This study explores the possibilities of classifying language as governing or not. The ground premise is to examine how detecting and quantifying governing conditions from thousands of financial grants in appropriation directions can be performed automatically, as well as creating a data set to perform machine learning for this text classification task. In this study, automatic classification is performed along with an annotation process extracting and labelling data. Automatic classification can be performed by using a variety of data, methods and tasks. The classification task aims to mainly divide conditions into being governing of the conducting of the specific agency or not. The data consists of text from the specific chapter in the appropriation directions regarding financial grants. The text is split into sentences, keeping only sentences longer than 15 words. An iterative annotation process is then performed in order to receive labelled conditions, involving three expert annotators for the final data set, and laymen annotations for initial experiments. Given the data extracted from the annotation process, SVM, BiLSTM and KB-BERT classifiers are trained and evaluated. All models are evaluated using no context information, with bullet points as an exception, where a previous, generally descriptive sentence is included. Apart from this default input representation type, context regarding preceding sentence along with the target sentence, as well as adding specific agency to the target sentence are evaluated as alternative data representation types. The final inter-annotator agreement was not optimal with Cohen’s Kappa scores that can be interpreted as representing moderate agreement. By using majority vote for the test set, the non-optimal agreement was somewhat prevented for this specific set. The best performing model all input representation types considered was the KB-BERT using no context information, receiving an F1-score on 0.81 and an accuracy score on 0.89 on the test set. All models gave a better performance for sentences classed as governing, which might be partially due to the final annotated data sets being skewed. Possible future studies include further iterative annotation and working towards a clear and as objective definition of how a governing condition can be defined, as well as exploring the possibilities of using data augmentation to counteract the uneven distribution of classes in the final data sets.

The work deals with the generation of ECG signals using generative adversarial networks (GAN). It examines in detail the basics of artificial neural networks and the principles of their operation. It theoretically describes the use and operation and the most common types of failures of generative adversarial networks. In this work, a general procedure of signal preprocessing suitable for GAN training was derived, which was used to compile a database. In this work, a total of 3 different GAN models were designed and implemented. The results of the models were visually displayed and analyzed in detail. Finally, the work comments on the achieved results and suggests further research direction of methods dealing with the generation of ECG signals.

The continuous integration infrastructure (CI servers) is commonly used as a shared test environment due to the need for collaborative and distributive development for the software products under growing scale and complexity in recent years. To ensure the stability of the CI servers, with the help of the constantly recorded measurement data of the servers, fault prediction is of great interest to software development companies. However, the lack of fault data is a typical challenge in learning the fault patterns directly. Alternatively, predicting the standard observations that represent the normal behavior of the CI servers can be viewed as an initial step toward fault prediction. Faults can then be identified and predicted by studying the difference between observed data and predicted standard data with enough fault data in the future. In this thesis, a long short-term memory (LSTM), a bidirectional LSTM (BiLSTM), and a vector autoregressive (VAR) models are developed. The models are compared on both one-step-ahead prediction and iteratively long-range prediction up to 60 steps (corresponds to 15 minutes for the CI servers analyzed in the thesis). To account for the uncertainties in the predictions, the LSTM-based models are trained to estimate predictive variance. The prediction intervals obtained are then compared with the VAR model. Moreover, since there are many servers in the CI infrastructure, it is of interest to investigate whether a model trained on one server can represent other servers. The investigation is carried out by applying the one-step-ahead LSTM model on a set of other servers and comparing the results. The LSTM model performs the best overall with only slightly better than the VAR model, whereas the BiLSTM model performs the worst in the one-step-ahead prediction. When taking the uncertainties into account, the LSTM model seems to estimate the assumed distribution the best with the highest log-likelihood. For long-range prediction, the VAR model surprisingly performs the best across almost all range lengths. Lastly, when applying the LSTM one-step-ahead model on the other servers, the performance differs from server to server, which indicates that it is less likely to achieve competitive performance when applying the same model on all servers.

With the rise of social media and publicly available data, opinion mining is more accessible than ever. It is valuable for content creators, companies and advertisers to gain insights into what users think and feel. This work examines comments on YouTube videos, and builds a deep learning classifier to automatically determine their sentiment. Four Long Short-Term Memory-based models are trained and evaluated. Experiments are performed to determine which deep learning model performs with the best accuracy, recall, precision, F1 score and ROC curve on a labelled YouTube Comment dataset. The results indicate that a BiLSTM-based model has the overall best performance, with the accuracy of 89%. Furthermore, the four LSTM-based models are evaluated on an IMDB movie review dataset, achieving an average accuracy of 87%, showing that the models can predict the sentiment of different textual data. Finally, a statistical analysis is performed on the YouTube videos, revealing that videos with positive sentiment have a statistically higher number of upvotes and views. However, the number of downvotes is not significantly higher in videos with negative sentiment.

