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  • 1.
    Abid, Muhammad Adil
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Amouzad Mahdiraji, Saeid
    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, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Mihailescu, Radu-Casian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Petersson, Jesper
    Department of Health Care Management, Region Skåne, 21428 Malmö, Sweden; Department of Neurology, Lund University, 22242 Malmö, Sweden.
    A Genetic Algorithm for Optimizing Mobile Stroke Unit Deployment2023Ingår i: Procedia Computer Science, ISSN 1877-0509, Vol. 225, s. 3536-3545Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A mobile stroke unit (MSU) is an advanced ambulance equipped with specialized technology and trained healthcare personnel to provide on-site diagnosis and treatment for stroke patients. Providing efficient access to healthcare (in a viable way) requires optimizing the placement of MSUs. In this study, we propose a time-efficient method based on a genetic algorithm (GA) to find the most suitable ambulance sites for the placement of MSUs (given the number of MSUs and a set of potential sites). We designed an efficient encoding scheme for the input data (the number of MSUs and potential sites) and developed custom selection, crossover, and mutation operators that are tailored according to the characteristics of the MSU allocation problem. We present a case study on the Southern Healthcare Region in Sweden to demonstrate the generality and robustness of our proposed GA method. Particularly, we demonstrate our method's flexibility and adaptability through a series of experiments across multiple settings. For the considered scenario, our proposed method outperforms the exhaustive search method by finding the best locations within 0.16, 1.44, and 10.09 minutes in the deployment of three MSUs, four MSUs, and five MSUs, resulting in 8.75x, 16.36x, and 24.77x faster performance, respectively. Furthermore, we validate the method's robustness by iterating GA multiple times and reporting its average fitness score (performance convergence). In addition, we show the effectiveness of our method by evaluating key hyperparameters, that is, population size, mutation rate, and the number of generations.

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  • 2.
    Alkhabbas, Fahed
    et al.
    Malmö högskola, Fakulteten för teknik och samhälle (TS). Malmö högskola, Internet of Things and People (IOTAP).
    Ayyad, Majed
    Mihailescu, Radu-Casian
    Malmö högskola, Fakulteten för teknik och samhälle (TS). Malmö högskola, Internet of Things and People (IOTAP).
    Davidsson, Paul
    Malmö högskola, Fakulteten för teknik och samhälle (TS). Malmö högskola, Internet of Things and People (IOTAP).
    A Commitment-Based Approach to Realize Emergent Configurations in the Internet of Things2017Ingår i: Software Architecture Workshops (ICSAW), 2017 IEEE International Conference on, IEEE, 2017, s. 88-91Konferensbidrag (Refereegranskat)
    Abstract [en]

    The Internet of Things (IoT) involves intelligent, heterogeneous, autonomous and often distributed things which interact and collaborate to achieve common goals. A useful concept for supporting this effort is Emergent Configuration (EC), which consists of a dynamic set of things, with their functionalities and services, that cooperate temporarily to achieve a goal. In this paper we introduce a commitment-based approach that exploits the concept of commitments to realize ECs. More specifically, (i) we present a conceptual model for commitment-based ECs, (ii) we use the smart meeting room scenario to illustrate how ECs are realized via commitments.

  • 3.
    Amouzad Mahdiraji, Saeid
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Abid, Muhammad Adil
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Holmgren, Johan
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Mihailescu, Radu-Casian
    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).
    Petersson, Jesper
    Skåne University Hospital, Fritz Bauersgatan 5, 21428, Malmö, Sweden; Lund University, Entrégatan 7, 22242, Lund, Sweden.
    An Optimization Model for the Placement of Mobile Stroke Units2023Ingår i: Advanced Research in Technologies, Information, Innovation and Sustainability: Third International Conference, ARTIIS 2023, Madrid, Spain, October 18–20, 2023, Proceedings, Part I / [ed] Teresa Guarda; Filipe Portela; Jose Maria Diaz-Nafria, Springer, 2023, s. 297-310Konferensbidrag (Refereegranskat)
    Abstract [en]

    Mobile Stroke Units (MSUs) are specialized ambulances that can diagnose and treat stroke patients; hence, reducing the time to treatment for stroke patients. Optimal placement of MSUs in a geographic region enables to maximize access to treatment for stroke patients. We contribute a mathematical model to optimally place MSUs in a geographic region. The objective function of the model takes the tradeoff perspective, balancing between the efficiency and equity perspectives for the MSU placement. Solving the optimization problem enables to optimize the placement of MSUs for the chosen tradeoff between the efficiency and equity perspectives. We applied the model to the Blekinge and Kronoberg counties of Sweden to illustrate the applicability of our model. The experimental findings show both the correctness of the suggested model and the benefits of placing MSUs in the considered regions.

