o presente estudo teve por objetivo investigar a influência de duas diferentes resiliências de materiais de amortecimento e de dois tipos de cabedais de calçados esportivos na cinemática e cinética de membro inferior e na percepção do conforto durante a corrida. Também investigamos as possíveis relações entre o conforto percebido e as variáveis biomecânicas capturadas. Para tal, foram avaliados 42 corredores recreacionais adultos, com no mínimo de um ano de experiência em corrida de rua, com mínimo de dois treinos regulares por semana, e com volume de treino semanal superior a 5 km. Foram avaliadas quatro condições de calçados aleatorizadas para cada corredor (material de amortecimento de baixa resiliência e cabedal estruturado, material amortecimento de alta resiliência e cabedal estruturado, material de amortecimento de baixa resiliência e cabedal minimalista, e material amortecimento de alta resiliência e cabedal minimalista). Após avaliação antropométrica e postural do complexo tornozelo/pé, os corredores realizaram corridas em uma pista de 25 metros em laboratório. A avaliação biomecânica foi realizada usando seis câmeras infravermelhas (VICON T-40, Oxford, UK) a 300 Hz, sincronizadas a duas plataformas de força (AMTI BP-600600, Watertown, USA) para aquisição da força reação do solo a 1200 Hz, e palmilhas instrumentas com sensores capacitivos (Pedar X System, Novel, Munique, Alemanha) a 100 Hz. A percepção subjetiva de conforto em cada condição foi avaliada por meio de um questionário de conforto para calçados. As comparações estatísticas entre os calçados foram verificadas por meio de análises de variância (ANOVAs) para medidas repetidas, e correlação de Pearson para verificar as relações entre o conforto e as variáveis biomecânicas (a=O,05). Realizou-se uma análise de Machine Learning para capturar variáveis da série temporal completa das curvas de cinemática e cinética que discriminassem os calçados estudados. Construímos uma matriz de entrada nas dimensões 1080 x 1242 para a análise por Machine learning. Os resultados demonstram que há uma interação entre as condições de cabedal e material de amortecimento que faz com que as comparações de resiliência se comportem de forma distinta para cabedais minimalistas e para cabedais estruturados. Contrariamente ao esperado, para os calçados de cabedal estruturado, as resiliências não foram diferentes entre si, e para o cabedal minimalista, os corredores apresentaram impactos mais altos com o material de baixa resiliência. A estrutura de cabedal influenciou a absorção de impacto, onde o cabedal minimalista apresentou impactos mais altos que o cabedal estruturado. Sobre o conforto, a condição de cabedal minimalista e material de baixa resiliência obteve as piores notas em cinco de nove quesitos do questionário. Em alguns quesitos ele foi o pior avaliado dentre todas as demais condições (como no amortecimento do calcanhar e no conforto geral). O cabedal minimalista recebeu pior avaliação que os cabedais estrutura dos no quesito controle médio-lateral da avaliação de conforto. Observou-se que a correlação entre as variáveis biomecânicas e as variáveis de conforto considerando todos os calçados conjuntamente, apesar de apresentarem valores significativos para algumas associações, foram sempre correlações fracas, abaixo de 30%. Ao se analisar cada condição de calçado isoladamente, em algumas se observou correlação moderada entre as variáveis biomecânicas e o conforto (r >31%, p < O,05), o que não se verificou em outras condições de calçados. Cada calçado gera condições particulares que favorecem ou não a associação entre conforto e repostas biomecânicas. Sobre a análise de Machine Learning, a metodologia foi capaz de diferenciar com sucesso os dois materiais de resiliência diferentes utilizando 200 (16%) variáveis biomecânicas disponíveis com uma precisão de 84,8%, e os dois cabedais com uma precisão de 93,9%. A discriminação da resiliência da entressola resultou em níveis de acurácia mais baixos do que a discriminação dos cabedais de calçados. Em ambos os casos, no entanto, as forças de reação do solo estavam entre as 25 variáveis mais relevantes. As 200 variáveis mais relevantes que discriminaram as duas resiliências estavam distribuídas em curtas janelas de tempo, ao longo de toda série temporal da cinemática e força. Estas janelas corresponderam