Modern software systems are increasingly more connected, pervasive, and dynamic, as such, they are subject to more runtime variations than legacy systems. Runtime variations affect system properties, such as performance and availability. The variations are difficult to anticipate and thus mitigate in the system design.

Self-adaptive software systems were proposed as a solution to monitor and adapt systems in response to runtime variations. Research has established a vast body of knowledge on engineering self-adaptive systems. However, there is a lack of systematic process support that leverages such engineering knowledge and provides for systematic reuse for self-adaptive systems development. 

This thesis proposes the Autonomic Software Product Lines (ASPL), which is a strategy for developing self-adaptive software systems with systematic reuse. The strategy exploits the separation of a managed and a managing subsystem and describes three steps that transform and integrate a domain-independent managing system platform into a domain-specific software product line for self-adaptive software systems.

Applying the ASPL strategy is however not straightforward as it involves challenges related to variability and uncertainty. We analyzed variability and uncertainty to understand their causes and effects. Based on the results, we developed the Autonomic Software Product Lines engineering (ASPLe) methodology, which provides process support for the ASPL strategy. The ASPLe has three processes, 1) ASPL Domain Engineering, 2) Specialization and 3) Integration. Each process maps to one of the steps in the ASPL strategy and defines roles, work-products, activities, and workflows for requirements, design, implementation, and testing. The focus of this thesis is on requirements and design.

We validate the ASPLe through demonstration and evaluation. We developed three demonstrator product lines using the ASPLe. We also conducted an extensive case study to evaluate key design activities in the ASPLe with experiments, questionnaires, and interviews. The results show a statistically significant increase in quality and reuse levels for self-adaptive software systems designed using the ASPLe compared to current engineering practices.

Software quality is critical in today's software systems. A challenge is the trade-off situation architects face in the design process. Designers often have two or more alternatives, which must be compared and put into context before a decision is made. The challenge becomes even more complex for dynamic software product lines, where domain designers have to take runtime variations into consideration as well. To address the problem we propose extensions to an architectural reasoning framework with constructs/artifacts to define and model a domain's scope and dynamic variability. The extended reasoning framework encapsulates knowledge to understand and reason about domain quality behavior and self-adaptation as a primary variability mechanism. The framework is demonstrated for a self-configuration property, self-upgradability on an educational product-line.

Software architecture serves as a foundation for the design and development of software systems. Designing an architecture requires extensive analysis and reasoning. The study presented herein focuses on the architectural analysis and reasoning in support of engineering self-adaptive software systems with systematic reuse. Designing self-adaptive software systems with systematic reuse introduces variability along three dimensions; adding more complexity to the architectural analysis and reasoning process. To this end, the study presents an extended Architectural Reasoning Framework with dedicated reasoning support for self-adaptive systems and reuse. To evaluate the proposed framework, we conducted an initial feasibility case study, which concludes that the proposed framework assists the domain architects to increase reusability, reduce fault density, and eliminate differences in skills and experiences among architects, which were our research goals and are decisive factors for a system's overall quality.

Advances in computing technologies are pushing software systems and their operating environments to become more dynamic and complex. The growing complexity of software systems coupled with uncertainties induced by runtime variations leads to challenges in software analysis and design. Self-Adaptive Software Systems (SASS) have been proposed as a solution to address design time complexity and uncertainty by adapting software systems at runtime. A vast body of knowledge on engineering self-adaptive software systems has been established. However, to the best of our knowledge, no or little work has considered systematic reuse of this knowledge. To that end, this study contributes an Autonomic Software Product Lines engineering (ASPLe) methodology. The ASPLe is based on a multi-product lines strategy which leverages systematic reuse through separation of application and adaptation logic. It provides developers with repeatable process support to design and develop self-adaptive software systems with reuse across several application domains. The methodology is composed of three core processes, and each process is organized for requirements, design, implementation, and testing activities. To exemplify and demonstrate the use of the ASPLe methodology, three application domains are used as running examples throughout the report.

This work studies systematic reuse in the context of self-adaptive software systems. In our work, we realized that managing variability for such platforms is different compared to traditional platforms, primarily due to the run-time variability and system uncertainties. Motivated by the fact that recent trends show that self-adaptation will be used more often in future system generation and that software reuse state-of-practice or research do not provide sufficient support, we have investigated the problems and possibly resolutions in this context. We have analyzed variability for these systems, using a systematic reuse prism, and identified a research gap in variability management. The analysis divides variability handling into four activities: (1) identify variability, (2) constrain variability, (3) implement variability, and (4) manage variability. Based on the findings we envision a reuse framework for the specific domain and present an example framework that addresses some of the identified challenges. We argue that it provides basic support for engineering self-adaptive software systems with systematic reuse. We discuss some important avenues of research for achieving the vision.

Designing a software architecture requires architectural reasoning, i.e., activities that translate requirements to an architecture solution. Architectural reasoning is particularly challenging in the design of product-lines of self-adaptive systems, which involve variability both at development time and runtime. In previous work we developed an extended Architectural Reasoning Framework (eARF) to address this challenge. However, evaluation of the eARF showed that the framework lacked support for rigorous reasoning, ensuring that the design complies to the requirements. In this paper, we introduce an analytical framework that enhances eARF with such support. The framework defines a set of artifacts and a series of activities. Artifacts include templates to specify domain quality attribute scenarios, concrete models, and properties. The activities support architects with transforming requirement scenarios to architecture models that comply to required properties. Our focus in this paper is on architectural reasoning support for a single product instance. We illustrate the benefits of the approach by applying it to an example client-server system, and outline challenges for future work. © 2016 IEEE.

More than two decades of research have demonstrated an increasing need for software systems to be self-adaptive. Self-adaptation is required to deal with runtime dynamics which are difficult to predict before deployment. A vast body of knowledge to develop Self-Adaptive Software Systems (SASS) has been established. We, however, discovered a lack of process support to develop self-adaptive systems with reuse. To that end, we propose a domain-engineering based methodology, Autonomic Software Product Lines engineering (ASPLe), which provides step-by-step guidelines for developing families of SASS with systematic reuse. The evaluation results from a case study show positive effects on quality and reuse for self-adaptive systems designed using the ASPLe compared to state-of-the-art engineering practices.

