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Digital transformation is rapidly changing the business landscape, compellingcompanies to invest in it due to its several benefits, such as reduced time-to-value, improved quality, and the ability to overcome hardware limitations. Aprominent technology enabling digital transformation is digital twin. A digitaltwin is a virtual representation of a physical asset, system, or process, usedfor monitoring and controlling purposes. Implementing digital twins requirestackling challenging engineering tasks such as establishing accurate digitalrepresentations of physical systems or ensuring live data synchronization. Suchchallenges are exacerbated by a lack of research on software architecturesthat meet required quality attributes, their actual implementation, and otherengineering tasks. Software architectures serve as a blueprint and foundationfor building software-intensive systems that meet specific requirements. Theyprovide a comprehensive overview of a system components, their properties,and relationships. Reference architectures, which are based on establishedbest practices in a given domain, help in reasoning about a class of softwarearchitectures and facilitate the engineering of software-intensive systems such asdigital twins. Given the above premises, the ultimate goal of this research is todefine a standard-based reference architecture for digital twins in manufacturingto improve interoperability. We use existing standards as common ground tofacilitate technology transfer between academia and industry. In addition, wefocus on interoperability as it is a crucial quality attribute affecting the functionalsuitability and performance efficiency of digital twins.

Esports has grown quickly in recent years, and the business has produced a ton of specifications-based data that is simple to obtain. Because of the aforementioned traits, data mining and deep learning techniques can be used to direct participants and create winning strategies. As one of the world’s most famous e-sports, Dota 2 has grown over time by luring many players from all over the world, and it recently held its tenth professional international tournament. A Dota2 game consists of a drafting and a playing phase. The drafting phase can influence the outcome of the game. However, players frequently struggle to select the strongest lineup to participate. In order to address this gap, we present in this article an NLP and Deep Learning hybrid model as an alternative approach for optimal drafting. We report state-of-the-art results on the problem of predicting the outcome of the match in the hero select phase of the game. Hero vectors are produced by the model using the Continuous Bag of Words (CBOW) in the Word2vec model. The context of a word in a sentence can be predicted by the CBOW model. The model used in this article suggests a predictive tool for players that can provide a more reliable method for the drafting phase. Accordingly, a word is changed into a hero, a phrase into a lineup, and a word vector into a hero vector. The improved Long short-term memory (LSTM) model is used to solve the influence of timing relationships affecting selection on the prediction of winning rate.

Cloud computing is a model that provide on-demand access to a range of computing services and resources such as servers, networks, storage, and application through the internet. Companies utilizing cloud computing can use different services based on their needs without initial capital expenditure. For the companies adopting cloud, business models have changed the competitive edge it offers to the business. In this thesis, we study the impacts of cloud transformation on the business models, using Osterwalder’s business model canvas as framework. To give a holistic view of the cloud transformation, the study also presents the motivations, challenges and factors that should be considered by the companies for their transformation journey.We carried out this study by using qualitative research, i.e., by interviewing experienced leaders from software development unit in companies from different industries. In our findings, we conclude that cloud transformation brings positive impacts in the customer relationships as it offers promising and advantageous value propositions due to the technological advancement in the key activities and resources. Therefore, it could attract new customer segments. Furthermore, with the new potential customers and with the value propositions it offers, cloud computing could unleash numerous new revenue streams.However, companies must be aware of the challenges that one might encounter during the cloud transformation journey. Prioritization of software assets, clear communication and compatibility, security are the main factors that need to be in focus.For future research, we recommend studying the financial impact of cloud transformation, such as additional revenue and profitability as these aspects are the real main drive of any business innovation. We also recommend studying the impact of data protection laws such as GDPR laws or regulations that could impact the organizational businesses.