a padrões individuais de respostas biomecânicas, ou a um grupo de indivíduos que apresentaram as mesmas respostas biomecânicas frente aos diferentes materiais de amortecimento. Como conclusão, destacamos que o cabedal tem maior influência que o material de amortecimento quando se trata da biomecânica da corrida e conforto subjetivo. Nos cabedais estruturados, a resiliência do material da entressola não diferenciou a biomecânica da corrida. A resiliência do material de amortecimento causa efeitos importantes sobre o impacto do calcanhar (menores loading rate, frequência mediana, pico de pressão em retropé) durante a corrida em cabedais com pouca estrutura. Alterações biomecânicas devido à resiliência do material de amortecimento parecem ser dependentes do sujeito, enquanto as relacionadas à estrutura de cabedal parecem ser mais sujeito independente. Sugere-se ter cautela ao afirmar que um calçado mais confortável também gerará respostas positivas biomecânicas, pois as associações entre essas variáveis analisando todos os calçados conjuntamente foram sempre correlações fracas. As correlações moderadas e particulares de cada condição de calçado com determinadas variáveis de conforto nos levam a concluir que os materiais aplicados nos calçado favorecem mais ou menos a percepção de determinada característica de conforto / The aim of this study was to investiga te the influence of two cushioning materiais with different resiliencies and two types of uppers of sportive shoes on kinematics and kinetics of lower limb and on the subjective perception of comfort during running. We also investigated the potential relationship between the perceived comfort and biomechanical variables analyzed. For this purpose, 42 adult recreational runners were evaluated. lhey had at least one year of experience on running, minimum of two regular running workouts per week, and weekly training volume above 5 km. We evaluated four randomized shoes conditions for each athlete (Iow resilience cushioning material and structured upper, high resilience cushioning material and structured upper, low resilience cushioning material and minimalist upper, and high resilience cushioning material and minimalist upper). After anthropometric and postura I assessment of the foot/ankle complex, runners held trials on a 25 meters long indoor track. Biomechanical data were collected by six infrared cameras (VICON l-40, Oxford, UK) at 300 Hz, synchronized with two force platforms (AMll BP-600600, Watertown, USA) at 1200Hz, and in- shoe plantar pressure insoles (Pedar X System, Nove\" Munich, Germany) at 100 Hz. Subjective perception of comfort in each shoe condition was assessed by a questionnaire of footwear comfort. lhe statistical comparisons between the shoes were verified by analysis of variance (ANOVA) for repeated measures and Pearson\'s correlation to verify the relationship between comfort and biomechanical variables (a=0.05). We conducted a Machine Learning analysis to capture variables from the complete kinematics and kinetics time series, which would be able to discriminate the studied footwear. We build an input matrix in the dimensions of 1080 x 1242 for Machine Learning analysis. There was an interaction between the upper structure and the resilience of cushioning material that made comparisons between resiliencies to behave differently for minimal uppers and for structured uppers. Contrary to expectation, for structured uppers, resiliencies were not different from each other, and for the minimal upper, runners had higher impact with the low-resilience material. lhe upper structure influenced the absorption of impact, in which the minimalist upper presented higher impacts than the structured upper. About comfort, minimalist upper condition and low resilience materiais had the worst grades for five of nine questions of the questionnaire. In some questions it was the worst of ali conditions (such as for the comfort in the heel cushioning and overall comfort). lhe minimalist upper received worse assessment than the structured uppers in the question about the mediolateral control. It was observed that the correlation between biomechanical variables and comfort, considering ali shoe conditions together, despite having significant values for some correlations were weak correlations (r <30%, p <0.05). When each shoe condition is