Indiana University-Purdue University Indianapolis (IUPUI) / With the proliferation of computer science in recent years in modern society, the number of computer science-related employment is expanding quickly. Software engineer has been chosen as the best job for 2023 based on pay, stress level, opportunity for professional growth, and balance between work and personal life. This was decided by a rankings of different news, journals, and publications. Computer science occupations are anticipated to be in high demand not just in 2023, but also for the foreseeable future. It's not surprising that the number of computer science students at universities is growing and will continue to grow. The enormous increase in student enrolment in many subdisciplines of computers has presented some distinct issues. If computer science is to be incorporated into the K-12 curriculum, it is vital that K-12 educators are competent. But one of the biggest problems with this plan is that there aren't enough trained computer science professors. Numerous new fields and applications, for instance, are being introduced to computer science. In addition, it is difficult for schools to recruit skilled computer science instructors for a variety of reasons including low salary issue. Utilizing the K-12 teachers who are already in the schools, have a love for teaching, and consider teaching as a vocation is therefore the most effective strategy to improve or fix this issue. So, if we want teachers to quickly grasp computer science topics, we need to give them an easy way to learn about computer science. To simplify and expedite the study of computer science, we must acquaint school-treachers with the terminology associated with computer science concepts so they can know which things they need to learn according to their profile. If we want to make it easier for schoolteachers to comprehend computer science concepts, it would be ideal if we could provide them with a tree of words and phrases from which they could determine where the phrases originated and which phrases are connected to them so that they can be effectively learned. To find a good concept word or phrase, we must first identify concepts and then establish their connections or linkages. As computer science is a fast developing field, its nomenclature is also expanding at a frenetic rate. Therefore, adding all concepts and terms to the knowledge graph would be a challenging endeavor. Cre- ating a system that automatically adds all computer science domain terms to the knowledge graph would be a straightforward solution to the issue. We have identified knowledge graph use cases for the schoolteacher training program, which motivates the development of a knowledge graph. We have analyzed the knowledge graph's use case and the knowledge graph's ideal characteristics. We have designed a webbased system for adding, editing, and removing words from a knowledge graph. In addition, a term or phrase can be represented with its children list, parent list, and synonym list for enhanced comprehension. We' ve developed an automated system for extracting words and phrases that can extract computer science idea phrases from any supplied text, therefore enriching the knowledge graph. Therefore, we have designed the knowledge graph for use in teacher education so that school-teachers can educate K-12 students computer science topicses effectively.

Negation is a common property of languages, in that there are few languages, if any, that lack means to revert the truth-value of a statement. A challenge to cross-lingual studies of negation lies in the fact that languages encode and use it in different ways. Although this variation has been extensively researched in linguistics, little has been done in automated language processing. In particular, we lack computational models of processing negation that can be generalized across language. We even lack knowledge of what the development of such models would require. These models however exist and can be built by means of existing cross-lingual resources, even when annotated data for a language other than English is not available. This thesis shows this in the context of detecting string-level negation scope, i.e. the set of tokens in a sentence whose meaning is affected by a negation marker (e.g. 'not'). Our contribution has two parts. First, we investigate the scenario where annotated training data is available. We show that Bi-directional Long Short Term Memory (BiLSTM) networks are state-of-the-art models whose features can be generalized across language. We also show that these models suffer from genre effects and that for most of the corpora we have experimented with, high performance is simply an artifact of the annotation styles, where negation scope is often a span of text delimited by punctuation. Second, we investigate the scenario where annotated data is available in only one language, experimenting with model transfer. To test our approach, we first build NEGPAR, a parallel corpus annotated for negation, where pre-existing annotations on English sentences have been edited and extended to Chinese translations. We then show that transferring a model for negation scope detection across languages is possible by means of structured neural models where negation scope is detected on top of a cross-linguistically consistent representation, Universal Dependencies. On the other hand, we found cross-lingual lexical information only to help very little with performance. Finally, error analysis shows that performance is better when a negation marker is in the same dependency substructure as its scope and that some of the phenomena related to negation scope requiring lexical knowledge are still not captured correctly. In the conclusions, we tie up the contributions of this thesis and we point future work towards representing negation scope across languages at the level of logical form as well.

The thesis presents named-entity recognition in Czech historical newspapers from Modern Access to Historical Sources Project. Our goal was to create a specific corpus and annotation manual for the project and evaluate neural networks methods for named-entity recognition within the task. We created the corpus using scanned Czech historical newspapers. The scanned pages were converted to digitize text by optical character recognition (OCR) method. The data were preprocessed by deleting some OCR errors. We also defined specific named entities types for our task and created an annotation manual with examples for the project. Based on that, we annotated the final corpus. To find the most suitable neural networks model for our task, we experimented with different neural networks architectures, namely long short-term memory (LSTM), bidirectional LSTM and CNN-BiLSTM models. Moreover, we experimented with randomly initialized word embeddings that were trained during the training process and pretrained word embeddings for contemporary Czech published as open source by fastText. We achieved the best result F1 score 0.444 using CNN-BiLSTM model and the pretrained word embeddings by fastText. We found out that we do not need to normalize spelling of our historical texts to get closer to contemporary language if we use the neural network model. We provided a qualitative analysis of observed linguistics phenomena as well. We found out that some word forms and pair of words which were not frequent in our training data set were miss-tagged or not tagged at all. Based on that, we can say that larger data sets could improve the results.

