The Internet of Things has become a central and exciting research area encompassing many fields in information and communication technologies and adjacent domains. IoT systems involve interactions with heterogeneous, distributed, and intelligent things, both from the digital and physical worlds including the human in the loop. Thanks to the increasingly wide spectrum of applications and cheap availability of both network connectivity and devices, a number of different stakeholders from industry, academia, society and government are part of the IoT ecosystem.

A set of important criteria to consider when evaluating potential road user charging system (RUCS) are identified. These criteria are grouped into five categories: charging precision, system costs & societal benefits, flexibility & modifiability, operational aspects, and security & privacy. The criteria are then used in a comparative analysis of five RUCS candidates for heavy goods vehicles. Two solutions are position-based systems and one is based on tachographs. The two remaining solutions are based on fuel taxes. For each of the solutions we estimate how well it fulfils each of the criteria. One way of making general comparisons of the approaches is to give each of the criteria a specific weight corresponding to how important it is. We show that these weights heavily influence the outcome of the comparison. We conclude by pointing out a number of important issues needing attention in the process of developing RUCS.

Social phenomena simulation in the area of agent-based modeling and simulation concerns the emulation of the individual behavior of a group of social entities, typically including their cognition, actions, and interaction.

This book constitutes the refereed proceedings of the 15th International Conference on Practical Applications of Scalable Multi-Agent Systems, PAAMS 2017, held in Porto, Portugal, in June 2017.The 11 revised full papers, 11 short papers, and 17 Demo papers were carefully reviewed and selected from 63 submissions. The papers report on the application and validation of agent-based models, methods, and technologies in a number of key application areas, including day life and real world, energy and networks, human and trust, markets and bids, models and tools, negotiation and conversation, scalability and resources.  

We study several problems of clearing subgraphs by mobile agents in digraphs. The agents can move only along directed walks of a digraph and, depending on the variant, their initial positions may be pre-specified. In general, for a given subset S of vertices of a digraph D and a positive integer k, the objective is to determine whether there is a subgraph H = (V-H, A(H)) of D such that (a) S subset of V-H, (b) H is the union of k directed walks in D, and (c) the underlying graph of H includes a Steiner tree for S in D. Since a directed walk is a not necessarily a simple directed path, the problem is actually on covering with paths. We provide several results on the polynomial time tractability, hardness, and parameterized complexity of the problem. Our main fixed-parameter algorithm is randomized. (C) 2018 Elsevier Inc. All rights reserved.

During the COVID-19 crisis there have been many difficult decisions governments and other decision makers had to make. E.g. do we go for a total lock down or keep schools open? How many people and which people should be tested? Although there are many good models from e.g. epidemiologists on the spread of the virus under certain conditions, these models do not directly translate into the interventions that can be taken by government. Neither can these models contribute to understand the economic and/or social consequences of the interventions. However, effective and sustainable solutions need to take into account this combination of factors. In this paper, we propose an agent-based social simulation tool, ASSOCC, that supports decision makers understand possible consequences of policy interventions, but exploring the combined social, health and economic consequences of these interventions.

Large development projects and programs are conducted using agile development methods, with an increasing body of advice from practitioners and from research. This sixth workshop showed in increasing interest in scaling frameworks and in topics related to achieving business agility. This article summarizes four contributed papers, discussions in "open space" format and also presents a revised research agenda for large-scale agile development.

Uncertainty is intrinsic in most of the complex systems, especially when the systems have to interact with the physical environment; therefore, handling uncertainty is critical in the Internet of Things (IoT). In this paper, we propose a semantic-based approach to build the belief network in IoT systems to handle the uncertainties. Semantics is the functionality description of any system component. Semantic Match mechanisms can construct the appropriate structures to compare the consistency between different sources of data based on the same functionality. In the approach, we define the belief property of every system component and develop the related algorithms to update the belief value. Furthermore, the related mechanisms and algorithms for data fusion and fault detection based on the belief property are described to explain how the approach works in the IoT systems. Several simulation experiments are used to evaluate the proposed approach, and the results indicate that the approach can work as expected. More accurate data are fused from the inaccurate devices and the fault in one node is automatically detected.

