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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
101

Étude de modèles neuronaux de questions-réponses

Archambault, Jean 08 1900 (has links)
Dans le domaine du traitement automatique du langage naturelle, la tâche question-réponse (Question-Answer (QA)) consistant à développer des systèmes générant une réponse plausible à une question posée en langage naturel par un utilisateur depuis une source d’information, demeure d’actualité. Elle présente de nombreuses applications pratiques dont la recherche affinée d’information sur le web. Aujourd’hui, suite à une requête, les moteurs de recherche actuels retournent des listes classées de documents mais ils ne fournissent pas de réponse à la question de l’utilisateur. Depuis plus de cinquante ans, différentes approches et technologies ont été développées, qui ont mené à des avancées significatives en QA. Parmi celles-ci, les embeddings de mots, des vecteurs numériques représentant la signification des mots dans des contextes, des ensembles de données modèles et l’utilisation de réseaux neuronaux (RN) ont permis le développement de systèmes QA performants. Dans ce contexte, ce mémoire a porté sur l’étude du modèle BIDAF (Bidirectional Attention Flow), un des systèmes QA à base de RN les plus performants au début de ces travaux, testé au moyen de SQuAD (Stanford Question Answering Dataset), un des benchmarks les plus populaires en QA. L’étude initiale de BIDAF a démontré un certain nombre de différences structurelles notables entre sa description littéraire et son implémentation. Différentes variantes de BIDAF ont donc été développées et testées auxquelles on a ajouté l’entraînement des embeddings de mots durant l’entraînement du modèle ainsi que la modulation cyclique du taux d’apprentissage. Les modèles de structures similaires à la description littéraire de BIDAF avec entraînement des embeddings de mots et une version simplifiée ont démontré de meilleures performances, soit de 59.56% en exact match (EM) et 67.09% en F1, et EM = 60.20% et F1 = 67.69%, respectivement. Cette performance a été améliorée par la modulation du taux d’apprentissage à EM = 61.42% et F1 = 68.46%. / In the field of natural language processing, the question-answer (QA) task involving the development of systems generating a plausible answer to a question asked in natural language by a user from an information source, remains a hot topic. It has many practical applications including fine-grained information retrieval on the web. Today, following a query, current search engines return lists of classified documents but they do not provide an answer to the user’s question. Since more than fifty years, a number of approaches and technologies have been developed which have led to significant advances in QA. Among these, word embeddings, numerical vectors representing the meaning of words in contexts, the development of model or benchmark datasets and the use of neural networks (NN) have enabled the development of efficient QA systems. In this context, this thesis focused on the study of the BIDAF (Bidirectional Attention Flow) model, one of the most efficient NN-based QA systems at the start of this work, tested using SQuAD (Stanford Question Answering Dataset), one of the most popular benchmarks in QA. BIDAF’s initial study showed a number of notable structural differences between its literary description and its implementation. Different variants of BIDAF were therefore developed and tested to which were added word embedding training during model training as well as cyclic modulation of the learning rate. The models of structures similar to the literary description of BIDAF with training of word embeddings and a simplified version showed better performances, i.e. 59.56% in exact match (EM) and 67.09% in F1, and EM = 60.20% and F1 = 67.69 %, respectively. This performance was improved by modulating the learning rate to EM = 61.42% and F1 = 68.46%.
102

Effects of Intervention on Text-Implicit Questions for d/Deaf and Hard of Hearing Students