Forests are essential for life, providing various ecological, social, and economic benefits worldwide. However, one of the main challenges faced by the world is the forest damage caused by biotic and abiotic factors. In any case, the forest damages threaten the environment, biodiversity, and ecosystem. Climate change and anthropogenic activities, such as illegal logging and industrial waste, are among the principal elements contributing to forest damage. To achieve the United Nations' Sustainable Development Goals (SDGs) related to forests and climate change, detecting and analyzing forest damages, and taking appropriate measures to prevent or reduce the damages are essential. To that end, we envision establishing a Smart Forest Observatories (SFOs) network, as shown below, which can be either a local area or a wide area network involving remote forests. The basic idea is to use Monitor, Analyze, Plan, Execute, and Knowledge (MAPE-K) architecture from autonomic computing and self-adaptive software systems domain to design and develop the SFOs network. The SFOs are planned to collect, analyze, and share the collected data and analysis results using state-of-the-art methods. The principal objective of the SFOs network is to provide accurate and real-time data to policymakers and forest managers, enabling them to develop effective policies and management strategies for global forest conservation that help to achieve SDGs related to forests and climate change.

Facial recognition technology has become a ubiquitous tool in security and personal identification. However, the rise of this technology has been accompanied by concerns over inherent biases, particularly regarding ethnic and gender. This thesis examines the extent of these biases by focusing on the influence of dataset imbalances in facial recognition algorithms. We employ a structured methodological approach that integrates AI-generated images to enhance dataset diversity, with the intent to balance representation across ethnics and genders. Using the ResNet and Vgg model, we conducted a series of controlled experiments that compare the performance impacts of balanced versus imbalanced datasets. Our analysis includes the use of confusion matrices and accuracy, precision, recall and F1-score metrics to critically assess the model’s performance. The results demonstrate how tailored augmentation of training datasets can mitigate bias, leading to more equitable outcomes in facial recognition technology. We present our findings with the aim of contributing to the ongoing dialogue regarding AI fairness and propose a framework for future research in the field.

Food waste is becoming an increasing threat to the environment and the economy. Estimates indicate that annually, a third of the food produced around the world ends up being wasted. Only one-fourth of that food is enough to take nearly a billion people out of starvation. Food waste is especially higher in more developed countries, including most of the states in the European Union and the USA. Sector-wise, food is being lost from field to fork, with households topping the charts. Overbuying, not knowing what already is in the fridge, unaware of the food until it eventually expires, are among the most common reasons that contribute to the food waste. The potential prevention of such massive waste could significantly reduce the amount of greenhouse gas emissions around the world and help the economy of the households including all the parties involved in food production, distributing and retailing.

On the other hand, technology has progressed in very rapid steps. The advancement of AI, ML, IoT, and voice-enabled devices has revolutionized many industries and has made us more efficient as human beings. Unfortunately, these advancements haven't yet had any significant impact in assisting families with their food choices and in preventing them from overbuying and throwing food away. Most of the proposed solutions addressing this issue, do not get integrated into everyday life. That is because they require a lot of manual input, rely entirely on mobile phones, do not show immediate results to keep users motivated, and on top of all, for the sole fact that modern lives are quite complex, and although an important issue, food waste is not an everyday cause of concern of an average person.

This thesis takes into account all of the shortcomings of the previous works and aims to create a more sustainable solution by exploring new ways of food inventory management in the households by automating the process so that users don't have to manually enter the data themselves. The proposed solution consists of a device that should be easily mounted into any fridge and acts as an interface between users and their food inventory. The device contains a bar-code scanner for the item input and a back-end that is capable of recognizing the item and can in return show user-friendly and valuable information such as the approximate price of the item, the approximate due date etc. and notifies users when an item is about to expire so that they can take appropriate actions.

7 out of 9 participants in the final conceptual design study said they would use this solution in their homes. The rest of the results from the designed test cases indicate a clear excitement and interest in participants and a willingness to see the prototype in the finished state, all the comments and insights together with the future work and how the feedback will be used into the next iteration are part of the final discussion of this thesis.

Software is the heartbeat of modern technology. To keep up with the new demands and expansion of requirements, changes are constantly introduced to the software, i.e., changes can also be made to fix an existing fault/defect. However, these changes might also cause further faults/defects in the software. This study aims to investigate the possible correlation between change-proneness and fault-proneness in object- oriented systems. Forty releases of five different open-source systems are analysed to quantify change- and fault-proneness; Beam, Camel, Ignite, Jenkins, and JMe- ter, then statistic evidence is presented as to answer the following: (1) Is there is a relationship between change-proneness and fault-proneness for classes in object- oriented systems? (2) Is there a relationship between size and fault-proneness for classes in object-oriented systems? and (3) Is there a relationship between size and change-proneness for classes in object-oriented systems? Using the Wilcoxon rank- sum test, the results show that: (1) there is a correlation between change- and fault- proneness at a statistically significant level and (2) a correlation also exists betweenclass size and its change- and fault-proneness at a statistically significant level.

Historical remains in Swedish forests are at risk of being damaged by heavy machineryduring regular soil preparation, scarification, and regeneration activities. Thereason for this is that the exact locations of these remains are often unknown or theirrecords are inaccurate. Some of the most vulnerable historical remains are the tracesleft after years of charcoal production. In this thesis, we design and implement acomputer vision artificial intelligent model capable of identifying these traces usingtwo accessible visualizations of Light Detection and Ranging (LIDAR) data. Themodel we used was the ResNet34 Convolutional Neural Network pre-trained on theImageNet dataset. The model took advantage of the image segmentation approachand required only a small number of annotations distributed on original images fortraining. During the process of data preparation, the original images were heavilyaugmented, which bolstered the training dataset. Results showed that the model candetect charcoal burners sites and mark them on both types of LIDAR visualizations.Being implemented on modern frameworks and featured with state-of-art machinelearning techniques, the model may reduce the costs of surveys of this type of historicalremains and thereby help save cultural heritage.

