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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]

Conversational interfaces that interact with humans need to continuously establish, maintain and repair common ground in task-oriented dialogues. Uncertainty, repairs and acknowledgements are expressed in user behaviour in the continuous efforts of the conversational partners to maintain mutual understanding. Users change their behaviour when interacting with systems in different forms of embodiment, which affects the abilities of these interfaces to observe users’ recurrent social signals. Additionally, humans are intellectually biased towards social activity when facing anthropomorphic agents or when presented with subtle social cues. Two studies are presented in this paper examining how humans interact in a referential communication task with wizarded interfaces in different forms of embodiment. In study 1 (N = 30), we test whether humans respond the same way to agents, in different forms of embodiment and social behaviour. In study 2 (N = 44), we replicate the same task and agents but introduce conversational failures disrupting the process of grounding. Findings indicate that it is not always favourable for agents to be anthropomorphised or to communicate with non-verbal cues, as human grounding behaviours change when embodiment and failures are manipulated.

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

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

By the prediction of future location for a vehicle in Internet of Vehicles (IoV), data forwarding schemes can be further improved. Major parameters for vehicle position prediction includes traffic density, motion, road conditions, and vehicle current position. In this paper, therefore, our proposed system enforces the accurate prediction with the help of real-time traffic from the vehicles. In addition, the proposed Neural Network Model assists Edge Controller and centralized controller to compute and predict vehicle future position inside and outside of the vicinity, respectively. Last but not least, in order to get real-time data, and to maintain a quality of experience, the edge controller is explored with Software Defined Internet of Vehicles. In order to evaluate our framework, SUMO simulator with Open Street map is considered and the results prove the importance of vehicle position prediction for vehicular networks.

As common vulnerabilites and exposures are detected, they are also discussed in various social platforms. The problem is that only a few of the posts made about them, are getting enough attention. This leads to an unawareness of potential and critical threats against systems. It is therefore important to look for patterns that make certain vulnerabilites more or less discussed. To do so, a framework was made for collecting discussions around cybersecurity and more specific vulnerabilites/exposures called CVE from Reddit. In addition, some of the desired data was collected from Twitter. Thereafter, the sentiments of the collected posts were calculated to see patterns between popular subreddits and the attitude shown in them. This was done with three methods: Flair, TextBlob and Vader. The results showed for instance that general discussions about information security were considered to be more positive than discussions of common vulnerabilites and exposures. Another result showed that the spread of CVEs that have a partial impact, are higher in Reddit, and is increasing almost exponentially. CVSS scores showed that a CVE with a CVSS score of around 7 is more likely to appear. Many CVEs in Reddit was also discussed before and after they were disclosed. The implication of this work might be that more and more people might use Reddit to discuss specific types of CVEs in a suitable subreddit, as well as being aware of common vulnerabilites and exposures, in order to prevent future threats.

In recent years, sentiment analysis (SA) has gained popularity among researchers in various domains, including the education domain. Particularly, sentiment analysis can be applied to review the course comments in massive open online courses (MOOCs), which could enable instructors to easily evaluate their courses. This article is a systematic literature review on the use of sentiment analysis for evaluating students’ feedback in MOOCs, exploring works published between January 1, 2015, and March 4, 2021. To the best of our knowledge, this systematic review is the first of its kind. We have applied a stepwise PRISMA framework to guide our search process, by searching for studies in six electronic research databases (ACM, IEEE, ScienceDirect, Springer, Scopus, and Web of Science). Our review identified 40 relevant articles out of 440 that were initially found at the first stage. From the reviewed literature, we found that the research has revolved around six areas: MOOC content evaluation, feedback contradiction detection, SA effectiveness, SA through social network posts, understanding course performance and dropouts, and MOOC design model evaluation. In the end, some recommendations are provided and areas for future research directions are identified.

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.

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.

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.

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.