  • 4.
    Amouzad Mahdiraji, Saeid
    et al.
    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, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Alshaban, Ala’a
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    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).
    Petersson, Jesper
    Lund University; Region Skåne.
    Al Fatah, Jabir
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    A Framework for Constructing Discrete Event Simulation Models for Emergency Medical Service Policy Analysis2022Ingår i: Procedia Computer Science, E-ISSN 1877-0509, Vol. 210, s. 133-140Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Constructing simulation models can be a complex and time-consuming task, in particular if the models are constructed from scratch or if a general-purpose simulation modeling tool is used. In this paper, we propose a model construction framework, which aims to simplify the process of constructing discrete event simulation models for emergency medical service (EMS) policy analysis. The main building blocks used in the framework are a set of general activities that can be used to represent different EMS care chains modeled as flowcharts. The framework allows to build models only by specifying input data, including demographic and statistical data, and providing a care chain of activities and decisions. In a case study, we evaluated the framework by using it to construct a model for the simulation of the EMS activities related to acute stroke. Our evaluation shows that the predefined activities included in the framework are sufficient to build a simulation model for the rather complex case of acute stroke.

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  • 5.
    Amouzad Mahdiraji, Saeid
    et al.
    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, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Mihailescu, Radu-Casian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Petersson, Jesper
    Region Skåne; Lund University.
    A Micro-Level Simulation Model for Analyzing the Use of MSUs in Southern Sweden2022Ingår i: Procedia Computer Science, E-ISSN 1877-0509, Vol. 198, s. 132-139Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A mobile stroke unit (MSU) is a special type of ambulance, where stroke patients can be diagnosed and provided intravenous treatment, hence allowing to cut down the time to treatment for stroke patients. We present a discrete event simulation (DES) model to study the potential benefits of using MSUs in the southern health care region of Sweden (SHR). We included the activities and actions used in the SHR for stroke patient transportation as events in the DES model, and we generated a synthetic set of stroke patients as input for the simulation model. In a scenario study, we compared two scenarios, including three MSUs each, with the current situation, having only regular ambulances. We also performed a sensitivity analysis to further evaluate the presented DES model. For both MSU scenarios, our simulation results indicate that the average time to treatment is expected to decrease for the whole region and for each municipality of SHR. For example, the average time to treatment in the SHR is reduced from 1.31h in the baseline scenario to 1.20h and 1.23h for the two MSU scenarios. In addition, the share of stroke patients who are expected to receive treatment within one hour is increased by a factor of about 3 for both MSU scenarios.

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  • 6.
    Casserfelt, Karl
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Mihailescu, Radu-Casian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    An investigation of transfer learning for deep architectures in group activity recognition2019Ingår i: 2019 IEEE International Conference On Pervasive Computing and Communications Workshops (Percom Workshops), IEEE, 2019, s. 58-64Konferensbidrag (Refereegranskat)
    Abstract [en]

    Pervasive technologies permeating our immediate surroundings provide a wide variety of means for sensing and actuating in our environment, having a great potential to impact the way we live, but also how we work. In this paper, we address the problem of activity recognition in office environments, as a means for inferring contextual information in order to automatically and proactively assists people in their daily activities. To this end we employ state-of-the-art image processing techniques and evaluate their capabilities in a real-world setup. Traditional machine learning is characterized by instances where both the training and test data share the same distribution. When this is not the case, the performance of the learned model is deteriorated. However, often times, the data is expensive or difficult to collect and label. It is therefore important to develop techniques that are able to make the best possible use of existing data sets from related domains, relative to the target domain. To this end, we further investigate in this work transfer learning techniques in deep learning architectures for the task of activity recognition in office settings. We provide herein a solution model that attains a 94% accuracy under the right conditions.

