The development of autonomous vehicles has prompted an interest in exploring various techniques in navigation. One such technique is simultaneous localization and mapping (SLAM), which enables a vehicle to comprehend its surroundings, build a map of the environment in real time, and locate itself within that map. Although traditional techniques have been used to perform SLAM for a long time, recent advancements have seen the incorporation of neural network techniques into various stages of the SLAM pipeline. This review article provides a focused analysis of the recent developments in neural network techniques for SLAM-based localization of autonomous ground vehicles. In contrast to the previous review studies that covered general navigation and SLAM techniques, this paper specifically addresses the unique challenges and opportunities presented by the integration of neural networks in this context. Existing review studies have highlighted the limitations of conventional visual SLAM, and this article aims to explore the potential of deep learning methods. This article discusses the functions required for localization, and several neural network-based techniques proposed by researchers to carry out such functions. First, it presents a general background of the issue, the relevant review studies that have already been done, and the adopted methodology in this review. Then, it provides a thorough review of the findings regarding localization and odometry. Finally, it presents our analysis of the findings, open research questions in the field, and a conclusion. A semisystematic approach is used to carry out the review.

On-demand transport services are often envisioned as stand-alone modes or as a replacement for conventional public transport modes. This leads to a comparison of service efficiencies, or direct competition for passengers between them. The results of this work point to the positive effects of the inclusion of DRT into the public transport network. We simulate a day of operation of a DRT service in a rural area and demonstrate that a DRT system that focuses on increasing accessibility for travellers with poor public transport access can be quite efficient, especially for reducing environmental impact. We show that DRT, while it produces more vehicle kilometres than private cars would inside the DRT operating zone, can help to reduce the vehicle kilometres travelled for long-distance trips. The results of this study indicate the need for a more systemic evaluation of the impact of the new mobility modes.

Sweden is seen as one of the most research-driven and educated countries in the world. Thus, Doctoral education is considered one of Sweden's most important parts of higher education. This position paper reflects upon the process and product of doctoral education in Computer Science and Software Engineering in Sweden. The paper provides an overview of doctoral education in Sweden, followed by a practical demonstration of how supervisors and doctoral students could use constructive alignment to achieve the learning outcomes of doctoral education using learning activities and assessment methods to evaluate the learning activities and, by extension, the learning outcomes.

Digital transformation (DT) in Higher Education Institutions (HEIs) affects the learning environment with digitally-enhanced teaching methods, student assistance, and administration processes. HEIs develop strategies to exploit the chances offered by DT. Our study investigates the connection between the strategic work of HEIs on DT and how students perceive the results of this work in their daily studies. We applied a case study design on a Swedish HEI to gather our results. Results show that students are somewhat positive about the strategic work but still perceive digitalization as a barrier to collaborating with peers and lecturers. Our research contributes to knowing if the time and effort spent on an HEI’s DT impact the student stakeholder group. By bringing forth ways of improvement, we generate new knowledge about DT processes in HEIs. Thus, we inspire educators and administrators in this industry by putting forward lessons learned and improvements.

This article explores monetization and networking strategies within the consolidating creator economy. Through a large-scale study of linking practices on YouTube, we investigate how creators seek to build their online presence across multiple platforms and widen their income streams. In particular, we build on a near-complete sample of 153,000 "elite" YouTube channels with at least 100,000 subscribers, retrieved at the end of 2019, and investigate the URLs found in 137 million video descriptions to analyze traces of these strategies. We first situate our study within relevant literature around the creator economy, the role of platforms, and issues such as social capital building and economic precarity. We then outline our data and analytical approach, followed by a presentation of our findings. The article finishes with a discussion on how monetization and networking strategies via placing URLs in video descriptions have become more important over time, but also differ substantially between channel sizes, content categories, and geographic locations. Our empirical analysis shows that YouTube, as a highly unequal platformed media system, thrives on the economic pressures it exerts on its creators.

User location is becoming an increasingly common and important feature for a wide range of services. Smartphone owners increasingly use location-based services, as service providers add context-enhanced functionality such as car-driving routes, COVID-19 tracking, crowdedness indicators, and suggestions for nearby points of interest. However, positioning a user indoors is still problematic due to the fading of the radio signal caused by multipath and shadowing, where both have complex dependencies on the indoor environment. Location fingerprinting is a common positioning method where Radio Signal Strength (RSS) measurements are compared to a reference database of previously stored RSS values. Due to the size of the reference databases, these are often stored in the cloud. However, server-side positioning computations make preserving the user's privacy problematic. Given the assumption that a user does not want to communicate his/her location, we pose the question of whether a passive system with client-side computations can substitute fingerprinting-based systems, which commonly use active communication with a server. We compared two passive indoor location systems based on multilateration and sensor fusion using an Unscented Kalman Filter (UKF) with fingerprinting and show how these may provide accurate indoor positioning without compromising the user's privacy in a busy office environment.