Smooth traffic presupposes fine coordination between different actors, such as pedestrians, cyclists and car drivers. When autonomous vehicles join regular traffic, they need to coordinate with humans on the road. Prior work has often studied and designed for interaction with autonomous vehicles in structured environments such as traffic intersections. This paper describes aspects of coordination also in less structured situations during mundane maneuvers such as overtaking. Taking an ethnomethodological and conversation analytic approach, the paper analyzes video recordings of self-driving shuttle buses in Sweden. Initial findings suggest that the shuttle buses currently do not comply with cyclists’ expectations of social coordination in traffic. The paper highlights that communication and coordination with human road users is crucial for smooth flow of traffic and successful deployment of autonomous vehicles also in less structured traffic environments. Funding: Swedish Research CouncilSwedish Research CouncilEuropean Commission [2016-00827]

We introduce a multi-label text classifier with per-label attention for the classification of Electronic Health Records according to the International Classification of Diseases. We apply the model on two Electronic Health Records datasets with Discharge Summaries in two languages with fewer resources than English, Spanish and Swedish. Our model leverages the BERT Multilingual model (specifically the Wikipedia, as the model have been trained with 104 languages, including Spanish and Swedish, with the largest Wikipedia dumps) to share the language modelling capabilities across the languages. With the per-label attention, the model can compute the relevance of each word from the EHR towards the prediction of each label. For the experimental framework, we apply 157 labels from Chapter XI – Diseases of the Digestive System of the ICD, which makes the attention especially important as the model has to discriminate between similar diseases. 1 https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages

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)

The use of pretrained language models, fine-tuned to perform a specific downstream task, has become widespread in NLP. Using a generic language model in specialized domains may, however, be sub-optimal due to differences in language use and vocabulary. In this paper, it is investigated whether an existing, generic language model for Swedish can be improved for the clinical domain through continued pretraining with clinical text. The generic and domain-specific language models are fine-tuned and evaluated on three representative clinical NLP tasks: (i) identifying protected health information, (ii) assigning ICD-10 diagnosis codes to discharge summaries, and (iii) sentence-level uncertainty prediction. The results show that continued pretraining on in-domain data leads to improved performance on all three downstream tasks, indicating that there is a potential added value of domain-specific language models for clinical NLP.

Open access to research data is increasingly important for accelerating research. Grant authorities therefore request detailed plans for how data is managed in the projects they finance. We have recently developed such a plan for the EU-H2020 BIG-MAP project - a cross-disciplinary project targeting disruptive battery-material discoveries. Essential for reaching the goal is extensive sharing of research data across scales, disciplines and stakeholders, not limited to BIG-MAP and the European BATTERY 2030+ initiative but within the entire battery community. The key challenges faced in developing the data management plan for such a large and complex project were to generate an overview of the enormous amount of data that will be produced, to build an understanding of the data flow within the project and to agree on a roadmap for making all data FAIR. This paper describes the process we followed and how we structured the plan., 12 pages, 4 figures; the full DMP is published as Supporting Information

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

Paleobiologists and paleoecologists interested in studying biodiversity dynamics over broad spatial and temporal scales have built multiple community-curated data resources, each emphasizing a particular spatial domain, timescale, or taxonomic group(s). This multiplicity of data resources is understandable, given the enormous diversity of life across Earth's history, but creates a barrier to achieving a truly global understanding of the diversity and distribution of life across time. Here we present the Earth Life Consortium Application Programming Interface (ELC API), a lightweight data service designed to search and retrieve fossil occurrence and taxonomic information from across multiple paleobiological resources. Key endpoints include Occurrences (returns spatiotemporal locations of fossils for selected taxa), Locales (returns information about sites with fossil data), References (returns bibliographic information), and Taxonomy (returns names of subtaxa associated with selected taxa). Data objects are returned as JSON or CSV format. The ELC API supports tectonic-driven shifts in geographic position back to 580 Ma using services from Macrostrat and GPlates. The ELC API has been implemented first for the Paleobiology Database and Neotoma Paleoecology Database, with a test extension to the Strategic Environmental Archaeology Database. The ELC API is designed to be readily extensible to other paleobiological data resources, with all endpoints fully documented and following open-source standards (e.g., Swagger, OGC). The broader goal is to help build an interlinked and federated ecosystem of paleobiological and paleoenvironmental data resources, which together provide paleobiologists, macroecologists, biogeographers, and other interested scientists with full coverage of the diversity and distribution of life across time.

