Ever since the rapid growth of the Internet, recommender systems have become essential in helping online users to search and retrieve relevant information they need. Just like the situation that people rely heavily on recommendation in their daily decision making processes, online users may identify desired documents more effectively and efficiently through recommendation of other users who exhibit similar interests, and/or through extracting crucial features of the users¡¦ past preferences. Typical recommendation approaches can be classified into collaborative filtering and content-based filtering. Both approaches, however, have their own drawbacks. The purpose of this research is thus to propose a hybrid approach for text recommendations. We combine collaborative input and document content to facilitate the creation of extended content-based user profiles. These profiles are then rearranged with the technique of latent semantic indexing. Two experiments are conducted to verify our proposed approach. The objective of these experiments is to compare the recommendation results from our proposed approach with those from the other two approaches. The results show that our approach is capable of distinguishing different degrees of document preference, and makes appropriate recommendation to users or does not make recommendation to users for uninterested documents. The application of our proposed approach is justified accordingly.

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Americans spent approximately $47.7 billion on pet products and services in 2010, an increase of 4.8% over 2009, making the pet industry a market segment ripe with opportunity for entrepreneurial small business venture (American Pet Products Association, 2013). Small businesses invite innovation, create and provide new jobs, foster entrepreneurial spirit and creativity, and create competition that drives future business endeavors (Hillary, 2001). The pet dog industry is a salient example of entrepreneurial activity in which the pressures of business, economics, and learning coalesce. Because small businesses bolster about half of the private-sector economy and represent more than 99% of all business firms (Small Business Administration, 2013), it is useful for small business owners to learn and prosper as entrepreneurs. "Entrepreneurship is a learning process, and a theory of entrepreneurship requires a theory of learning" (Minniti, 2010, p. 9). However, there is still limited knowledge and understanding of the interaction between learning and entrepreneurship, and such a process remains one of the most neglected areas of entrepreneurial research and thus understanding (Deakins and Freel, 1999). This study explored entrepreneurial decision making by using the construct of David A. Kolb's Learning Style Inventory to examine an entrepreneurial operation in the pet dog-training industry. The researcher worked hand-in-hand with the entrepreneur in a collaborative partnership to explore the phenomenon using narrative inquiry research methods. A series of semi-structured interviews were used to collect and analyze stories and identify key considerations for learning style in relation to entrepreneurship. The results showed the entrepreneur's preferred learning style aligned with his expressed style and demonstrated a keen sense of operations awareness. Additionally, the entrepreneur had learned how to leverage his strengths over time while recognizing and compensating for his weaknesses. For a novice or someone with a desire to learn more about their own entrepreneurial inclinations, results from a learning style instrument could provide such understandings with helpful implications for small business ownership. Future studies could contribute to entrepreneurial research and add greater voice to the pet dog industry. / Ph. D.

The diploma thesis deals with learning styles of learners in English language teaching namely in one-to-one courses. The theoretical part deals with the theory of learning styles which is contrasted with the theory of learning strategies. In the theoretical part it is described how learning styles and learning strategies are classified by different authors. How to identify learning styles of one particular student is explained. Apart from that the theoretical part deals with teaching styles in order to find the relationship between learning styles and teaching styles. One-to-one teaching is described as the research takes place in one-to-one teaching environment. The empirical part of the thesis is based on qualitative research of learning styles namely case studies of 7 students of different levels of English who acquire English in one-to-one courses. Students´ learning styles will be analysed through data obtained from an interview. The author of the thesis will use participant observation and will keep account of learning styles of every particular student. Results acquired from the interview will be compared with results from observation in one-to-one teaching. When students´ learning styles are analysed, appropriate teaching methods and exercises are proposed to satisfy students´ ascertained learning styles.

