The COVID-19 pandemic forced a transition to digital teaching in higher education institutions (HEIs) as it was the only safe method for higher education (HE) teaching during the pandemic. However, this crisis emphasized the barriers students face worldwide. For digital HE teaching to survive in the future, these barriers should be overcome. The present paper aimed to systematically identify these barriers and present recommendations to overcome them. For this purpose, a quantitative survey (n = 369) was conducted with students in three countries, and qualitative student statements were analyzed. Possible countermeasures for corresponding barriers are described, and related stakeholders are identified. Thus, the study provided an overview of recommendations for stakeholders to overcome the barriers. The recommendations to resolve most barriers entail offering hybrid formats, adjusting lecture design, and ensuring proper communication.

AbstractIn Sweden as well as internationally the teaching and research nexus has been described as the defining charac-teristics of higher education promoting generic skills such as information analysis and critical reflection. Vertically Integrated Projects has been proposed as one educational strategy where research and teaching are linked by in-viting students to take active part in actual research projects. The strategy is well aligned to Scholarship of teaching and learning enabling the transition from a teacher-centred accepted knowledge to a student-centred perspective where students are invited as producers of knowledge. The aim of the current study was to explore students’ experiences of participation in a research-based learning activity with academia and industrial partners, designed as a qualitative explorative study using focus group interviews. Findings describe not only factors students find motivating for learning, but also their experience of being part of professional life with its benefits and challenges.

During the COVID-19 pandemic, the rapid transition to digital teaching enabled higher education institutions (HEIs) to continue teaching. The strict execution exposed barriers that both teachers and students have faced towards digital higher education teaching around the world. In this paper, the barriers from both perspectives are identified and systematically processed. For this purpose, a quantitative survey of 396 students from HEIs in Sweden, Türkiye, and Germany was conducted. The students' barriers were identified and assigned to categories based on teachers' barrier categories, which were analyzed in a pre-study by conducting a literature review. The teachers' barrier categories could be confirmed by the students' survey. However, within the subcategories, the two perspectives differ. All categories and subcategories are described in detail so that this contribution offers an overview of barriers that have to be overcome if digital higher education teaching will be followed in the future. © 2022 Gesellschaft fur Informatik (GI). All rights reserved.

Artificial Intelligence (AI) engineering is an engineering discipline that is concerned with all aspects of development and evolution of AI systems (that is, systems that include AI components). AI engineering is primarily an extension of software engineering, but it also includes methods and technologies from data science and AI in general.

Games are complex, multi-faceted systems that share common elements and underlying narratives, such as the conflict between a hero and a big bad enemy or pursuing a goal that requires overcoming challenges. However, identifying and describing these elements together is non-trivial as they might differ in certain properties and how players might encounter the narratives. Likewise, generating narratives also pose difficulties when encoding, interpreting, and evaluating them. To address this, we present TropeTwist, a trope-based system that can describe narrative structures in games in a more abstract and generic level, allowing the definition of games’ narrative structures and their generation using interconnected tropes, called narrative graphs. To demonstrate the system, we represent the narrative structure of three different games. We use MAP-Elites to generate and evaluate novel quality-diverse narrative graphs encoded as graph grammars, using these three hand-made narrative structures as targets. Both hand-made and generated narrative graphs are evaluated based on their coherence and interestingness, which are improved through evolution.  

Narratives are a predominant part of games, and their design poses challenges when identifying, encoding, interpreting, evaluating, and generating them. One way to address this would be to approach narrative design in a more abstract layer, such as narrative structures. This paper presents Story Designer, a mixed-initiative co-creative narrative structure tool built on top of the Evolutionary Dungeon Designer (EDD) that uses tropes, narrative conventions found across many media types, to design these structures. Story Designer uses tropes as building blocks for narrative designers to compose complete narrative structures by interconnecting them in graph structures called narrative graphs. Our mixed-initiative approach lets designers manually create their narrative graphs and feeds an underlying evolutionary algorithm with those, creating quality-diverse suggestions using MAP-Elites. Suggestions are visually represented for designers to compare and evaluate and can then be incorporated into the design for further manual editions. At the same time, we use the levels designed within EDD as constraints for the narrative structure, intertwining both level design and narrative. We evaluate the impact of these constraints and the system’s adaptability and expressiveness, resulting in a potential tool to create narrative structures combining level design aspects with narrative.  

I propose and discuss some principles that I believe are substantial for percep- tion, various kinds of memory, expectations and the capacity for imagination in the mammal brain, as well as for the design of a biologically inspired artificial cognitive architecture. I also suggest why these same principles could explain our ability to represent novel concepts and imagine non-existing and perhaps impossible objects, while there are still limits to what we can imagine and think about. Some ideas re- garding how these principles could be relevant for an autonomous agent to become functionally conscious are discussed as well.

I propose a number of principles that I believe are substantial for various faculties of the mammalian brain, such as perception, expectations, imagery, and memory. The same principles are also of interest when designing an artificial but biologically inspired cognitive architecture. Moreover, I discuss how the same principles may lie behind the ability to represent new concepts and to imagine fictitious and impossible objects, while also giving us reasons to believe that there are limits to our imagination and to what it is possible for us to think about. Some ideas regarding how these principles could be relevant for an autonomous agent to become functionally conscious are discussed as well.

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.

