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  • Jerčić, Petar
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Hagelbäck, Johan
    Linnéuniversitetet, SWE.
    Lindley, Craig
    Computational Modelling Group, Data61, CSIRO, AUS.
    An affective serious game for collaboration between humans and robots2019In: Entertainment Computing, ISSN 1875-9521, E-ISSN 1875-953X, Vol. 32, article id 100319Article in journal (Refereed)
    Abstract [en]

    Elicited physiological affect in humans collaborating with their robot partners was investigated to determine its influence on decision-making performance in serious games. A turn-taking version of the Tower of Hanoi game was used, where physiological arousal and valence underlying such human-robot proximate collaboration were investigated. A comparable decision performance in the serious game was found between human and non-humanoid robot arm collaborator conditions, while higher physiological affect was found in humans collaborating with such robot collaborators. It is suggested that serious games which are carefully designed to take into consideration the elicited physiological arousal might witness a better decision-making performance and more positive valence using non-humanoid robot partners instead of human ones. © 2019 The Authors

  • Ludwig Barbosa, Vinícius
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Schlosser, Edson
    Machado, Renato
    Heckler, Marcos Vinício Thomas
    Beamforming of a Linear Array Applying PSO Algorithm with Restrictive Approach2016In: Journal of Communication and Information Systems, ISSN 1980-6604, Vol. 31, no 1, p. 118-126Article in journal (Refereed)
    Abstract [en]

    This paper presents a four-element linear arraycomposed of E-shaped microstrip antennas designed to switched-beam application in ISM band. Particle Swarm Optimization(PSO) algorithm is applied to optimize four different sets ofamplitude and progressive phase shift to achieve four distinctradiation patterns controlling the major lobe direction andsidelobe level. For this application, two restrictive approachesare presented for the implementation of PSO algorithm in orderto improve the algorithm convergence to feasible solutions.

  • Berglund Snodgrass, Lina
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Spatial Planning.
    Mukhtar-Landgren, Dalia
    Lund University, SWE.
    Paulsson, Alexander
    Lund University, SWE.
    Experiment för hållbar mobilitet. Vad innoveras det (inte) kring i svenska kommuner?2019In: INNOVATION OCH STADSUTVECKLING : En forskningsantologi om organiseringsutmaningar för stad och kommun / [ed] Algehed, Jessica; Eneqvist, Erica; Jensen, Christian; Lööf, Jenny, Sverige: Stema , 2019, p. 89-102Chapter in book (Other academic)
    Abstract [sv]

    Våra städer har under de senaste 100 åren till stor del präglats av bilen(Urry, 2004). Bilismen har påverkat allt från den moderna stadsplaneringens utformning, till hur vi konsumerar, våra boendemönster, arbetsmarknadsre- gionernas utbredning och inte minst dagens fossilberoende och utsläpp av växthusgaser. Traditionellt sett har svensk kommunal planering varit inriktad på att försöka tillgodose en prognostiserad ökad efterfrågan på bilresor, och bilen har varit en given parameter att ta hänsyn till i stadsplaneringen.

    Vi vet idag att en omställning av transportsektorn är avgörande för uppfyllandet av de klimatpolitiska målen och FN:s 2030-mål. Såväl forskare som praktiker har därför framhävt behovet av att öka andelen energieffektiva transportslag som gång, cykel och kollektivtrafik. Med god vilja kan det här betraktas som framväxten av ett nytt mer hållbart paradigm - som förvisso är omstritt - men som istället lyfter fram vikten av ett mer hållbart resande och en planeringspraktik där den privatägda bilen inte står i fokus.

    (...)

    I det här kapitlet undersöker vi specifikt vad kommuner experimenterar om inom mobilitetsområdet, och vilka förväntningar som tillskrivs experimenten. Vilket slags samhälle föreställer de sig? Och hur radikalt annorlunda, eller disruptivt, är detta samhälle ur ett hållbarhetsperspektiv?