Santoro, Carly Rae January 2020 (has links)
No description available.
103

[en] CORPUS FOR ACADEMIC DOMAIN: MODELS AND APPLICATIONS / [pt] CORPUS PARA O DOMÍNIO ACADÊMICO: MODELOS E APLICAÇÕES

IVAN DE JESUS PEREIRA PINTO 16 November 2021 (has links)
[pt] Dados acadêmicos (e.g., Teses, Dissertações) englobam aspectos de toda uma sociedade, bem como seu conhecimento científico. Neles, há uma riqueza de informações a ser explorada por modelos computacionais, e que podem ser positivos para sociedade. Os modelos de aprendizado de máquina, em especial, possuem uma crescente necessidade de dados para treinamento, que precisam ser estruturados e de tamanho considerável. Seu uso na área de processamento de linguagem natural é pervasivo nas mais diversas tarefas. Este trabalho realiza o esforço de coleta, construção, análise do maior corpus acadêmico conhecido na língua portuguesa. Foram treinados modelos de vetores de palavras, bag-of-words e transformer. O modelo transformer BERTAcadêmico apresentou os melhores resultados, com 77 por cento de f1-score na classificação da Grande Área de conhecimento e 63 por cento de f1-score na classificação da Área de conhecimento nas categorizações de Teses e Dissertações. É feita ainda uma análise semântica do corpus acadêmico através da modelagem de tópicos, e uma visualização inédita das áreas de conhecimento em forma de clusters. Por fim, é apresentada uma aplicação que faz uso dos modelos treinados, o SucupiraBot. / [en] Academic data (i.e., Thesis, Dissertation) encompasses aspects of a whole society, as well as its scientific knowledge. There is a wealth of information to be explored by computational models, and that can be positive for society. Machine learning models in particular, have an increasing need for training data, that are efficient and of considerable size. Its use in the area of natural language processing (NLP) is pervasive in many different tasks. This work makes the effort of collecting, constructing, analyzing and training of models for the biggest known academic corpus in the Portuguese language. Word embeddings, bag of words and transformers models have been trained. The Bert-Academico has shown the better result, with 77 percent of f1-score in Great area of knowledge and 63 percent in knowledge area classification of Thesis and Dissertation. A semantic analysis of the academic corpus is made through topic modelling, and an unprecedented visualization of the knowledge areas is presented. Lastly, an application that uses the trained models is showcased, the SucupiraBot.
104

USING RULE-BASED METHODS AND MACHINE LEARNING FOR SHORT ANSWER SCORING

Pihlqvist, Fredrik, Mulongo, Benedith January 2018 (has links)
Automatiskt rättning av korta texter är ett område som spänner allt från naturlig språkbehandling till maskininlärning. Projektet behandlar maskininlärning för att förutsäga korrektheten av svar i fritext. Naturlig språkbehandling används för att analysera text och utvinna viktiga underliggande relationer i texten. Det finns idag flera approximativa lösningar för automatiskt rättning av korta svar i fritext. Två framstående metoder är maskininlärning och regelbaserad metod. Vi kommer att framföra en alternativ metod som kombinerar maskininlärning med en regelbaserad metod för att approximativt lösa förenämnda problemet. Studien handlar om att implementera en regelbaserad metod, maskininlärning metod och en slutgiltig kombination av båda dessa metoder. Utvärderingen av den kombinerade metoden utförs genom att titta på de relativa ändringarna i prestanda då vi jämför med den regelbaserade och maskininlärning metoden. De erhållna resultaten har visat att det inte finns någon ökning av noggrannheten hos den kombinerade metoden jämfört med endast maskininlärning metoden. Den kombinerade metoden använder emellertid en liten mängd märkta data med en noggrannhet som är nästan lika metoden med maskininlärning, vilket är positivt. Ytterligare undersökning inom detta område behövs, denna uppsats är bara ett litet bidrag till nya metoder i automatisk rättning. / Automatic correction of short text answers is an area that involves everything from natural language processing to machine learning. Our project deals with machine learning for predicting the correctness of candidate answers and natural language processing to analyse text and extract important underlying relationships in the text. Given that today there are several approximative solutions for automatically correcting short answers, ranging from rule-based methods to machine learning methods. We intend to look at how automatic answer scoring can be solved through a clever combination of both machine learning methods and rule-based method for a given dataset. The study is about implementing a rule-based method, a machine learning method and a final combination of both these methods. The evaluation of the combined method is done by measuring its relative performance compared to the rule-based method and machine learning method. The results obtained have shown that there is no increase in the accuracy of the combined method compared to the machine learning method alone. However, the combined method uses a small amount of labeled data with an accuracy almost equal to the machine learning, which is positive. Further investigation in this area is needed, this thesis is only a small contribution, with a new approaches and methods in automatic short answer scoring.
105

Extracting relevant answer phrases from text : For usage in reading comprehension question generation / Extrahering av relevanta svarsfraser från text : För användning vid generering av läsförståelsefrågor

