Payment channels allow parties to utilize the blockchain to send transactions for a cheaper fee. Previous work has analyzed to which degree a party can profit by facilitating the transaction process. The aim is to increase the usability of the network and to be rewarded for providing this service. However, previous work focuses on maximizing the reward of the individual player in isolation, a model that we aim to expand. That is why in this work we extend the action space to allow other parties to act and react, and observe the impact this has on the rewards of the player that would otherwise act in isolation.
Testing existing placement strategies by performing channel placement games, we can assess the difference in the reward that indicates the potential loss that competition may cause when operating in the Bitcoin Lightning Network.
Furthermore, we have developed a new strategy that is able to improve the performance in the multi-actor model.

With the increasing demand for high- quality data in the field of Machine Learning and AI, the availability of such data has become a major bottleneck for further advancements. This paper proposes a novel approach to extract valuable data from comic illustrations, aiming to address the scarcity of labeled datasets. By leveraging popular comic series such as Dilbert, which contain thousands of comic strips with multiple panels, text boxes, characters, and settings, we aim to create a pipeline for data labeling and manipulation. This pipeline will enable experiments in various areas, including generative comics, humor detection, translation, and more. The paper focuses on two key research questions: 1) How accurately can we get current OCR models to extract text from the comics, and 2) How can we create the ability to edit and delete existing text boxes. By accurately segmenting the panels and text boxes within the comics, we expect to improve OCR performance by reducing noise and addressing unique text formats. Object detection models will be employed to zoom into text boxes, further enhancing OCR text extraction accuracy. Evaluation metrics such as Latent Dirichlet allocation (LDA), Character Error Rate (CER), and Word Error Rate (WER) will be used to measure the effectiveness of the proposed techniques. In the end, utilizing a dataset of 500 labelled comic panels, we achieve accuracies of 94.07% for CER (up 9.86% from 84.21% baseline), 88.35% for WER (up 8.35% from 80.0% baseline), and 98.0% for LDA (up 3.77% from 94.23% baseline). Similarly, editing and deleting of text boxes inside the comic panels prove to be successful in a vast majority of instances. We believe these results are more than adequate for select use cases.

Federated learning enables the construction of machine learning models, while adhering to privacy constraints and without sharing data between different devices. It is achieved by creating a machine learning model on each device that contains data, and then combining these models through an aggregation algorithm without sharing the data. Federated learning is currently a hot topic, and a lot of research has gone into implementing accurate aggregation algorithms. The original algorithm is FedAvg, and since then many different algorithms have been introduced. In this paper, I will compare the performance of five different aggregation algorithms: FedAvg, FedProx, FedYogi, FedMedian and q-FedAvg. The algorithms are compared on different data sets, namely MNIST and a kinase inhibition data set, as well as on different data distributions and number of clients. The experiments indicate that among these five algorithms, FedYogi achieves the best performance, both in terms of highest final accuracy as well as in terms of convergence rate.

Federated learning (FL) has emerged as a promis-ing approach for training machine learning models using geographically distributed data. This paper presents a comprehensive comparative study of var-ious machine learning models in the context of FL. The aim is to evaluate the efficacy of these models in different data distribution scenarios and provide
practical insights for practitioners in the field. The findings highlight the performance and limitations of linear and non-linear models on MNIST and Ki-nase datasets.

Synthetic art is the end result of artificial intelligence models that have been trained to generate images from text prompts. "Comic synthesis" is one such use case, where comic illustrations are produced from textual descriptions. Previous attempts at comic synthesis have utilized conditional Generative Adversarial Networks (cGANs), but this approach has encountered challenges in generating consistent and visually appealing comic panels. Strict data requirements and quality limitations have left room for improvement. We propose a novel approach to comic synthesis using Stable Diffusion, a powerful generative modelling technique. The study investigates the fine-tuning of the stable diffusion model specifically for the generation of Dilbert Comics from textual prompts. We explore different techniques to fine-tune the stable diffusion model for comic synthesis including Dreambooth and LoRA. Through extensive experimentation and analysis, with an FID score of 123, results produced using the Lora technique outperformed Dreambooth, excelling in understanding the Dilbert style, while Dreambooth struggled with multiple-subject training. Results are also compared with previous approaches based on conditional GANs. While the quality and detail greatly improved, the transition from conditionals to text descriptions meant the results were less accurate. The results show the potential of stable diffusion in generating appealing Dilbert Comic panels while highlighting the need for further exploration to enhance the alignment between textual descriptions and the generated images.

Federated Learning is a machine learning paradigm for decentralized training over different clients. The training happens in rounds where each client learns a specific model which is then aggregated by a central server and passed back to the clients. Since the paradigm’s inception, many frameworks that provide Federated Learning tools and infrastructure have appeared. This leads to the question of ”How do different Federated Learning frameworks compare?”, which is the research question of this paper. The paper’s main contribution will be helping developers new to the Federated Learning field decide between NVidia Flare, OpenFL, and Flower, three popular federated learning frameworks.

