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  • 81.
    Zhu, G.
    et al.
    Maritime College, Zhejiang Ocean University, Zhoushan 316022, China..
    Ma, Y.
    Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China.
    Li, Z.
    School of Engineering, Ocean University of China, Qingdao 266110, China, and also with the Yonsei Frontier Lab, Yonsei University, Seoul 03722, Republic of Korea.
    Malekian, Reza
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Sotelo, M.
    Department of Computer Engineering. University of Alcalá, 28806 Alcalá de Henares, Spain.
    Event-Triggered Adaptive Neural Fault-Tolerant Control of Underactuated MSVs With Input Saturation2022Ingår i: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 23, nr 7, s. 7045-7057Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper investigates the tracking control problem of marine surface vessels (MSVs) in the presence of uncertain dynamics and external disturbances. The facts that actuators are subject to undesirable faults and input saturation are taken into account. Benefiting from the smoothness of the Gaussian error function, a novel saturation function is introduced to replace each nonsmooth actuator saturation nonlinearity. Applying the hand position approach, the original motion dynamics of underactuated MSVs are transformed into a standard integral cascade form so that the vector design method can be used to solve the control problem for underactuated MSVs. By combining the neural network technique and virtual parameter learning algorithm with the vector design method, and introducing an event triggering mechanism, a novel event-triggered indirect neuroadaptive fault-tolerant control scheme is proposed, which has several notable characteristics compared with most existing strategies: 1) it is not only robust and adaptive to uncertain dynamics and external disturbances but is also tolerant to undesirable actuator faults and saturation; 2) it reduces the acting frequency of actuators, thereby decreasing the mechanical wear of the MSV actuators, via the event-triggered control (ETC) technique; 3) it guarantees stable tracking without the a priori knowledge of the dynamics of the MSVs, external disturbances or actuator faults; and 4) it only involves two parameter adaptations--a virtual parameter and a lower bound on the uncertain gains of the actuators--and is thus more affordable to implement. On the basis of the Lyapunov theorem, it is verified that all signals in the tracking control system of the underactuated MSVs are bounded. Finally, the effectiveness of the proposed control scheme is demonstrated by simulations and comparative results. 

  • 82.
    Bugeja, Joseph
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Jacobsson, Andreas
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Davidsson, Paul
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    The Ethical Smart Home: Perspectives and Guidelines2022Ingår i: IEEE Security and Privacy, ISSN 1540-7993, E-ISSN 1558-4046, Vol. 20, nr 1, s. 72-80Artikel i tidskrift (Refereegranskat)
  • 83.
    Mihailescu, Radu-Casian
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmo Univ, Dept Comp Sci, Internet Things & People Res Ctr, S-20506 Malmo, Sweden..
    A weakly-supervised deep domain adaptation method for multi-modal sensor data2021Ingår i: 2021 IEEE GLOBAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INTERNET OF THINGS (GCAIOT), IEEE , 2021, s. 45-50Konferensbidrag (Refereegranskat)
    Abstract [en]

    Nearly every real-world deployment of machine learning models suffers from some form of shift in data distributions in relation to the data encountered in production. This aspect is particularly pronounced when dealing with streaming data or in dynamic settings (e.g. changes in data sources, behaviour and the environment). As a result, the performance of the models degrades during deployment. In order to account for these contextual changes, domain adaptation techniques have been designed for scenarios where the aim is to learn a model from a source data distribution, which can perform well on a different, but related target data distribution. In this paper we introduce a variational autoencoder-based multi-modal approach for the task of domain adaptation, that can be trained on a large amount of labelled data from the source domain, coupled with a comparably small amount of labelled data from the target domain. We demonstrate our approach in the context of human activity recognition using various IoT sensing modalities and report superior results when benchmarking against the effective mSDA method for domain adaptation.

  • 84.
    Tegen, Agnes
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Davidsson, Paul
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Persson, Jan A.
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Active Learning and Machine Teaching for Online Learning: A Study of Attention and Labelling Cost2021Ingår i: 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), Institute of Electrical and Electronics Engineers (IEEE), 2021Konferensbidrag (Refereegranskat)
    Abstract [en]

    Interactive Machine Learning (ML) has the potential to lower the manual labelling effort needed, as well as increase classification performance by incorporating a human-in-the loop component. However, the assumptions made regarding the interactive behaviour of the human in experiments are often not realistic. Active learning typically treats the human as a passive, but always correct, participant. Machine teaching provides a more proactive role for the human, but generally assumes that the human is constantly monitoring the learning process. In this paper, we present an interactive online framework and perform experiments to compare active learning, machine teaching and combined approaches. We study not only the classification performance, but also the effort (to label samples) and attention (to monitor the ML system) required of the human. Results from experiments show that a combined approach generally performs better with less effort compared to active learning and machine teaching. With regards to attention, the best performing strategy varied depending on the problem setup.

