Högskolan i Skövde

The metaverse presents a new opportunity to construct personalized learning paths and to promote practices that scale the development of future skills and collective intelligence. The attitudes, knowledge and skills that are necessary to face the challenges of the 21st century should be developed through iterative cycles of continuous learning, where learners are enabled to experience, reflect, and produce new ideas while participating in a collective creativity process. In this paper, we propose an architecture to develop a metaverse-intensive learning experience (MiLEx) platform with an illustrative scenario that reinforces the development of 21st century career practices and collective intelligence. The learning ecosystem of MiLEx integrates four key elements: (1) key players that define the main actors and their roles in the learning process; (2) a learning context that defines the learning space and the networks of expected interactions among human and non-human objects; (3) experiential learning instances that deliver education via a real-life–virtual merge; and (4) technology support for building practice communities online, developing experiential cycles and transforming knowledge between human and non-human objects within the community. The proposed MiLEx architecture incorporates sets of technological and data components to (1) discover/profile learners and design learner-centric, theoretically grounded and immersive learning experiences; (2) create elements and experiential learning scenarios; (3) analyze learner’s interactive and behavioral patterns; (4) support the emergence of collective intelligence; (5) assess learning outcomes and monitor the learner’s maturity process; and (6) evaluate experienced learning and recommend future experiences. We also present the MiLEx continuum as a cyclic flow of information to promote immersive learning. Finally, we discuss some open issues to increase the learning value and propose some future work suggestions to further shape the transformative potential of metaverse-based learning environments.

Studies on Critical Success Factors (CSFs) in Healthcare Information Systems (HIS) development projects have traditionally often been limited to retrospectively identifying CSFs in a finished project. In this paper, we focus on how to prospectively apply and manage CSFs in HIS projects. Based on a holistic perspective and systems thinking, an inductive research strategy was applied and a single in-depth case study was conducted. The findings include detailed descriptions that contribute to further understanding of how to prospectively apply and manage CSFs in HIS projects. The analysis reveals that CSFs must be applied differently and managed on various system levels. Furthermore, it shows how interactions exist between different system levels, both in the case of a specific CSF and between different CSFs on various system levels. Our analysis framework and findings indicate new directions for future research: how to prospectively apply and manage CSFs in HIS development projects can now be investigated both in a more holistic way and more in detail. Finally, healthcare practitioners can use the descriptions as practical checklists for guiding them in how to realize situational adaptation of CSFs in HIS projects across different system levels.

Citation is how we acknowledge our debt to those who came before; those who helped us find our way when the way was obscured because we deviated from the paths we were told to follow. Sara Ahmed reminds us that just citational practices recognize the knowledge contributions of less dominant, routinely overlooked voices. Pursuing citational justice, then, entails moving away from individualistic views of authorship and toward a shared, reciprocal understanding of how knowledge is produced. Drawing from our experiences working within HCI, we extend an invitation for a just citational practice—one that makes space for the diversity of human experience and recognizes that human-computer interactions must be responsive to cultural and geographic differences. We outline parts of our ongoing conversations as a collective to motivate a careful citation practice across our field, interrogating how we can best honor one another’s ideas and labor without alienation or appropriation.

Chatbots are mainly data-driven and usually based on utterances that might be sensitive. However, training deep learning models on shared data can violate user privacy. Such issues have commonly existed in chatbots since their inception. In the literature, there have been many approaches to deal with privacy, such as differential privacy and secure multi-party computation, but most of them need to have access to users' data. In this context, Federated Learning (FL) aims to protect data privacy through distributed learning methods that keep the data in its location. This paper presents Fedbot, a proof-of-concept (POC) privacy-preserving chatbot that leverages large-scale customer support data. The POC combines Deep Bidirectional Transformer models and federated learning algorithms to protect customer data privacy during collaborative model training. The results of the proof-of-concept showcase the potential for privacy-preserving chatbots to transform the customer support industry by delivering personalized and efficient customer service that meets data privacy regulations and legal requirements. Furthermore, the system is specifically designed to improve its performance and accuracy over time by leveraging its ability to learn from previous interactions.

Background: The increasing up-trend of software size brings about challenges related to release planning and maintainability. Foreseeing the growth of software metrics can assist in taking proactive decisions regarding different areas where software metrics play vital roles. For example, source code metrics are used to automatically calculate technical debt related to code quality which may indicate how maintainable a software is. Thus, predicting such metrics can give us an indication of technical debt in the future releases of software. Objective: Estimation or prediction of software metrics can be performed more meaningfully if the relationships between different domains of metrics and relationships between the metrics and different domains are well understood. To understand such relationships, this empirical study has collected 25 metrics classified into four domains from 9572 software revisions of 20 open source projects from 8 well-known companies. Results: We found software size related metrics are most correlated among themselves and with metrics from other domains. Complexity and documentation related metrics are more correlated with size metrics than themselves. Metrics in the duplications domain are observed to be more correlated to themselves on a domain-level. However, a metric to domain level relationship exploration reveals that metrics with most strong correlations are in fact connected to size metrics. The Overall correlation ranking of duplication metrics are least among all domains and metrics. Contribution: Knowledge earned from this research will help to understand inherent relationships between metrics and domains. This knowledge together with metric-level relationships will allow building better predictive models for software code metrics. © 2017 Association for Computing Machinery.