Tabular data is widely used in various fields and applications, making the synthesis of such data an active area of research. One important aspect of this research is the development of methods for privacy-preserving data synthesis, which aims to generate synthetic data that retains statistical properties while protecting the privacy of individuals in the dataset. Recently, Diffusion Generative Models, such as Gaussian Diffusion Model, have significantly improved image synthesis, but their effectiveness in synthesizing tables is limited, because of using One-Hot encoding for representing categorical attributes with many categories. Furthermore, it needs the private data to be centrally collected for training, thus violating the privacy-preserving criteria. In this paper, we propose a new decentralized tabular synthesizing framework, which has three key features: (i) a decentralized Autoencoder comprised of an encoder and a decoder to map discrete features into the continuous space and back, (ii) a tabular diffusion model trained in a decentralized manner and (iii) incorporating differential privacy on central stochastic gradient training. We conduct extensive experimental studies that focus on sampling quality and diversity, using 9 tabular datasets and 4 state-of-the-art synthesizers. The results show that our method outperforms existing central methods by 10.7% and 31.4% in data quality and diversity on average, and 6.8% and 21.1% in data quality and diversity in scenarios facing non-IID data.

Quantitative cardiac MRI is an increasingly important diagnostic tool for cardiovascular diseases. Yet, it is essential to have correct image registration for good accuracy and precision of quantitative mapping. Registering all baseline images from a quantitative cardiac MRI sequence, however, is nontrivial because the patient is moving, leading to simultaneous changes in motion, intensity, and contrast. The changes in image contrast, in particular, make it challenging to design a reliable registration metric for optimization.
In this paper, we propose a novel approach based on robust principle component analysis (rPCA) that decomposes quantitative cardiac MRI into low-rank and sparse components, in combination with a groupwise CNN-based registration backbone. The proposed framework aims for fast, robust motion correction for contrast-agnostic sequences, which benefits registration. We evaluated our proposed method on cardiac T1 mapping sequences, both pre-contrast and post-contrast. Additionally, we synthesize the numerical phantoms with gold standard to test the performance. Our experiments showed that our method effectively improved registration performance over baseline methods without rPCA, and reduced quantitative mapping error in both in-domain and out-of-domain MRI sequences. The proposed rPCA framework is generic and can be easily incorporated into existing registration methods and other clinical applications.

Data processing systems have become increasingly important in modern computing, as the volume and complexity of data that needs to be analyzed has grown dramatically. Multiple data processing systems have been and are being developed, that are scalable, resilient and performant.

However, despite the advances made in data processing technology, there are still challenges that need to be addressed in order to optimize the performance, energy efficiency as well as the practical- ity of these systems. One such challenge is the need to effectively manage the underlying system’s resources, including the system’s throughput and the amount of work that each operator has to do and to use optimal data-structures that would lead in faster task processing speeds.

To address this challenge, this thesis proposes the implementation of a high-performance data processing system that exposes the underlying system’s metrics to the application level and applys an innovative way for operator communication, by utilizing an efficient thread-safe data structure. By providing underlying system’s metrics to the application’s scheduler, the scheduler can schedule the tasks optimally according to the current system’s state and adjust the system’s resources during run- time. This alleviates the developers from having to fine-tune the system beforehand and allows the system to tackle fluctuating input workload more efficiently.

This thesis will explore the design and implementation of such system, as well as its impact on the performance, energy-efficiency and resiliency of data processing applications. We provide perfor- mance measurements as well as a qualitative comparison of our system compared to other state-of-the art systems, proving our hypotheses.

Blockchain technology has proven to be a promising solution for decentralized systems in various industries. At the core of a blockchain system is the peer-to-peer (P2P) overlay, which facilitates communication be- tween parties in the blockchain system. Recently, there is increasing evidence that this P2P overlay plays a major role in limited transaction throughput, limited scalability, and security threats in a blockchain system. In this work, it is explained that this bottleneck is caused by the fact that currently deployed P2P overlays cannot fully capture the complexity of a blockchain system. We propose a novel approach to address these issues by introducing a novel overlay architecture. Our approach, Sovereign Domains P2P Overlays, allows for nodes in a single network to belong to multiple heterogeneous groups (called domains), each with their own set of protocols tailored to the characteristics and needs of the nodes in that domain. To demonstrate the effectiveness of the proposed overlay architecture, two novel node discovery protocols, FedKad and SovKad, were designed and implemented. The simulations conducted in a custom-built simulator show that SovKad outperforms node discovery in a Structured P2P Overlay (Kademlia) and node discovery in a Federated P2P Overlay (FedKad), providing evidence of the potential of the Sovereign Domains P2P Overlay architecture in blockchain systems.