analyzed alone, some footwear conditions had moderate correlation between comfort and biomechanical variables (r >31%, p <0.05L although the same behavior was not observed in other shoe conditions. Each shoe represents a specific condition that favor or not the association between comfort and biomechanical responses. On Machine Learning analysis, the method was able to successfully distinguish between the two different resiliencies using 200 (16%) of available biomechanical variables with an accuracy of 84.8%, and between the 2 uppers with an accuracy of 93.9 %. Discrimination of the resiliencies resulted in lower levels of accuracy than the discrimination of shoe uppers. In both cases, however, the ground reaction forces were among the 25 most important features. The 200 most relevant features which discriminate the two resiliencies were distribuited in short time windows along the kinematic and force time series. These windows corresponded to individual biomechanical patterns, or patterns of a group of people with similar behavior. In conclusion, we emphasize that the upper has greater influence than the resilience of cushioning material when it is about biomechanics of running and subjective comfort of the shoes. In structured uppers, the biomechanics did not differenciate the resiliencies of the midsole materiais. The resilience of the cushioning material has important effects on the heel impact (Iower loading rate, median frequency, peak pressure in rearfoot) during running on shoes with little structure on the upper. Biomechanical changes due to the resilience of the cushioning material seems to be dependent on the subject, while related to the upper structure seems to be more independent of the subject. It is suggested to be cautious to affirm that more comfortable footwear will also let to positive biomechanical responses. That is because the correlations between these variables when analyzing ali the footwear together were always weak. Moderate and positive correlations of each shoe condition with some of comfort variables lead us to conclude that the materiais applied on each footwear favors more or less the comfort perception

One of the challenges fairly common in motivational interviewing is patient resistance to health behavior change. Hence, automated dialog systems aimed at counseling patients need to be capable of detecting resistance and appropriately altering dialog. This thesis focusses primarily on the development of such a system for automatic identification of patient resistance to behavioral change. This enables the dialogue system to direct the discourse towards a more agreeable ground and helping the patient overcome the obstacles in his or her way to change. This thesis also proposes a dialogue system framework for health behavior change via natural language analysis and generation. The proposed framework facilitates automated motivational interviewing from clinical psychology and involves three broad stages: rapport building and health topic identification, assessment of the patient’s opinion about making a change, and developing a plan. Using this framework patients can be encouraged to reflect on the options available and choose the best for a healthier life.

In dieser Arbeit wird ein Programm zur Sprachidentifikation von Web-Dokumenten vorgestellt. Das Verfahren nutzt Worthäufigkeitslisten als Trainingsdaten, um anhand dieser Dokumentenklassifikation in Sprachen vorzunehmen. Somit gehört dieses Werkzeug zu den supervised-learning-Systemen. Die zu klassifizierenden Web-Dokumente wurden mittels des von der Abteilung fur Automatische Sprachverarbeitung entwickelten Tools 'FindLinks' heruntergeladen. Das Programm ist somit in die Nachverarbeitung bestehender Rohdaten einzuordnen. / This BSc Thesis presents a program for automatic language identification of web-documents called LangSepa. The procedure uses training-data which is based on word-frequency-tables of over 350 natural languages. Thus this tool can be subsumed under supervised learning systems. The documents for the classification-task were crawled by an information-retrieval system called FindLinks, which is developed at the Natural Language Processing group at the University of Leipzig. Therefore the presented program will be employed for the postprocessing of existent raw data.

No description available.

