This paper explores the possibilities and challenges of realizing a context-aware travel planner with bidirectional information exchange between the actor and the traveller during unplanned traffic disturbances. A prototype app is implemented and tested to identify potential benefits. The app uses data from open APIs, and beacons to detect the traveller context (which train or train platform the traveller is currently on). Alternative travel paths are presented to the user, and each alternative is associated with a certainty factor reflecting the reliability of the travel time prognoses. The paper also presents an interview study that investigates PT actors’ views on the potential use for actors and travellers of new information about certainty factors and travellers’ contexts, during unplanned traffic disturbances. The results show that this type of travel planner can be realized and that it enables travellers to find ways to reach their destination, in situations where the public t ravel planner only suggests infeasible travel paths. The value for the traveller of the certainty factors are also illustrated. Additionally, the results show that providing actors with information about traveller context and certainty factors opens up for the possibility of more advanced support for both the PT actor and the traveller.

Cooperative work differs depending on contexts and tasks, whether co-located, synchronous, or distributed in time and space. New technology allows new opportunities to support cooperation. A central aspect of cooperation is the relation to individual work; when co-located, people enter and exit cooperation seamlessly. This dissertation explores how technology, situation, and context interplay in various forms of cooperation. It addresses two research questions: (1) How do people get engaged in cooperative work? and (2) How can engagement in distributed cooperative work be supported?

The work focuses on ethnographic empirical studies that analyse the interaction between humans and technology across various domains. Workplace studies have been conducted in different fields. Emergency service work, truck driver's work, building maintenance workers, and visitor's technology use at a music festival. The workplace studies in the dissertation imply that field studies are conducted to document and analyse how people use technology and how this use takes place. Common to all studies is the work about activities distributed in time and space.

These research findings inform the development of new perspectives, concepts, and design challenges for distributed collaboration. The dissertation discusses two primary ways to engage in cooperative work are identified: requesting and choosing to engage through shared materials and artefacts support awareness and enable cooperative work. The results identify four factors to facilitate engagement in remote cooperative environments: supporting requests and choices to engage, providing opportunities to use artefacts, promoting shareability, and incorporating awareness technology.

The dissertation contributes new insights into the interplay between technology, situation, and context in cooperation. Providing design insights for distributed collaboration, and the exploration of design concepts and analysis models. The contributions emphasize the dynamic nature of collaboration and the importance of understanding the relationship between individual and cooperative work to support distributed and remote collaboration effectively.

Introduction: Social robots are accompanied by high expectations of what they can bring to society and in the healthcare sector. So far, promising assumptions have been presented about how and where social robots are most relevant. We know that the industry has used robots for a long time, but what about social uptake outside industry, specifically, in the healthcare sector? This study discusses what trends are discernible, to better understand the gap between technology readiness and adoption of interactive robots in the welfare and health sectors in Europe.

Methods: An assessment of interactive robot applications at the upper levels of the Technology Readiness Level scale is combined with an assessment of adoption potential based on Rogers' theory of diffusion of innovation. Most robot solutions are dedicated to individual rehabilitation or frailty and stress. Fewer solutions are developed for managing welfare services or public healthcare.

Results: The results show that while robots are ready from the technological point of view, most of the applications had a low score for demand according to the stakeholders.

Discussion: To enhance social uptake, a more initiated discussion, and more studies on the connections between technology readiness and adoption and use are suggested. Applications being available to users does not mean they have an advantage over previous solutions. Acceptance of robots is also heavily dependent on the impact of regulations as part of the welfare and healthcare sectors in Europe.

The use of artificial intelligence (AI) is increasing in our everyday applications. One emerging field within AI is image recognition. Research that has been devoted to predicting fires involves predicting its behaviour. That is, how the fire will spread based on environmental key factors such as moisture, weather condition, and human presence. The result of correctly predicting fire spread can help firefighters to minimise the damage, deciding on possible actions, as well as allocating personnel effectively in potentially fire prone areas to extinguish fires quickly. Using neural networks (NN) for active fire detection has proven to be exceptional in classifying smoke and being able to separate it from similar patterns such as clouds, ground, dust, and ocean. Recent advances in fire detection using NN has proved that aerial imagery including drones as well as satellites has provided great results in detecting and classifying fires. These systems are computationally heavy and require a tremendous amount of data. A NN model is inextricably linked to the dataset on which it is trained. The cornerstone of this study is based on the data dependencieds of these models. The model herein is trained on two separate datasets and tested on three dataset in total in order to investigate the data dependency. When validating the model on their own datasets the model reached an accuracy of 92% respectively 99%. In comparison to previous work where an accuracy of 94% was reached. During evaluation of separate datasets, the model performed around the 60% range in 5 out of 6 cases, with the outlier of 29% in one of the cases. 

