由於網際網路的普及造成資訊量愈來愈大，在資訊的搜尋、整理與閱讀上會耗費許多時間，因此本研究提出一應用文本主題及關係探勘的方法，將多份文件自動生成一篇摘要，以幫助使用者能降低資訊的閱讀時間，並能快速理解文件所欲表達之意涵。 本研究以電影評論文章為例，結合文章結構的概念，將影評摘要分為「電影資訊」、「電影劇情介紹」及「心得結論」三部分，其中「電影資訊」及「心得結論」為透過本研究建置之電影領域相關詞庫比對得出。接著將餘下之段落歸屬於「電影劇情介紹」，並透過LDA主題模型將段落分群，再運用主題關係地圖的概念挑選各群之代表段落並排序，最後將各段落去除連接詞及將代名詞還原為其所指之主詞，以形成一篇列點式影評摘要。 研究結果顯示，本研究所實驗之三部電影，產生之摘要能涵蓋較多的資訊內容，提升了摘要之多樣性，在與最佳範本摘要的相似度比對上，分別提升了10.8228%、14.0123%及25.8142%，可知本研究方法能有效掌握文件之重點內容，生成之摘要更為全面，藉由此方法讓使用者自動彙整電影評論文章，以生成一精簡之摘要，幫助使用者節省其在資訊的搜尋及閱讀的時間，以便能快速了解相關電影之資訊及評論。 / The rapid development of information technology over the past decades has dramatically increased the amount of online information. Because of the time-wasting on absorbing large amounts of information for users, we would like to present a method in this thesis by using text topic and relationship mining for multi-document summarization to help users grasp the theme of multiple documents quickly and easily by reading the accurate summary without reading the whole documents. We use movie reviews as an example of multi-document summarization and apply the concept of article structures to categorize summary into film data, film orientation and conclusion by comparing the thesaurus of movie review field built by this thesis. Then we cluster the paragraphs in the structure of film orientation into different topics by Latent Dirichlet Allocation (LDA). Next, we apply the concept of text relationship map, a network of paragraphs and the node in the network referring to a paragraph and an edge indicating that the corresponding paragraphs are related to each other, to extract the most important paragraph in each topic and order them. Finally, we remove conjunctions and replace pronouns with the name it indicates in each extracted paragraph s and generate a bullet-point summary. From the result, the summary produced by this thesis can cover different topics of contents and improve the diversity of the summary. The similarities compared with the produced summaries and the best-sample summaries raise of 10.8228%, 14.0123% and 25.8142% respectively. The method presented in this thesis grasps the key contents effectively and generates a comprehensive summary. By providing this method, we try to let users aggregate the movie reviews automatically and generate a simplified summary to help them reduce the time in searching and reading articles.

Un modèle complet permettant de simuler la dynamique d'un nano-aérosol est présenté et discuté. On considère une équation Eulérienne de type « Diffusion-Inertia » réécrite en moments en incluant un terme source de coagulation. Le phénomène de dépôt est pris en compte par l'intermédiaire d'une condition aux limites sur le flux de moments à la paroi. L'expression de la granulométrie en moments permet d'obtenir une très bonne efficacité de calcul et rend ainsi le modèle utilisable pour des applications industrielles ou en santé au travail. L'implémentation de cette approche dans un code de CFD est validée sur des cas simples par comparaison avec une méthode des classes ainsi que des données expérimentales. La méthode des moments n'introduit pas de biais particulier et les résultats numériques sont en accord avec les résultats expérimentaux. Un nouveau dispositif expérimental, qui consiste en une enceinte ventilée, est également proposé afin de maîtriser au mieux l'écoulement et de caractériser la morphologie des agrégats générés. La confrontation entre les résultats numériques et expérimentaux met en évidence le fait que la détermination des paramètres fractals est un élément clé de la modélisation / A complete CFD model for nano-aerosol dynamics is presented and discussed. It consists in an Eulerian "Diffusion-Inertia" equation including a coagulation source term which is rewritten in terms of moments. Deposition phenomenon is taken into account by means of a boundary condition on the flux of moments at walls. The moment transformation allows good computational performances and makes thus the model tractable for industrial and occupational health applications. The implementation of this approach into a CFD code is assessed for simple cases by comparison with sectional approach results and experimental data. These comparisons show that the method of moments does not induce particular bias and that numerical results are in good agreement with available experimental data. An experimental set-up, which consists in a ventilated chamber, is also proposed for allowing a good control of the flow and for allowing the investigation of aggregates morphology. The confrontation between numerical and experimental results highlights that the determination of the fractal parameters is a modelling key point