This paper explores the role of anticipated futures of automation in public administration. Engaging with ethnographic research with stakeholders from roughly ten municipalities in Sweden, this paper examines the tension between different ways of imagining automated work futures and the extent to which they are associated with (or not) technologies. Automating data-driven processes is believed to alleviate administrative drudgery and support a goal-driven, efficient public sector. Various stakeholders participate in the implementation of automation systems, including corporate actors, managers, politicians, and civil servants. This group of stakeholders has diverse perspectives and expectations regarding the future of work automation and its role in the organisation of public services. Some see automated work processes as a way to boost efficiency, productivity, and precision through algorithmic data processing; others, however, see them as ways to allow professionals to spend less time on repetitive, rule-based, and seemingly tedious tasks, so that they can focus on their core professional practice. Challenging established narratives about work automation, this paper suggests how automation can be used to visualise, think about, and communicate organisational change without involving any technology per se, but rather as an empty signifier to which future-making practices can be affixed and legitimised. By emphasising social expectations and experiences, the paper interrogates emerging automated work futures in ways that move beyond techno-optimism and economic-political goals of efficiency and optimisation, not the least by showing that automation is situated, social and contingent.

A major concern in the use of Internet of Things (IoT) technologies in general is their reliability in the presence of security threats and cyberattacks. Particularly, there is a growing recognition that IoT environments featuring virtual sensing and interactive machine learning may be subject to additional vulnerabilities when compared to traditional networks and classical batch learning settings. Partly, this is as adversaries could more easily manipulate the user feedback channel with malicious content. To this end, we propose a data-centric anomaly-based detection system, based on machine learning, that facilitates the process of identifying anomalies, particularly those related to poisoning integrity attacks targeting the user feedback channel of interactive machine learning setups. We demonstrate the capabilities of the proposed system in a case study involving a smart campus setup consisting of different smart devices, namely, a smart camera, a climate sensmitter, smart lighting, a smart phone, and a user feedback channel over which users could furnish labels to improve detection of correct system states, namely, activity types happening inside a room. Our results indicate that anomalies targeting the user feedback channel can be accurately detected at 98% using the Random Forest classifier.

Collaborating with public or private sector organizations in project-based courses equips design students with key skills to future-proof their careers, but this gives only one side of the story as the key feature of these partnerships is that they are mutualistic collaborations. However, the benefits to organizations of collaborating are not fully explored. This paper presents a case study of partnerships with four different public organizations in a service design course over a five-year period. It argues that collaborating in project-based courses serves as risk-free experimentation and paves the way for organizational learning. The paper first reviews the existing research on collaboration in design education and organizational learning. Then, three types of learning that emerged from the data are analyzed. Next, the steps to successful collaboration are discussed, noting the ups and downs of managing the project partnerships. Finally, the challenges of teaching a collaborative project-based design course are discussed.

Digital Transformation is a ubiquitous process envisioned to enhance operations, business models, and customer contact. Practically, barriers stand in the way of these enhancements. In a Master’s course on Business Informatics, a research-based learning design was used to research these barriers. Furthermore, the research-based learning design was expected to generate a positive student engagement, as the course had to be adopted to Covid-19 teaching policies. Results of the data generated throughout the course show a high explanation power of the applied barrier model as the adjusted R² is 0,648. In the evaluation of the course design, students perceive the effects of the research-based design on their understanding and their critical thinking as excellent.

Immediate treatment is of extreme importance for stroke patients. However, providing fast enough treatment for stroke patients is far from trivial, mainly due to logistical challenges and difficulties in diagnosing the correct stroke type. One way to reduce the time to treatment is to use so-called Mobile Stroke Units (MSUs), which allows to diagnose and provide treatment for stroke patients already at the patient scene. A well-designed stroke transport policy is vital to improve the access to treatment for stroke patients. Simulation and mathematical optimization are useful approaches for assessing and optimizing stroke transport policies, without endangering the health of the patients.

The main purpose of this thesis is to contribute to improving the situation for stroke patients and to reducing the social impacts of stroke. The aim is to study how to use simulation and optimization to achieve improved analysis and planning of prehospital stroke care. In particular, we focus on assessing the potential use of MSUs in a geographic area. In this thesis, optimization is used to identify the optimal locations of MSUs, and simulation is used to assess different stroke transport policies, including MSU locations. The results of this thesis aim to support public health authorities when making decisions in the prehospital stroke care domain.

In order to fulfill the aim of this thesis, we develop and analyze a number of different simulation and optimization models. First, we propose a macro-level simulation model, an average time to treatment estimation model, used to estimate the expected time to treatment for different parts of a geographic region. Using the proposed model, we generate two different MSU scenarios to explore the potential benefits of employing MSUs in Sweden’s southern healthcare region (SHR).  

Second, we present an optimization model to identify the best placement of MSUs while making a trade-off between the efficiency and equity perspectives, providing maximum population coverage and equal service for all patients, respectively. The trade-off function used in the model makes use of the concepts of weighted average time to treatment to model efficiency and the time difference between the expected time to treatment for different geographical areas to model equity. In a scenario study applied in the SHR, we evaluate our optimization model by comparing the current situation with three MSU scenarios, including 1, 2, and 3 MSUs.

Third, we present a micro-level discrete event simulation model to assess stroke transport policies, including MSUs, allowing us to model the behaviors of individual entities, such as patients and emergency vehicles, over time. We generate a synthetic set of stroke patients using a Poisson distribution, used as input in a scenario study.

Finally, we present a modeling framework with reusable components, which aims to facilitate the construction of discrete event simulation models in the emergency medical services domain. The framework consists of a number of generic activities, which can be used to represent healthcare chains modeled in the form of flowcharts. As the framework includes activities and policies modeled on the general level, the framework can be used to create models only by providing input data and a care chain specification. We evaluate the framework by using it to build a model for simulating EMS activities related to the complex case of acute stroke.