Microscope cameras possess high zoom capabilities and are frequently used for quality control in various industries. Visus Technology is a company that manufactures and sells these cameras, along with control software. The problem is that these cameras can only be set to one focus depth at a time. Therefore, we were tasked with implementing a fast focus stacking algorithm that combines multiple input images with different focus depths, resulting in a composite image with consistent focus throughout. This will later be integrated into the control software.

We initially approached the problem by choosing pixels with the highest Laplacian intensity. This resulted in very noisy output images since the blurry images could sometimes produce high-intensity pixels where they were not supposed to be. To address this, we implemented a grid-based focus stacking algorithm. The algorithm divides the input images into tiles, filtering out the blurriest tiles to exclude them from the stacking process. The result is a composite image with significantly reduced blur.

The increasing demand for efficient and secure public transportation systems necessi-tates innovative solutions. This research investigates facial recognition technology in pub-lic bus transportation as an advanced alternative to traditional RFID and QR code methods.The study addresses the limitations of existing identification and fare collection methods,such as the potential for lost, stolen, or shared RFID cards and fare evasion with QR codes.We designed and implemented a facial recognition-based system using a Raspberry Pi withan HQ-Camera, OpenCV for real-time image processing, and FaceNet for facial recogni-tion. The system was tested in a simulated bus boarding environment, focusing on accu-racy, processing time, and robustness under varying conditions. Results demonstrated thatthe proposed system significantly enhances passenger verification speed and security, of-fering a more efficient and secure solution. Specifically, our system achieved an accuracyrate of 0.75 after analyzing 15 images of 13 participants, with a notable improvement inprocessing speed and robustness. Additionally, user feedback indicated general acceptanceof the technology despite privacy concerns. This research contributes to developing moreefficient and secure public transportation systems, paving the way for broader applicationsof biometric technology in daily commutes. With this system, we can look forward to afuture of faster, more secure public transportation.

Java applications consume energy, which has become a controversial topic since it limits the number of machines and increases the cost of data centers. This paper investigates the potential relationship between energy consumption and some quality attributes for Java Collections and Sorting algorithms in order to raise awareness about using energy-efficient programs. In addition, introduce to the developers the most and least efficient Java Collection and Sorting algorithm in terms of energy consumption, memory, and CPU usage. This was achieved by conducting a controlled experiment to measure these terms. The data obtained for the results was used to acquire Statistical and Efficiency Analysis to answer the research questions.

Scientific and technological innovations have become increasingly important as we face the benefits and challenges of both globalization and a knowledge-based economy. Still, enrolment rates in STEM degrees are low in many European countries and consequently there is a lack of adequately educated workforce in industries. We believe that this can be mainly attributed to pedagogical issues, such as the lack of engaging hands-on activities utilized for science and math education in middle and high schools. In this paper, we report our work in the SciChallenge European project, which aims at increasing the interest of pre-university students in STEM disciplines, through its distinguishing feature, the systematic use of social media for providing and evaluation of the student-generated content. A social media-aware contest and platform were thus developed and tested in a pan-European contest that attracted >700 participants. The statistical analysis and results revealed that the platform and contest positively influenced participants STEM learning and motivation, while only the gender factor for the younger study group appeared to affect the outcomes (confidence level – p<.05).

Single-board computers as hardware for container orchestration have been a growing subject. Previous studies have investigated their potential of running production-grade technologies in various environments where low-resource, cheap, and flexible clusters may be of use. This report investigates the appliance of methods and processes prevalent in cluster, container orchestration, and cloud-native environments. The motivation being that if single-board computers are able to run clusters to a satisfactory degree, they should also be able to fulfill the methods and processes which permeate the same cloud-native technologies. Investigation of the subject will be conducted through the creation of different criteria for each method and process. They will then act as an evaluation basis for an experiment in which a single-board computer cluster will be built, provisioned, configured, and monitored. As a summary, the investigation has been successful, instilling more confidence in single-board computer clusters and their ability to implement cluster related methodologies and processes.

Background: Research on medical vocabulary expansion from large corpora has primarily been conducted using text written in English or similar languages, due to a limited availability of large biomedical corpora in most languages. Medical vocabularies are, however, essential also for text mining from corpora written in other languages than English and belonging to a variety of medical genres. The aim of this study was therefore to evaluate medical vocabulary expansion using a corpus very different from those previously used, in terms of grammar and orthographics, as well as in terms of text genre. This was carried out by applying a method based on distributional semantics to the task of extracting medical vocabulary terms from a large corpus of Japanese patient blogs. Methods: Distributional properties of terms were modelled with random indexing, followed by agglomerative hierarchical clustering of 3x100 seed terms from existing vocabularies, belonging to three semantic categories: Medical Finding, Pharmaceutical Drug and Body Part. By automatically extracting unknown terms close to the centroids of the created clusters, candidates for new terms to include in the vocabulary were suggested. The method was evaluated for its ability to retrieve the remaining n terms in existing medical vocabularies. Results: Removing case particles and using a context window size of 1 + 1 was a successful strategy for Medical Finding and Pharmaceutical Drug, while retaining case particles and using a window size of 8 + 8 was better for Body Part. For a 10n long candidate list, the use of different cluster sizes affected the result for Pharmaceutical Drug, while the effect was only marginal for the other two categories. For a list of top n candidates for Body Part, however, clusters with a size of up to two terms were slightly more useful than larger clusters. For Pharmaceutical Drug, the best settings resulted in a recall of 25 % for a candidate list of top n terms and a recall of 68 % for top 10n. For a candidate list of top 10n candidates, the second best results were obtained for Medical Finding: a recall of 58 %, compared to 46 % for Body Part. Only taking the top n candidates into account, however, resulted in a recall of 23 % for Body Part, compared to 16 % for Medical Finding. Conclusions: Different settings for corpus pre-processing, window sizes and cluster sizes were suitable for different semantic categories and for different lengths of candidate lists, showing the need to adapt parameters, not only to the language and text genre used, but also to the semantic category for which the vocabulary is to be expanded. The results show, however, that the investigated choices for pre-processing and parameter settings were successful, and that a Japanese blog corpus, which in many ways differs from those used in previous studies, can be a useful resource for medical vocabulary expansion.