Artificial intelligence and machine learning (ML) in particular increasingly impact human life by creating value from collected data. This assetisation affects all aspectsof human life, from choosing a significant other to recommending a product for us to consume. This type of ML-based system thrives because it predicts human behaviour based on average case performance metrics (like accuracy). However, its usefulnessis more limited when it comes to being transparent about its internal knowledge representations for singular decisions, for example, it is not good at explaining why ithas suggested a particular decision in a specific context.The goal of this work is to let end users be in command of how ML systems are used and thereby combine the strengths of humans and machines – machines which can propose transparent decisions. Artificial neural networks are an interesting candidate for a setting of this type, given that this technology has been successful in building knowledge representations from raw data. A neural network can be trained by exposing it to data from the target domain. It can then internalise knowledge representations from the domain and perform contextual tasks. In these situations, the fragment of the actual world internalised in an ML system has to be contextualised by a human to beuseful and trustworthy in non-static settings.This setting is explored through the overarching research question: What challenges and opportunities can emerge when an end user uses neural networks in context to support singular decision-making? To address this question, Research through Design is used as the central methodology, as this research approach matches the openness of the research question. Through six design experiments, I explore and expand on challenges and opportunities in settings where singular contextual decisions matter. The initial design experiments focus on opportunities in settings that augment human cognitive abilities. Thereafter, the experiments explore challenges related to settings where neural networks can enhance human cognitive abilities. This part concerns approaches intended to explain promoted decisions.This work contributes in three ways: 1) exploring learning related to neural networks in context to put forward a core terminology for contextual decision-making using ML systems, wherein the terminology includes the generative notions of true-to-the-domain, concept, out-of-distribution and generalisation; 2) presenting a number of design guidelines; and 3) showing the need to align internal knowledge representations with concepts if neural networks are to produce explainable decisions. I also argue that training neural networks to generalise basic concepts like shapes and colours, concepts easily understandable by humans, is a path forward. This research direction leads towards neural network-based systems that can produce more complex explanations that build on basic generalisable concepts.

Neuropathic pain is a difficult condition to treat and would require reliable biomarkers to personalise and optimise treatments. To date, pain levels are mostly measured with subjective scales, but research has shown that electroencephalography (EEG) and heart rate variability (HRV) can be linked to those levels. Internet of Things technology could allow embedding EEG and HRV in easy-to-use systems that patients can use at home in their daily life. We have developed a system for home monitoring that includes a portable EEG device, a tablet application to guide patients through imaginary motor tasks while recording EEG, a wearable HRV sensor and a mobile phone app to report pain levels. We are using this system in a clinical study involving 15 spinal cord injury patients for one month. Preliminary results show that relevant data are being collected, with inter and intra-patients variability for both HRV and pain levels, and that the mobile phone app is perceived as usable, of good quality and useful. However, because of its complexity, the system requires some effort from patients, is sometimes unreliable and the collected EEG signals are not always of the desired quality.

Asthma monitoring is an important aspect of patient self-management. However, due to its repetitive nature, patients can find long-term monitoring tedious. Mobile health can provide an avenue to monitor asthma without needing high levels of active engagement, and instead rely on passive monitoring. In our recent AAMOS-00 study, we collected mobile health data over six months from 22 asthma patients using passive and active monitoring technology, including smartwatch, peak flow measurements, and daily asthma diaries.

Compliance to smartwatch monitoring was found to lie between the compliance to complete daily asthma diaries and measuring daily peak flow. However, some study participants faced technical issues with the devices which could have affected the relative compliance of the monitoring tasks.

Moreover, as evidenced by standard usability questionnaires, we found that the AAMOS-00 study’s data collection system was similar in quality to other studies and published apps.

The currently dominating artificial intelligence and machine learning technology, neural networks, builds on inductive statistical learning processes. Being void of knowledge that can be used deductively these systems cannot distinguish exemplars part of the target domain from those not part of it. This ability is critical when the aim is to build human trust in real-world settings and essential to avoid usage in domains wherein a system cannot be trusted. In the work presented here, we conduct two qualitative contextual user studies and one controlled experiment to uncover research paths and design openings for the sought distinction. Through our experiments, we find a need to refocus from average case metrics and benchmarking datasets toward systems that can be falsified. The work uncovers and lays bare the need to incorporate and internalise a domain ontology in the systems and/or present evidence for a decision in a fashion that allows a human to use our unique knowledge and reasoning capability. Additional material and code to reproduce our experiments can be found at https://github.com/k3larra/ood.

Self-driving cars enable dynamic shared mobility, where customers are independent of schedules and fixed stops. This study aims to investigate the potential effects shared mobility can have on future transportation. We simulate multiple scenarios to analyze the effects different service designs might have on vehicle kilometers, on the required number of shared vehicles, on the potential replacement of private cars, and on service metrics such as waiting times, travel times, and detour levels. To demonstrate how simulation can be used to analyze future mobility, we present a case study of the city of Gothenburg in Sweden, where we model travel demand in the morning hours of a workday. The results show that a significant decrease of vehicle kilometers can be achieved if all private car trips are replaced by rideshare and that shared vehicles can potentially replace at least 5 private cars during the morning peak.