Social media has proven to affect the dynamics of the stock market directly. The potential influence of social media makes it an excellent tool for stock market manipulation, especially in the era of online misinformation. Therefore, the work in this thesis aims to provide a better understanding of company discussion on social media. More precisely, collecting a dataset of company-specific discussion from Twitter, Reddit, Seeking Alpha, and Citron Research enabled us to perform time series analysis using smoothing and clustering, where associated events were identified, categorized, and summarized. A selected companies' time series was also more closely and qualitatively analyzed to build intuition and understanding of the dynamics. The results show that the dynamics of company discussion on social media can be evaluated using the discussion intensities. They also show the possibility of measuring whether a specific social media is, in general, reactive or proactive to abnormal events in the stock market. Moreover, external events seem to trigger discussion activity which propagates through all social media. Furthermore, the results seem to depend on the choice of bandwidth used for smoothing and the number of clusters given to the clustering algorithm, which requires further refinement as a continuation of this thesis.

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)

Aim Road users, such as cyclists and car drivers, are constantly adapting to each other and to the road infrastructure. This study aimed to investigate what trade-offs can be observed when drivers and cyclists adapt to other road users and to the infrastructure. Methods A semi-controlled study was carried out in an urban traffic environment, where all participants travelled the same route twice, once as a cyclist and once as a car driver. The extended control model was used as an analytical framework to examine how people adapt depending on their main and current road-user role, experiencing different infrastructure and complexity. Results The results revealed possible trade-offs made while adapting, mainly in relation to rule-following. No differences in adaptive behaviour between people who mostly drive and people who mostly cycle were found. The current situational demands influence both the need to adapt and the adaptation carried out. A more complex traffic environment led to increased information intake, measured by glance behaviour.

This paper continues previous efforts in understanding the problems students face when learning concurrency. In this paper, we explore students' understanding of the subject using phenomenography in order to gain insights that can aid in explaining the underlying causes for common student mistakes in concurrency, which has been studied in depth previously. Students' experience of concurrency and critical sections were analyzed using a phenomenographic study based on interviews with students attending one of two courses on concurrency and operating systems. We present 6 categories describing students' experience of concurrency, and 4 categories describing students' experience of critical sections in this paper. Furthermore, these categories are related to previous results, both to explore how misconceptions in the categores relate to student mistakes and to estimate how common it is for each category to be discerned.

Optimization problems lie at the core of scientific and engineering endeavors. Solutions to these problems are often compute-intensive. To fulfill their compute-resource requirements, graphics processing unit (GPU) technology is considered a great opportunity. To this end, we focus on linear programming (LP) problem solving on GPUs using revised simplex method (RSM). This method has potentially GPU-friendly tasks, when applied to large dense problems. Basis update (BU) is one such task, which is performed in every iteration to update a matrix called basis-inverse matrix. The contribution of this paper is two-fold. Firstly, we experimentally analyzed the performance of existing GPU-based BU techniques. We discovered that the performance of a relatively old technique, in which each GPU thread computed one element of the basis-inverse matrix, could be significantly improved by introducing a vector-copy operation to its implementation with a sophisticated programming framework. Second, we extended the adapted element-wise technique to develop a new BU technique by using three inexpensive vector operations. This allowed us to reduce the number of floating-point operations and conditional processing performed by GPU threads. A comparison of BU techniques implemented in double precision showed that our proposed technique achieved 17.4% and 13.3% average speed-up over its closest competitor for randomly generated and well-known sets of problems, respectively. Furthermore, the new technique successfully updated basis-inverse matrix in relatively large problems, which the competitor was unable to update. These results strongly indicate that our proposed BU technique is not only efficient for dense RSM implementations but is also scalable.

In air traffic control (ATC), teamwork is a key component among air traffic control operators (ATCOs) to safely direct theaircraft through the sky and on the ground. To be able to design for future ATC systems, we must understand how ATCOswork together, their teamwork, and how they experience and perceive teamwork, in different ATC environments. We conductedinterviews with 16 ATCOs working in four different ATC environments (en-route control, terminal area control, towercontrol for a small airport and tower control for a large airport in Sweden) and analysed the results in the light of the “BigFive” model of teamwork. The main contributions of this paper are to show: (1) how eight teamwork factors are differentlymanifested by the ATCOs in the different ATC environments, (2) that teamwork in ATC is important during routine operations,during stressful work, and during abnormal situations, and (3) that the design of the organisation, the environment,and the tools, affects teamwork and the importance of different teamwork factors. Funding agencies: Swedish Transport Administration; LFV Air Navigation Services of Sweden; NFFP (National Aviation Research Programme) - VINNOVA (Swedish Governmental Agency for Innovation Systems) [NFFP6-2013-01201]; Swedish Armed Forces; Swedish Defence Materiel Administ

