In their seminal work on Multidimensional Sorting, Goodman and Pollack introduced the so-called order type, which for each ordered triple of a point set in the plane gives its orientation, clockwise or counterclockwise. This information is sufficient to solve many problems from discrete geometry where properties of point sets do not depend on the exact coordinates of the points but only on their relative positions. Goodman and Pollack showed that an efficient way to store an order type in a matrix λ of quadratic size (w.r.t. the number of points) is to count for every oriented line spanned by two points of the set how many of the remaining points lie to the left of this line.

We generalize the concept of order types to bicolored point sets (every point has one of two colors). The bicolored order type contains the orientation of each bicolored triple of points, while no information is stored for monochromatic triples. Similar to the uncolored case, we store the number of blue points that are to the left of an oriented line spanned by two red points or by one red and one blue point in λ_B. Analogously the number of red points is stored in λ_R.

We show that the equivalence of the information contained in the orientation of all bicolored point triples and the two matrices λ_B and λ_R also holds in the colored case. This is remarkable, as in general the bicolored order type does not even contain sufficient information to determine all extreme points (points on the boundary of the convex hull of the point set).

We present an O(n² · min{m, n}) time and O(n · min{m, n}) storage algorithm to compute the minsum set of m watchmen routes given their starting points in a Stiegl polygon ― a staircase polygon where the floor solely consists of one horizontal and one vertical edge ― with n vertices.

The development in smart meter technology has made grid operations more efficient based on fine-grained electricity usage data generated at different levels of time granularity. Consequently, machine learning algorithms have benefited from these data to produce useful models for important grid operations. Although machine learning algorithms need historical data to improve predictive performance, these data are not readily available for public utilization due to privacy issues. The existing smart grid data simulation frameworks generate grid data with implicit privacy concerns since the data are simulated from a few real energy consumptions that are publicly available. This paper addresses two issues in smart grid. First, it assesses the level of privacy violation with the individual household appliances based on synthetic household aggregate loads consumption. Second, based on the findings, it proposes two privacy-preserving mechanisms to reduce this risk. Three inference attacks are simulated and the results obtained confirm the efficacy of the proposed privacy-preserving mechanisms.

The integration of the Internet of Things (IoT) and Machine Learning (ML) technologies has opened up for the development of novel types of systems and services. Federated Learning (FL) has enabled the systems to collaboratively train their ML models while preserving the privacy of the data collected by their IoT devices and objects. Several FL frameworks have been developed, however, they do not enable FL in open, distributed, and heterogeneous IoT environments. Specifically, they do not support systems that collect similar data to dynamically discover each other, communicate, and negotiate about the training terms (e.g., accuracy, communication latency, and cost). Towards bridging this gap, we propose ART4FL, an end-to-end framework that enables FL in open IoT settings. The framework enables systems' users to configure agents that participate in FL on their behalf. Those agents negotiate and make commitments (i.e., contractual agreements) to dynamically form federations. To perform FL, the framework deploys the needed services dynamically, monitors the training rounds, and calculates agents' trust scores based on the established commitments. ART4FL exploits a blockchain network to maintain the trust scores, and it provides those scores to negotiating agents' during the federations' formation phase.

Water pollution is a global issue that has an impact on the entire ecosystems’ life cycles. Traditional sampling and laboratory testing techniques are labor-intensive and error-prone, making them ineffective for quickly detecting changes in water quality. This paper presents the development of a low-cost, portable and efficient prototype sensor-based system for monitoring water quality in real-time. The system consists of a microcontroller, temperature, turbidity, pH, and distance sensors, and an application for a visual representation of the data. Extensive testing was carried out to ensure uninterrupted operation. The prototype is a user-friendly sensor system that can be positioned close to the target area in order to assist in preventing environmental and biological harm. This can ensure safe, healthy, and sustainable water supplies for the communities, environment, and the economy. Continuous monitoring of water parameters can also help avoid critical situations. The experimental results demonstrate a successful development of a smart water quality monitoring system with potential applications in various scenarios.