This paper investigates the dynamic event-triggered adaptive neural coordinated disturbance rejection for marine vehicles with external disturbances as the sinusoidal superpositions with unknown frequencies, amplitudes and phases. The vehicle movement mathematical models are transformed into parameterized expressions with the neural networks approximating nonlinear dynamics. The parametric exogenous systems are exploited to express external disturbances, which are converted into the linear canonical models with coordinated changes. The adaptive technique together with disturbance filters realize the disturbance estimation and rejection. By using the vectorial backstepping, the dynamic event-triggered adaptive neural coordinated disturbance rejection controller is derived with the dynamic event-triggering conditions being incorporated to reduce execution frequencies of vehicle's propulsion systems. The coordinated formation control can be achieved with the closed-loop semi-global stability. The dynamic event-triggered adaptive disturbance rejection scheme achieves the disturbance estimation and cancellation without requiring the a priori marine vehicle's model dynamics. Illustrative simulations and comparisons validate the proposed scheme.

This paper investigates the control issue of marine surface vehicles (MSVs) subject to internal and external uncertainties without velocity information. Utilizing the specific advantages of adaptive neural network and disturbance observer, a classification reconstruction idea is developed. Based on this idea, a novel adaptive neural-based state observer with disturbance observer is proposed to recover the unmeasurable velocity. Under the vector-backstepping design framework, the classification reconstruction idea and adaptive neural-based state observer are used to resolve the control design issue for MSVs. To improve the control performance, the serial-parallel estimation model is introduced to obtain a prediction error, and then a composite learning law is designed by embedding the prediction error and estimate of lumped disturbance. To reduce the mechanical wear of actuator, a dynamic event triggering protocol is established between the control law and actuator. Finally, a new dynamic event-triggered composite learning adaptive neural output feedback control solution is developed. Employing the Lyapunov stability theory, it is strictly proved that all signals in the closed-loop control system of MSVs are bounded. Simulation and comparison results validate the effectiveness of control solution.

Vehicle utilization analysis is an essential tool for manufacturers to understand customer needs, improve equipment uptime, and to collect information for future vehicle and service development. Typically today, this behavioral modeling is done on high-resolution time-resolved data with features such as GPS position and fuel consumption. However, high-resolution data is costly to transfer and sensitive from a privacy perspective. Therefore, such data is typically only collected when the customer pays for extra services relying on that data. This motivated us to develop a multi-task ensemble approach to transfer knowledge from the high-resolution data and enable vehicle behavior prediction from low-resolution but high dimensional data that is aggregated over time in the vehicles. This study proposes a multi-task snapshot-stacked ensemble (MTSSE) deep neural network for vehicle behavior prediction by considering vehicles’ low-resolution operational life records. The multi-task ensemble approach utilizes the measurements to map the low-frequency vehicle usage to the vehicle behaviors defined from the high-resolution time-resolved data. Two data sources are integrated and used: high-resolution data called Dynafleet, and low-resolution so-called Logged Vehicle Data (LVD). The experimental results demonstrate the proposed approach’s effectiveness in predicting the vehicle behavior from low frequency data. With the suggested multi-task snapshot-stacked ensemble deep network, it is shown how low-resolution sensor data can highly contribute to predicting multiple vehicle behaviors simultaneously while using only one single training process.

Artificial intelligence (AI) and the use of machine learning (ML) and deep learning (DL) technologies are becoming increasingly popular in companies. These technologies enable companies to leverage big quantities of data to improve system performance and accelerate business development. However, despite the appeal of ML/DL, there is a lack of systematic and structured methods and processes to help data scientists and other company roles and functions to develop, deploy and evolve models. In this paper, based on multi-case study research in six companies, we explore practices and challenges practitioners experience in developing ML/DL models as part of large software-intensive embedded systems. Based on our empirical findings, we derive a conceptual framework in which we identify three high-level activities that companies perform in parallel with the development, deployment and evolution of models. Within this framework, we outline activities, iterations and triggers that optimize model design as well as roles and company functions. In this way, we provide practitioners with a blueprint for effectively integrating ML/DL model development into the business to achieve better results than other (algorithmic) approaches. In addition, we show how this framework helps companies solve the challenges we have identified and discuss checkpoints for terminating the business case.

Continuous experimentation (CE) refers to a set of practices used by software companies to rapidly assess the usage, value, and performance of deployed software using data collected from customers and systems in the field using an experimental methodology. However, despite its increasing popularity in developing web-facing applications, CE has not been studied in the development process of business-to-business (B2B) mission-critical systems. By observing the CE practices of different teams, with a case study methodology inside Ericsson, we were able to identify the different practices and techniques used in B2B mission-critical systems and a description and classification of the four possible types of experiments. We present and analyze each of the four types of experiments with examples in the context of the mission-critical long-term evolution (4G) product. These examples show the general experimentation process followed by the teams and the use of the different CE practices and techniques. Based on these examples and the empirical data, we derived the HURRIER process to deliver high-quality solutions that the customers value. Finally, we discuss the challenges, opportunities, and lessons learned from applying CE and the HURRIER process in B2B mission-critical systems. 

