52 research outputs found

    Cluster to User Profile Ontology Mapping

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    In this paper, we present an approach that uses cluster analysis techniques to extend the ontology of an E-learning domain. This approach is significantly different from any current information retrieval systems, it uses a global ontology model that represents the whole E-learning domain combined with clusters’ centroids vocabularies (terms) to extend the core ontology model. The most important advantage of clustering from the personalization perspective is that the clusters are later used as automatically constructed labels for each user profile. Hence, depending on the document collection and its evolution, both the user profiles and their underlying ontology labels are allowed to change or evolve accordingly. Our proposed approach has been implemented on the HyperMany-Media1 platform at Western Kentucky University, USA

    Automatic Photography Categorization

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    Tato práce se zabývá automatickou kategorizací fotografií podle obrazového obsahu. Cílem práce bylo vytvořit aplikaci, která je schopna s dostatečnou přesností a rychlostí tuto úlohu naplnit. Základní řešení obnáší detekci význačných bodů a extrakci lokálních příznaků, tvorbu vizuálního slovníku shlukováním metodou k-means a jeho reprezentaci pomocí k-dimenzionálního stromu.  Fotografie je reprezentována pomocí histogramu početnosti výskytu vizuálních slov (bag of words). Úlohu vlastního klasifikátoru plní SVM (support vector machines). Dále je základní řešení obohaceno o dělení obrazu na části se samostatným zpracováním, využití barevných korelogramů pro doplňkový popis obrazu, extrakci lokálních  příznaků v opponent color space a měkké přiřazení vizuálních slov k extrahovaným příznakovým vektorům. Závěr práce je věnován experimentům se zmíněnými technikami a vyhodnocování výsledků kategorizace při jejich použití.This thesis deals with content based automatic photo categorization. The aim of the work is to create an application, which is would be able to achieve sufficient precision and computation speed of categorization. Basic solution involves detection of interesting points, extraction of feature vectors, creation of visual codebook by clustering, using k-means algorithm and representing visual codebook by k-dimensional tree. Photography is represented by bag of words - histogram of presence of visual words in a particular photo. Support vector machines (SVM) was used in role of classifier. Afterwards the basic solution is enhanced by dividing picture into cells, which are processed separately, computing color correlograms for advanced image description, extraction of feature vectors in opponent color space and soft assignment of visual words to extracted feature vectors. The end of this thesis concerns to experiments of of above mentioned techniques and evaluation of the results of image categorization on their usage.

    Induction of the morphology of natural language : unsupervised morpheme segmentation with application to automatic speech recognition

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    In order to develop computer applications that successfully process natural language data (text and speech), one needs good models of the vocabulary and grammar of as many languages as possible. According to standard linguistic theory, words consist of morphemes, which are the smallest individually meaningful elements in a language. Since an immense number of word forms can be constructed by combining a limited set of morphemes, the capability of understanding and producing new word forms depends on knowing which morphemes are involved (e.g., "water, water+s, water+y, water+less, water+less+ness, sea+water"). Morpheme boundaries are not normally marked in text unless they coincide with word boundaries. The main objective of this thesis is to devise a method that discovers the likely locations of the morpheme boundaries in words of any language. The method proposed, called Morfessor, learns a simple model of concatenative morphology (word forming) in an unsupervised manner from plain text. Morfessor is formulated as a Bayesian, probabilistic model. That is, it does not rely on predefined grammatical rules of the language, but makes use of statistical properties of the input text. Morfessor situates itself between two types of existing unsupervised methods: morphology learning vs. word segmentation algorithms. In contrast to existing morphology learning algorithms, Morfessor can handle words consisting of a varying and possibly high number of morphemes. This is a requirement for coping with highly-inflecting and compounding languages, such as Finnish. In contrast to existing word segmentation methods, Morfessor learns a simple grammar that takes into account sequential dependencies, which improves the quality of the proposed segmentations. Morfessor is evaluated in two complementary ways in this work: directly by comparing to linguistic reference morpheme segmentations of Finnish and English words and indirectly as a component of a large (or virtually unlimited) vocabulary Finnish speech recognition system. In both cases, Morfessor is shown to outperform state-of-the-art solutions. The linguistic reference segmentations were produced as part of the current work, based on existing linguistic resources. This has resulted in a morphological gold standard, called Hutmegs, containing analyses of a large number of Finnish and English word forms.reviewe

    Automatic Photography Categorization

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    Tato práce se zabývá automatickou kategorizací fotografií podle obrazového obsahu. Cílem práce bylo experimentovat s pokročilými technikami reprezentace obrazu a vytvoření klasifikátoru, který je schopen zpracovat s dostatečnou přesnosí a rychlostí velkou sadu obrazových dat. Základní řešení s využitím vizuálních slovníků je obohaceno o dělení obrazu na části se samostatným zpracováním, využití barevných příznaků pro popis obrazu, měkké přiřazení vizuálních slov k extrahovaným příznakovým vektorům a využití segmentace při budování vizuálního slovníku. Pro dosažní efektivity klasifikátoru jsou využity lineární SVM s explicitním vložením dat. Závěr práce je věnován experimentům se zmíněnými technikami a vyhodnocování výsledků kategorizace při jejich použití.This thesis deals with content based automatic photo categorization. The aim of the work is to experiment with advanced techniques of image represenatation and to create a classifier which is able to process large image dataset with sufficient accuracy and computation speed. A traditional solution based on using visual codebooks is enhanced by computing color features, soft assignment of visual words to extracted feature vectors, usage of image segmentation in process of visual codebook creation and dividing picture into cells. These cells are processed separately. Linear SVM classifier with explicit data embeding is used for its efficiency. Finally, results of experiments with above mentioned techniques of the image categorization are discussed.