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

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

Effective monitoring of the slope deformation of an open-pit mine is essential for preventing catastrophic collapses. It is a challenging task to accurately predict slope deformation. To this end, this article proposed a new machine-learning method for slope deformation prediction. Ground-based interferometric radar (GB-SAR) was employed to collect the slope deformation data from an open-pit mine. Then, an ensemble learner, which aggregated a set of weaker learners, was proposed to mine the GB-SAR field data, delivering a slope deformation prediction model. The evaluation of the field data acquired from the Anjialing open-pit mine demonstrates that the proposed slope deformation model was able to precisely predict the slope deformation of the monitored mine. The prediction accuracy of the super learner was superior to those of all the independent weaker learners.

Public transport is an efficient way to transport large volumes of travellers. However, there are systemic issues that make it hard for conventional public transport to provide efficient service on finer levels, like first- and last-mile problems or low-demand areas. One of the potential solutions that has been getting a lot of attention recently in research and real practice is Demand-Responsive Transport(DRT). The main difference between demand-responsive services and conventional public transport is the need for explicit requests for a trip from the travellers. The service then adapts the routes of the vehicles to satisfy the requests as efficiently as possible. One of the aims of such transport services is to combine the flexibility and accessibility of travel modes like taxis and private cars with the efficiency of buses achieved through ride-sharing.DRT has the potential to improve public transport in, for example, low population density areas or for people with mobility limitations who could request a trip directly to a home door. Historically DRT has been extensively used for special transportation while the recent trend in research and practice explores the possibility of using this service type for the general population.The history of DRT shows a large degree of discontinued trials and services together with low utilisation of vehicles and limited efficiency levels. In practice, this leads to measures restricting the trip destination, times when service is available, or eligibility to use the service at all in case of special transport DRT. Due to the limited use of DRT services, there is little data collected on the efficiency of the service and transport agencies exploring the possibility of introducing this new service type face difficulties in estimating its potential.The main goal of this thesis is to contribute towards developing a decisionsupport method for transport analysts, planners, or decision-makers who want to evaluate the systemic effect of a DRT service such as costs, emissions and effecton society. Decision-makers should be able to evaluate and compare a large variety of DRT design choices like booking time restrictions, vehicle fleet type, target trip quality level, or stop allocation pattern. Using a design science, we develop a simulation approach which is evaluated with two simulation experiments. The simulation experiments themselves provide valuable insight into the potential of DRT services, explore the niche where DRT could provide the most benefits and advocate taking into account the sustainability perspective for a comprehensive comparison of transport modes.

The findings from the simulation experiments indicate that DRT, even in its extreme forms like fully autonomous shared taxis, does not show the level of efficiency that could result in a revolution in transportation — it is hard to compete inefficiency with conventional public transport in urban zones. However, in scenarios with lower demand levels, it could be more efficient to replace conventional buses with a DRT service when considering costs and emissions. We also show that, when integrated with conventional public transport, DRT could help alleviate the last-mile problem by improving accessibility to long-distance lines. Additionally, if car users are attracted to public transport with the help of DRT, there is a potential to significantly reduce the total level of emissions.

The simulation results indicate that the proposed simulation method can be applied for the evaluation of DRT. The implementation of trip planning combining DRT and conventional public transport is a major contribution of this thesis. We show that the integration between services may be important for the efficiency of the service, especially when considering the sustainability aspects.

Finally, this thesis indicates the direction for further research. The proposed simulation approach is suitable for the estimation of the potential of DRT but lacks the ability to make a prediction of the demand for DRT. Integration of a realistic mode choice model and day-to-day simulations are important for making predictions. We also note the complexity of the DRT routing for large-scale problems which prohibits a realistic estimation with simulation and the efficient operation of the service.

