This study investigates the use of Multi-Objective Natural Evolution Strategies (MONES) to optimise water management control policies in the Nile River Basin, focusing on four key objectives: minimising irrigation deficits for Egypt and Sudan, maximising hydropower production for Ethiopia, and maintaining water level in the High Aswan Dam (HAD). The developed Nile River simulation was integrated with MONES and used to train an agent for making release decisions. Performance metrics including hypervolume, epsilon-indicator, and Inverted Generational Distance Plus (IGD+) were employed to compare MONES with the EMODPS baseline. The results indicate that while MONES identifies feasible solutions, it falls short in exploration and overall performance compared to EMODPS. The study contributes to the development of water management strategies by open-sourcing Nile River framework compatible with reinforcement learning and demonstrating the potential and limitations of MONES in multi-objective optimisations.

Efficient management of water resources is increasingly critical in the face of growing challenges such as climate change and population growth. This research paper introduces RL4Water, an adaptable framework for simulating water management systems using multi-objective reinforcement learning (MORL). Adhering to the Gymnasium API standard, RL4Water ensures seamless integration with existing MORL algorithms. The framework includes diverse facility classes to accurately model the physical components of water networks. Its generalizability is enhanced by allowing users to modify both the physical properties of these components and the key features of the MORL simulations. RL4Water's capabilities are demonstrated through two case studies: simulations of the Nile River and the Susquehanna River, validating its accuracy and flexibility in managing both large, distributed water systems and centralized systems with complex reservoirs. By bridging the gap between water management and reinforcement learning, RL4Water offers a unified platform for developing and researching water management simulations.

Water management systems (WMSs) are complex systems in which often multiple conflicting objectives are at stake. Reinforcement Learning (RL), where an agent learns through punishments and rewards, can find trade-offs between these objectives. This research studies three case studies of WMS simulations in the context of RL problems and notes their similarities and differences. Based on these, core properties of WMSs are defined and used to formulate a general WMS as a RL problem. This bottom-up approach uses Gymnasium to implement the RL problem. The result is compared to a simulation from one of the case studies and produces the same results. While maintaining this level of accuracy, it is applicable to a much wider range of WMSs. It thereby contributes to generalisation of WMSs in the context of RL, and removes the need to rewrite simulations each time.

Poor battery life is one of smartphone users’ top frustrations about their devices. This fact, in combination with the limited supply of battery minerals, the working
conditions of mining, and its environmental impact, has led to high interest in reducing smartphone energy consumption. Smartphone manufacturers have introduced power-saving features on their products and guide developers to use energy- efficient software engineering practices.

In literature, the increased awareness of the need to reduce energy consumption and reduce emissions has led to the birth of a Green Software research field. Focusing on mobile software, prior research has focused on quantifying energy use and finding instances of software using more energy than is reasonable for its intended purpose. However, the ubiquitous power-saving features of smartphones have hardly been studied until now. In particular, bugs stemming from Android’s Battery Saver mode, a power-saving technology introduced in 2017 by Google, have not been studied at all. This thesis aims to address this research gap.

To do so, we first characterize these issues by systematically collecting documentation pages, bug reports, and forum questions relating to these bugs. We find 13 separate problems, most of which (9 out of 13) have considerable user impact. Additionally, most problems are reported multiple times independently (mean re- porting frequency = 4.1) outside of Google documentation. Four of the problems have never been officially documented.

Driven by this characterization, we build a static analysis tool that detects one of the characterized issues. It builds a Conditional Call Graph to find invocations of the implicated Application Programming Interfaces (APIs) without asserting this API is available. The tool is fast and does not require source code to be available. It runs on the Java Virtual Machine for portability.

To evaluate the tool, we use a two-pronged approach. We first determine the ground truth for all 1,472 Google Play Store applications that are also available in the FDroid repository. We write a script to identify all suspicious API invocations, the bug candidates. We find and attempt to reproduce 178 of these bug candidates. We manually review all candidates to determine the ground truth. We evaluate our tool using the same data set. The tool reaches a precision of 0.911 and a recall of 0.911 and identifies 41 reproducible issues.

Lastly, we report the reproduced issues identified by the tool to the developers of the affected applications. To date, nine issues have been confirmed by the developers, and two issues have already been addressed.