Die Habilitation befasst sich mit neuen Verfahren und Anwendungen des Thermischen Spritzens, beleuchtet anhand einer internationalen Umfrage den Forschungsbedarf der Branche und liefert zahlreiche Lösungen, die im Rahmen von grundlagenorientierten und industrienahen Forschungsvorhaben erarbeitet wurden. Der Fokus der Arbeit liegt auf der Verbesserung individueller Arbeitsschritte der Prozesskette des Thermischen Spritzens, wobei Ergebnisse aus den Bereichen Werkstoffentwicklung, Prozessoptimierung, Qualitätssicherung und Nachbearbeitung zusammengeführt und durch konkrete Anwendungsbeispiele hinterlegt werden. Im Detail werden die Aspekte Herstellung leistungsfähiger und preiswerter Spritzzusätze (Wasserverdüsung von Metallpulvern, Hochenergiekugelmahlen, Agglomerieren und Sintern sowie Fülldrahtherstellung), die Verbesserung der Prozessführung (numerisch optimierte Spritzbrenner und automatisierbare Online-Prozessdiagnostikmethoden) und die Steigerung der Leistungsfähigkeit der Beschichtungen durch mechanische Nachbearbeitung sowie Versieglung behandelt. Anwendungsbezogen werden das Beschichten von Hochleistungspolymeren und CFC-Leichtbaustrukturen untersucht. Um Anknüpfungspunkte für weiterführende Forschungsarbeiten zu schaffen, schließt die Arbeit mit der Darstellung von Entwicklungstrends und zeigt Arbeitsgebiete auf, die perspektivisch von thermisch gespritzten Schichten profitieren können.

Flow rate measurements are a common topic for process monitoring in chemical engineering and food industry. To achieve the requested low uncertainties of 0:1% for flow rate measurements, a precise measurement of the shear layers of such flows is necessary. The Laser Doppler Velocimeter (LDV) is an established method for measuring local flow velocities. For exact estimation of the flow rate, the flow profile in the shear layer is of importance. For standard LDV the axial resolution and therefore the number of measurement points in the shear layer is defined by the length of the measurement volume. A decrease of this length is accompanied by a larger fringe distance variation along the measurement axis which results in a rise of the measurement uncertainty for the flow velocity (uncertainty relation between spatial resolution and velocity uncertainty). As a unique advantage, the laser Doppler profile sensor (LDV-PS) overcomes this problem by using two fan-like fringe systems to obtain the position of the measured particles along the measurement axis and therefore achieve a high spatial resolution while it still offers a low velocity uncertainty. With this technique, the flow rate can be estimated with one order of magnitude lower uncertainty, down to 0:05% statistical uncertainty.1 And flow profiles especially in film flows can be measured more accurately. The problem for this technique is, in contrast to laboratory setups where the system is quite stable, that for industrial applications the sensor needs a reliable and robust traceability to the SI units, meter and second. Small deviations in the calibration can, because of the highly position depending calibration function, cause large systematic errors in the measurement result. Therefore, a simple, stable and accurate tool is needed, that can easily be used in industrial surroundings to check or recalibrate the sensor. In this work, different calibration methods are presented and their in uences to the measurement uncertainty budget of the sensor is discussed. Finally, generated measurement results for the film flow of an impinging jet cleaning experiment are presented.

