Model-driven engineering promotes the migration from code-centric to model-based software development. Systems consist of model collections integrating different concerns and perspectives, while semi-automated model transformations generate executable code combining the information from these. Increasing the abstraction level to models required appropriate management technologies supporting the various software development activities. Among these, model comparison represents one of the most challenging tasks and plays an essential role in various modelling activities. Its hardness led researchers to propose a multitude of approaches adopting different approximation strategies and exploiting specific knowledge of the involved models. However, almost no support is provided for their evaluation against specific scenarios and modelling practices. This thesis presents Benji, a framework for the automated generation of model comparison benchmarks. Given a set of differences and an initial model, users generate models resulting from the application of the first on the latter. Differences consist of preconditions, actions and postconditions expressed using a dedicated specification language. The generator converts benchmark specifications to design-space exploration problems and produces the final solutions along with a model-based description of their differences with respect to the initial model. A set of representative use cases is used to evaluate the framework against its design principles, which resemble the essential properties expected from model comparison benchmark generators.

The purpose of this thesis is to study security for digital home visits, where traditional home visits are replaced by digital home visits using digital technology. The report examines the safety aspects for welfare technology solutions where data is collected from sensor systems and digital platforms and examines di↵erent Swedish laws that implies on a digital home visit. The study proposes an implementation of a prototype application to support users, relatives, and healthcare professionals to conduct digital home visits in a safe manner. The chosen scenario of the digital home visit was to check whether the person has eaten food during the day or not. This was done in a lab kitchen at Lule°a University of Technology with help of Z-wave sensors and a implemented systems. The result is displayed on a secure website. The solution is discussed and compared to other technical solutions of this problem and also to several Swedish laws. This paper finishes with a section aimed to provide a variety of recommendations when implementing a similar system.

Today’s embedded systems require more and more performance but they are still required to meet power constraints. Single processor systems can deliver high performance but this leads to high power consumption. One solution to this problem is to use a multiprocessor system instead which is able to provide high performance and at the same time meet the power constraints. The reason that such a system can meet the power constraints is that it can have a lower clock frequency than a similar single processor system. The focus of the project is to explore possibilities when developing new multiprocessor systems. The project makes a comparison of asymmetric multiprocessing (AMP) systems and symmetric multiprocessing (SMP) systems in terms of task management and communication between the processors. A comparison is made between the Advanced High-performance Bus (AHB) interface and the Advanced eXtensible Interface (AXI). The fixed priority and round-robin arbitration algorithms is also compared. The project also contains a practical part where a demo is developed to show that an inter-processor communication using exclusive access is possible to implement. The theoretical part of the project containing the comparisons result in good comparisons that can be used to get an overview of what to use when developing new Multiprocessor System on Chip (MPSoC) designs. The demo developed in this project failed to meet the requirement of having a fully functional spinlock. This problem can be solved in the future if new hardware is developed. / Dagens inbyggda system kräver mer och mer prestanda men de måste fortfarande klara av kraven kring strömförbrukning. System med en processor kan leverera hög prestanda men detta leder till hög strömförbrukning. En lösning till detta problem är att använda ett multiprocessorsystem istället som klarar av att leverera hög prestanda och samtidigt klara av kraven kring strömförbrukning. Anledningen till att denna typ av system klarar av kraven kring strömförbrukning är att de kan använda en lägre klockfrekvens än ett system med en processor. Fokuset på detta projektet ligger på att utforska möjligheterna som finns när nya multiprocessorsystem ska utvecklas. Projektet gör en jämförelse mellan asymmetriska och symmetriska multiprocessorsystem i termer av uppgiftshantering och kommunikation mellan processorerna. En jämförelse har gjorts mellan Advanced High-Performance Bus (AHB) gränssnittet och Advanced eXtensible Interface (AXI) gränssnittet. Fixed priority och round-robin algoritmerna för hantering av krockar mellan processorerna har också jämförts. Det finns även en praktisk del i projektet där en demo har utvecklats för att visa en fungerande kommunikation mellan processorer som använder funktionaliteten för exklusiv åtkomst till den gemensamma bussen. Den teoretiska delen av projektet som innehåller jämförelserna resulterar i bra jämförelser som kan användas när nya multiprocessorsystem utvecklas. Demon som har utvecklats i detta projekt misslyckades med att klara av kravet kring att ha ett fullt fungerande lås. Detta problemet kan lösas i framtiden ifall ny hårdvara utvecklas.

