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Davidsson, Paul, ProfessorORCID iD iconorcid.org/0000-0003-0998-6585
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Tegen, A., Davidsson, P. & Persson, J. A. (2024). Activity Recognition through Interactive Machine Learning in a Dynamic Sensor Setting. Personal and Ubiquitous Computing, 28(1), 273-286
Öppna denna publikation i ny flik eller fönster >>Activity Recognition through Interactive Machine Learning in a Dynamic Sensor Setting
2024 (Engelska)Ingår i: Personal and Ubiquitous Computing, ISSN 1617-4909, E-ISSN 1617-4917, Vol. 28, nr 1, s. 273-286Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

The advances in Internet of things lead to an increased number of devices generating and streaming data. These devices can be useful data sources for activity recognition by using machine learning. However, the set of available sensors may vary over time, e.g. due to mobility of the sensors and technical failures. Since the machine learning model uses the data streams from the sensors as input, it must be able to handle a varying number of input variables, i.e. that the feature space might change over time. Moreover, the labelled data necessary for the training is often costly to acquire. In active learning, the model is given a budget for requesting labels from an oracle, and aims to maximize accuracy by careful selection of what data instances to label. It is generally assumed that the role of the oracle only is to respond to queries and that it will always do so. In many real-world scenarios however, the oracle is a human user and the assumptions are simplifications that might not give a proper depiction of the setting. In this work we investigate different interactive machine learning strategies, out of which active learning is one, which explore the effects of an oracle that can be more proactive and factors that might influence a user to provide or withhold labels. We implement five interactive machine learning strategies as well as hybrid versions of them and evaluate them on two datasets. The results show that a more proactive user can improve the performance, especially when the user is influenced by the accuracy of earlier predictions. The experiments also highlight challenges related to evaluating performance when the set of classes is changing over time.

Ort, förlag, år, upplaga, sidor
Springer, 2024
Nyckelord
machine learning, interactive machine learning, active learning, machine teaching, online learning, sensor data
Nationell ämneskategori
Annan data- och informationsvetenskap Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mau:diva-17434 (URN)10.1007/s00779-020-01414-2 (DOI)000538990600002 ()2-s2.0-85086152913 (Scopus ID)
Anmärkning

Correction available: https://doi.org/10.1007/s00779-020-01465-5

Tillgänglig från: 2020-06-07 Skapad: 2020-06-07 Senast uppdaterad: 2024-03-06Bibliografiskt granskad
Jevinger, Å., Zhao, C., Persson, J. A. & Davidsson, P. (2024). Artificial intelligence for improving public transport: a mapping study. Public Transport, 16(1), 99-158
Öppna denna publikation i ny flik eller fönster >>Artificial intelligence for improving public transport: a mapping study
2024 (Engelska)Ingår i: Public Transport, ISSN 1866-749X, E-ISSN 1613-7159, Vol. 16, nr 1, s. 99-158Artikel i tidskrift (Refereegranskat) Published
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.

Ort, förlag, år, upplaga, sidor
Springer, 2024
Nyckelord
Artifcial intelligence · Machine learning · Public transit · Mass transit · Public transport · Literature review
Nationell ämneskategori
Datavetenskap (datalogi) Transportteknik och logistik
Forskningsämne
Transportstudier
Identifikatorer
urn:nbn:se:mau:diva-64419 (URN)10.1007/s12469-023-00334-7 (DOI)001104065400001 ()2-s2.0-85177171423 (Scopus ID)
Projekt
AI and public transport: potential and hindrances
Forskningsfinansiär
Vinnova, VINNOVA
Anmärkning