In many countries the news media landscape has changed significantly in recent decades, particularly the locally based media have decreased in number or become more geographically remote. Hyperlocal media – locally based, community- oriented news with a high level of audience-participation possibilities – has been proposed as a potential replacement for established media that are quickly moving away from recognized norms, but have demonstrated a variety of possibilities as well as challenges in terms of structure, resources, and sustainability. Based on an extensive mapping of the Swedish news media landscape, this study traces the consistency and change of legacy and alternative media over the period 2016-2021. The aim is to make sense of the declining stability, the emergence of “news deserts'', and alternative forms of news media, by focusing on the structuring forces behind hyperlocal operations.

The results show a transforming media landscape, with a reduced presence of legacy media, and a variety of hyperlocal news operations; from economically viable, small-scale organizations to idealistic individual projects, based on public, non-profit models. The restructuring of the landscape does not turn out to be so much about growing news deserts, as a thinning of news scrutiny, where legacy and hyperlocal media go in different directions in terms of business strategies: Where legacy media consolidates, the hyperlocals lean on idealism and localism. While the latter sometimes can be described as poor business skills, they can also be viewed as low-risk strategies, enabling trial and error and organic growth. Yet, highly personal and locally anchored rationales behind many hyperlocal initiatives make them challenging from a sustainability perspective. The conclusion is that the transforming media landscape can be described as a form of mutually related restructuring, where established and alternative media coexist in new ways. 

INTRODUCTION: Supported self-management empowering people with asthma to detect early deterioration and take timely action reduces the risk of asthma attacks. Smartphones and smart monitoring devices coupled with machine learning could enhance self-management by predicting asthma attacks and providing tailored feedback.We aim to develop and assess the feasibility of an asthma attack predictor system based on data collected from a range of smart devices.

METHODS AND ANALYSIS: A two-phase, 7-month observational study to collect data about asthma status using three smart monitoring devices, and daily symptom questionnaires. We will recruit up to 100 people via social media and from a severe asthma clinic, who are at risk of attacks and who use a pressurised metered dose relief inhaler (that fits the smart inhaler device).Following a preliminary month of daily symptom questionnaires, 30 participants able to comply with regular monitoring will complete 6 months of using smart devices (smart peak flow meter, smart inhaler and smartwatch) and daily questionnaires to monitor asthma status. The feasibility of this monitoring will be measured by the percentage of task completion. The occurrence of asthma attacks (definition: American Thoracic Society/European Respiratory Society Task Force 2009) will be detected by self-reported use (or increased use) of oral corticosteroids. Monitoring data will be analysed to identify predictors of asthma attacks. At the end of the monitoring, we will assess users' perspectives on acceptability and utility of the system with an exit questionnaire.

ETHICS AND DISSEMINATION: Ethics approval was provided by the East of England - Cambridge Central Research Ethics Committee. IRAS project ID: 285 505 with governance approval from ACCORD (Academic and Clinical Central Office for Research and Development), project number: AC20145. The study sponsor is ACCORD, the University of Edinburgh.Results will be reported through peer-reviewed publications, abstracts and conference posters. Public dissemination will be centred around blogs and social media from the Asthma UK network and shared with study participants.

Supporting customers after they acquire the product is essential for companies producing and selling software-intensive embedded systems products. Generally, customer support is the first interaction point between the product users and the product vendor. Customer support is often engaged with answering customers' questions, troubleshooting, fault identification, and fixing product faults. While continuous deployment advocates for closer cooperation between the ones operating the software and the ones developing it, the means of such collaboration in general and the role of customer support, in particular, has not been addressed in the context of software-intensive embedded systems. Therefore, to better understand the impact that continuous deployment has on customer support and the role customer support should play in this context, we conducted a case study at a multinational company developing and selling telecommunications networks infrastructure. We focused on the 4th and 5th Generation (4G and 5G) Radio Access Networks (RAN) products, which can be considered a high volume product as they cover more than 80% of the world's population. Our study reveals that customer support needs to transition from a transaction-based and passive function triggered by customer support requests, to take an active role characterized by being proactive and preemptive to cope with the shorter operational time of a software version introduced by continuous deployment. In addition, customer support plays an essential role in making the feedback actionable by aggregating and consolidating feedback data to the R&D organization.

AI in the Engineering, Procurement and Construction (EPC) industry has not yet a proven track record in large-scale projects. Since AI solutions for industrial applications became available only recently, deployment experience and lessons learned are still to be built up. Several research papers exist describing the potential of AI, and many surveys and white papers have been published indicating the challenges of AI deployment in the EPC industry. However, there is a recognizable shortage of in-depth studies of deployment experience in academic literature, particularly those focusing on the experiences of EPC companies involved in large-scale project execution with high safety standards, such as the petrochemical or energy sector. The novelty of this research is that we explore in detail the challenges and obstacles faced in developing and deploying AI in a large-scale project in the EPC industry based on real-life use cases performed in an EPC company. Those identified challenges are not linked to specific technology or a company's know-how and, therefore, are universal. The findings in this paper aim to provide feedback to academia to reduce the gap between research and practice experience. They also help reveal the hidden stones when implementing AI solutions in the industry.