Deep reinforcement learning has advanced signifi-cantly in recent years, and it is now used in embedded systems in addition to simulators and games. Reinforcement Learning (RL) algorithms are currently being used to enhance device operation so that they can learn on their own and offer clients better services. It has recently been studied in a variety of industrial applications. However, reinforcement learning, especially when controlling a large number of agents in an industrial environment, has been demonstrated to be unstable and unable to adapt to realistic situations when used in a real-world setting. To address this problem, the goal of this study is to enable multiple reinforcement learning agents to independently learn control policies on their own in dynamic industrial contexts. In order to solve the problem, we propose a dynamic multi-agent reinforcement learning (dynamic multi-RL) method along with adaptive exploration (AE) and vector-based action selection (VAS) techniques for accelerating model convergence and adapting to a complex industrial environment. The proposed algorithm is tested for validation in emergency situations within the telecommunications industry. In such circumstances, three unmanned aerial vehicles (UAV-BSs) are used to provide temporary coverage to mission-critical (MC) customers in disaster zones when the original serving base station (BS) is destroyed by natural disasters. The algorithm directs the participating agents automatically to enhance service quality. Our findings demonstrate that the proposed dynamic multi-RL algorithm can proficiently manage the learning of multiple agents and adjust to dynamic industrial environments. Additionally, it enhances learning speed and improves the quality of service.

This paper proposes a novel agglomerated privacy-preservation model integrated with data mining and evolutionary Genetic Algorithm (GA). Privacy-pReservIng with Minimum Epsilon (PRIMϵ) delivers minimum privacy budget (ϵ) value to protect personal or sensitive data during data mining and publication. In this work, the proposed Pattern identification in the Locale of Users with Mining (PLUM) algorithm, identifies frequent patterns from dataset containing users’ sensitive data. ϵ-allocation by Differential Privacy (DP) is achieved in PRIMϵ with GA PRIMϵ , yielding a quantitative measure of privacy loss (ϵ) ranging from 0.0001 to 0.045. The proposed model maintains the trade-off between privacy and data utility with an average relative error of 0.109 on numerical data and an Earth Mover’s Distance (EMD) metric in the range between [0.2,1.3] on textual data. PRIMϵ model is verified with Probabilistic Computational Tree Logic (PCTL) and proved to accept DP data only when ϵ ≤ 0.5. The work demonstrated resilience of model against background knowledge, membership inference, reconstruction, and privacy budget attack. PRIMϵ is compared with existing techniques on DP and is found to be linearly scalable with worst time complexity of O(n log n) .

Designing MMLA systems is a complex task requiring a wide range of considerations. In this paper, we identify key considerations that are essential for designing MMLA systems. These considerations include data management, human factors, sensors and modalities, learning scenarios, privacy and ethics, interpretation and feedback, and data collection. The implications of these considerations are twofold: 1) The need for flexibility in MMLA systems to adapt to different learning contexts and scales, and 2) The need for a researcher-centered approach to designing MMLA systems. Unfortunately, the sheer number of considerations can lead to a state of "analysis paralysis," where deciding where to begin and how to proceed becomes overwhelming. This synthesis paper asks researchers to rethink the design of MMLA systems and aims to provide guidance for developers and practitioners in the field of MMLA.

Games have always been popular for connecting people, from local single-player and couch co-op, to massively multiplayer online. Throughout the COVID-19 pandemic, remote games that involved and fostered social interactions and connections were a highlight among strategies for staying connected. For this year’s games and play SIG, we come together to discuss the relevant and timely topic of social and playful technologies, and how they can be designed to best foster meaningful social connections over a distance. We bring together attendees from not only the games community, but also those in the broader field of CHI focusing on social and playful technologies.  

In this paper, we highlight how including technology, movement or play can boost a design process but with unbalanced amounts can also hamper the process. We provide a set of examples where we miscalculated the amount of technology, movement, or play that was needed in a design activity in such a way that it became counterproductive and for each example mention possible adaptations. Finally, we highlight three existing approaches that can balance the overabundance of technology, movement, and play in design processes: activity-centered design, somaesthetic design, and perspective-changing movement-based design.  