La motivation de ce travail est la détection des pannes matérielles pouvant se produire à l'intérieur d'une unité centrale (UC) intégrée (microprocesseur). Certaines des différentes étapes de la méthodologie dépassant ce cadre (pouvant être utilisées avec profit pour d'autres problèmes de test que celui du test d'une UC) mais étant tournées vers des problèmes de tests, le caractère intégré étant une constante des circuits étudiés, le titre de ce document situe ledit travail dans un cadre plus vaste, brièvement résumé par : Test et LSI

Η ραγδαία ανάπτυξη του Παγκόσμιου Ιστού προσέφερε σε όλους τους χρήστες ανά τον κόσμο τη δυνατότητα άμεσης, γρήγορης και αποτελεσματικής προσπέλασης κάθε είδους πληροφορίας. Καθημερινά πραγματοποιούνται εκατομμύρια καταχωρήσεις πληροφοριών στο Διαδίκτυο με αποτέλεσμα ο όγκος της διακινούμενης πληροφορίας να αυξάνει με εκθετικούς ρυθμούς. Με το πάτημα ενός κουμπιού, μια πληθώρα πληροφοριών, ακόμη και για το πιο εξειδικευμένο θέμα, βρίσκεται μπροστά στην οθόνη του χρήστη, έτοιμη προς ανάγνωση και επεξεργασία. Αυτή ακριβώς η «υπερδιάθεση» πληροφοριών καθιστά πολύ δύσκολη έως αδύνατη οποιουδήποτε είδους επεξεργασία των δεδομένων από το χρήστη, έστω και σε επίπεδο απλής ανάγνωσης. Η ύπαρξη ενός εργαλείου ανάκτησης κειμένου και εξαγωγής όρων και κανόνων από μια υπερμεγέθη συλλογή κειμένων θα έδινε τη δυνατότητα στο χρήστη να ανακτήσει χρήσιμες πληροφορίες γρήγορα, χωρίς να είναι απαραίτητη η ανάγνωση και η φυσική επεξεργασία όλων αυτών των κειμένων. Ειδικότερα στο ευαίσθητο πεδίο των Βιο-Επιστημών όπου η αδυναμία επεξεργασίας της διαθέσιμης πληροφορίας και της εξαγωγής χρήσιμων συνδέσεων και συμπερασμάτων επηρεάζει αρνητικά την επιστημονική έρευνα, είναι επιτακτική η ανάγκη παρουσίας εργαλείων που θα διευκολύνουν τη διαδικασία εξόρυξης γνώσης από κείμενα με βιολογικό περιεχόμενο. Στην παρούσα διπλωματική εργασία γίνεται μια παρουσίαση τεχνικών με τις οποίες είναι δυνατή η εξαγωγή γνώσης και κανόνων από κείμενα ηλεκτρονικής μορφής στο Διαδίκτυο τα οποία αφορούν στο επιστημονικό πεδίο της Βιολογίας. Η προσπάθειά μας επικεντρώνεται κυρίως στη δυνατότητα εξόρυξης γνώσης από κείμενα που αναφέρονται σε ένα συγκεκριμένο θέμα Βιολογίας (π.χ. μεταγραφικοί παράγοντες) και που η πραγματοποίηση του στόχου αυτού θα ήταν διαφορετικά από δύσκολη έως αδύνατη καθώς το πλήθος των κειμένων είναι απαγορευτικό για την αναλυτική μελέτη τους από ειδικό ή ομάδα ειδικών, πόσο μάλλον από έναν απλό χρήστη. Αρχικά, περιγράφουμε τον τρόπο ανάκτησης των κειμένων που αναφέρονται στο συγκεκριμένο θέμα του ενδιαφέροντός μας από την ηλεκτρονική βιβλιοθήκη National Library of Medicine και τη δημιουργία της προς επεξεργασία συλλογής κειμένων. Η συλλογή αυτή υπόκειται σε λεξικολογική ανάλυση και επεξεργασία κατά τη διάρκεια της οποίας διατηρούνται από κάθε κείμενο οι πιο σημαντικοί όροι, ενώ οι υπόλοιποι απορρίπτονται. Με τον τρόπο αυτό δημιουργείται ένα σύνολο από τους πιο αντιπροσωπευτικούς όρους ανά κείμενο με τη συχνότητα εμφάνισής τους σε αυτά. Στη συνέχεια, εφαρμόζουμε τεχνικές ομαδοποίησης δεδομένων με στόχο τη δημιουργία ομάδων όρων, αλλά και ομάδων κειμένων. Στα πλαίσια της προσπάθειας αυτής, πειραματιστήκαμε με διάφορες γνωστές τεχνικές ομαδοποίησης (αλγόριθμοι k-means και ιεραρχικός μονής σύνδεσης), ενώ υλοποιήσαμε εκ νέου τον αλγόριθμο ISODATA σε περιβάλλον ανάπτυξης Matlab. Η έρευνά μας ολοκληρώνεται με την εφαρμογή της τεχνικής του Latent Semantic Indexing πριν τη ομαδοποίηση των δεδομένων και τη σύγκριση των αποτελεσμάτων. Μέσα από τις ομάδες που δημιουργούνται με αυτή τη διαδικασία, διαπιστώνουμε την παρουσία συνδέσεων μεταξύ όρων και κειμένων και, ακόμη περισσότερο, τη δυνατότητα εξαγωγής συμπερασμάτων, αλλά και εξόρυξης πραγματικά νέας γνώσης επάνω σε συγκεκριμένα πεδία της επιστήμης της Βιολογίας. / The rapid growth of World Wide Web offered every user around the globe the ability to have immediate, quick and effective access to every kind of information. Daily, millions of records of information about every subject are added on Internet, giving the volume of available information an exponential boost. Simply by pressing only one single button, a plethora of information – even about the most sophisticated topic - is laid out in front of user’s screen ready to be read and processed. This plethora is exactly the reason that makes it difficult or even impossible for a simple user to process all the available data, or even just read it. It is clear that the presence of a tool that will make feasible the retrieval of documents and the extraction of terms and rule-associations from a huge document collection would give users the ability to retrieve valuable information quickly, without even reading or pre-processing all these documents. Especially in Bio-sciences, the inability of processing the available information and extracting useful connections and assumptions is an obstacle in scientific research. Therefore, there is a crying need for tools that will facilitate the process of text mining from documents with biological concept. In the present master thesis we present techniques for extracting knowledge and rules from documents in a digital format retrieved from Internet, with special reference to the scientific field of Biology. Our attempt is mainly focused on knowledge extraction from documents with specific biological concept (e.g. transcription factors), which is a really difficult – in some cases even impossible – task to accomplish due to the huge amount of available documents that an expert or a group of experts should read and process – imagine what a simple user could do. First, we describe the retrieval of documents referring to the specific biological concept we are interested about, from the National Library of Medicine and the construction of our document set. This set will be lexicological processed and only the most important term from each document will be kept while the rest will be ignored. This way, a set of the most representative terms per document will be created, along with the frequency in which the terms appear in each document. Secondly, we apply clustering techniques over this terms-by-document set in order to produce clusters of terms as well as clusters of documents. During this step, many well known clustering techniques are being tested, such as the k-means algorithm and the hierarchical-single linkage algorithm. We also describe our implementation, the ISODATA algorithm. The implementation of all clustering algorithms tested here was done on Matlab 6p5. Our research ends with the application of Latent Semantic Indexing (LSI) technique over our terms-by-documents set before the clustering step; we compare the resulting clusters with those taken without performing LSI before clustering. It is in those clusters that we find many connections between terms and documents and - even more – we discover the ability of extracting not only conclusions about the concept of the documents in each cluster but also truly new knowledge referring to specific scientific fields of Biology.