  • 7.
    Ekedahl, Ulrik
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    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).
    Ma, Zhizhong
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Lessons Learned from Adapting "Things" to IoT Platforms in Research and Teaching2018Ingår i: SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, ACM Digital Library, 2018, s. 1457-1460Konferensbidrag (Refereegranskat)
    Abstract [en]

    This study presents lessons learned based on practical experiences of connecting devices to internet-of-things platforms in the context of research and academic coursework. The experiences are gathered from six research projects, one undergraduate course, and a few undergraduate theses over a three-year period. The lessons learned include: the trade-off of rapid prototyping over security is very common, example source code is not up to production standards, adherence to standards speeds development, debugging support for IoT systems is lacking, open source licenses varies, poor platform interoperability, and the array of service fees among platform providers obstruct cost comparisons.

  • 8.
    Florea, George Albert
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    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).
    Multimodal Deep Learning for Group Activity Recognition in Smart Office Environments2020Ingår i: Future Internet, E-ISSN 1999-5903, Vol. 12, nr 8, artikel-id 133Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Deep learning (DL) models have emerged in recent years as the state-of-the-art technique across numerous machine learning application domains. In particular, image processing-related tasks have seen a significant improvement in terms of performance due to increased availability of large datasets and extensive growth of computing power. In this paper we investigate the problem of group activity recognition in office environments using a multimodal deep learning approach, by fusing audio and visual data from video. Group activity recognition is a complex classification task, given that it extends beyond identifying the activities of individuals, by focusing on the combinations of activities and the interactions between them. The proposed fusion network was trained based on the audio-visual stream from the AMI Corpus dataset. The procedure consists of two steps. First, we extract a joint audio-visual feature representation for activity recognition, and second, we account for the temporal dependencies in the video in order to complete the classification task. We provide a comprehensive set of experimental results showing that our proposed multimodal deep network architecture outperforms previous approaches, which have been designed for unimodal analysis, on the aforementioned AMI dataset.

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  • 9.
    Gabelaia, David
    et al.
    Department of Mathematical logic, Razmadze Mathematical Institute, Tbilisi, Georgia.
    Kuznetsov, Evgeny
    Department of Mathematical logic, Razmadze Mathematical Institute, Tbilisi, Georgia.
    Mihailescu, Radu-Casian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Razmadze, Konstantine
    Faculty of Exact and Natural Sciences, Tbilisi State University, Tbilisi, Georgia.
    Uridia, Levan
    Department of Mathematical logic, Razmadze Mathematical Institute, Tbilisi, Georgia.
    Temporal logic of surjective bounded morphisms between finite linear processes2024Ingår i: Journal of Applied Non-Classical Logics, ISSN 1166-3081, E-ISSN 1958-5780, Vol. 34, nr 1, s. 1-30Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper, we study temporal logic for finite linear structures and surjective bounded morphisms between them. We give a characterisation of such structures by modal formulas and show that every pair of linear structures with a bounded morphism between them can be uniquely characterised by a temporal formula up to an isomorphism. As the main result, we prove Kripke completeness of the logic with respect to the class of finite linear structures with bounded morphisms between them. 

  • 10.
    Holmgren, Johan
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Ghaffari, Zahra
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Mihailescu, Radu-Casian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    An optimization model for group formation in project-based learning2020Ingår i: Proceedings of the 53rd Hawaii International Conference on System Sciences / [ed] Tung X. Bui, Hawaii, 2020, s. 62-70Konferensbidrag (Refereegranskat)
    Abstract [en]

    We propose an optimization model to tackle the problem of determining how projects are assigned to student groups based on a bidding procedure. In order to improve student experience in project-based learning we resort to actively involving them in a transparent and unbiased project allocation process. To evaluate our work, we collected information about the students' own views on how our approach influenced their level of learning and overall learning experience and provide a detailed analysis of the results. The results of our evaluation show that the large majority of students (i.e., 91%) increased or maintained their satisfaction ratings with the proposed procedure after the assignment was concluded, as compared to their attitude towards the process before the project assignment occurred.

