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Imagination Studios is a company specialized in motion capturing and animation. Part of their daily business is working at trade shows where they have a booth to keep close contact with existing customers and also to find new ones. However, usually only two to three people will be working at the booth, and frequently, these people will be in meetings with potential customers. During a time like this, nobody is free to attend to other people checking out the booth. This can result in a potential loss of a new customer. This project seeks a way to alleviate that problem.The idea behind this project was to create an application that trade show visitors can interact with in a playful and innovative way while also giving them a feel of what Imagination Studios is all about while looking for information about the company. To do this it was decided to let users interact with the system by using a Microsoft Kinect. The Kinect allows for easy implementation of a user interface based on motion capturing while also being very cost effective. A new user interface was to be designed as well, without copying already existing solutions and without simply expanding a traditional UI with new elements. To achieve this several design sketches were made, and the most interesting ones were then turned into storyboards. These were then used to decide on the final design, which was then elaborated on by use of video sketches and a collage in Adobe Photoshop.Several tools were used during the actual implementation. For the actual visualization and graphical design, the Unreal Engine 3 in combination with UDK was decided upon. To connect Kinect and Unreal Engine 3, a third party addon called NIUI which makes use of the open source SDK OpenNI was used. For ease of debugging and programming in Unrealscript, the programming language used by the Unreal Engine 3, an addon for Microsoft Visual Studio 2010 called nFringe (Pixel Mine, Inc., 2010) was used.

Industrial cybersecurity has risen as an important topic of research nowadays. The heavy connectivity by the Internet of Things (IoT) and the growth of cyberattacks against industrial assets cause this risen and attract attention to the cybersecurity field. While fostering current software applications and use-cases, the ubiquitous access to the Internet has also exposed operational technologies to new and challenging security threats that need to be addressed. As the number of attacks increases, their visibility decreases. An attack can modify the Cyber-Physical Systems (CPSs) quality to avoid proper quality assessment. They can disrupt the system design process and adversely affect a product’s design purpose. This working progress paper presents our approach to modeling, analyzing, and mitigating cyberattacks in CPS. We model the normal behavior of the application as well as cyberattacks with the help of Microsoft Security Development Lifecycle (SDL) and threat modeling approach (STRIDE). Then verify the application and attacks model using a model checking tool and propose mitigation strategies to decrease the risk of vulnerabilities. The results can be used to improve the system design to overcome the vulnerabilities.

Concurrent programming puts demands on software debugging and testing, as concurrent software may exhibit problems not present in sequential software, e.g., deadlocks and race conditions. In aiming to increase efficiency and effectiveness of debugging and bug-fixing for concurrent software, a deep understanding of concurrency bugs, their frequency and fixing-times would be helpful. Similarly, to design effective tools and techniques for testing and debugging concurrent software, understanding the differences between non-concurrency and concurrency bugs in real-word software would be useful. This paper presents an empirical study focusing on understanding the differences and similarities between concurrency bugs and other bugs, as well as the differences among various concurrency bug types in terms of their severity and their fixing time, and reproducibility. Our basis is a comprehensive analysis of bug reports covering several generations of five open source software projects. The analysis involves a total of 11860 bug reports from the last decade, including 351 reports related to concurrency bugs. We found that concurrency bugs are different from other bugs in terms of their fixing time and severity while they are similar in terms of reproducibility. Our findings shed light on concurrency bugs and could thereby influence future design and development of concurrent software, their debugging and testing, as well as related tools.

Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activity Recognition (HAR) is one of the fields that has seen an explosive research interest among the ML community due to its wide range of applications. HAR is one of the most helpful technology tools to support the elderly’s daily life and to help people suffering from cognitive disorders, Parkinson’s disease, dementia, etc. It is also very useful in areas such as transportation, robotics and sports. Deep learning (DL) is a branch of ML based on complex Artificial Neural Networks (ANNs) that has demonstrated a high level of accuracy and performance in HAR. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DL models widely used in the recent years to address the HAR problem. The purpose of this paper is to investigate the effectiveness of their integration in recognizing daily activities, e.g., walking. We analyze four hybrid models that integrate CNNs with four powerful RNNs, i.e., LSTMs, BiLSTMs, GRUs and BiGRUs. The outcomes of our experiments on the PAMAP2 dataset indicate that our proposed hybrid models achieve an outstanding level of performance with respect to several indicative measures, e.g., F-score, accuracy, sensitivity, and specificity. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

The edge-cloud computing continuum effectively uses fog and cloud servers to meet the quality of service (QoS) requirements of tasks when edge devices cannot meet those requirements. This paper focuses on the workflow offloading problem in edge-cloud computing and formulates this problem as a nonlinear mathematical programming model. The objective function is to minimize the monetary cost of executing a workflow while satisfying constraints related to data dependency among tasks and QoS requirements, including security and deadlines. Additionally, it presents a genetic algorithm for the workflow offloading problem to find near-optimal solutions with the cost minimization objective. The performance of the proposed mathematical model and genetic algorithm is evaluated on several real-world workflows. Experimental results demonstrate that the proposed genetic algorithm can find admissible solutions comparable to the mathematical model and outperforms particle swarm optimization, bee life algorithm, and a hybrid heuristic-genetic algorithm in terms of workflow execution costs.

A hybrid cloud is an efficient solution to deal with the problem of insufficient resources of a private cloud when computing demands increase beyond its resource capacities. Cost-efficient workflow scheduling, considering security requirements and data dependency among tasks, is a prominent issue in the hybrid cloud. To address this problem, we propose a mathematical model that minimizes the monetary cost of executing a workflow and satisfies the security requirements of tasks under a deadline. The proposed model fulfills data dependency among tasks, and data transmission time is formulated with exact mathematical expressions. The derived model is a Mixed-integer linear programming problem. We evaluate the proposed model with real-world workflows over changes in the input variables of the model, such as the deadline and security requirements. This paper also presents a post-optimality analysis that investigates the stability of the assignment problem. The experimental results show that the proposed model minimizes the cost by decreasing inter-cloud communications for dependent tasks. However, the optimal solutions are affected by the limitations that are imposed by the problem constraints. 