It is well known now that there exists a coupling between functional brain activity and regional blood flow response in the somatosensory cortex and other cortical areas. Various modalities, including functional magnetic resonance imaging and optical imaging (intrinsic signals as well as fluorescence), have been developed in the past to map functional brain activity. The complexity and fundamental physical constraints of the instruments preclude functional imaging in awake, behaving small animals. This thesis presents the method of Laser Speckle Imaging (LSI) of brain with high spatial and temporal resolution, and potential for imaging awake and behaving animals. The method has the potential to map brain activation with high sensitivity and spatiotemporal resolution without using any exogenous contrast agents. In LSI, scattered laser light with different paths produces a random interference pattern known as speckle, fluctuations of which contain information about the motion of particles in the underlying medium. A post-processing step is needed to extract information out of the speckle images, two of which we introduce in details. Our first method is based on Laser speckle contrast analysis (LASCA), which has been demonstrated as a full-field method for imaging the cerebral blood flow (CBF). However, conventional LASCA is limited to extremely low dynamic range because of the ambient background field, dark current and anomalies in the circuits of CCD camera, which makes it difficult to analyze the spatiotemporal variabilities in CBF. In this study, we propose an enhanced laser speckle contrast analysis (eLASCA) method to improve the dynamic range of LASCA based on monotonic point transformation (MPT). In addition, eLASCA greatly improves the CBF visualization, which is very helpful in demonstrating the details of CBF change. Our second method involves the second order features (SOFs) of the image; they are derived from the cooccurrence matrix that in turn was calculated over the same spatial and temporal window than for the contrast. The image quality metrics - equivalent number of looks, entropy and objective quality – showed superior performance of the SOFs comparing to the contrast analysis. / --