User behavior is a key aspect of cybersecurity and it is well documented that insecure user behavior is the root cause of the majority of all cybersecurity incidents. Security Education, Training, and Awareness (SETA) is described by practitioners and researchers as the most important tool for improving cybersecurity behavior and has been for several decades. Further, there are several ways to work with SETA found in academic literature and a lot of research into various aspects of SETA effectiveness. However, the problem of insecure user behavior remains revealing a need for further research in the domain. While previous research have looked at the users’ experience of SETA, this study looks at SETA adoption from the perspective of the adopting organization. For this purpose, a survey was sent out to all Nordic municipalities with the intent of measuring if and how SETA is conducted, and how the respondents would ideally like to conduct SETA. The results show that a majority of the participating organizations use SETA and that e-learning is the most common delivery method. However, the results also show that gamification and embedded training is seldom used in practice nor a part of the participants’ picture of ideal SETA.

Federated learning (FL) enables edge nodes to collaboratively contribute to constructing a global model without sharing their data. This is accomplished by devices computing local, private model updates that are then aggregated by a server. However, computational resource constraints and network communication can become a severe bottleneck for larger model sizes typical for deep learning (DL) applications. Edge nodes tend to have limited hardware resources (RAM, CPU), and the network bandwidth and reliability at the edge is a concern for scaling federated fleet applications. In this paper, we propose and evaluate a FL strategy inspired by transfer learning in order to reduce resource utilization on devices, as well as the load on the server and network in each global training round. For each local model update, we randomly select layers to train, freezing the remaining part of the model. In doing so, we can reduce both server load and communication costs per round by excluding all untrained layer weights from being transferred to the server. The goal of this study is to empirically explore the potential trade-off between resource utilization on devices and global model convergence under the proposed strategy. We implement the approach using the FL framework FEDn. A number of experiments were carried out over different datasets (CIFAR-10, CASA, and IMDB), performing different tasks using different DL model architectures. Our results show that training the model partially can accelerate the training process, efficiently utilizes resources on-device, and reduce the data transmission by around 75% and 53% when we train 25%, and 50% of the model layers, respectively, without harming the resulting global model accuracy. Furthermore, our results demonstrate a negative correlation between the number of participating clients in the training process and the number of layers that need to be trained on each client’s side. As the number of clients increases, there is a decrease in the required number of layers. This observation highlights the potential of the approach, particularly in cross-device use cases.

Social robots are expected gradually to be used by more and more people in a widerrange of settings, domestic as well as professional. As a consequence, the features and qualityrequirements on human–robot interaction will increase, comprising possibilities to communicateemotions, establishing a positive user experience, e.g., using touch. In this paper, the focus is ondepicting how humans, as the users of robots, experience tactile emotional communication with theNao Robot, as well as identifying aspects affecting the experience and touch behavior. A qualitativeinvestigation was conducted as part of a larger experiment. The major findings consist of 15 differentaspects that vary along one or more dimensions and how those influence the four dimensions ofuser experience that are present in the study, as well as the different parts of touch behavior ofconveying emotions.

The ANEMONE is a methodological approach for user experience (UX) evaluation of action and intention recognition in human-robot interaction that has activity theory as its theoretical lens in combination with the seven stages of action model and UX evaluation methodology. ANEMONE has been applied in a case where a prototype has been evaluated. The prototype was a workstation in assembly in manufacturing consisting of a collaborative robot, a pallet, a tablet, and a workbench, where one operator is working in the same physical space as one robot. The purpose of this paper is to provide guidance on how to use ANEMONE, with a particular focus on the data analysis part, through describing a real example together with lessons learned and recommendations.

The industrial transition to Industrie 4.0 and subsequently Industrie 5.0 requires robots to be able to share physical and social space with humans in such a way that interaction and coexistence are positively experienced by the humans and where it is possible for the human and the robot to mutually perceive, interpret and act on each other's actions and intentions. To achieve this, strategies for humanrobot interaction are needed that are adapted to operators’ needs and characteristics in an industrial context, i.e., Operator 5.0. This paper presents a research design for the development of a framework for human-robot interaction strategies based on ANEMONE, which is an evaluation framework based on activity theory, the seven stages of action model, and user experience (UX) evaluation methodology. At two companies, ANEMONE is applied in two concrete use cases, collaborative kitting and mobile robot platforms for chemical laboratory assignments. The proposed research approach consists of 1) evaluations of existing demonstrators, 2) development of preliminary strategies that are implemented, 3) re-evaluations and 4) cross-analysis of results to produce an interaction strategy framework. The theoretically and empirically underpinned framework-to-be is expected to, in the long run, contribute to a sustainable work environment for Operator 5.0.

The education of students to become competent user experience designers is a delicate matter as students need to obtain a multitude of knowledge, skills, and judgmental abilities. In this paper, our effort to manage this multiplicity in a bachelor’s program in user experience design is shared along with our experiences and teaching practices influenced by theories of situated and embodied cognition together with reflective teaching. The program was followed up through interviews with eight alumni and a company representative that employs user experience designers. The results show that the program overall works well, although some of the identified issues need to be addressed in the future. The interpretation is that our program curricula and teaching practices are fruitful, which hopefully can contribute to thoughts and discussions for other teachers in the field of user experience design and human-computer interaction.

Industries are under development with new upcoming tools that will further streamline the work of operators, not least in assembly. Assembly instructions are usually visualized by traditional paper or databases. A new way of showing instruction is provided by augmented reality (AR). The focus of this paper is the user experience (UX) of AR-based instructions for assembly. In order to study the UX in AR, an evaluation matrix and an AR prototype has been developed and evaluated in a UX test, where data regarding both hedonic and pragmatic qualities was collected. The UX test yielded a result of three out of nine sub-goals completed while six did not. There was a general low degree of cognitive load while assembling but not low enough. However, there are promising results for AR-based instructions, though the technology still needs improvement and more testing is also necessary. The assembly scenario for this study was somewhat simple and could be one reason why this study generated ambiguous results.