Information from the Internet of Medical Things (IoMT) domain demands building safeguards against illegitimate access and identification. Existing user identification schemes suffer from challenges in detecting impersonation attacks which leave systems vulnerable and susceptible to misuse. Significant advancement has been achieved in the domain of biometrics and health informatics. This can take a step ahead with the usage of multimodal biometrics for the identification of healthcare system users. With this aim, the proposed work explores the fingerprint and iris modality to develop a multimodal biometric data identification and access control system for the healthcare ecosystem. In the proposed approach, minutiae-based fingerprint features and a combination of local and global iris features are considered for identification. Further, an index space based on the dimension of the feature vector is created, which gives a 1-D embedding of the high-dimensional feature set. Next, to minimize the impact of false rejection, the approach considers the possible deviation in each element of the feature vector and then stores the data in possible locations using the predefined threshold. Besides, to reduce the false acceptance rate, linking of the modalities has been done for every individual data. The modality linking thus helps in carrying out an efficient search of the queried data, thereby minimizing the false acceptance and rejection rate. Experiments on a chimeric iris and fingerprint bimodal database resulted in an average of 95% reduction in the search space at a hit rate of 98%. The results suggest that the proposed indexing scheme has the potential to substantially reduce the response time without compromising the accuracy of identification.

A shift in water monitoring approach from traditional grab sampling to novel wireless sensors is gaining in popularity not only among researchers but also in the market. These latest technologies readily enable numerous advantageous monitoring arrangements like remote, continuous, real-time, and spatially-dense and broad in coverage measurements, and identification of long-term trends of parameters of interest. Thus, a WSN system is implemented in a river in Kosovo as part of the InWaterSense project to monitor its water quality parameters. It is one of the first state of the art technology demonstration systems of its kind in the domain of water monitoring in developing countries like Kosovo. Water quality datasets are transmitted at pre-programmed intervals from sensing stations deployed in the river to the server at university via the GPRS network. Data is then made available through a portal to different target groups (policy-makers, water experts, and citizens). Moreover, the InWaterSense system behaves intelligently like staying in line with water quality regulatory standards.

A shift in the water monitoring approach from traditional grab sampling to novel wireless sensors is gaining in popularity not only among researchers but also in the market. These latest technologies readily enable numerous advantageous monitoring arrangements like remote, continuous, real-time, and spatially dense and broad in coverage measurements, and identification of long-term trends of parameters of interest. Thus, a WSN system is implemented in a river in Kosovo as part of the InWaterSense project to monitor its water quality parameters. It is one of the first state-of-the-art technology demonstration systems of its kind in the domain of water monitoring in developing countries like Kosovo. Water quality datasets are transmitted at pre-programmed intervals from sensing stations deployed in the river to the server at university via the GPRS network. Data is then made available through a portal to different target groups (policymakers, water experts, and citizens). Moreover, the InWaterSense system behaves intelligently like staying in line with water quality regulatory standards. 

Husqvarna AB is one of the leading producers of outdoor products such as autonomous lawn mowers. One important feature of these products is the ability toquickly respond to environmental factors such as slippy areas. A reliable slip detector is needed for this mission and many different technologies exists for detectingslip events. A common technique is to check the wheel motor current, which clearlydeviates when the lawn mower is subjected to slipping. The on-board sensors opensup for an alternative solution which utilizes the loop sensors as the main slip detector. This thesis covers the construction of a slip detection prototype which is basedon the loop sensors. In the end, Husqvarna AB was provided with a new alternativesolution, which was successfully compared to the exiting solution. It proved to bea reliable slip detector for manually induced slipping indoors, outdoor performancewere not investigated. Ultimately, the implemented prototype outperformed the existing solution in the intended environment of indoor testing.

Tactile sensors are among the most important devices used in industrial and biomedical fields. Sensors' profiles are significantly affected by their structures and material used. This article presents a robust, low-cost, low noise, accurate and simple fabrication capacitive tactile sensor as a single taxel fabricated on foam. This highly scalable design provides excellent noise immunity, accuracy, and due to a unique printable elastic conductor, it is flexible and stretchable with more than 200% strain. Furthermore, the taxel is based on the capacitive Wheatstone bridge. As a result, noise immunity and stability in case of temperature fluctuation is accomplished. Additionally, the sensor's innovative, simple fabrication, made of Polyurethane foam and printable elastic conductor, allows the system to adapt and achieve relevant results necessary for the purpose of the sensor's application. Therefore, the proposed sensor has potential applications in industrial and biomedical contexts, such as sleep monitoring, etc.

Medical image analysis has undergone significant advancements with the integration of machine learning techniques, particularly in the realm of bone anomaly detection. The availability of recent datasets and the lack of benchmarking and explainability components provide numerous opportunities in this domain. This study proposes a benchmarking approach to a recently published FracAtlas dataset utilizing state-of-the-art deep-learning models coupled with explainable artificial intelligence (XAI) having two distinct modules. The first module involves the binary classification of fractures in different body parts and explains the decision-making process of the best-performing model using an XAI technique known as EigenCAM. EigenCAM generates heatmaps on every layer of the YOLOv8m model to explain how the model reached a conclusion and localizes the fracture using a heatmap. To verify the heatmap, we also detected fractures using the YOLOv8m detection model, which achieved a mAP@O.5 of 59.5%, outperforming the baseline results on this dataset. The second module involves a multi-class classification task to categorize images into one of the five anatomical regions. The best-performing model for binary classification is the YOLOv8m model, with an accuracy of 83.1%, whereas the best-performing model for multi-class classification is the YOLOv8s, achieving an accuracy of 96.2%.