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  • 11.
    Jamali, Mahtab
    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).
    Khoshkangini, Reza
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Ljungqvist, Martin Georg
    Axis Communications AB, Lund, Sweden.
    Mihailescu, Radu-Casian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Specialized Indoor and Outdoor Scene-specific Object Detection Models2023Ingår i: SPIE Digital Library, 2023Konferensbidrag (Refereegranskat)
  • 12.
    Kurasinski, Lukas
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Mihailescu, Radu-Casian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Towards Machine Learning Explainability in Text Classification for Fake News Detection2020Ingår i: 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, 2020Konferensbidrag (Refereegranskat)
    Abstract [en]

    The digital media landscape has been exposed in recent years to an increasing number of deliberately misleading news and disinformation campaigns, a phenomenon popularly referred as fake news. In an effort to combat the dissemination of fake news, designing machine learning models that can classify text as fake or not has become an active line of research. While new models are continuously being developed, the focus so far has mainly been aimed at improving the accuracy of the models for given datasets. Hence, there is little research done in the direction of explainability of the deep learning (DL) models constructed for the task of fake news detection.In order to add a level of explainability, several aspects have to be taken into consideration. For instance, the pre-processing phase, or the length and complexity of the text play an important role in achieving a successful classification. These aspects need to be considered in conjunction with the model's architecture. All of these issues are addressed and analyzed in this paper. Visualizations are further employed to grasp a better understanding how different models distribute their attention when classifying fake news texts. In addition, statistical data is gathered to deepen the analysis and to provide insights with respect to the model's interpretability.

  • 13.
    Mahdiraji, Saeid Amouzad
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Dahllöf, Oliver
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Hofwimmer, Felix
    Region Skåne.
    Holmgren, Johan
    Region Skåne.
    Mihailescu, Radu-Casian
    Region Skåne.
    Petersson, Jesper
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Lund University.
    Mobile stroke units for acute stroke care in the south of sweden2021Ingår i: Cogent Engineering, E-ISSN 2331-1916, Vol. 8, nr 1, artikel-id 1874084Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A Mobile stroke unit (MSU) is a type of ambulance deployed to promote the rapid delivery of stroke care. We present a computational study using a time to treatment estimation model to analyze the potential benefits of using MSUs in Sweden's Southern Health Care Region (SHR). In particular, we developed two scenarios (MSU1 and MSU2) each including three MSUs, which we compared with a baseline scenario containing only regular ambulances. For each MSU scenario, we assessed how much the expected time to treatment is estimated to decrease for the whole region and each subregion of SHR, and how the population is expected to benefit from the deployment of MSUs. For example, the average time to treatment in SHR was decreased with 20,4 and 15,6 minutes, respectively, in the two MSU scenarios. Moreover, our computational results show that the locations of the MSUs significantly influence what benefits can be expected. While MSU1 is expected to improve the situation for a higher share of the population, MSU2 is expected to have a higher impact on the patients who currently have the longest time to treatment.

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  • 14.
    Mahdiraji, Saeid Amouzad
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Holmgren, Johan
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Mihailescu, Radu-Casian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Petersson, Jesper
    Region Skåne; Lund University.
    An Optimization Model for the Tradeoff Between Efficiency and Equity for Mobile Stroke Unit Placement2021Ingår i: Innovation in Medicine and Healthcare: Proceedings of 9th KES-InMed 2021, Springer, 2021, s. 183-193Konferensbidrag (Refereegranskat)
    Abstract [en]

    A mobile stroke unit (MSU) is an ambulance, where stroke patients can be diagnosed and treated. Recently, placement of MSUs has been studied focusing on either maximum population coverage or equal service for all patients, termed efficiency and equity, respectively. In this study, we propose an unconstrained optimization model for the placement of MSUs, designed to introduce a tradeoff between efficiency and equity. The tradeoff is based on the concepts of weighted average time to treatment and the time difference between the expected time to treatment for different geographical areas. We conduct a case-study for Sweden’s Southern Health care Region (SHR), generating three scenarios (MSU1, MSU2, and MSU3) including 1, 2, and 3 MSUs, respectively. We show that our proposed optimization model can tune the tradeoff between the efficiency and equity perspectives for the MSU(s) allocation. This enables a high level of equal service for most inhabitants, as well as reducing the time to treatment for most inhabitants of a geographic region. In particular, placing three MSUs in the SHR with the proposed tradeoff, the share of inhabitants who are expected to receive treatment within an hour potentially improved by about a factor of 14 in our model.

