1,721,011 research outputs found
Nametones
No full text"Nametones Generator" is an Android application where the user can generate an unique Nametone based on his/her name, birthdate and the selected music style. This is based on the idea of Bach that every name is to be transformed into a classic musical piece.Software TechnologyElectrical Engineering, Mathematics and Computer Scienc
Planning applicatie voor Tango
No full textTechnische InformaticaElectrical Engineering, Mathematics and Computer Scienc
Evolving Biologically Inspired Classifiers
No full textThis thesis argues that natural complex systems can provide an inspiring example for creating software which incorporates emergent, self-organizing and adaptive properties. The advantages of complex sys- tems are their natural resilience, redundancy and adaptivity. A generalization of neural networks and boolean networks called computational networks is presented as a model for complex systems. It is argued that this model satisfies the required properties for modeling complex systems. Furthermore, it is asserted that a computational network, being a network of mathematical functions, is appropriate for solving classification problems. For the design of computational networks an evolutionary design algorithm is constructed. Additionally, four extensions of this algorithm are presented. Each extension is inspired by natural evolution and theories from the evolutionary computing literature. An impor- tant component is a novel generative representation which can reuse substructures of computational networks. Experiments with this component have shown that it facilitates a higher level of complexity in the solution space, improving the computational network performance for more complex problems. Other components steer the evolutionary process towards a desired solution, either by introducing spe- cial stages during evolution, or by smoothing the fitness landscape. The experiments show that complex systems can be evolutionary designed to act as a classifier. The resulting computational network has a better performance on the Iris dataset compared to every classifier in the Weka classifier collection. Furthermore, an experiment was conducted using the TIMIT read speech dataset, the classifier was evo- lutionary designed using only 13 MFCC features, and a very small train set. Although the performance is not good enough to be of any practical use, the results are adequate given the limitations of the train data.Man-Machine InteractionDepartment of MediamaticsElectrical Engineering, Mathematics and Computer Scienc
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Comparing Feature Sets and Classifiers for Sentiment Analysis of Opinionated Free Text
No full textThis master thesis is about the sentiment analysis of the societal theme documents and categorizing them in positive or negative groups. The application of this thesis can be widely used in review blogs, public polls and etc. In this study, we have compared different feature sets as well as different classifiers on datasets of opinionated texts with societal themes. These datasets consist of one large and 6 small sets in terms of number of documents. By considering the often used “Bag of Words” feature set as the base line we have tested 4 other models and came to this conclusion that selecting features with their part of speech tags can always improve the results of sentiment classification while adjective and negation tags can describe the opinionated documents more informatively in much smaller matrices which saves a lot of memory and processing time. Moreover, by selecting these tags according to their PMI ranks in positive and negative labeled documents, we obtained the most informative sentimental words. On the other hand, based on the obtained results, in contrast with the predominant attitude in the sentiment analysis field that support vector machine (SVC) is the best classifier for binary classification of opinionated documents, we found the linear discriminate classifier (LDC) can perform as well as support vector machine but 10 times faster. The consumed time is convincing enough to substitute SVC with LDC in sentiment analysis when we have a large number of features as is the case in a Bag of Words model due to the fact that the time that SVC needs for predicting labels is quadratic in terms of number of documents. The feature vectors obtained through PMI analysis are relatively small, we found that as a consequence, the k-nearest Neighbor Classifier (KNNC) could train well and gave the most accurate results in comparison with LDC and SVC in both large and small datasets. It should be stressed that Principal Component Analysis (PCA) is used in this study in order to extract mathematically the most common features in all the models.Computer ScienceMan-Machine InteractionElectrical Engineering, Mathematics and Computer Scienc
Automatic Creation of Opinion-Based Summary Representations
No full textCustomers interested in buying a product, can search on the internet for reviews about that product. For many products, an enormous amount of information and opinions is available. Customers gets overwhelmed by this information and systems are needed to filter out the essential information. In this research, a model is developed to automatically create a graphical summary of a set of Dutch product reviews. This model is divided into three sub modules. The first module is meant to select sentences expressing opinions. The second module groups those sentences based on their subjects. Finally, the third module generates a graphical representation of the groups of sentences and presents it to the customer. The most important parts of the model are implemented into a system. An user experiment is conducted to evaluate the performance of this system. This experiment shows that the first module has an accuracy of 81%. This is comparable with systems developed for other languages. Information shown in the graphical summary representation is judged as relevant in 56% of the cases. Although further research is needed, this model is a good basis to provide insight to the customer about essential information in product reviews.Section Interactive IntelligenceIntelligent SystemsElectrical Engineering, Mathematics and Computer Scienc
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Modelling context in automatic speech recognition
No full textSpeech is at the core of human communication. Speaking and listing comes so natural to us that we do not have to think about it at all. The underlying cognitive processes are very rapid and almost completely subconscious. It is hard, if not impossible not to understand speech. For computers on the other hand, recognising speech is a daunting task. It has to deal with a large number of different voices "influenced, among other things, by emotion, moods and fatigue" the acoustic properties of different environments, dialects, a huge vocabulary and an unlimited creativity of speakers to combine words and to break the rules of grammar. Almost all existing automatic speech recognisers use statistics over speech sounds "what is the probability that a piece of audio is an a-sound" and statistics over word combinations to deal with this complexity. The results of those systems are impressive but unfortunately not good enough for most applications of speech recognition. This thesis proposes to put context information in the models of speech recognition to achieve better recognition results. Context is defined as knowledge of the speaker, such as gender and dialect, knowledge of the conversation and knowledge of the world. The influence of each of those categories is investigated using data analysis and case studies and new models for speech recognition are defined. In particular, a model that dynamically adapts the vocabulary of the recogniser to the topic of a conversation, which it can automatically determine, is presented.Electrical Engineering, Mathematics and Computer Scienc
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