The ongoing integration of Internet of Things (IoT) within Fifth-generation (5G) mobile ecosystem promises to provide smart and intelligent services meant to foster vertical industry digitalization. 5G-IoT private networks have become a popular choice of adoption by industry verticals in accelerating digital transformation across various sectors, thereby culminating in Industry 4.0. Research and development on 5G-IoT private networks has mainly focused on the technical aspect with less attention on the business side. This paper discusses viable business model options for 5G-IoT private networks for applications in diverse industry sectors and the attainment of economic value in terms of revenue maximization, cost minimization and sustainability.

In this paper an experimental system assisted by emerging digital technologies is developed, for monitoring and controlling invasive water hyacinth weed and rescue operation of freshwater lakes in Africa. The system is designed to integrate fifth generation ultra-reliable low latency communication (5G URLLC), unmanned aerial vehicles (UAV), underwater robots, smart environmental sensing with internet of things (IoT) and machine learning techniques for real time monitoring, managing, controlling and predicting the expansion of invasive water hyacinth weed. The experimental system for sensor data collection implemented on lake Tana in Ethiopia will be expanded to other fresh water lakes of Africa affected by water hyacinth weed. System modeling and data analytics based on sensor data will be performed to generate decision inference for controlling the growth of water hyacinth in the water bodies of the lake. Environmental data collection from other local sources will be integrated with sensor data for further system modeling and critical action analysis and implementation using machine learning algorithms to remove the main causes for the rapid expansion of water hyacinth throughout the lake.

International Telecommunications Unions (ITU) and Global System for Mobile Communications (GSMA) statistics show that over half of the world's population is still not connected to broadband Internet, let alone to get services based on emerging digital technologies. Most of these digitally excluded communities are situated in rural emerging economy countries and experience the brunt of the adverse effects of the digital divide. This paper presents the Council for Scientific and Industrial Research (CSIR) Television White Space (TVWS) deployment ecosystem and the complementary projects for the provision of affordable broadband connectivity in rural and township communities in South Africa. Subsequently, the paper presents the socio-economic impact achieved by the four women and youth-owned beneficiary Small, Micro, and Medium Enterprises (SMMEs) Rural Network Operators (RNOs) which were supported by the CSIR in Phase I of the project. Furthermore, this paper briefly introduces the nine women and youth-owned beneficiary RNOs that will be supported in Phase II of the project.

In data societies, as everyday activities are mediated by digital technologies, individuals are thrown into a digital existence, even if they are not aware of their digital interactions. Digital technologies are not value-free or unbiased. Contemporary discourses about digital natives and late adopters contribute to reinforcing negative stereotypes about older users of digital technologies and influence the design, development, marketing and usage of digital technologies. Such discourses disregard how digital trajectories and personal circumstances influence media use in all stages of everyday life. Hence, occasional digital technology users, and older adults in particular, stand a higher risk of exclusion and loss of autonomy. In this chapter, we briefly introduce ageism and digital ageism in data societies, definitions and previous research as a background and introduction to the following chapters. Our aim is to underline how socio-technical and cultural analyses may contribute to raising awareness about digital ageism in data societies. Only by initiating a discussion may existing power relationships be challenged and contemporary inequalities understood.

This anthology contributes to creating awareness on how digital ageism operates in relation to the widely spread symbolic representations of old and young age around digital technologies, the (lack of) representation of diverse older individuals in the design, development, and marketing of digital technologies and in the actual algorithms and datasets that constitute them. It also shows how individuals and institutions deal with digital ageism in everyday life.

In the past decades, digital technologies permeated most aspects of everyday life. With a focus on how age is represented and experienced in relation to digital technologies leading to digital ageism, digitalisation’s reinforcement of spirals of exclusion and loss of autonomy of some collectives is explored, when it could be natural for a great part of society and represent a sort of improvement.

The book addresses social science students and scholars interested in everyday digital technologies, society and the power struggles about it, providing insights from different parts of the globe. By using different methods and touching upon different aspects of digital ageism and how it plays out in contemporary connected data societies, this volume will raise awareness, challenge power, initiate discussions and spur further research into this field.