Internet of Things (IoT) systems are complex systems that can manage mission-critical, costly operations or the collection, storage, and processing of sensitive data. Therefore, security represents a primary concern that should be considered when engineering IoT systems. Additionally, several challenges need to be addressed, including the following ones. IoT systems' environments are dynamic and uncertain. For instance, IoT devices can be mobile or might run out of batteries, so they can become suddenly unavailable. To cope with such environments, IoT systems can be engineered as goal-driven and self-adaptive systems. A goal-driven IoT system is composed of a dynamic set of IoT devices and services that temporarily connect and cooperate to achieve a specific goal. Several approaches have been proposed to engineer goal-driven and self-adaptive IoT systems. However, none of the existing approaches enable goal-driven IoT systems to automatically detect security threats and autonomously adapt to mitigate them. Toward bridging these gaps, this paper proposes a distributed architectural Approach for engineering goal-driven IoT Systems that can autonomously SElf-adapt to secuRity Threats in their environments (ASSERT). ASSERT exploits techniques and adopts notions, such as agents, federated learning, feedback loops, and blockchain, for maintaining the systems' security and enhancing the trustworthiness of the adaptations they perform. The results of the experiments that we conducted to validate the approach's feasibility show that it performs and scales well when detecting security threats, performing autonomous security adaptations to mitigate the threats and enabling systems' constituents to learn about security threats in their environments collaboratively.

Sparrow Search Algorithm (SSA) is a kind of novel swarm intelligence algorithm, which has been applied in-to various domains because of its unique characteristics, such as strong global search capability, few adjustable parameters, and a clear structure. However, the SSA still has some inherent weaknesses that hinder its further development, such as poor population diversity, weak local searchability, and falling into local optima easily. This manuscript proposes an improved chaos sparrow search optimization algorithm (ICSSOA) to overcome the mentioned shortcomings of the standard SSA. Firstly, the Cubic chaos mapping is introduced to increase the population diversity in the initialization stage. Then, an adaptive weight is employed to automatically adjust the search step for balancing the global search performance and the local search capability in different phases. Finally, a hybrid strategy of Levy flight and reverse learning is presented to perturb the position of individuals in the population according to the random strategy, and a greedy strategy is utilized to select individuals with higher fitness values to decrease the possibility of falling into the local optimum. The experiments are divided into two modules. The former investigates the performance of the proposed approach through 20 benchmark functions optimization using the ICSSOA, standard SSA, and other four SSA variants. In the latter experiment, the selected 20 functions are also optimized by the ICSSOA and other classic swarm intelligence algorithms, namely ACO, PSO, GWO, and WOA. Experimental results and corresponding statistical analysis revealed that only one function optimization test using the ICSSOA was slightly lower than the CSSOA and the WOA among the 20-function optimization. In most cases, the values for both accuracy and convergence speed are higher than other algorithms. The results also indicate that the ICSSOA has an outstanding ability to jump out of the local optimum.

Museums have always used different media to communicate, widen perspectives and bring new knowledge, but in the era of digital media, their various offerings are increasingly part of the media ecosystem. Our research interventions explored the possibility of reusing existing digitised material in a participatory setting. The aim was to explore the object-centred audience participatory method in digital settings. We held a series of digital and in-person workshops that invited the participants to “imagine” narratives about the provenance of the museum’s objects and journeys to Sweden in a playful and creative exploration. We could observe how the virtual workshop setting supported focused discussions, and allowed zooming, drawing and remixing of digital photographs to facilitate conversation. The workshop participants on-site worked with the museum objects on display to remediate them through photos, drawings, clay modelling, and writing down thoughts and questions about the objects on discussion postcards. The participants’ contributions were included in the virtual collection database (Carlotta), under the same collection as the other museum objects, making the remediation process circular. We argue that object-centred methods enable audience participation in digital media ecosystems both in museums and with other media makers. The audience’s expectations and experiences from using other media bring them to the digital museum platforms with a willingness to explore, remix and integrate.

Multimodal journey planners are used worldwide to support travelers in planning and executing their journeys. Generated travel plans usually involve local mobility service providers, consider some travelers' preferences, and provide travelers information about the routes' current status and expected delays. However, those planners cannot fully consider the special situations of individual cities when providing travel planning services. Specifically, authorities of different cities might define customizable regulations or constraints of movements in the cities (e.g., due to construction works or pandemics). Moreover, with the transformation of traditional cities into smart cities, travel planners could leverage advanced monitoring features. Finally, most planners do not consider relevant information impacting travel plans, for instance, information that might be provided by travelers (e.g., a crowded square) or by mobility service providers (e.g., changing the timetable of a bus). To address the aforementioned shortcomings, in this paper, we propose ROUTE, a framework for customizable smart mobility planners that better serve the needs of travelers, local authorities, and mobility service providers in the dynamic ecosystem of smart cities. ROUTE is composed of an architecture, a process, and a prototype developed to validate the feasibility of the framework. Experiments' results show that the framework scales well in both centralized and distributed deployment settings.

The outbreak of COVID-19 has affected people all around the world. Governments had no choice but to put people in self-isolation to stop the spread of the virus. As a result, all companies and educational institutions switched to working or studying from home. The purpose of the study is to investigate the impact of COVID-19 on student teaching and learning in the context of Malmo university. Furthermore, the study proposes recommendations for sustainable post-pandemic education at Malmo University. The study includes ten semi-structured interviews with students followed by a workshop with ten senior lecturers teaching bachelor's and master's courses. The study uses snowball sampling to select students for the interviews and senior lecturers for the workshop. A qualitative data analysis technique, thematic analysis, is used for data analysis on the data collected from interviews with students and the workshop with senior lecturers. The results from the study suggested that online education leads to several benefits for students, such as better time management, higher lecture attendance, flexibility, and discipline in their studies. However, the shift to online education has caused a communication deterioration between students and teachers. Less social interaction with other students leads to depression, anxiety, and stress. The recommendations for post-pandemic education include the unified selection of digital learning tools across courses, a designated budget for digital learning tools, training support, and hybrid learning methods. In conclusion, the study proposes blended and hybrid learning to improve higher education at the university, requiring digital tools to minimize students' communication barriers.