    Automatic Photography Categorization

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    Hlavním cílem práce je návrh a implementace klasifikačního nástroje pro účely automatické organizace fotografií, založeného na metodě Bag of Words. Nástroj je implementován jako XnView zásuvný modul, který klasifikuje vybrané fotografie a zapisuje název nejlépe ohodnocené kategorie jako klíčové slovo do IPTC metadat obrazového souboru.Purpose of this thesis is to design and implement a tool for automatic categorization of photos. The proposed tool is based on the Bag of Words classification method and it is realized as a plug-in for the XnView image viewer. The plug-in is able to classify a selected group of photos into predefined image categories. Subsequent notation of image categories is written directly into IPTC metadata of the picture as a keyword.

    Drawing Elena Ferrante's Profile. Workshop Proceedings, Padova, 7 September 2017

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    Elena Ferrante is an internationally acclaimed Italian novelist whose real identity has been kept secret by E/O publishing house for more than 25 years. Owing to her popularity, major Italian and foreign newspapers have long tried to discover her real identity. However, only a few attempts have been made to foster a scientific debate on her work. In 2016, Arjuna Tuzzi and Michele Cortelazzo led an Italian research team that conducted a preliminary study and collected a well-founded, large corpus of Italian novels comprising 150 works published in the last 30 years by 40 different authors. Moreover, they shared their data with a select group of international experts on authorship attribution, profiling, and analysis of textual data: Maciej Eder and Jan Rybicki (Poland), Patrick Juola (United States), Vittorio Loreto and his research team, Margherita Lalli and Francesca Tria (Italy), George Mikros (Greece), Pierre Ratinaud (France), and Jacques Savoy (Switzerland). The chapters of this volume report the results of this endeavour that were first presented during the international workshop Drawing Elena Ferrante's Profile in Padua on 7 September 2017 as part of the 3rd IQLA-GIAT Summer School in Quantitative Analysis of Textual Data. The fascinating research findings suggest that Elena Ferrante\u2019s work definitely deserves \u201cmany hands\u201d as well as an extensive effort to understand her distinct writing style and the reasons for her worldwide success

    Current Challenges in Modeling Cellular Metabolism

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    Mathematical and computational models play an essential role in understanding the cellular metabolism. They are used as platforms to integrate current knowledge on a biological system and to systematically test and predict the effect of manipulations to such systems. The recent advances in genome sequencing techniques have facilitated the reconstruction of genome-scale metabolic networks for a wide variety of organisms from microbes to human cells. These models have been successfully used in multiple biotechnological applications. Despite these advancements, modeling cellular metabolism still presents many challenges. The aim of this Research Topic is not only to expose and consolidate the state-of-the-art in metabolic modeling approaches, but also to push this frontier beyond the current edge through the introduction of innovative solutions. The articles presented in this e-book address some of the main challenges in the field, including the integration of different modeling formalisms, the integration of heterogeneous data sources into metabolic models, explicit representation of other biological processes during phenotype simulation, and standardization efforts in the representation of metabolic models and simulation results