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

Tillgänglig från: 2023-12-14 Skapad: 2023-12-14 Senast uppdaterad: 2024-04-11Bibliografiskt granskad
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)
Öppna denna publikation i ny flik eller fönster >>A Comparison of Machine Learning Prediction Models to Estimate the Future Heat Demand
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2023 (Engelska)Ingår i: 2023 IEEE 13th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), Institute of Electrical and Electronics Engineers (IEEE), 2023Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2023
Serie
IEEE International Conference on Consumer Electronics-Berlin, ISSN 2166-6814, E-ISSN 2166-6822
Nationell ämneskategori
Sannolikhetsteori och statistik
Identifikatorer
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)
Konferens
2023 IEEE 13th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), Berlin, Germany, 03-05 September 2023
Tillgänglig från: 2024-01-09 Skapad: 2024-01-09 Senast uppdaterad: 2024-01-09Bibliografiskt granskad
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)
Öppna denna publikation i ny flik eller fönster >>ART4FL: An Agent-Based Architectural Approach for Trustworthy Federated Learning in the IoT
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2023 (Engelska)Ingår i: 2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC), Institute of Electrical and Electronics Engineers (IEEE), 2023Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2023
Nationell ämneskategori
Datorsystem
Identifikatorer
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)
Konferens
2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC), Tartu, Estonia, 18-20 September 2023
Tillgänglig från: 2023-11-20 Skapad: 2023-11-20 Senast uppdaterad: 2023-12-28Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Human Factors in Interactive Online Machine Learning
2023 (Engelska)Ingår i: HHAI 2023: Augmenting Human Intellect / [ed] Paul Lukowicz; Sven Mayer; Janin Koch; John Shawe-Taylor; Ilaria Tiddi, IOS Press, 2023, s. 33-45Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
IOS Press, 2023
Serie
Frontiers in Artificial Intelligence and Application, ISSN 0922-6389, E-ISSN 1879-8314 ; 368
Nyckelord
interactive machine learning, online learning, human factors
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
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)
Konferens
HHAI 2023, the 2nd International Conference on Hybrid Human-Artificial Intelligence, 26-30 June 2023, Munich, Germany
Tillgänglig från: 2023-07-06 Skapad: 2023-07-06 Senast uppdaterad: 2024-02-26Bibliografiskt granskad
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)
Öppna denna publikation i ny flik eller fönster >>Integrate, not compete! On Potential Integration of Demand Responsive Transport Into Public Transport Network
2023 (Engelska)Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Bilbao, Bizkaia, Spain: Institute of Electrical and Electronics Engineers (IEEE), 2023
Nyckelord
Simulation, Demand-Responsive Transport, Public transport
Nationell ämneskategori
Transportteknik och logistik Datavetenskap (datalogi)
Forskningsämne
Transportstudier
Identifikatorer
urn:nbn:se:mau:diva-62399 (URN)
Konferens
26th IEEE International Conference on Intelligent Transportation Systems ITSC 2023
Tillgänglig från: 2023-09-08 Skapad: 2023-09-08 Senast uppdaterad: 2023-09-15Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Interoperability, Scalability, and Availability of Energy Types in Hybrid Heating Systems
2023 (Engelska)Ingår i: New Technologies, Development and Application VI: Volume 2, Springer, 2023, s. 3-13Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Springer, 2023
Serie
Lecture Notes in Networks and Systems, ISSN 2367-3370, E-ISSN 2367-3389 ; 707
Nationell ämneskategori
Energiteknik
Identifikatorer
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)
Konferens
New Technologies and Applications(NT-2023), Sarajevo, 22-24 June 2023
Tillgänglig från: 2023-12-12 Skapad: 2023-12-12 Senast uppdaterad: 2023-12-12Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Shaping IoT Systems Together: The User-System Mixed-Initiative Paradigm and Its Challenges
2023 (Engelska)Ingår i: 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, s. 221-229Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Springer, 2023
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 14212
Nyckelord
Mixed-initiative paradigm, User-System Collaboration, Intelligent IoT Systems, Novel Experiences, Goal-driven IoT Systems
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
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)
Konferens
17th European Conference, ECSA 2023, Istanbul, Turkey, September 18–22, 2023
Tillgänglig från: 2023-12-12 Skapad: 2023-12-12 Senast uppdaterad: 2023-12-12Bibliografiskt granskad
Jamali, M., Davidsson, P., Khoshkangini, R., Ljungqvist, M. G. & Mihailescu, R.-C. (2023). Specialized Indoor and Outdoor Scene-specific Object Detection Models. In: SPIE Digital Library: . Paper presented at International Conference on Machine Vision (ICMV 2023), Nov. 15-18, 2023, Yerevan, Armenia.
Öppna denna publikation i ny flik eller fönster >>Specialized Indoor and Outdoor Scene-specific Object Detection Models
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2023 (Engelska)Ingår i: SPIE Digital Library, 2023Konferensbidrag, Publicerat paper (Refereegranskat)
Serie
Studies in Computer Science
Nyckelord
object detection, YOLOv5, indoor object detection, outdoor object detection, scene classification
Nationell ämneskategori
Annan elektroteknik och elektronik
Identifikatorer
urn:nbn:se:mau:diva-66441 (URN)
Konferens
International Conference on Machine Vision (ICMV 2023), Nov. 15-18, 2023, Yerevan, Armenia
Anmärkning

The paper has not been published yet

Tillgänglig från: 2024-03-22 Skapad: 2024-03-22 Senast uppdaterad: 2024-03-27Bibliografiskt granskad
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.
Öppna denna publikation i ny flik eller fönster >>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 (Engelska)Ingår i: Applied Sciences, E-ISSN 2076-3417, Vol. 13, nr 11, artikel-id 6516Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
MDPI, 2023
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:mau:diva-60144 (URN)10.3390/app13116516 (DOI)001004726600001 ()2-s2.0-85163091186 (Scopus ID)
Tillgänglig från: 2023-06-07 Skapad: 2023-06-07 Senast uppdaterad: 2023-09-05Bibliografiskt granskad
Projekt
Forskningsprofilen Internet of Things and People; Malmö universitet; Publikationer
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.
Smarta Offentliga Miljöer II; Malmö universitetEdge vs. Cloud Computing; Malmö universitetFramtidens Intelligenta Mobilitet i Greater Copenhagen; Publikationer
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 universitet; Publikationer
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.
Framtidens integrerade och adaptiva kollektivtrafik; Publikationer
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)
AVANS projekt: "Internet of Things Master's Program"; Malmö universitetInteraktion mellan människor och omgivning i Internet of Things-ekosystem: Design av uppkopplade system för energi-management i smarta byggnader för hållbarhet; Malmö universitet, Internet of Things and People (IOTAP)Mot mer tillförlitliga prognoser: Multimodell-ensembler för simulering av corona-pandemin; Malmö universitetKontextmedvetet resestöd vid störningar i kollektivtrafiken
Organisationer
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0003-0998-6585

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