  • 15.
    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.

  • 16.
    Mihailescu, Radu-Casian
    et al.
    Malmö högskola, Fakulteten för teknik och samhälle (TS). Malmö högskola, Internet of Things and People (IOTAP).
    Davidsson, Paul
    Malmö högskola, Fakulteten för teknik och samhälle (TS). Malmö högskola, Internet of Things and People (IOTAP).
    Integration of Smart Home Technologies for District Heating Control in Pervasive Smart Grids2017Ingår i: 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), IEEE, 2017, s. 515-520Konferensbidrag (Refereegranskat)
    Abstract [en]

    Pervasive technologies permeating our immediate surroundings provide a wide variety of low-cost means of sensing and actuating in our environment. This paper presents an approach for leveraging insights onto the lifestyle and routines of the users in order to control heating in a smart home through the use of individual climate zones, while ensuring system efficiency at a grid-level scale. Organizing smart living spaces into controllable individual climate zones allows us to exert a more fine-grained level of control. Thus, the system can benefit from a higher degree of freedom to adjust the heat demand according to the system objectives. Whereas district heating planing is only concerned with balancing heat demand among buildings, we extend the reach of these systems inside the home through the use of pervasive sensing and actuation. That is to say, we bridge the gap between traditional district heating systems and pervasive technologies in the home designed to maintain the thermal comfort of the user, in order to increase efficiency. The objective is to automate heating based on the user's preferences and behavioral patterns. The control scheme proposed applies a learning algorithm to take advantage of the sensing data inside the home in combination with an optimization procedure designed to trade-off the discomfort undertaken by the user and heating supply costs. We report on preliminary simulation results showing the effectiveness of our approach and describe the setup of our forthcoming field study.

  • 17.
    Mihailescu, Radu-Casian
    et al.
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Davidsson, Paul
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Eklund, Ulrik
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    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).
    A survey and taxonomy on intelligent surveillance from a system perspective2018Ingår i: Knowledge engineering review (Print), ISSN 0269-8889, E-ISSN 1469-8005, Vol. 33, artikel-id e4Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Recent proliferation of surveillance systems is mostly attributed to advances in both image-processing techniques and hardware enhancement of smart cameras, as well as the ubiquity of sensor-driven architectures. Owing to these capabilities, new aspects are coming to the forefront. This paper addresses the current state-of-the-art and provides researchers with an overview of existing surveillance solutions, analyzing their properties as a system and drawing attention to relevant challenges when developing, deploying and managing them. Also, some of the more prominent application domains are highlighted here. In an effort to understand the development of the advanced solutions, based on their most distinctive characteristics, we propose a taxonomy for surveillance systems to help classify them and reveal gaps in existing research. We conclude by identifying promising future research lines.

  • 18.
    Mihailescu, Radu-Casian
    et al.
    Malmö högskola, Fakulteten för teknik och samhälle (TS). Malmö högskola, Internet of Things and People (IOTAP).
    Davidsson, Paul
    Malmö högskola, Fakulteten för teknik och samhälle (TS). Malmö högskola, Internet of Things and People (IOTAP).
    Persson, Jan
    Malmö högskola, Fakulteten för teknik och samhälle (TS). Malmö högskola, Internet of Things and People (IOTAP).
    Multiagent model for agile context inference based on artificial immune systems and sparse distributed representations2016Ingår i: Multi-Agent Systems and Agreement Technologies, Springer, 2016, s. 82-87Konferensbidrag (Refereegranskat)
    Abstract [en]

    The ubiquity of sensor infrastructures in urban environments poses new challenges in managing the vast amount of data being generated and even more importantly, deriving insights that are relevant and actionable to its users and stakeholders. We argue that understanding the context in which people and things are connected and interacting is of key importance to this end. In this position paper, we present ongoing work in the design of a multiagent model based on immunity theory concepts with the scope of enhancing sensor-driven architectures with context-aware capabilities. We aim to demonstrate our approach in a real-world scenario for processing streams of sensor data in a smart building

