Bakgrund Följsamhet till hemträning hos patienter inom primärvården är ofta undermålig. För att en behandling ska få ett önskvärt resultat är det nödvändigt att patienten följer rekommendationerna från sin fysioterapeut. Syfte Syftet var att undersöka vilka erfarenheter fysioterapeuter, verksamma inom primärvården, har kring vilka faktorer i det kliniska arbetet som ökar patienters följsamhet till icke övervakad träning samt hur de utvärderar graden av följsamhet. Design och metod Studien har kvalitativ deskriptiv design med semistrukturerade intervjuer som datainsamlingsmetod. Databearbetning genomfördes enligt Graneheim och Lundmans modell för innehållsanalys. Resultat Totalt intervjuades sex fysioterapeuter. De huvudsakliga påverkande faktorerna var; användning av olika behandlingsstrategier, genomförande av uppföljning, patientens hälsostatus och förutsättningar, fysiologiska förklaringsmodeller, verksamhetens resurser, fysioterapeutens karaktäristika samt patienters ålder och kön. Utvärderingen av följsamhet skedde genom samtal och utvärdering vid återbesök med patienten. Konklusion En del av faktorerna kopplat till följsamhet kan vara svåra att påverka som fysioterapeut, exempelvis patientens hälsostatus, medan det går bättre att påverka andra faktorer i fysioterapeutens kliniska arbete, såsom att individanpassa träningen, använda sig utav förklaringsmodeller och ta hjälp av andra professioner. / Background Adherence to non-supervised training in primary care patients is often insufficient and there are no clear methods for improving and evaluating adherence. For a treatment to have a desirable result, it is necessary that the patient follows the recommendations from his physiotherapist. Aim The purpose of the study was to investigate what experiences physiotherapists who work in primary care have about what factors in their clinical work that increase patient adherence to non-supervised training and how they evaluate the degree of adherence. Design och methods The study has a qualitative and descriptive design with semi-structured interviews as data collection method. Data processing was carried out according to Graneheim and Lundman's content analysis model. Results A total of six physiotherapists were interviewed. The main impact factors were; use of different treatment strategies, follow-ups, patient’s health status and conditions, explanatory models, resources, physiotherapist's characteristics and age and gender of the patient. The evaluation of adherence is basically done through conversations and evaluation during patient follow up.  Conclusions Some of the factors linked to adherence may be difficult to influence as a physiotherapist, such as the patient's health status, while it is easier to influence other factors in the physiotherapist's clinical work, such as; personalizing the training, using explanatory models and utilizing other professionals.

Automated human activity recognition has attracted increasing attention in the past decade. However, the application of machine learning and probabilistic methods for activity recognition problems has been studied only in the past couple of years. For the first time, this thesis explores the application of semi-supervised and active learning in activity recognition. We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs),a probabilistic graphical model. In real-world applications such as activity recognition, unlabeled sensor traces are relatively easy to obtain whereas labeled examples are expensive and tedious to collect. Furthermore, the ability to automatically select a small subset of discriminatory features from a large pool can be advantageous in terms of computational speed as well as accuracy. We introduce the semi-supervised virtual evidence boosting (sVEB)algorithm for training CRFs — a semi-supervised extension to the recently developed virtual evidence boosting (VEB) method for feature selection and parameter learning. sVEB takes advantage of the unlabeled data via mini-mum entropy regularization. The objective function combines the unlabeled conditional entropy with labeled conditional pseudo-likelihood. The sVEB algorithm reduces the overall system cost as well as the human labeling cost required during training, which are both important considerations in building real world inference systems. Moreover, we propose an active learning algorithm for training CRFs is based on virtual evidence boosting and uses entropy measures. Active virtual evidence boosting (aVEB) queries the user for most informative examples, efficiently builds up labeled training examples and incorporates unlabeled data as in sVEB. aVEB not only reduces computational complexity of training CRFs as in sVEB, but also outputs more accurate classification results for the same fraction of labeled data. Ina set of experiments we illustrate that our algorithms, sVEB and aVEB, benefit from both the use of unlabeled data and automatic feature selection, and outperform other semi-supervised and active training approaches. The proposed methods could also be extended and employed for other classification problems in relational data. / Science, Faculty of / Computer Science, Department of / Graduate

