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Nowadays, wireless sensor networks can be found in many new application areas. In these sensor networks there may exit a part of the network which are difficult to access or lie in a wide area, far apart. A change in the software (e.g., function update or bug fix) can entail reprogramming of all sensor nodes. This is very time consuming and labour intensive, if the patching has to be done manually for each individual sensor nodes. In the area of mobile phones, the over the air (OTA) update function has been established very well with good reliability. In embedded systems such as sensor nodes, where resources are severely restricted, an update cannot be stored but must be programmed directly with the transfer. For this to be possible, a lot of basic functionality is needed to be established to correct errors or to be able to resume a failed programming. Within the framework of this thesis a concept for the transmission and distribution of the firmware and programming the sensor node is established. Focus here is to optimize the use of resources and to provide basic functionality within the programming mode.

The thesis evaluates the results of experimental protocol involving the fertility sparing treatment procedure in early stage cervical carcinoma (LAP I protocol). Sentinel lymph node detection and experimental extirpation of afferent channels using laparoscopy and its technical aspects were analysed in prospective group of 85 women. The oncologic results and early/late morbidity show that established surgical procedures can be considered safe with minimal morbidity, provided that the indication criteria are met. The second part analyses the results of 148 women with no further pregnancy plans suffering from cervical tumors less than 2 cm in size with invasion less than half of the stroma (LAP II protocol). The oncological results in our defined group are very good and comparable to 'standard' procedure of modified radical hysterectomy type B or C with lower morbidity. In the separate section the thesis analyses the possibilities of laparoscopy in endometrial cancer treatment including the potentials of use of sentinel lymph node detection and technical aspects of laparoscopy in obese women. Currently the biggest controversy is the use of laparoscopy in malignant ovarian tumors. Our oncogynaecological study group at FN Motol prefers the laparotomic approach and we chose to include the set of advanced...

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Knowledge Graphs are fascinating concepts in machine learning as they can hold usefully structured information in the form of entities and their relations. Despite the valuable applications of such graphs, most knowledge bases remain incomplete. This missing information harms downstream applications such as information retrieval and opens a window for research in statistical relational learning tasks such as node classification and link prediction. This work proposes a deep learning framework based on existing relational convolutional (R-GCN) layers to learn on highly multi-relational data characteristic of realistic knowledge graphs for node property classification tasks. We propose a deep and improved variant, Deep-RGCNs, with dense and residual skip connections between layers. These skip connections are known to be very successful with popular deep CNN-architectures such as ResNet and DenseNet. In our experiments, we investigate and compare the performance of Deep-RGCN with different baselines on multi-relational graph benchmark datasets, AIFB and MUTAG, and show how the deep architecture boosts the performance in the task of node property classification. We also study the training performance of Deep-RGCNs (with N layers) and discuss the gradient vanishing and over-smoothing problems common to deeper GCN architectures.

In recent years, knowledge graphs(KGs) have become essential tools for visualizing concepts and retrieving contextual information. However, constructing KGs for new and specialized domains like Positive Energy Districts (PEDs) presents unique challenges, particularly when dealing with unstructured texts and ambiguous concepts from academic articles. This study focuses on various strategies for constructing and inferring KGs, specifically incorporating entities related to PEDs, such as projects, technologies, organizations, and locations. We utilize visualization techniques and node embedding methods to explore the graph's structure and content and apply filtering techniques and t-SNE plots to extract subgraphs based on specific categories or keywords. One of the key contributions is using the longest path method, which allows us to uncover intricate relationships, interconnectedness between entities, critical paths, and hidden patterns within the graph, providing valuable insights into the most significant connections. Additionally, community detection techniques were employed to identify distinct communities within the graph, providing further understanding of the structural organization and clusters of interconnected nodes with shared themes. The paper also presents a detailed evaluation of a question-answering system based on the KG, where the Universal Sentence Encoder was used to convert text into dense vector representations and calculate cosine similarity to find similar sentences. We assess the system's performance through precision and recall analysis and conduct statistical comparisons of graph embeddings, with Node2Vec outperforming DeepWalk in capturing similarities and connections. For edge prediction, logistic regression, focusing on pairs of neighbours that lack a direct connection, was employed to effectively identify potential connections among nodes within the graph. Additionally, probabilistic edge predictions, threshold analysis, and the significance of individual nodes were discussed. Lastly, the advantages and limitations of using existing KGs(Wikidata and DBpedia) versus constructing new ones specifically for PEDs were investigated. It is evident that further research and data enrichment is necessary to address the scarcity of domain-specific information from existing sources.