AIM: Technology affects almost all aspects of modern eldercare. Ensuring ethical decision-making is essential as eldercare becomes more digital; each decision affects a patient's life, self-esteem, health and wellness.

METHODS: We conducted a survey and interviews with eldercare professionals to better understand the behavioural ethics and decision making involved in the digital transition of eldercare.

CONCLUSION: Our qualitative analysis showed three recurrent roles among eldercare professionals in regard to digital service transformation; makers, implementers and maintainers. All three encountered challenging and stressful ethical dilemmas due to uncertainty and a lack of control. The matter of power relations, the attempts to standardize digital solutions and the conflict between cost efficiency and if digital care solutions add value for patients, all caused moral dilemmas for eldercare professionals. The findings suggest a need for organizational infrastructure that promotes ethical conduct and behaviour, ethics training and access to related resources. Implications for rehabilitation The transition to digital care service is not neutral, but value-laden. Digital transformation affects ethical behaviour and decision-making. The decision as to which digital services should be developed and deployed must include eldercare professionals and not lay solely in the hands of managers, technologists and economists. We must move away from attempting to fit standardized solutions to a heterogenous group of older patients; accommodating the pluralism of patients' needs and wants protects their dignity, autonomy and independence. As digital care practices evolve, so too must organizational structures that promote ethical conduct.

Reinforcement learning, particularly deep reinforcement learning, has made remarkable progress in recent years and is now used not only in simulators and games but is also making its way into embedded systems as another software-intensive domain. However, when implemented in a real-world context, reinforcement learning is typically shown to be fragile and incapable of adapting to dynamic environments. In this paper, we provide a novel dynamic reinforcement learning algorithm for adapting to complex industrial situations. We apply and validate our approach using a telecommunications use case. The proposed algorithm can dynamically adjust the position and antenna tilt of a drone-based base station to maintain reliable wireless connectivity for mission-critical users. When compared to traditional reinforcement learning approaches, the dynamic reinforcement learning algorithm improves the overall service performance of a drone-based base station by roughly 20%. Our results demonstrate that the algorithm can quickly evolve and continuously adapt to the complex dynamic industrial environment.

Modern release engineering practices such as continuous integration and delivery have allowed software development companies to transition from a long release cycle to a shorter one. The shorter release cycle has led to more software releases available to customers. At the same time, companies developing high-volume software-intensive embedded systems often deliver patch releases and maintenance releases on top of major and minor releases to customers who pick and choose what releases apply to them and decide when to upgrade the system, if to upgrade at all. While release engineering has been studied before in web-based, desktop-based, and embedded software, the focus has been on pre-release activities. Few studies have investigated what happens after the release, particularly the role of tracing software from release to deployment in high-volume software-intensive embedded systems. To address this gap, we conducted a qualitative case study at a multi-national telecommunications systems provider focusing on Radio Access Network (RAN) software. RAN software is a complex and large-scale embedded software used in mobile networks Base Stations (BS), providing software functionality for RAN mobile technologies ranging from 2G to 5G. Our study shed light on post-release software traceability and how it is used in the release engineering process.

For companies in the embedded systems domain, digitalization and digital technologies allow endless opportunities for new business models and continuous value delivery. While physical products still provide the core revenue, these are rapidly being complemented with offerings that allow for recurring revenue and that are based on software, data and artificial intelligence (AI). However, while new digital offerings allow for fundamentally new and recurring revenue streams and continuous value delivery to customers, the creation of these proves to be a challenging endeavour. In this paper, we study how companies explore ways to create new or additional value with the intention to complement their product portfolio with offerings that allow for recurring revenue. Based on multi-case study research, we identify the key challenges that companies in the embedded systems domain experience and we derive four organizational patterns that we see slow down innovation. Second, we present a framework outlining alternative types of offerings to customers. Third, we provide a value taxonomy in which we detail the different types of offerings and the value these provide to customers. For each value offering, we indicate whether this offering is (1) static or evolving, (2) bundled or unbundled, (3) free or monetized, and we provide examples from the case companies we studied.

In order to discuss, evaluate, and address social consequences of digitalization, we need to study and understand key people and events behind today’s digital innovations. This research contributes to an ongoing discussion within critical data studies by focusing on humans and meeting places shaping digital innovations that are/will be realized in this connected and data-saturated society we find ourselves in. The focus will be on angel investors and venture capitalist, pitching events and conferences where innovators and investors meet and intermingle. I will present conclusions from pilot studies conducted in Sweden (Malmö, at MINC-Malmö Incubator), South Africa (Stellenbosch, at the LaunchLab) and the US (Austin, at SXSW – South by southwest conference & Silicon Valley, at Facebook and Google headquarters). The overall research question is how key people and events contribute to, and shape, current and future digital innovations. With my expertise coming from the Social Sciences, the focus will be on culture (in an anthropological understanding of culture) which in this project operationalized through norms, values, rituals, and imaginaries surrounding humans and meeting places behind digital innovations. What consequences does these norms, values, rituals, and imaginaries have in our digitalized societies?  