Demand Responsive Transport (DRT) is seen as a means to providing mobility for passengers living in lowdensity population areas and impaired passengers with a reasonable cost. Conventional public transport istoo expensive to provide a desired level of mobility for these categories of passengers. Hence DRT hasbeen introduced in order to replace or supplement existing transportation schemes. However, multiple DRTschemes were discontinued due to a high cost or poor patronage. In this work we argue that a simulationtool is required to analyze DRT applicability in given conditions before implementing it. As a first steptowards this tool, we describe the requirements that DRT impose on a simulator.

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.

For the provision of efficient and high-quality public transport services in rural areas with a low population density, the introduction of Demand Responsive Transport (DRT) services is reasonable. The optimal design of such services depends on various socio-demographical and environmental factors, which is why the use of simulation is feasible to support planning and decision-making processes. A key challenge for sound simulation results is the generation of realistic demand, i.e., requests for DRT journeys. In this paper, a method for modelling and simulating commuting activities is presented, which is based on statistical real-world data. It is applied to Sjöbo and Tomelilla, two rural municipalities in southern Sweden.

This article demonstrates an approach to the simulation of Demand Responsive Transport (DRT) – a flexible transport mode that typically operates as a combination of taxi and bus modes. Travellers request individual trips and DRT is capable of adjusting its routes or schedule to the needs of travellers. It has been seen as a part of the public transport network, which has the potential to reduce operational costs of public transport services, to provide better service quality for population groups with limited mobility and to improve transport fairness. However, a DRT service needs to be thoroughly planned to target the intended user groups, attract a sufficient demand level and maintain reasonable operational costs. As the demand for DRT is dynamic and heterogeneous, it is difficult to simulate it with a macro approach. To address this problem, we develop and evaluate an individual-based simulation comprising models of traveller behaviour for both supply and demand sides. Travellers choose a trip alternative with a mode choice model and DRT vehicle routing utilises a model of travellers’ mode choice behaviour to optimise routes. This allows capturing supply-side operational costs and demand-side service quality for every individual, what allows for designing a personalised service that can prioritise needy groups of travellers improving transport fairness. By simulating different setups of DRT services, the simulator can be used as a decision support tool.

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

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

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.  

This study presents lessons learned based on practical experiences of connecting devices to internet-of-things platforms in the context of research and academic coursework. The experiences are gathered from six research projects, one undergraduate course, and a few undergraduate theses over a three-year period. The lessons learned include: the trade-off of rapid prototyping over security is very common, example source code is not up to production standards, adherence to standards speeds development, debugging support for IoT systems is lacking, open source licenses varies, poor platform interoperability, and the array of service fees among platform providers obstruct cost comparisons.

Agile software development principles enable companies to successfully and quickly deliver software by meeting their customers' expectations while focusing on high quality. Many companies working with pure software systems have adopted these principles, but implementing them in companies dealing with non-pure software products is challenging. We identified a set of goals and practices to support large-scale agile development in companies that develop software-intense mechatronic systems. We used an inductive approach based on empirical data collected during a longitudinal study with six companies in the Nordic region. The data collection took place over two years through focus group workshops, individual on-site interviews, and complementary surveys. The primary benefit of large-scale agile development is improved quality, enabled by practices that support regular or continuous integration between teams delivering software, hardware, and mechanics. In this regard, the most beneficial integration cycle for deliveries is every four weeks; while continuous integration on a daily basis would favor software teams, other disciplines does not seem to benefit from faster integration cycles. We identified 108 goals and development practices supporting agile principles among the companies, most of them concerned with integration; therefrom, 26 agile practices are unique to the mechatronics domain to support adopting agile beyond pure software development teams. 16 of these practices are considered as key enablers, confirmed by our control cases.