Tuberculosis (TB) is an infectious disease which is a global public health problem with over 9 million new cases annually. Tuberculosis treatment, with patient supervision and support is an element of the global plan to stop TB designed by the World Health Organization in 2006. The plan requires prediction of patient treatment course destination. The prediction outcome can be used to determine how intensive the level of supplying services and supports in frame of DOTS therapy should be. No predictive model for the outcome has been developed yet and only limited reports of influential factors for considered outcome are available. To fill this gap, this thesis develops a machine learning approach to predict the outcome of tuberculosis treatment course, which includes, firstly, data of 6,450 Iranian TB patients under DOTS (directly observed treatment, short course ) therapy were analysed to initially diagnose the significant predictors by correlation analysis; secondly, these significant features were applied to find the best classification approach from six examined algorithms including decision tree, Bayesian network, logistic regression, multilayer perceptron, radial basis function, and support vector machine; thirdly, the prediction accuracy of these existing techniques was improved by proposing and developing a new integrated method of k-mean clustering and classification algorithms. Finally, a cluster-based simplified decision tree (CSDT) was developed through an innovative hierarchical clustering and classification algorithm. CSDT was built by k-mean partitioning and the decision tree learning. This innovative method not only improves the prediction accuracy significantly but also leads to a much simpler and interpretative decision tree. The main results of this study included, firstly, finding seventeen significantly correlated features which were: age, sex, weight, nationality, area of residency, current stay in prison, low body weight, TB type, treatment category, length of disease, TB case type, recent TB infection, diabetic or HIV positive, and social risk factors like history of imprisonment, IV drug usage, and unprotected sex ; secondly, the results by applying and comparing six applied supervised machine learning tools on the testing set revealed that decision trees gave the best prediction accuracy (74.21%) compared with other methods; thirdly, by using testing set, the new integrated approach to combine the clustering and classification approach leads to the prediction accuracy improvement for all applied classifiers; the most and least improvement for prediction accuracy were shown by logistic regression (10%) and support vector machine (4%) respectively. Finally, by applying the proposed and developed CSDT, cluster-based simplified decision trees were optioned, which reduced the size of the resulting decision tree and further improved the prediction accuracy. Data type and having normal distribution have created an opportunity for the decision tree to outperform other algorithms. Pre-learning by k-mean clustering to relocate the objects and put similar cases in the same group can improve the classification accuracy. The compatible feature of k-mean partitioning and decision tree to generate pure local regions can simplify the decision trees and make them more precise through creating smaller sub-trees with fewer misclassified cases. The extracted rules from these trees can play the role of a knowledge base for a decision support system in further studies.

The main aim of this dissertation is to address the security issues of the physical layer of Cyber Physical Systems. The network security is first assessed using a 5-level Network Security Evaluation Scheme (NSES). The network security is then enhanced using a novel Intrusion Detection System that is designed using Supervised Machine Learning. Defined as a complete architecture, this framework includes a complete packet analysis of radio traffic of Routing Protocol for Low-Power and Lossy Networks (RPL). A dataset of 300 different simulations of RPL network is defined for normal traffic, hello flood attack, DIS attack, increased version attack and decreased rank attack. The IDS is a multi-model detection model that provides an efficient detection against the known as well as new attacks. The model analysis is done with the cross-validation method as well as using the new data from a similar network. To detect the known attacks, the model performed at 99% accuracy rate and for the new attack, 85% accuracy is achieved. / Graduate