Concepts are powerful human mental representations used to explain, reason and understand. In this work, we use theories on concepts as an analytical lens to compare internal knowledge representations in neural networks to human concepts. In two image classification studies we find an unclear alignment between these, but more pronounced, we find the need to further develop explanation methods that incorporate concept ontologies. 

Current heavy vehicles are equipped with hundreds of sensors that are used to continuously collect data in motion. The logged data enables researchers and industries to address three main transportation issues related to performance (e.g. fuel consumption, breakdown), environment (e.g., emission reduction), and safety (e.g. reducing vehicle accidents and incidents during maintenance activities). While according to the American Transportation Research Institute (ATRI), the operational cost of heavy vehicles is around 59%59% of overall costs, there are limited studies demonstrating the specific impacts of external factors (e.g. weather and road conditions, driver behavior) on vehicle performance. In this work, vehicle usage modeling was studied based on time to determine the different usage styles of vehicles and how they can affect vehicle performance. An ensemble clustering approach was developed to extract vehicle usage patterns and vehicle performance taking into consideration logged vehicle data (LVD) over time. Analysis results showed a strong correlation between driver behavior and vehicle performance that would require further investigation.

Game design is a hard and multi-faceted task that intertwines different gameplay mechanics, audio, level, graphic, and narrative facets. Games' facets are developed in conjunction with others with a common goal that makes games coherent and interesting. These combinations result in plenty of games in diverse genres, which usually require a collaboration of a diverse group of designers. Collaborators can take different roles and support each other with their strengths resulting in games with unique characteristics. The multi-faceted nature of games and their collaborative properties and requirements make it an exciting task to use Artificial Intelligence (AI). The generation of these facets together requires a holistic approach, which is one of the most challenging tasks within computational creativity. Given the collaborative aspect of games, this thesis approaches their generation through Human-AI collaboration, specifically using a mixed-initiative co-creative (MI-CC) paradigm. This paradigm creates an interactive and collaborative scenario that leverages AI and human strengths with an alternating and proactive initiative to approach a task. However, this paradigm introduces several challenges, such as Human and AI goal alignment or competing properties.

In this thesis, game design and the generation of game facets by themselves and intertwined are explored through Human-AI collaboration. The AI takes a colleague's role with the designer, arising multiple dynamics, challenges, and opportunities. The main hypothesis is that AI can be incorporated into systems as a collaborator, enhancing design tools, fostering human creativity, and reducing workload. The challenges and opportunities that arise from this are explored, discussed, and approached throughout the thesis. As a result, multiple approaches and methods such as quality-diversity algorithms and designer modeling are proposed to generate game facets in tandem with humans, create a better workflow, enhance the interaction, and establish adaptive experiences.

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.

Digital media conditions shape new forms of reading. We read on a daily basis on various digital platforms: we read books, we search for information while reading on screens, we use apps on our smartphones, and we read audiobooks. A number of these practices challenge the former print-biased definitions of reading to which we are accustomed, and foreground intermedial practices of aesthetic works. They also challenge how we understand the sensory input that is activated while we read such works and in what contexts such reading takes place. The aim of this paper is to present our analysis of  these extended practises of reading in a digital landscape by proposing reading as a travelling concept (Bal 2002) that moves across different media contexts and moves inbetween disciplinary concerns. Central to our analysis is the material and intermedial interplay between medium and material affordances which in turn shapes the reading experience (Hayles 2005). 

By bringing selected research fields and contributions regarding reading into dialogue with each other, we will exemplify what we see are common scholarly issues when analyzing digital reading today, specifically the multisensory address inherent in many digital texts: we are invited to touch, listen, watch, possibly take part in movement and interaction, look at images and text, listen to the timbre of voices of an audiobook reading and so forth. These elements must, we argue, play a larger role when analyzing these distinctly digital reading conditions (reimagining Jerome McGann’s 1991 analyses of the textual condition). In this paper, through analyses of digital reading situations in Tender Claws Pry (2014) and Sally Rooney’s Beautiful World, Where Are You (2021) read by Aoife MaMahon that challenge the print bias that is still the foundation of the reading concept, we explore the assumptions and value judgments that imbue the concept of reading. 

In the Nordic countries, which are known for their expansive welfare systems, public administration is increasingly being viewed as a potential automation site. Data-driven process automation is believed to alleviate administrative drudgery and support a goal-driven, efficient public sector. In the implementation of automation systems, a variety of stakeholders participate, including corporate actors, managers, consultants, politicians and civil servants. As stakeholders, these groups have diverse perspectives and expectations regarding the anticipated future of automation and its role in the organization of public services. The idea of automated work processes is accepted by some as a way to boost efficiency, productivity, and precision through algorithmic data processing; others, however, see such systems as ways to let professionals spend less time on repetitive, rule-based, and seemingly tedious tasks so that they can focus on their core professional practice. This paper engages with the tensions between the different ways of imagining automatized futures by reporting from ethnographic research with stakeholders from roughly ten municipalities in Sweden, as well as digital technographic studies of two world- leading work automation platforms. Using the sociology of expectations as a theoretical framework to illuminate these tensions, this paper explores the role of anticipated automation when organizations imagine change. The paper shows that automation can be used to help envision, think about, and communicate organizational change without involving any technology, but rather as a metaphor to which future-making practices can be attached and legitimized. Work automation is understood in the paper as fundamentally social and structured as well as affected by expectations regarding future benefits and potential risks. By explicitly paying attention to expectations and experiences, the paper allows for an interrogation of emerging automated work futures that goes beyond techno- optimism and economic-political goals of efficiency and optimisation, not the least by illustrating work automation’s situated and contingent nature.