In our connected world, recommender services have become widely known for their ability to provide expert and personalize information to participants of diverse applications. The excessive growth of social networks, a new kind of services are being embraced which are termed as "group based recommendation services", where recommender services can be utilized to discover sub-communities within implicit social groups and provide referrals to new participants in order to join various sub-communities of other participants who share similar preferences or interests. Nevertheless, protecting participants' privacy in recommendation services is a quite crucial aspect which might prevent participants from exchanging their own data with these services, which in turn detain the accuracy of the generated referrals. So in order to gain accurate referrals, recommendation services should have the ability to discover previously unknown sub-communities from different social groups in a way to preserve privacy of participants in each group. In this paper, we present a middleware that runs on end-users' mobile phones to sanitize their profiles' data when released for generating referrals, such that computation of referrals continues over the sanitized version of their profiles' data. The proposed middleware is equipped with cryptography protocols to facilitate private discovery of sub-communities from the sanitized version of participants' profiles in a university scenario. Location data are added to participants' profiles to improve the awareness of surrounding sub-communities, so the offered referrals can be filtered based on adjacent locations for participant's location. We performed a number of different experiments to test the efficiency and accuracy of our protocols. We also developed a formal model for the tradeoff between privacy level and accuracy of referrals. As supported by the experiments, the sub-communities were correctly identified with good accuracy and an acceptable privacy level.

Digitalizing tabletop games for general game playing(GGP) AI research is a continuously growing field. TabletopGames Framework (TAG) is a framework developed to simplifythe process of implementing tabletop board games to digital form.Sushi Go! is a game that combines simultaneous action selectionand complete information. This creates a unique combination ofmechanics, which presents a new challenge for GGP agents. Byimplementing Sushi Go! into TAG, we can test different agent’sperformance using these mechanics and compare them to theirexisting performances in the other games of TAG. Results ofthis testing are presented, which display that the framework iscapable of implementing Sushi Go! and that the agents performwith mixed results. Further developing heuristics for the agentsshould prove to increase their performance when faced with thesetypes of games.

In recent years design with augmented reality applications for cultural heritage purposes have increased and their usefulness for informal learning and tourist experiences is improving (Haugstvedt and Krogstie, 2012; Liestøl 2014). However, there are still significant challenges with using Augmented Reality technology for cultural heritage applications in open urban environments using GPS location. Even if the potential for rich experiences is great, the continued lack of precision of available GPS location and direction in smart phones create particular challenges for the interaction and experience design. This paper presents the experiences from a project that underwent several iterations in 2017 and 2018, using mobile Augmented Reality and 360 panoramic photography in a mobile application that foregrounded historical narratives in urban heritage environments. Specifically, the narratives were about the colonial past in the Danish capital Copenhagen, a past whose traces are still present in the architecture and history of noted places such as the famous Tivoli in the city as well as in archives and museums. This contested and fragmented colonial past live in digital archives that require design and exhibition practices in order to find their way to a larger audience. 

Our project Finding Alberta was one such intervention. The extended reality (XR) web-based application, using a now depreciated platform called Argon (Speiginer et al 2015) but which was created using web programming and therefore is transferable, was part of a larger set of experiences, workshops and installations that brought to life black persons who were once taken to Denmark from the Virgin Islands, then under Danish rule. The point of the urban AR experience was to let the visitor follow in the footsteps of two children - Victor and Alberta - in order to better comprehend their lives and ultimately their fate in Denmark, from the human exhibition to early death of Alberta in 1917. However, the difficulty of properly leading visitors to GPS points and understand fully in what directions they are facing once they reach those points proved a design challenge that we were only partially able to successfully work around. This paper presents some of the design choices we made in order to still create a compelling experience while working around the limits of the affordances of mobile AR.

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.

In this article, the authors examine the aesthetics of immersion in two emerging media forms: 360° video and 3D VR. Their goal is to move beyond addressing technical affordances, to consider the techniques and choices that producers of 360° video and 3D VR are making to exploit these affordances, and what resulting effects those viewing experiences have. They discuss the tension between transparency and reflectivity in two contrasting examples, in particular: the Danish company Makropol’s Anthropia (2017) and Arora and Unseld’s The Day the World Changed (2018). The authors argue that technical affordances are part of a complex process of mediation that includes both experimentation with the technology at hand and a reliance on earlier media forms. It is critical, they argue, to understand the creative tension between established forms and new ones that underscore new aesthetic and narrative experiences in VR and 360° formats.