Task offloading is a prominent problem in edge−cloud computing, as it aims to utilize the limited capacityof fog servers and cloud resources to satisfy the QoS requirements of tasks, such as meeting their deadlines.This paper formulates the task offloading problem as a nonlinear mathematical programming model to maximizethe number of independent IoT tasks that meet their deadlines and to minimize the deadline violationtime of tasks that cannot meet their deadlines. This paper proposes two Q-learning algorithms to solve theformulated problem. The performance of the proposed algorithms is experimentally evaluated with respect toseveral algorithms. The evaluation results demonstrate that the proposed Q-learning algorithms perform wellin meeting task deadlines and reducing the total deadline violation time.

In today's ever-automating world, businesses are increasingly focused on optimizing network operations, including configuration, management, and troubleshooting. This study scrutinizes the comparison between manual and automated methodologies within the framework of the cloud-based Cisco Meraki platform. Our objective is to establish decisive criteria for identifying the most advantageous circumstances for automation, particularly in network configuration. To achieve this, we constructed a network consisting of three Meraki devices. Automation of network configuration was facilitated through the development of Python scripts leveraging the Meraki API library in conjunction with configuration files. The configuration files were constructed in various file formats–Excel, CSV, JSON, and YAML– in order to assess their impact on automation effectiveness. Moreover, manual configuration tasks were undertaken by three network technicians with varying levels of Meraki proficiency. A comprehensive series of tests, encompassing both automated and manual methods, was subsequently conducted, focusing on metrics such as execution time and keystroke count. Through meticulous analysis, we provided valuable insights into the effectiveness of automation in network configuration processes. Results demonstrated that the use of JSON files in the automation process yielded the shortest completion time and required the fewest keystrokes compared to other automation methods. Moreover, manual configuration via the Meraki dashboard GUI exhibited comparable efficiency to automation methods in networks with a small number of devices. Nonetheless, owing to the capacity to reuse configuration files with minor adjustments for similar setups, the true advantage of automation over manual configuration becomes apparent as the device count increases.

Recent advancements of multiprocessor architectures have led to increasing use of multiprocessors in real-time embedded systems. The two most popular real-time scheduling approaches in multiprocessors are global and partitioned scheduling. Cluster based multiprocessor scheduling can be seen as a hybrid approach combining benefits of both partitioned and global scheduling. Virtual clustering further enhances it by providing dynamic cluster resource allocation duringrun-time and applying hierarchical scheduling to ensure temporal isolation between different software components. Over the years, the study of virtual clustered-based multiprocessor scheduling has been limited to theoretical analysis. In this thesis, we implemented a Virtual-Clustered Hierarchical Scheduling Framework (VC-HSF) in Linux without modifying the base Linux kernel. This work includes complete design, implementation and experimentation of this framework in a multiprocessor platform. Our main contributions are twofold: (i) to the best of our knowledge, our work is the first implementation of any virtual-clustered real-time multiprocessor scheduling in an operating system, (ii) our design and implementation gives practical insights about challenges of implementing any virtual-clustered algorithms for real-time scheduling.

This study explores the potential of artificial intelligence (AI) in enhancing the diagnosis of brain tumours, specifically through a comparative analysis of two advanced deep learning (DL) models, ResNet50 and YOLOv8, applied to detect and classify brain tumours in MRI images. The study addresses the critical need for rapid and accurate diagnostic tools in the medical field, given the complexity and diversity of brain tumours. The research was motivated by the potential benefits AI could offer to medical diagnostics, particularly in terms of speed and accuracy, which are crucial for effective patient treatment and outcomes. The performance of the ResNet50 and YOLOv8 models was evaluated on a dataset of 7023 MRI images across four tumour types. Key metrics used were accuracy, precision, recall, specificity, F1-score, and processing time, to identify which model performs better in detecting and classifying brain tumours. The findings demonstrates that although both models exhibit high performance, YOLOv8 surpasses ResNet50 in most metrics, particularly showing advantages in speed. The findings highlight the effectiveness advanced DL models in medical image analysis, providing a significant advancement in brain tumour diagnosis. By offering a thorough comparative analysis of two commonly used DL models, aligning with ongoing approaches to integrate AI into practical medical application, and highlighting their potential uses, this study advances the area of medical AI providing insight into the knowledge required for the deployment of future AI diagnostic tools.

Most of the hardware products today, especially those that people interact with, are controlled by software. There are many devices with inbuilt software which many people do not bother to notice. Software may be critical in terms of strict quality requirements due to failures which enable risks of endangering the production and more importantly the lives of people. The testing team of Westermo Network Technologies AB faced a challenge with not being able to identify the coverage of tested cases in software. Identifying the coverage of total executed test cases enables the possibility of knowing the potential future quality of a software. By reaching such a stage a software will likely not suffer from failures due to higher quality, therefore the lives of people and the production at stake will not get harmed. The essential problem is that test cases are both tested and skipped, which makes it more difficult for the company to identify what has been actually tested and skipped. The purpose of this thesis is to identify and calculate the coverage of test cases, the process was mainly to understand the essence in the identification of executed test cases. The ethical aspect of putting people's lives at stake is what inspired us to investigate software failures. Software is typically tested before a release, therefore our investigation was to research testing process of software. When investigating test result over many days and test systems we developed a coverage calculator system which helps Westermo to decide and determine the release of tested software, either the software test result is acceptable for a release or not.