How can we, as social scientists, make sense of the promises and implications of automated and data-driven systems that are becoming increasingly ubiquitous and essential for the Nordic welfare states? What are the theoretical and methodological tensions and possibilities that these systems pose to research when they assemble and disassemble existing structures, organisational logics and dependencies?

Over the last few years, critical social science research has established that data harvesting and digital tracking, in particular, pose a general societal challenge that risks undermining Nordic values of autonomy and equity and the overall welfare of people. At the same time, the welfare state and welfare provision are increasingly characterised by processes of datafication, promoting uses of data analytics and automated decision-making (ADM). Researchers have flagged datafication as a specific concern for the public sector in relation to questions of ADM systems, and other forms of data-driven optimization. Despite the burgeoning literature on various concerns and the ethical guidelines and regulatory initiatives that try to respond to them, however, we have engaged so far with a limited range of theoretical and methodological approaches to explore the social dynamics at play in concrete contexts of ADM.

This roundtable brings together key scholars that engage critically with the social aims and implications of datafication to address how ADM is imagined, practised and experienced in different empirical contexts and across various organisational levels in the Nordics. The roundtable will open with short ’provocations’ through which the speakers present and contextualise concepts they have used or would like to promote in the study of emerging automated and data-driven systems. The provocations are followed by a joint discussion about how these concepts can support sociological research that studies the promises and implications of automated and data-driven systems as part of the myths and realities of the Nordic welfare states, now and in the future.

In Mixed-Initiative Co-Creative tools, the human is mostly in control of what will and can be created, delegating the AI to a more suggestive role instead of a colleague in the co-creative process. Allowing more control and agency for the AI might be an interesting path in co-creative scenarios where AI could direct and take more initiative within the co-creative task. However, the relationship between AI and human designers in creative processes is delicate, as adjusting the initiative or agency of the AI can negatively affect the user experience. In this paper, different degrees of agency for the AI are explored within the Evolutionary Dungeon Designer (EDD) to further understand MI-CC tools. A user study was performed using EDD with three varying degrees of AI agency. The study highlighted elements of frustration that the human designer experiences when using the tool and the behavior in the AI that led to possible strains on the relationship. The paper concludes with the identified issues and possible solutions and suggested further research.

Games are complex, multi-faceted systems that share common elements and underlying narratives, such as the conflict between a hero and a big bad enemy or pursuing a goal that requires overcoming challenges. However, identifying and describing these elements together is non-trivial as they might differ in certain properties and how players might encounter the narratives. Likewise, generating narratives also pose difficulties when encoding, interpreting, and evaluating them. To address this, we present TropeTwist, a trope-based system that can describe narrative structures in games in a more abstract and generic level, allowing the definition of games' narrative structures and their generation using interconnected tropes, called narrative graphs. To demonstrate the system, we represent the narrative structure of three different games. We use MAP-Elites to generate and evaluate novel quality-diverse narrative graphs encoded as graph grammars, using these three hand-made narrative structures as targets. Both hand-made and generated narrative graphs are evaluated based on their coherence and interestingness, which are improved through evolution.

First, we present a new algorithm for the single-source shortest paths problem (SSSP) in edge-weighted directed graphs, with n vertices, m edges, and both positive and negative real edge weights. Given a positive integer parameter t, in O(tm) time the algorithm finds for each vertex v a path distance from the source to v not exceeding that yielded by the shortest path from the source to v among the so called t+ light paths. A directed path between two vertices is t+ light if it contains at most t more edges than the minimum edge-cardinality directed path between these vertices. For t= O(n), our algorithm yields an O(nm)-time solution to SSSP in directed graphs with real edge weights matching that of Bellman and Ford. Our main contribution is a new, output-sensitive algorithm for the all-pairs shortest paths problem (APSP) in directed acyclic graphs (DAGs) with positive and negative real edge weights. The running time of the algorithm depends on such parameters as the number of leaves in (lexicographically first) shortest-paths trees, and the in-degrees in the input graph. If the trees are sufficiently thin on the average, the algorithm is substantially faster than the best known algorithm. Finally, we discuss an extension of hypothetical improved upper time-bounds for APSP in non-negatively edge-weighted DAGs to include directed graphs with a polynomial number of large directed cycles. © 2022, Springer Nature Switzerland AG.

Family life is no longer confined to geographically shared spaces. More often, families are separated. T echnology offers countless means of keeping families connected, which has been subject of extensive research. Yet, connection between families goes beyond interpersonal communication. Being separated from extended family means to be separated from familiar rituals, habits, and values. In this paper we present an ethnographic study of mobile families to understand how families are dealing with this kind of separation in their everyday life. We analyze situated practices and discuss how these families create a sense of connectedness to their country of origin. Our observations show that design for connectedness should address practices and materialities that are part of the family home. Furthermore, we argue that there should be more consideration for what the family connects to: Instead of connecting between people, connectedness can also be seen as staying in touch with familiar routines, customs, and environments.

