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A powerful and efficient algorithm has been designed to deal with the critical timing problem of the MIDI messages. This algorithm can convert note events stored in a natural way to MIDI messages dynamically. Only limited memory space (the buffer) is required to finish the conversion work, and the size of the buffer is independent of the size of the original sequence (notes). This algorithm's real-time variable properties suggest not only the flexible real-time controls in the use of musical aspects, but also the expandability to interactive multi-media applications. A compositional environment called MusicSculptor has been implemented in terms of this algorithm.

Táto diplomová práca sa venuje problematike využitia strojového učenia na detekciu anomálií na základe analýzy systémových logov. Navrhnuté modely sú založené na algoritmoch strojového učenia s učiteľom, bez učiteľa a na hlbokom učení. Funkčnosť a správanie týchto algoritmov sú objasnené ako teoreticky, tak aj prakticky. Okrem toho boli využité metódy a postupy na predspracovanie dát predtým, než boli vložené do modelov strojového učenia. Navrhnuté modely sú na konci porovnané s využitím viacerých metrík a otestované na syslogoch, ktoré modely predtým nevideli. Najpresnejší výkon podali modely Klasifikátor rozhodovacích stromov, Jednotriedny podporný vektorový stroj a model Hierarchické zoskupovanie, ktoré správne označili 93,95%, 85,66% a 85,3% anomálií v uvedenom poradí.

This thesis concentrates on the UMTS cellular network and the possibilities of its monitoring. There are several methods of monitoring the UMTS. The technique used in this project is based on monitoring through a cellular terminal. The theoretical part is devoted to the description of the UMTS system and the WCDMA technique. The practical part then deals with the method of UMTS monitoring through a cellular terminal and describes in detail individual screens of the FTD (Field Test Display) program, the Netmonitor functions and their parameters. The next part of this thesis analyses the RRC messages, which were recorded during the realization of services (video calls, calls, data transfers) in the UMTS network. The last chapters of the thesis are briefly dealing with software and hardware servicing of cellular terminals. The attachment then offers two lab tasks, in which the students get acquainted with the UMTS network structure and the behaviour of the cellular terminal in this network.

Thesis is adressed to the principals of a satellite navigation. It is focused on a determination of a longitude, latitude and altitude of a single point on the surface of the Earth. It contains analysis of navigation messages and a manner of processing of navigation signals in GPS receivers. Realization of an emulator of the navigation signal of GPS system in Matlab is also described. The purpose of this emulator is a generation of navigation messages, which could be transmitted by universal software radio. The last part of thesis contains the analysis of these signals, which were captured by second software radio.

La classification automatique des messages courts est de plus en plus employée de nos jours dans diverses applications telles que l'analyse des sentiments ou la détection des « spams ». Par rapport aux textes traditionnels, les messages courts, comme les tweets et les SMS, posent de nouveaux défis à cause de leur courte taille, leur parcimonie et leur manque de contexte, ce qui rend leur classification plus difficile. Nous présentons dans cette thèse deux nouvelles approches visant à améliorer la classification de ce type de message. Notre première approche est nommée « forêts sémantiques ». Dans le but d'améliorer la qualité des messages, cette approche les enrichit à partir d'une source externe construite au préalable. Puis, pour apprendre un modèle de classification, contrairement à ce qui est traditionnellement utilisé, nous proposons un nouvel algorithme d'apprentissage qui tient compte de la sémantique dans le processus d'induction des forêts aléatoires. Notre deuxième contribution est nommée « IGLM » (Interactive Generic Learning Method). C'est une méthode interactive qui met récursivement à jour les forêts en tenant compte des nouvelles données arrivant au cours du temps, et de l'expertise de l'utilisateur qui corrige les erreurs de classification. L'ensemble de ce mécanisme est renforcé par l'utilisation d'une méthode d'abstraction permettant d'améliorer la qualité des messages. Les différentes expérimentations menées en utilisant ces deux méthodes ont permis de montrer leur efficacité. Enfin, la dernière partie de la thèse est consacrée à une étude complète et argumentée de ces deux prenant en compte des critères variés tels que l'accuracy, la rapidité, etc. / Automatic short text classification is more and more used nowadays in various applications like sentiment analysis or spam detection. Short texts like tweets or SMS are more challenging than traditional texts. Therefore, their classification is more difficult owing to their shortness, sparsity and lack of contextual information. We present two new approaches to improve short text classification. Our first approach is "Semantic Forest". The first step of this approach proposes a new enrichment method that uses an external source of enrichment built in advance. The idea is to transform a short text from few words to a larger text containing more information in order to improve its quality before building the classification model. Contrarily to the methods proposed in the literature, the second step of our approach does not use traditional learning algorithm but proposes a new one based on the semantic links among words in the Random Forest classifier. Our second contribution is "IGLM" (Interactive Generic Learning Method). It is a new interactive approach that recursively updates the classification model by considering the new data arriving over time and by leveraging the user intervention to correct misclassified data. An abstraction method is then combined with the update mechanism to improve short text quality. The experiments performed on these two methods show their efficiency and how they outperform traditional algorithms in short text classification. Finally, the last part of the thesis concerns a complete and argued comparative study of the two proposed methods taking into account various criteria such as accuracy, speed, etc.