  • 85.
    Holmberg, Lars
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Generalao, Stefan
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Hermansson, Adam
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    The Role of Explanations in Human-Machine Learning2021Ingår i: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, 2021, s. 1006-1013Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, we study explanations in a setting where human capabilities are in parity with Machine Learning (ML) capabilities. If an ML system is to be trusted in this situation, limitations in the trained ML model’s abilities have to be exposed to the end-user. A majority of current approaches focus on the task of creating explanations for a proposed decision, but less attention is given to the equally important task of exposing limitations in the ML model’s capabilities, limitations that in turn affect the validity of created explanations. Using a small-scale design experiment we compare human explanations with explanations created by an ML system. This paper explores and presents how the structure and terminology of scientific explanations can expose limitations in the ML models knowledge and be used as an approach for research and design in the area of explainable artificial intelligence.

  • 86.
    Stefansson, Petter
    et al.
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Karlsson, Fredrik
    Sony Network Communications, 223 62 Lund, Sweden.
    Persson, Magnus
    Sony Network Communications, 223 62 Lund, Sweden.
    Olsson, Carl Magnus
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Synthetic generation of passive infrared motion sensor data using a game engine2021Ingår i: Sensors, E-ISSN 1424-8220, Vol. 21, nr 23, artikel-id 8078Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Quantifying the number of occupants in an indoor space is useful for a wide variety of applications. Attempts have been made at solving the task using passive infrared (PIR) motion sensor data together with supervised learning methods. Collecting a large labeled dataset containing both PIR motion sensor data and ground truth people count is however time-consuming, often requiring one hour of observation for each hour of data gathered. In this paper, a method is proposed for generating such data synthetically. A simulator is developed in the Unity game engine capable of producing synthetic PIR motion sensor data by detecting simulated occupants. The accuracy of the simulator is tested by replicating a real-world meeting room inside the simulator and conducting an experiment where a set of choreographed movements are performed in the simulated environment as well as the real room. In 34 out of 50 tested situations, the output from the simulated PIR sensors is comparable to the output from the real-world PIR sensors. The developed simulator is also used to study how a PIR sensor’s output changes depending on where in a room a motion is carried out. Through this, the relationship between sensor output and spatial position of a motion is discovered to be highly non-linear, which highlights some of the difficulties associated with mapping PIR data to occupancy count. 

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  • 87.
    Alassadi, Abdulrahman
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Lorig, Fabian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Holmgren, Johan
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    An Agent-based Model for Simulating Travel Patterns of Stroke Patients2021Ingår i: DIGITAL 2021: Advances on Societal Digital Transformation / [ed] Wanwan Li; Manuela Popescu, ThinkMind , 2021, s. 11-16Konferensbidrag (Refereegranskat)
    Abstract [en]

    For patients suffering from a stroke, the time until the start of the treatment is a crucial factor with respect to the recovery from this condition. In rural regions, transporting the patient to an adequate hospital typically delays the diagnosis and treatment of a stroke, worsening its prognosis. To reduce the time to treatment, different policies can be applied. This includes, for instance, the use of Mobile Stroke Units (MSUs), which are specialized ambulances that can provide adequate care closer to where the stroke occurred. To simulate and assess different stroke logistics policies, such as the use of MSUs, a major challenge is the realistic modeling of the patients. In this article, we present an approach for generating an artificial population of stroke patients to simulate when and where strokes occur. We apply the model to the region of Skåne, where we investigated the relevance of travel behavior on the spatial distribution of stroke patients.