    심층학습을 이용한 액체계의 성질 예측

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    학위논문(박사)--서울대학교 대학원 :자연과학대학 화학부,2020. 2. 정연준.최근 기계학습 기술의 급격한 발전과 이의 화학 분야에 대한 적용은 다양한 화학적 성질에 대한 구조-성질 정량 관계를 기반으로 한 예측 모형의 개발을 가속하고 있다. 용매화 자유 에너지는 그러한 기계학습의 적용 예중 하나이며 다양한 용매 내의 화학반응에서 중요한 역할을 하는 근본적 성질 중 하나이다. 본 연구에서 우리는 목표로 하는 용매화 자유 에너지를 원자간의 상호작용으로부터 구할 수 있는 새로운 심층학습 기반 용매화 모형을 소개한다. 제안된 심층학습 모형의 계산 과정은 용매와 용질 분자에 대한 부호화 함수가 각 원자와 분자들의 구조적 성질에 대한 벡터 표현을 추출하며, 이를 토대로 원자간 상호작용을 복잡한 퍼셉트론 신경망 대신 벡터간의 간단한 내적으로 구할 수 있다. 952가지의 유기용질과 147가지의 유기용매를 포함하는 6,493가지의 실험치를 토대로 기계학습 모형의 교차 검증 시험을 실시한 결과, 평균 절대 오차 기준 0.2 kcal/mol 수준으로 매우 높은 정확도를 가진다. 스캐폴드-기반 교차 검증의 결과 역시 0.6 kcal/mol 수준으로, 외삽으로 분류할 수 있는 비교적 새로운 분자 구조에 대한 예측에 대해서도 우수한 정확도를 보인다. 또한, 제안된 특정 기계학습 모형은 그 구조 상 특정 용매에 특화되지 않았기 때문에 높은 양도성을 가지며 학습에 이용할 데이터의 수를 늘이는 데 용이하다. 원자간 상호작용에 대한 분석을 통해 제안된 심층학습 모형 용매화 자유 에너지에 대한 그룹-기여도를 잘 재현할 수 있음을 알 수 있으며, 기계학습을 통해 단순히 목표로 하는 성질만을 예측하는 것을 넘어 더욱 상세한 물리화학적 이해를 하는 것이 가능할 것이라 기대할 수 있다.Recent advances in machine learning technologies and their chemical applications lead to the developments of diverse structure-property relationship based prediction models for various chemical properties; the free energy of solvation is one of them and plays a dominant role as a fundamental measure of solvation chemistry. Here, we introduce a novel machine learning-based solvation model, which calculates the target solvation free energy from pairwise atomistic interactions. The novelty of our proposed solvation model involves rather simple architecture: two encoding function extracts vector representations of the atomic and the molecular features from the given chemical structure, while the inner product between two atomistic features calculates their interactions, instead of black-boxed perceptron networks. The cross-validation result on 6,493 experimental measurements for 952 organic solutes and 147 organic solvents achieves an outstanding performance, which is 0.2 kcal/mol in MUE. The scaffold-based split method exhibits 0.6 kcal/mol, which shows that the proposed model guarantees reasonable accuracy even for extrapolated cases. Moreover, the proposed model shows an excellent transferability for enlarging training data due to its solvent-non-specific nature. Analysis of the atomistic interaction map shows there is a great potential that our proposed model reproduces group contributions on the solvation energy, which makes us believe that the proposed model not only provides the predicted target property, but also gives us more detailed physicochemical insights.1. Introduction 1 2. Delfos: Deep Learning Model for Prediction of Solvation Free Energies in Generic Organic Solvents 7 2.1. Methods 7 2.1.1. Embedding of Chemical Contexts 7 2.1.2. Encoder-Predictor Network 9 2.2. Results and Discussions 13 2.2.1. Computational Setup and Results 13 2.2.2. Transferability of the Model for New Compounds 17 2.2.3. Visualization of Attention Mechanism 26 3. Group Contribution Method for the Solvation Energy Estimation with Vector Representations of Atom 29 3.1. Model Description 29 3.1.1. Word Embedding 29 3.1.2. Network Architecture 33 3.2. Results and Discussions 39 3.2.1. Computational Details 39 3.2.2. Prediction Accuracy 42 3.2.3. Model Transferability 44 3.2.4. Group Contributions of Solvation Energy 49 4. Empirical Structure-Property Relationship Model for Liquid Transport Properties 55 5. Concluding Remarks 61 A. Analyzing Kinetic Trapping as a First-Order Dynamical Phase Transition in the Ensemble of Stochastic Trajectories 65 A1. Introduction 65 A2. Theory 68 A3. Lattice Gas Model 70 A4. Mathematical Model 73 A5. Dynamical Phase Transitions 75 A6. Conclusion 82 B. Reaction-Path Thermodynamics of the Michaelis-Menten Kinetics 85 B1. Introduction 85 B2. Reaction Path Thermodynamics 88 B3. Fixed Observation Time 94 B4. Conclusions 101Docto

    Towards Reliable and Inclusive Natural Language Generation

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    Natural language generation (NLG) is an important subfield of natural language processing (NLP) that produces natural language output. Despite notable advancements made by large-scale pre-trained language models in NLG, there remain several unresolved challenges. This thesis aims to enhance NLG from two significant aspects: reliability and inclusiveness. For reliability, on the one hand, we introduce novel training objectives that improve the alignment of language generation models with desired model behaviors. To improve the answerability of model-generated questions, we use a question answering model to provide additional rewards to a question generation model, encouraging the production of more answerable questions. In addition, we propose to train language models with a mixture of forward and reverse cross-entropies, demonstrating that the resulting models yield better generated text without complex decoding strategies. On the other hand, we propose novel evaluation methods to assess the performance of NLG models accurately and comprehensively. By combining human and automatic evaluations, we strike a balance between reliability and reproducibility. We delve into the unexplored issue of unfaithfulness in extractive summaries and conclude that extractive summarization does not guarantee faithfulness. For inclusiveness, we extend the coverage of NLG techniques to low-resource or endangered languages. We develop the first machine translation system for supporting translation between Cherokee, an endangered Native American language, and English, and we propose a roadmap for utilizing NLP to support language revitalization efforts. Additionally, we investigate the underrepresentation of low-resource languages during multilingual tokenization, a crucial data preprocessing step in training multilingual NLG models, and we present best practices for training multilingual tokenizers. Overall, this thesis works towards enhancing the trustworthiness of NLG models in practice and facilitating support for a more diverse range of languages worldwide.Doctor of Philosoph
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