Kärrfelt, Filippa January 2022 (has links)
This report presents a method for extracting answer phrases, suitable as answers to reading comprehension questions, from Swedish text. All code used to produce the results is available on github*. The method is developed using a Swedish BERT, a pre-trained language model based on neural networks. The BERT model is fine-tuned for three different tasks; two variations of token classification for answer extraction, and one for sentence classification with the goal of identifying relevant sentences. The dataset used for fine-tuning consists of 1814 question and answer pairs posed on 598 different texts, partitioned into a training, a validation and a test set. The models are assessed individually and are furthermore combined, using a method based on roundtrip consistency, into a system for filtering extracted answer phrases. The results for each of the models, and for the system combining them are evaluated both on quantitative measures (precision, recall and Jaccard index) and qualitative measures. Within the qualitative evaluation we both look at results produced by the models and conduct structured human evaluation with the help of four external evaluators. The final answer extraction model achieves a precision of 0.02 and recall of 0.95, with an average Jaccard index of 0.55 between the extracted answer phrases and the targets. When applying the system for filtering the precision is 0.03, the recall 0.50 and the Jaccard index 0.62 on a subset of the test data. The answer extraction model achieves the same results as the baseline on precision, outperforms it on recall by a large margin, and has worse results than the baseline on Jaccard index. The method applying filtering, which is evaluated on a subset of the test set, has worse precision than the baseline but outperform it on both recall and Jaccard index. In the qualitative evaluation we detect some flaws in the grammatical correctness of the extracted answers, as over 50% of them are classified as not grammatically correct. The joint result of the two evaluators on suitability show that 32% of the grammatically correct answers are suitable as answer phrases. / I rapporten presenteras en metod för extrahering av svarsfraser lämpliga som svar till läsförståelsefrågor på svensk text. All kod använd för att producera resultaten finns tillgänglig på github*. Metoden utgår från en svensk BERT, en tränad språkmodell baserad på neurala nätverk. BERT-modellen är finjusterad (“fine-tuned“) för tre olika uppgifter; två varianter av “token classification“ för extrahering av svarsfraser samt en för “sentence classification“ med målet att identifiera relevanta meningar. Datasetet som används för finjusteringen innehåller 1814 fråge- och svarspar baserade på 598 texter, uppdelat i ett tränings-, valideringsoch testset. Resultaten utvärderas separat för varje modell, och också för ett kombinerat system av de tre modellerna. I det kombinerade systemet extraherar en modell potentiella svarsfraser medans de andra två agerar som ett filter, baserat på en variant av “roundtrip consistency“. Resultaten för varje modell och för systemet för filtrering utvärderas både kvantitativt (på “precision“, “recall“ och Jaccard index) och kvalitativt. Fyra externa utvärderare rekryterades för utvärdering av resultaten på kvalitativa grunder. Modellen med bäst resultat når en precision av 0.02 och recall av 0.95, med ett snittvärde för Jaccard index av 0.55 mellan de extraherade och korrekta svarsfraserna. Med applicering av systemet för filtrering blir resultaten för precision 0.03, recall 0.50 och Jaccard index 0.62 på en delmängd av testdatat. Den BERT-baserade modellen för extrahering av svarsfraser når samma resultat som baseline på precision, bättre resultat på recall samt sämre resultat på Jaccard index. Resultaten för metoden med filtrering, som är utvärderad på en delmängd av testdatat, har sämre resultat än baseline på precision, men bättre resultat på recall och Jaccard index. I den kvalitativa utvärderingen upptäcker vi brister i den grammatiska korrektheten av de extraherade svarsfraserna, då mer än 50% av dem klassificeras som grammatiskt felaktiga. De sammantagna resultaten av utvärderingen av svarsfrasernas lämplighet visar att 32% av de svarsfraser som är grammatiskt korrekta är lämpliga som svarsfraser.
106

Разработка инструмента для планирования и контроля заказов на производстве литий-ионных аккумуляторов в «1C: ERP Управление предприятием» : магистерская диссертация / Development of software tool for schedule and resources planning laity-ions buttery for order control and management base on ERP-system “1C: ERP Enterprise”

Бородулина, А. Д., Borodulina, A. D. January 2023 (has links)
В рамках работы были исследованы методы построения систем планирования и управления предприятием класса ERP-систем. Цель работы – разработка программного инструмента в «1С: ERP Управление предприятием 2», позволяющего вести учет всех заказов, предстоящих и уже выполняющихся на предприятии. В ходе исследования был разработан рабочий прототип учета и планирования производства заказов на предприятии по производству литий-ионных аккумуляторов с использованием ERP-системы «1С-Предприятие». / Within the framework of this work, a study was made of the existing types of scheduler and enterprise resources planning system methods. An analysis of the existing question-answer systems was carried out. The goal of work id development software tool for schedule and resources planning for order control and management of laity-ions buttery base on ERP-system “1C: ERP Enterprise”. In the course of the study, a working prototype of schedule and resources planning for order control and management base on ERP-system “1C: ERP Enterprise” was developed.
107

A Sample-to-Answer Polymer Lab-on-a-Chip with Superhydrophilic Surfaces using a Spray Layer-by-Layer Nano-Assembly Method

Lee, Kang Kug January 2013 (has links)
No description available.
108

Разработка экспертной системы для планирования застройки садового участка : магистерская диссертация / Development of expert system for planning of garden plot development

Касоян, Н. Ф., Kasoyan, N. F. January 2023 (has links)
В рамках работы были исследованы методы построения систем планирования и застройки садовых, земельных участков. Цель работы – разработка программного инструмента, позволяющего решать планирования застройки садового участка с учетом ограничений в части пожарной безопасности и санитарных норм. В качестве основы разработки экспертной системы планирования использовались продукционная модель представления знаний и язык Python. / Within the framework of this work, a study was made of the existing types of planning of garden plot development. An analysis of the existing planning systems was carried out. The goal of work id development software tool for planning of garden plot development with limitations of fire security and sanitary norms. Planning expert system based on production knowledge base and Python language.
109