JavaScript has in recent years become one of the most utilized programming languages for developing different kinds of applications. However, even though it has received a lot of praise for its simplicity, versatility and highly active community, it lacks some functionalities and features that a lot of programmers highly value, like static and strict typing, compile-time debugging, and to not be required to make use of third-party libraries to integrate crucial functionality. However, several new languages built on top of JavaScript have been developed to address and resolve these issues developers find with JavaScript without losing the benefits that come with it. One of these super- set languages is Reason, the new syntax and toolchain powered by the OCaml compiler. This thesis aims to address whether there are scenarios where Reason could act as a reasonable substitute of JavaScript by investigating how the languages compare in regards to different criteria. The criteria examined are writability, data structures and typing, reliability and testing, community support, market demand, portability, and performance. The findings show that using Reason over JavaScript could result in higher reliability and robustness due to static type checking, compile-time debugging, and other usable feature like pattern matching and explicitly defined custom data structures, which is convenient when dealing with advanced data. On the other hand, Reason’s interoperability with JavaScript is something that is not very straight-forward and makes it difficult to integrate Reason into an existing JavaScript codebase or include one of the thousand of JavaScript- written dependencies available on npm. Furthermore, with Reason being a rather young language yet to be used by a larger audience, both the community support and market demand are a lot smaller than that of JavaScript and have yet to see a significant growth, which leads to questions about the overall survival of the language. Both of the languages have a significant role and contribute with different kind of functionality. However, with the non-straightforward interoperability, Reason loses a lot of potential benefits to be gained from JavaScript, which could be problematic in the long run and could impact the future of Reason. JavaScript har under senare år blivit ett av de mest använda programmeringsspråken för att utveckla olika typer av applikationer. Men även om det har fått mycket beröm för sin enkelhet, mångsidighet och mycket aktivt nätgemenskap, saknar det vissa funktioner och egenskaper som många programmerare uppskattar mycket, som statisk och strikt typning, felsökning under kompilering och att inte vara I behov av tredjepartsbibliotek för att integrera grundläggande funktionalitet. Flera nya språk som är byggda ovanpå JavaScript har utvecklats för att lösa de problem som utvecklare har med JavaScript, utan att förlora fördelarna med det. Ett av dessa supersetsspråk är Reason, den nya syntaxen och verktygskedjan som drivs av OCaml-kompilatorn. Denna avhandling syfte är att utvärdera om det finns scenarier där Reason kan fungera som en rimlig ersättare för JavaScript, detta kommer genomföras genom att undersöka hur språken jämförs med varandra med avseende på olika kriterier. Kriterierna som undersöks är skrivbarhet, datastrukturer och typning, tillförlitlighet och testning, nätgemenskap, marknadsförfrågan, portabilitet, och prestanda. Resultaten visar att användning av Reason över JavaScript kan leda till högre tillförlitlighet på grund av statisk typkontroll, felsökning under kompilering och andra användbara funktioner som mönstermatchning och anpassade datastrukturer, båda underlättar vid hantering avancerade och komplexa datastrukturer. Däremot är Reasons kompabilitet med JavaScript komplicerad, vilket gör det svårt att integrera Reason i en befintlig JavaScript- kodbas eller inkludera ett av de tusentals JavaScript-skrivna biblioteken som finns tillgängliga på npm. Eftersom Reason är ett ganska ungt språk som ännu inte används på en större skala, så är både nätgemenskapen och marknadens efterfrågan mycket mindre än JavaScript och har ännu inte sett en betydande tillväxt, vilket leder till frågor om den långsiktiga överlevnaden av språket. Båda språken har en betydande roll och bidrar med olika slags funktioner. Men med den komplicerade kompatibiliteten med JavaScript så förlorar Reason en hel del potentiella fördelar som kan uppnås genom JavaScript, vilket kan vara problematiskt på lång sikt och kan påverka Reasons framtid.

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.

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.

