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In molecular analysis, Spatial Distribution Functions (SDF) are fundamental instruments in answering questions related to spatial occurrences and relations of atomic structures over time. Given a molecular trajectory, SDFs can, for example, reveal the occurrence of water in relation to particular structures and hence provide clues of hydrophobic and hydrophilic regions. For the computation of meaningful distribution functions, the definition of molecular reference structures is essential. Therefore we introduce the concept of an internal frame of reference (IFR) for labeled point sets that represent selected molecular structures, and we propose an algorithm for tracking the IFR over time and space using a variant of Kabschs algorithm. This approach lets us generate a consistent space for the aggregation of the SDF for molecular trajectories and molecular ensembles. We demonstrate the usefulness of the technique by applying it to temporal molecular trajectories as well as ensemble datasets. The examples include different docking scenarios with DNA, insulin, and aspirin. Funding agencies:This work was supported through grants from the ExcellenceCenter at Link¨oping and Lund in Information Technology(ELLIIT) and the Swedish e-Science Research Centre(SeRC). The authors thanks the Swedish National Infrastructurefor Computing (SNIC) for providing computingresources.

Topological and geometric segmentation methods provide powerful concepts for detailed field analysis and visualization. However, when it comes to a quantitative analysis that requires highly accurate geometric segmentation, there is a large discrepancy between the promising theory and the available computational approaches. In this paper, we compare and evaluate various segmentation methods with the aim to identify and quantify the extent of these discrepancies. Thereby, we focus on an application from quantum chemistry: the analysis of electron density fields. It is a scalar quantity that can be experimentally measured or theoretically computed. In the evaluation we consider methods originating from the domain of quantum chemistry and computational topology. We apply the methods to the charge density of a set of crystals and molecules. Therefore, we segment the volumes into atomic regions and derive and compare quantitative measures such as total charge and dipole moments from these regions. As a result, we conclude that an accurate geometry determination can be crucial for correctly segmenting and analyzing a scalar field, here demonstrated on the electron density field. Funding: Excellence Center at Linkoping and Lund in Information Technology (ELLIIT); Swedish e-Science Research Centre (SeRC); Swedish Research Council (VR)Swedish Research Council [2018-07085, 2019-05487]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [2018-05973]

Reasoning about uncertainty is one of the main cornerstones of Knowledge Representation. Formal representations of uncertainty are numerous and highly varied due to different types of uncertainty intended to be modeled such as vagueness, imprecision and incompleteness. There is a rich body of theoretical results that has been generated for many of these approaches. It is often the case though, that pragmatic tools for reasoning with uncertainty lag behind this rich body of theoretical results. Rough set theory is one such approach for modeling incompleteness and imprecision based on indiscernibility and its generalizations. In this paper, we provide a pragmatic tool for constructively reasoning with generalized rough set approximations that is based on the use of Answer Set Programming (Asp). We provide an interpretation of answer sets as (generalized) approximations of crisp sets (when possible) and show how to use Asp solvers as a tool for reasoning about (generalized) rough set approximations situated in realistic knowledge bases. The paper includes generic Asp templates for doing this and also provides a case study showing how these techniques can be used to generate reducts for incomplete information systems. Complete, ready to run clingo Asp code is provided in the Appendix, for all programs considered. These can be executed for validation purposes in the clingo Asp solver. Funding: ELLIIT Network Organization for Information and Communication Technology, Sweden; Swedish Foundation for Strategic Research SSF(Smart Systems Project) [RIT15-0097]; Jinan University (Zhuhai Campus); National Science Centre PolandNational Science Centre, Poland [2017/27/B/ST6/02018] ELLIIT Smart Systems Project RIT15-0097

This article examines how the unique characteristics of Marvinter, an inclusive transmedia Advent calendar, affected its production and the individual media workers involved in this. In the native transmedia Marvinter project, a radio series and a mobile game were created and released simultaneously. Using ‘partly shared’ resources, the project was developed by two collaborating, non-profit organisations. Each chapter of the digital game had to match the corresponding radio episode. The whole had to be designed to provide a shared cultural experience that included people with hearing or visual impairments. The authors of the present article were part of the game development team and thus directly experienced the complexity of developing a digital game as part of a transmedia project. This article presents the results of a case study with an insider perspective. It is supplemented by semi-structured interviews with key people in the project. The transmedia nature of Marvinter was justified by the need to include people with complementary disabilities. Although sometimes associated with negative connotations of ‘naked commercialisation’ in transmedia contexts, the marketing strategy was here an important element in promoting inclusivity. However, owing to the concurrent work processes, game production became overly complex. CC BY 4.0 Inkluderande julkalender

In recent years, the use of information technologies in Critical Infrastructures is gradually increasing. Although this brings benefits, it also increases the possibility of security attacks. Despite the availability of various advanced incident handling techniques and tools, there is still no easy, structured, standardized and trusted way to manage and forecast interrelated cybersecurity incidents. This paper introduces CyberSANE, a novel dynamic and collaborative, warning and response system, which supports security officers and operators to recognize, identify, dynamically analyse, forecast, treat and respond to security threats and risks and and it guides them to handle effectively cyber incidents. The components of CyberSANE are described along with a description of the CyberSANE data flow. The main novelty of the CyberSANE system is the fact that it enables the combination of active incident handling approaches with reactive approaches to support incidents of compound, highly dependent Critical Information Infrastructures. The benefits and added value of using CyberSANE is described with the aid of a set of cyber-attack scenarios.

Urban planning typically involves multiple actors and stakeholders with conflicting opinions and diverging preferences. The proposed development plans and actions greatly affect the quality of life of the local community at different spatial scales and time horizons. Consequently, it is important for decision-makers to understand and analyse the conflicting needs and priorities of the local community. This paper presents a decision analytic framework for evaluating stakeholder conflicts in urban planning. First, the stakeholders state their preferences regarding the actions in terms of a set of criteria and estimate the weight of each criterion. Then, a conflict index and overall value for each action is calculated. Next, a set of Pareto efficient portfolios of actions are generated by solving an optimization problem with different levels of conflict as a resource constraint. Finally, a sensitivity analysis of the actions is performed. The framework is demonstrated using real-world survey data collected in the municipality of Upplands Väsby, Sweden.