Les applications futures de la robotique, en particulier pour des robots de service à la personne, exigeront des capacités d’adaptation continue à l'environnement, et notamment la capacité à reconnaître des nouveaux objets et apprendre des nouveaux mots via l'interaction avec les humains. Bien qu'ayant fait d'énormes progrès en utilisant l'apprentissage automatique, les méthodes actuelles de vision par ordinateur pour la détection et la représentation des objets reposent fortement sur de très bonnes bases de données d’entrainement et des supervisions d'apprentissage idéales. En revanche, les enfants de deux ans ont une capacité impressionnante à apprendre à reconnaître des nouveaux objets et en même temps d'apprendre les noms des objets lors de l'interaction avec les adultes et sans supervision précise. Par conséquent, suivant l'approche de le robotique développementale, nous développons dans la thèse des approches d'apprentissage pour les objets, en associant leurs noms et leurs caractéristiques correspondantes, inspirées par les capacités des enfants, en particulier l'interaction ambiguë avec l’homme en s’inspirant de l'interaction qui a lieu entre les enfants et les parents.L'idée générale est d’utiliser l'apprentissage cross-situationnel (cherchant les points communs entre différentes présentations d’un objet ou d’une caractéristique) et la découverte de concepts multi-modaux basée sur deux approches de découverte de thèmes latents: la Factorisation en Natrices Non-Négatives (NMF) et l'Allocation de Dirichlet latente (LDA). Sur la base de descripteurs de vision et des entrées audio / vocale, les approches proposées vont découvrir les régularités sous-jacentes dans le flux de données brutes afin de parvenir à produire des ensembles de mots et leur signification visuelle associée (p.ex le nom d’un objet et sa forme, ou un adjectif de couleur et sa correspondance dans les images). Nous avons développé une approche complète basée sur ces algorithmes et comparé leur comportements face à deux sources d'incertitudes: ambiguïtés de références, dans des situations où plusieurs mots sont donnés qui décrivent des caractéristiques d'objets multiples; et les ambiguïtés linguistiques, dans des situations où les mots-clés que nous avons l'intention d'apprendre sont intégrés dans des phrases complètes. Cette thèse souligne les solutions algorithmiques requises pour pouvoir effectuer un apprentissage efficace de ces associations de mot-référent à partir de données acquises dans une configuration d'acquisition simplifiée mais réaliste qui a permis d'effectuer des simulations étendues et des expériences préliminaires dans des vraies interactions homme-robot. Nous avons également apporté des solutions pour l'estimation automatique du nombre de thèmes pour les NMF et LDA.Nous avons finalement proposé deux stratégies d'apprentissage actives: la Sélection par l'Erreur de Reconstruction Maximale (MRES) et l'Exploration Basée sur la Confiance (CBE), afin d'améliorer la qualité et la vitesse de l'apprentissage incrémental en laissant les algorithmes choisir les échantillons d'apprentissage suivants. Nous avons comparé les comportements produits par ces algorithmes et montré leurs points communs et leurs différences avec ceux des humains dans des situations d'apprentissage similaires. / Future applications of robotics, especially personal service robots, will require continuous adaptability to the environment, and particularly the ability to recognize new objects and learn new words through interaction with humans. Though having made tremendous progress by using machine learning, current computational models for object detection and representation still rely heavily on good training data and ideal learning supervision. In contrast, two year old children have an impressive ability to learn to recognize new objects and at the same time to learn the object names during interaction with adults and without precise supervision. Therefore, following the developmental robotics approach, we develop in the thesis learning approaches for objects, associating their names and corresponding features, inspired by the infants' capabilities, in particular, the ambiguous interaction with humans, inspired by the interaction that occurs between children and parents.The general idea is to use cross-situational learning (finding the common points between different presentations of an object or a feature) and to implement multi-modal concept discovery based on two latent topic discovery approaches : Non Negative Matrix Factorization (NMF) and Latent Dirichlet Association (LDA). Based on vision descriptors and sound/voice inputs, the proposed approaches will find the underlying regularities in the raw dataflow to produce sets of words and their associated visual meanings (eg. the name of an object and its shape, or a color adjective and its correspondence in images). We developed a complete approach based on these algorithms and compared their behavior in front of two sources of uncertainties: referential ambiguities, in situations where multiple words are given that describe multiple objects features; and linguistic ambiguities, in situations where keywords we intend to learn are merged in complete sentences. This thesis highlights the algorithmic solutions required to be able to perform efficient learning of these word-referent associations from data acquired in a simplified but realistic acquisition setup that made it possible to perform extensive simulations and preliminary experiments in real human-robot interactions. We also gave solutions for the automatic estimation of the number of topics for both NMF and LDA.We finally proposed two active learning strategies, Maximum Reconstruction Error Based Selection (MRES) and Confidence Based Exploration (CBE), to improve the quality and speed of incremental learning by letting the algorithms choose the next learning samples. We compared the behaviors produced by these algorithms and show their common points and differences with those of humans in similar learning situations.

With the vast amount of information available on the Internet today, helping users find relevant content has become a prioritized task in many software products that recommend news articles. One such product is Opera for Android, which has a news feed containing articles the user may be interested in. In order to easily determine what articles to recommend, they can be categorized by the topics they contain. One approach of categorizing articles is using Machine Learning and Natural Language Processing (NLP). A commonly used model is Latent Dirichlet Allocation (LDA), which finds latent topics within large datasets of for example text articles. An extension of LDA is hierarchical Latent Dirichlet Allocation (hLDA) which is an hierarchical variant of LDA. In hLDA, the latent topics found among a set of articles are structured hierarchically in a tree. Each node represents a topic, and the levels represent different levels of abstraction in the topics. A further extension of hLDA is constrained hLDA, where a set of predefined, constrained topics are added to the tree. The constrained topics are extracted from the dataset by grouping highly correlated words. The idea of constrained hLDA is to improve the topic structure derived by a hLDA model by making the process semi-supervised. The aim of this thesis is to create a hLDA and a constrained hLDA model from a dataset of articles provided by Opera. The models should then be evaluated using the novel metric word frequency similarity, which is a measure of the similarity between the words representing the parent and child topics in a hierarchical topic model. The results show that word frequency similarity can be used to evaluate whether the topics in a parent-child topic pair are too similar, so that the child does not specify a subtopic of the parent. It can also be used to evaluate if the topics are too dissimilar, so that the topics seem unrelated and perhaps should not be connected in the hierarchy. The results also show that the two topic models created had comparable word frequency similarity scores. None of the models seemed to significantly outperform the other with regard to the metric.