Many studies have been conducted on the experience of play in MMOgames to determine what makes them attractive to players. To deepenthis understanding and to suggest a potential longitudinal study, literaturehas been reviewed to determine key factors of what makesMMOs attractive to queer players. Social play, character creation,gameplay, jobs/classes, tasks, gestures, queer representation and thepossibilities of queer play as well aesthetic appearance was revealedto be important for queer players. These key factors were then appliedto Final Fantasy XIV to determine if it differs from or adheres to thestandard of MMO design.

In this paper, the author uses her personal experience of encounteringand engaging with same-sex romances in games as a way to discuss the positives and negatives of same-sex romances in games. Like all people, LGBTQ players look to role models in media for guidance and solidarity, and romantic relationships in video games can create particularly strong attachments for players to characters. Yet LGBTQ representations in games are rife with negative stereotypes or worse: complete exclusion. The paper argues for the potential power of games tohelp minorities explore their identities.

Active mobility, such as biking, faces a common challenge in Swedish municipalities due to the lack of adequate lighting during the dark winter months. Insufficient lighting infrastructure hinders individuals from choosing bicycles, despite the presence of well-maintained bikepaths and a willingness to cycle. To address this issue, a project has been undertaken in the Swedish municipality of Skara for an alternative lighting solution using drones. A series of tests have been conducted based on drone prototypes developed for the selected bike paths. Participants were invited to cycle in darkness illuminated by drone lighting and share their mobility preferences and perception. This paper summarizes the users' perception of drone lighting as an alternative to fixed lighting on bike paths, with a special focus on the impact on travel habits and the perceived sense of security and comfort. Most participants were regular cyclists who cited bad weather, time, and darkness as significant factors that deterred them from using bicycles more frequently, reducing their sense of security. With drone lighting, the participants appreciated the illumination's moonlight-like quality and its ability to enhance their sense of security by illuminating the surroundings. On the technology side, they gave feedback on reducing the drone's sound and addressing lighting stability issues. In summary, the test results showcase the potential of drone lighting as a viable alternative to traditional fixed lighting infrastructure, offering improved traffic safety, sense of security, and comfort. The results show the feasibility and effectiveness of this innovative approach, supporting transformation towards active and sustainable mobility, particularly in regions facing lighting challenges.

The Magic Lantern of Memories: On Ingmar Bergman’s Story Laterna Magica

Abstract: Laterna magica (1987) is the first book Ingmar Bergman (1918–2007) published whose content was not intended to be developed into film or theater. This autobiographical book has often been used in research as a reference in various contexts concerning Bergman’s life and art. From a research point of view, however, it is difficult to use the memory book as evidence for facts or truths, as there are many examples of it simply being fiction. It is therefore interesting today to read Laterna magica as a work of art in its own right and thus perhaps find a different “truth” than the one about Ingmar Bergman’s life. For his story of himself, Bergman has very consciously chosen a literary form that structures personal memories interspersed with more contemporary events. What memories then does his literary laterna magica show and what do they mean? Why are memories and events structured in a certain way? Is there any unity in the total of more than a hundred memory images in various forms that the book consists of? My reading of Laterna magica takes a point of departure in Adriana Cavarero’s (2000) theory of the “narrative self.” In Laterna magica the narrative self creates an identity through the story that consists of the merged memory images. It then becomes a story where Bergman’s narrative self is looking for answers to the question “who am I?” The reading of the “autobiography”also assumes that it is a literary work. Thus, the story of the magical lantern of memories can be analyzed as a literary composition where staging and truth-seeking are two important concepts.

Correct classification of fractures according to their patterns is critical for developing a treatment plan in orthopaedic surgery. Unfortunately, for proximal humeral fractures (PHF), methods for proper classification have remained a jigsaw puzzle that has not yet been fully solved despite numerous proposed classifications and diagnostic methods. Recently, many studies have suggested that three-dimensional printed models (3DPM) can improve the interobserver agreement on PHF classifications. Moreover, Virtual Reality (VR) has not been properly studied for classification of shoulder injuries. The current study investigates the PHF classification accuracy relative to an expert committee when using either 3DPM or equivalent models displayed in VR among 36 orthopaedic surgery residents from different hospitals. We designed a multicentric randomised controlled trial in which we created two groups: a group exposed to a total of 34 3DPM and another exposed to VR equivalents. Association between classification accuracy and group assignment (VR/3DPM) was assessed using mixed effects logistic regression models. The results showed VR can be considered a non-inferior technology for classifying PHF when compared to 3DPM. Moreover, VR may be preferable when considering possible time and resource savings along with potential uses of VR for presurgical planning in orthopaedics. 

Background: Foam rolling has been extensively investigated, showing benefits in performance and recovery. Recently, vibration has been added to foam rollers, with hypothesized advantages over conventional foam rollers. However, there is no systematic evidence in this regard.

Objective: To carry out a systematic review and meta-analysis about the effects of vibration foam roller (VFR) on performance and recovery.

Methods: A systematic search was conducted in PubMed/MEDLINE, Web of Science and SportDiscus according to the PRISMA guidelines. The outcomes included performance (jump, agility and strength) and recovery variables (blood flow, pain and fatigue) measured after an intervention with VFR. The methodological quality was assessed with the PEDro scale. A random-effects model was used to perform the meta-analysis.

Results: Initially, 556 studies were found and after the eligibility criteria 10 studies were included in the systematic review and 9 in the meta-analysis. There was no significant effects on jump performance (SMD = 0.14 [95% CI − 0.022 to 0.307]; p = 0.101; I2 = 1.08%) and no significant beneficial effects were reported on isokinetic strength (SMD = 0.16 [95% CI − 0.041 to 0.367]; p = 0.117; I2 = 9.7%). Recovery appears to be enhanced after VFR interventions, but agility does not seem to increase after VFR interventions.

