Very Small Enterprises (VSEs) are currently the backbone of all industries, since they account for roughly 80% of the world’s economy. In some countries, they account for over 95% of all companies and enterprises. As such, VSEs are a fundamental element in any given industry. Because of this, understanding how VSEs function and how, as a software developer, work and think is crucial to facilitate the software development process. A software unaware VSE (SUVSE) is a company which has little tangency with understanding software in general. The problem lies in how to properly and efficiently communicate with SUVSE, due to their limitations, as well as the general lack of knowledge regarding software development.   This report will cover the topic of challenges faced when working with a very small company for gathering domain knowledge (DK) and facilitating the communication and exchange of meaningful information, in the context of developing a software product, using agile methodologies. In the report, some of the more popular agile methodologies will be covered and chosen, in addition to existing ISO standards for VSEs.   The purpose of the research is to answer the following questions:   What topics are software unaware very small enterprise (SUVSE) aware of, in the software development process?   Which aspects of the software development process must be known by a software unaware domain expert of a very small enterprise (VSE), so that a software development team can capture the required domain knowledge (DK)?     How is the software unaware very small enterprises (SUVSEs) understanding of the software development process affected by their involvement in an agile development methodology?   To fulfill the purpose of the study, a case study was performed using 3 techniques to gather data: questionnaire, observation and interview. The participants were employees and CEOs from a few VSEs, contacted either via phone or face-to-face meetings.   The study showed the problems faced when working with SUVSE, what steps should be performed to facilitate communication with domain experts for gathering DK, and how the VSE can benefit from the knowledge and experience gained because of actively participating in the software development process. It concludes with practices on how to improve communication between software developers and VSE stakeholders to optimize the exchange of meaningful data and knowledge in the context of VSEs, and recommends future work based on the findings.

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Architectural degeneration is an ever-present threat to software systems with no exception based on the domain or tools used. This thesis focus on the architectural degeneration in systems written in multi-paradigm run-time evaluated languages like Python. The focus on Python in this kind of investigation is to our knowledge the first of its kind; thus the thesis investigates if the methods for measuring architectural degeneration also applies to run-time evaluated languages like Python as believed by other researchers. Whom in contrast to our research have only researched this phenomenon in systems written in compiled languages such as Java, C, C++ and C#. In our research a tool PySmell has been developed to recover architectures and identify the presence of architectural smells in a system. PySmell has been used and evaluated on three different projects Django, Flask and PySmell itself. The results of PySmell are promising and of great interest but in need of further investigating and fine-tuning to reach the same level as the architectural recovery tools available for compiled languages. The thesis presents the first step into this new area of detecting architectural degeneration in interpreted languages, revealing issues such as that of extracting dependencies and how that may affect the architectural smell detection.

Multiple camera arrays are beginning to see more widespread use in a variety of different applications, be it for research purposes or for enhancing the view- ing experience in entertainment. However, when using multiple cameras the images produced are often not color consistent due to a variety of different rea- sons such as differences in lighting, chip-level differences e.t.c. To address this there exists a multitude of different color calibration algorithms. This paper ex- amines two different color calibration algorithms one targeted and one target- less. Both methods were implemented in Python using the libraries OpenCV, Matplotlib, and NumPy. Once the algorithms had been implemented, they were evaluated based on two metrics; color range homogeneity and color ac- curacy to target values. The targeted color calibration algorithm was more ef- fective improving the color accuracy to ground truth then the target-less color calibration algorithm, but the target-less algorithm deteriorated the color range homogeneity less than the targeted color calibration algorithm. After both methods where tested, an improvement of the targeted color calibration al- gorithm was attempted. The resulting images were then evaluated based on the same two criteria as before, the modified version of the targeted color cal- ibration algorithm performed better than the original targeted algorithm with respect to color range homogeneity while still maintaining a similar level of performance with respect to color accuracy to ground truth as before. Further- more, when the color range homogeneity of the modified targeted algorithm was compared with the color range homogeneity of the target-less algorithm. The performance of the modified targeted algorithm performed similarly to the target-less algorithm. Based on these results, it was concluded that the targeted color calibration was superior to the target-less algorithm.

Gateways play a vital role in the Internet of Things (IoT) infrastructure. Gateways are widely used and their usage range from industries to smart homes. As they have expanded nowadays to have internet connectivity which raises certain security issues like being exposed to various threats and cyber-attacks such as rootkits, bootkits. Many researches and developmentswere made to countermeasure these problems. One of the significant developments was Root ofTrust (RoT). RoT can be enabled both by using hardware such as a chip named Trusted PlatformModule (TPM) and using software like Trusted Execution Environment (TEE).We propose amethod on how to enable RoT using the chip TPM 2.0 on a product, a Gateway named GWen from the company HMS Industrial Networks AB using the TPM software stack TSS2.0. We have performed tests on cryptographic operations such as key generation, key usage, and secure key storage and have shown the results in this report. Based on our results, adding and enabling the TPM 2.0 to the GWen enhances the security of its Linux distribution and makes it possible tosecurely identify and authenticate the gateway based on its secret keys that are stored securelyinside it’s TPM.

I rapporten undersöks och testas psuedoslumptalsgeneratorer för att undersöka deras egenskaper, en av dem var periodlängd. För att göra detta valde vi ut fyra kända psuedoslumptalsgeneratorer och fyra olika testsviter som av olika anledning var av intresse, samt att vi själva utvecklade en pseudoslumptalsgenerator som vi utsatte för samma test. Genom att göra detta har vi kunnat dra slutsatsen att pseudoslumptalsgeneratorns viktigaste egenskap är en lång slumptalssekvens som inte periodiseras.

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The  rapid  digitization  in  the  health  care  sector  leads  to  an  increaseof  data.  This  routine  collected  data  in  the  form  of  electronic  healthrecords (EHR) is not only used by medical professionals but also hasa  secondary  purpose:  health  care  research.  It  can  be  opportune  touse this EHR data for predictive modeling in order to support medi-cal professionals in their decisions. However, using routine collecteddata  (RCD)  often  comes  with  subtle  biases  that  might  risk  efficientlearning of predictive models. In this thesis the effects of RCD on theprediction performance are reviewed.In particular we thoroughly investigate and reason if the performanceof  particular  prediction  models  is  consistent  over  a  range  of  hand-crafted sub-populations within the data.Evidence  is  presented  that  the  overall  prediction  score  of  the  algo-rithms trained by EHR significantly differ for some groups of patientsin  the  data.  A  method  is  presented  to  give  more  insight  why  thesegroups of patients have different scores.

No description available.

When developing applications for iOS devices it is very common to use Apple’s own Core Data database system which is a framework handling database persistence for iOS devices among other things, but since there so many different kinds of applications it might not be the best option to use the same database system every time. Realm is another database system for iOS devices, it is very lightweight and a big rival to Core Data. This work was conducted with the goal of finding differences between the database systems Core Data and Realm that might show that one or the other is better used in some cases. The comparison between the systems was divided into two different parts, one theoretical comparison focused on reading and analyzing documentation and development of a test application. The test application tested time of create, read, update and delete operations in relation to increasing number of objects and increased number of properties in each object. The tests on Core Data were made with two different implementations to get the aspect of time difference based on implementation included. The results were fairly similar on the different operations with a slight advantage to Core Data. The big difference was seen in implementation difficulty and usability. The included features in the database system were also considered. Realm included more of commonly used and important features but Core Data gives the user the ability to add most of these in the implementation, this results in a question of user case.