Existing approaches to solving combinatorial optimization problems on graphs suffer from the need to engineer each problem algorithmically, with practical problems recurring in many instances. The practical side of theoretical computer science, such as computational complexity, then needs to be addressed. Relevant developments in machine learning research on graphs are surveyed for this purpose. We organize and compare the structures involved with learning to solve combinatorial optimization problems, with a special eye on the telecommunications domain and its continuous development of live and research networks., 29 pages, 1 figure, open access journal publication

How different cultures react and respond given a crisis is predominant in a society’s norms and political will to combat the situation. Often, the decisions made are necessitated by events, social pressure, or the need of the hour, which may not represent the nation’s will. While some are pleased with it, others might show resentment. Coronavirus (COVID-19) brought a mix of similar emotions from the nations towards the decisions taken by their respective governments. Social media was bombarded with posts containing both positive and negative sentiments on the COVID-19, pandemic, lockdown, and hashtags past couple of months. Despite geographically close, many neighboring countries reacted differently to one another. For instance, Denmark and Sweden, which share many similarities, stood poles apart on the decision taken by their respective governments. Yet, their nation’s support was mostly unanimous, unlike the South Asian neighboring countries where people showed a lot of anxiety and resentment. The purpose of this study is to analyze reaction of citizens from different cultures to the novel Coronavirus and people’s sentiment about subsequent actions taken by different countries. Deep long short-term memory (LSTM) models used for estimating the sentiment polarity and emotions from extracted tweets have been trained to achieve state-of-the-art accuracy on the sentiment140 dataset. The use of emoticons showed a unique and novel way of validating the supervised deep learning models on tweets extracted from Twitter. This work is licensed under a Creative Commons Attribution 4.0 License.

International audience; This article presents an overview and analysis of the key cybersecurity problems, challenges and requirements to be addressed in the future, which we derived through 63 interviews with European stakeholders from security-critical sectors including Open Banking, Supply Chain, Privacy-preserving Identity Management, Security Incident Reporting, Maritime Transport, Medical Data Exchange, and Smart Cities. We show that common problems, challenges and requirements across these sectors exist in relation to building trust, implementing privacy and identity management including secure and useable authentication, building resilient systems, standardisation and certification, achieving security and privacy by design, secure and privacy-compliant data and information sharing, and government regulations. Our results also indicate cybersecurity trends and allow to derive directions for future research and innovation activities that will be of high importance for Europe.

Patients’ online access to their EHR together with the rapid proliferation of medical information on the Internet has changed how patients use information to learn about their health. Patients’ tendency to turn to the Internet to find information about their health and care is well-documented. However, little is known about patients’ information seeking behavior when using online EHRs. By using information horizons as an analytical tool this paper aims to investigate the information behavior of cancer patients who have chosen to view their EHRs (readers) and to those who have not made that option (non-readers). Thirty interviews were conducted with patients. Based on information horizons, it seems that non-reading is associated with living in a narrower information world in comparison to readers. The findings do not suggest that the smallness would be a result of active avoidance of information, or that it would be counterproductive for the patients. The findings suggest, however, that EHRs would benefit from comprehensive linking to authoritative health information sources to help users to understand their contents. In parallel, healthcare professionals should be more aware of their personal role as a key source of health information to those who choose not to read their EHRs. CC BY 4.0© The Author(s) 2021.Corresponding author: Isto Huvila, Department of ALM, Uppsala University, Thunbergsvägen 3H, Uppsala 75105, Sweden. Email: isto.huvila@abm.uu.seThe author(s) disclosed of the following financial support for the research, authorship, and/or publication of this article. This work was partly supported by NordForsk through the funding to Nordic eHealth for Patients: Benchmarking and Developing for the Future (NORDeHEALTH), project number 100477.

The graphical and interactive design of a consent form helps individuals to keep control and pay attention to the information that they are disclosing. In the context of mobile apps we propose and test alternative interaction design solutions for selecting personal information on permission dialogues, namely using checkboxes, a drag-and-drop selection, and a swiping action. We test each proposed design and compare the results in terms of their usability and effectiveness in helping users to be more attentive and aware of their data flow, in other words, to provide their informed consent. This study demonstrates that checkboxes while speedy do not engage the user as much as drag-and-drop or swiping. User satisfaction is positively impacted by these newer ways of giving consent.

Combinatorial interaction testing (CIT) is a useful testing technique to address the interaction of input parameters in software systems. In many applications, the technique has been used as a systematic sampling technique to sample the enormous possibilities of test cases. In the last decade, most of the research activities focused on the generation of CIT test suites as it is a computationally complex problem. Although promising, less effort has been paid for the application of CIT. In general, to apply the CIT, practitioners must identify the input parameters for the Software-under-test (SUT), feed these parameters to the CIT tool to generate the test suite, and then run those tests on the application with some pass and fail criteria for verification. Using this approach, CIT is used as a black-box testing technique without knowing the effect of the internal code. Although useful, practically, not all the parameters having the same impact on the SUT. This paper introduces a different approach to use the CIT as a gray-box testing technique by considering the internal code structure of the SUT to know the impact of each input parameter and thus use this impact in the test generation stage. We applied our approach to five reliable case studies. The results showed that this approach would help to detect new faults as compared to the equal impact parameter approach., Comment: 28 pages

PurposeThis paper aims to describe the cyber-insurance market in Norway but offers conclusions that are interesting to a wider audience.Design/methodology/approachThe study is based on semi-structured interviews with supply-side actors: six general insurance companies, one marine insurance company and two insurance intermediaries.FindingsThe Norwegian cyber-insurance market supply-side has grown significantly in the past two years. The General Data Protection Regulation (GDPR) is found to have had a modest effect on the market so far but has been used by the supply-side as an icebreaker to discuss cyber-insurance with customers. The NIS Directive has had little or no impact on the Norwegian cyber-insurance market until now. Informants also indicate that Norway is still the least mature of the four Nordic markets.Practical implicationsSome policy lessons for different stakeholders are identified.Originality/valueEmpirical investigation of cyber-insurance is still rare, and the paper offers original insights on market composition and actor motivations, ambiguity of coverage, the NIS Directive and GDPR.