Within the context of social networks, existing methods for document classification tasks typically only capture textual semantics while ignoring the text’s metadata, e.g., the users who exchange emails and the communication networks they form. However, some work has shown that incorporating the social network information in addition to information from language is useful for various NLP applications, including sentiment analysis, inferring user attributes, and predicting interpersonal relations. In this thesis, we present empirical studies of incorporating social network information from the underlying communication graphs for various text classification tasks. We show different graph representations for different problems. Also, we introduce social network features extracted from these graphs. We use and extend graph embedding models for text classification. Our contributions are as follows. First, we have annotated large datasets of emails with fine-grained business and personal labels. Second, we propose graph representations for the social networks induced from documents and users and apply them on different text classification tasks. Third, we propose social network features extracted from these structures for documents and users. Fourth, we exploit different methods for modeling the social network of communication for four tasks: email classification into business and personal, overt display of power detection in emails, hierarchical power detection in emails, and Reddit post classification. Our main findings are: incorporating the social network information using our proposed methods improves the classification performance for all of the four tasks, and we beat the state-of-the-art graph embedding based model on the three tasks on email; additionally, for the fourth task (Reddit post classification), we argue that simple methods with the proper representation for the task can outperform a state-of-the-art generic model.

Some human actions are linked to the decline of the environment on a planetary scale. In order to motivate individuals to adopt pro-environmental behaviors (PEBs), it is important to understand how individuals react when exposed to persuasive messages. The goal of this program of research was to examine the influence of images of climate change causes, consequences, and solutions on the relationships between environmental motivation, psychological discomfort, discomfort compensation strategies, as well as changes in pro-environmental attitude and PEBs. In Study 1 (N = 199), I identified visual stimuli (pictures) depicting causes, consequences or solutions to global warming to be used in Study 2. Also, I examined the relationship between environmental motivation and competency on the perception of these pictures. The results indicated that the pictures depicting causes or consequences were perceived more negatively than pictures depicting solutions. In addition, findings showed that regardless of the individual’s perceived level of environmental competence and their type of motivation towards PEBs, individuals had a negative perception of pictures depicting causes and consequences to global warming as well as a positive perception of pictures related to solutions to global warming. In Study 2 (N = 312), I examined the relationships between environmental motivation, psychological discomfort, discomfort compensation strategies, as well as changes in pro-environmental attitude and PEBs following the exposition to images identified in Study 1. Three models based on Self-Determination Theory (SDT), Action-Based Model, and the Hierarchical Action-Based model of Inconsistency Compensation in the Environment (HABICE) domain were examined. The results indicated that exposure to pictures alone is not enough to generate a significant change in pro-environmental attitude or PEBs. The findings showed that the SDT model was best suited to explain the process leading to PEBs changes when exposed to pictures depicting causes of global warming. Finally, the results indicated that the HABICE model was best suited to explain changes in pro-environmental attitude when individuals are exposed to pictures depicting consequences. The HABICE was also a good model to explain the relationships among the different variables when individuals are exposed to pictures depicting solutions to global warming. Overall, this program of research contributed to both SDT and the HABICE models by supporting their conceptual framework.