We report here on an application of reality media(virtual and augmented reality) to digital culturalheritage. The particular challenge we address is:how to combine VR and AR to bridge the gap betweenthe center (the museum housing cultural artifacts)and periphery (the heritage site where theartifacts were found) while at the same time attendingto, even enhancing, the aura of both artifacts andsites? Our proposed solution is to implement thecultural heritage technique known as situated simulation(sitsim) in combination with a social virtual environmentcalled Hubs. Our case study is a sitsim ofthe Acropolis in Athens, which can function on locationand remotely and offers real-time conferencingcapabilities for its participants.

This paper presents an ongoing experiment using WebXR to create something analogous to a non-fiction book in AR and VR; an immersive, interactive experience that stands on its own, rather than merely complementing a traditional book. The book introduces the reader/user to AR and VR both as technologies and as media. The printed book is one of the more influential communicative interfaces in history. AR and VR have the potential to remediate several genres of printed books, but somewhat different conventions may need to be developed for different combinations of genre and modality. The lessons learned through this experiment should contribute to the establishment of guidelines for this new form of multimedia, in particular conventions that facilitate the reader/user’s transition from discursive to immersive modes and back.

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.

A re-materialisation of the visual in terms of viscosity is provided by this article. The argument is grounded in practical design processes from on-going research in the integration of archival material into AR/MR environments (Augmented Reality and Mixed Reality). This is an approach to emergent materiality not because new materials are invented but because existing visual, digital and traditional craft materials are re-configured. The archival material we use for this project is visual rather than textual, and it portrays moving bodies. The re-materialisation happens through experimentation with materials, affect and perception. Visual materialities, in this case viscosity, rely on a phenomenological approach to vision whereby design materials cannot be separated from the active perception of the designers, the participants and even the materials themselves. This article outlines the final iteration of the AffeXity project where glass was used as a design material to enhance viscous materiality. Viscosity is experienced as depth, layers, stickiness, reflections, motion, and an affective quality of dreaminess or the passage of time.

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.

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

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

Indoor positioning is important for applications within Internet of Things, such as equipment tracking and indoor maps. Inexpensive Bluetooth-beacons have become common for such applications, where the distance is estimated using the Received Signal Strength. Large installations require substantial efforts, either in determining the exact location of all beacons to facilitate lateration, or collecting signal strength data from a grid over all locations to facilitate fingerprinting. To reduce this initial setup cost, one may infer the positions using Simultaneous Location and Mapping. In this paper, we use a mobile phone equipped with an Inertial Measurement Unit, a Bluetooth receiver, and an Unscented Kalman Filter to infer beacon positions. Further, we apply adaptive noise modeling in the filter based on the estimated distance of the beacons, in contrast to using a fixed noise estimate which is the common approach. This gives us more granular control of how much impact each signal strength reading has on the position estimates. The adaptive model decreases the beacon positioning errors by 27% and the user positioning errors by 21%. The positioning accuracy is 0.3 m better compared to using known beacon positions with fixed noise, while the effort to setup and maintain the position of each beacon is also substantially reduced. Therefore, adaptive noise modeling of Received Signal Strength is a significant improvement over static noise modeling for indoor positioning.

Our phones have many uses for positioning technologies, such as navigation, LocationBased Services (LBS), emergency positioning, fitness applications, and advertising. We trust our phones and wearables to be location-aware. However, as soon as we enter a building, we can no longer use GPS signals, as their already weak signals are well below the background noise of the environment. This requires us to develop alternatives, such as installing active radio beacons, using existing radio infrastructure, applying environmental sensing based on barometric pressure and magnetic fields, or utilizing Inertial Measurement Units (IMUs) to estimate the user location. This licentiate thesis aims to evaluate beacon-based indoor positioning, where we assume installing a set of small battery-powered Bluetooth low-energy (BLE) beacons are possible. In particular, the thesis addresses essential factors such as installation effort, accuracy, the privacy aspects of an Indoor Positioning System(IPS), and mitigation of accuracy issues related to radio signal shadowing in complex indoor environments. The goal is to solve some obstacles to the widespread adoption of indoor positioning solutions.