To overcome the hurdles associated with space management and security controls in a housing system, research was projected to study and analyze the necessary factors of accomplishment. Over time, different processes were observed and reviewed to make this a possible deal. Various residents were interviewed on the daily constraints in parking and managing their vehicles within their housing premises. The reported daunting concern was majorly the gate access and personal hunts for the space to keep the individual resident’s cars. Every resident would always have to stop and hoot at the housing gate for the assigned personnel to check and open the gate. While this would waste every resident’s time, the visitors even face more delay often time. Hitherto, car access and parking constraint become a thing of worry that no one would want to engage the housing service anymore. The interest has got dwindled. And to re-awaken the high patronage of the housing system, a gap must be bridged with an immediate solution to space management with a gating system. These were subsequently given a classical thought, while a prototype solution was demonstrated and reviewed with the various residents of some selected housing. This received a high welcoming embracement and was beckoned to be made real by the logical heuristic. At this point, nothing was further considered than using the Internet of things (IoT) technology to implement Vehicular Access Management for the control and integration of intended space provisioning in any housings. Consequently, the number plate of every vehicle becomes the automatic access tag and would be used for security control within the housing location. Vehicles’ numbers would be captured and used to manage the residents passing through the automated gating system. With it, records would be made for all permitted residents and the visitors that own a car. Thus, a proper arrangement would be allotted accordingly, as provisioned by the gating system administrator.

However, to allegories the above-proffered solution, this project work is divided into six sections. The introductory section introduces the project rationale, lists the objectives, explores related works, and introduces how IoT and vehicular systems can be merged. The second section delves into these vehicular systems. It introduces the Automatic License Plate Recognition System (ALRP) and the Raspberry Pi and highlights the merits of the Integrated Vehicular Access Security System. Open-CV and machine learning are also introduced. Section three covers the solution design, while section four is the implementation phase. Section five covers the testing and implementation of the solution. The final section summarizes the project. The project successfully models an automated solution for the security of tenants and vehicle users against unauthorized access to residential estates and buildings.

Internet of Things (IoT) devices have increased rapidly in recent years, revolutionizing many industries, including healthcare, manufacturing, and transportation, and bringing benefits to both individuals and industries. However, this increase in IoT device usage has exposed IoT ecosystems to numerous security threats and digital forensic challenges. This thesis investigates the most common IoT security threats and attacks, students’ awareness of them and their mitigation strategies, and the key challenges associated with IoT forensic investigations. A mixed-method approach is adopted in this thesis combining a literature review and a survey study. The survey assesses students’ knowledge of IoT security threats, mitigation techniques, and perceptions of the most effective ways to enhance IoT security. The survey also emphasizes the importance of user training and awareness in mitigating IoT threats, highlighting the most effective strategies, such as stronger regulations and improved device security by manufacturers. The literature review provides a comprehensive overview of the most common IoT security threats and attacks, such as malware, malicious code injection, replay attacks, Man in the Middle (MITM), botnets, and Distributed Denial of Service Attacks (DDoS). The mitigation techniques to these threats are overviewed as well as real-world incidents and crimes, such as the Mirai botnet, St. Jude Medical implant cardiac devices hack, and the Verkada hack, are examined to understand the consequences of these attacks. Moreover, this work also highlights the definition and the process of digital and IoT forensics, the importance of IoT forensics, and different data sources in IoT ecosystems. The key challenges associated with IoT forensics and how they impact the effectiveness of digital investigations in the IoT ecosystem are examined in detail. Overall, the results of this work contribute to ongoing research to improve IoT device security, highlight the importance of increased awareness and user training, and address the challenges associated with IoT forensic investigations.

This thesis addresses the challenges of large software and data-intensive systems. We will discuss a Big Data software that consists of quite a bit of Linux configuration, some Scala coding and a set of frameworks that work together to achieve the smooth performance of the system. Moreover, the thesis focuses on the Apache Spark framework and the challenging of measuring the lazy evaluation of the transformation operations of Spark. Investigating the challenges are essential for the performance engineers to increase their ability to study how the system behaves and take decisions in early design iteration. Thus, we made some experiments and measurements to achieve this goal. In addition to that, and after analyzing the result we could create a formula that will be useful for the engineers to predict the performance of the system in production.

The increased interest for self-tracking through the use of technology has given birth to the Quantified Self movement. The movement empowers users to gain self-knowledge from their own data. The overall idea is fairly recent and as such it provides a vast space for exploration and research. This project contributes to the Quantified self movement by proposing a concept for visualization of personal data using an avatar. The overall work finds inspiration in Chernoff faces visualization and it uses parts of the presentation method within the project design.  

This thesis presents a visualization approach for Quantified Self data using avatars. It tests the proposed concept through a user study with two iterations. The manuscript holds a detailed overview of the designing process, questionnaire for the data mapping, implementation of the avatars, two user studies and the analysis of the results. The avatars are evaluated using Spotify data. The implementation offers a visualization library that can be reused outside of the scope of this thesis.

The project managed to deliver an avatar that presents personal data through the use of facial expressions. The results show that the users can understand the proposed mapping of data. Some of the users were not able to gain meaningful insights from the overall use of the avatar, but the study gives directions for further improvements of the concept. 

Approaches to determining the factuality of diagnoses and findings in clinical text tend to rely on dictionaries of marker words for uncertainty and negation. Here, a method for semi-automatically expanding a dictionary of marker words using distributional semantics is presented and evaluated. It is shown that ranking candidates for inclusion according to their proximity to cluster centroids of semantically similar seed words is more successful than ranking them according to proximity to each individual seed word. 

With the recent advances of manufacturing technologies, referred to as Industry 4.0, maintenance approaches have to be developed to fulfill the new de-mands. The technological complexity associated to Industry 4.0 makes designing maintenance solutions particularly challenging. This paper proposes a novel maintenance framework leveraging principles from self-adaptation and software architecture. The framework was tested in an operational scenario where a bearing condition in an electrical motor needs to be managed, the results showed a proper operation. As a conclusion, the proposed framework could be used to develop maintenance systems for Industry 4.0.

With the recent advances of manufacturing technologies, referred to as Industry 4.0, maintenance approaches have to be developed to fulfill the new de-mands. The technological complexity associated to Industry 4.0 makes designing maintenance solutions particularly challenging. This paper proposes a novel maintenance framework leveraging principles from self-adaptation and software architecture. The framework was tested in an operational scenario where a bearing condition in an electrical motor needs to be managed, the results showed a proper operation. As a conclusion, the proposed framework could be used to develop maintenance systems for Industry 4.0.