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  • 88.
    Frank, Dignum
    et al.
    Department of Computing Science, Umeå University, SE-901 87, Umeå, Sweden.
    Loïs, Vanhée
    Department of Computing Science, Umeå University, SE-901 87, Umeå, Sweden; GREYC, Université de Caen, 14000, Caen, France.
    Maarten, Jensen
    Department of Computing Science, Umeå University, SE-901 87, Umeå, Sweden.
    Christian, Kammler
    Department of Computing Science, Umeå University, SE-901 87, Umeå, Sweden.
    René, Mellema
    Department of Computing Science, Umeå University, SE-901 87, Umeå, Sweden.
    Lorig, Fabian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Păstrăv, Cezara
    Department of Computing Science, Umeå University, SE-901 87, Umeå, Sweden.
    van den Hurk, Mijke
    Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands.
    Melchior, Alexander
    Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands; Ministry of Economic Affairs and Climate Policy and Ministry of Agriculture, Nature and Food Quality, The Netherlands, Bezuidenhoutseweg 73, 2594 AC, Den Haag, The Netherlands.
    Ghorbani, Ahmine
    Faculty of Technology, Policy and Management, TU Delft, Jaffalaan 5, 2628 BX, Delft, The Netherlands.
    de Bruin, Bart
    Faculty of Technology, Policy and Management, TU Delft, Jaffalaan 5, 2628 BX, Delft, The Netherlands.
    Kreulen, Kurt
    Faculty of Technology, Policy and Management, TU Delft, Jaffalaan 5, 2628 BX, Delft, The Netherlands.
    Verhagen, Harko
    Department of Computer and Systems Sciences, Stockholm University, PO Box 7003, 16407, Kista, Sweden.
    Davidsson, Paul
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Introduction2021Ingår i: Social Simulation for a Crisis: Results and Lessons from Simulating the COVID-19 Crisis / [ed] Frank Dignum, Cham: Springer, 2021, s. 3-13Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    The introduction of this book sets the stage of performing social simulations in a crisis. The contents of the book are based on the experience of creating a large scale and complex social simulation for the Covid-19 crisis. However, the contents are reaching much further than just this experience. We will show the general contribution that social simulations based on fundamental social-psychological principles can have in times of crises. In times of big societal changes due to a pandemic or other disaster, these simulations can give handles to support decision makers in their difficult task to act in a very short time with many uncertainties. Besides giving our results, we also will indicate why the results are trustworthy and interesting. Finally we also look what challenges should be picked up to convert the successful project into a sustainable research area.

  • 89.
    Serrano Iglesias, Sergio
    et al.
    GSIC-EMIC Research Group, Universidad de Valladolid, Spain.
    Spikol, Daniel
    Departments of Computer Science and Science Education, University of Copenhagen, Denmark.
    Bote Lorenzo, Miguel Luis
    GSIC-EMIC Research Group, Universidad de Valladolid, Spain.
    Ouhaichi, Hamza
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Gómez Sánchez, Eduardo
    GSIC-EMIC Research Group, Universidad de Valladolid, Spain.
    Vogel, Bahtijar
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Adaptable Smart Learning Environments supported by Multimodal Learning Analytics2021Ingår i: Proceedings of the LA4SLE 2021 Workshop: Learning Analytics for Smart Learning Environmentsco-located with the 16th European Conference on Technology Enhanced Learning 2021 (ECTEL 2021) / [ed] Davinia Hernández-Leo, Elise Lavoué, Miguel L. Bote-Lorenzo, Pedro J. Muñoz-Merino, Daniel Spikol, 2021, s. 24-30Konferensbidrag (Refereegranskat)
    Abstract [en]

    Smart Learning Environments and Learning Analytics hold promise of providing personalized support to learners according to their individual needs and context. This support can be achieved by collecting and analyzing data from the different learning tools and systems that are involved in the learning experience. This paper presents a first exploration of requirements and considerations for the integration of two systems: MBOX, a Multimodal Learning Analytics system for the physical space (human behavior and learning context), and SCARLETT, an SLE for the support during across-spaces learning situations combining different learning systems. This integration will enable the SLE to have access to a new and wide range of information, notably students’ behavior and social interactions in the physical learning context (e.g. classroom). The integration of multimodal data with the data coming from the digital learning environments will result in a more holistic system, therefore producing learning analytics that trigger personalized feedback and learning resources. Such integration and support is illustrated with a learning scenario that helps to discuss how these analytics can be derived and used for the intervention by the SLE.

        

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  • 90.
    Maus, Benjamin
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Olsson, Carl Magnus
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Salvi, Dario
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Privacy Personas for IoT-Based Health Research: A Privacy Calculus Approach2021Ingår i: Frontiers in Digital Health, E-ISSN 2673-253X, Vol. 3, s. 1-12, artikel-id 675754Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The reliance on data donation from citizens as a driver for research, known as citizen science, has accelerated during the Sars-Cov-2 pandemic. An important enabler of this is Internet of Things (IoT) devices, such as mobile phones and wearable devices, that allow continuous data collection and convenient sharing. However, potentially sensitive health data raises privacy and security concerns for citizens, which research institutions and industries must consider. In e-commerce or social network studies of citizen science, a privacy calculus related to user perceptions is commonly developed, capturing the information disclosure intent of the participants. In this study, we develop a privacy calculus model adapted for IoT-based health research using citizen science for user engagement and data collection. Based on an online survey with 85 participants, we make use of the privacy calculus to analyse the respondents' perceptions. The emerging privacy personas are clustered and compared with previous research, resulting in three distinct personas which can be used by designers and technologists who are responsible for developing suitable forms of data collection. These are the 1) Citizen Science Optimist, the 2) Selective Data Donor, and the 3) Health Data Controller. Together with our privacy calculus for citizen science based digital health research, the three privacy personas are the main contributions of this study.

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