Deep learning (DL) based software systems are difficult to develop and maintain in industrial settings due to several challenges. Data management is one of the most prominent challenges which complicates DL in industrial deployments. DL models are data-hungry and require high-quality data. Therefore, the volume, variety, velocity, and quality of data cannot be compromised. This study aims to explore the data management challenges encountered by practitioners developing systems with DL components, identify the potential solutions from the literature and validate the solutions through a multiple case study. We identified 20 data management challenges experienced by DL practitioners through a multiple interpretive case study. Further, we identified 48 articles through a systematic literature review that discuss the solutions for the data management challenges. With the second round of multiple case study, we show that many of these solutions have limitations and are not used in practice due to a combination of four factors: high cost, lack of skill-set and infrastructure, inability to solve the problem completely, and incompatibility with certain DL use cases. Thus, data management for data-intensive DL models in production is complicated. Although the DL technology has achieved very promising results, there is still a significant need for further research in the field of data management to build high-quality datasets and streams that can be used for building production-ready DL systems. Furthermore, we have classified the data management challenges into four categories based on the availability of the solutions.(c) 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Digital Transformation is one of the megatrends to re-shape industries, non-profit sectors, higher education, and even societies. Digital technologies merge with physical production processes provoking changes in value creation processes. These changes may lead to positive effects such as more effectiveness, revised business models, and closer customer contact. However, there are also negative effects hindering the digital transformation journey. As many firms still struggle, it is essential to understand the barriers which slow down or even stop a digital transformation. Based on qualitative data from 525 respondents working in different sectors, we reevaluate and extend an existing model by using a triangulation approach. The result is a holistic socio-technical model of the barriers to digital transformation consisting of seven distinct dimensions. The model can serve as input for future research instruments. Firms can use it to reflect barriers in their digital transformation.

Higher education institutions (HEIs) are facing significant progress in their digitalization. Covid-19 is an external affordance in which digitalization helps to secure social distance. Internal affordances are requirements to enhance the students’ learning experience. We analyze students’ attitudes toward the digitalization of their HEI based on empirical data from two groups of students within the same study program during the pandemic. Before the pandemic, the first group started on-campus and was forced into online teaching. The second group started online. Our results show that students, to a high degree, perceive harm in their learning success. At the same time, they have trust in the HEI’s data handling. Generally, the group, which started online, shows a slightly higher negative perception. The differences between the two groups show a low to medium degree. Our work contributes to clarifying the impact of having to start studies online, which seems to be minor.

With the Internet of Things paradigm, the data generated by the rapidly increasing number of connected devices lead to new possibilities, such as using machine learning for activity recognition in smart environments. However, it also introduces several challenges. The sensors of different devices might be mobile and of different types, i.e. there is a need to handle streaming data from a dynamic and heterogeneous set of sensors. In machine learning, the performance is often linked to the availability and quality of annotated data. Annotating data is in general costly, but it can be even more challenging if there is not any, or a very small amount of, annotated data to train the model on at the start of learning. To handle these issues, we implement interactive and adaptive systems. By including human-in-the-loop, which we refer to as interactive machine learning, the input from users can be utilized to build the model. The type of input used in interactive machine learning is typically annotations of the data, i.e. correctly labelled data points. Generally, it is assumed that the user always provides correct labels in accordance with the chosen interactive learning strategy. In many real-world applications these assumptions are not realistic however, as users might provide incorrect labels or not provide labels at all in line with the chosen strategy.

In this thesis we explore which interactive learning strategy types are possible in the given scenario and how they affect performance, as well as the effect of machine learning algorithms on the performance. We also study how a user who is not always reliable, i.e. who does not always provide a correct label when expected to, can affect performance. We propose a taxonomy of interactive online machine learning strategies and test how the different strategies affect performance through experiments on multiple datasets. Simulated experiments are compared to experiments with human participants, to verify the results. The findings show that the overall best performing interactive learning strategy is one where the user provides labels when current estimations are incorrect, but that the best performing machine learning algorithm depends on the problem scenario. The experiments also show that a decreased reliability of the user leads to decreased performance, especially when there is a limited amount of labelled data. The robustness of the machine learning algorithms differs, where e.g. Naïve Bayes classifier is better at handling a lower reliability of the user. We also present a systematic literature review on machine teaching, a subfield of interactive machine learning where the human is proactive in the interaction. The study shows that the area of machine teaching is rapidly evolving with an increased number of publications in recent years. However, as it is still maturing, there exists several open challenges that would benefit from further exploration, e.g. how human factors can affect performance.

Continuous deployment has become a widely used practice in web-based software applications. Deploying a new software version to production is a seamless automated process executed thousands of times per day. Continuous deployment reduces the time between a code commit and that commit is active in production. While continuous deployment promises many advantages to software development organizations, the adoption of continuous deployment in the software-intensive embedded systems industry is limited. Several empirical studies have highlighted the challenges associated with software-intensive embedded systems. However, very few studies, if any at all, have attempted to provide a practical approach to realize continuous deployment to these systems. This paper proposes a Controlled Continuous Deployment (CCD) approach, which considers the constraints software-intensive embedded systems have, such as high reliability and availability requirements, limited possibility for rollback after deployment, and the high volume of in-service systems in the market. We derived the approach by conducting a case study at Ericsson AB, focusing on three Radio Access Networks (RAN) technologies embedded software used in 3G, 4G, and 5G mobile networks.  