Predicting breakdowns is becoming one of the main goals for vehicle manufacturers so as to better allocate resources, and to reduce costs and safety issues. At the core of the utilization of vehicle sensors is the fact that early detection of anomalies facilitates the prediction of potential breakdown issues, which, if otherwise undetected, could lead to breakdowns and warranty claims. However, the making of such predictions is too complex a challenge to solve using simple predictive models. The strength of heuristic optimization techniques in solving np-hard problems, and the recent success of ensemble approaches to various modeling problems, motivated us to investigate a hybrid optimization- and ensemble-based approach to tackle the complex task. In this study, we propose a snapshot-stacked ensemble deep neural network (SSED) approach to predict vehicle claims (in this study, we refer to a claim as being a breakdown or a fault) by considering vehicle operational life records. The approach includes three main modules: Data pre-processing, Dimensionality Reduction, and Ensemble Learning. The first module is developed to run a set of practices to integrate various sources of data, extract hidden information and segment the data into different time windows. In the second module, the most informative measurements to represent vehicle usage are selected through an adapted heuristic optimization approach. Finally, in the last module, the ensemble machine learning approach utilizes the selected measurements to map the vehicle usage to the breakdowns for the prediction. The proposed approach integrates, and uses, the following two sources of data, collected from thousands of heavy-duty trucks: Logged Vehicle Data (LVD) and Warranty Claim Data (WCD). The experimental results confirm the proposed system's effectiveness in predicting vehicle breakdowns. By adapting the optimization and snapshot-stacked ensemble deep networks, we demonstrate how sensor data, in the form of vehicle usage history, contributes to claim predictions. The experimental evaluation of the system on other application domains also indicated the generality of the proposed approach.

Digitisation is among the macro-trends that significantly influence the business world in the twenty-first century. Firms striving to succeed in this environment must develop new strategic approaches. The accelerating development of information technology (IT) drives digitisation. Therefore, IT and business strategies must be integrated. In this context, the information systems literature promotes the concept of digital business strategies (DBSs), reflecting a fusion between IT and business strategies. However, knowledge of the types and characteristics of such DBSs is currently scarce. Therefore, we developed a conceptually and empirically grounded typology of DBS based on the well-known business strategy classification by Miles and Snow (1978). Using a dataset comprising 192 firms worldwide, we conducted a cluster analysis, identified basic types of DBS, and evaluated their effects on firm performance. Moreover, we identified four types of DBS: non-digital reactor, analyser, digital opportunist, and digital producer. The study contributes to a better understanding of new business strategy concepts in the digitisation context.

Continuous deployment has been practiced for many years by companies developing web- and cloud-based applications. To succeed with continuous deployment, these companies have a strong collaboration culture between the operations and development teams. In addition, these companies use AI, analytics, and big data to assist with time-consuming postdeployment activities such as continuous monitoring and fault identification. Thus, the term AIOps has evolved to highlight the importance and difficulty of maintaining highly available applications in a complex and dynamic environment. In contrast, software-intensive embedded systems often provide customer product-related services, such as maintenance, optimization, and support. These services are critical for these companies as they provide significant revenue and increase customer satisfaction. Therefore, the objective of our study is to gain an in-depth understanding of the impact of continuous deployment on product-related services provided by software-intensive embedded systems companies. In addition, we aim to understand how AIOps can support continuous deployment in the context of software-intensive embedded systems. To address this objective, we conducted a case study at a large and multinational telecommunications systems provider focusing on the radio access network (RAN) systems for 4G and 5G networks. The company provides RAN products and three complementing services: rollout, optimization, and customer support. The results from the case study show that the boundaries between product-related services become blurry with continuous deployment. In addition, product-related services, which were conducted in sequence by independent projects, converge with continuous deployment and become part of the same project. Further, AIOps platforms play an important role in reducing costs and increasing postdeployment activities' efficiency and speed. These results show that continuous deployment has a profound impact on the software-intensive system's provider service organization. The service organization becomes the connection between the R&D organization and the customer. In order to cope with the increased speed of releases, deployment and postdeployment activities need to be largely automated. AIOps platforms are seen as a critical enabler in managing the increasing complexity without increasing human involvement.