This paper explores the application of landmark-based planning algorithms, specifically focusing on AND/OR landmark extraction methods. Drawing from classical planning principles and recent advancements, we investigate the effectiveness of landmark extraction in guiding the search for solutions to planning problems. Our research questions center on identifying effective domains for landmark extraction, assessing the utility of extracted landmarks, and comparing our implementation with existing literature. Utilizing the Symbolicplanners.jl framework, we implement AND/OR landmark extraction and evaluate its performance across various domains. Due to challenges in implementation, the landmarks we were able to extract had limited meaning. We propose future work to refine the AND/OR method and expand our analysis to include the Hm procedure.

The Fast Downward planning system is currently mainly used for solving classical problems. Another alternative to Fast Downward is SymbolicPlanners, which sacrifices speed for generality and extensibility. SymbolicPlanners is missing landmark based planners and landmark extraction algorithms. The research question we are trying to answer in this research paper is: What design choices can be made to adapt the forward propagation extraction algorithm into SymbolicPlanners?
The forward propagation landmark generation design choices are discussed and implemented in SymbolicPlanners. The runtime performance of the implementation is only about two times slower than the Fast Downward implementation. Another aspect of the implementation is the incorrect amount of landmarks generated in complex problems caused by limitation in the relaxed planning graph from SymbolicPlanners.

A lot of research has been conducted to make the task of plan generation more efficient. One idea to do so is the use of landmarks, which are sub-goals that must be true in every solution to the problem. The approximation of landmarks has a lower complexity than solving the task itself, and they can be used to guide the planner in the right direction.
In previous work, ideas to order landmarks are proposed and compared to algorithms that do not use them. We verify if this comparison is fair by testing both algorithms implemented in the same language and framework. In our experiment not many problem instances finish in time, but those that do are in line with previous experiments in that on average planners using landmarks produce longer solutions than planners that do not use them.

Landmarks are propositions or actions that must be true at some point in every valid solution plan [16]. Using landmarks, planners can develop solutions more efficiently. Different algorithms exist to extract landmarks from a planning problem. The one used in this study is FULL [13], a landmark extraction algorithm by Marzal et al. from 2011.
In this research, the performance of the FULL algorithm is analysed by comparing the total number of landmarks found to two other landmark extraction algorithms, namely forward propagation by Zhu and Givan [22] and backward propagation by Porteous et al. [16].
The original FULL algorithm is slightly modified, by removing orderings and disjunctive landmark extraction. FULL is implemented using Julia and was run on five different domains from the International Planning Competitions.
All of these domains are logical and 15 problems were randomly selected from them.
FULL managed to extract more landmarks in two out of the five domains, Grid and Logistics, compared to the two aforementioned algorithms.
In the three other domains, FULL matched the number of landmarks found by the best out of the two. The two domains where FULL performed well, were both transportation domains and this is where FULL's performance excels.
Runtime was not an issue when extracting landmarks in four of the five domains. Freecell consistently exceeded the timeout put in place, likely due to a bug.
Furthermore, a higher number of landmarks is also a desired outcome due to its use in planners, either as heuristics or intermediary goals.

Algorithmic planners occasionally waste effort and thus computing time trying to solve certain tasks, as they often lack the human ability to recognize essential paths. These essential paths, termed landmarks, are vital for optimizing planning processes. This study revisits landmark-based planning methods introduced by Richter, Helmert, and Westphal in their 2008 paper, adapting and implementing them within a different framework, SymbolicPlanners, using the Julia programming language. The primary research question explores the performance of using landmarks as intermediary goals and pseudo-heuristics in the SymbolicPlanner framework. Sub-questions delve into the effectiveness of specific planning strategies, such as A^∗ Planner with GoalCount and HAdd heuristics, as well as planners utilizing landmarks. Evaluation over diverse domains reveals that LM^Local  and LM^LocalSmart outperform the basic GoalCount
heuristic and are on par with the HAdd heuristic. LM^Count, despite solving fewer instances, exhibits speed improvements over GoalCount in the instances that they both solve. Discussion highlights limitations, such as the non-exhaustive interference check in LM^LocalSmart and limiting factors in the SymbolicPlanner framework.