Purpose: A crucial decision in financial services is how to classify credit or loan applicants into good and bad applicants. The purpose of this paper is to propose a four-stage hybrid data mining approach to support the decision-making process. Design/methodology/approach: The approach is inspired by the bagging ensemble learning method and proposes a new voting method, namely two-level majority voting in the last stage. First some training subsets are generated. Then some different base classifiers are tuned and afterward some ensemble methods are applied to strengthen tuned classifiers. Finally, two-level majority voting schemes help the approach to achieve more accuracy. Findings: A comparison of results shows the proposed model outperforms powerful single classifiers such as multilayer perceptron (MLP), support vector machine, logistic regression (LR). In addition, it is more accurate than ensemble learning methods such as bagging-LR or rotation forest (RF)-MLP. The model outperforms single classifiers in terms of type I and II errors; it is close to some ensemble approaches such as bagging-LR and RF-MLP but fails to outperform them in terms of type I and II errors. Moreover, majority voting in the final stage provides more reliable results. Practical implications: The study concludes the approach would be beneficial for banks, credit card companies and other credit provider organisations. Originality/value: A novel four stages hybrid approach inspired by bagging ensemble method proposed. Moreover the two-level majority voting in two different schemes in the last stage provides more accuracy. An integrated evaluation criterion for classification errors provides an enhanced insight for error comparisons.

Detta är en lärobok i grundläggande programmering. Den fokuserar på att lära ut det som är gemensamt för de flesta programmeringsspråk – de grundläggande elementen och programkonstruktionerna och hur dessa relaterar till varandra, oberoende av språket. Vilket språk programmet skrivs i måste vara underordnat den uppgift programmet skall lösa. Boken går därför igenom grunderna i strukturerad programmering och visar otaliga exempel i såväl flödesdiagram, strukturdiagram, pseudokod som i källkod. I slutet av boken finns kapitel som går igenom grunderna även i objektorienterad programmering.

Boken tonar ner inlärningen av ett specifikt programmeringsspråk, men i slutändan måste program ändå skrivas i något språk. Exemplen är skrivna i C# som har slagit igenom som ett praktiskt programmeringsspråk på mycket kort tid och redan börjat göra sitt intåg i högskolekurser i programmering.

I första hand är boken avsedd för nybörjare i programmering på högskolenivå, som vill lära sig programmeringens grunder.

The massive increase in 5G usage for video streaming can cause short but intense spikes in data transfer rates, potentially causing network congestion. Congestion detection and prediction are essential for ensuring user satisfaction and enhancing the performance of real-time applications. By leveraging historical network measurement data such as Round Trip Time (RTT), Jitter, and packet loss ratio, machine learning algorithms can analyze traffic patterns, predict congestion based on the attributes, and provide adaptive solutions. We have focused primarily on ML models due to their adaptability to learn from historical patterns, a capability where traditional rule-based methods may fall short. Furthermore, past researchers faced difficulties in obtaining measurement data for fully comprehensive real-world networks. We have collected real-world network measurement datasets over a six-month period for a complete network. Using the datasets and topology information, we trained Support Vector Regression (SVR), Long Short-TermMemory (LSTM), and XGBRegressor (Extreme Gradient Boosting) to forecast the RTT values of each router. To predict network anomalies based on packet loss and high RTT values, Random Forest and Support Vector Classifier (SVC) models have been developed. By analyzing the outputs of these two models, a method is proposed to predict network congestion. While this study is limited by data quantity and the use of simpler ML models, it offers a comprehensive solution and a solid foundation for predicting network congestion symptoms. Network congestion prediction can be further improved by exploring areas such as model ensembling, explainability in deep learning models, transfer learning approaches, and integrating multi-source data.

Tests are important artifacts in the software development process. Testing activities such as test automation, test maintenance, and test suite optimization mainly rely on an in-depth understanding of test specifications. The manual process of writing testspecifications in natural language can create many different quality issues such as ambiguous, incomplete, redundant, or inconsistent test cases. Nowadays, the concept of test smells is proposed by several researchers to be used as indicators of low-qualityattributes in test specifications. Quality assurance processes for test specifications often rely on manual reviews to detect these smells. The manual process of detecting these smells is considered time consuming and costly. However, there is currently no work that implements a comprehensive quality model that classifies and identifies these smells by using a systematic strategy. As a result, there is a need for machine-supported analytical measures that decrease the time and effort needed to detect these smells manually, especially when it comes to reviewing and validating large test specifications.This study aims to investigate which natural language smell metrics implemented in NALABS can be found in test specifications and to measure the sufficiency of those smellmetrics. It also aims to extend these smell metrics by exploring, proposing, or combining with new bad smell metrics to cover more aspects of natural language test quality. The results of the study show that the smell metrics exists in real-world test specifications and can uncover many potential quality issues by assisting test designers in identifying certain types of quality issues pertaining to for example the understandability and complexity of test specifications. Moreover, the results show thatthe list of smell metrics implemented in NALABS is incomplete and can be extended to cover more aspects of test quality.

The Internet traffic volume continues to grow at a great rate, now driven by video and TV distribution. For network operators it is important to avoid congestion in the network, and to meet service level agreements with their customers.  This thesis presents work on two methods operators can use to reduce links loads in their networks: traffic engineering and content caching.

This thesis studies access patterns for TV and video and the potential for caching.  The investigation is done both using simulation and by analysis of logs from a large TV-on-Demand system over four months.

The results show that there is a small set of programs that account for a large fraction of the requests and that a comparatively small local cache can be used to significantly reduce the peak link loads during prime time. The investigation also demonstrates how the popularity of programs changes over time and shows that the access pattern in a TV-on-Demand system very much depends on the content type.

For traffic engineering the objective is to avoid congestion in the network and to make better use of available resources by adapting the routing to the current traffic situation. The main challenge for traffic engineering in IP networks is to cope with the dynamics of Internet traffic demands.

This thesis proposes L-balanced routings that route the traffic on the shortest paths possible but make sure that no link is utilised to more than a given level L. L-balanced routing gives efficient routing of traffic and controlled spare capacity to handle unpredictable changes in traffic.  We present an L-balanced routing algorithm and a heuristic search method for finding L-balanced weight settings for the legacy routing protocols OSPF and IS-IS. We show that the search and the resulting weight settings work well in real network scenarios.