The use of Internet of Things (IoT) devices has experienced an increase since its inception and is expected to continue to do so. However, this growth has also attracted individuals with malicious intentions. Botnet attacks on IoT devices have become more potent each year, exploiting new vulnerabilities and attacking more devices. Therefore, it is imperative to improve countermeasures. N-BaIoT is a frequently used dataset that covers botnet attacks in various stages of the botnet life cycle. Nevertheless, when examining the state-of-the-art utilizing the dataset, there are certain limitations that need to be addressed.

One limitation is the lack of detailed feature analysis in most studies. This results in less comprehension of the behavior of the malicious and benign data in the dataset, leading to a lack of feature optimization. Feature optimization is crucial as it improves the computational time of the model and makes it efficient to deploy in real-life applications. Another limitation is the uneven distribution of the malicious and benign data, resulting in unreliable evaluation scores. This issue has not been addressed in many studies.

The main contribution of this thesis is the development of a hybrid ensemble model to detect IoT botnet attacks faster and accurately. Additionally, the aim of this study is to provide a clear analysis of the behavior of the malicious and benign data and optimize the number of selected features. In the hybrid ensemble model, a minimal number of features will be utilized to evaluate performance. The comparison will be achieved by taking into account both the unbalanced and balanced datasets used for training and testing. By considering both datasets, the limitation of the distribution can be highlighted by examining the distinct performance of each dataset, making the comparison extensive and reliable.

The results indicate that the selected features and detection models proposed in this research outperform those of other studies. In both cases using the balanced and unbalanced sets, the performance score and computational time are improved. In addition, this is achieved by using fewer features compared to several studies.

As the amount of information available in the world grows, Information Retrieval (IR) systems have become an integral part of day to day life. They determine what subset of the large pool of information is shown to people. IR algorithms determine which items should be returned in response to a query and rank the results in a ranked list.
Recently, concerns about the fairness of IR algorithms have surfaced. In particular, research is being done into whether IR algorithms are fair to producers, people or organizations that provide the items that are retrieved by IR algorithms. The higher an item is in the ranked list, the more attention it receives from users. This attention translates to benefits for the producers, e.g. fame or financial compensation.
In this thesis we investigate the fairness of IR algorithms in terms of a specific measure for provider fairness: the Expected Exposure Loss (EEL). This measure measures whether the providers of equally relevant items receive the same amount of attention in expectation. EEL was first proposed as part of the 2020 TREC Fair Ranking track (FAIR-TREC), which also provided a matching dataset. We investigate for two IR systems whether they achieve fairness on this dataset. We conduct a failure analysis and propose improvements for both systems.
We find that for a system that always returns the same ranking it is not useful to improve its accuracy, but rather that it benefits most from fairness-aware post-processing. By contrast, a fairness-aware systems does benefit from a higher accuracy, since EEL requires that equally relevant items are treated the same. We note that the generalizability of our investigation is limited due to the small size of the FAIR-TREC 2020 dataset and recommend that a larger dataset be made available.

Large­scale machine learning frameworks can accelerate training of a neural network by per­ forming distributed training on a cluster using multiple GPUs per node and multiple nodes. Because distributed training on a cluster involves many nodes which need to communicate and load and exchange data, a machine learning framework may at certain times during training not fully utilize the available hardware of the system. Various techniques are as­ sessed in their capability to measure the performance of specific parts of the hardware of a cluster. We present ML Board, a tool that measures and visualizes the utilization of the system while training a neural network model using some of the previously assessed tech­niques, and does so without requiring any changes to the used machine learning framework. ML Board can be used to identify straggling nodes, and by subsequently letting the user select different nodes using the Slurm job scheduler, can help to decrease the training time of a ResNet model by between 15 and 45% when using an ImageNet or CIFAR­10 dataset. Furthermore, the energy used by the GPUs can be measured and used to identify and replace GPUs to reduce the total used energy by between 5 to 16%.

Modern systems generate a tremendous amount of data, making manual investigations infeasible, hence requiring automating the process of analysis. However, running automated log analysis pipelines is far from straightforward, due to the changing nature of software ecosystems caused by the constant need to adapt to user requirements. In practice, these are comprised of a series of steps that collectively aim at turning raw logs into actionable insights. The first step is log parsing which aims to abstract away from raw logs toward structured information. Log parsing is paramount, as it influences the performance of all subsequent downstream tasks that rely on its output. Although previous works have investigated the performance of log parsing, given the increase in data heterogeneity witnessed over the past decades, the validity of current estimates is questionable, as there is a lack of understanding of how log parsing methods perform in modern contexts. Consequently, we investigate the field and, in the process, we discover that misleading metrics are adopted, which produce incomplete performance estimates. Furthermore, motivated by an industry use case within the infrastructure of a large international financial institution, we discover that the current log parsing paradigm is not aligned with what is required in practice. Consequently, to address these current limitations, in this work we contribute with the following. We (1) evaluate the field of log parsing, (2) propose a new log parsing paradigm and create a benchmark dataset to facilitate future research, and (3) propose and evaluate a machine learning model that solves log parsing within the new paradigm.