Replacing a data acquisition system (DAQ) is a substantial and long-term investment. Siemens Energy’s R&D department is considering a new DAQ system. The equipment should be relevant for at least ten years. The system performance needs to reflect today’s requirements but also consider those of the future. Together with measurement engineers at the company, a list of specifications is written, highlighting the DAQ system requirements. Also, two parallel use cases are defined. The general architecture of a data acquisition system is analyzed. Different implementation techniques are compared when navigating through the DAQ system design process. The comparison forms a relation between the architectural constraints and the requirements. Several tradeoffs in DAQ system design have been found and discussed. Furthermore, the review of market trends indicates the technical direction of modern DAQ systems Thus, questioning the current instrument/measurement strategy.

I Software defined networking (SDN) separeras kontrollplanet och dataplanet. Separationenintroducerar säkerhetsrisker, vilket öppnar dörren för olika attacktyper. Denial-of-Service (DoS) är enattacktyp som används för att belasta nätverksresurser, genom att skicka en stor mängd trafik tillnätverket. Det finns många olika metoder för att utföra DoS-attacker, vilket gör det svårt att stoppaattackerna. En DoS-attacktyp som kan vara kritisk mot SDN är SYN-flood. Den här typen av attackriktar sig mot Three-way handshake-processen, som uppstår när en klient vill ansluta mot en server.Avslutas inte processen på rätt sätt kan det missbruka serverns resurser, och påverkanätverkstillgängligheten. Det finns olika säkerhetslösningar som motarbetar SYN-flood-attacker. Deflesta säkerhetslösningarna faller inom kategorierna upptäcka och hantera. Säkerhetslösningar inomkategorin förhindra är relativt få i jämförelse med kategorierna upptäcka och hantera. Syftet med vårtarbete var att jämföra tre typer av SYN-flood-attacker, och undersöka säkerhetslösningar som förhindrarattacktypernas påverkan på SDN. De varianter vi jämförde var SYN-flood, ACK-flood och SYN-ACK-flood. Säkerhetslösningarna vi implementerade i vår studie var Rate-limit, CPU-limit och enkombination av båda. Genom en noggrant utformad fallstudie lyckades vi identifiera SYN-ACK-floodsom attacktypen med störst påverkan på SDN. Kombinationen av Rate-limit och CPU-limit varsäkerhetslösningen som var effektivast på att motarbeta attacktyperna. Vårt arbete bidrar till en ökadkunskap inom SDN-säkerhet.

Procedural level generation has long since been prevalent in video games. A desire to make the methods used more generalised has recently sparked an interest in adapting machine learning for this purpose. However, this field is still relatively nascent and has several open questions. As such, this study investigated several metrics for the evaluation of machine learning assisted level generators for Super Mario Bros. This was done by using a generative adversarial network (GAN) together with evolutionary programming to generate levels that maximize the aforementioned metrics individually. Then, in order to establish correlative relationships, a user study was conducted. In this user study, participants were asked to play through the generated levels and rate them according to enjoyment, aesthetics, and difficulty. We show significant correlations between several metrics and the three dimensions of quality; some such correlations are also, seemingly, independent of prior gaming experience. We contribute to the field of machine learning assisted level generation by 1) reinforcing certain metrics’ validity for use in the evaluation of level generators, and 2) by demonstrating that this evolutionary approach can be used to control difficulty effectively.

Today the IT support could be overwhelmed with loads of requests from the clients within the Health care center Hjärtat (Also known as HCCH) through email, which is an inefficient way to store and manage information of each complaint. Tracking down the list of requests is also an issue. For the goals to ensure the achievements of expanding to new clients, IT support needs a better alternative way to manage, store, and retrieve information. This thesis reports a prototype of a complaint management system implemented for the Healthcare center Helsingborg Hjärtat. The software architect pattern in this project is Model view control and applied on the Visual studio .Net core with the programming language of C-sharp and HTML. Test results showcase the prototype of the complaint management system as an administrator, and the result of the system handles the complaint cases well. The IT support experience keeps track of the complaints more efficiently sent from the client users.