Within a community, social relationships are paramount to profile individuals’ conduct. For instance, an individual within a social network might be compelled to embrace a behaviour that his/her companion has recently adopted. Such social attitude is labelled social influence, which assesses the extent by which an individual’s social neighbourhood adopt that individual’s behaviour. We suggest an original approach to influence maximization using a fuzzy-logic based model, which combines influence-weights associated with historical logs of the social network users, and their favourable location in the network. Our approach uses a two-phases process to maximise influence diffusion. First, we harness the complexity of the problem by partitioning the network into significantly-enriched community-structures, which we then use as modules to locate the most influential nodes across the entire network. These key users are determined relatively to a fuzzy-logic based technique that identifies the most influential users, out of which the seed-set candidates to diffuse a behaviour or an innovation are extracted following the allocated budget for the influence campaign. This way to deal with influence propagation in social networks, is different from previous models, which do not compare structural and behavioural attributes among members of the network. The performance results show the validity of the proposed partitioning-approach of a social network into communities, and its contribution to “activate” a higher number of nodes overall. Our experimental study involves both empirical and real contemporary social-networks, whereby a smaller seed set of key users, is shown to scale influence to the high-end compared to some renowned techniques, which employ a larger seed set of key users and yet they influence less nodes in the social network.

The enormous impact of artificial intelligence has been realized in transforming the fashion and apparel industry in the past decades. However, the research in this domain is scattered and mainly focuses on one of the stages of the supply chain. Due to this, it is difficult to comprehend the work conducted in the distinct domain of the fashion and apparel industry. Therefore, this paper aims to study the impact and the significance of artificial intelligence in the fashion and apparel industry in the last decades throughout the supply chain. Following this objective, we performed a systematic literature review of research articles (journal and conference) associated with artificial intelligence in the fashion and apparel industry. Articles were retrieved from two popular databases ‘‘Scopus’’ and ‘‘Web of Science’’ and the article screening was completed in five phases resulting in 149 articles. This was followed by article categorization which was grounded on the proposed taxonomy and was completed in two steps. First, the research articles were categorized according to the artificial intelligence methods applied such as machine learning, expert systems, decision support system, optimization, and image recognition and computer vision. Second, the articles were categorized based on supply chain stages targeted such as design, fabric production, apparel production, and distribution. In addition, the supply chain stages were further classified based on business-to-business (B2B) and business-to-consumer (B2C) to give a broader outlook of the industry. As a result of the categorizations, research gaps were identified in the applications of AI techniques, at the supply chain stages and from a business (B2B/B2C) perspective. Based on these gaps, the future prospects of the AI in this domain are discussed. These can benefit the researchers in academics and industrial practitioners working in the domain of the fashion and apparel industry. Author 1 and 2 are equal contributing authors.

Road condition evaluation is a critical part of gravel road maintenance. One of the assessed parameters is the amount of loose gravel, as this determines the driving quality and safety. Loose gravel can cause tires to slip and the driver to lose control. An expert assesses the road conditions subjectively by looking at images and notes. This method is labor-intensive and subject to error in judgment, therefore, its reliability is questionable. Road management agencies look for automated and objective measurement systems. In this study, acoustic data on gravel hitting the bottom of a car was used. The connection between the acoustics and the condition of loose gravel on gravel roads was assessed. Traditional supervised learning algorithms and convolution neural network (CNN) were applied, and their performances are compared for the classification of loose gravel acoustics. The advantage of using a pre-trained CNN is that it selects relevant features for training. In addition, pre-trained networks offer the advantage of not requiring days of training or colossal training data. In supervised learning, the accuracy of the ensemble bagged tree algorithm for gravel and non-gravel sound classification was found to be 97.5%, whereas, in the case of deep learning, pre-trained network GoogLeNet accuracy was 97.91% for classifying spectrogram images of the gravel sounds.

Traditional methods to study visual politics have been limited in geographical, media and temporal coverage. Recent advances in deep learning have the potential to dramatically extend the scope of the field especially with respect to making sense of the contemporary social and political developments in digital media. While some early adopters may be tempted to take the new computational tools at face value, others see their black-box character as cause for concern. This paper argues that the integration of deep learning into the study of visual politics must be approached still more critically and boldly. On the one hand, the complexity of visual political themes requires a more substantial human involvement if compared with other applications of deep neural networks. Therefore, a question is how the scientist and the network should best interact. On the other hand, it is important to acknowledge that a deep learning tool will never simply replace specific tasks inside a research pro- cess: its adoption has implications for the broader process from the delineation of the object of analysis, to data collection, to the interpretation and communication of results. We examine the conditions of integrating a deep learning tool for image classification into the large-scale study of visual politics in digital and social media along these two dimensions.

As a business model, the essence of games is to provide a service to satisfy the player experience. From a business perspective, development in the game industry has led to the application of Business Intelligence (BI) becoming more and more extensive. However, related research lacks systematic examination and precise classification. This paper provides a comprehensive literature review of BI used in the game industry, focusing primarily on game analytics. This research mainly studies and discusses five aspects. First, we explore game analytics aspects in the available literature based on the traditional game value chain. Second, we find out the main purposes of using analytics in the game industry. Third, we present the problems or challenges in the game area, which can be addressed by using game analytics. Fourth, we also list different algorithms that have been used in game analytics for prediction. Finally, we summarize the research areas that have already been covered in literature but need further development. Based on the categories established after the mapping and the review findings, we also discuss the limitations of game analytics and propose potential research points for future research. CC BY 4.0 This research was supported by the University of Skövde, Sweden Game Arena and the Game Hub Scandinavia 2.0 (NYPS 20201849) project under the EU regional development fund Interreg Öresund-Kattegat-Skagerrak.Published online: 1 November 2020 Game Hub Scandinavia 2.0 (NYPS 20201849)

As the Internet of Things (IoT) paradigm gets more attention from academia and industry, implementation tools of IoT will be explored more and more. One example is the applicability of blockchain systems to provide security and privacy of IoT networks, which is the topic of this article. Blockchain systems are on the rise, as crypto-currency payment systems (such as Bitcoin, Litecoin, etc.) boomed in the last few years due to their attractive de-centralized and anonymous features. As in every transaction, access of the users to IoT systems needs to be controlled. However, these systems are peer-to-peer systems and do not have centralized control, which means that traditional access control techniques will not be optimal. As a result, distributed access control schemes are needed and this paper aims at providing the state of the art in the literature. Thereby, we introduce and discuss the details and applicability of centralized (role-based) and distributed (threshold-signature, reputation, trusted-computing, identity, capability, ACL, group-signature, and hybrid) access control schemes to blockchain systems under the IoT ecosystems. Moreover, permissioned vs. permissionless blockchain systems are also discussed. Finally, challenges and research directions related to the application of all those presented blockchain systems to IoT are discussed.