Background: Untreated patients and discontinuity in treatments are problems that mental health care is facing. Even though people seek care, there is still a pattern of patients who do not attend their scheduled appointments, referred to as No-shows. Noshows result in prolonged waiting times for patients and decreased efficiency and workflow for healthcare professionals. Moreover, causing great financial costs and losses for the healthcare sector. Using machine learning to predict potential No-shows beforehand could be a possible solution to minimize No-shows, while enhancing treatment adherence. Aim: The aim is to explore the best-performing algorithm for No-show predictions in digital psychotherapy. Furthermore, gaining a deeper knowledge of common behaviors in patient demographic and appointment data that may explain the reasons behind No-shows in digital mental health care services. Methods: A quantitative experimental research methodology and design with an inductive approach were utilized, incorporating computational methods, tools, and techniques. The Knowledge Discovery in Databases process was used as a guidance in the data mining process. Results: An observational relationship was found between No-shows and the following features age, day of the week of the appointment, date in a month of the appointment, month of the appointment, and waiting time. The best-performing algorithms to predict No-shows were Gradient Boosting Decision Tree and Random Forest. The date in a month was the most impactful feature for both classifiers, followed by the appointment month, the day of the week, and the number of waiting days. Conclusion: Machine learning has the potential to predict No-shows in digital psychotherapy and can be used to identify the underlying factors and patterns behind Noshows while providing useful information to support and improve digital mental health care delivery, treatment adherence, and patient outcomes. Thus, predicting No-shows beforehand is highly relevant for enhancing treatment adherence in digital psychotherapy and mental health care.

<p>Every 12 minutes, 12 women are diagnosed with breast cancer in the US, and 1 dies out of  it. Globally, every 46 seconds, a woman loses her life due to breast cancer, meaning more than  1,800 deaths every day. The condition makes the prediction of breast cancer very important. To  achieve the goal, supervised machine learning (ML) methods are used for breast cancer  likelihood predictions. However, due to imbalance in the real-world data with very low portion  of positive cases, the prediction accuracy of ML models for positive cancer cases was limited. Two procedures were done to address the issues in the study. Firstly, four supervised ML  models, including Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), using WEKA, the industry-standard software, were  applied to the Breast Cancer Surveillance Consortium (BCSC) dataset to assess the impact of the  data imbalance on breast cancer prediction. Secondly, the data was manually built as balanced  (24,558 cases, 12,279 for each class-positive and negative) and unbalanced (99,000 cases for  negative) training datasets and a non-overlapping testing dataset (11,000 cases) based on the  same dataset and a decision support system was developed for two ML models, NB and LR to  tackle the class imbalance issue for breast cancer prediction. Overall, the results indicate that  MLP had the best performance on positive breast cancer prediction with 0.959 sensitivity and  0.907 PPV and balanced dataset predicted better results for all ML models than unbalanced  dataset. Furthermore, the proposed method improved the sensitivity of positive cancer case  prediction from 0.687 to 0.936 using the NB model and from 0.358 to 0.8306 using the LR  model. The improvement demonstrated that the approach provided higher confidence ML-based  predictions and filtered weaker ones, and the technique could efficiently address the class  imbalance issue in breast cancer likelihood prediction and be used in clinical practice.</p>

Artificial Intelligence is a fuzzy concept. My role, as I see it, is to put down a working definition, a criterion, and a set of assumptions to set up equations for a workable methodology. This research introduces the notion of Artificial Intelligent Agency, denoting the application of Artificial General Intelligence. The problem being handled by mathematics and logic, and only thereafter semantics, is Self-Supervised Machine Learning (SSML) towards Intuitive Vehicle Health Management, in the domain of cybernetic-physical science. The present work stems from a broader engagement with a major multinational automotive OEM, where Intelligent Vehicle Health Management will dynamically choose suitable variants only to realise predefined variation points. Physics-based models infer properties of a model of the system, not properties of the implemented system itself. The validity of their inference depends on the models’ degree of fidelity, which is always an approximate localised engineering abstraction. In sum, people are not very good at establishing causality. To deduce new truths from implicit patterns in the data about the physical processes that generate the data, the kernel of this transformative technology is the intersystem architecture, occurring in-between and involving the physical and engineered system and the construct thereof, through the communication core at their interface. In this thesis it is shown that the most practicable way to establish causality is by transforming application models into actual implementation. The hypothesis being that the ideal source of training data for SSML, is an isomorphic monoid of indexical facts, trace-preserving events of natural kind.

