Wireless Sensor Networks (WSNs) have recently attracted much interest in the research community because of their wide range of applications. One emerging application for WSNs involves their use in healthcare where they are generally termed Wireless Medical Sensor Networks (WMSNs). In a hospital, fitting patients with tiny, wearable, wireless vital sign sensors would allow doctors, nurses and others to continuously monitor the state of those in their care. In the healthcare industry, patients are expected to be treated in reasonable time and any loss in data availability can result in further decline in the patient's condition or can even lead to death. Therefore, the availability of data is more important than security concerns. The overwhelming priority is to take care of the patient, but the privacy and confidentiality of that patient's medical records cannot be neglected. In current healthcare applications, there are many problems concerning security policy violations such as unauthorised denial of use, unauthorised information modification and unauthorised information release of medical data in the real world environment. Current WSN access control models used the traditional Role-Based Access Control (RBAC) or cryptographic methods for data access control but the systems still need to predefine attributes, roles and policies before deployment. It is, however, difficult to determine in advance all the possible needs for access in real world applications because there may be unanticipated situations at any time. This research proceeds to study possible approaches to address the above issues and to develop a new access control model to fill the gaps in work done by the WSN research community. Firstly, the adaptive access control model is proposed and developed based on the concept of discretionary overriding to address the data availability issue. In the healthcare industry, there are many problems concerning unauthorised information release. So, we extended the adaptive access control model with a prevention and detection mechanism to detect security policy violations, and added the concept of obligation to take a course of action when a restricted access is granted or denied. However, this approach does not consider privacy of patients' information because data availability is prioritised. To address the conflict between data availability and data privacy, this research proposed the Trust-based Adaptive Access Control (TBA2C) model that integrates the concept of trust into the previous model. A simple user behaviour trust model is developed to calculate the behaviour trust value which measures the trustworthiness of the users and that is used as one of the defined thresholds to override access policy for data availability purpose, but the framework of the TBA2C model can be adapted with other trust models in the research community. The trust model can also protect data privacy because only a user who satisfies the relevant trust threshold can get restricted access in emergency and unanticipated situations. Moreover, the introduction of trust values in the enforcement of authorisation decisions can detect abnormal data access even from authorised users. Ponder2 is used to develop the TBA2C model gradually, starting from a simple access control model to the full TBA2C. In Ponder2, a Self-Managed Cell (SMC) simulates a sensor node with the TBA2C engine inside it. Additionally, to enable a full comparison with the proposed TBA2C model, the Break-The-Glass Role Based Access Control (BTGRBAC) model is redesigned and developed in the same platform (Ponder2). The proposed TBA2C model is the first to realise a flexible access control engine and to address the conflict between data availability and data privacy by combining the concepts of discretionary overriding, the user behaviour trust model, and the prevention and detection mechanism.

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In the last decade Video on Demand (VOD) services are becoming the centre of audiovisual entertainment consumption. American company Netflix is currently the biggest player on the market. Thanks to growing base of viewers it has become big competitor even for big hollywood studios. Those studios don't want to lose the track in this new, popular segment of on-line entertainment, therefore they are also coming out with their own platforms. Viewer's habits on VOD services has many specifics, for example in ways of consumption or in choosing the content. The subject of this thesis is to capture allround behaviour of czech user on VOD platforms.

In this thesis, the process of developing an application for segmenting customers with the use of machine learning is described. The project was carried out at a company which provides a booking platform for beauty and health services. Data about customers were analyzed and processed in order to train two classification models able to segment customers into three different customer groups. The performance of the two models, a Logistic Regression model and a Support Vector Classifier, were evaluated with different numbers of features and compared to classifications made by human experts working at the company. The results shows that the logistic regression model achieved an accuracy of 71% when classifying users into the three groups, which was more accurate than the experts manual classification. A web API where the model is provided has been developed and presented to the company. The results of the study showed that machine learning is a useful technique for performing customer segmentation based on behavioral data. Even in the case where the classes are not naturally divisible, the application provides valuable insights on user behaviour that can help the company become more data-driven.