Conclusion: This systematic review and meta-analysis suggest that VFR could have great potential for increasing jump performance, agility, strength and enhancing recovery. Further research is needed to confirm the effects of VFR on performance and recovery.

Trial Registration This investigation was registered in PROSPERO with the code CRD42021238104.

The European Union (EU), as well as the entire world, is facing emerging challenges in the cybersecurity domain. Two of the most prominent challenges are citizens’ cybersecurity awareness which is the first line of defense against cybersecurity incidents, and the cybersecurity skill gap expected to lead to a future shortage of cybersecurity professionals. This paper presents an effort to combat those issues through the implementation of an intra-European course on cybersecurity awareness. The course engages university students from four EU member states who learn about increasing cybersecurity awareness while practically developing cybersecurity awareness activities for preadolescents. The paper provides an overview of the course and lessons learned from implementing it in international cooperation. The intention is to provide a guide for the development of such courses and outline success factors others can adopt and pitfalls that should be avoided.

Recommendation systems in fashion are used to provide recommendations to users on clothing items, matching styles, and size or fit. These recommendations are generated based on user actions such as ratings, reviews or general interaction with a seller. There is an increased adoption of implicit feedback in models aimed at providing recommendations in fashion. This paper aims to understand the nature of implicit user feedback in fashion recommendation systems by following guidelines to group user actions. Categories of user actions that characterize implicit feedback are examination, retention, reference, and annotation. Each category describes a specific set of actions a user takes. It is observed that fashion recommendations using implicit user feedback mostly rely on retention as a user action to provide recommendations.

A growing attention in the empirical literature has been paid on the incidence of climate shocks and change on migration decisions. Previous literature leads to different results and uses a multitude of traditional empirical approaches. This paper proposes a tree-based Machine Learning (ML) approach to analyze the role of the weather shocks toward an individual’s intention to migrate in the six agriculture-dependent-economy countries such as Burkina Faso, Ivory Coast, Mali, Mauritania, Niger, and Senegal. We performed several tree-based algorithms (e.g., XGB, Random Forest) using the train-validation-test workflow to build robust and noise-resistant approaches. Then we determine the important features showing in which direction they influence the migration intention. This ML-based estimation accounts for features such as weather shocks captured by the Standardized Precipitation-Evapotranspiration Index (SPEI) for different timescales and various socioeconomic features/covariates. We find that (i) the weather features improve the prediction performance, although socioeconomic characteristics have more influence on migration intentions, (ii) a country-specific model is necessary, and (iii) the international move is influenced more by the longer timescales of SPEIs while general move (which includes internal move) by that of shorter timescales.

Studies have found serious games to be good tools for programming education. As anoutcome from such research, several game solutions for learning computer programming have appeared. Most of these games are only used in the research field where only a few are published and made available for the public. There are however numerous examples of programming games in commercial stores that have reached a large audience.This article presents a systematic review of publicly available and popular programming games. It analyses which fundamental software development concepts, as defined by theACM/IEEE Computer Science Curricula, are represented in these games and identifies game design patterns used to represent these concepts.This study shows that fundamental programming concepts and programming methods have a good representation in mainstream games. There is however a lack of games addressing data structures, algorithms and design. There is a strong domination of puzzle games. Only two of the 20 studied games belong to a different genre. The eleven game design patterns identified in this study have potential to contribute to future efforts in creating engaging serious games for programming education.

Digitalization is forcing the industry to rethink current practices in all business domains, pushing for a digital transformation of business and operations at a high rate and, thus, paving the way for new business models and making others redundant. For small and medium-sized companies (SME), in particular, it is an enormous challenge to keep up with the pace of technological development. Several initiatives have argued the industry’s need for continuous digitalization, innovation, transformation ability, and future skills and competencies development. However, the advancement of the Swedish industry in this area has been uneven, where larger organizations have begun their digital transformation journey to some extent, but SMEs risk falling behind. In addition to the technological transformation, the challenges regarding the industries’ skills supply need to be solved, where a workforce with the right competencies, knowledge, and skill sets are equally, if not more, important for remaining competitive. One of the key elements to face these challenges in the companies will be to recruit knowledgeable employees or re-skill the existing ones. Efficient access to relevant knowledge and skills is still a major concern for companies that will surely affect their competitiveness for a long time to come. This paper elaborates on the opportunities and challenges that Swedish universities face in the context of lifelong learning and education for industry professionals. The paper presents results and experiences gained from a lifelong learning project for industry professionals at the University of Skövde in collaboration with ten industry partners. The results from the project show that in addition to pedagogical methods, current structures and policies within academia need to be further developed to effectively serve industry professionals. The paper also presents a concept of education for industry professionals in the lifelong learning context based on the results and experience gained from the project.

The COVID-19 pandemic has had a devastating effect on many industries around the world including tourism and policy makers are interested in mapping out what the recovery path will look like. We propose a novel statistical methodology for generating scenario-based probabilistic forecasts based on a large survey of 443 tourism experts and stakeholders. The scenarios map out pessimistic, most-likely and optimistic paths to recovery. Taking advantage of the natural aggregation structure of tourism data due to geographic locations and purposes of travel, we propose combining forecast reconciliation and forecast combinations implemented to historical data to generate robust COVID-free counterfactual forecasts, to contrast against. Our empirical application focuses on Australia, analyzing international arrivals and domestic flows. Both sectors have been severely affected by travel restrictions in the form of international and interstate border closures and regional lockdowns. The two sets of forecasts, allow policy makers to map out the road to recovery and also estimate the expected effect of the pandemic.