    (från introduktion)

  • Kransberg, Lena
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Problembaserat lärande2019Report (Other academic)
    Abstract [sv]

    Sedan några år tillbaka arbetar jag i sjuksköterskeprogrammet på Institutionen

    för Hälsa. Institutionen har gjort ett aktivt val av pedagogisk utgångspunkt

    med problembaserat lärande (PBL). Att arbeta med studenterna i basgruppsarbetet

    är både givande och utmanande.

  • Folino, Emil
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Självrättande programmeringstenta2019Report (Other academic)
    Abstract [sv]

    Hur kan vi på bästa sätt examinera grundläggande programmeringskunskaper

    i en inledande programmeringskurs? Vi skapade en självrättande

    examinationsform där studenterna under tentan kan få feedback och

    ökade genomströmningen med 20%.

  • Mattsson, Linda
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Nyqvist, Robert
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    En helhetssyn på lärande via matematisk problemlösning2019Report (Other academic)
    Abstract [sv]

    Syftet med kursen Matematisk problemlösning

    är att genom utmanande problemlösning

    få studenter att träna på generella

    arbets- och förhållningssätt som är hållbara och

    värdefulla för fortsatta (matematik)studier.

    Genom övnings- och examinationsuppgifter

    som kräver kreativa matematiska resonemang

    tvingas studenter att fokusera på de

    matematiska komponenternas grunder vilket

    möjliggör utveckling av högre matematiska

    kompetenser.* Vidare skapar kursupplägget

    förutsättningar för utveckling av personliga

    och professionella förmågor* väsentliga för

    lärande i tillämpningsämnen och utövande av

    framtida yrkesroll.

  • Peng, Cong
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    What Can Teachers Do to Make the Group Work Learning Effective: a Literature ReviewManuscript (preprint) (Other academic)
    Abstract [en]

    Group work-based learning is encouraged in higher education on account of both ped-agogical benefits and industrial employers’s requirements. However, although a plenty ofstudies have been performed, there are still various factors that will affect students’ groupwork-based learning in practice. It is important for the teachers to understand which fac-tors are influenceable and what can be done to influence. This paper performs a literaturereview to identify the factors that has been investigated and reported in journal articles. Fif-teen journal articles were found relevant and fifteen factors were identified, which could beinfluenced by instructors directly or indirectly. However, more evidence is needed to sup-port the conclusion of some studies since they were performed only in one single course.Therefore, more studies are required on this topic to investigate the factors in differentsubject areas. 

  • Peng, Cong
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Good Record Keeping for Conducting Research Ethically CorrectManuscript (preprint) (Other academic)
  • Reheman, Wureguli
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering.
    Ståhle, Per
    Divison of soild mechancis, LTH, SWE.
    Kao-Walter, Sharon
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering.
    On instabilities of growing bi-material interfaces2019In: Procedia Structural Integrity, ICIS2019, Elsevier, 2019, Vol. 17, p. 850-856Conference paper (Refereed)
    Abstract [en]

    This study concerns with the evolution of morphological patterns that often arise on the interface of bi-material, so called metal-precipitate phase, due to the instability of the interfaces. The instability leads to growth or retraction of small perturbation, which may determine the formation of a variety of morphological patterns initially arising on surfaces of growing precipitates at small length scales. To better understand the cause of different patterns on the bi-material interfaces, an analytical study of the stability of the precipitate-matrix interface is performed. First, a wavy interface perturbation is used to examine the spontaneous variations that occur at the precipitate-matrix interface. Then, the analysis utilises Cerruti?s solution to compute the perturbed stress field surrounding the interface. It is shown that a virtually flat interface subjected to tension is in general unstable. The amplitude of sinusoidal perturbations decays for short wave lengths and grow for longer wave lengths. Both a critical wave length for which the perturbation amplitude is unaffected and a specific ditto which obtain maximum perturbation growth rate are derived

  • Westphal, Florian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Lavesson, Niklas
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    A Case for Guided Machine Learning2019In: Machine Learning and Knowledge Extraction / [ed] Andreas Hozinger, Peter Kieseberg, A Min Tjoa and Edgar Weippl, Springer, 2019, p. 353-361Conference paper (Refereed)
    Abstract [en]

    Involving humans in the learning process of a machine learning algorithm can have many advantages ranging from establishing trust into a particular model to added personalization capabilities to reducing labeling efforts. While these approaches are commonly summarized under the term interactive machine learning (iML), no unambiguous definition of iML exists to clearly define this area of research. In this position paper, we discuss the shortcomings of current definitions of iML and propose and define the term guided machine learning (gML) as an alternative.