The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a huge amount of data related to students in digital education. This educational data can be used with artificial intelligence and machine learning techniques to improve digital education. This study makes two main contributions. First, the study follows a repeatable and objective process of exploring the literature. Second, the study outlines and explains the literature's themes related to the use of AI-based algorithms in digital education. The study findings present six themes related to the use of machines in digital education. The synthesized evidence in this study suggests that machine learning and deep learning algorithms are used in several themes of digital learning. These themes include using intelligent tutors, dropout predictions, performance predictions, adaptive and predictive learning and learning styles, analytics and group-based learning, and automation. artificial neural network and support vector machine algorithms appear to be utilized among all the identified themes, followed by random forest, decision tree, naive Bayes, and logistic regression algorithms.

Focal application of transcranial static magnetic field stimulation (tSMS) over the human motor cortex induces local changes in cortical excitability. Whether tSMS can also induce distant network effects, and how these local and distant effects may vary over time, is currently unknown. In this study, we applied 10 min tSMS over the left motor cortex of healthy subjects using a real/sham parallel design. To measure tSMS effects at the sensori-motor network level, we used resting-state fMRI. Real tSMS, but not sham, reduced functional connectivity within the stimulated sensori-motor network. This effect of tSMS showed time-dependency, returning to sham levels after the first 5 min of fMRI scanning. With 10 min real tSMS over the motor cortex we did not observe effects in other functional networks examined (default mode and visual system networks). In conclusion, 10 min of tSMS over a location within the sensori-motor network reduces functional connectivity within the same functional network.

This article introduces “digital technography” as a methodology to interrogate and voice emerging digital technologies and their anticipated futures. I demonstrate, with reference to recent research on wearable self-tracking devices, digital food technologies, and platforms for work automation, how one can gain an understanding of these technologies by attending to the materials in which they are promoted; and actively engaging with them imaginatively and self-reflexively as a social scientist. This article outlines a digital technographic methodology centered around the three conceptual anchors of specification, valorization, and anticipation, all of which pertain to how a digital technology aims and perhaps even aspires to become a part of everyday life. 

With the increased availability of new and better computer processing units (CPUs) as well as graphical processing units (GPUs), the interest in statistical learning and deep learning algorithms for classification tasks has grown exponentially. These classification algorithms often require the presence of fully labeled instances during the training period for maximum classification accuracy. However, in industrial applications, data is commonly not fully labeled, which both reduces the prediction accuracy of the learning algorithms as well as increases the project cost to label the missing instances. The purpose of this paper is to survey the current state-of-the-art literature on machine learning algorithms that are used for assisted or automatic labeling and to understand where these are used. We performed a systematic mapping study and identified 52 primary studies relevant to our research. This paper provides three main contributions. First, we identify the existing machine learning algorithms for labeling and we present a taxonomy of these algorithms. Second, we identify the datasets that are used to evaluate the algorithms and we provide a mapping of the datasets based on the type of data and the application area. Third, we provide a process to support people in industry to optimally label their dataset. The results presented in this paper can be used by both researchers and practitioners aiming to improve the missing labels with the aid of machine algorithms or to select appropriate datasets to compare new state-of-the art algorithms in their respective application area.

We propose modeling designer style in mixed-initiative game content creation tools as archetypical design traces. These design traces are formulated as transitions between design styles; these design styles are in turn found through clustering all intermediate designs along the way to making a complete design. This method is implemented in the Evolutionary Dungeon Designer, a research platform for mixed-initiative systems to create adventure and dungeon crawler games. We present results both in the form of design styles for rooms, which can be analyzed to better understand the kind of rooms designed by users, and in the form of archetypical sequences between these rooms, i.e., Designer Personas.

The main emphasis of this work is placed on the problem of collaborative coverage path planning for unmanned surface mapping vehicles (USMVs). As a result, the collaborative coverage improved BA* algorithm (CCIBA*) is proposed. In the algorithm, coverage path planning for a single vehicle is achieved by task decomposition and level map updating. Then a multiple USMV collaborative behavior strategy is designed, which is composed of area division, recall and transfer, area exchange and recognizing obstacles. Moverover, multiple USMV collaborative coverage path planning can be achieved. Consequently, a high-efficiency and high-quality coverage path for USMVs can be implemented. Water area simulation results indicate that our CCIBA* brings about a substantial increase in the performances of path length, number of turning, number of units and coverage rate.

In rural areas with low demand, demand responsive transport (DRT) can provide an alternative to the regular public transport bus lines, which are expensive to operate in such conditions. With simulation, we explore the potential effects of introducing a DRT service that replaces existing bus lines in Lolland municipality in Denmark, assuming that the existing demand remains unchanged. We set up the DRT service in such a way that its service quality (in terms of waiting time and in-vehicle time) is comparable to the replaced buses. The results show that a DRT service can be more cost efficient than regular buses and can produce significantly less CO2 emissions when the demand level is low. Additionally, we analyse the demand density at which regular buses become more cost efficient and explore how the target service quality of a DRT service can affect operational characteristics. Overall, we argue that DRT could be a more sustainable mode of public transport in low demand areas.

A fuzzy Petri net (FPN) is a powerful tool to model and analyze knowledge-based systems containing vague information. This paper systematically reviews recent developments of the FPN model from the following three perspectives: knowledge representation using FPN, reasoning mechanisms using an FPN framework, and the latest industrial applications using FPN. In addition, some specific modeling and reasoning approaches to FPN to solve the 'state-explosion problem' are illustrated. Furthermore, detailed analysis of the discussed aspects are shown to reveal some interesting findings, as well as their developmental history. Finally, we present conclusions and suggestions for future research directions.