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.

Our dependency on Global Navigation Satellite System (GNSS) for getting directions, tracking items, locating friends, or getting maps of the world has increased tremendously over the last decade. However, as soon as we enter a building, the signal strength of the satellites is too low, and we need to resort to other technologies to achieve the same goals. An Indoor Positioning System (IPS) may utilize a wide range of methods for positioning a device, such as fingerprinting, multilateration, or sensor fusion, while using one or several radio technologies to measure Received Signal Strength (RSS) or Time of Arrival(ToA). Sensor fusion is an efficient approach where an Inertial Measurement Unit (IMU) is combined with, e.g., RSS measurements converted to distances. But this approach has significant drawbacks in areas where, e.g., walls or large objects obstruct the signal path, which introduces bias in the distance estimates. This paper addresses the bias caused by signal path obstruction by compensating the measured RSS with localized RSS attenuation adjustments and thereby increasing the accuracy of the sensor fusion model significantly. We also show that a system can learn the compensation parameters over time, reducing the installationefforts and achieving higher accuracy than a fingerprinting-based system.

A first step towards emotional well-being is to monitor, understand and reflect upon one’s feelings and emotions. A number of personal emotion-tracking applications are available today. In this paper we describe an examination of these applications which indicates that many of the applications do not provide sufficient support for monitoring a full spectrum of emotional data or for analysing or using the data that is provided. To design applications that better support emotional well-being, the full capabilities of the Internet of Things should be utilized. The paper concludes with a description of how Internet of Things technologies can enable the development of systems that can more accurately capture emotional data and support personal learning in the area of emotional health.

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.

Accurately learning what customers value is critical for the success of every company. Despite the extensive research on identifying customer preferences, only a handful of software companies succeed in becoming truly data-driven at scale. Benefiting from novel approaches such as experimentation in addition to the traditional feedback collection is challenging, yet tremendously impactful when performed correctly. In this thesis, we explore how software companies evolve from data-collectors with ad-hoc benefits, to trustworthy data-driven decision makers at scale. We base our work on a 3.5-year longitudinal multiple-case study research with companies working in both embedded systems domain (e.g. engineering connected vehicles, surveillance systems, etc.) as well as in the online domain (e.g. developing search engines, mobile applications, etc.). The contribution of this thesis is three-fold. First, we present how software companies use data to learn from customers. Second, we show how to adopt and evolve controlled experimentation to become more accurate in learning what customers value. Finally, we provide detailed guidelines that can be used by companies to improve their experimentation capabilities. With our work, we aim to empower software companies to become truly data-driven at scale through trustworthy experimentation. Ultimately this should lead to better software products and services.

Online Controlled Experiments (OCEs) are the norm in data-driven software companies because of the benefits they provide for building and deploying software. Product teams experiment to accurately learn whether the changes that they do to their products (e.g. adding new features) cause any impact (e.g. customers use them more frequently). Experiments also help reduce the risk from deploying software by minimizing the magnitude and duration of harm caused by software bugs, allowing software to be shipped more frequently. To make informed decisions in product development, experiment analysis needs to be granular with a large number of metrics over heterogeneous devices and audiences. Discovering experiment insights by hand, however, can be cumbersome. In this paper, and based on case study research at a large-scale software development company with a long tradition of experimentation, we (1) describe the standard process of experiment analysis, and (2) introduce an artifact to improve the effectiveness and comprehensiveness of this process.