As this article is being drafted, the SARS-CoV-2/COVID-19 pandemic is causing harm and disruption across the world. Many countries aimed at supporting their contact tracers with the use of digital contact tracing apps in order to manage and control the spread of the virus. Their idea is the automatic registration of meetings between smartphone owners for the quicker processing of infection chains. To date, there are many contact tracing apps that have already been launched and used in 2020. There has been a lot of speculations about the privacy and security aspects of these apps and their potential violation of data protection principles. Therefore, the developers of these apps are constantly criticized because of undermining users’ privacy, neglecting essential privacy and security requirements, and developing apps under time pressure without considering privacy- and security-by-design. In this study, we analyze the privacy and security performance of 28 contact tracing apps available on Android platform from various perspectives, including their code’s privileges, promises made in their privacy policies, and static and dynamic performances. Our methodology is based on the collection of various types of data concerning these 28 apps, namely permission requests, privacy policy texts, run-time resource accesses, and existing security vulnerabilities. Based on the analysis of these data, we quantify and assess the impact of these apps on users’ privacy. We aimed at providing a quick and systematic inspection of the earliest contact tracing apps that have been deployed on multiple continents. Our findings have revealed that the developers of these apps need to take more cautionary steps to ensure code quality and to address security and privacy vulnerabilities. They should more consciously follow legal requirements with respect to apps’ permission declarations, privacy principles, and privacy policy contents.

Autonomous, or self-driving, cars are emerging as the solution to several problems primarily caused by humans on roads, such as accidents and traffic congestion. However, those benefits come with great challenges in the verification and validation (V&V) for safety assessment. In fact, due to the possibly unpredictable nature of Artificial Intelligence (AI), its use in autonomous cars creates concerns that need to be addressed using appropriate V&V processes that can address trustworthy AI and safe autonomy. In this study, the relevant research literature in recent years has been systematically reviewed and classified in order to investigate the state-of-the-art in the software V&V of autonomous cars. By appropriate criteria, a subset of primary studies has been selected for more in-depth analysis. The first part of the review addresses certification issues against reference standards, challenges in assessing machine learning, as well as general V&V methodologies. The second part investigates more specific approaches, including simulation environments and mutation testing, corner cases and adversarial examples, fault injection, software safety cages, techniques for cyber-physical systems, and formal methods. Relevant approaches and related tools have been discussed and compared in order to highlight open issues and opportunities.

Energy use for thermal comfort in housing accounts for a large share of total energy use in many countries. The housing sector has the potential to lower the demand for heating and cooling through better building designs, such as the passive house concept. This concept integrates building technologies, energy systems and activities performed by end-users in their everyday lives to minimise the use of external energy sources for thermal comfort. In this study we propose a methodology based on the combination of personal diary data and their in-context interactive visualization as a promising approach to study and better understand thermal comfort in private spheres. We collected activity diaries from residents in a new display city district called Vallastaden in Linköping, Sweden. Passive house designs were promoted in Vallastaden and around 10 percent of the new housing was built with this concept. Activity and thermal comfort data from the diaries were digitalised and coded. Following this the data were represented and visually explored using an adapted, for the purpose, version of a visual analysis tool. Vallastaden energidesign

Clouds play an important role in enhancing the realism of outdoor scenes in computer graphics (CG). Realistic cloud generation is a challenging task, which entails processes such as modeling, photorealistic rendering and simulation of the clouds. To these ends, several techniques have been proposed within the CG community in the last 4 decades with one or more of the above stated focuses. The growth of modern hardware has also enabled development of techniques that can achieve cloud display and animation at interactive frame rates. In this survey, we review the prominent work in the domain and also summarize the evolution of the research over the time.

The proliferation of the Internet of Things (IoT) caused new application needs to emerge as rapid response ability is missing in the current IoT end-devices. Therefore, Fog Computing has been proposed to be an edge component for the IoT networks as a remedy to this problem. In recent times, cyber-attacks are on the rise, especially towards infrastructure-less networks, such as IoT. Many botnet attack variants (Mirai, Torii, etc.) have shown that the tiny microdevices at the lower spectrum of the network are becoming a valued participant of a botnet, for further executing more sophisticated attacks against infrastructural networks. As such, the fog devices also need to be secured against cyber-attacks, not only software-wise, but also from hardware alterations and manipulations. Hence, this article first highlights the importance and benefits of fog computing for IoT networks, then investigates the means of providing hardware security to these devices with an enriched literature review, including but not limited to Hardware Security Module, Physically Unclonable Function, System on a Chip, and Tamper Resistant Memory.

Visual relationship detection is fundamental for holistic image understanding. However, the localization and classification of (subject, predicate, object) triplets remain challenging tasks, due to the combinatorial explosion of possible relationships, their long-tailed distribution in natural images, and an expensive annotation process. This paper introduces a novel weakly-supervised method for visual relationship detection that relies on minimal image-level predicate labels. A graph neural network is trained to classify predicates in images from a graph representation of detected objects, implicitly encoding an inductive bias for pairwise relations. We then frame relationship detection as the explanation of such a predicate classifier, i.e. we obtain a complete relation by recovering the subject and object of a predicted predicate. We present results comparable to recent fully- and weakly-supervised methods on three diverse and challenging datasets: HICO-DET for human-object interaction, Visual Relationship Detection for generic object-to-object relations, and UnRel for unusual triplets; demonstrating robustness to non-comprehensive annotations and good few-shot generalization., Published at the European Conference on Computer Vision, ECCV 2020 (Poster)

In this paper, forecasting sales model for truck components using machine learning algorithms is proposed. The forecasting model helps companions (i.e. Volvo Trucks) in the activity of trade and business. It also plays a major role for firms in decision-making operations in the areas corresponding to sales, production, purchasing, finance, and accounting. In order to achieve good forecasting sales mode, firstly, a normalization approach is performed on the time-series data to reduce and eliminate the data redundancy. After that, feature extraction and selection techniques are employed on the normalized data. Finally, different machine learning methods such as Support Vector Machine Regression, Ridge Regression, Gradient Boosting Regression and Random Forest Regression have been applied to the features of the normalized time-series data. Results depict that ridge regression method gives the most promising forecasting sale results of truck components compared to the other machine learning methods. © 2020 IEEE. open access