We examine the antecedents of customer satisfaction in the restaurant sector, paying particular attention to perceived value and price level. Using Latent Dirichlet Allocation, we extract latent topics from the text of Yelp! reviews, then analyze the relationship between these topics and satisfaction, measured as the difference between review rating and user average review rating.

AJ Knowledge of the soft facial tissues is the basis of any craniofacial reconstruction. It is of a great importance mainly for forensic practice, but it plays an important role in other fields, for example aesthetic surgery. Defining the thickness of facial tissues for different sexes, age and ethnic groups is an important aspect of forensic anthropology. The thesis specialises in finding out the thickness of soft facial tissues in modern czech population, it takes into consideration the sex, age and assymetry. The main source of information are the CT scans of the heads of 46 adult women and 56 adult men of czech nationality ranging between ages 21 to 83. 80 landmarks are defined in each scan, therefore 40 linear measurements between corresponding points have been evaluated. Data were analysed using the PCA, Hotelling test, linear discrimination analysis, Kolmogorov-Smirnov test, MANOVA, Kruskal-Wallis test and Wilcoxon paired test. Retrieved thicknesses of soft tissues will serve as the standards for the current czech population. Sexual dimorphism has been proven regarding the whole face, as well as both upper and lower parts of the face. The success of classification on the upper part of the face decresases significantly. Aging has been proven to have strong effect on the thickness of soft...

根據過去研究指出，社群網站上的貼文訊息會對群眾情緒造成影響，進而影響股市波動，故對於投資者而言，如果能快速分析大量社群網站的財經文本來推測投資情緒進而預測股市走勢，將可提升投資獲利。 過去文本情感分析的研究中已證實監督式學習方法可以透過簡單量化的方式達到良好的分類效果，但監督式學習方法所使用的訓練資料集須有事先定義好的已知類別，故其有無法預期未知類別的限制，所以本研究透過深度學習方法，從巨量資料及裡抓出有關於股市之文章，並透過財經文本的混合監督式學習與非監督式學習之情感分析方法，透過非監督式學習對微博財經貼文進行文本主題判別、情緒指數計算與情緒傾向標注，並且透過監督式學習的方式，建立分類模型以預測上海指數走勢，最後配合視覺化工具作趨勢線圖分析，找出具有領先指標特性之主題。 在實驗結果中，深度學習方面，本研究透過word2vec抓取有效之股市主題文章，有效篩選了需要分析之文本，主題模型方面，我們最後使用LDA作為本研究標註主題之方法，因為其文本數量大於議題詞數量造成TFIDF矩陣過於稀疏，造成Kmeans分群效果不佳，故後續採用LDA主題模型進行主題標注。情緒傾向標注方面，透過擴充後的情感詞集比起NTUSD有更好的詞性分數判斷效果，計算出的情緒指數之趨勢線能有效預測上海指數之趨勢。此外，並非所有主題模型之情緒指數皆具有領先特性，僅公司表現與上海指數之主題模型的情緒指數能提前反應上海指數趨勢，故本研究用此二主題之文本的情緒指數來建立分類模型。 本研究透過比較情緒指數與單純指數指標分類模型的準確度，前者較後者高出7%的準確率。故證實了情感分析確實能有效提升上海指數趨勢預測準確度，幫助投資者增加股市報酬率。

Using best-practices and supports that apply the least dangerous assumption (LDA) is a powerful tool for increasing overall student quality of life and keeping alive a vision of high achievement for all students. This presentation will focus on research and evidence based strategies to promote the LDA for students with significant disabilities regarding accessing personally relevant academic instruction with meaningful student centered outcomes. OBJECTIVES: By the end of this session, participants will be able to: a) identify four criteria to promote Least Dangerous Assumption b) discuss ways to successfully implement the four criteria in their classroom to meet the needs of a wide range of diverse students with significant disabilities c) identify resources that incorporate these four criteria and are applicable to students from diverse backgrounds