  • Public defence: 2019-11-14 13:30 J1640, Karlskrona
    Guo, Yang
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Heterogeneous Knowledge Sharing in eHealth: Modeling, Validation and Application2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Knowledge sharing has become an important issue in the eHealth field for improving the quality of healthcare service. However, since eHealth subject is a multidisciplinary and cross-organizational area, knowledge sharing is a serious challenge when it comes to developing eHealth systems. Thus, this thesis studies the heterogeneous knowledge sharing in eHealth and proposes a knowledge sharing ontology. The study consists of three main parts: modeling, validation and application.

    In the modeling part, knowledge sharing in eHealth is studied from two main aspects: the first aspect is the heterogeneous knowledge of different healthcare actors, and the second aspect is the interactivities among various healthcare actors. In this part, the contribution is to propose an Activity Theory based Ontology (ATO) model to highlight and represent these two aspects of eHealth knowledge sharing, which is helpful for designing efficient eHealth systems.

    In the validation part, a questionnaire based survey is conducted to practically validate the feasibility of the proposed ATO model. The survey results are analyzed to explore the effectiveness of the proposed model for designing efficient knowledge sharing in eHealth. Further, a web based software prototype is constructed to validate the applicability of the ATO model for practical eHealth systems. In this part, the contribution is to explore and show how the proposed ATO model can be validated.

    In the application part, the importance and usefulness of applying the proposed ATO model to solve two real problems are addressed. These two problems are healthcare decision making and appointment scheduling. There is a similar basic challenge in both these problems: a healthcare provider (e.g., a doctor) needs to provide optimal healthcare service (e.g., suitable medicine or fast treatment) to a healthcare receiver (e.g., a patient). Here, the optimization of the healthcare service needs to be achieved in accordance with eHealth knowledge which is distributed in the system and needs to be shared, such as the doctor’s competence, the patient’s health status, and priority control on patients’ diseases. In this part, the contribution is to propose a smart system called eHealth Appointment Scheduling System (eHASS) based on ATO model.

    This research work has been presented in eight conference and journal papers, which, along with an introductory chapter, are included in this compilation thesis.

  • Guo, Yang
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science. Blekinge institute of Technology.
    Yao, Yong
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    On Performance of Prioritized Appointment Scheduling for Healthcare2019In: Journal of Service Science and Management, ISSN 1940-9893, E-ISSN 1940-9907, Vol. 12, p. 589-604Article in journal (Refereed)
    Abstract [en]

    Designing the appointment scheduling is a challenging task for the development of healthcare system. The efficient solution approach can provide high-quality healthcare service between care providers (CP)s and care receivers (CR)s. In this paper, we consider the healthcare system with the heterogeneous CRs in terms of urgent and routine CRs. Our suggested model assumes that the system gives the service priority to the urgent CRs by allowing them to interrupt the ongoing routine appointments. An appointment handoff scheme is suggested for the interrupted routine appointments, and thus the routine CRs can attempt to re-establish the appointment scheduling with other available CPs. With these considerations, we study the scheduling performance of the system by using the Markov chains based modeling approach. The numerical analysis is reported and the simulation experiment is conducted to validate the numerical results.