Training support vector machines (SVMs) for pixel-based feature extraction purposes from aerial images requires selecting representative pixels (instances) as a training dataset. In this research, locality-sensitive hashing (LSH) is adopted for developing a new instance selection method which is referred to as D R . L S H . The intuition of D R . L S H rests on rapidly finding similar and redundant training samples and excluding them from the original dataset. The simple idea of this method alongside its linear computational complexity make it expeditious in coping with massive training data (millions of pixels). D R . L S H is benchmarked against two recently proposed methods on a dataset for building extraction with 23,750,000 samples obtained from the fusion of aerial images and point clouds. The results reveal that D R . L S H outperforms them in terms of both preservation rate and maintaining the generalization ability (classification loss). The source code of D R . L S H can be found in https://github.com/mohaslani/DR.LSH .

The number of people that have been in touch with drugs is continuously increasing. Excessive intake of drugs becomes problematic when it turns into disorderly behaviors, such as addictions. In order to treat these disorderly behaviors, treatment plans often adhere to a one-size-fits-all approach with fixed and standardized steps. However, for effective treatment of disorderly behaviors it has been acknowledged that personalized treatment programs are necessary. The personality of people has been argued to be a factor that plays an important role in setting up effective treatment plans. In this work we explored the predictability of people's personality traits based on their drug consumption profile. Based on self-reported consumption frequencies of "abusable psychoactive drugs," we found among 1878 respondents that drug consumption profiles can be used to predict people's personality traits. The prediction of personality traits can be used to circumvent intruding questionnaires and to implicitly create personalized treatment programs.

Background Although mental health issues constitute an increasing global burden affecting a large number of people, the mental health care industry is still facing several care delivery barriers such as stigma, education, and cost. Connected mental health (CMH), which refers to the use of information and communication technologies in mental health care, can assist in overcoming these barriers. Objective The aim of this systematic mapping study is to provide an overview and a structured understanding of CMH literature available in the Scopus database. Methods A total of 289 selected publications were analyzed based on 8 classification criteria: publication year, publication source, research type, contribution type, empirical type, mental health issues, targeted cohort groups, and countries where the empirically evaluated studies were conducted. Results The results showed that there was an increasing interest in CMH publications; journals were the main publication channels of the selected papers; exploratory research was the dominant research type; advantages and challenges of the use of technology for mental health care were the most investigated subjects; most of the selected studies had not been evaluated empirically; depression and anxiety were the most addressed mental disorders; young people were the most targeted cohort groups in the selected publications; and Australia, followed by the United States, was the country where most empirically evaluated studies were conducted. Conclusions CMH is a promising research field to present novel approaches to assist in the management, treatment, and diagnosis of mental health issues that can help overcome existing mental health care delivery barriers. Future research should be shifted toward providing evidence-based studies to examine the effectiveness of CMH solutions and identify related issues.

This paper introduces a new image-based handwritten historical digit dataset named Arkiv Digital Sweden (ARDIS). The images in ARDIS dataset are extracted from 15,000 Swedish church records which were written by different priests with various handwriting styles in the nineteenth and twentieth centuries. The constructed dataset consists of three single-digit datasets and one-digit string dataset. The digit string dataset includes 10,000 samples in red–green–blue color space, whereas the other datasets contain 7600 single-digit images in different color spaces. An extensive analysis of machine learning methods on several digit datasets is carried out. Additionally, correlation between ARDIS and existing digit datasets Modified National Institute of Standards and Technology (MNIST) and US Postal Service (USPS) is investigated. Experimental results show that machine learning algorithms, including deep learning methods, provide low recognition accuracy as they face difficulties when trained on existing datasets and tested on ARDIS dataset. Accordingly, convolutional neural network trained on MNIST and USPS and tested on ARDIS provide the highest accuracies $$58.80\%$$ 58.80 % and $$35.44\%$$ 35.44 % , respectively. Consequently, the results reveal that machine learning methods trained on existing datasets can have difficulties to recognize digits effectively on our dataset which proves that ARDIS dataset has unique characteristics. This dataset is publicly available for the research community to further advance handwritten digit recognition algorithms.

Cutting-edge sensors and devices are increasingly deployed within urban areas to make-up the fabric of transmission control protocol/internet protocol con- nectivity driven by Internet of Things (IoT). This immersion into physical urban environments creates new data streams, which could be exploited to deliver novel cloud-based services. Connected vehicles and road-infrastructure data are leveraged in this article to build applications that alleviate notorious parking and induced traffic-congestion issues. To optimize the utility of parking lots, our proposed SmartPark algorithm employs a discrete Markov-chain model to demystify the future state of a parking lot, by the time a vehicle is expected to reach it. The algorithm features three modular sections. First, a search pro- cess is triggered to identify the expected arrival-time periods to all parking lots in the targeted central business district (CBD) area. This process utilizes smart-pole data streams reporting congestion rates across parking area junc- tions. Then, a predictive analytics phase uses consolidated historical data about past parking dynamics to infer a state-transition matrix, showing the transfor- mation of available spots in a parking lot over short periods of time. Finally, this matrix is projected against similar future seasonal periods to figure out the actual vacancy-expectation of a lot. The performance evaluation over an actual busy CBD area in Stockholm (Sweden) shows increased scalability capa- bilities, when further parking resources are made available, compared to a baseline case algorithm. Using standard urban-mobility simulation packages, the traffic-congestion-aware SmartPark is also shown to minimize the journey duration to the selected parking lot while maximizing the chances to find an available spot at the selected lot. SmartPark

Oz is a programming language designed to support multiple programming paradigms in a clean factored way that is easy to program despite its broad coverage. It started in 1991 as a collaborative effort by the DFKI (Germany) and SICS (Sweden) and led to an influential system, Mozart, that was released in 1999 and widely used in the 2000s for practical applications and education. We give the history of Oz as it developed from its origins in logic programming, starting with Prolog, followed by concurrent logic programming and constraint logic programming, and leading to its two direct precursors, the concurrent constraint model and the Andorra Kernel Language (AKL). We give the lessons learned from the Oz effort including successes and failures and we explain the principles underlying the Oz design. Oz is defined through a kernel language, which is a formal model similar to a foundational calculus, but that is designed to be directly useful to the programmer. The kernel language is organized in a layered structure, which makes it straightforward to write programs that use different paradigms in different parts. Oz is a key enabler for the book Concepts, Techniques, and Models of Computer Programming (MIT Press, 2004). Based on the book and the implementation, Oz has been used successfully in university-level programming courses starting from 2001 to the present day.