Les logiciels de correction grammaticale commettent parfois des détections illégitimes (fausses alertes), que nous appelons ici surdétections. La présente étude décrit les expériences de mise au point d’un système créé pour identifier et mettre en sourdine les surdétections produites par le correcteur du français conçu par la société Druide informatique. Plusieurs classificateurs ont été entraînés de manière supervisée sur 14 types de détections faites par le correcteur, en employant des traits couvrant di-verses informations linguistiques (dépendances et catégories syntaxiques, exploration du contexte des mots, etc.) extraites de phrases avec et sans surdétections. Huit des 14 classificateurs développés sont maintenant intégrés à la nouvelle version d’un correcteur commercial très populaire. Nos expériences ont aussi montré que les modèles de langue probabilistes, les SVM et la désambiguïsation sémantique améliorent la qualité de ces classificateurs. Ce travail est un exemple réussi de déploiement d’une approche d’apprentissage machine au service d’une application langagière grand public robuste. / Grammar checking software sometimes erroneously flags a correct word sequence as an error, a problem we call overdetection in the present study. We describe the devel-opment of a system for identifying and filtering out the overdetections produced by the French grammar checker designed by the firm Druide Informatique. Various fami-lies of classifiers have been trained in a supervised way for 14 types of detections flagged by the grammar checker, using features that capture diverse linguistic phe-nomena (syntactic dependency links, POS tags, word context exploration, etc.), extracted from sentences with and without overdetections. Eight of the 14 classifiers we trained are now part of the latest version of a very popular commercial grammar checker. Moreover, our experiments have shown that statistical language models, SVMs and word sense disambiguation can all contribute to the improvement of these classifiers. This project is a striking illustration of a machine learning component suc-cessfully integrated within a robust, commercial natural language processing application.

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal.

In this work, the possibility of realising object tracking by implementing predictive methods with static object detectors is explored. The static object detectors are obtained as models trained on a machine learning algorithm, or in other words, a deep neural network. Specifically, it is the single shot detector inception v2 network that will be used to train such models. Predictive methods will be incorporated to the end of improving the obtained models’ precision, i.e. their performance with respect to accuracy. Namely, Lagrangian mechanics will be employed to derived equations of motion for three different scenarios in which the object is to be tracked. These equations of motion will be implemented as predictive methods by discretising and combining them with four different iterative formulae. In ch. 1, the fundamentals of supervised machine learning, neural networks, convolutional neural networks as well as the workings of the single shot detector algorithm, approaches to hyperparameter optimisation and other relevant theory is established. This includes derivations of the relevant equations of motion and the iterative formulae with which they were implemented. In ch. 2, the experimental set-up that was utilised during data collection, and the manner by which the acquired data was used to produce training, validation and test datasets is described. This is followed by a description of how the approach of random search was used to train 64 models on 300×300 datasets, and 32 models on 512×512 datasets. Consecutively, these models are evaluated based on their performance with respect to camera-to-object distance and object velocity. In ch. 3, the trained models were verified to possess multi-scale detection capabilities, as is characteristic of models trained on the single shot detector network. While the former is found to be true irrespective of the resolution-setting of the dataset that the model has been trained on, it is found that the performance with respect to varying object velocity is significantly more consistent for the lower resolution models as they operate at a higher detection rate. Ch. 3 continues with that the implemented predictive methods are evaluated. This is done by comparing the resulting deviations when they are let to predict the missing data points from a collected detection pattern, with varying sampling percentages. It is found that the best predictive methods are those that make use of the least amount of previous data points. This followed from that the data upon which evaluations were made contained an unreasonable amount of noise, considering that the iterative formulae implemented do not take noise