  • Alégroth, Emil
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Gorschek, Tony
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Petersen, Kai
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Mattsson, Michael
    Characteristics that affect Preference of DecisionModels for Asset Selection: An Industrial Questionnaire Survey - Appendix A: Questionnaire Introduction. Decision-making in Practice / Appendix B: Survey results2019Data set
  • Paladi, Nicolae
    et al.
    Lund University and RISE.
    Svenningsson, Jakob
    RISE.
    Medina, Jorge
    New Jersey Institute of Technology.
    Arlos, Patrik
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Protecting OpenFlow Flow Tables with Intel SGX2019In: Proceedings of the ACM SIGCOMM 2019 Conference Posters and Demos, Beijing: ACM Publications, 2019, p. 146-147Conference paper (Refereed)
  • Nordahl, Christian
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Monitoring Household Electricity Consumption Behaviour for Mining Changes2019In: 3rd International Workshop on Aging, Rehabilitation and Independent Assisted Living (ARIAL), International Joint Conferenec on Artificial Intelligence (IJCAI), August 10-16, 2019, Macao, China., 2019Conference paper (Refereed)
    Abstract [en]

    In this paper, we present an ongoing work on using a household electricity consumption behavior model for recognizing changes in sleep patterns. The work is inspired by recent studies in neuroscience revealing an association between dementia and sleep disorders and more particularly, supporting the hypothesis that insomnia may be a predictor for dementia in older adults. Our approach initially creates a clustering model of normal electricity consumption behavior of the household by using historical data. Then we build a new clustering model on a new set of electricity consumption data collected over a predefined time period and compare the existing model with the built new electricity consumption behavior model. If a discrepancy between the two clustering models is discovered a further analysis of the current electricity consumption behavior is conducted in order to investigate whether this discrepancy is associated with alterations in the resident’s sleep patterns. The approach is studied and initially evaluated on electricity consumption data collected from a single randomly selected anonymous household. The obtained results show that our approach is robust to mining changes in the resident daily routines by monitoring and analyzing their electricity consumption behavior model.

  • García Martín, Eva
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Rodrigues, Crefeda Faviola
    University of Manchester, GBR.
    Riley, Graham
    University of Manchester, GBR.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Estimation of energy consumption in machine learning2019In: Journal of Parallel and Distributed Computing, ISSN 0743-7315, E-ISSN 1096-0848, p. 75-88Article in journal (Refereed)
    Abstract [en]

    Energy consumption has been widely studied in the computer architecture field for decades. While the adoption of energy as a metric in machine learning is emerging, the majority of research is still primarily focused on obtaining high levels of accuracy without any computational constraint. We believe that one of the reasons for this lack of interest is due to their lack of familiarity with approaches to evaluate energy consumption. To address this challenge, we present a review of the different approaches to estimate energy consumption in general and machine learning applications in particular. Our goal is to provide useful guidelines to the machine learning community giving them the fundamental knowledge to use and build specific energy estimation methods for machine learning algorithms. We also present the latest software tools that give energy estimation values, together with two use cases that enhance the study of energy consumption in machine learning.

  • Minniti, Maria
    et al.
    Martin J. Whitman School of Management, USA.
    Andersson, Martin
    Blekinge Institute of Technology, Faculty of Engineering, Department of Industrial Economics.
    Braunerhjelm, Pontus
    KTH, SWE.
    Delmar, Frederic
    EMLYON Business School, FRA.
    Rickne, Annika
    Linköping University, SWE.
    Thorburn, Karin
    Wharton School at University of Pennsylvania, USA.
    Wennberg, Karl
    Linköping University, SWE.
    Stenkula, Mikael
    Research Institute of Industrial Economics (IFN), SWE.
    Boyan Jovanovic: recipient of the 2019 Global Award for Entrepreneurship Research2019In: Small Business Economics, ISSN 0921-898X, E-ISSN 1573-0913, Vol. 53, no 3, p. 547-553Article in journal (Refereed)
    Abstract [en]

    The 2019 Global Award for Entrepreneurship Research has been awarded to Professor Boyan Jovanovic at New York University in the USA. Boyan Jovanovic has developed pioneering research that advances our understanding of the competitive dynamics between incumbent firms and new entrants, entrepreneurial learning and selection processes, and the importance of entrepreneurship for the economy. Key perspectives in his research are that the entrepreneur makes employment choices based on the comparative advantage of his or her skills and that entrepreneurial firms are vehicles of technological change and knowledge diffusion that influence industry dynamics and, in turn, economic growth. © 2019, The Author(s).