The project departs from the importance attributed to digital innovations, the promise they bring with a more connected world where digital innovations are believed to solve most, if not all, problems that our society faces such as climate change, infection tracing, increased polarization, and intolerance. I am still conducting these pilot studies (the last will be in June) and by the time of the conference I will have results to present. 

With the rise of modern technologies, digital transformation (DT) has become an issue in nearly all industries. In enterprises, DT means to digitalize internal processes, offer digital services and products, and enhance the customer experience. However, complex barriers hinder the successful transformation. Our study follows an explorative mixed-method design. Through a qualitative data collection with 46 participants and a quantitative data collection with 824 participants, we sequentially identify the barriers to DT and transfer them into a testable research model. This paper offers a proposal for an instrument to measure barriers to DT. The identified predictors explain 50% of the alteration problems of the transformation process. The results offer further research an existing instrument to build upon. Also, the instrument is helpful for managers to measure hinders in DT processes and to develop counter-measurements. Our results indicate that many barriers depend on organizational issues rather than the individual level. 

The concept of deep reading is defined as the application of higher-order thinking skills to the process of reading. It includes analogical skills, critical analysis, reflection, and insight. Deep reading is also often associated with particular media, primarily printed books, preferably certain kinds of literature. This chapter discusses some of the prevalent ideas surrounding notions of focused, critical and valued reading modes and how these are connected to media technologies, implicitly or explicitly. Some scholars, such as Nicholas Carr, have suggested that digital media in general and the kinds of distracted, quick, or hypertextual reading that the Internet provides in particular are detrimental to our ability to focus and engage deeply. Within media studies, however, research has pointed to other equally important aspects of engagement that must be redefined so as not to be inextricably linked to a particular medium or genre.

The concept of deep reading is defined as the application of higher-order thinking skills to the process of reading. It includes analogical skills, critical analysis, reflection, and insight. Deep reading is also often associated with particular media, primarily printed books, preferably certain kinds of literature. This chapter discusses some of the prevalent ideas surrounding notions of focused, critical and valued reading modes and how these are connected to media technologies, implicitly or explicitly. Some scholars, such as Nicholas Carr, have suggested that digital media in general and the kinds of distracted, quick, or hypertextual reading that the Internet provides in particular are detrimental to our ability to focus and engage deeply. Within media studies, however, research has pointed to other equally important aspects of engagement that must be redefined so as not to be inextricably linked to a particular medium or genre.

Media materiality matters for how reading happens, through digital technologies, print, audio, and so on. However, equally important for our understanding of what happens in each individual reader's experience is the place and situation in which the reading occurs. The surroundings, what the reader is doing while reading, what occurs around them are part of what we discuss in this chapter as situated reading. Our interests go beyond the reading mediation itself to address how the reader's sensing body experiences each reading instance. We seek to decouple the naturalized link between our understanding of what constitutes reading, the medium, and the situations in which reading occurs.

This chapter discusses the digital reading condition and multisensory reading from the perspective of journalism, which implies other implications and dimensions than educational or literary reading. Global as well as local news organizations around the world are experimenting with new digital opportunities for presence and engagement of users beyond the written word, and the chapter gives some examples. Framed by the term “immersive journalism,” the chapter presents examples of the use of immersive technologies like 3D models, Augmented and Virtual Reality, and 360° photography and videography. It also suggests to include digital audio formats as examples of journalistic products that are able to create different kinds of sensory and social experiences of presence.

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

Fast and reliable connectivity is essential to enhance situational awareness and operational efficiency for public safety mission-critical (MC) users. In emergency or disaster circumstances, where existing cellular network coverage and capacity may not be available to meet MC communication demands, deployable-network-based solutions such as cells-on-wheels/wings can be utilized swiftly to ensure reliable connection for MC users. In this paper, we consider a scenario where a macro base station (BS) is destroyed due to a natural disaster and an unmanned aerial vehicle carrying BS (UAV-BS) is set up to provide temporary coverage for users in the disaster area. The UAV-BS is integrated into the mobile network using the 5G integrated access and backhaul (IAB) technology. We propose a framework and signalling procedure for applying machine learning to this use case. A deep reinforcement learning algorithm is designed to jointly optimize the access and backhaul antenna tilt as well as the three-dimensional location of the UAV-BS in order to best serve the on-ground MC users while maintaining a good backhaul connection. Our result shows that the proposed algorithm can autonomously navigate and configure the UAV-BS to improve the throughput and reduce the drop rate of MC users.

Accent invariance in speech recognition is a chal- lenging problem especially in the are of aviation. In this paper a speech recognition system is developed to transcribe accented speech between pilots and air traffic controllers. The system allows handling of accents in continuous speech by modelling phonemes using Hidden Markov Models (HMMs) with Gaussian mixture model (GMM) probability density functions for each state. These phonemes are used to build word models of the NATO phonetic alphabet as well as the numerals 0 to 9 with transcriptions obtained from the Carnegie Mellon University (CMU) pronouncing dictionary. Mel-Frequency Cepstral Co-efficients (MFCC) with delta and delta-delta coefficients are used for the feature extraction process. Amplitude normalisation and covariance scaling is implemented to improve recognition accuracy. A word error rate (WER) of 2% for seen speakers and 22% for unseen speakers is obtained.