Today video and TV distribution dominate Internet traffic and the increasing demand for high-bandwidth multimedia services puts pressure on Internet service providers. In this paper we simulate TV distribution with time-shift and investigate the effect of introducing a local cache close to the viewers. We study what impact TV program popularity, program set size, cache replacement policy and other factors have on the caching efficiency. The simulation results show that introducing a local cache close to the viewers significantly reduces the network load from TV-on-Demand services. By caching 4% of the program volume we can decrease the peak load during prime time by almost 50%. We also show that the TV program type and how program popularity changes over time can have a big influence on cache hit ratios and the resulting link loads

IPTV, where television is distributed over the Internet Protocol in a single operator network, has become popular and widespread. Many telecom and broadband companies have become TV providers and distribute TV channels using multicast over their backbone networks. IPTV also means an evolution to time-shifted television where viewers now often can choose to watch the programs at any time. However, distributing individual TV streams to each viewer requires a lot of bandwidth and is a big challenge for TV operators. In this paper we present an empirical IPTV workload model, simulate IPTV distribution with time-shift, and show that local caching can limit the bandwidth requirements significantly.

Today increasingly large volumes of TV and video are distributed over IP-networks and over the Internet. It is therefore essential for traffic and cache management to understand TV program popularity and access patterns in real networks.

In this paper we study access patterns in a large TV-on-Demand system over four months. We study user behaviour and program popularity and its impact on caching. The demand varies a lot in daily and weekly cycles. There are large peaks in demand, especially on Friday and Saturday evenings, that need to be handled.

We see that the cacheability, the share of requests that are not first-time requests, is very high. Furthermore, there is a small set of programs that account for a large fraction of the requests. We also find that the share of requests for the top most popular programs grows during prime time, and the change rate among them decreases. This is important for caching. The cache hit ratio increases during prime time when the demand is the highest, and aching makes the biggest difference when it matters most.

We also study the popularity (in terms of number of requests and rank) of individual programs and how that changes over time. Also, we see that the type of programs offered determines what the access pattern will look like.

The Covid-19 crisis has highlighted the vulnerability of access to food and the need for local and circular food supply chains in urban environments. Nowadays, Indoor Vertical Farming has been increased in large cities and started deploying Artificial Intelligence to control vegetations remotely. This thesis aims to monitor and control the vertical farm by scheduling the farming activities by solving a newly proposed Job-shop scheduling problem to enhance food productivity. The Job-shop scheduling problem is one of the best-known optimization problems as the execution of an operation may depend on the completion of another operation running at the same time. This paper presents an efficient method based on genetic algorithms developed to solve the proposed scheduling problem. To efficiently solve the problem, a determination of the assignment of operations to the processors and the order of each operation so that the execution time is minimized. An adaptive penalty function is designed so that the algorithm can search in both feasible and infeasible regions of the solution space. The results show the effectiveness of the proposed algorithm and how it can be applied for monitoring the farm remotely.

Development in the field of software architecture, from the early days in the mid-80’s, has been significant. From purely technical descriptions to decision based architectural knowledge, software architecture has seen fundamental changes to its methodologies and techniques. Architectural knowledge is a resource that is managed and stored by companies, this resource is valuable because it can be reused and analysed to improve future development. Companies today are interested in the reasoning behind the software architecture. This reasoning is mainly formulated through the architectural decisions made during development. For architectural decisions to be easier to analyse they need to be stored in a way that enables use of common analytical tools so that comparisons between decisions are consistent and relevant. Additionally, it is also important to have enough data, which leads us to the problem that, preferably, all the individual architectural knowledge cases must be structured and stored. To do this we present a tool that uses graph generation algorithms to generate architectural knowledge as graphs based on an architectural decision canvas called GRADE. This enables multiple decision cases to be encoded through graphs that can be used to analyse relationships and balances between different architectural knowledge elements represented through nodes and edges within a graph.

Most of the critical challenges seen in the past decades have impacted citizens in a global way. Given shrinking resources, educationists find preparing students for the global market place a formidable challenge. Hence exposing students to multi-lateral educational initiatives are critical to their growth, understanding and future contributions. This paper focuses on European Union's Erasmus Mundus programs, involving academic cooperation amongst international universities in engineering programs. A phased undergraduate engineering program with multiple specializations is analyzed within this context. Based on their performance at the end of first phase, selected students were provided opportunities using scholarship to pursue completion of their degree requirements at various European universities. This paper will elaborate the impact of differing pedagogical interventions, language and cultural differences amongst these countries on students in diverse engineering disciplines. The data presented is based on on the feedback analysis from Eramus Mundus students (N = 121) that underwent the mobility programs. The findings have given important insights into the structure of the initiative and implications for academia and education policy makers for internationalizing engineering education. These included considering digital interventions such as MOOCs (Massive Open Online Courses) and Virtual Laboratory (VL) initiatives for systemic reorganization of engineering education.

While ontology comparison and alignment have been extensively researched in the last decade, there are still some challenges to these disciplines, such as incomplete ontologies, those that cover only a portion of a domain, and differences in domain modeling due to varying viewpoints. Although the literature has compared ontological concepts from the same domain, comparisons of concepts from different domains (e.g., security and safety) remain unexplored. To compare the concepts of security and safety domains, a security ontology must first be created to bridge the gap between these domains. Therefore, this paper presents a Combined Security Ontology (CSO) based on the Unified Foundational Ontology (UFO) that could be compared to or aligned with other ontologies. This CSO includes the core ontological concepts and their respective relationships that had been extracted through a previous systematic literature review. The CSO concepts and their relationships were mapped to the UFO to get a common terminology that facilitates to bridge the gap between the security and safety domains. Since the proposed CSO is based on the UFO, it could be compared to or aligned with other ontologies from different domains.