Semantic embeddings have advanced the state of the art for countless natural language processing tasks, and various extensions to multimodal domains, such as visual-semantic embeddings, have been proposed. While the power of visual-semantic embeddings comes from the distillation and enrichment of information through machine learning, their inner workings are poorly understood and there is a shortage of analysis tools. To address this problem, we generalize the notion of probing tasks to the visual-semantic case. To this end, we (i) discuss the formalization of probing tasks for embeddings of image-caption pairs, (ii) define three concrete probing tasks within our general framework, (iii) train classifiers to probe for those properties, and (iv) compare various state-of-the-art embeddings under the lens of the proposed probing tasks. Our experiments reveal an up to 12% increase in accuracy on visual-semantic embeddings compared to the corresponding unimodal embeddings, which suggest that the text and image dimensions represented in the former do complement each other., Comment: Submitted July 1 2020, COLING 2020 main conference

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

Design choices related to development of data- driven models significantly impact or degrade predictive performance of the models. One of the essential steps during development and evaluation of such models is the choice of feature selection and dimension reduction techniques. That is imperative especially in cases dealing with multimodal data gathered from different sources. In this paper, we will investigate the behavior of Partial Least Squares (PLS) regression for dimension reduction and prediction of motor states of Parkinson’s disease (PD) patients, using upper limb motor data gathered by means of a smartphone. The results in terms of correlations between smartphone-based and clinician-derived scores were compared to a previous study using the same data where principal component analysis (PCA) and support vector machines (SVM) were used. The findings from this study show that PLS is superior in terms of prediction performance of motor states in PD than combining PCA and SVM. This indicates that PLS could be considered as a useful methodology in problems where data-driven analysis is needed.

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)

In forestry and agricultural crop growing there has always been a need to identify plant diseases. The purpose of this is to faster remove a harmful plant within a larger group to reduce its effect on the crop yield. Currently, the major way to identify plant diseases is through manual labor where an plant pathology expert identifies diseases by looking at the plant. The aim of this project is to explore how image analysis can be utilised for faster identification of plant diseases. The report will also present descriptions of different relevant plant diseases, the relevant data sets that have been used, and the validity of the results.

This thesis examines and evaluates whether it is possible to measure the quality of a software project and introduces a metric that will evaluate if the quality of every test performed when developing software can be measured. This subject is examined because it can be hard to conclude how well a project and all its parts performed, both during implementation and after it is done. To facilitate this need, this thesis provides a possible solution. To try and answer these questions and meet the needs of this tool a prototype has been developed. The prototype is automated and runs through all the software development tests selected for this project. It sums up the test results and later translates them with the help of a metric to tell its quality grade. The metric is calculated with the help of an arbitrary formula developed for this thesis. Once the metric is concluded the development team working with the project will have an overview of how well each test area is performing and how well the project's end result was. With the help of this metric it is also easier to see if the quality achieved meets the company’s standards and the customer’s wishes. The prototype aims to be sustainable because the solution should last for a long term and also because sustainability means a smoother and more efficient way for developers and other people involved to work with the prototype since not much extra work will be required when updates need to be implemented or other necessary implementations. The prototype is applied on a second project, which is larger and more advanced than the project created for this thesis, to get a better and accurate understanding if the implementation is correct and if the metric can be used as a value to describe a project. The metric results are compared and evaluated. The results of this thesis conclude a proof-of-concept and can be seen as a first step in a longer evaluation and process in determining the quality of tests. The results conclusion is that more parameters and more weighing of each tests importance are needed in order to achieve a reliable metric result. This tool is meant to ease and help developers to quickly come to a conclusion about how good the work is. It could also be beneficial for a company with focus on web development and IT-solutions though it will be easier to follow and set a standard for the services they provide.

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

We present a novel learning-based approach to graph representations of road networks employing state-of-the-art graph convolutional neural networks. Our approach is applied to realistic road networks of 17 cities from Open Street Map. While edge features are crucial to generate descriptive graph representations of road networks, graph convolutional networks usually rely on node features only. We show that the highly representative edge features can still be integrated into such networks by applying a line graph transformation. We also propose a method for neighborhood sampling based on a topological neighborhood composed of both local and global neighbors. We compare the performance of learning representations using different types of neighborhood aggregation functions in transductive and inductive tasks and in supervised and unsupervised learning. Furthermore, we propose a novel aggregation approach, Graph Attention Isomorphism Network, GAIN. Our results show that GAIN outperforms state-of-the-art methods on the road type classification problem. Funding agencies: The computations handling was enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council through grant agreement no. 2018-05973.