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

We extended the Mobistudy app for clinical research in order to gather data about Parkinson’s motor and non-motor symptoms. We developed 5 tests that make use of the phone’s embedded sensors and 3 questionnaires. We show through data collected by healthy individuals simulating PD symptoms that the tests are able to identify the presence of symptoms.

Emerging 5G and future networks (5GFNs) will change the mobile communication business ecosystem by introducing multi-service wireless applications with diverse specifications. These smart and future-oriented wireless networks are intended to address not only consumer-based smartphone applications, but also the needs of various vertical industry markets (e.g., education, energy, healthcare, transportation, manufacturing, agriculture, and so forth). 5GFNs are aimed at attaining economic value for all key stakeholders, including customers, mobile network operators (MNOs), equipment vendors, public institutions, private enterprises, digital business start-ups and various third parties. This paper discusses business model options for 5GFNs from the MNOs’ perspective.  We describe current MNOs’ business models and their shortcomings. Thereafter, we present emerging business models for 5GFNs which MNOs should consider when rolling out these networks.  

Advancements in technology, such as smartphones and wearabledevices, can be used for collecting movement data through embeddedsensors. This paper focuses on linking Parkinson’s Disease severitywith data collected from mobile phones in the mPower study. As referencefor symptoms’ severity, we use the answers provided to part 2 ofthe standard MDS-UPDRS scale. As input variables, we use the featurescomputed within mPower from the raw data collected during 4 phonebasedactivities: walking, rest, voice and finger tapping. The features arefiltered in order to remove unreliable datapoints and associated to referencevalues. After pre-processing, 5 Machine Learning algorithms areapplied for predictive analysis. We show that, notwithstanding the noisedue to the data being collected in an uncontrolled manner, the regressedsymptom levels are moderately to strongly correlated with the actualvalues (highest Pearson’s correlation = 0.6). However, the high differencebetween the values also implies that the regressed values can not beconsidered as a substitute for a conventional clinical assessment (lowestmean absolute error = 5.4).

The conditions of reading are shaped by materialities of that which is read. In the wake of digital publishing, reading activities have been impacted by the affordances of digital technologies, and the chapter “Reading and materiality: conditions of digital reading” charts some of the influential ideas on the material nature of digital reading, and arguing that print-centric notions of what constitutes “good” reading have at times overshadowed an in-depth reckoning of the role that digital technologies play today. The perceived dichotomy between so-called digitally born and digitized materials does not delineate a border between “digital” and “print” reading, even though many of the assumptions about the latter still permeate perceptions of what is more valuable to read. The digital reading condition that the chapter introduces does not exclude any forms. Rather, the current media moment includes print, audiobooks, printed books in all forms, as well as a multitude of digital forms in a complex, interlocking media economy.

This volume offers a critical overview of digital reading practices and scholarly efforts to analyze and understand reading in the mediatized landscape. Building on research about digital reading, born-digital literature, and digital audiobooks, The Digital Reading Condition explores reading as part of a broader cultural shift encompassing many forms of media and genres.

Bringing together research from media and literary studies, digital humanities, scholarship on reading and learning, as well as sensory studies and research on multimodal and multisensory media reception, the authors address and challenge print-biased conceptions of reading that are still prevalent in research, whether the reading medium is print or digital. They argue that the act of reading itself is changing, and rather than rejecting digital media as unsuitable for sustained or focused reading practices, they argue that the complex media landscape challenges us to rethink how to define reading as a mediated practice.

Presenting a truly interdisciplinary perspective on digital reading practices, this volume will appeal to scholars and graduate students in communication, media studies, new media and technology, literature, digital humanities, literacy studies, composition, and rhetoric.

In this paper, we consider the variant of the art gallery problem where the input polygon is a staircase polygon. Previous works on this problem gave a 2-approximation for point guarding a staircase polygon (where guards can be placed anywhere in the interior of the polygon and we wish to guard the entire polygon). It is still unknown whether this point guarding variant is NP-hard. In this paper we consider the vertex guarding problem, where guards are only allowed to be placed at the vertices of the polygon, and we wish to guard only the vertices of the polygon. We show that this problem is NP-hard, and we give a polynomial-time 2-approximation algorithm. 

Humanity is faced with another revolution now: “Industry 4.0”. In addition to economic elements, other dimensions of societal structures are also fundamentally reorganized by this novel revolution. As a natural consequence of this development, digital transformation became a substantial issue for business organizations, the crucial players of the economy in society. Several studies have investigated the antecedents, processes, and consequences associated with digitalization in organizations. However, the overwhelming majority of these studies discussed the issue from the perspective of white-collar employees, who are the decision-makers or decision developers. Thus, it could be suggested that there is a significant gap in digitalization literature about the views of blue-collar employees, who are bound by the consequences of the decisions by the white collars. Therefore, the current study aims to emphasize the “non-tackled” conditions of blue-collar workers during organizational digital transformation. Furthermore, only a few studies solidified (or warranted) their claims with a theoretical approach. Thus, the present study also aims to discuss whether the textures of Scandinavian Institutionalism and digital transformation are consistent to test Scandinavian Institutionalism as an alternative theoretical foundation for future studies that will search for an adequate theory to understand this phenomenon better. Finally, the current study also aims to recommend certain research avenues to combine the digital transformation of blue-collar employees and Scandinavian Institutionalism.