This thesis examines some of the currently available programs for password guessing, in terms of designs and strengths. The programs Hashcat, OMEN, PassGAN, PCFG and PRINCE were tested for effectiveness, in a series of experiments similar to real-world attack scenarios. Those programs, as well as the program TarGuess, also had their design examined, in terms of the extent of how they use different important parameters. It was determined that most of the programs use different models to deal with password lists, in order to learn how new, similar, passwords should be generated. Hashcat, PCFG and PRINCE were found to be the most effective programs in the experiments, in terms of number of correct password guessed each second. Finally, a program for automated password guessing based on the results was built and implemented in the cyber range at the Swedish defence research agency.

The world of industrial manufacturing has changed a lot during the past decades. It has gone from a labour-intensive process of manual control of machines to a fully connected and automated process. The next big leap in industrial manufacturing is known as industry 4.0 or smart manufacturing. With industry 4.0 comes increased integration between IT systems and the factory floor. This change has proven challenging to implement into existing factories many with the intended lifespan of several decades. One of the single most important parameters to measure is the operating hours of each machine. This information can help companies better utilize their resources and save huge amounts of money.  The goal is to develop a solution which can track the operating hours of the machines using image analysis and the signal lights already mounted on the machines. Using methods commonly used for traffic light recognition in autonomous cars, a system with an accuracy of over 99% during the specified conditions, has been developed. It is believed that if more diverse video data becomes available a system, with high reliability that generalizes well, could be developed using similar methodology. / Industriell tillverkning har förändrats mycket under de senaste decennierna. Det har gått från en process som krävt mycket manuellt arbete till en process som är nästan helt uppkopplad och automatiserad. Nästa stora steg inom industriell tillverkning går under benämningen industri 4.0 eller smart tillverkning. Med industri 4.0 kommer en ökad integration mellan IT-system och fabriksgolvet. Denna förändring har visat sig vara särskilt svår att implementera i redan existerande fabriker som kan ha en förväntad livstid på flera årtionden. En av de viktigaste parametrarna att mäta inom industriell tillverkning är varje maskins operativa timmar. Denna information kan hjälpa företag att bättre utnyttja tillgängliga resurser och därigenom spara stora summor pengar. Målet är att utveckla en lösning som, med hjälp av bildanalys och de signalljus som maskinerna kommer utrustade med, kan mäta maskinernas operativa timmar. Med hjälp av metoder som vanligen används för trafikljusigenkänning i autonoma fordon har ett system med en träffsäkerhet på över 99% under de förutsättningar som presenteras i rapporten utvecklats. Om mer video med större variation blir tillgänglig är det mycket troligt att det går att utveckla ett system som har hög pålitlighet i de flesta produktionsmiljöer.

Big data of type spatial is growing exponentially with the highest rate due to extensive growth in usage of sensors, IoT and mobile devices’ spatial data generation, therefore maintaining, processing and using such data efficiently, effectively with high performance has become one of the top priorities for Database management system providers, hence spatial database features and datatypes have become serious criteria in evaluating database management systems that are supposed to work as the back-end for spatial applications and services. With exponential growth of data and introducing of new types of data, “Big Data” has become strongly focused area that has gained the attention of different sectors e.g. academia, industries and governments to other organizations and studies. The rising trend in high resolution and large-scale geographical information systems have resulted in more companies providing location-based applications and services, therefore finding a proper database management system solution that can support spatial big data features, with multi-model big data support that is reliable and affordable has become a business need for many companies. Concerning the fact that choosing proper solution for any software project can be crucial due to the total cost and desired functionalities that any product could possibly bring into the solution. Migration is also a very complicated and costly procedure that many companies should avoid, which justifies the criticality of choosing the right solution based on the specific needs of any organization. Companies providing spatial applications and services are growing with the common concern of providing successful solutions and robust services. One of the most significant elements that ensures services’ and hence the providers’ reputation and positive depiction is services’ high availability. The possible future work for the thesis could be to develop the framework into a decision support solution for IT businesses with emphasize on spatial features. Another possibility for the future works would be to evaluate the framework by testing the evaluation framework on many other different DBMSs.