The first part of thesis focuses on theoretical introduction to the methods of text mining -- Information retrieval, classification and clustering. LSA method is presented as an advanced model for representing textual data. Furthermore, the work describes source data and methods for their preprocessing and preparation used to enhance the effectiveness of text mining methods. For each chosen text mining method there are defined evaluation metrics and used already existing, or newly implemented, programs are presented. The results of experiments comparing the effects of different preprocessing type and use of different models of the source data are then demonstrated and discussed in the conclusion.

This master’ thesis deals with the design of the front landing gear for Zlín 40 aircrafts. The main emphasis in the research part of the work is placed on the selection of a new nose landing gear and the subsequent design of the structure. For the structural design was prepared a new mass analysis and load cases. The load-bearing capacity of the structure is verified using analytical and numerical methods of FEM. An important step of the work is the design concept of structure. The aim of the work is to point out the new possibilities of the nose landing gear structure and verify its feasibility for the case of future implementation.

Rekommendationssystem är idag väsentliga för att navigera den enorma mängd produkter tillgängliga via internet. Då social media i form av Twitter vid tidigare tillfällen använts för att generera filmrekommendationer har detta främst varit för att hantera cold-start, ett vanligt drabbande problem för collaborative-filtering. I detta arbete adresseras istället hur top-k rekommendationer påverkas vid integrering av social media data i rekommendationssystemet. För att svara på denna fråga har en prototyp av nytt slag utvecklats inom processmodellen för Design Science. Systemet rankar om top-k rekommendationer baserat på resultatet av social matchning där användares Tweets matchas med nyckelord för filmer genom latent semantic indexing (LSI) similarity. Prototypen evalueras genom experiment som adresserar funktionalitet, noggrannhet, konsekvens och prestanda. Resultatet visar att mätetalen NDCG och MAP för top-k rekommendationer förbättras med social matching jämfört med att enbart använda collaborative filtering. / Recommender systems are a crucial part of navigating the vast number of products on the internet. Social media, in the form of Twitter microblogs, has been previously used to produce movie recommendations, yet this has mainly been to solve cold-start, a common problem in collaborative filtering environments. This work addresses how top-k recommendations in a collaborative filtering environment are affected when augmented with social media data. To answer this question a novel prototype is developed following a design science process model. This system re-ranks top-k recommendations based on a social matching process where Tweets are matched with movie keywords through latent semantic indexing (LSI) similarity. The prototype is evaluated through experiments regarding functionality, accuracy, consistency, and performance. The results show that NDCG and MAP metrics of the top-k recommendations improve with social matching compared to only using the collaborative filtering algorithms.