Cloud Security and Privacy is the most concerned area in the development of newly advance technological domains like cloud computing, the cloud of things, the Internet of Things. However, with the growing popularity and diverse nature of these technologies, security and privacy are becoming intricate matters and affect the adoption of cloud computing. Many small and large enterprises are in conflict while migrating towards cloud technology and this is because of no proper cloud adoption policy guideline, generic solutions for system migration issues, systematic models to analyze security and privacy performance provided by different cloud models. Our proposed literature review focuses on the problems and identifies solutions in the category of security and privacy. A comprehensive analysis of various identified techniques published during 2010 – 2018 has been presented. We have reviewed 51 studies including research papers and systematic literature reviews on the factors of security and privacy. After analyzing, the papers have been classified into 5 major categories to get an appropriate solution for our required objectives of this study. This work will facilitate the researchers and as well the companies to select appropriate guideline while adopting cloud services. 

We advocate a novel concept of dependable intelligent edge systems (DIES) i.e., the edge systems ensuring a high degree of dependability (e.g., security, safety, and robustness) and autonomy because of their applications in critical domains. Building DIES entail a paradigm shift in architectures for acquiring, storing, and processing potentially large amounts of complex data: data management is placed at the edge between the data sources and local processing entities, with loose coupling to storage and processing services located in the cloud. As such, the literal definition of edge and intelligence is adopted, i.e., the ability to acquire and apply knowledge and skills is shifted towards the edge of the network, outside the cloud infrastructure. This paradigm shift offers flexibility, auto configuration, and auto diagnosis, but also introduces novel challenges. © 2019 ACM.

In recent years, it is becoming more common for people to use applications or devices that keep track of their life and activities, such as physical fitness, places they visited, the music they listen to, or pictures they took. This generates data that are used by the service providers for a variety of (usually analytics) purposes, but commonly there are limitations on how the users themselves can also explore or interact with these data. Our position paper describes a new approach of visualizing such Quantified Self data, in a meaningful and enjoyable way that can give the users personal insights into their own data. The visualization of the information is proposed as an avatar that maps the different activities the user is engaged with, along with each such activity level, as graphical features. An initial prototype (both in terms of graphical design and software architecture) as well as possible future extensions are discussed.

This paper presents our initial efforts towards the development of a framework for facilitating public engagement through the use of mobile Augmented Reality (mAR), that fall under the overall project title "Augmented Reality for Public Engagement" (PEAR). We present the concept, implementation, and discuss the results from the deployment of a mobile phone app (PEAR 4 VXO). The mobile app was used for a user study in conjunction with a campaign carried out by Växjö municipality (Sweden) while exploring how to get citizens more engaged in urban planning actions and decisions. These particular activities took place during spring 2016.One of the salient features of our approach is that it combines novel ways of using mAR together with social media, online databases, and sensors, to support public engagement. In addition, the data collection process and audience engagement were tested in a follow-up limited deployment.The analysis and outcomes of our initial results validate the overall concept and indicate the potential usefulness of the app as a tool, but also highlight the need for an active campaign from the part of the stakeholders.Our future efforts will focus on addressing some of the problems and challenges that we have identified during the different phases of this user study.

In recent years, data visualization has become a major area in Digital Humanities research, and the same holds true also in linguistics. The rapidly increasing size of corpora, the emergence of dynamic real-time streams, and the availability of complex and enriched metadata have made it increasingly important to facilitate new and innovative approaches to presenting and exploring primary data. This demonstration showcases the uses of Virtual Reality (VR) in the visualization of geospatial linguistic data using data from the Nordic Tweet Stream (NTS) project (see Laitinen et al 2017). The NTS data for this demonstration comprises a full year of geotagged tweets (12,443,696 tweets from 273,648 user accounts) posted within the Nordic region (Denmark, Finland, Iceland, Norway, and Sweden). The dataset includes over 50 metadata parameters in addition to the tweets themselves.

We demonstrate the potential of using VR to efficiently find meaningful patterns in vast streams of data. The VR environment allows an easy overview of any of the features (textual or metadata) in a text corpus. Our focus will be on the language identification data, which provides a previously unexplored perspective into the use of English and other non-indigenous languages in the Nordic countries alongside the native languages of the region.

Our VR prototype utilizes the HTC Vive headset for a room-scale VR scenario, and it is being developed using the Unity3D game development engine. Each node in the VR space is displayed as a stacked cuboid, the equivalent of a bar chart in a three-dimensional space, summarizing all tweets at one geographic location for a given point in time (see: https://tinyurl.com/nts-vr). Each stacked cuboid represents information of the three most frequently used languages, appropriately color coded, enabling the user to get an overview of the language distribution at each location. The VR prototype further encourages users to move between different locations and inspect points of interest in more detail (overall location-related information, a detailed list of all languages detected, the most frequently used hashtags). An underlying map outlines country borders and facilitates orientation. In addition to spatial movement through the Nordic areas, the VR system provides an interface to explore the Twitter data based on time (days, weeks, months, or time of predefined special events), which enables users to explore data over time (see: https://tinyurl.com/nts-vr-time).

In addition to demonstrating how the VR methods aid data visualization and exploration, we will also briefly discuss the pedagogical implications of using VR to showcase linguistic diversity.

We demonstrate an approach that utilizes immersive virtual reality (VR) to explore and interact with corpus linguistics data. Our case study focuses on the language identification parameter in the Nordic Tweet Stream corpus, a dynamic corpus of Twitter data where each tweet originated within the Nordic countries. We demonstrate how VR can provide previously unexplored perspectives into the use of English and other non-indigenous languages in the Nordic countries alongside the native languages of the region and showcase its geospatial variation. We utilize a head-mounted display (HMD) for a room-scale VR scenario that allows 3D interaction by using hand gestures. In addition to spatial movement through the Nordic areas, the interface enables exploration of the Twitter data based on time (days, weeks, months, or time of predefined special events), making it particularly useful for diachronic investigations.

In addition to demonstrating how the VR methods aid data visualization and exploration, we briefly discuss the pedagogical implications of using VR to showcase linguistic diversity. Our empirical results detail students’ reactions to working in this environment. The discussion part examines the benefits, prospects and limitations of using VR in visualizing corpus data.