Safety and security ontologies quickly become essential support for integrating heterogeneous knowledge from various sources. Today, there is little standardization of ontologies and almost no discussion of how to compare concepts and their relationships, establish a general approach to create relationships or model them in general. However, concepts with similar names are not semantically similar or compatible in some cases. In this case, the problem of correspondence arises among the concepts and relationships found in the ontologies. To solve this problem, a comparison between the Hazard Ontology (HO) and the Combined Security Ontology (CSO) is proposed, in which the value of equivalence between their concepts and their relationships was extracted and analyzed. Although the HO covers the concepts related to the safety domain and the CSO includes securityrelated concepts, both are based on the Unified Foundational Ontology (UFO). For this study, HO and CSO were compared, and the results were summarized in the form of comparison tables. Our main contribution involves the comparisons among the concepts in HO and CSO to identify equivalences and differences between the two. Due to the increasing number of ontologies, their mapping, merging, and alignment are primary challenges in bridging the gaps that exist between the safety and security domains. 

Security ontologies have been developed to facilitate the organization and management of security knowledge. A comparison and evaluation of how these ontologies relate to one another is challenging due to their structure, size, complexity, and level of expressiveness. Differences between ontologies can be found on both the ontological and linguistic levels, resulting in errors and inconsistencies (i.e., different concept hierarchies, types of concepts, definitions) when comparing and aligning them. Moreover, many concepts related to security ontologies have not been thoroughly explored and do not fully meet security standards. By using standards, we can ensure that concepts and definitions are unified and coherent. In this study, we address these deficiencies by reviewing existing security ontologies to identify core concepts and relationships. The primary objective of the systematic literature review is to identify core concepts and relationships that are used to describe security issues. We further analyse and map these core concepts and relationships to five security standards (i.e., NIST SP 800-160, NIST SP 800-30 rev.1, NIST SP 800-27 rev.A, ISO/IEC 27001 and NISTIR 8053). As a contribution, this paper provides a set of core concepts and relationships that comply with the standards mentioned above and allow for a new security ontology to be developed.

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 analyse quality attributes and generate executable code combining the information from these. Raising the abstraction level to models requires 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. In this respect, almost no support is provided for the systematic evaluation of comparison approaches against specific scenarios and modelling practices, namely benchmarks. In this article we propose Benji, a framework for the automated generation of model comparison benchmarks. In particular, by giving a set of difference patterns and an initial model, users can generate model manipulation scenarios resulting from the application of the patterns on the model. The generation support provided by the framework obeys specific design principles that are considered as essential properties for the systematic evaluation of model comparison solutions, and are inherited from the general principles coming from evidence-based software engineering. The framework is validated through representative scenarios of model comparison benchmark generations.

Model comparison is a critical task in model-driven engineering. Its correctness enables an effective management of model evolution, synchronisation, and even other tasks, such as model transformation testing. The literature is rich as concerns comparison algorithms approaches, however the same cannot be said for their systematic evaluation. In this paper we present Benji, a tool for the generation of model comparison benchmarks. In particular, Benji provides domain-specific languages to design experiments in terms of input models and possible manipulations, and based on those generates corresponding benchmark cases. In this way, the experiment specification can be exploited as a systematic way to evaluate available comparison algorithms against the problem under study.

Domain-specific modelling languages defined by extending or constraining the Unified Modelling Language (UML) through the profiling mechanism have historically relied on graphical notations to maximise human understanding and facilitate communication among stakeholders. Other notations, such as text-, form-, or table-based are, however, often preferred for specific modelling purposes, due to the nature of a specific domain or the available tooling, or for personal preference. Currently, the state of the art support for UML-based languages provides an almost completely detached, or even entirely mutually exclusive, use of graphical and textual modelling. This becomes inadequate when dealing with the development of modern systems carried out by heterogeneous stakeholders. Our intuition is that a modelling framework based on seamless blended multi-notations can disclose several benefits, among which: flexible separation of concerns, multi-view modelling based on multiple notations, convenient text-based editing operations (inside and outside the modelling environment), and eventually faster modelling activities. In this paper we report on: (i) a proof-of-concept implementation of a framework for UML and profiles modelling using blended textual and graphical notations, and (ii) an experiment on the framework, which eventually shows that blended multi-notation modelling performs better than standard single-notation modelling.

The increasing complexity of systems and the demand for increasing implemented functionalit-ies within the systems necessitate a paradigm shift in development approaches. While traditionalsystems engineering relies heavily on textual documentation, the future calls for a more stream-lined, digital method such as Model-Based Systems Engineering (MBSE). MBSE utilises models tostore data, mitigating textual ambiguity and providing stakeholders with a unified source of inform-ation. Despite its potential, the adoption of MBSE presents challenges because of the need of newpractitioner skill-sets.

The thesis investigates the feasibility of implementing an easily adoptable MBSE, using a limitedset of SysML elements and diagrams, approach within the space industry. Through the developmentof guidelines, steps, and goals, a model of an industry-related system is constructed. The createdmodel serves as a demonstration to industry engineers, evaluating the suitability of the proposedguidelines.

The thesis concludes the need for maturity of MBSE, its tools, and the need for harmonisationwithin the space industry to establish acceptance among the practitioners. These are the crucialpoints where development and refinements are needed to gain the trust and interest of the end usersof the modelling activities and adopting the proposed methods.i   

Embedded systems are typically resource constrained, i.e., resources such as processors, I/O devices, shared buffers or shared memory can be limited for tasks in the system. Therefore, techniques that enable an efficient usage of such resources are of great importance.

In the industry, typically large and complex software systems are divided into smaller parts (applications) where each part is developed independently. Migration towards multiprocessor platforms has become inevitable from an industrial perspective. Due to such migration and to efficient use of system resources, these applications eventually may be integrated on a shared multiprocessor platform. In order to facilitate the integration phase of the applications on a shared platform, the timing and resource requirements of each application can be provided in an interface when the application is developed. The system integrator can benefit from such provided information in the interface of each application to ease the integration process. In this thesis, we have provided the resource and timing requirements of each application in their interfaces for applications that may need several processors to be allocated on when they are developed.