This chapter zooms in on the Silicon Valley, the US centre for innovative technology and home to 2000 technology companies. Inspired by the terminology of Sassen (2014), this chapter will describe how the technology industry has created a system of multiple modes of expulsions of “older” workers – from work relations, workspaces, ideologies and values, as well as digital products and services. The main purpose is to propose a theoretical framework guiding future empirical and critical research into the phenomenon of ageism, as well as other systems of oppression and discrimination in the technology industry. In this chapter, we propose a concept of “Silicon Valley Ageism” which is understood as negative attitudes, beliefs and behaviours towards adults perceived as “older” and manifested in interpersonal relations and institutional practices, as well as their narratives. This type of ageism can affect people already in their 30s. The aim of the chapter is to explore (1) what narratives of “older” age are constructed in Silicon Valley, (2) how this relates to workplace practices in the Valley and (3) how this has a bearing on the products and services coming out of Silicon Valley.

This chapter focuses on how technology culture, from the beginning, has been geared towards the youth. Early hacking, for example, had a teenage rebellion to it, anti-authoritarian prankish boys in, first, the computer labs of established American universities and later in the garages in middle-class Silicon Valley suburbia. This resonates in the industry's more recent turn towards entrepreneurship. The chapter thus provides a backdrop to how ageism in digital technologies can be understood and made sense from a more historical and cultural perspective. The chapter also discusses how technology culture's many different roots and influences have made certain tensions apparent, for example, left-leaning hippies versus libertarian entrepreneurs. How such tensions are navigated also points towards a fundamentally youth-oriented culture. Empirically the chapter is based on an extensive interview study conducted between 2018 and 2020

This presentation attends to tensions and negotiations surrounding the introduction and development of a news-ranking algorithm in a Swedish daily. Approaching algorithms as culture, being composed of collective human practices, the study emphasizes socio-institutional dynamics in the everyday life of the algorithm. The focus on tensions and negotiations is justified from an institutional perspective and operationalized through an analytical framework of logics. Empirically the study is based on interviewswith 14 different in-house workers at the daily, journalists as well as programmers andmarket actors. The study shows that logics connected to both journalism and programming co-developed the news-ranking algorithm. Tensions and their negotiations around these logics contributed to its very development.One example is labeling of the algorithm as editor-led, allowing journalists to oversee some of its parameters. Social practices in the newsroom, such as Algorithm-Coffee, was also important for its development. In other words, different actors, tensions between them and how these were negotiated, co-constituted by the algorithm itself.

This presentation focuses on Uganda, a country infamous for its state-sanctioned homophobia. This international attention has steadily increased the number of LGBT+ organizations in the country. In this article, we set out to study what organizations are more central and more peripheral in the Ugandan LGBT+ Twittersphere. Following an analytical framework around rationalities of mediated participation, we have studied with whom Ugandan LGBT+ organizations relate through mapping retweets and @mentions. The network maps reveal a dividing line between more well-funded and internationally connected organizations and younger, more peripheral organizations. Complementing these maps with qualitative data, we conclude that access to funds and negotiating visibility are rationales behind the network structure. The article reveals an interesting use of Twitter, both as an instrument for advocacy work and for expressing and negotiating themselves as part of a larger LGBT+ community.  

This article unpacks different meanings of visibility and adds to a more complex and nuanced understanding of visibility and its role in LGBT+ activism in Uganda, a notorious case of political homophobia. Public visibility has a central, although contested, role here. The aim of the study is to explore how visibility is understood and navigated among international development actors, local LGBT+ activists, and unaffiliated people with same-sex desires. Interviews conducted in Kampala December 2021 – January 2022 reveal different and surprising narratives surrounding visibility. Local unaffiliated individuals and activists agreed on the importance of making the LGBT+ rights struggle more visible. This, however, did not translate into a wish to “come out” themselves. International development actors, on the other hand, expressed a need for caution regarding their own visibility. 