In recent years, the security of many corporate networks have been compromised by hackers who managed to obtain important information by leveraging the vulnerabilities of those networks. Such attacks can have a strong economic impact and affect the image of the entity whose network has been attacked. Various tools are used by network security analysts to study and improve the security of networks. Attack graphs are among these tools. Attack graphs are graphs that show all the possible chains of exploits an attacker could follow to access an important host on a network. While attack graphs are useful for network security, they may become hard to read because of their size when networks become larger. Previous work tried to deal with this issue by applying simplification algorithms on graphs. Experience shows that even if these algorithms can help improve the visualization of attack graphs, we believe that improvements can be made, especially by relying on Machin Learning (ML) algorithms. Thus, the goal of this thesis is to investigate how ML can help improve the visualization of attack graphs and the security analysis of networks based on their attack graph. To reach this goal, we focus on two main areas. First we used graph clustering which is the process of creating a partition of the nodes based on their position in the graph. This improves visualization by allowing network analysts to focus on a set of related nodes instead of visualizing the whole graph. We also design several metrics for security analysis based on attack graphs. We show that the ML algorithms in both areas. The ML clustering algorithms even produce better clusters than non-ML algorithms with respect to the coverage metric, at the cost of computation time. Moreover, the ML security evaluation algorithms show faster computation times on dense attack graphs than the non-ML baseline, while producing similar results. Finally, a user interface that permits the application of the methods presented   in the thesis is also developed, with the goal of making the use of such methods easier by network analysts. / Under de senaste åren har säkerheten för många företagsnätverk äventyrats av hackare som lyckats få fram viktig information genom att utnyttja sårbarheterna i dessa nätverk. Sådana attacker kan ha en stark ekonomisk inverkan och påverka bilden av den enhet vars nätverk har angripits. Olika verktyg användes av nätverkssäkerhetsanalytiker för att studera och förbättra säkerheten i nätverken. Attackgrafer ät bland dessa verktyg. Attackgrafer är diagram som visar alla möjliga kedjor av utnyttjande en angripare kan följa för att komma åt en viktig värd i ett nätverk. Även om attackgrafer är användbara för nätverkssäkerhet, kan de bli svåra att läsa på grund av deras storlek när nätverk blir större. Tidigare arbete försökte hantera detta problem genom att tillämpa förenklingsalgoritmer på grafer. Erfarenheten visar att även om dessa algoritmer kan hjälpa till att förbättra visualiseringen av attackgrafer tror vi att förbättringar kan göras, särskilt genom att förlita sig på Machine Learning  (ML) algoritmer. Således är målet med denna avhandling att undersöka hur ML kan hjälpa till att förbättra visualiseringen av attackgrafer och säkerhetsanalys av nätverk baserat på deras attackgraf. För att nå detta mål fokuserar vi på två huvudområden. Först använder vi grafklustering som är processen för att skapa en partition av noderna baserat på deras position i grafen. Detta förbättrar visualiseringen genom att låta nätverksanalytiker fokusera på en uppsättning relaterade noder istället för att visualisera hela grafen. Vi utformar också flera mätvärden för säkerhetsanalys baserat på attackgrafer. Vi visar att ML-algoritmerna är lika effektiva som icke-LM-algoritmer inom båda områdena. Klusteringsalgoritmerna ML producerar till och med bättre kluster än icke-ML-algoritmer med avseende på täckningsvärdet, till kostnaden för beräkningstid. Dessutom visar ML säkerhetsutvärderingsalgoritmerna snabbare beräkningstider på täta attackgrafer än icke-ML baslinjen, samtidigt som de ger liknande resultat. Slutligen utvecklas också ett användargränssnitt som tillåter tillämpning av metoderna som presenteras i avhandlingen, med målet att göra användningen av sådana metoder enklare för nätverksanalytiker.

Mobile computing is becoming increasingly important, due to the rise in the number of portable computers and the desire to have continuous network connectivity to the Internet, irrespective of the physical location of the node. We have also seen a steady growth of the market for wireless communication devices. Such devices can only have the effect of increasing the options for making connections to the global Internet. The Internet infrastructure is built on top of a collection of protocols called the TCP/IP protocol suite. Transmission Control Protocol (TCP) and Internet Protocol (IP) are the core protocols in this suite. There are currently two standards: one to support the current IPv4 and one for the upcoming IPv6 [1]. IP requires the location of any node connected to the Internet to be uniquely identified by an assigned IP address. This raises one of the most important issues in mobility because, when a node moves to another physical location, it has to change its IP address. However, the higher-level protocols require the IP address of a node to be fixed for identifying connections. The Mobile Internet Protocol (Mobile IP) is an extension to the Internet Protocol proposed by the Internet Engineering Task Force (IETF) that addresses this issue. It enables mobile devices to stay connected to the Internet regardless of their locations, without changing their IP addresses and, therefore, an ongoing IP session will not be interrupted [2, 3, 4]. More precisely, Mobile IP is a standard protocol that builds on the Internet Protocol by making mobility transparent to applications and higher-level protocols like TCP. However, before Mobile IP can be broadly deployed, there are still several technical barriers, such as long handover periods and packet loss that have to be overcome, in addition to other technical obstacles, including handover performance, security issues and routing efficiency [7]. This study presents an investigation into developing new handover mechanisms based on link layer information in Mobile IP and fast handover in Mobile IPv6 environments. The main goal of the developed mechanisms is to improve the overall IP mobility performance by reducing packet loss, minimizing signalling overheads and reducing the handover processing time. These models include the development of a cross-layer handover scheme using link layer information and Mobile Node (MN) location information to improve the performance of the communication system by reducing transmission delay, packet loss and registration signalling overheads. Finally, the new schemes are developed, tested and validated through a set of experiments to demonstrate the relative merits and capabilities of these schemes.