Log messages are one of the most valuable sources of information in the cloud and other software systems. These logs can be used for audits and ensuring system security. Many millions of log messages are produced each day which makes anomaly detection challenging. Automating the detection of anomalies can save time and money as well as improve detection performance. In this dissertation, Deep Learning (DL) methods called Auto-LSTM, Auto-BLSTM and Auto-GRU are developed for log message anomaly detection. They are evaluated using four data sets, namely BGL, Openstack, Thunderbird and IMDB. The first three are popular log data sets while the fourth is a movie review data set which is used for sentiment classification. The results obtained show that Auto-LSTM, Auto-BLSTM and Auto-GRU perform better than other well-known algorithms. Dealing with imbalanced data is one of the main challenges in Machine Learning (ML)/DL algorithms for classification. This issue is more important with log message data as it is typically very imbalanced and negative logs are rare. Hence, a model is proposed to generate text log messages using a Sequence Generative Adversarial Network (SeqGAN) network. Then features are extracted using an Autoencoder and anomaly detection is done using a GRU network. The proposed model is evaluated with two imbalanced log data sets, namely BGL and Openstack. Results are presented which show that oversampling and balancing data increases the accuracy of anomaly detection and classification. Another challenge in anomaly detection is dealing with unlabeled data. Labeling even a small portion of logs for model training may not be possible due to the high volume of generated logs. To deal with this unlabeled data, an unsupervised model for log message anomaly detection is proposed which employs Isolation Forest and two deep Autoencoder networks. The Autoencoder networks are used for training and feature extraction, and then for anomaly detection, while Isolation Forest is used for positive sample prediction. The proposed model is evaluated using the BGL, Openstack and Thunderbird log message data sets. The results obtained show that the number of negative samples predicted to be positive is low, especially with Isolation Forest and one Autoencoder. Further, the results are better than with other well-known models. A hybrid log message anomaly detection technique is proposed which uses pruning of positive and negative logs. Reliable positive log messages are first identified using a Gaussian Mixture Model (GMM) algorithm. Then reliable negative logs are selected using the K-means, GMM and Dirichlet Process Gaussian Mixture Model (BGM) methods iteratively. It is shown that the precision for positive and negative logs with pruning is high. Anomaly detection is done using a Long Short-Term Memory (LSTM) network. The proposed model is evaluated using the BGL, Openstack, and Thunderbird data sets. The results obtained indicate that the proposed model performs better than several well-known algorithms. Last, an anomaly detection method is proposed using radius-based Fuzzy C-means (FCM) with more clusters than the number of data classes and a Multilayer Perceptron (MLP) network. The cluster centers and a radius are used to select reliable positive and negative log messages. Moreover, class probabilities are used with an expert to correct the network output for suspect logs. The proposed model is evaluated with three well-known data sets, namely BGL, Openstack and Thunderbird. The results obtained show that this model provides better results than existing methods. / Graduate

When studying the novel Les Années by the novelist Annie Ernaux we have chosen to have acloser look upon the translation of the novel to Swedish following the strategies of translationelaborated by Brynja Svane. These strategies are based on the study of cultural expressions. Ascultural expressions we have chosen those expressions which are typical for the world situationand for the daily life during this period of time, that is 70 between 1940 and 2008. We want tofind out whether these strategies can help the translator to convey the message of the novel. Thethree most often used strategies are addition, transfer with explanation and semantic adaption.Sometimes the translation needs to add words to dramatize the text in a way that is easily donein a few words in the source language. Finally, we find that using a lot of words sometimesmake the translated text a bit heavy and clumsy, which might hide and weaken the message ofthe novel