The use of physical systems and Virtual Machines has become inefficient and expensive for creating tailored, hands-on exercises for providing cyber security training. The main purpose of this project is to directly address these issues faced in cyber security education with the help of Docker containers. Using Docker, a lightweight and automated platform was developed for creating, sharing, and managing hands-on exercises. With the help of orchestration tools, this platform provides a centralized point to monitor and control the systems and exercises with a high degree of automation. In a classroom/lab environment, this infrastructure enables instructors and students not only to share exercises but also helps create and deploy exercises more easily. By streamlining the end to end delivery and deployment of the exercises, instructors can now efficiently make use of the class/lab hours in educating the students rather than performing system administration tasks.

The control of type-I error is a focal point for clinical trials. On the other hand, it is also critical to be able to detect a truly efficacious treatment in a clinical trial. With recent success in supervised learning (classification and regression problems), artificial intelligence (AI) and machine learning (ML) can play a vital role in identifying efficacious new treatments. However, the high performance of the AI methods, particularly the deep learning neural networks, requires a much larger dataset than those we commonly see in clinical trials. It is desirable to develop a new ML method that performs well with a small sample size (ranges from 20 to 200) and has advantages as compared with the classic statistical models and some of the most relevant ML methods. In this dissertation, we propose a Similarity-Principle-Based Machine Learning (SBML) method based on the similarity principle assuming that identical or similar subjects should behave in a similar manner. SBML method introduces the attribute-scaling factors at the training stage so that the relative importance of different attributes can be objectively determined in the similarity measures. In addition, the gradient method is used in learning / training in order to update the attribute-scaling factors. The method is novel as far as we know. We first evaluate SBML for continuous outcomes, especially when the sample size is small, and investigate the effects of various tuning parameters on the performance of SBML. Simulations show that SBML achieves better predictions in terms of mean squared errors or misclassification error rates for various situations under consideration than conventional statistical methods, such as full linear models, optimal or ridge regressions and mixed effect models, as well as ML methods including kernel and decision tree methods. We also extend and show how SBML can be flexibly applied to binary outcomes. Through numerical and simulation studies, we confirm that SBML performs well compared to classical statistical methods, even when the sample size is small and in the presence of unmeasured predictors and/or noise variables. Although SBML performs well with small sample sizes, it may not be computationally efficient for large sample sizes. Therefore, we propose Recursive SBML (RSBML), which can save computing time, with some tradeoffs for accuracy. In this sense, RSBML can also be viewed as a combination of unsupervised learning (dimension reduction) and supervised learning (prediction). Recursive learning resembles the natural human way of learning. It is an efficient way of learning from complicated large data. Based on the simulation results, RSBML performs much faster than SBML with reasonable accuracy for large sample sizes.

Document classification is used to sort and label documents. This gives users quicker access to relevant data. Users that work with large inflow of documents spend time filing and categorizing them to allow for easier procurement. The Automatic Classification and Document Filing (ACDF) system proposed here is designed to allow users working with files or documents to rely on the system to classify and store them with little manual attention. By using a system built on Hidden Markov Models, the documents in a smaller desktop environment are categorized with better results than the traditional Naive Bayes implementation of classification.

Quality of Experience (QoE) is the degree of delight or annoyance of the user of an application or service [1]. To ensure a proper level of QoE for end users, networks and service providers have to continuously monitor their systems in terms of technical parameters, which can then be used to estimate QoE. Especially for video streaming services, which consume a large amount of traffic, network problems such as bandwidth fluctuations quickly develop into annoying artefacts visible to the users, which may lead to abandonment of services. Internet Service Providers (ISPs) are therefore continuously monitoring video network streams in order to provide the better QoE. In this regard to conduct the user behavioral studies, the ISPs spend a large amount of money and energy every time. To avoid this, we are using existing user behavioral studies and simulating the user behavior in an automated set-up and try to measure the impact of network conditions. In our current studies based on the user-behavioral model used [5], we can conclude that low upload speeds don’t affect on simulated user behavior unless they are in high download speed networks. Simulated users with the mid-range download and upload bandwidth tend to face more stalling and quality switches compared to both low and high-bandwidth users. Key quality indicators(KQIs) of video QoE also depends on the number of videos we measure in a single session. Reloading of player helps to reduce stalling for mid and high bandwidths. Reloading worsens the situation in low bandwidth scenarios. / Kvalitet av erfarenhet (QoE) definieras som: "Graden av fröjd eller förargelse av användaren av en applikation eller en service. Den resulterar från uppfyllelsen av hans eller hennes förväntningar med hänsyn till hjälpmedlet, och/eller njutning av applikationen eller servicen i ljuset av användarens personlighet och aktuella tillstånd" [1]. Att att se till en riktig nivå av QoE för slut användare, nätverk och tjänstefamiljeförsörjare måste övervakar fortlöpande deras system när det gäller tekniska parametrar, som kan därefter vara den van vid bedömningen QoE. Speciellt för videoen som strömmar service, som konsumerar ett stort belopp av trafik, framkallar nätverksproblem liksom bandbreddväxlingar snabbt in i förargliga artefacts som är synliga till användarena, som kan leda till övergivande av service. Internetleverantörer (ISPs) är därför fortlöpande videopp nätverksströmmar för övervakning för att att ge den bättre QoEen. I detta avseende att föra de beteendestudierna för användaren, spenderar ISPsna en stor mängd pengar och energi varje gång. Att undvika denna, använder simulerar vi beteendestudier för existerande användare och användareuppförandet i en automatiserat aktivering och försök för att mäta inverkan av nätverksvillkor. I våra aktuella studier som baseras på den använda användare-beteendemodellen [5], oss kan avsluta som laddar upp lågt hastigheter inte påverkar på simulerat användareuppförande, om inte de är i höga nedladdninghastighetsnätverk. Simulerade användare med mitt–området nedladdar och laddar upp bandbredd ansar för att vända mot mer avbrottsoch kvalitetsströmbrytare som jämförs till både låga och höga bandbreddanvändare. Nyckelkvalitetsindikatorer (KQIs) av video QoE beror också på numret av video som vi mäter i en enkel period. Tillbakaläggande av spelaren hjälper att förminska avbrott för mittoch höga bandbredder. Tillbakaläggande försämrar läget i scenarion för låg bandbredd.