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Thesis (Ph. D.)--University of Texas at Austin, 1998. / Vita. Includes bibliographical references (leaves 382-405). Available also in a digital version from Dissertation Abstracts.

Thesis (M.S. in Program Management)--Naval Postgraduate School, December 2002. / Thesis advisor(s): Michael W. Boudreau, Richard G. Rhoades. Includes bibliographical references (p. 63-65). Also available online.

Thesis (Ph. D.)--Washington State University, August 2006. / Includes bibliographical references (p. 230-241).

This diploma thesis describes a design and implementation of a system for monitoring events in passive optical networks, specifically in GPON networks. The main technologies used in the implementation of this system are Apache Kafka, Docker and the Python programming language. Within the created application, several filters are implemented. This filters obtain essential information from the captured frames in terms of traffic analysis on the given network. The result of the thesis is a functional system that from the captured GPON frames obtains information about the network traffic and stores them in the Apache Kafka, where the stored data is accessible for further processing. The work also provides examples of how to process the stored data, along with information about their meaning and structure.

This Master´s thesis deals with the design and implementation of System for remote measuring and data storing in central databese. The system consists of server, which collects data and clients that send data to the server. On the server is installed program Apache in which runs utility script for collecting and evaluating data, written in PHP. Data are stored in MySQL relational database and can be clearly displayed. The client consists of microcontroller ATmega16, which evaluates data from sensors. These data are then transferred via Ethernet using the HTTP protocol through the module XPort.

Content is generated on the web at an increasing rate. The type of content varies from text on a traditional webpage to text on social media portals (e.g., social network sites and microblogs). One such example of social media is the microblogging site Twitter. Twitter is known for its high level of activity during live events, natural disasters, and events of global importance. Challenges with the data in the Twitter universe include the limit of 140 characters on the text length. Because of this limitation, the vocabulary in the Twitter universe includes short abbreviations of sentences, emojis, hashtags, and other non-standard usage. Consequently, traditional text classification techniques are not very effective on tweets. Fortunately, sophisticated text processing techniques like cleaning, lemmatizing, and removal of stop words and special characters will give us clean text which can be further processed to derive richer word semantic and syntactic relationships using state of the art feature selection techniques like Word2Vec. Machine learning techniques, using word features that capture semantic and context relationships, can be of benefit regarding classification accuracy. Improving text classification results on Twitter data would pave the way to categorize tweets relative to human defined real world events. This would allow diverse stakeholder communities to interactively collect, organize, browse, visualize, analyze, summarize, and explore content and sources related to crises, disasters, human rights, inequality, population growth, resiliency, shootings, sustainability, violence, etc. Having the events classified into different categories would help us study causality and correlations among real world events. To check the efficacy of our classifier, we would compare our experimental results with an Association Rules (AR) classifier. This classifier composes its rules around the most discriminating words in the training data. The hierarchy of rules, along with an ability to tune to a support threshold, makes it an effective classifier for scenarios where short text is involved. Traditionally, developing classification systems for these purposes requires a great degree of human intervention. Constantly monitoring new events, and curating training and validation sets, is tedious and time intensive. Significant human capital is required for such annotation endeavors. Also, involved efforts are required to tune the classifier for best performance. Developing and tuning classifiers manually using human intervention would not be a viable option if we are to monitor events and trends in real-time. We want to build a framework that would require very little human intervention to build and choose the best among the available performing classification techniques in our system. Another challenge with classification systems is related to their performance with unseen data. For the classification of tweets, we are continually faced with a situation where a given event contains a certain keyword that is closely related to it. If a classifier, built for a particular event, due to overfitting to what is a biased sample with limited generality, is faced with new tweets with different keywords, accuracy may be reduced. We propose building a system that will use very little training data in the initial iteration and will be augmented with automatically labelled training data from a collection that stores all the incoming tweets. A system that is trained on incoming tweets that are labelled using sophisticated techniques based on rich word vector representation would perform better than a system that is trained on only the initial set of tweets. We also propose to use sophisticated deep learning techniques like Convolutional Neural Networks (CNN) that can capture the combination of the words using an n-gram feature representation. Such sophisticated feature representation could account for the instances when the words occur together. We divide our case studies into two phases: preliminary and final case studies. The preliminary case studies focus on selecting the best feature representation and classification methodology out of the AR and the Word2Vec based Logistic Regression classification techniques. The final case studies focus on developing the augmented semi-supervised training methodology and the framework to develop a large collection learning optimizer to generate a highly performant classifier. For our preliminary case studies, we are able to achieve an F1 score of 0.96 that is based on Word2Vec and Logistic Regression. The AR classifier achieved an F1 score of 0.90 on the same data. For our final case studies, we are able to show improvements of F1 score from 0.58 to 0.94 in certain cases based on our augmented training methodology. Overall, we see improvement in using the augmented training methodology on all datasets. / Master of Science / Content is generated on social media at a very fast pace. Social media content in the form of tweets that is generated by the microblog site Twitter is quite popular for understanding the events and trends that are prevalent at a given point of time across various geographies. Categorizing these tweets into their real-world event categories would be useful for researchers, students, academics and the government. Categorizing tweets to their real-world categories is a challenging task. Our framework involves building a classification system that can learn how to categorize tweets for a given category if it is provided with a few samples of the relevant and non-relevant tweets. The system retrieves additional tweets from an auxiliary data source to further learn what is relevant and irrelevant based on how similar a tweet is to a positive example. Categorizing the tweets in an automated way would be useful in analyzing and studying the events and trends for past and future real-world events.

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Diese Arbeit behandelt die Entstehung eines Prototypen für die Übertragung von hochsensiblen Daten zwischen verschieden Firmen. Dabei geht Sie auf alle Schritte bei der Entwicklung ein von der Anforderungsanalyse über die Evaluierung einer passenden Technologie und die eigentliche Implementierung bis hin zum Test und der Administration.