Authenticated safety beacons in Vehicular Communication (VC) systems ensure awareness among neighboring vehicles. However, the verification of beacon signatures introduces significant processing overhead for resource-constrained vehicular On-Board Units (OBUs). Even worse in dense neighborhood or when a clogging Denial of Service (DoS) attack is mounted. The OBU would fail to verify for all received (authentic or fictitious) beacons. This could significantly delay the verifications of authentic beacons or even affect the awareness of neighboring vehicle status. In this paper, we propose an efficient cooperative beacon verification scheme leveraging efficient symmetric key based authentication on top of pseudonymous authentication (based on traditional public key cryptography), providing efficient discovery of authentic beacons among a pool of received authentic and fictitious beacons, and can significantly decrease waiting times of beacons in queue before their validations. We show with simulation results that our scheme can guarantee low waiting times for received beacons even in high neighbor density situations and under DoS attacks, under which a traditional scheme would not be workable. rights reserved. QC 20190904

Virtual and augmented reality is increasingly prevalent in industrial applications, such as remote control of industrial machinery,due to recent advances in head-mounted display technologies and low-latency communications via 5G. However, theinfluence of augmentations and camera placement-based viewing positions on operator performance in telepresence systemsremains unknown. In this paper, we investigate the joint effects of depth-aiding augmentations and viewing positionson the quality of experience for operators in augmented telepresence systems. A study was conducted with 27 non-expertparticipants using a real-time augmented telepresence system to perform a remote-controlled navigation and positioningtask, with varied depth-aiding augmentations and viewing positions. The resulting quality of experience was analyzed viaLikert opinion scales, task performance measurements, and simulator sickness evaluation. Results suggest that reducing thereliance on stereoscopic depth perception via camera placement has a significant benefit to operator performance and qualityof experience. Conversely, the depth-aiding augmentations can partly mitigate the negative effects of inferior viewingpositions. However the viewing-position based monoscopic and stereoscopic depth cues tend to dominate over cues basedon augmentations. There is also a discrepancy between the participants’ subjective opinions on augmentation helpfulness,and its observed effects on positioning task performance. Quality of Experience for Augmented Telepresence (KK-foundation 20160194)

The focus of this paper is the design and player reception of a serious game called Missing released on Google Play with the intention of spreading awareness of trafficking and its impact on individuals and society. The aim of the paper is to investigate how the game has been received by its players, focusing on its trafficking theme, by analyzing player metrics and app store data available from the Google Play digital distribution system. The paper presents results focusing on three main knowledge contributions: the identification and characterization of the tension between the designer’s intention with a game’s mechanics and how they help to convey the message of the game, the identification of the complexity of finding relevant reviews relating to the serious theme of the game and the identification and characterization of the tension between the star rating and the content of the reviews. One of the conclusions is that even a negative review can mirror a positive result in terms of fulfillment of the purpose. CC BY-NC-ND 4.0 Game Hub Scandinavia

In this paper, we propose a multi-agent framework to deal with situations involving uncertain or inconsistent information located in a distributed environment which cannot be combined into a single knowledge base. To this end, we introduce an inquiry dialogue approach based on a combination of possibilistic logic and a formal argumentation-based theory, where possibilistic logic is used to capture uncertain information, and the argumentation-based approach is used to deal with inconsistent knowledge in a distributed environment. We also modify the framework of earlier work, so that the system is not only easier to implement but also more suitable for educational purposes. The suggested approach is implemented in a clinical decision-support system in the domain of dementia diagnosis. The approach allows the physician to suggest a hypothetical diagnosis in a patient case, which is verified through the dialogue if sufficient patient information is present. If not, the user is informed about the missing information and potential inconsistencies in the information as a way to provide support for continuing medical education. The approach is presented, discussed, and applied to one scenario. The results contribute to the theory and application of inquiry dialogues in situations where the data are uncertain and inconsistent.

Automated warehouses, as a form of cyber-physical systems (CPSs), require several components to work collaboratively to address the common business objectives of complex logistics systems. During the collaborative operations, a number of key performance indicators (KPI) can be monitored to understand the proficiency of the warehouse and control the operations and decisions. It is possible to drive and monitor these KPIs by looking at both the state of the warehouse components and the operations carried out by them. Therefore, it is necessary to represent this knowledge in an explicit and formally-specified data model and provide automated methods to derive the KPIs from the representation. In this paper, we implement a minimalistic data model for a subset of warehouse resources using linked data in order to monitor a few KPIs, namely sustainability, safety and performance. The applicability of the approach and the data model is illustrated through a use case. We demonstrate that it is possible to develop minimalistic data models through Open Services for Lifecycle Collaboration (OSLC) resource shapes which enables compatibility with the declarative and procedural knowledge of automated warehouse agents specified in Planning Domain Definition Language (PDDL). QC 20180530 SCOTT

Wireless self-backhauling is a promising solution for dense millimeter wave (mmWave) small cell networks, the system efficiency of which, however, depends upon the balance of resources between the backhaul link and access links of each small cell. In this paper, we address the discrete power control and non-unified transmission duration allocation problem for self-backhauling mmWave cellular networks, in which each small cell is allowed to adopt individual transmission duration allocation ratio according to its own channel and load conditions. We first formulate the considered problem as a non-cooperative game ${\mathcal {G}}$ with a common utility function. We prove the feasibility and existence of the pure strategy Nash equilibrium (NE) of game $ {\mathcal {G}}$ under some mild conditions. Then, we design a centralized resource allocation algorithm based on the best response dynamic and a decentralized resource allocation algorithm (DRA) based on control-plane/user-plane split architecture and log-linear learning to obtain a feasible pure strategy NE of game ${\mathcal {G}}$ . For speeding up convergence and reducing signaling overheads, we reformulate the considered problem as a non-cooperative game ${{\mathcal {G}}'}$ with local interaction, in which only local information exchange is required. Based on DRA, we design a concurrent DRA to obtain the best feasible pure strategy NE of game ${\mathcal {G}'}$ . Furthermore, we extend the proposed algorithms to the discrete power control and unified transmission duration allocation optimization problem. Extensive simulations are conducted with different system configurations to demonstrate the convergence and effectiveness of the proposed algorithms.