Although many scheduling techniques have been studied for multiprocessor systems, these techniques are usually based on the assumption that tasks are independent, i.e. do not share resources other than the processors. This assumption is typically not true. In this thesis, we provide an extension to such systems to handle sharing of resources other than processor among tasks. Two traditional approaches exist for multiprocessor systems to schedule tasks on processors. A recent scheduling approach for multiprocessors has combined the two traditional approaches and achieved a hybrid more efficient approach compared to the two previous one. Due to the complex nature of this scheduling approach the conventional approaches for resource sharing could not be used straight forwardly. In this thesis, we have modified resource sharing approaches such that they can be used in such hybrid scheduling systems. A second concern is that enabling resource sharing in the systems can cause unpredictable delays and variations in response time of tasks which can degrade system performance. Therefore, it is of great significance to improve the resource handling techniques to reduce the effect of imposed delays caused by resource sharing in a multiprocessor platform. In this thesis we have proposed alternative techniques for resource handling that can improve system performance for special setups.

Various approaches can be utilized upon resource locking for mutually exclusive resource access in multiprocessor platforms. So far two conventional approaches exist for dealing with tasks that are blocked on a global resource in a multi-processor platform. Either the blocked task performs a busy wait, i.e. spins, at the highest priority level until the resource is released, or it is suspended. Although both approaches provide mutually exclusive access to resources, they can introduce long blocking delays to tasks, which may be unacceptable for many industrial applications. In this paper, we propose a general spin-based model for resource sharing in multiprocessor platforms in which the priority of the blocked tasks during spinning can be selected arbitrarily. Moreover, we provide the analysis for two selected spin-lock priorities and we show by means of a general comparison as well as specific examples that these solutions may provide a better performance for higher priority tasks.

Resource efficient approaches are of great importance for resource constrained embedded systems. In this paper, we present an approach targeting systems where tasks of a critical application are partitioned on a multi-core platform and by using resource reservation techniques, the remaining bandwidth capacity on each core is utilized for one or a set of non-critical application(s). To provide a resource efficient solution and to exploit the potential parallelism of the extra applications on the multi-core processor, global scheduling is used to schedule the tasks of the non-critical applications. Recently a specific instantiation of such a system has been studied where tasks do not share resources other than the processor. In this paper, we enable semaphore-based resource sharing among tasks within critical and non-critical applications using a suspension-based synchronization protocol. Tasks of non-critical applications have partial access to the processor bandwidth. The paper provides the systems schedulability analysis where blocking due to resource sharing is bounded. Further, we perform experimental evaluations under balanced and unbalanced allocation of tasks of a critical application to cores.

Two traditional approaches exist for a task that is blocked on a global resource; a task either performs a non-preemptive busy wait, i.e., spins, or suspends and releases the processor. Previously, we have shown that both approaches can be viewed as spinning either at the highest priority HP or at the lowest priority on the processor LP, respectively. Based on this view, previously we have generalized a task's blocking behavioral model, as spinning at any arbitrary priority level. In this paper, we focus on a particular class of spin-lock protocols from the introduced flexible spin-lock model where spinning is performed at a priority equal to or higher than the highest local ceiling of the global resources accessed on a processor referred to as CP spin-lock approach. In this paper, we assume that all tasks of a specific processor are spinning on the same priority level. Given this class and assumption, we show that there exists a spin-lock protocol in this range that dominates the classic spin-lock protocol which tasks spin on highest priority level (HP). However we show that this new approach is incomparable with the CP spin-lock approach. Moreover, we show that there may exist an intermediate spin-lock approach between the priority used by CP spin-lock approach and the new introduced spin-lock approach that can make a task set schedulable when those two cannot. We provide an extensive evaluation results comparing the HP, CP and the new proposed approach.

Two traditional approaches exist for a task that is blocked on a global resource; a task either performs a non-preemptive busy wait, i.e., spins, or suspends and releases the processor. Previously, we have shown that both approaches can be viewed as spinning either at the highest priority HP or at the lowest priority on the processor LP, respectively. Based on this view, previously we have generalized a task's blocking behavioral model, as spinning at any arbitrary priority level. In this paper, we focus on a particular class of spin-lock protocols from the introduced flexible spin-lock model where spinning is performed at a priority equal to or higher than the highest local ceiling of the global resources accessed on a processor referred to as CP spin-lock approach. In this paper, we assume that all tasks of a specific processor are spinning on the same priority level. Given this class and assumption, we show that there exists a spin-lock protocol in this range that dominates the classic spin-lock protocol which tasks spin on highest priority level (HP). However we show that this new approach is incomparable with the CP spin-lock approach. Moreover, we show that there may exist an intermediate spin-lock approach between the priority used by CP spin-lock approach and the new introduced spin-lock approach that can make a task set schedulable when those two cannot. We provide an extensive evaluation results comparing the HP, CP and the new proposed approach.

A major challenge for the European electronic industry is to enhance productivity by ensuring quality of development, integration and maintenance while reducing the associated costs. Model-Driven Engineering (MDE) principles and techniques have already shown promising capabilities, but they still need to scale up to support real-world scenarios implied by the full deployment and use of complex electronic components and systems. Moreover, maintaining efficient traceability, integration, and communication between two fundamental system life cycle phases (design time and runtime) is another challenge requiring the scalability of MDE. This paper presents an overview of the ECSEL 1 project entitled “MegaModelling at runtime – Scalable model-based framework for continuous development and runtime validation of complex systems” (MegaM@Rt2), whose aim is to address the above mentioned challenges facing MDE. Driven by both large and small industrial enterprises, with the support of research partners and technology providers, MegaM@Rt2 aims to deliver a framework of tools and methods for: 1) system engineering/design and continuous development, 2) related runtime analysis and 3) global models and traceability management. Diverse industrial use cases (covering strategic domains such as aeronautics, railway, construction and telecommunications) will integrate and demonstrate the validity of the MegaM@Rt2 solution. This paper provides an overview of the MegaM@Rt2 project with respect to its approach, mission, objectives as well as to its implementation details. It further introduces the consortium as well as describes the work packages and few already produced deliverables.