Collections of time series formed via aggregation are prevalent in many fields. These are commonly referred to as hierarchical time series and may be constructed cross-sectionally across different variables, temporally by aggregating a single series at different frequencies, or even generalised beyond aggregation as time series that respect linear constraints. When forecasting such time series, a desirable condition is for forecasts to be coherent: to respect the constraints. The past decades have seen substantial growth in this field with the development of reconciliation methods that ensure coherent forecasts and improve forecast accuracy. This paper serves as a comprehensive review of forecast reconciliation and an entry point for researchers and practitioners dealing with hierarchical time series. The scope of the article includes perspectives on forecast reconciliation from machine learning, Bayesian statistics and probabilistic forecasting, as well as applications in economics, energy, tourism, retail demand and demography. 

The aim of this paper is to provide a thinking road-map and a practical guide to researchers and practitioners working on hierarchical forecasting problems. Evaluating the performance of hierarchical forecasts comes with new challenges stemming from both the structure of the hierarchy and the application context. We discuss several relevant dimensions for researchers and analysts: the scale and units of the time series, the issue of intermittency, the forecast horizon, the importance of multiple evaluation windows and the multiple objective decision context. We conclude with a series of practical recommendations. 

Within a community, social relationships are paramount to profile individuals’ conduct. For instance, an individual within a social network might be compelled to embrace a behaviour that his/her companion has recently adopted. Such social attitude is labelled social influence, which assesses the extent by which an individual’s social neighbourhood adopt that individual’s behaviour. We suggest an original approach to influence maximization using a fuzzy-logic based model, which combines influence-weights associated with historical logs of the social network users, and their favourable location in the network. Our approach uses a two-phases process to maximise influence diffusion. First, we harness the complexity of the problem by partitioning the network into significantly-enriched community-structures, which we then use as modules to locate the most influential nodes across the entire network. These key users are determined relatively to a fuzzy-logic based technique that identifies the most influential users, out of which the seed-set candidates to diffuse a behaviour or an innovation are extracted following the allocated budget for the influence campaign. This way to deal with influence propagation in social networks, is different from previous models, which do not compare structural and behavioural attributes among members of the network. The performance results show the validity of the proposed partitioning-approach of a social network into communities, and its contribution to “activate” a higher number of nodes overall. Our experimental study involves both empirical and real contemporary social-networks, whereby a smaller seed set of key users, is shown to scale influence to the high-end compared to some renowned techniques, which employ a larger seed set of key users and yet they influence less nodes in the social network.

Critical infrastructures (CIs) are becoming increasingly sophisticated with embedded cyber-physical systems (CPSs) that provide managerial automation and autonomic controls. Yet these advances expose CI components to new cyber-threats, leading to a chain of dysfunctionalities with catastrophic socio-economical implications. We propose a comprehensive architectural model to support the development of incident management tools that provide situation-awareness and cyber-threats intelligence for CI protection, with a special focus on smart-grid CI. The goal is to unleash forensic data from CPS-based CIs to perform some predictive analytics. In doing so, we use some AI (Artificial Intelligence) paradigms for both data collection, threat detection, and cascade-effects prediction. 

This document reports a technical description of ELVIRA project results obtained as part of Work- package 4.1 entitled “Multi-agent systems for power Grid monitoring”. ELVIRA project is a collaboration between researchers in School of IT at University of Skövde and Combitech Technical Consulting Company in Sweden, with the aim to design, develop and test a testbed simulator for critical infrastructures cybersecurity. This report outlines intelligent approaches that continuously analyze data flows generated by Supervisory Control And Data Acquisition (SCADA) systems, which monitor contemporary power grid infrastructures. However, cybersecurity threats and security mechanisms cannot be analyzed and tested on actual systems, and thus testbed simulators are necessary to assess vulnerabilities and evaluate the infrastructure resilience against cyberattacks. This report suggests an agent-based model to simulate SCADA- like cyber-components behaviour when facing cyber-infection in order to experiment and test intelligent mitigation mechanisms. 

Cutting-edge sensors and devices are increasingly deployed within urban areas to make-up the fabric of transmission control protocol/internet protocol con- nectivity driven by Internet of Things (IoT). This immersion into physical urban environments creates new data streams, which could be exploited to deliver novel cloud-based services. Connected vehicles and road-infrastructure data are leveraged in this article to build applications that alleviate notorious parking and induced traffic-congestion issues. To optimize the utility of parking lots, our proposed SmartPark algorithm employs a discrete Markov-chain model to demystify the future state of a parking lot, by the time a vehicle is expected to reach it. The algorithm features three modular sections. First, a search pro- cess is triggered to identify the expected arrival-time periods to all parking lots in the targeted central business district (CBD) area. This process utilizes smart-pole data streams reporting congestion rates across parking area junc- tions. Then, a predictive analytics phase uses consolidated historical data about past parking dynamics to infer a state-transition matrix, showing the transfor- mation of available spots in a parking lot over short periods of time. Finally, this matrix is projected against similar future seasonal periods to figure out the actual vacancy-expectation of a lot. The performance evaluation over an actual busy CBD area in Stockholm (Sweden) shows increased scalability capa- bilities, when further parking resources are made available, compared to a baseline case algorithm. Using standard urban-mobility simulation packages, the traffic-congestion-aware SmartPark is also shown to minimize the journey duration to the selected parking lot while maximizing the chances to find an available spot at the selected lot.