Constrained Application Protocol (CoAP) has become the de-facto web standard for the IoT. Unlike traditional wireless sensor networks, Internet-connected smart thing deployments require security. CoAP mandates the use of the Datagram TLS (DTLS) protocol as the underlying secure communication protocol. In this paper we implement DTLS-protected secure CoAP for both resource-constrained IoT devices and a cloud backend and evaluate all three security modes (pre-shared key, raw-public key, and certificate-based) of CoAP in a real cloud-connected IoT setup. We extend SicsthSense– a cloud platform for the IoT– with secure CoAP capabilities, and compliment a DTLS implementation for resource-constrained IoT devices with raw-public key and certificate-based asymmetric cryptography. To the best of our knowledge, this is the first effort toward providing end-to-end secure communication between resource-constrained smart things and cloud back-ends which supports all three security modes of CoAP both on the client side and the server side. SecureSense– our End-to-End (E2E) secure communication architecture for the IoT– consists of all standard-based protocols, and implementation of these protocols are open source and BSD-licensed. The SecureSense evaluation benchmarks and open source and open license implementation make it possible for future IoT product and service providers to account for security overhead while using all standardized protocols and while ensuring interoperability among different vendors. The core contributions of this paper are: (i) a complete implementation for CoAP security modes for E2E IoT security, (ii) IoT security and communication protocols for a cloud platform for the IoT, and (iii) detailed experimental evaluation and benchmarking of E2E security between a network of smart things and a cloud platform. Funding details: VINNOVA; Funding text: This research has partly been funded by the Strategic Innovation Program for Internet of Things (SIP-IoT), a joint venture of VINNOVA, Formas och Energimyndigheten; and partly by the EU H2020 project NobelGrid under grant no. 646184.CEBOT;NOBELGRIDVINNOVA;EU, Horisont 2020

We present a framework for system-level analysis of electronic systems whose runtime behaviors depend on uncertain parameters. The proposed approach thrives on hierarchical interpolation guided by an advanced adaptation strategy, which makes the framework general and suitable for studying various metrics that are of interest to the designer. Examples of such metrics include the end-to-end delay, total energy consumption, and maximum temperature of the system under consideration. The framework delivers a light generative representation that allows for a straightforward, computationally efficient calculation of the probability distribution and accompanying statistics of the metric at hand. Our technique is illustrated by considering a number of uncertainty-quantification problems and comparing the corresponding results with exhaustive simulations.

Acute coronary syndrome (ACS) is responsible for the obstruction of coronary arteries, resulting in the loss of lives. The onset of ACS can be determined by looking at the various signs and symptoms of a patient. However, the accuracy of ACS determination is often put into question since there exist different types of uncertainties with the signs and symptoms. Belief rule-based expert systems (BRBESs) are widely used to capture uncertain knowledge and to accomplish the task of reasoning under uncertainty by employing belief rule base and evidential reasoning. This article presents the process of developing a BRBES to determine ACS predictability. The BRBES has been validated against the data of 250 patients suffering from chest pain. It is noticed that the outputs created from the BRBES are more dependable than that of the opinion of cardiologists as well as other two expert system tools, namely artificial neural networks and support vector machine. Hence, it can be argued that the BRBES is capable of playing an important role in decision making as well as in avoiding costly laboratory investigations. A procedure to train the system, allowing its enhancement of performance, is also presented. Validerad;2018;Nivå 2;2018-10-15 (svasva) BRBWSN

This paper analyzes how an experimenter can balance errors in subjective video quality tests between the statistical power of finding an effect if it is there and not claiming that an effect is there if the effect it is not there i.e. balancing Type I and Type II errors. The risk of committing Type I errors increases with the number of comparisons that are performed in statistical tests. We will show that when controlling for this and at the same time keeping the power of the experiment at a reasonably high level, it will require more test subjects than are normally used and recommended by international standardization bodies like the ITU. Examples will also be given for the influence of Type I error on the statistical significance of comparing objective metrics by correlation.

Nowadays Cloud Computing and Cloud Sourcing is on the agenda in many organizations. Many Chief Information Officers (CIOs) that urge for alternatives to traditional outsourcing are interested in how they can take advantage from Cloud Computing, by sourcing Information Technology (IT) from the cloud. This paper provides an overview of the research direction of Cloud Sourcing in the IS field. A literature review based on selected papers from top Information Systems (IS) journals and conferences were conducted. Findings from the review indicate that the attention of Cloud Sourcing in IS literature has mainly been directed towards security and risk as well as adoption issues, and that Cloud Sourcing is claimed to be the next generation of outsourcing. Unfortunately, this is where this strong claim ends without any further evidence, which indicate that there is a need for more research on Cloud Sourcing, especially in the direction of investigating relationships and implications when organizations start using Cloud Sourcing.

Part 2: Workshop and Tutorial Papers; International audience; In the context of big data analytics, the possibilities and demands of online data services may change rapidly, and with it change scenarios related to the processing of personal data. Such changes may pose challenges with respect to legal requirements such as a transparency and consent, and therefore call for novel methods to address the legal and conceptual issues that arise in its course. We define the concept of ‘dynamic consent’ as a means to meet the challenge of acquiring consent in a commercial use case that faces change with respect to re-purposing the processing of personal data with the goal to implement new data services. We present a prototypical implementation that facilitates incremental consent forms based on dynamic consent. We report the results gained via two focus groups which we used to evaluate our design, and derive from our findings implications for future directions.