  • Östling, Dan
    et al.
    Sandvik Coromant Trondheim AS, NOR.
    Magnevall, Martin
    Modelling the dynamics of a large damped boring bar in a lathe.2019In: Procedia CIRP, Elsevier B.V. , 2019, Vol. 82, p. 285-289Conference paper (Refereed)
    Abstract [en]

    Boring bars with tuned mass dampers have a passive damper tuned with respect to the frequency of the first bending mode of the tool. When the tool is clamped into the machine tool there is a stiffness loss that lowers the natural frequency of the bar compared to ideal clamping conditions. For large tools the difference can be more than 35%, depending on clamping structure, tool size and overhang. In this paper we investigate a simple two-degree-of-freedom model for the tool-machine interaction consisting of a bending mode coupled with a rotational stiff mode. The model gives good insight into the system behavior and fits well with measurements. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of The 17th CIRP Conference on Modelling of Machining Operations

  • Moraes, Ana Luiza Dallora
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Kvist, Ola
    KI, SWE.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Ruiz, Sandra
    KI, SWE.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis2019In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 14, no 7, article id e0220242Article, review/survey (Refereed)
    Abstract [en]

    Background The assessment of bone age and skeletal maturity and its comparison to chronological age is an important task in the medical environment for the diagnosis of pediatric endocrinology, orthodontics and orthopedic disorders, and legal environment in what concerns if an individual is a minor or not when there is a lack of documents. Being a time-consuming activity that can be prone to inter- and intra-rater variability, the use of methods which can automate it, like Machine Learning techniques, is of value. Objective The goal of this paper is to present the state of the art evidence, trends and gaps in the research related to bone age assessment studies that make use of Machine Learning techniques. Method A systematic literature review was carried out, starting with the writing of the protocol, followed by searches on three databases: Pubmed, Scopus and Web of Science to identify the relevant evidence related to bone age assessment using Machine Learning techniques. One round of backward snowballing was performed to find additional studies. A quality assessment was performed on the selected studies to check for bias and low quality studies, which were removed. Data was extracted from the included studies to build summary tables. Lastly, a meta-analysis was performed on the performances of the selected studies. Results 26 studies constituted the final set of included studies. Most of them proposed automatic systems for bone age assessment and investigated methods for bone age assessment based on hand and wrist radiographs. The samples used in the studies were mostly comprehensive or bordered the age of 18, and the data origin was in most of cases from United States and West Europe. Few studies explored ethnic differences. Conclusions There is a clear focus of the research on bone age assessment methods based on radiographs whilst other types of medical imaging without radiation exposure (e.g. magnetic resonance imaging) are not much explored in the literature. Also, socioeconomic and other aspects that could influence in bone age were not addressed in the literature. Finally, studies that make use of more than one region of interest for bone age assessment are scarce. Copyright: © 2019 Dallora et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

  • Christiansen, Line
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Lindberg, Catharina
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Skär, Lisa
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Health-related quality of life and related factors among a sample of older people with cognitive impairment2019In: Nursing Open, E-ISSN 2054-1058, Vol. 6, no 3, p. 849-859Article in journal (Refereed)
    Abstract [en]

    Aim: This study aimed to identify factors affecting health-related quality of life (HRQoL) of older adults with cognitive impairment and to describe the association of these factors with different components of HRQoL. Design: A cross-sectional, descriptive research design was used. Methods: Data were collected from 247 individuals aged 60 years and older from a Swedish longitudinal cohort study. The Short-Form Health Survey-12 (SF-12) and EuroQol (EQ-5D) were used to assess HRQoL. The data were analysed using descriptive and comparative statistics. Results: The present study identified several factors that influenced HRQoL of older adults with cognitive impairment. The results of a multiple logistic regression analysis revealed that the following factors were associated with physical and mental HRQoL: dependency in activities of daily living (ADL), receiving informal care and feelings of loneliness and pain. © 2019 The Authors. Nursing Open published by John Wiley & Sons Ltd.