The Circular Economy is a growing research field and policy agenda. Yet, integrating the social dimensions of sustainability into the Circular Economy remains a challenge. The significance of reactions to an implemented Circular Economy is poorly understood.  Contrary to the narrative that consumer demand shapes supply, affordance theory stresses the significance of considering the exogenous physical context that shapes user decisions. Building on affordance theory and insights from the social sciences, this study develops an agent-based model, TransportTransform, to analyse the interactions of the individual-, meso-, and system-levels. The agent-based model connects individual mobility choices with network decision-making mechanisms. Looking at user decision-making on transportation modes, the model yields insights into the interaction of mode occupancy and social norms to assess system patterns of user behaviour. The model design was informed by eleven interviews with researchers in the field and is initialized with data from an empirical survey conducted in Germany. The TransportTransform agent-based model confirms the importance of affordances as an important factor in modal choice. Model results show the relevance of including habitual behaviour when modelling transport mode choice, with the car being the most popular mode, followed by biking and public transport. Incorporating mode occupancy significantly reduces car usage, offering potential policy avenues for redirecting mode capacities towards desired modes. The impact of social norms on mode choice was less pronounced, highlighting the need to further explore norm internalisation and the indirect effects of social norms in future model iterations. The study emphasizes the need for further model expansions to better understand the impact of Circular Economy policies on user decision-making. Overall, the study highlights the importance of considering the social dimensions of sustainability in the Circular Economy and provides a valuable framework and implemented agent-based model for analysing user behaviour in this context.

This study aims to create a Machine Learning model and investigate its performance of detecting fraudulent user behaviour on an e-commerce platform. The user data was analysed to identify and extract critical features distinguishing regular users from fraudulent users. Two different types of user data were used; Event Data and Screen Data, spanning over four weeks. A Principal Component Analysis (PCA) was applied to the Screen Data to reduce its dimensionality. Feature Engineering was conducted on both Event Data and Screen Data. A Random Forest model, a supervised ensemble method, was used for classification. The data was imbalanced due to a significant difference in number of frauds compared to regular users. Therefore, two different balancing methods were used: Oversampling (SMOTE) and changing the Probability Threshold (PT) for the classification model.  The best result was achieved with the resampled data where the threshold was set to 0,4. The result of this model was a prediction of 80,88% of actual frauds being predicted as such, while 0,73% of the regular users were falsely predicted as frauds. While this result was promising, questions are raised regarding the validity since there is a possibility that the model was over-fitted on the data set. An indication of this was that the result was significantly less accurate without resampling. However, the overall conclusion from the result was that this study shows an indication that it is possible to distinguish frauds from regular users, with or without resampling. For future research, it would be interesting to see data over a more extended period of time and train the model on real-time data to counter changes in fraudulent behaviour.