This paper analyzes how flocking behavior in fish can be used to develop target protection algorithms. This starts from the hypothesis that fish aggregate into coordinated flocks in order to protect themselves from predatory attacks. In order to test the protection capabilities of fish, a Prey-Predator instance is developed in which faults are introduced. Prey fish try to protect their faulty flock-mates and themselves from predatory attacks while Predators hunt the fish in order to stay alive. The simulated fish are developed using Boids that emerge in a 70 prey versus 7 predators ecosystem [4]. Results are then extracted using multiple attacking and protection strategies as well as different faulty Boid configurations.
The results initially show no improvement when flocking around faulty prey but when a genetic approach is introduced, the prey gains a clear advantage against the predators. This implies that fish flocking (as opposed to individualistic behavior) is an optimal protection strategy against attacks and could be used in other instances such as military operations or agriculture automation.

One of the key problems of swarm robotics is how the mobile robots can navigate accurately in a given environment. To achieve this, the mobile robots need to accurately determine where they are globally, or relative to other robots and landmarks. This paper is going to be an investigation of the Monte Carlo Localization algorithm in an environment containing 3 anchors. In addition, an alternative modification is suggested to this localization algorithm to improve the localization performance. An experiment is devised to assess different aspects of localization performances of these algorithms. With the experimental results, a quantitative analysis of performance figures are compared and analyzed.

A majority of existing single-anchor localization algorithms make use of antenna arrays or special antenna systems. However, the need for specialized antenna systems incurs higher costs, complexity and power consumption. This paper presents a novel single-anchor localization algorithm, which does not require antenna arrays or special antennas. The algorithm is implemented in a two-robot system, where one of the robots acts as the anchor and the other acts as the target. The localization algorithm uses velocity measurements of each robot and distance measurements between robots. Using the change in distance between the robots and the velocity of the target robot relative to the anchor robot, the target robot can be localized relative to the anchor robot. The localization algorithm uses a Kalman filter as a state estimator of the movement of the target robot and a Savitzky-Golay filter to filter the distance measurements. A simulation with Gaussian noise in the distance measurements is performed to display the impact of the filtering methods. Another simulation showed an average localization error of 1.38 meters when the distance measurements are acquired through RSSI measurements, which is comparable to existing RSSI-based localization methods.

In this paper, we explore the creation of control algorithms for swarms of robots playing the role of either predator or prey in an environment filled with static obstacles. The paper devel- ops on a famous flock simulation model proposed by Craig Reynolds called boids. The paper analyzes a zero-sum game situation, in which one swarm of robots, the prey, is trying to reach a certain pre-determined target, while another swarm of robots, the predator, is trying to prevent it from reaching its objective. Swarm control algorithms for both the predator and the prey scenarios are analyzed in an arms race manner. The robots are modelled as point-mass holonomic entities, that can move in arbitrary directions. The proposed algorithms are tested on characteristics such as success rate and time in a simulated environment. As a result, a set of algorithms for both the predator and prey are proposed and their strength and weaknesses are discussed.

Robotic swarms provide a great many uses within a world increasingly relying on autonomous systems. Alas these swarms are also very vulnerable to faults, even the smallest fault can cripple the performance of a whole swarm. Such a fault could be one of several types; those that have to do with sensors, communication, ... This paper will try to discover the effect that these faults have on the performance of a robotic swarm attempting to complete a specified task, e.g. a multiple destination routing problem or a variant of a foraging task. With the experiments concluded and the data gathered it proved possible to observe correlations between fault and performance, but this dependency heavily changes based on the algorithm used and fault introduced.

Serverless computing is a relatively recent paradigm that promises fine-grained billing and ease-of-use by abstracting away cloud infrastructure for developers. There is an increasing interest in using the serverless paradigm to execute data analysis tasks. Serverless functions often interact with external services, which can be considered similar to the concept of side-effects in regular programming. Haskell uses monads to isolate side-effects and to structure the composition of functions using side-effects. This thesis explores whether the concept of the monad can be applied in a serverless computing environment, ideally in a way that is flexible with regards to platform. An abstraction of side-effects was developed in the form of a monadic layer that is added to serverless functions. The monadic layer interacts with monads using an interface and exposes the API of the monads to the user. A monadic implementation was also created for a platform-independent function composition mechanism using orchestrator functions. An implementation of a shared state side-effect has also been created as a practical use-case for the monadic layer, and to explore the usability of monads on platforms with more restricted composition frameworks. The implementations are evaluated on performance, expressiveness and usability.