This presentation focuses on Uganda, a country infamous for its state-sanctioned homophobia. This international attention has steadily increased the number of LGBT+ organizations in the country. In this article, we set out to study what organizations are more central and more peripheral in the Ugandan LGBT+ Twittersphere. Following an analytical framework around rationalities of mediated participation, we have studied with whom Ugandan LGBT+ organizations relate through mapping retweets and @mentions. The network maps reveal a dividing line between more well-funded and internationally connected organizations and younger, more peripheral organizations. Complementing these maps with qualitative data, we conclude that access to funds and negotiating visibility are rationales behind the network structure. The article reveals an interesting use of Twitter, both as an instrument for advocacy work and for expressing and negotiating themselves as part of a larger LGBT+ community.

How do professionals at the forefront of digital technologies perceive their own work? Conducting 39 interviews with programmers around the world I asked them to describe their workday and then reflect upon an ideal workday.  Ideals revolved around the pleasure of solving difficult problems, to disrupt and to innovate, but ultimately to make the world a better place through their work. Many talked about a pleasurable state of “flow” in which they almost merged with the computer (their work tool). The empirical material reveals two interesting differences; one is between freelance programmers and those employed in big tech. Freelance programmers, in general, valued a work-life balance, clearly separating home and office, while big tech employees, on the other hand, tended to be younger (without kids), spending time in offices that blurred boundaries between home and office, providing employees with everything from ice cream parlors to fitness centers. Second, while most programmers looked at their profession as a vocation, programmers growing up in Asia (India and China) approached their profession as a means to a comfortable and exciting life (in terms of salary and working outside of their home countries). From this study, I will suggest that sustainable socio-technical work futures will be shaped around the new, the innovative and the meaningful. Work will, also in the future, be understood as a means to earn a living, but a meaningful one, and meaningful while earning it, as well as flexible and individually adaptable.   

Departing from popular imaginations around artificial intelligence (AI), this presentation engages in the I in the AI acronym but from perspectives outside of mathematics, computer science and machine learning. When intelligence is attended to here, it most often refers to narrow calculating tasks. This connotation to calculation provides AI an image of scientificity and objectivity, particularly attractive in societies with a pervasive desire for numbers. However, as is increasingly apparent today, when employed in more general areas of our messy socio-cultural realities, AI- powered automated systems often fail or have unintended consequences. This article will contribute to this critique of AI by attending to Nicholas of Cusa and his treatment of intelligence. According to him, intelligence is equally dependent on an ability to handle the unknown as it unfolds in the present moment. This suggests that intelligence is organic which ties Cusa to more contemporary discussions in tech philosophy, neurology, evolutionary biology, and cognitive sciences in which it is argued that intelligence is dependent on having—and acting through—an organic body. Understanding intelligence as organic thus suggests an oxymoronic relationship to artificial

This article attends to tensions and negotiations surrounding the introduction and development of a news-ranking algorithm in a Swedish daily. Approaching algorithms as culture, being composed of collective human practices, the study emphasizes socio-institutional dynamics in the everyday life of the algorithm. The focus on tensions and negotiations is justified from an institutional perspective and operationalized through an analytical framework of logics. Empirically the study is based on interviews with 14 different in-house workers at the daily, journalists as well as programmers and market actors. The study shows that logics connected to both journalism and programming co-developed the news-ranking algorithm. Tensions and their negotiations around these logics contributed to its very development. One example is labeling of the algorithm as editor-led, allowing journalists to oversee some of its parameters. Social practices in the newsroom, such as Algorithm-Coffee, was also important for its development. In other words, different actors, tensions between them and how these were negotiated, co-constituted by the algorithm itself.