Accelerating Digital Transformation with the Cloud and the Internet of Things (IoT) is a reference for IT engineers and decision-makers who may engage in IoT platform pilot projects. The resources covered in this book help establish plans for sustainable operations and management and assist with the long-term procurement of relevant IoT technologies. The aim of the book is to be exhaustive and holistic by pointing out numerous issues and related solution options that guide with daily challenges when deploying and running IoT platforms.

The book is divided into three parts where each part includes relevant theoretical chapters and applied case studies. Part One focuses on architectural and federation options for the design and implementation of IoT platforms that foster strategic collaboration opportunities. Part Two addresses vertical security challenges across IoT platform layers. Finally, Part Three shows how IoT is driving the digital transformation wheel through existing and forthcoming case studies.

The concept of Smart Cities is an emerging social and technology innovation, attracting large public and private investments at a global scale, arguing for the effective exploitation of digital technologies to drive quality of living and sustainable growth. However, these investments mainly focus in smart technical infrastructure, and they have yet to be systematically complemented with efforts to prepare the human capital of future smart cities in terms of core competences anticipated for exploiting their potential. In this context, this chapter introduces “cyber-physical learning” as a generic overarching model to cultivate Digital Smart Citizenship competence. The proposed approach exploits the potential of Internet of Things technologies to create authentic blended and augmented learning experiences. Proof-of-concept case studies of the proposed cyber-physical learning approach, to develop smart household energy management competences, are presented and discussed as a field of application. Finally, the findings of a survey with university students for eliciting their attitudes to engage with cyber-physical learning environments for enhancing their digital smart citizenship competences are reported.

Joint pick and place tasks occur in many interpersonal scenarios, such as when two people pick up and pass dishes. Previous studies have demonstrated that low-dimensional models can accurately capture the dynamics of pick and place motor behaviors in a controlled 2D environment. The current study models the dynamics of pick-up and pass decisions within a less restrictive virtual reality mediated 3D joint pick and place task. Findings indicate that reach-normalized distance measures, between participants and objects/targets, could accurately predict pick-up and pass decisions. Findings also reveal that participants took longer to pick-up objects where division of labor boundaries were less obvious and tended to pass in locations maximizing the dyad's efficiency. This study supports the notion that individuals are more likely to engage in interpersonal behavior when a task goal is perceived as difficult or unattainable (i.e., not afforded). Implications of findings for human-artificial agent interactions are discussed. 

Games for health is one of the most prominent areas for serious games, i.e. games with a purpose beyond only entertainment. The purpose of a health game may be to inform about health related issues; promote healthy lifestyles and even to drive behavioral change. This paper outlines the initial game design considerations and some future research directions for a game focusing on cardiovascular health. As the overall aim of the project is to promote a healthy lifestyle through diet and physical activity to prevent future cardiovascular disease, we focused on “taking care of your heart” as the basis for the game. Hence we call the game Happy Heart and use a heart symbol as a non-playable character (NPC) that the player needs to take care of. To some extent we are inspired by the electronic Tamagochi toys (Bandai) where players need to take care of a digital pet. The heart symbol is universal and is also an ideograph that expresses the concept of love and as such it transcends language barriers.

The burden of non-communicable diseases (NCD) is rapidly increasing across the world. Today they are the main drivers of morbidity, disability, and mortality in low- and middle income countries (LMICs), and are expected to increase due to unhealthy lifestyles in the wake of ongoing societal changes [1]. Among the major risk factors in many LMICs are poor diet, insufficient physical activity, tobacco and alcohol consumption, and exposure to hazardous substances, e.g. from air pollution. LMICs currently contribute three quarters of the deaths from NCD.

Among the NCD, cardiovascular diseases are the most common cause of hospitalization in Nepal [1]. Digitalization and collaboration with the education sector (e.g. community schools) in health promotion interventions could further improve children’s behavior by targeting factors that affect their lifestyle outside the family environment [4]. Hence, the Digital Game Based Learning approach.

This report contains a technical description of the result of the S.A.R.E.K (Simulation – Ambulance – Research – Education - Kinship) collaboration project and the Sim2020 project. The projects are collaborations between researchers in healthcare and IT, and prehospital care practitioners, with the aim to design, develop and test a contextualized simulation environment for prehospital care. We built a simulation environment representing the full depth and width of a prehospital care process. Breadth refers to including all phases of a prehospital mission, from dispatch to handover; while depth refers to detailed representations and recreation of artefacts, information and context for each of these phases. This report outlines the details of the overall design, all equipment and practical solutions used to create this.  

Apart from the installation which is described in this report we have also developed methods and carried out a variety of tests and experiments which are reported elsewhere. The focus of this report is the system and its components.

Rapporten bygger på tre delstudier med olika datainsamlingstekniker:

• En bibliometristudie av svensk spelforskning baserad på data från Scopus respektive SwePub.
• En enkätstudie över svenska spelforskningsmiljöer.
• En intervjustudie om svenska spelforskarmiljöer.

Bibliometristudiens ansats har varit bred. Materialet inkluderar därmed även poster som endast har en perifer koppling till spel, men returnerar i fallet SwePub färre än 3000 poster. Spelforskning bedrivs på många lärosäten i Sverige och publiceras inom många ämnesområden. Totalt 95 forskare har en publicering som omfattar 10 eller fler spelpublikationer1. Det finns starka lokala kluster av forskare som sampublicerar men sampubliceringen mellan svenska och nordiska lärosäten är inte så omfattande. Jämfört med Finland har Sverige färre publikationer med ett tydligt fokus på spelforskning.