O comportamento do usuário afeta o impacto ambiental gerado durante a fase de uso das edificações e de produtos em geral. O Design para o Comportamento Sustentável (DCS) é uma abordagem que visa tornar o comportamento do usuário mais sustentável através do design do produto. Apesar de estar sendo estudada principalmente nas duas últimas décadas, poucos estudos examinam a eficácia dessa abordagem e a aplicam em edificações escolares. A aplicação de estratégias de DCS em edificações escolares pode trazer benefícios, uma vez que as crianças têm influência no ambiente em que estão inseridas e no comportamento dos adultos ao seu redor. O objetivo deste estudo é propor e avaliar uma solução desenvolvida a partir de estratégias de DCS para criar um ambiente escolar que torne o comportamento do usuário mais sustentável. Primeiramente, os comportamentos que afetam o impacto ambiental nas edificações escolares foram identificados. Após, um kit de adesivos para sanitários (EcoSticker) foi proposto e implementado em duas escolas. O consumo de recursos (energia, papel higiênico, papel toalha e sabão) antes e após a intervenção foi medido. Posteriormente, entrevistas, questionários e um workshop foram realizados para entender melhor os dados de consumo coletados e a percepção dos usuários sobre o kit. O consumo de energia diminuiu nas duas escolas, demonstrando que o EcoSticker pode alterar positivamente o comportamento do usuário. A partir deste trabalho foram obtidos os potenciais benefícios e limitações da aplicação de estratégias de DCS em edificações escolares. / The user behaviour affects the environmental impact generated during the usage phase of buildings and products in general. Design for Sustainable Behaviour (DfSB) is an approach that focus on influence users to behave more sustainably through the product design. Although it has received attention particularly in the last two decades, few studies examined the effectiveness of such approach and apply it on school buildings. The application of DfSB strategies in school buildings can bring some benefits, once children have influence on their environment and on how adults behave. The goal of this study is to propose and test an artefact developed according to the DfSB strategies to create a school environment that leads to a more sustainable behaviour. First, behaviours on school buildings that affect the environmental impact were identified. Then, a toolkit for washrooms (EcoSticker) was proposed and implemented in two schools. The resources consumption (energy, toilet paper, paper towel and soap) prior and after the intervention was measured. Afterwards, interviews, questionnaires and a workshop were carried out to further understand the consumption data collected and users’ perception of the toolkit. The consumption of energy have decreased in both schools, demonstrating that the EcoSticker toolkit can change user behaviour positively. At the end, the potential benefits and constrains in applying DfSB strategies on school buildings were discussed.

This thesis has several aims, the main to offer the reader an overview on specific social networks. To address the main aim, we will describe commonly used social networks, provide a general history of social networks, and list the most famous social networks in the world and Czech, as well as describe their main histories. Next, we are going to specify the main topic groups in which we can divide the social networks. The second aim is to introduce how we can use social networks for business and marketing intensions, including the techniques used by marketing employees. In the next part, we are going to discuss development trends from the users' perspective and we will also focus on main trends, which are important for the firms operating on social networks. In the last theoretical part, we will describe risks and hazards on social networks. In the practical part, we will demonstrate how people share their own private information and what area of interest is the most useful for gathering this information.

As the fast development of mobile communication technology, mobile value added service (VAS) kept booming, the maturation of the mobile payment solutions also promoted the industry to become more consummate and profitable. As the cost of duplicating e-products is low while the return on investment is considerable high in this industry, many mobile VAS company are searching for bigger and challenging market such as China. The general aim of this thesis project is to provide practical solutions for these companies to successfully enter into the Chinese market. To fulfill this goal, the thesis studied behavior patterns of Chinese mobile VAS users by questionnaires, results indicated that variables and factors assessed in this study can significantly affect customers’ consume behaviours. Furthermore feasible business models and corresponding cooperative strategies were proposed and discussed in perspectives of value network, profitability and product control, which are tightly correlated to the commercialization of product. Moreover, this thesis introduced different marketing strategies to support the implementation of business models and introduced open and closed innovation conceptions for strategic development of mobile VAS companies’ growth in future.

As Twitter launched with their unique way of limiting posts to only 140 characters the usage of link shorteners was brought forth. This was the only way to fit long URLs in tweets until Twitter solved this by providing their own integrated link shortener. This study investigates how links are used on Twitter. The study include both care fulldata collection including multiple APIs and analysis of the collected data providing new insight into this topic. It was found that a small set of internet domains account for a large part of the links found in posted tweets. This set of top occurring domains did not necessarily reflect the top domains typically on common internet top lists. When looking at link shorteners in posted tweets we found that “bit.ly” was the most common one. Due to our method of collecting data we had the possibility of looking up the amount of clicks “bit.ly” links had received. We compared the click data to the amount of retweets the tweets containing these links had received and this led to some interesting discoveries regarding the ratio between these two.