In this paper, we treat VR as a new writing space in the long tradition of inscription. Constructing Virtual Reality (VR) narratives can then be understood as a process of inscribing text in space, and consuming them as a process of "reading" the space. Our research objective is to explore the meaning-making process afforded by spatial narratives-to test whether VR facilitates traditional ways of weaving complex, multiple narrative strands and provides new opportunities for leveraging space. We argue that, as opposed to the linear space of a printed book, a VR narrative space is similar to the physical space of a museum and can be analyzed on three distinct levels: (1) the architecture of the space itself, (2) the collection, and (3) the individual artifacts. To provide a deeper context for designing VR narratives, we designed and implemented a testbed called RealityMedia to explore digital remediations of traditional narrative devices and the spatial, immersive, and interactive affordances of VR. We conducted task-based user study using a VR headset and follow-up qualitative interviews with 20 participants. Our results highlight how the three semantic levels (space, collection, and artifacts) can work together to constitute meaningful narrative experiences in VR.

Data security has remained a challenging problem in cloud storage, especially in multiowner data sharing scenarios. As one of the most effective solutions for secure data sharing, multikeyword ranked searchable encryption (MRSE) has been widely used. However, most of the existing MRSE schemes have some shortcomings in multiowner data sharing, such as index trees generated by data owners, relevance scores in plaintext form, and lack of verification function. In this article, we propose a verifiable and efficient secure sharing scheme in multiowner multiuser settings, where the index tree is generated by the trusted authority. To achieve verifiable functionality, the blockchain-based smart contract is adopted to execute the search algorithm. Based on a distributed two-trapdoor public-key cryptosystem, the data uploaded and used are in ciphertext form, and the proposed algorithms are secure in our scheme. For improving efficiency, the encrypted data are aggregated according to the category and the Category ID-based index tree is generated. Extensive experiments are conducted to demonstrate that it can reduce the time cost of index construction by 75% and the time cost of search by 53%, approximately. Moreover, multithreaded optimization is introduced in our scheme, which can reduce the time cost of index construction by 76% and the time cost of search by 67%, approximately (with 16 threads).

Through a mixed-methods approach consisting of a directed content analysis of five established LGBT+ organizations' use of Twitter and Facebook during a month in 2022, and semi-structured qualitative interviews with social media content producers, the study attempts to understand the role of self-controlled social media spaces in challenging the Uganda society's logics of oppression. The results indicate that self-controlled spaces are not used for disrupting the basis for repression - the local logic of oppression - or its cocoon of collective post-colonial amnesia. Nor were spaces used for re-constructive engaging with transnational and development partners' unwitting impact on global south actors' agency and legitimacy. Instead, with a few exceptions, spaces displayed a conspicuous uniform human rights advocacy rhetoric, and Western identity labels summarized in the LGBT+ acronym. The interviews with social media content producers suggest that the LGBT+ community's dependency on international support may sway actors into what we call performative visibility, in self-controlled spaces. The study concludes that future analysis of Global South based activist's use of social media spaces' affordances including its potential for supporting de-colonialization efforts, must approach use as relational to actors' dependency on key resources such as funding and protection through affiliation.

The world is gearing towards renewable energy sources, due to the numerous negative repercussions of fossil fuels. There is a need to increase the efficiency of power generation, transmission, distribution, and use. The proposed work intends to decrease household electricity use and provide an intelligent home automation solution with ensembled machine learning algorithms. It also delivers organized information about the usage of each item while automating the use of electrical appliances in a home. Experimental results show that with XGBoost and Random Forest classifiers, electricity usage can be fully automated at an accuracy of 79%, thereby improving energy utilization efficiency and improving quality of life of the user.

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.

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.

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.

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.

Sverige ses som ett av de mest forskningsdrivna och utbildade länderna i världen. Forskarutbildningen anses därmed vara en av Sveriges viktigaste delar av den högre utbildningen. Detta ställningstagande reflekterar över forskarutbildningens process och produkt i Datavetenskap och programvarusystem i Sverige. Artikeln ger en översikt över forskarutbildningen i Sverige, följt av en praktisk demonstration av hur handledare och doktorander kan använda konstruktiv anpassning för att uppnå lärandemålen för forskarutbildningen med hjälp av inlärningsaktiviteter och bedömningsmetoder för att utvärdera lärandeaktiviteterna och i förlängningen lärandemålen. 

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.

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.

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.

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.

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.

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.

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.