With the breakthrough of machine learning techniques, the research concerning music emotion classification has been getting notable progress combining various audio features and state-of-the-art machine learning models. Still, it is known that the way to preprocess music samples and to choose which machine classification algorithm to use depends on data sets and the objective of each project work. The collaborating company of this thesis, Ichigoichie AB, is currently developing a system to categorize music data into positive/negative classes. To enhance the accuracy of the existing system, this project aims to figure out the best model through experiments with six audio features (Mel spectrogram, MFCC, HPSS, Onset, CENS, Tonnetz) and several machine learning models including deep neural network models for the classification task. For each model, hyperparameter tuning is performed and the model evaluation is carried out according to pareto optimality with regard to accuracy and execution time. The results show that the most promising model accomplished 95% correct classification with an execution time of less than 15 seconds. / I och med genombrottet av maskininlärningstekniker har forskning kring känsloklassificering i musik sett betydande framsteg genom att kombinera olikamusikanalysverktyg med nya maskinlärningsmodeller. Trots detta är hur man förbehandlar ljuddatat och valet av vilken maskinklassificeringsalgoritm som ska tillämpas beroende på vilken typ av data man arbetar med samt målet med projektet. Denna uppsats samarbetspartner, Ichigoichie AB, utvecklar för närvarande ett system för att kategorisera musikdata enligt positiva och negativa känslor. För att höja systemets noggrannhet är målet med denna uppsats att experimentellt hitta bästa modellen baserat på sex musik-egenskaper (Mel-spektrogram, MFCC, HPSS, Onset, CENS samt Tonnetz) och ett antal olika maskininlärningsmodeller, inklusive Deep Learning-modeller. Varje modell hyperparameteroptimeras och utvärderas enligt paretooptimalitet med hänsyn till noggrannhet och beräkningstid. Resultaten visar att den mest lovande modellen uppnådde 95% korrekt klassificering med en beräkningstid på mindre än 15 sekunder.

This thesis explores the development and validation of advanced Machine Learning (ML) and Deep Learning (DL) algorithms for detecting Distributed Denial of Service (DDoS) attacks within 5th Generation (5G) telecommunications networks. As 5G technologies expand, the vulnerability of these networks to cyber threats that compromise service integrity increases, necessitating robust detection mechanisms. The primary aim of this research is to develop and validate ML and DL algorithms that effectively detect DDoS attacks within 5G telecommunications networks. These algorithms will leverage real-time data processing to enhance network security protocols and improve resilience against cyber threats. A robust simulated environment using free 5GC and UERANSIM was established to mimic the complex dynamics of 5G networks. This facilitated the controlled testing of various ML and DL models under both normal and attack conditions. The models developed and tested include Bidirectional Encoder Representations from Transformer (BERT), Bidirectional Long Short-Term Memory (BiLSTM), Multilayer Perceptron (MLP), a Custom Convolutional Neural Network (CNN), Random Forest, Support Vector Machine (SVM), and XGBoost. The ensemble model combining Random Forest and XGBoost showed superior performance, making it suitable for the dynamic 5G environment. However, the study also highlights the complications of ensemble models, such as increased computational complexity and resource demands, which may limit their practicality in resource-constrained settings. This thesis addresses a critical research gap by evaluating modern DL techniques, traditional ML models, and ensemble methods within a simulated 5G environment. This comparative analysis helps identify the most effective approach for real-time DDoS detection, balancing accuracy, complexity, and resource efficiency. The findings indicate that the tailored ML, DL and Ensemble models developed are highly effective in detecting DDoS attacks, demonstrating high accuracy and efficiency in real-time threat detection. This highlights the potential for these models to be adapted for real-world applications in modern telecommunications infrastructures. In conclusion, this thesis contributes substantially to the field of cybersecurity in 5G networks by demonstrating that ML and DL models, developed and tested in a sophisticated simulated environment, can significantly enhance network security protocols. These models offer promising approaches to securing emerging telecommunications infrastructures against continuously evolving cyber threats, thus supporting the stability and reliability of 5G networks globally.

<p><strong>Graph Neural Networks</strong> (GNNs) have become excessively popular and prominent deep learning techniques to analyze structural graph data for their ability to solve complex real-world problems. Because graphs provide an efficient approach to contriving abstract hypothetical concepts, modern research overcomes the limitations of classical graph theory, requiring prior knowledge of the graph structure before employing traditional algorithms. GNNs, an impressive framework for representation learning of graphs, have already produced many state-of-the-art techniques to solve node classification, link prediction, and graph classification tasks. GNNs can learn meaningful representations of graphs incorporating topological structure, node attributes, and neighborhood aggregation to solve supervised, semi-supervised, and unsupervised graph-based problems. In this study, the usefulness of GNNs has been analyzed primarily from two aspects - <strong>clustering and classification</strong>. We focus on these two techniques, as they are the most popular strategies in data mining to discern collected data and employ predictive analysis.</p>