  • 19.
    Mihailescu, Radu-Casian
    et al.
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Hurtig, David
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Olsson, Charlie
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    End-to-end anytime solution for appliance recognition based on high-resolution current sensing with few-shot learning2020Ingår i: Internet of Things, ISSN 2543-1536, Vol. 11, artikel-id 100263Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    With the steady rise of home and building automation management system, it is becoming paramount to gain access to information that reflects consumption patterns with devicelevel granularity. Various application-level services can then makes use of this data for monitoring and controlling purposes in an efficient manner. In this paper we report on the design and development of an Internet of Things (IoT) end-to-end solution for electric appliance recognition that can operate in real-time and entails low hardware cost. For the task of identifying various appliance signatures we also provide a comparative analysis, where on the one hand, we investigate the suitability of several machine learning approaches given publicly available datasets, that generally provide months worth of data with a relatively low sampling frequency. On the other hand, we proceed to evaluate their discriminative effectiveness for our particular scenario, where the goal is to provide rapid identification of the appliance signature in real-time based on a reduced training dataset (few-shot learning). This is particularly important in the context of appliance recognition, where due to the high variance in consumption patterns within each class, in order to achieve high accuracy, data points often need to be collected for each individual appliance or device that would need to be later identified. Clearly, this data collection process is often expensive and difficult to perform, especially in large-scale settings, hence few-shot learning is key. Besides presenting our end-to-end IoT solution that meets the abovementioned desiderata, the paper also provides an analysis of the computational demand of such an approach with regard to cost and real-time performance, which is often critical to low-powered IoT solutions. (C) 2020 The Authors. Published by Elsevier B.V.

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  • 20.
    Mihailescu, Radu-Casian
    et al.
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Kyriakou, Georgios
    Mirado Consulting, 11144 Stockholm, Sweden.
    Papangelis, Angelos
    Axis Communications, 22369 Lund, Sweden.
    Natural Language Understanding for Multi-Level Distributed Intelligent Virtual Sensors2020Ingår i: IoT, E-ISSN 2624-831X, Vol. 1, nr 2, s. 494-505Artikel i tidskrift (Övrigt vetenskapligt)
    Abstract [en]

    In this paper we address the problem of automatic sensor composition for servicing human-interpretable high-level tasks. To this end, we introduce multi-level distributed intelligent virtual sensors (multi-level DIVS) as an overlay framework for a given mesh of physical and/or virtual sensors already deployed in the environment. The goal for multi-level DIVS is two-fold: (i) to provide a convenient way for the user to specify high-level sensing tasks; (ii) to construct the computational graph that provides the correct output given a specific sensing task. For (i) we resort to a conversational user interface, which is an intuitive and user-friendly manner in which the user can express the sensing problem, i.e., natural language queries, while for (ii) we propose a deep learning approach that establishes the correspondence between the natural language queries and their virtual sensor representation. Finally, we evaluate and demonstrate the feasibility of our approach in the context of a smart city setup.

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  • 21.
    Mihailescu, Radu-Casian
    et al.
    Malmö högskola, Fakulteten för teknik och samhälle (TS). Malmö högskola, Internet of Things and People (IOTAP).
    Ossowski, Sascha
    Klusch, Matthias
    ECOOP: Applying Dynamic Coalition Formation to the Power Regulation Problem in Smart Grids2017Ingår i: Computational intelligence, ISSN 0824-7935, E-ISSN 1467-8640, Vol. 33, nr 3, s. 401-427Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this work, we focus on one particular area of the smart grid, namely, the challenges faced by distribution network operators in securing the balance between supply and demand in the intraday market, as a growing number of load-controllable devices and small-scale, intermittent generators coming from renewables are expected to pervade the system. We introduce a multiagent design to facilitate coordinating the various actors in the grid. The underpinning of our approach consists of an online cooperation scheme, ECOOP, where agents learn a prediction model regarding potential coalition partners and so can respond in an agile manner to situations that are occurring in the grid, by means of negotiating and formulating speculative solutions, with respect to the estimated behavior of the system. We provide a computational characterization for our solution in terms of complexity, as well as an empirical analysis against real consumption data sets, based on the macro-model of the Australian energy market, showing a performance improvement of about 17%.