Jägersro is a horse racing track situated in Malmö, Sweden. A public body funded this organization to start its Machine Learning (ML) journey. Jägersro wants to use this opportunity to initiate its journey towards a digitally transformed organization. During its ML journey, the institution will face different barriers. This study aims to analyze the start of the organization’s journey. A sociomaterial perspective is applied during the analysis to generalize the findings. A project like this is an ideal start for a digital transformation as ML is a well-defined area. Results indicate a strong need for making implementation efforts visible and material to spark further development of a DT.

Developments within digital technology are redefining how spectators will experience sport in the future. Combined with current crises, it creates new demands on how sports arenas can generate visitors to their events. An alternative can be virtual arenas. Therefore, this study aimed to understand the visitor’s expectations of a virtual arena and identify key factors that affected potential spectators’ intentions to visit a virtual arena. This qualitative study collected empirical data through focus groups. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) was used as a theoretical foundation for the analysis. This study results in an enhanced hypothetical model arguing for additional elements affecting a spectator’s intention to visit a virtual arena. Our research contributes to helping shape future research on and practical implementation of virtual arenas.

Digital Transformation (DT) impacts industries, non-profit sectors, higher education, and even societies. As connectivity technologies blend with physical assets, modifications in value creation processes are provoked. These modifications may have positive impacts such as higher effectivity, enhanced business models, and improved customer connection. However, realizing a DT is a complex endeavor. Specific properties, so-called barriers, hinder the DT journey. Thus, it is essential to grasp the barriers and indicate ways to overcome them. We develop a decision matrix for overcoming barriers using qualitative data from participants working in different sectors. This work builds upon a pre-study developing a barrier classification and enhances it with specific recommendations such as “define clear DT responsibilities”. Thus, our work takes the development of plain barrier classifications further. From a theoretical perspective, this work contributes to developing hypothetical models of the effects of recommendations to overcome barriers in the future. From a practical perspective, companies can use the recommendations to plan actions to take.

Data has been claimed to be the new oil of the 21st century as it has seen to be able both to improve the existing products and services as well as to create new revenue streams for its utilizing company with a secondary customers base. However, while there is active streams of research for developing machine learning and data science methods, considerably less has been done to understand and characterize data business activities in the software-intensive companies. This study uses a multiple case study approach in the software-intensive embedded system domain. Four large international embedded system companies were selected as the case study subjects. The objective is to understand how the case companies are developing their activities for successful utilization of the data. The study identifies six distinct stages with their own challenges. In addition, this study serves as a starting for further work for supporting software-intensive embedded system companies to start data business.

The purpose of this chapter is to identify and classify proposed business model innovation tools through a literature review of academic publications. We classified the tools into three categories: innovation process stage, strategic objective and size of firm. Our main findings are that the overall number of business model innovation (BMI) tools described in the research literature is limited mostly to facilitate design processes rather than for test or implementation purposes. Moreover, the identified tools are based on conceptual reasoning and not on empirical studies of tools used by managers. Implications for managers are that most BMI tools are intended for, and have their strength in, experimenting with the design or redesign of a business model, placing it in a value chain and value network ecosystem. As most of these BMI tools have a visual representation, such as the business model canvas, they facilitate creativity and analysis work in management groups with different functions, e.g., in workshops with visual boards. Moreover, while most BMI tools assume the profit objective it is important for managers to make the objective of the BMI process explicit as many of the tools may be used also for growth or creating new businesses. Making the objective explicit might be especially important for managers in government or non-profit organizations as these organizations often have other objectives than profit and growth. A final implication for managers is that BMI tools are mainly developed to support managers when established companies need to change their business model in three types of situations; changes due to digitalization, to develop and sharpen the company’s competitive advantages and when developing new businesses, i.e., corporate venturing.

Traffic flows play a very important role in transportation engineering. In particular, link flows are a source of information about the traffic state, which is usually available from the authorities that manage road networks. Link flows are commonly used in both short-term and long-term planning models for operation and maintenance, and to forecast the future needs of transportation infrastructure. In this paper, we propose a model to study how traffic flow in one location can be expected to reflect the traffic flow in a nearby region. The statistical basis of the model is derived from link flows to find estimates of the distribution of traffic flows in junctions. The model is evaluated in a numerical study, which uses real link flow data from a transportation network in southern Sweden. The results indicate that the model may be useful for studying how large departing flows from a node reflect the link flows in a neighboring geographic region.