Background Patients and citizens need access to their health information to get a retrospective as well as a prospective view on their care and rehabilitation processes. However, patients’ health information is stored in several health information systems and interoperability problems often hamper accessibility. In Sweden a national health information exchange (HIE) platform has been developed that enables information exchange between different health information systems. The aim of this study is to explore the opportunities and limitations of accessing and interacting with important health information through the Swedish national HIE platform. Methods A single case study approach was used for this study as an in-depth understanding of the subject was needed. A fictive patient case with a pseudo-name was created based on an interview with a stroke coordinator in Stockholm County. Information access through the national health information exchange platform and available service contracts and application programming interfaces were studied using different scenarios. Results Based on the scenarios created in this study, patients would be able to access some health related information from their electronic health records using the national health information exchange platform. However, there is necessary information which is not retrievable as it is either stored in electronic health records and eHealth services which are not connected to the national health information exchange platform or there is no service contract developed for these types of information. In addition, patients are not able to share information with healthcare professionals. Conclusion The national Swedish HIE platform provides the building blocks needed to allow patients online access to their health information in a fragmented and distributed health system. However, more complex interaction scenarios allowing patients to communicate with their health care providers or to update their health related information are not yet supported. Therefore it is of great importance to involve patients throughout the design and evaluation of eHealth services on both national and local levels to ensure that their needs for interoperability and information exchange are met. Electronic supplementary material The online version of this article (10.1186/s12911-019-0816-x) contains supplementary material, which is available to authorized users.

In this article we present a novel dictionary learning framework designed for compression and sampling of light fields and light field videos. Unlike previous methods, where a single dictionary with one-dimensional atoms is learned, we propose to train a Multidimensional Dictionary Ensemble (MDE). It is shown that learning an ensemble in the native dimensionality of the data promotes sparsity, hence increasing the compression ratio and sampling efficiency. To make maximum use of correlations within the light field data sets, we also introduce a novel nonlocal pre-clustering approach that constructs an Aggregate MDE (AMDE). The pre-clustering not only improves the image quality but also reduces the training time by an order of magnitude in most cases. The decoding algorithm supports efficient local reconstruction of the compressed data, which enables efficient real-time playback of high-resolution light field videos. Moreover, we discuss the application of AMDE for compressed sensing. A theoretical analysis is presented that indicates the required conditions for exact recovery of point-sampled light fields that are sparse under AMDE. The analysis provides guidelines for designing efficient compressive light field cameras. We use various synthetic and natural light field and light field video data sets to demonstrate the utility of our approach in comparison with the state-of-the-art learning-based dictionaries, as well as established analytical dictionaries.

Cyber insurance has attracted much attention from both practitioners, policymakers and academics in the past few years. However, it also faces some challenges before it can reach its full potential as a tool for better cyber risk management. One such challenge is the gap between what customers expect and what insurers really offer. This paper investigates this gap empirically, based on interviews with informant companies in Norway and Sweden considering cyber insurance. The expectations expressed in the interviews are compared to anonymized incident claims reports and claims statistics for 2018 from a global insurance intermediary. The results show no obvious pattern of discrepancies between different domains. However, informant expectations on business interruption coverage are much greater than one would expect from its share of claims. In this respect, informant expectations on business interruption coverage are more aligned with some recently published scenarios on possible major business interruptions.

Governments across the world spend vast resources on implementing digital technology. Electronic, or digital, government is the use and study of Internet-based information and communication technology in the public sector. A point of departure in this study is that investments in technology are not value-free, they require allocation of limited resources and trade-offs between values. The purpose of this paper was to investigate how values are prioritized in the Swedish digital government. This research was conducted by using quantitative data from a survey administered to Swedish municipalities and national agencies. In addition, qualitative data from a database was used to exemplify value operationalization. The research utilized a theoretical framework based on four value positions: professionalism, efficiency, service, and engagement. The findings reveal that service and quality, and productivity and legality have a high priority, while engagement values are less prioritized. Differences based on organization type and size are also discussed. Moreover, the study suggests that professionalism and efficiency are distinct value positions, while service and engagement are closely related through citizen centricity. The qualitative material suggests that citizen centricity can manifest itself as a form of service logic, but also in the form of educational digital inclusion activities for vulnerable groups. The paper concludes by suggesting that future research should further refine the concept of citizen centricity in relation to digital government values, since its current meaning is ambiguous.

As numerous Industrial Internet platforms emerge, manufacturing services are shared among multiple stakeholders more frequently than ever. The optimal selection of shared manufacturing service composition (MSC) should both promise the task completion and the stakeholders’ satisfaction. However, as commercial entities, stakeholders concentrate on not only the temporary benefits but also the long-term acquisitions. Most of the existing MSC problems neglect the stakeholders’ prospect on the manufacturing service sharing. This leads to the disappointment and dissatisfaction of the stakeholders with long-term expectations, who will abandon the participation in Industrial Internet platform. Therefore, the long/short-term preferences of various stakeholders should be satisfied and balanced. In this paper, the long/short-term preferences of three sides (provider, consumer, and operator) are discussed. And the models considering short-term utility of a consumer and long-term utility of providers, are established. The potential tasks assigned to providers are taken into account to estimate the long-term utility if the current task is accepted. Then, to solve the bi-objective optimization problem, an improved Non-dominated Sorting Genetic Algorithm-II algorithm, combining Tabu search and improved K-means mechanism, is proposed to find the optimal solution set. Finally, the effectiveness of the method is verified by the experimental results in terms of solution diversity, astringency and stability, in which a finding is further observed that the changes of consumers’ preferences have little impact on the long-term utility of providers. Funding agencies: National Natural Science Foundation of China [51875030, 51805020]; National Key Research and Development Program of China [2016YFB1101700]; Industrial Internet Innovation and Development Project

Light field technology, which emerged as a solution to the increasing demands of visually immersive experience, has shown its extraordinary potential for scene content representation and reconstruction. Unlike conventional photography that maps the 3D scenery onto a 2D plane by a projective transformation, light field preserves both the spatial and angular information, enabling further processing steps such as computational refocusing and image-based rendering. However, there are still gaps that have been barely studied, such as the light field demosaicing process. In this paper, we propose a depth-assisted demosaicing method for light field data. First, we exploit the sampling geometry of the light field data with respect to the scene content using the ray-tracing technique and develop a sampling model of light field capture. Then we carry out the demosaicing process in a layered object space with object-space sampling adjacencies rather than pixel placement. Finally, we compare our results with state-of-art approaches and discuss about the potential research directions of the proposed sampling model to show the significance of our approach.