovers an ambiguity in the reviewed literature related to the goal of model explainability; is it understanding the ML model or, is it actionable explanations useful in the deployment domain? 

National Category
Computer Sciences
Identifiers
urn:nbn:se:mau:diva-64797 (URN)10.48550/arXiv.2301.00189 (DOI)
Conference
36th AAAI Conference on Artificial Intelligence, February 28-March 1 2022, Vancouver, Canada
Available from: 2023-12-29 Created: 2023-12-29 Last updated: 2023-12-29Bibliographically approved
Dytckov, S., Persson, J. A., Lorig, F. & Davidsson, P. (2022). Potential Benefits of Demand Responsive Transport in Rural Areas: A Simulation Study in Lolland, Denmark. Sustainability, 14(6), Article ID 3252.
Open this publication in new window or tab >>Potential Benefits of Demand Responsive Transport in Rural Areas: A Simulation Study in Lolland, Denmark
2022 (English)In: Sustainability, E-ISSN 2071-1050, Vol. 14, no 6, article id 3252Article in journal (Refereed) Published
Abstract [en]

In rural areas with low demand, demand responsive transport (DRT) can provide an alternative to the regular public transport bus lines, which are expensive to operate in such conditions. With simulation, we explore the potential effects of introducing a DRT service that replaces existing bus lines in Lolland municipality in Denmark, assuming that the existing demand remains unchanged. We set up the DRT service in such a way that its service quality (in terms of waiting time and in-vehicle time) is comparable to the replaced buses. The results show that a DRT service can be more cost efficient than regular buses and can produce significantly less CO2 emissions when the demand level is low. Additionally, we analyse the demand density at which regular buses become more cost efficient and explore how the target service quality of a DRT service can affect operational characteristics. Overall, we argue that DRT could be a more sustainable mode of public transport in low demand areas.

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
demand-responsive transport, microsimulation, operational costs
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:mau:diva-51183 (URN)10.3390/su14063252 (DOI)000774348100001 ()2-s2.0-85126302018 (Scopus ID)
Projects
Towards integrated and adaptive public transport
Available from: 2022-04-28 Created: 2022-04-28 Last updated: 2024-02-05Bibliographically approved
Projects
Internet of Things and People Research Profile; Malmö University; Publications
Banda, L., Mjumo, M. & Mekuria, F. (2022). Business Models for 5G and Future Mobile Network Operators. In: 2022 IEEE Future Networks World Forum (FNWF): . Paper presented at IEEE Future Networks World Forum FNWF 2022, Montreal, QC, Canada, 10-14 October 2022. IEEE, Article ID M17754.
Smart Public Environments II; Malmö UniversityEdge vs. Cloud Computing; Malmö UniversityIntelligent Mobility of the Future in Greater Copenhagen; Publications
Dytckov, S., Persson, J. A., Lorig, F. & Davidsson, P. (2022). Potential Benefits of Demand Responsive Transport in Rural Areas: A Simulation Study in Lolland, Denmark. Sustainability, 14(6), Article ID 3252.
Dynamic Intelligent Sensor Intensive Systems; Malmö University; Publications
Persson, J. A., Bugeja, J., Davidsson, P., Holmberg, J., Kebande, V. R., Mihailescu, R.-C., . . . Tegen, A. (2023). The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning. Applied Sciences, 13(11), Article ID 6516.
Towards integrated and adaptive public transport; Publications
Dytckov, S., Davidsson, P. & Persson, J. A. (2023). Integrate, not compete! On Potential Integration of Demand Responsive Transport Into Public Transport Network. In: : . Paper presented at 26th IEEE International Conference on Intelligent Transportation Systems ITSC 2023. Bilbao, Bizkaia, Spain: Institute of Electrical and Electronics Engineers (IEEE)
Internet of Things Master's Program; Malmö UniversityHuman-environment interaction in the Internet of Things ecosystems: Design of a connected energy management system in smart buildings for sustainability; Malmö University, Internet of Things and People (IOTAP)Towards More Reliable Predictions: Multi-model Ensembles for Simulating the Corona Pandemic; Malmö UniversityContext-aware travel support in public transport disturbances
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ORCID iD: ORCID iD iconorcid.org/0000-0003-0998-6585

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