The exploratory testing (ET) approach is commonly applied in industry, but lacks scientific research. The scientific community needs quantitative results on the performance of ET taken from realistic experimental settings. The objective of this paper is to quantify the effectiveness and efficiency of ET vs. testing with documented test cases (test case based testing, TCT). We performed four controlled experiments where a total of 24 practitioners and 46 students performed manual functional testing using ET and TCT. We measured the number of identified defects in the 90-minute testing sessions, the detection difficulty, severity and types of the detected defects, and the number of false defect reports. The results show that ET found a significantly greater number of defects. ET also found significantly more defects of varying levels of difficulty, types and severity levels. However, the two testing approaches did not differ significantly in terms of the number of false defect reports submitted. We conclude that ET was more efficient than TCT in our experiment. ET was also more effective than TCT when detection difficulty, type of defects and severity levels are considered. The two approaches are comparable when it comes to the number of false defect reports submitted.

Despite the general acceptance that software engineering datasets often contain noisy, irrele- vant or redundant variables, very few benchmark studies of feature subset selection (FSS) methods on real-life data from software projects have been conducted. This paper provides an empirical comparison of state-of-the-art FSS methods: information gain attribute ranking (IG); Relief (RLF); principal com- ponent analysis (PCA); correlation-based feature selection (CFS); consistency-based subset evaluation (CNS); wrapper subset evaluation (WRP); and an evolutionary computation method, genetic program- ming (GP), on five fault prediction datasets from the PROMISE data repository. For all the datasets, the area under the receiver operating characteristic curve—the AUC value averaged over 10-fold cross- validation runs—was calculated for each FSS method-dataset combination before and after FSS. Two diverse learning algorithms, C4.5 and na ??ve Bayes (NB) are used to test the attribute sets given by each FSS method. The results show that although there are no statistically significant differences between the AUC values for the different FSS methods for both C4.5 and NB, a smaller set of FSS methods (IG, RLF, GP) consistently select fewer attributes without degrading classification accuracy. We conclude that in general, FSS is beneficial as it helps improve classification accuracy of NB and C4.5. There is no single best FSS method for all datasets but IG, RLF and GP consistently select fewer attributes without degrading classification accuracy within statistically significant boundaries.

In enterprise-scale companies, where processing complex test results is often a time-consuming task for testers and developers, the need for an Application Programming Interface (API) testing visualization tool is vital. Furthermore, as various stakeholders require insights into the API testing results, a clear dashboard can facilitate effective communication among them. 

This thesis aims to fulfill the need for a generic API testing visualization tool within the mobile network industry through the implementation of TestUp, a user-friendly solution. TestUp simplifies the analysis of complex API test results for various stakeholders, including testers, developers, managers, and product owners. To accomplish the objectives of this thesis, an industrial case study was conducted at Nokia, beginning with a pre-study phase to explore industry needs and challenges. TestUp was designed based on the Agile methodology, evolving through feedback from industry and academia. Additionally, a post-tool questionnaire was employed to gather data on the tool’s effectiveness and limitations. Subsequently, the data analysis was conducted using the thematic analysis approach. 

The thesis demonstrates that TestUp provides valuable benefits to stakeholders by effectively addressing critical issues found in existing tools. TestUp is compatible with a range of test automation frameworks and serves as a user-friendly tool for visualizing test coverage. These facts fulfill the company's requirements. All in all, this thesis improves the analysing of API testing results within the mobile network management sector. Additionally, future work can involve enhancing the tool by adding extra features, such as export functionality and the ability to indicate timeout requests. Also, integrating TestUp with the continuous integration pipelines is a potential area for future work.

Date: 2008-06-04

Purpose: The primary goal of this research is to describe the IT function and examine how its alignment to an organisations strategy affects the way it is organised.

Method: The chosen method was a purely theoretical examination with the use of the case study of Windham International as primary resource and secondary resources such as book and literature review used for the thesis.

Research Questions: How does the alignment of IT to business strategy affect the organisation of the IT function?

Conclusion: One of the findings was that a few researchers seem to suggest that the centralized mode of organising IT was symbolic of the past, while outsourcing and decentralization are the modern approach to organising IT. Wyndham International shows the opposite, after the introduction of the CIO in 2002, centralization was the chosen mode of organisation because that was what would best fit the new strategic approach of the organisation. Insourcing rather than outsourcing proved to be a winning formula.

In this thesis work, our main goal is to improve IoT communications of an edge platform by selecting suitable solutions to work with the MQTT protocol. Our primary focus in this thesis is to investigate two quality attributes: data throughput and latency of message brokers.There are two research questions that are to be answered: how do protocols like MQTT and their architecture contribute to messaging in IoT communications? andHow to Enhance MQTT Architecture to Match the Requirements for ABB IoT Edge Platform?.  This thesis work comprises a mixed-methods research methodology that includes both quantitative and qualitative phases. The quantitative phase involves experimental work on examining data throughput and latency of a message broker, while the qualitative phase includes interviews with stakeholders and literature reviews. According to the qualitative and quantitative data, we found that event-driven architecture, publisher-subscriber design pattern, and NATS message broker are suitable choices to work with the MQTT-based architecture of IoT edge platforms. Since NATS showed a decent result in terms of high throughput and low latency in comparison to other message brokers. The results of this thesis work identify the best practices of message brokers in an MQTT-based IoT platform, for software architects, specifically the ABB IoT edge platform.