The aim of the present study has been to examine users' experience of Chatbot from a business perspective and a consumer perspective. The study has also focused on highlighting what limitations a Chatbot can have and possible improvements for future development. The study is based on a qualitative research method with semi-structured interviews that have been analyzed on the basis of a thematic analysis. The results of the interview material have been analyzed based on previous research and various theoretical perspectives such as Artificial Intelligence (AI), Natural Language Processing (NLP). The results of the study have shown that the experience of Chatbot can differ between businesses that offer Chatbot, which are more positive and consumers who use it as customer service. Limitations and suggestions for improvements around Chatbotar are also a consistent result of the study.

Getting incorrect website content has increased in recent years, which is a reflection of the web security status on the Internet. However, when It comes to government and other professional organisations websites, they should have the best security requirements and follow security recommendations. This research will study websites located in the SE zone. The total number of investigated websites is 1166. The testing process was done in two ways. The firstway is a Dutch test website tool called Internet.nl. The second is using a tool developed as part of the research. The investigation focuses on Swedish websites and nine security extensions. These extensions prevent Man in the middle attack(MITM), downgrade attacks, Cross-Site Scripting (XSS), Click-jacking, and ensure that the correct information is obtained when a client requests a website. The paper evaluated the security between 2014 and 2022. What are the types of security taken and which sector has the best security awareness. The using of security headers had increased in 2022, the total use of tested security standards in the SE zone is around 50%, and banks have the best security awareness.

In recent years with the advances in Machine Learning and Artificial Intelligence, the demand for ever smarter automation solutions could seem insatiable. One such demand was identified by Fortnox AB, but undoubtedly shared by many other industries dealing with contractual services, who were looking for an intelligent solution capable of predicting the expiration date of a contractual period. As there was no clear evidence suggesting that Machine Learning models were capable of learning the patterns necessary to predict a contract's expiration, it was deemed desirable to determine subject feasibility while also investigating whether it would perform better than a commonplace rule-based solution, something that Fortnox had already investigated in the past. To do this, two different solutions capable of predicting a contractual expiration were implemented. The first one was a rule-based solution that was used as a measuring device, and the second was a Machine Learning-based solution that featured Tree Decision classifier as well as Neural Network models. The results suggest that Machine Learning models are indeed capable of learning and recognizing patterns relevant to the problem, and with an average accuracy generally being on the high end. Unfortunately, due to a lack of available data to use for testing and training, the results were too inconclusive to make a reliable assessment of overall accuracy beyond the learning capability. The conclusion of the study is that Machine Learning-based solutions show promising results, but with the caveat that the results should likely be seen as indicative of overall viability rather than representative of actual performance.

The number of Internet of Things (IoT) devices is rising and Wireless Fidelity (Wi-Fi) networks are still widely used in IoT networks. Security protocols such as Wi-Fi Protected Access 2 (WPA2) are still in use in most Wi-Fi networks, but Wi-Fi Protected Access 3 (WPA3) is making its way as the new security standard. These security protocols are crucial in Wi-Fi networks with energy and memory-constrained devices because of adversaries that could breach confidentiality, integrity, and availability of networks through various attacks. Many research papers exist on single Wi-Fi attacks, and the strengths and weaknesses of security protocols and Wi-Fi standards. This thesis aims to provide a detailed overview of Wi-Fi attacks and corresponding mitigation techniques against IoT Wi-Fi networks in a comprehensive taxonomy. In addition tools are mentioned for each Wi-Fi attack that allows, e.g., professionals or network administrators to test the chosen Wi-Fi attacks against their IoT networks. Four types of attack (categories) were defined, Man-in-the-Middle (MitM), Key-recovery, Traffic Decryption, and Denial of Service (DoS) attacks. A set of Wi-Fi attack features were defined and decribed. The features included the security protocol and security mode, the layer (physical or data-link) that an attack targets, and the network component interaction required to allow a Wi-Fi attack to execute successfully. In total, 20 Wi-Fi attacks were selected with relevance to IoT in Wi-Fi networks based on some criteria. Additonally, each Wi-Fi attack consist of a description of possible consequences/results an adversary can achieve, such as eavesdropping, data theft, key recovery, and many more. Flow charts were also added to give the reader a visual perspective on how an attack works. As a result, tables were created for each relevant security protocol and the Open Systems Interconnection (OSI) layers to create a overview of mitigations and available tools for each attack. Furthermore, WPA3 was discussed on how it solves some shortcomings of WPA2 but has vulnerabilities of it own that lie in the design of the 4-way and dragonfly handshake itself. In conclusion, development and proper vulnerability tests on the Wi-Fi standards and security protocols have to be conducted to improve and reduce the possibility of current and upcoming vulnerabilities.

One of the novel threats to the internet is the slow HTTP Denial of Service (DoS) attack on the application level targeting web server software. The slow HTTP attack can leave a high impact on web server availability to normal users, and it is affordable to be established compared to other types of attacks, which makes it one of the most feasible attacks against web servers. This project investigates the slow HTTP attack impact on the Apache and Nginx servers comparably, and review the available configurations for mitigating such attack. The performance of the Apache and NGINX servers against slow HTTP attack has been compared, as these two servers are the most globally used web server software. Identifying the most resilient web server software against this attack and knowing the suitable configurations to defeat it play a key role in securing web servers from one of the major threats on the internet. From comparing the results of the experiments that have been conducted on the two web servers, it has been found that NGINX performs better than the Apache server under slow rate DoS attack without using any configured defense mechanism. However, when defense mechanisms have been applied to both servers, the Apache server acted similarly to NGINX and was successful to defeat the slow rate DoS attack.

We present a machine learning based method for noise classification using a low-power and inexpensive IoT unit. We use Mel-frequency cepstral coefficients for audio feature extraction and supervised classification algorithms (that is, support vector machine and k-nearest neighbors) for noise classification. We evaluate our approach experimentally with a dataset of about 3000 sound samples grouped in eight sound classes (such as, car horn, jackhammer, or street music). We explore the parameter space of support vector machine and k-nearest neighbors algorithms to estimate the optimal parameter values for classification of sound samples in the dataset under study. We achieve a noise classification accuracy in the range 85% -- 100%. Training and testing of our k-nearest neighbors (k = 1) implementation on Raspberry Pi Zero W is less than a second for a dataset with features of more than 3000 sound samples.