Today, the Internet of Things is widely used in various fields, such as factories, public facilities, and even homes. The use of the Internet of Things involves a large number of sensor devices that collect various types of data in real time, such as machine voltage, current, and temperature. These devices will generate a large amount of streaming sensor data. These data can be used to make the data analysis, which can discover hidden relation such as monitoring operating status of a machine, detecting anomalies and alerting the company in time to avoid significant losses. Therefore, the application of anomaly detection in the field of data mining is very extensive. This paper proposes an anomaly detection method based on multiple streaming sensor data and performs anomaly detection on three data sets which are from the real company. First, this project proposes the state transition detection algorithm, state classification algorithm, and the correlation analysis method based on frequency. Then two algorithms were implemented in Python, and then make the correlation analysis using the results from the system to find some possible meaningful relations which can be used in the anomaly detection. Finally, calculate the accuracy and time complexity of the system, and then evaluated its feasibility and scalability. From the evaluation result, it is concluded that the method

In recent years, cyber attacks and data fraud have become major issues to companies, businesses and nation states alike. The need for more accurate and reliable risk management models is therefore substantial. Today, cybersecurity risk management is often carried out on a qualitative basis, where risks are evaluated to a predefined set of categories such as low, medium or high. This thesis aims to challenge that practice, by presenting a model that quantitatively assesses risks - therefore named MaRiQ (Manage Risks Quantitatively). MaRiQ was developed based on collected requirements and contemporary literature on quantitative risk management. The model consists of a clearly defined flowchart and a supporting tool created in Excel. To generate scientifically validated results, MaRiQ makes use of a number of statistical techniques and mathematical functions, such as Monte Carlo simulations and probability distributions. To evaluate whether our developed model really was an improvement compared to current qualitative processes, we conducted a workshop at the end of the project. The organization that tested MaRiQexperienced the model to be useful and that it fulfilled most of their needs. Our results indicate that risk management within cybersecurity can and should be performed using more quantitative approaches than what is praxis today. Even though there are several potential developments to be made, MaRiQ demonstrates the possible advantages of transitioning from qualitative to quantitative risk management processes.

The objective of this study has been to answer three questions. The questions are Can you navigate in a different way than with your fingers, Do you lose any functionality in the application when you are not using your fingers and is the solution a reasonable way to navigate. The study consist of two parts, the first is to create a navigation library with different navigation methods and the second part is to evaluate the navigation methods using user tests. The study shows that it is possible to navigate without the use of fingers and address problems and solution to navigate without the use of fingers. The study shows how different navigation methods perform compared to each other and compared to navigating with the use of fingers.

Knowledge Graph is a rising topic in the field of Artificial Intelligence. As the current trend of knowledge representation, Knowledge graph research is utilizing the large knowledge base freely available on the internet. Knowledge graph also allows inspection, analysis, the reasoning of all knowledge in reality. To enable the ambitious idea of modeling the knowledge of the world, different theory and implementation emerges. Nowadays, we have the opportunity to use freely available information from Wikipedia and Wikidata. The thesis investigates and formulates a theory about learning from Knowledge Graph. The thesis researches probabilistic knowledge graph. It only focuses on a branch called latent feature models in learning probabilistic knowledge graph. These models aim to predict possible relationships of connected entities and relations. There are many models for such a task. The metrics and training process is detailed described and improved in the thesis work. The efficiency and correctness enable us to build a more complex model with confidence. The thesis also covers possible problems in finding and proposes future work.