into account. Moreover, the lower resolution models were found to benefit more than those trained on the higher resolution datasets because of the higher detection frequency they can employ. In ch. 4, it is argued that the concept of combining predictive methods with static object detectors to the end of obtaining an object tracker is promising. Moreover, the models obtained on the single shot detector network are concluded to be good candidates for such applications. However, the predictive methods studied in this thesis should be replaced with some method that can account for noise, or be extended to be able to account for it. A profound finding is that the single shot detector inception v2 models trained on a low-resolution dataset were found to outperform those trained on a high-resolution dataset in certain regards due to the higher detection rate possible on lower resolution frames. Namely, in performance with respect to object velocity and in that predictive methods performed better on the low-resolution models. / I detta arbete undersöks möjligheten att åstadkomma objektefterföljning genom att implementera prediktiva metoder med statiska objektdetektorer. De statiska objektdetektorerna erhålls som modeller tränade på en maskininlärnings-algoritm, det vill säga djupa neurala nätverk. Specifikt så är det en modifierad version av entagningsdetektor-nätverket, så kallat entagningsdetektor inception v2 nätverket, som används för att träna modellerna. Prediktiva metoder inkorporeras sedan för att förbättra modellernas förmåga att kunna finna ett eftersökt objekt. Nämligen används Lagrangiansk mekanik för härleda rörelseekvationer för vissa scenarion i vilka objektet är tänkt att efterföljas. Rörelseekvationerna implementeras genom att låta diskretisera dem och därefter kombinera dem med fyra olika iterationsformler. I kap. 2 behandlas grundläggande teori för övervakad maskininlärning, neurala nätverk, faltande neurala nätverk men också de grundläggande principer för entagningsdetektor-nätverket, närmanden till hyperparameter-optimering och övrig relevant teori. Detta inkluderar härledningar av rörelseekvationerna och de iterationsformler som de skall kombineras med. I kap. 3 så redogörs för den experimentella uppställning som användes vid datainsamling samt hur denna data användes för att producera olika data set. Därefter följer en skildring av hur random search kunde användas för att träna 64 modeller på data av upplösning 300×300 och 32 modeller på data av upplösning 512×512. Vidare utvärderades modellerna med avseende på deras prestanda för varierande kamera-till-objekt avstånd och objekthastighet. I kap. 4 så verifieras det att modellerna har en förmåga att detektera på flera skalor, vilket är ett karaktäristiskt drag för modeller tränade på entagninsdetektor-nätverk. Medan detta gällde för de tränade modellerna oavsett vilken upplösning av data de blivit tränade på, så fanns detekteringsprestandan med avseende på objekthastighet vara betydligt mer konsekvent för modellerna som tränats på data av lägre upplösning. Detta resulterade av att dessa modeller kan arbeta med en högre detekteringsfrekvens. Kap. 4 fortsätter med att de prediktiva metoderna utvärderas, vilket de kunde göras genom att jämföra den resulterande avvikelsen de respektive metoderna innebar då de läts arbeta på ett samplat detektionsmönster, sparat från då en tränad modell körts. I och med denna utvärdering så testades modellerna för olika samplingsgrader. Det visade sig att de bästa iterationsformlerna var de som byggde på färre tidigare datapunkter. Anledningen för detta är att den insamlade data, som testerna utfördes på, innehöll en avsevärd mängd brus. Med tanke på att de implementerade iterationsformlerna inte tar hänsyn till brus, så fick detta avgörande konsekvenser. Det fanns även att alla prediktiva metoder förbättrade objektdetekteringsförmågan till en högre utsträckning för modellerna som var tränade på data av lägre upplösning, vilket följer från att de kan arbeta med en högre detekteringsfrekvens. I kap. 5, argumenteras det, bland annat, för att konceptet att kombinera prediktiva metoder med statiska objektdetektorer för att åstadkomma objektefterföljning är lovande. Det slutleds även att modeller som erhålls från entagningsdetektor-nätverket är lovande kandidater för detta applikationsområde, till följd av deras höga detekteringsfrekvenser och förmåga att kunna detektera på flera skalor. Metoderna som användes för att förutsäga det efterföljda föremålets position fanns vara odugliga på grund av deras oförmåga att kunna hantera brus. Det slutleddes därmed att dessa antingen bör utökas till att kunna hantera brus eller ersättas av lämpligare metoder. Den mest väsentliga slutsats detta arbete presenterar är att lågupplösta entagninsdetektormodeller utgör bättre kandidater än de tränade på data av högre upplösning till följd av den ökade detekteringsfrekvens de erbjuder.