Enkätstudien visar att spelforskarmiljöerna oftast består av ett antal forskare med olika ämnesmässig bakgrund där de själva definierar sig som spelforskare snarare än att organisationen som sådan gör det. Miljöerna har sammantaget och högt räknat runt 30 aktiva doktorander men siffran är osäker. 94% av respondenterna anger att deras forskning ligger inom ”spel för andra områden än underhållning” och endast 6% anger ”spelteknikfokus”. 44% anger ”spelbranschen”. Det är tydligt att spel för just underhållning inte ligger fokus för svensk spelforskning. Detta är remarkabelt då den svenska datorspelsindustrin är en multimiljardindustri fokuserad på just spel som underhållning. Det finns alltså en mycket stor diskrepans mellan industri och svensk spelforskning.

Intervjustudien ger en bild av att spelforskningen bedrivs inom splittrade miljöer, av forskare med olika ämnesmässig bakgrund. Det finns en tydlig upplevd brist på forskningsfinansiering inom adekvata områden och det finns brister i de finansieringsinstrument som finns idag. Detta påverkar såväl vilken typ av forskning som kan bedrivas inom spel och rekryteringen av forskarstuderande. Respondenterna är överlag mycket positivt inställda till idén om en nationell forskarskola. Flera ser också Svenska Spelforskarrådet som en viktig aktör för att stärka svensk spelforskning både nationellt och internationellt.

Rekommendationer:

• Det bör finnas en tydligare egen hemvist för forskning och utbildning inom spel för att tydliggöra kärnan i området.
• Fokus för forskningen behöver förändras så att spel för underhållning kommer mer i fokus.
• Finansieringsinstrumenten för spelforskning behöver förändras för att möjliggöra en sådan förändring.
• En nationell forskarskola inom spel bör inrättas.
• Det finns anledning att lära av framgångsrika miljöer i Finland och Danmark.

Gamification is the application of game design elements in non-gaming contexts to reach some purposes. It has been proposed as a solution in several domains, such as teaching, corporate training and healthy living. This paper presents the design and evaluation of the skill tree method which is a part of a gamified approach to motivate and support NEET-youth (Not in Education, Employment or Training) to leave their isolation and take steps towards better inclusion in society. The overall approach is based on two principles: 1) it targets adolescents with an interest in games 2); it utilizes their interest in games as the starting point for the intervention. Part of the intervention is based on a skill tree which maps in-game skills to out-of-game skills and visualizes the output so that the participant can become better aware of how their interest can drive personal development towards societal participation. The paper presents an evaluation of the skill tree method from the project staff perspective. The findings are positive but some need for improvements in the method as well as the tool were identified.

This paper aims to analyze international migrations in western African countries using irregular multivariate monthly time series containing a few values. Existing methods of filling in missing values have limitations because there are not enough values to infer them. In this study, we explore two approaches to solve this problem. One approach is to aggregate the values annually to eliminate missing values. The other is to use the Random Forest (RF) based approach to fill in the missing values. Then, we predict the international migration intentions using deep learning approaches and time series dataset. We demonstrate that a RF-based imputation outperforms a zero filling approach (used as the baseline) with Long Short-Term Memory (LSTM) method. Moreover, we show that analyzing the monthly subregion-based time series provides better insights than the yearly country-based time series. 

Climate change has impacted global temperatures, triggering extreme weather and adverse environmental effects. In Sweden, these changes have caused shifts in weather patterns, leading to disruptions in infrastructure. This, in turn, has influenced water turbidity levels, negatively impacting water quality. To tackle these issues, a study was conducted using machine learning to predict turbidity with six meteorological variables collected for two years. Our preliminary research showed a substantial influence of seasonal changes on water turbidity, especially air temperature. Identifying supporting indicators such as lagged features is crucial and considerably improved the turbidity prediction performance for two of the machine learning models used. However, the study also identified challenges like data collection and uncertainty issues. We recommend improving data collection quality with higher frequency, minimizing geographical gaps between data collection points, sharing calibration assumptions, checking the sensors regularly, and accounting for data anomalies. Understanding these challenges and their potential implications could lead to more methodological enhancements.

In this survey, 105 papers related to interactive clustering were reviewed according to seven perspectives: (1) on what level is the interaction happening, (2) which interactive operations are involved, (3) how user feedback is incorporated, (4) how interactive clustering is evaluated, (5) which data and (6) which clustering methods have been used, and (7) what outlined challenges there are. This article serves as a comprehensive overview of the field and outlines the state of the art within the area as well as identifies challenges and future research needs.

The steel-making process in a Basic Oxygen Furnace (BOF) must meet a combination of target values such as the final melt temperature and upper limits of the carbon and phosphorus content of the final melt with minimum material loss. An optimal blow end time (cut-off point), where these targets are met, often relies on the experience and skill of the operators who control the process, using both collected sensor readings and an implicit understanding of how the process develops. If the precision of hitting the optimal cut-off point can be improved, this immediately increases productivity as well as material and energy efficiency, thus decreasing environmental impact and cost. We examine the usage of standard machine learning models to predict the end-point targets using a full production dataset. Various causes of prediction uncertainty are explored and isolated using a combination of raw data and engineered features. In this study, we reach robust temperature, carbon, and phosphorus prediction hit rates of 88, 92, and 89 pct, respectively, using a large production dataset. © 2020, The Author(s).

The problem of using machine learning (ML) to predict the process endpoint for a Basic Oxygen Furnace (BOF) process used for steelmaking has been largely studied. However, current research often lacks both the usage of a rich dataset and does not address revealing influential factors that explain the process. The process is complex and difficult to control and has a multi-objective target endpoint with a proper range of heat temperature combined with sufficiently low levels of carbon and phosphorus. Reaching this endpoint requires skilled process operators, who are manually controlling the heat throughout the process by using both implicit and explicit control variables in their decisions. Trained ML models can reach good BOF target prediction results, but it is still a challenge to extract the influential factors that are significant to the ML prediction accuracy. Thus, it becomes a challenge to explain and validate an ML prediction model that claims to capture the process well. This paper makes use of a complex and full production dataset to evaluate and compare different approaches for understanding how the data can determine the process target prediction. One approach is based on the collected process data and the other on the ML approach trained on that data to find the influential factors. These complementary approaches aim to explain the BOF process to reveal actionable information on how to improve process control.