  • 22.
    Mihailescu, Radu-Casian
    et al.
    Malmö högskola, Fakulteten för teknik och samhälle (TS). Malmö högskola, Internet of Things and People (IOTAP).
    Persson, Jan
    Malmö högskola, Fakulteten för teknik och samhälle (TS). Malmö högskola, Internet of Things and People (IOTAP).
    Davidsson, Paul
    Malmö högskola, Fakulteten för teknik och samhälle (TS). Malmö högskola, Internet of Things and People (IOTAP).
    Eklund, Ulrik
    Malmö högskola, Fakulteten för teknik och samhälle (TS). Malmö högskola, Internet of Things and People (IOTAP).
    Towards Collaborative Sensing using Dynamic Intelligent Virtual Sensors2016Ingår i: Proceedings of the 10th International Symposium on Intelligent Distributed Computing – IDC 2016, Paris, France, October 10-12 2016, Springer, 2016, s. 217-227Konferensbidrag (Refereegranskat)
    Abstract [en]

    The recent advent of ’Internet of Things’ technologies is set to bring about a plethora of heterogeneous data sources to our immediate environment. In this work, we put forward a novel concept of dynamic intelligent virtual sensors (DIVS) in order to support the creation of services designed to tackle complex problems based on reasoning about various types of data. While in most of works presented in the literature virtual sensors are concerned with homogeneous data and/or static aggregation of data sources, we define DIVS to integrate heterogeneous and distributed sensors in a dynamic manner. This paper illustrates how to design and build such systems based on a smart building case study. Moreover, we propose a versatile framework that supports collaboration between DIVS, via a semantics- empowered search heuristic, aimed towards improving their performance.

  • 23.
    Mihailescu, Radu-Casian
    et al.
    Malmö högskola, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö högskola, Internet of Things and People (IOTAP).
    Spalazzese, Romina
    Malmö högskola, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö högskola, Internet of Things and People (IOTAP).
    Davidsson, Paul
    Malmö högskola, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö högskola, Internet of Things and People (IOTAP).
    Heyer, Clint
    Malmö högskola, Internet of Things and People (IOTAP). Malmö högskola, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    A Role-Based Approach for Orchestrating Emergent Configurations in the Internet of Things2017Ingår i: Internet of Agents;2, 2017, s. 18-35Konferensbidrag (Refereegranskat)
    Abstract [en]

    The Internet of Things (IoT) is envisioned as a global net- work of connected things enabling ubiquitous machine-to-machine (M2M) communication. With estimations of billions of sensors and devices to be connected in the coming years, the IoT has been advocated as having a great potential to impact the way we live, but also how we work. How- ever, the connectivity aspect in itself only accounts for the underlying M2M infrastructure. In order to properly support engineering IoT sys- tems and applications, it is key to orchestrate heterogeneous ’things’ in a seamless, adaptive and dynamic manner, such that the system can ex- hibit a goal-directed behaviour and take appropriate actions. Yet, this form of interaction between things needs to take a user-centric approach and by no means elude the users’ requirements. To this end, contextu- alisation is an important feature of the system, allowing it to infer user activities and prompt the user with relevant information and interactions even in the absence of intentional commands. In this work we propose a role-based model for emergent configurations of connected systems as a means to model, manage, and reason about IoT systems including the user’s interaction with them. We put a special focus on integrating the user perspective in order to guide the emergent configurations such that systems goals are aligned with the users’ intentions. We discuss related scientific and technical challenges and provide several uses cases outlining the concept of emergent configurations.