Deep reinforcement learning has made significant development in recent years, and it is currently applied not only in simulators and games but also in embedded systems. However, when implemented in a real-world context, reinforcement learning is frequently shown to be unstable and incapable of adapting to realistic situations, particularly when directing a large number of agents. In this paper, we develop a decentralized architecture for reinforcement learning to allow multiple agents to learn optimal control policies on their own devices of the same kind but in varied environments. For such multiple agents, the traditional centralized learning algorithm usually requires a costly or time-consuming effort to develop the best-regulating policy and is incapable of scaling to a large-scale system. To address this issue, we propose a decentralized reinforcement learning algorithm (DecRL) and information exchange scheme for each individual device, in which each agent shares the individual learning experience and information with other agents based on local model training. We incorporate the algorithm into each agent in the proposed collaborative architecture and validate it in the telecommunication domain under emergency conditions, in which a macro base station (BS) is broken due to a natural disaster, and three unmanned aerial vehicles carrying BSs (UAV-BSs) are deployed to provide temporary coverage for missioncritical (MC) users in the disaster area. Based on the findings, we show that the proposed decentralized reinforcement learning algorithm can successfully support multi-agent learning, while the learning speed and service quality can be further enhanced.

Interactive machine learning (ML) adds a human-in-the-loop aspect to a ML system. Even though the input from human users to the system is a central part of the concept, the uncertainty caused by the human feedback is often not considered in interactive ML. The assumption that the human user is expected to always provide correct feedback, typically does not hold in real-world scenarios. This is especially important for when the cognitive workload of the human is high, for instance in online learning from streaming data where there are time constraints for providing the feedback. We present experiments of interactive online ML with human participants, and compare the results to simulated experiments where humans are always correct. We found combining the two interactive learning paradigms, active learning and machine teaching, resulted in better performance compared to machine teaching alone. The results also showed an increased discrepancy between the experiments with human participants and the simulated experiments when the cognitive workload was increased. The findings suggest the importance of taking uncertainty caused by human factors into consideration in interactive ML, especially in situations which requires a high cognitive workload for the human.

Digital technologies have enabled novel forms and reconfigurations of value creation, delivery, and capture. These new reconfigurations challenge the conventional notion of value creation with digital business models. On that premise, the widening of privacy concerns, alert us that organizations of the elite digital, like Netflix, Amazon, and Spotify, design technology to feed on personal data, based on algorithmic profiling capabilities. Then, privacy itself becomes their digital business model. In this paper we conceptualize the impact of digital business models on privacy concerns, by presenting a focused literature review that presents 4 waves of research on understanding privacy from the context of digital business models. With our initial findings, we recommend that future technological development should pay central attention to privacy-preserving digital business models, by making it possible that data privacy is envisioned with the right safeguards, targeting 'invisibility' of the user.

Monitoring asthma is essential for self-management. However, traditional monitoring methods require high levels of active engagement, and some patients may find this tedious. Passive monitoring with mobile-health devices, especially when combined with machine-learning, provides an avenue to reduce management burden. Data for developing machine-learning algorithms are scarce, and gathering new data is expensive. A few datasets, such as the Asthma Mobile Health Study, are publicly available, but they only consist of self-reported diaries and lack any objective and passively collected data. To fill this gap, we carried out a 2-phase, 7-month AAMOS-00 observational study to monitor asthma using three smart-monitoring devices (smart-peak-flow-meter/smart-inhaler/smartwatch), and daily symptom questionnaires. Combined with localised weather, pollen, and air-quality reports, we collected a rich longitudinal dataset to explore the feasibility of passive monitoring and asthma attack prediction. This valuable anonymised dataset for phase-2 of the study (device monitoring) has been made publicly available. Between June-2021 and June-2022, in the midst of UK's COVID-19 lockdowns, 22 participants across the UK provided 2,054 unique patient-days of data.

Municipalities around the world have become increasingly reliant upon digital technologies in their everyday operations. In pursuit of a faster, cheaper, and more efficient local government, service platforms and applications that mediate citizen-government inter- actions, smart city infrastructures, and automated decision-making systems have proliferated. More recently, digital technologies are also sought to address socially complex issues and foster civic en- gagement. These ambitions, motivated by both rational and demo- cratic perspectives, however, confront many challenges such as de- signing with wide heterogeneous groups, navigating organizational structures, and dealing with the political agendas and conflicting perspectives of multiple stakeholders. Designing digital technolo- gies for municipalities, therefore, requires an ability to address the technical, social, institutional, and political challenges critically, practically, and holistically. This hybrid workshop aims to bring together researchers and practitioners to (1) explore how this could be achieved and (2) map the existing and emerging challenges and opportunities for designing public digital services and technologies.