Supervised deep neural networks have been successfully applied to many real-world measurement applications. However, their success relies on labeled data which is expensive and time-consuming to obtain, especially when domain expertise is required. For this reason, researchers have turned to semi-supervised learning for image classification tasks. Semi-supervised learning uses structural assumptions to automatically leverage unlabeled data, dramatically reducing manual labeling efforts. We conduct our research based on images from Enclosures Direct Inc. (EDI) which is a manufacturer of enclosures used to house and protect electronic devices. Their industrial robotics utilizes a computer vision system to guide a robot in a welding application employing a laser and a camera. The laser is combined with an optical line generator to cast a line of structured light across a joint to be welded. An image of the structured light is captured by the camera which needs to be located in the image in order to find the desired coordinate for the weld seam. The existing system failed due to the fact that the traditional machine vision algorithm cannot analyze the image correctly in unexpected imaging conditions or during variations in the manufacturing process. In this thesis, we purpose a novel algorithm for semi-supervised key-point detection for seam placement by a welding robot. Our deep learning based algorithm overcomes unfavorable imaging conditions providing faster and more precise predictions. Moreover, we demonstrate that our approach can work with as few as ten labeled images accepting a reduction of detection accuracy. In addition, we also purpose a method that can utilize full image resolution to enhance the accuracy of the key-point detection.

Dans la plupart des systèmes biométriques de l’état de l’art, les données biométrique sont souvent représentés par des vecteurs de grande dimensionalité. La dimensionnalité d'éléments biométriques génèrent un problème de malédiction de dimensionnalité. Dans la biométrie multimodale, différentes modalités biométriques peuvent former différents entrés des algorithmes de classification. La fusion des modalités reste un problème difficile et est généralement traitée de manière isolée à celui de dimensionalité élevée. Cette thèse aborde le problème de la dimensionnalité élevée et le problème de la fusion multimodale dans un cadre unifié. En vertu d'un paramètre biométrique multi-modale et les données non étiquetées abondantes données, nous cherchons à extraire des caractéristiques discriminatoires de multiples modalités d'une manière non supervisée. Les contributions de cette thèse sont les suivantes: Un état de l’art des algorithmes RMVD de l'état de l'art ; Un nouveau concept de RMVD: accord de la structure de données dans sous-espace; Trois nouveaux algorithmes de MVDR basée sur des définitions différentes de l’accord de la structure dans les sous-espace; L’application des algorithmes proposés à la classification semi-supervisée, la classification non supervisée, et les problèmes de récupération de données biométriques, en particulier dans un contexte de la reconnaissance de personne en audio et vidéo; L’application des algorithmes proposés à des problèmes plus larges de reconnaissance des formes pour les données non biométriques, tels que l'image et le regroupement de texte et la recherche. / Biometric data is often represented by high-dimensional feature vectors which contain significant inter-session variation. Discriminative dimensionality reduction techniques generally follow a supervised learning scheme. However, labelled training data is generally limited in quantity and often does not reliably represent the inter-session variation encountered in test data. This thesis proposes to use multi-view dimensionality reduction (MVDR) which aims to extract discriminative features in multi-modal biometric systems, where different modalities are regarded as different views of the same data. MVDR projections are trained on feature-feature pairs where label information is not required. Since unlabelled data is easier to acquire in large quantities, and because of the natural co-existence of multiple views in multi-modal biometric problems, discriminant, low-dimensional subspaces can be learnt using the proposed MVDR approaches in a largely unsupervised manner. According to different functionalities of biometric systems, namely, clustering, and retrieval, we propose three MVDR frameworks which meet the requirements for each functionality. The proposed approaches, however, share the same spirit: all methods aim to learn a projection for each view such that a certain form of agreement is attained in the subspaces across different views. The proposed MVDR frameworks can thus be unified into one general framework for multi-view dimensionality reduction through subspace agreement. We regard this novel concept of subspace agreement to be the primary contribution of this thesis.

Machine learning has facilitated many recent advances in natural language processing and information extraction. Unfortunately, most machine learning methods rely on costly labeled data, which impedes their application to new problems. Even in the absence of labeled data we often have a wealth of prior knowledge about these problems. For example, we may know which labels particular words are likely to indicate for a sequence labeling task, or we may have linguistic knowledge suggesting probable dependencies for syntactic analysis. This thesis focuses on incorporating such prior knowledge into learning, with the goal of reducing annotation effort for information extraction and natural language processing tasks. We advocate constraints on expectations as a flexible and interpretable language for encoding prior knowledge. We focus on the development of Generalized Expectation (GE), a method for learning with expectation constraints and unlabeled data. We explore the various flexibilities afforded by GE criteria, derive efficient algorithms for GE training, and relate GE to other methods for incorporating prior knowledge into learning. We then use GE to develop lightly supervised approaches to text classification, dependency parsing, sequence labeling, and entity resolution that yield accurate models for these tasks with minimal human effort. We also consider the incorporation of GE into interactive training systems that actively solicit prior knowledge from the user and assist the user in evaluating and analyzing model predictions.