The spread of fake news has negatively impacted society. Prior efforts in Natural Language Processing (NLP) have employed machine learning models and Pre-trained Language Models (PLMs) like BERT to automate fake news detection with promising results. These models excel at text classification tasks, but their dependence on large context-specific datasets presents a hurdle in the dynamic and ever-evolving context of fake news. The recent emergence of Large Language Models (LLMs) offers a potentially transformative innovation. LLMs have demonstrated great promise in NLP tasks with little additional training data. Compared to PLMs, LLMs have broader knowledge and enhanced reasoning capabilities, suggesting their suitability for fake news detection. This study proposes an investigation which considers multiple perspectives to probe the applicability of the effectiveness, opportunities and challenges associated with leveraging LLMs for automated fake news detection. We leverage multiple state-of-the-art LMMs by using them at multiple levels of guidance to validate their accuracy and task-specific bias. The main contribution of this work is a better understanding of the role LLMs can play in automated fake news detection, the pitfalls that should be avoided when leveraging them, the most effective approaches when employing them, and the future challenges that need to be addressed. Our key contributions include a better understanding of how to employ LLMs for fake news detection, their strengths and weaknesses, and some practical recommendations for deploying them in the real world. Our work shows that while LLMs hold great potential for advancing automated fake news detection, thoughtful consideration of their limitations and careful application refinement are essential for their effective deployment in the fight against fake news.

Image inpainting is a problem that has been well studied over the last decades. In contrast, for 3D reconstructions such as neural radiance fields (NeRFs), work in this area is still limited. Most existing 3D inpainting methods follow a similar approach: they perform image inpainting on the training images and use the inpainted images for further training of the 3D model. Due to inconsistencies in the different inpaintings of the images, the 3D inpainting often becomes blurry. With the advent of 3D Gaussian Splatting (3DGS), we identify a new opportunity for 3D inpainting. As 3DGS is more explicit in nature than NeRF, we can manipulate the 3D Gaussians directly rather than relying on image inpainting. Based on that key idea, we propose a method that works similar to the PatchMatch image inpainting algorithm. We first construct a nearest-neighbour field (NNF) by searching for nearest-neighbour patches throughout the scene that look similar to the area we want to inpaint. After constructing the NNF we copy the contents of the nearest-neighbour patches to the inpainting region and blend them together to obtain the inpainting result. In our experiments we found that our method performs well in terms of texture synthesis but struggles with structure synthesis, similar to the original PatchMatch algorithm. In cases where only texture synthesis is required to inpaint the area our method is able to provide good results, although in some cases pre-processing of the scene is necessary, as we found that better quality inputs (e.g. the scene itself, the surface mesh underlying the scene, and precise masks) drastically improve the results of our method. Moreover, some parameters of the algorithm are highly scene-dependent and by tailoring them to the scene we can further enhance the performance of the algorithm. Besides introducing a 3D inpainting method that directly manipulates the scene contents, our work offers valuable new insights into 3DGS editing in general.

Through the expansion of large-scale service systems and the exponential growth of data generated by complex IT infrastructure components, gaining a comprehensive overview of the different levels of service within an IT system has become increasingly challenging. In particular, this brought to the fore the question from a large commercial bank of how IT monitoring data streams generated by their complex IT infrastructure can be associated with one another.

In more detail, the data from the monitoring stream consists (among other things) of a message and a time stamp. Moreover, the monitoring data stream of this bank consists of two natures of information. These natures are either automatically generated warnings in the form of events or unplanned outages, referred to as incidents. The events and incidents are referred to as arrivals. As a first requirement to obtain better granularity, both event and incident messages with similar semantics should be grouped together. To this extent, the message component from each arrival is transformed into a numerical vector, the dimension of the obtained vector is reduced, and the collection of vectors is clustered. Once the individual arrival from the IT monitoring data stream is attached to a cluster based on their message component, the arrival is assigned a mark. This mark consists of a combination of the assigned cluster, the nature, and three different levels of service from the IT architecture on which the arrival occurred.

From a mathematical point of view, we can now view the monitoring data stream from different levels of service as a marked point process. Our primary focus centers on a specific category of marked point processes, known as marked Hawkes processes. Given the marked Hawkes process, we assume that each arrival from the IT monitoring data stream results in an instantaneous increase in the probability of some other arrivals in the near future. From here, we estimate the excitation matrix, representing the instantaneous increases among all assigned marks. Once the estimated excitation matrix is obtained, we decompose it into the different levels of service as defined within the mark. In particular, the decomposition has been performed through means of hierarchical linear models. Finally, the decomposition resulted in a comprehensive overview of the excitation behavior in large-scale service systems. This overview can directly be incorporated into the field of Software Architecture in order to uncover associations within complex IT infrastructures.