Online Controlled Experiments (OCEs, aka A/B tests) are one of the most powerful methods for measuring how much value new features and changes deployed to software products bring to users. Companies like Microsoft, Amazon, and Booking.com report the ability to conduct thousands of OCEs every year. However, the competences of the remainder of the online software industry remain unknown. The main objective of this paper is to reveal the current state of A/B testing maturity in the software industry based on a maturity model from our previous research. We base our findings on 44 responses from an online empirical survey. Our main contribution of this paper is the current state of experimentation maturity as operationalized by the ExG model for a convenience sample of companies doing online controlled experiments. Our findings show that, among others, companies typically develop in-house experimentation platforms, that these platforms are of various levels of maturity, and that designing key metrics - Overall Evaluation Criteria - remains the key challenge for successful experimentation.

Online Controlled Experiments (OCEs) are transforming the decision-making process of data-driven companies into an experimental laboratory. Despite their great power in identifying what customers actually value, experimentation is very sensitive to data loss, skipped checks, wrong designs, and many other 'hiccups' in the analysis process. For this purpose, experiment analysis has traditionally been done by experienced data analysts and scientists that closely monitored experiments throughout their lifecycle. Depending solely on scarce experts, however, is neither scalable nor bulletproof. To democratize experimentation, analysis should be streamlined and meticulously performed by engineers, managers, or others responsible for the development of a product. In this paper, based on synthesized experience of companies that run thousands of OCEs per year, we examined how experts inspect online experiments. We reveal that most of the experiment analysis happens before OCEs are even started, and we summarize the key analysis steps in three checklists. The value of the checklists is threefold. First, they can increase the accuracy of experiment setup and decision-making process. Second, checklists can enable novice data scientists and software engineers to become more autonomous in setting-up and analyzing experiments. Finally, they can serve as a base to develop trustworthy platforms and tools for OCE set-up and analysis.

Online controlled experiments (for example A/B tests) are increasingly being performed to guide product development and accelerate innovation in online software product companies. The benefits of controlled experiments have been shown in many cases with incremental product improvement as the objective. In this paper, we demonstrate that the value of controlled experimentation at scale extends beyond this recognized scenario. Based on an exhaustive and collaborative case study in a large software-intensive company with highly developed experimentation culture, we inductively derive the benefits of controlled experimentation. The contribution of our paper is twofold. First, we present a comprehensive list of benefits and illustrate our findings with five case examples of controlled experiments conducted at Microsoft. Second, we provide guidance on how to achieve each of the benefits. With our work, we aim to provide practitioners in the online domain with knowledge on how to use controlled experimentation to maximize the benefits on the portfolio, product and team level.

Online Controlled Experiments (OCEs) enable an accurate understanding of customer value and generate millions of dollars of additional revenue at Microsoft. Unlike other techniques for learning from customers, OCEs establish an accurate and causal relationship between a change and the impact observed. Although previous research describes technical and statistical dimensions, the key phases of online experimentation are not widely known, their impact and importance are obscure, and how to establish OCEs in an organization is underexplored. In this paper, using a longitudinal in-depth case study, we address this gap by (1) presenting the Experiment Lifecycle, and (2) demonstrating with four example experiments their profound impact. We show that OECs help optimize infrastructure needs and aid in project planning and measuring team efforts, in addition to their primary goal of accurately identifying what customers value. We conclude that product development should fully integrate the Experiment Lifecycle to benefit from the OCEs.

Companies need to know how much value their ideas deliver to customers. One of the most powerful ways to accurately measure this is by conducting online controlled experiments (OCEs). To run experiments, however, companies need to develop strong experimentation practices as well as align their organization and culture to experimentation. The main objective of this paper is to demonstrate how to run OCEs at large scale using the experience of companies that succeeded in scaling. Based on case study research at Microsoft, Booking.com, Skyscanner, and Intuit, we present our main contribution—The Experiment Growth Model. This four‐stage model addresses the seven critical aspects of experimentation and can help companies to transform their organizations into learning laboratories where new ideas can be tested with scientific accuracy. Ultimately, this should lead to better products and services.