Relaxation of Subgraph Queries Delivering Empty Results

Vasilyeva, Elena, Thiele, Maik, Mocan, Adrian, Lehner, Wolfgang 16 September 2022 (has links)
Graph databases with the property graph model are used in multiple domains including social networks, biology, and data integration. They provide schema-flexible storage for data of a different degree of a structure and support complex, expressive queries such as subgraph isomorphism queries. The exibility and expressiveness of graph databases make it difficult for the users to express queries correctly and can lead to unexpected query results, e.g. empty results. Therefore, we propose a relaxation approach for subgraph isomorphism queries that is able to automatically rewrite a graph query, such that the rewritten query is similar to the original query and returns a non-empty result set. In detail, we present relaxation operations applicable to a query, cardinality estimation heuristics, and strategies for prioritizing graph query elements to be relaxed. To determine the similarity between the original query and its relaxed variants, we propose a novel cardinality-based graph edit distance. The feasibility of our approach is shown by using real-world queries from the DBpedia query log.
110

Studies of physical activity in the Swedish population

Olsson, Sven Johan Gustav January 2016 (has links)
Background: Cheap and effective tools for measuring patients’ physical activity (PA) level are needed. The first aim in this thesis was therefore to assess the validity of two PA -questions, and their three associated answer modes, that are used within the Swedish health care system. Sitting, light intensity PA (LIPA), and moderate and vigorous intensity PA (MVPA), are associated with health and longevity, but detailed population data assessed with objective methods is needed. The second aim was thus to assess the above with motion sensor technology, in a middle-aged Swedish sample. Low self-perceived health is a strong predictor of morbidity and mortality, but this association may vary over time with changes in the society and our lifestyle. The third aim was to assess secular trends in the interrelations between self-perceived health, physical fitness, and selected covariates. The effects of PA on prescription (PAP) on health-related quality of life (HRQoL) in overweight adults are unclear, thus the fourth aim was to explore this. Methods: All data was collected in the Swedish population. Data from the PA -questions and accelerometers, aerobic fitness, counter movement jump, and balance tests, blood samples, and self-rated general health were collected in 365 participants, 21–66 yrs. The PA pattern was assessed in 948 individuals, 50‒64 yrs, from the SCAPIS pilot study. Self-perceived physical health, and measured aerobic fitness, counter movement jump height, and balance, and demographic and lifestyle data, was assessed in three independent samples from 1990, 2000 and 2013, including 3564 adults, 20‒65 yrs. The effects of Swedish PAP on HRQoL was assessed in a randomized controlled trial including 101 men and women, 67‒68 yrs, that were inactive, overweight (BMI>25 kg/m2), and had a waist circumference ≥102 cm (men) or ≥88 cm (women), who were randomized to an intervention group or a control group. The 36-item Short Form Health Survey (SF-36) was used to assess HRQoL. Results: The multiple choice answer mode of the two PA -questions was found to have the strongest validity, compared with the two other (an open mode, and one where PA minutes is specified per weekday). The validity is in line with many other established PA-questionnaires, but the open mode has limitations. The assessment of PA pattern showed that 61% of motion sensor wear time represented sitting, 35% LIPA, and 4% MVPA. Only 7% of the sample met the PA recommendations. The odds for describing perceived health as good was found to increase by 5% per each increment of 1 ml/kg/min in VO2max. This was stable across genders and all three LIV-samples (i.e. over time). Waist circumference, chronic disease, sleep problems, and level of satisfaction with one’s life, were also important correlates. The Swedish PAP group improved significantly more, and more participants displayed clinically relevant improvements (OR 2.43), in mental aspects of HRQoL, compared to the controls. Physical aspects of HRQoL improved in the PAP group, but not in the control group. Conclusions: The multiple choice answer mode has the strongest validity and Open mode the weakest. The PA -questions may be used in populations, or in individuals to determine appropriateness for treatment. The questions’ advantages and limitations must be considered and further reliability and validity studies are needed. The results regarding sitting, LIPA, MVPA and fulfillment of PA recommendations, are of high clinical relevance. A great challenge remains to further implement methods to increase the level of PA in the Swedish population. Physical fitness is related to self-perceived health independently of changes in society and lifestyle over time, and simple questions may be useful for the clinical assessment of physical fitness. Swedish PAP has a positive effect on mental aspects of HRQoL, measured by the SF-36. This finding supports the clinical use of the Swedish PAP model. / LIV 2013

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