We all know the possible consequences of global warming, rising temperatures, flooded cities and destroyed ecosystems. One of the causes is the emission of gases, predominantly CO2, which is increased by the growing E-commerce market. E-commerce companies rely on recommender systems to stimulate users to purchase products. We are convinced that we can use the core strength of recommender systems, influencing decision making, to steer users towards eco-friendly choices. Therefore, in this thesis, we research how greenness can be integrated into recommender systems. We present the first recommender system dataset that includes greenness, we benchmark several recommendation algorithms and we propose a strategy to increase recommendation greennness. To create the dataset, we annotate an existing recipe recommendation dataset with recipe greenness. For our benchmarking experiment, we propose metrics to measure recommendation greenness, which we use to show that no recommendation algorithm is fundamentally greener than others. Lastly, we propose a re-ranking method for improving the greenness of recommendation rankings. We use the method to explore the trade-off between accuracy and greenness and we show that it is possible improve the greenness of recommender systems significantly with little loss of accuracy.

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.

Common sense is knowledge that most humans have, but machines do not. Generally, computer knowledge bases make use of positive (known) knowledge. However, in addition to positive common sense knowledge, there is also negative. Negative knowledge represent facts that are known to be untrue, like "a cat does not have fins". This knowledge is important in order for a machine to make assumptions the same way a human does. This research proposes ways to combine and organize the positive and negative knowledge tuples in a unified way. The two main ways that are looked into are table-like and graph-like organizations. These are analyzed based on different requirements and important queries are defined. Based on the requirements a recommendation is made. The research also takes a look at a solution that makes use of complex number space and suggest how this solution could be further researched and improved in the future.

Search engines operate as an oracle between user queries and information access: the user types the input and receives back the information requested. To accomplish the task, search engines need to interpret human language and, most importantly, comprehend the underlying user intents of a query. With this process, they can retrieve the most appropriate sources of information. The purpose of our research is to introduce a new,
hierarchical taxonomy that better depicts the underlying intents of users asking questions online (on search engines and Q&A platforms). Throughout our study, we first review the prior work and findings on the topic. We assemble a new dataset with queries aggregated from MS Marco, AskReddit and Quora. We examine its questions and label them to construct a new fine-grained ontology. Our examination continues with the integration of
Deep Learning (DL) models and Active Learning (AL) to evaluate the quality of our work. The results show that the taxonomy can effectively assess users’ goals. Our taxonomy, the dataset composed and the codebase are publicly available to support future research.

Commonsense knowledge is a type of knowledge consisting of facts that humans use every day. Humans make queries in search engines with different user intents, and some of them can be answered by knowledge tuples. Different types of knowledge are stored differently in the knowledge bases. Being aware of the types of commonsense knowledge required to answer the queries can accelerate the process of finding corresponding knowledge for the search engines to give a response to users. For some queries with specific user intents, it is not possible to be answered solely with commonsense knowledge because some analysis and judgment from humans are needed. On the other hand, some queries can be answered with commonsense knowledge tuples and the user intents can have a strong indication of what the knowledge type is required to answer. The research is to look into how to map queries and their user intents to knowledge types and explore the impacts of user intents in the knowledge type classification. There was no existing dataset that had annotations on both user intents and knowledge types. In this work, the described dataset was created. Observations of the created dataset and experiments on three classifiers with accuracy being around 0.99 were conducted. The results show that user intents generally help the classification of the type of commonsense knowledge.

Commonsense knowledge plays a key role in human intelligence. It is knowledge possessed by most humans that helps them in everyday situations. One possible way is to store the knowledge in four types. Each piece is either positive or negative, and generative or discriminative. For efficient retrieval and storage, a uniform model is needed. Existing models for commonsense knowledge are not fit for negative and discriminative knowledge. The aim of this paper is to create a uniform model to store both positive and negative generative and discriminative knowledge tuples. Models are evaluated on a set of generalized queries as well as on the storage they require. Four possible models were evaluated of which two were the most promising: the generative model and the combined model. The generative model is efficient in storage and retrieving generative knowledge for concepts, but relatively slow in distinguishing concepts. Combining the generative model with discriminative tuples gives the combined model, a model that is the most efficient for all queries but expensive in storage. Which of the two models is most suitable depends on the application and the available resources.

Commonsense knowledge based question answer- ing is a recent topic that has seen a surge in inter- est. Yet most models obtain general data, this pa- per looks at obtaining query-specific similar con- cepts using first and second-order proximity to- gether with BERT-based retrieval. Using these query-specific concepts new commonsense knowl- edge can be obtained using a Game with a pur- pose. Results show that this current implementa- tion leaves room for improvement.