Non-additive measures generalize additive measures, and have been utilized in several applications. They are used to represent different types of uncertainty and also to represent importance in data aggregation. As non-additive measures are set functions, the number of values to be considered grows exponentially. This makes difficult their definition but also their interpretation and understanding. In order to support understability, this paper explores the topic of visualizing discrete non-additive measures using node-link diagram representations.

Today non-communicable diseases (NCD) are the main drivers of morbidity, disability, and mortality in low- and middle-income countries, and are expected to increase due to unhealthy lifestyles in the wake of ongoing societal changes. In Nepal, heart disease is the most common NCD, causing a majority of the country’s hospitalizations.

Previous research found that this issue stems from a lack of information regarding nutrition and healthy activities. Our project initially mapped out the details of this knowledge gap by producing a Knowledge, Attitude, and Practice report based on field studies conducted in Nepal. Based on this report, we have created a mobile serious game - Happy Heart - that aims to fill the identified gaps by providing information, but also by presenting it in a way that we hope changes peoples’ attitudes towards healthy lifestyles. The game presents four main challenges: identifying and categorizing food items based on nutrition, preparing healthy meals, and planning a healthy weekly calendar.

The project is a collaboration between the University of Skövde (Sweden), Kathmandu Medical College, and Patan Academy of Health Sciences (Nepal).

In serious game development, graphic design needs to be eye-catching, while also depicting subject matter content in a responsible, accurate, and clear way. Previous research has shown that abstract and symbolic game visuals seem to be preferable for learning and providing an engaging experience. Our research focuses on describing the challenges involved in creating effective visual communication through game graphics in cross cultures. In particular, we’re interested in examining if certain styles of visual communication are more or less effective between different cultural demographics. To examine this, we have created a serious game which aims to promote healthy food and nutrition habits to teenagers in both Nepalese and Swedish schools and by doing so also motivate behavioral changes toward healthier eating habits. We are currently conducting studies to see whether preferences and image recognition differ between the two demographical spheres. This paper will only discuss the exploratory study done in Nepal. Ultimately, this paper aims to contribute development guidelines that can aid developers in creating more effective visual communication in their serious games, and we primarily focus on exploring what we call the compromise of ‘clarity’ and ‘appeal’ in the creation of game graphics. We present an initial model for choosing at what level in terms of realism/abstraction and taxonomic hierarchy the graphical components of serious games optimally should be produced in order to solve the dilemma of precise, unmistakable, yet appealing visuals in serious games. It all comes down to two primary decisions: defining the taxonomic hierarchy of the items to depict, and choosing the style in which to depict them. With a better understanding of when different game visuals are more or less appropriate, both in terms of style and in which objects are represented, game developers will be able to balance production costs better while also creating something that strikes the compromise between clarity and appeal.

In serious game development, effective communication through both languages, sounds, and icons can be crucial for a game to have its intended impact. While this is also true for entertainment games, serious games have added layers of challenges as they are a) often played by audiences outside of the “typical” game ecosystem, and b) miscommunication can lead to players missing important lessons or even learning incorrect information. When a serious game is intended to be used in different parts of the world, however, clear visual communication gets an added layer of complexity: culturally informed symbol interpretation and visual preference. In order to examine how these might affect players’ experiences when playing serious games, this paper presents the results of a mixed-method study conducted in two schools in Nepal. The study included 10 participants, between 13-16 years old, who played a prototype of a mobile game currently in development, which has the purpose of teaching young players about food nutrition and healthy habits. After playing the prototype, they took a short survey where they were asked to identify different food types, and they were also interviewed to discuss their opinions of the game’s visual style. The results of the study indicate that, while higher fidelity images were much easier to correctly identify by the participants, the participants’ preference for visual fidelity varied to a large degree.

The visuals of a game is a crucial element when it comes to providing good player experiences. Visuals are also an incredibly complex subject in a game context since different modes of visual representation can be more or less “appropriate” for different settings. For example, while one can look at photo-realism as an impressive feature of a game due to its technical complexity and functionally accurate representation of real-world objects, it might still not be a fitting choice for different audiences, or for different pedagogical strategies. Serious game research seldom focuses on understanding the design of these components or their applicability to different types of learning, and it more often focuses on games’ mechanics and how well they manage to capture subject matter content while still being engaging. The aim of this paper is to explore the gap in visual communication research, describing what studies tend to focus on providing some valuable context. This review was conducted on papers that dealt with visual aspects of serious games. The results show that visual communication is rarely addressed in serious game development research. Future research would benefit from taking visual communication in detail to facilitate the effectiveness of serious games.

Urban planning is a complex process that involves many different stakeholders and has a very long time frame. The United Nations' Declaration of the Rights of the Child states that children should be given the opportunity to express their views and that these should be respected. The complexity of the urban planning process poses challenges on how to involve children. This article presents Stadsbyggarna - a board game designed with the explicit goal to help children understand the nature of urban planning. It has been used in citizen dialog in the development of a new 30-year city plan in a mid-sized Swedish city. Nineteen school classes in the municipality have played the game. The result shows that the gameplay encourage urban planning discussions. Role-playing is identified as a key element of the game. The digital component initially planned to be included in the gameplay was however found to be superfluous.