For acute medical conditions, for instance strokes, the time until the start of the treatment is a crucial factor to prevent a fatal outcome and to facilitate the recovery of the patient’s health. Hence, the planning and optimization of patient logistics is of high importance to ensure prompt access to healthcare facilities in case of medical emergencies. Computer simulation can be used to investigate the effects of different stroke logistics policies under realistic conditions without jeopardizing the health of the patients. The success of such policies greatly depends on the behavior of the individuals. Hence, agent-based simulation is particularly well-suited as it imitates human behavior and decision-making by means of artificial intelligence, which allows for investigating the effects of policies under different conditions. Agent-based simulation requires the generation of a realistic synthetic population, that adequately represents the population that shall be investigated such that reliable conclusions can be drawn from the simulation results. In this article, we propose a process for generating an artificial population of potential stroke patients that can be used to investigate the effects of stroke logistics policies using agent-based simulation. To illustrate how this process can be applied, we present the results from a case study in the region of Skåne in southern Sweden, where a synthetic population of stroke patients with realistic mobility behavior is simulated. 

In this paper, we developed a power line communication (PLC) system design, power measurement sensor design, light sensor design, temperature sensor design, and the integration of these components into an advanced sensor network to allow for energy metering and environment monitoring. A power measurement sensor was implemented through a current and voltage sensing circuitry was interfaced multi-plug power adapter to allow for non-invasive measurement of power usage of appliances. The sensors produce signals corresponding to the drawn voltage and current, which are then sampled and processed to estimate power usage. The PLC communications operated at an average accuracy of 95%. The power measurement sensor had an accuracy of 92%, which made it appropriate for home user estimations. The light sensor had an accuracy of between 91-97%, which was suitable for home lighting measurement.

Forecasting in financial markets is challenging due to the inherent randomness of financial data sources and the vast number of factors that affect the market trends. Thus, it is essential to find informative elements within the vast number of available factors to enhance the performance of the predictive models in such a vital context. This makes the feature selection process an integral part of the financial prediction. In this paper, we propose a multi-objective evolutionary algorithm to reduce the number of features employed to predict the yearly performance of the US stock market. The primary idea is to select a smaller set of features with the slightest similarity and the best prediction accuracy. In this practice, we have utilized genetic algorithm, XGBoost and correlation in order to obtain a more diverse set of features which increases the performance. Experiential results show that our proposed approach is able to reduce the number of features significantly while maintaining comparable prediction accuracy.

Augmented reality (AR) games are a rich environment for researching and testing computational systems that provide subtle user guidance and training. In particular computer systems that aim to augment a user’s situation awareness benefit from the range of sensors and computing power available in AR headsets. The main focus of this work-in-progress paper is the introduction of the concept of the individualized Situation Awareness-based Attention Guidance (SAAG) system used to increase humans’ situating awareness and the augmented reality version of the board game Carcassonne for validation and evaluation of SAAG. Furthermore, we present our initial work in developing the SAAG pipeline, the generation of game state encodings, the development and training of a game AI, and the design of situation modeling and eye-tracking processes.  

Artificial intelligence is increasingly becoming important to businesses since many companies have realized the benefits of applying Machine Learning (ML) and Deep Learning (DL) into their operations. Nevertheless, ML/DL technologies' industrial development and deployment examples are still rare and generally confined within a small cluster of large international companies who are struggling to apply ML more broadly and deploy their use cases at a large scale. Meanwhile, current AI market has started offering various solutions and services. Thus, organizations must understand how to acquire AI technology based on their business strategy and available resources. This paper discusses the industrial experience of developing and deploying ML/DL use cases to support organizations in their transformation towards AI. We identify how various factors, like cost, schedule, and intellectual property, can be affected by the choice of approach towards ML/DL project development and deployment within large international engineering corporations. As a research result, we present a framework that covers the trade-offs between those various factors and can support engineering companies to choose the best approach based on their long-term business strategies and, therefore, would help to accomplish their ML/DL project deployment successfully.  

MnemoRoom4U is an AR (Augmented Reality) tool that uses a memory-palace strategy for foreign-language training. A memory palace helps information recall with the aid of object association in visualizations of familiar spatial surroundings. In MnemoRoom4U, paper or digital flashcards are re-placed with virtual notes containing L1 words and their L2 translations that are placed on top of real physical objects inside a familiar environment, such as one’s room, home, office space, etc. The AR-supported notes aid associative memory by establishing a relationship between the physical objects in the user’s mind and the virtual lexis to be retained in L2. Learners first set up a path through their familiar environment, attaching virtual sticky notes—each containing a target word to be memorized together with its corresponding source-language translation—to real-life objects (e.g. furniture in their homes or offices). They then take the same path again, reviewing all the words, and finally carry out a retention test. MnemoRoom4U is a technological artifact designed for specific didactic purposes in the Unity game engine with the ARCore augmented-reality plug-in for Android. This work takes a Design-Science approach with phenomenological, exploratory underpinnings tracking back to the efficiency of spatial mnemonics previously reported quantitatively and combines it with AR technology to effect L2 vocabulary recall.