Utvecklingsmetoder är olika arbetsprocesser som företag kan använda för att förbättra och effektivisera sitt arbete. Idag är de agila utvecklingsmetoderna Scrum och Kanban två av de mer använda metoderna inom företag som utvecklar mjukvara. I mitt arbete undersöks det hur nära små utvecklingsteam inom mindre företag förhåller sig till Scrum eller Kanban i verkligheten. Undersökningen görs för att se om det finns förbättringar som mindre företag med små utvecklingsteam kan ta till sig från litteratur för att efterlikna hur teorin är beskriven, samt kunna diskutera kring delar relevans som inte efterlevs.

Syftet med detta arbete är att identifiera hur nära agila utvecklingsmetoder som Scrum eller Kanban efterlevs i praktiken, hos mindre företag med små utvecklingsteam, enligt hur metoderna är teoretiskt beskrivna. Målet i detta arbete är se om det finns förbättringar som små utvecklingsteam kan ta till sig från litteratur om utvecklingsmetoderna, Scrum eller Kanban, för att effektivisera sin arbetsprocess genom att efterleva mer enligt hur dessa utvecklingsmetoder beskrivs i litteraturen.

För att uppnå arbetets syfte och mål undersökas tre frågeställningar. Ena frågeställningen är om hur litteratur beskriver metoderna för agil mjukvaruutveckling för små utvecklingsteam i mindre företag, vilket besvarades genom en litteraturstudie. Genom intervjuer besvarades frågeställningen om hur utvecklare i små utvecklingsteam inom mindre företag beskriver sin utvecklingsmetod. Tillsammans med intervjuerna och en observationsstudie besvarades den sista frågeställningen angående hur små utvecklingsteam i mindre företag arbetar med utveckling av mjukvara i praktiken.

Resultatet visade att utvecklarna från det medverkande företaget i praktiken endast följde vissa delar från Scrum men inte alla centrala delar, trots att Scrum enligt litteratur är anpassad till att användas inom små utvecklingsteam. Genom att inte följa Scrum centrala delar går det inte att säga att de använder Scrum, och därmed riskerar att inte leva upp till alla de fördelar Scrum kan bidra med.

Autonoma agenter är beroende av information från den omgivande miljön för att agera. I en mängd av tillgängliga sensorer är troligtvis bildsensorn den mest mångsidiga, då den möjliggör särskillnad av färg, storlek, form och djup. För det sistnämnda är, i en dynamisk miljö utan krav på förkunskaper, stereovision en vanligt tillämpad teknik. Tolkning av bildinnehåll och extrahering av relevant information går under benämningen datorseende. Datorseende, och specifikt stereoalgoritmer, är redan för ett enskilt bildpar komplexa och beräkningsmässigt kostsamma, och ger resultat som, i termer av noggrannhet, är kvalitativt svåra att jämföra. Problematiken utökas vidare av kontinuerlig ström av bilder, med allt högre bildfrekvens och upplösning. För autonoma agenter krävs dessutom överväganden vad gäller realtidskrav, inbyggda system/resursbegränsade beräkningsplattformar, strömförbrukning och fysisk storlek, vilket summerar till ett otvetydigt utmanande problem.

Den här avhandlingen syftar till att åstadkomma högupplöst stereovision med hög uppdateringsfrekvens och låg latens på inbyggda system. Genom att närma sig problemet från två olika vinklar, hårdvaru- och algoritmmässigt, kan ett symbiotiskt förhållande däremellan säkerställas.Avhandlingens första bidrag är GIMME och GIMME2 inbyggda visionsplattformar, som erbjuder FPGA-baserad hårdvaruaccelerering, med särskilt fokus på stereoseende, i kontrast till för tidpunkten kommersiellt tillgängliga system.Det andra bidraget, härrörande stereoalgoritmer, är tudelat.Först hanteras skalbarhetproblemet, sammankopplat med disparitetsomfånget, genom att föreslå en segmentbaserad stereoalgoritm.I segmentrymden är matchningen oberoende av bildupplösningen, samt att disparitetsomfånget definieras i termer av segment, vilket antyder att relativt få hypoteser behövs för att omfatta hela scenen.I det andra bidraget på algoritmnivå, mer i linje med konventionella stereoalgoritmer för FPGAer, har Censustransformen (CT) identifierats som ett återkommande kostnadsmått för likhet. Här föreslås en optimering av CT genom att tillämpa genetisk algoritm (GA) - Genetisk Algoritm Census Transform (GACT). GACT visar lovande resultat för referensdataset jämfört med etablerade CT-metoder, men är samtidigt resurseffektiv.

The Census Transform (CT) is a well proven method for stereo vision that provides robust matching, with respect to object boundaries, outliers and radiometric distortion, at a low computational cost. Recent CT methods propose patterns for pixel comparison and sparsity, to increase matching accuracy and reduce resource requirements. However, these methods are bounded with respect to symmetry and/or edge length. In this paper, a Genetic algorithm (GA) is applied to find a new and powerful CT method. The proposed method, Genetic Algorithm Census Transform (GACT), is compared with the established CT methods, showing better results for benchmarking datasets. Additional experiments have been performed to study the search space and the correlation between training and evaluation data.

Rapporten är resultatet av mitt examensarbete i Textdesign vid Mälardalens högskola. På uppdrag av Europaskolan i Strängnäs har ett informationsmaterial utvecklats och resulterat i en broschyr. Målgruppen är elever i årskurs nio som ska söka gymnasieutbildning. Rapporten beskriver det tillvägagångssätt som använts vid

framtagningen av det nya informationsmaterialet. Med utgångspunkt i begreppen läsligt, läsbart och läsvärt har text, bild och layout utformats för att skapa en informativ och målgruppsanpassad broschyr. Metoder som använts är komparation, textanalys och litteraturstudier. Utprovningsmetoden som tillämpats är enskilda intervjuer med elever

på Europaskolan. Slutsatser som dragits är att behovet av designad information ökar i dagens mediebrus och att sändarens budskap till mottagaren måste genomgå en hermeneutisk process.