This paper analyses how fans engage on Twitter with the 22 players in the starting line up during the week of the 2019 UEFA Women's Champions League final. It explores fans' entangled representations of female professional footballers on Twitter from a postfeminist sensibility. Out of 200 tweets posted by the players during the day of the final and the week after, the research focuses on the 1468 fans replies to the 20 most engaged players' tweets. To facilitate this, we developed an analysis instrument called the 3Fs Spiral, which helps to disentangle the complex meanings of the fans' replies on Twitter. Results highlight the fans' entangled representations and the continuous flow of disruption and reinforcement of the gender order that emerge from them in a set of tweets. The decentralised nature of Twitter has the potential to slowly promote the change of dominant gender narratives and frames in female football.

Ugandan urban same-sex desiring individuals frequently encounter and navigate competing understandings of sexuality and sexual identity. Western essentialist understanding of sexual identity introduced by international development partners and transnational LGBT+ (Lesbian, Gay, Bi- and Transsexual) activism, as well as media, offer an alternative to Ugandan non-essentialist and fluid subject positions. This article seeks to understand how young individuals with same-sex -desires in Kampala navigate tensions between Western and local understandings concerning sexuality. We have interviewed 24 young individuals with same-sex desires (unaffiliated and individuals working in LGBT+ organizations) and asked how they approach their sexuality and experiences living with same-sex desires in contemporary Kampala. The results reveal how interview participants engaged in a complex navigation between local community expectations, their own same-sex desires, and embeddedness in a global LGBT+ culture. Although the participants engaged in what Westerners would label as a "double life," the article problematizes the prescriptive norms of authenticity and "coming out." The conclusion is that the fluid vs essentialist dichotomy is too simplistic to be helpful when trying to understand the lives and aspirations of young people with same-sex desires.

Tracking a person’s activities is relevant in a variety of contexts, from health and group-specific assessments, such as elderly care, to fitness tracking and human–computer interaction. In a clinical context, sensor-based activity tracking could help monitor patients’ progress or deterioration during their hospitalization time. However, during routine hospital care, devices could face displacements in their position and orientation caused by incorrect device application, patients’ physical peculiarities, or patients’ day-to-day free movement. These aspects can significantly reduce algorithms’ performances. In this work, we investigated how shifts in orientation could impact Human Activity Recognition (HAR) classification. To reach this purpose, we propose an HAR model based on a single three-axis accelerometer that can be located anywhere on the participant’s trunk, capable of recognizing activities from multiple movement patterns, and, thanks to data augmentation, can deal with device displacement. Developed models were trained and validated using acceleration measurements acquired in fifteen participants, and tested on twenty-four participants, of which twenty were from a different study protocol for external validation. The obtained results highlight the impact of changes in device orientation on a HAR algorithm and the potential of simple wearable sensor data augmentation for tackling this challenge. When applying small rotations (<20 degrees), the error of the baseline non-augmented model steeply increased. On the contrary, even when considering rotations ranging from 0 to 180 along the frontal axis, our model reached a f1-score of 0.85±0.110.85±0.11 against a baseline model f1-score equal to 0.49±0.120.49±0.12.

This paper concerns the novel concept of an Interactive Dynamic Intelligent Virtual Sensor (IDIVS), which extends virtual/soft sensors towards making use of user input through interactive learning (IML) and transfer learning. In research, many studies can be found on using machine learning in this domain, but not much on using IML. This paper contributes by highlighting how this can be done and the associated positive potential effects and challenges. An IDIVS provides a sensor-like output and achieves the output through the data fusion of sensor values or from the output values of other IDIVSs. We focus on settings where people are present in different roles: from basic service users in the environment being sensed to interactive service users supporting the learning of the IDIVS, as well as configurators of the IDIVS and explicit IDIVS teachers. The IDIVS aims at managing situations where sensors may disappear and reappear and be of heterogeneous types. We refer to and recap the major findings from related experiments and validation in complementing work. Further, we point at several application areas: smart building, smart mobility, smart learning, and smart health. The information properties and capabilities needed in the IDIVS, with extensions towards information security, are introduced and discussed.

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.

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.

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.

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.

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.