Noise is any undesired environmental sound. A sound at the same dB level may be perceived as annoying noise or as pleasant music. Therefore, it is necessary to go beyond the state-of-the-art approaches that measure only the dB level and also identify the type of noise. In this paper, we present a machine learning based method for urban noise identification using an inexpensive IoT unit. We use Mel-frequency cepstral coefficients for audio feature extraction and supervised classification algorithms (that is, support vector machine, k-nearest neighbors, bootstrap aggregation, and random forest) for noise classification. We evaluate our approach experimentally with a data-set of about 3000 sound samples grouped in eight sound classes (such as car horn, jackhammer, or street music). We explore the parameter space of the four algorithms to estimate the optimal parameter values for classification of sound samples in the data-set under study. We achieve a noise classification accuracy in the range 88% - 94%.

Education systems worldwide must strive to support the teaching of a set of New Media Literacies (NMLs). These literacies respond to the need for educating human capital within participatory cultures in a highly technologized world. In this paper, we present Collboard, a constructivist problem solving activity for fostering the development of specific NMLs in classrooms: collective intelligence, distributed cognition and transmedia navigation. Collboard encompasses successive individual and collaborative work phases that prompt active student participation and engagement. It integrates digitally augmented appliances, namely, digital pens as a means to support individual work, and interactive whiteboards as a collaborative knowledge construction space. We report on the conceptual design of Collboard, its different technological and software components, as well as our findings from experiences we conducted in a Swedish school with 12 students from a 7th grade maths class. Findings from the experience provide an indication that Collboard can be well integrated in classroom teaching, and that it can foster the development of collective intelligence, distributed cognition and transmedia navigation in different knowledge domains. (C) 2013 Elsevier Ltd. All rights reserved.

Security testing is a widely applied measure to evaluate and improve software security by identifying vulnerabilities and ensuring security requirements related to properties like confidentiality, integrity, and availability. A confidentiality policy guarantees that attackers will not be able to expose secret information. In the context of software programs, the output that attackers observe will not carry any information about the confidential input information. Integrity is the dual of confidentiality, i.e., unauthorized and untrusted data provided to the system will not affect or modify the system’s data. Availability means that systems must be available at a reasonable time. Information flow control is a mechanism to enforce confidentiality and integrity. An accurate security assessment is critical in an age when the open nature of modern software-based systems makes them vulnerable to exploitation. Security testing that verifies and validates software systems is prone to false positives, false negatives, and other such errors, requiring more resilient tools to provide an efficient way to evaluate the threats and vulnerabilities of a given system. Therefore, the newly developed tool Reax controls information flow in Java programs by synthesizing conditions under which a method or an application is secure. Reax is a command-line application, and it is hard to be used by developers. This project has its primary goal to integrate Reax by introducing a plugin for Java IDEs to perform an advanced analysis of security flaws. Specifically, by design, a graphical plugin performs advanced security analysis that detects and reacts directly to security flaws within the graphical widget toolkit environment (SWT). The project proposed a new algorithm to find the root cause of security violations through a graphical interface as a second important goal. As a result, developers will be able to detect security violations and fix their code during the implementation phase, reducing costs.

A major challenge in modelling and simulation is the need to combine expertise in both software technologies and a given scientific domain. When High-Performance Computing (HPC) is required to solve a scientific problem, software development becomes a problematic issue. Considering the complexity of the software for HPC, it is useful to identify programming languages that can be used to alleviate this issue. Because the existing literature on the topic of HPC is very dispersed, we performed a Systematic Mapping Study (SMS) in the context of the European COST Action cHiPSet. This literature study maps characteristics of various programming languages for data-intensive HPC applications, including category, typical user profiles, effectiveness, and type of articles. We organised the SMS in two phases. In the first phase, relevant articles are identified employing an automated keyword-based search in eight digital libraries. This lead to an initial sample of 420 papers, which was then narrowed down in a second phase by human inspection of article abstracts, titles and keywords to 152 relevant articles published in the period 2006–2018. The analysis of these articles enabled us to identify 26 programming languages referred to in 33 of relevant articles. We compared the outcome of the mapping study with results of our questionnaire-based survey that involved 57 HPC experts. The mapping study and the survey revealed that the desired features of programming languages for data-intensive HPC applications are portability, performance and usability. Furthermore, we observed that the majority of the programming languages used in the context of data-intensive HPC applications are text-based general-purpose programming languages. Typically these have a steep learning curve, which makes them difficult to adopt. We believe that the outcome of this study will inspire future research and development in programming languages for data-intensive HPC applications.

Mobile devices and mobile computing have made tremendous advances and become ubiquitous in the last few years. As a result, the landscape has become seriously fragmented which brings lots of challenges for the mobile development process. Whilst native approach of mobile development still is the predominant way to develop for a particular mobile platform, recently there is shifting towards cross-platform mobile development as well. In this paper, we have performed a survey of the literature to see the trends in cross-platform mobile development over the last few years. With the result of the survey, we argue that the web-based approach and in particular,hybrid approach, of mobile development serves the best for cross-platform development. The results of this work indicate that even though cross platform tools are not fully matured they show great potential. Thus we consider that cross-platform development offers great opportunities for rapid development of high-fidelity prototypes of the mobile application.

Personuppgiftshantering inom EU regleras av General Protection Data Regulation, GDPR, den är till för att skydda en persons integritet samtidigt som det fria flödet av data mellan medlemsländerna ska upprätthållas. Företag och organisationer idag lägger mycket resurser på en manuell hantering av personuppgifter, som skulle kunna läggas på andra värdeskapande aktiviteter om denna process var mer automatiserad. Därför har vi utfört en studie hos Meriworks, där deras system, Imagevault, hanterar bilder och samtycken i ett manuellt flöde. Genom intervjuer av organisationer som använder detta system samt en litteraturstudie på GDPR, sammanställdes krav för vad som ställs av en applikation som hanterar samtycken och personuppgifter, både ur ett tekniskt och användarperspektiv. Utifrån dessa krav tog vi fram ett tekniskt lösningsförslag som skulle kunna ersätta den manuella processen i vår fallstudie. Vår lista över de tekniska krav som GDPR ställer, är generell och kommer kunna användas som riktlinjer för fall även utanför denna studie.