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  • 24.
    O'Donnell, Jake
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Tan, Jason
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Mihailescu, Radu-Casian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Towards intent-aware and privacy-preserving image processing systems2020Ingår i: 10th International Conference on the Internet of Things Companion, Association for Computing Machinery (ACM), 2020Konferensbidrag (Refereegranskat)
    Abstract [en]

    Biometric solutions for access control is an active line of research. Specifically, when it comes to facial identification for access control, these systems can pose privacy concerns. For instance, identifying people that do not want to use the facial identification module. This work focuses on implementing an intent-aware system, which uses a hand gesture trigger to initiate the identification process. In order to evaluate the system, test cases were performed to verify accuracy of each hand gesture. Thereafter, a scenario was created to simulate an activation of the prototype system. The evaluation was used to determine the convenience and guidance when implementing intent-aware systems.  

     

  • 25.
    Persson, Jan A.
    et al.
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Bugeja, Joseph
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Davidsson, Paul
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Holmberg, Johan
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Kebande, Victor R.
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    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).
    Sarkheyli-Hägele, Arezoo
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Tegen, Agnes
    Malmö universitet, Internet of Things and People (IOTAP). Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    The Concept of Interactive Dynamic Intelligent Virtual Sensors (IDIVS): Bridging the Gap between Sensors, Services, and Users through Machine Learning2023Ingår i: Applied Sciences, E-ISSN 2076-3417, Vol. 13, nr 11, artikel-id 6516Artikel i tidskrift (Refereegranskat)
    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.

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  • 26.
    Skiöld, David
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Arora, Shivani
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Mihailescu, Radu-Casian
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Balaghi, Ramtin
    Volvo Cars, Gothenburg, Sweden..
    Forecasting key performance indicators for smart connected vehicles2022Ingår i: Advances in artificial intelligence: IBERAMIA 2022 / [ed] A C B Garcia, M Ferro, J C R Ribon, Springer, 2022, Vol. 13788, s. 414-415Konferensbidrag (Refereegranskat)
    Abstract [en]

    As connectivity has been introduced to the car industry, automotive companies have in-use cars which are connected to the internet. A key concern in this context represents the difficulty of knowing how the connection quality changes over time and if there are associated issues. In this work we describe the use of CDR data from connected cars supplied by Volvo to build and study forecasting models that predict how relevant KPIs change over time. Our experiments show promising results for this predictive task, which can lead to improving user experience of connectivity in smart vehicles.

  • 27.
    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).
    Mihailescu, Radu-Casian
    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, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
    Collaborative Sensing with Interactive Learning using Dynamic Intelligent Virtual Sensors2019Ingår i: Sensors, E-ISSN 1424-8220, Vol. 19, nr 3, artikel-id 477Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Although the availability of sensor data is becoming prevalent across many domains, it still remains a challenge to make sense of the sensor data in an efficient and effective manner in order to provide users with relevant services. The concept of virtual sensors provides a step towards this goal, however they are often used to denote homogeneous types of data, generally retrieved from a predetermined group of sensors. The DIVS (Dynamic Intelligent Virtual Sensors) concept was introduced in previous work to extend and generalize the notion of a virtual sensor to a dynamic setting with heterogenous sensors. This paper introduces a refined version of the DIVS concept by integrating an interactive machine learning mechanism, which enables the system to take input from both the user and the physical world. The paper empirically validates some of the properties of the DIVS concept. In particular, we are concerned with the distribution of different budget allocations for labelled data, as well as proactive labelling user strategies. We report on results suggesting that a relatively good accuracy can be achieved despite a limited budget in an environment with dynamic sensor availability, while proactive labeling ensures further improvements in performance.

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  • 28.
    Tell, Amanda
    et al.
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    Hägred, Carl
    Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
    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).
    Perceptions of Time: Determine the Time of an Analogue Watch using Computer Vision2022Ingår i: 2022 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), Institute of Electrical and Electronics Engineers (IEEE), 2022Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper explores the problem of determining the time of an analogue wristwatch by developing two systems and conducting a comparative study. The first system uses OpenCV to find the watch hands and applies geometrical techniques to calculate the time. The second system uses Machine Learning by building a neural network to classify images in Tensorflow using a multi-labelling approach. The results show that in a set environment the geometric-based approach performs better than the Machine Learning model. The geometric system predicted time correctly with an accuracy of 80% whereas the best Machine Learning model only achieves 74%. Experiments show that the accuracy of the neural network model did increase when using data augmentation, however there was no significant improvement when adding synthetic data to our training set.

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