<p> Exercise programs designed for cardiac patients frequently report high dropout rates. Little is known about the reasons for this high rate of dropout; further, little is known about health behavior patterns including physical activity subsequent to graduation or dropout from exercise programs. Identification of reasons for dropout and the pattern of physical activity after participation in formal exercise rehabilitation would provide information regarding achievement and maintenance of treatment goals.</p> <p> Entry characteristics were determined for 84 male cardiac patients (45 compliers and 39 dropouts) from the McMaster Cardiac Rehabilitation Exercise Program. Follow-up information pertaining to areas of: a) health; b) employment, smoking, activity, and dietary status; c) reasons for joining the program; d) perceived benefits achieved; and e) factors contributing to compliance with or dropout from the exercise program was obtained from 63 subjects (41 compliers and 22 dropouts) who responded to a questionnaire by mail.</p> <p> The dropout rate at the end of the 6 month program was 46.4% (39 of 84 subjects) with one-half of all dropout occurring within the first 2 months of the 6 month program. Upon entry into the exercise program, a significantly greater proportion of dropouts (43.6%, n=17) than compliers (8.9%, n=4) were found to be regular smokers. Likewise, a significantly greater proportion of dropouts (82.1%, n=32) than compliers (55.6%, n=25) were found to be inactive in their leisure habits upon entry. Dropouts were also more likely to be blue collar workers (71.8%, n=28), and younger in age (x̅ age = 48.4 years) when compared to compliers (37.8%, n=17; x̅ age = 54.3 years) upon entry into the exercise program. Upon follow-up, compliers were significantly more likely to report active leisure habits (85.4%, n=35) than were responding dropouts (45.5%, n=10). Compliers were also significantly more likely to report moderate work activity levels upon follow-up (54.8%, n=17) compared to dropouts (22.2%, n=4). Reasons for compliance to and withdrawal from the exercise program provided by respondents centred around psychosocial and personal convenience categories.</p> <p> Although statistically significant, the greater follow-up activity levels noted among compliers in this study appear to be only temporary, short-term patterns which tend to diminish with time. It is suggested that compliance-improving strategies be developed through further study with the aim of encouraging the long-term maintenance of desired behavior change.</p> / Thesis / Master of Science (MSc)

Ambiguity is inherent to human language. In particular, word sense ambiguity is prevalent in all natural languages, with a large number of the words in any given language carrying more than one meaning. Word sense disambiguation is the task of automatically assigning the most appropriate meaning to a polysemous word within a given context. Generally the problem of resolving ambiguity in literature has revolved around the famous quote “you shall know the meaning of the word by the company it keeps.” In this thesis, we investigate the role of context for resolving ambiguity through three different approaches. Instead of using a predefined monolingual sense inventory such as WordNet, we use a language-independent framework where the word senses and sense-tagged data are derived automatically from Wikipedia. Using Wikipedia as a source of sense-annotations provides the much needed solution for knowledge acquisition bottleneck. In order to evaluate the viability of Wikipedia based sense-annotations, we cast the task of disambiguating polysemous nouns as a monolingual classification task and experimented on lexical samples from four different languages (viz. English, German, Italian and Spanish). The experiments confirm that the Wikipedia based sense annotations are reliable and can be used to construct accurate monolingual sense classifiers. It is a long belief that exploiting multiple languages helps in building accurate word sense disambiguation systems. Subsequently, we developed two approaches that recast the task of disambiguating polysemous nouns as a multilingual classification task. The first approach for multilingual word sense disambiguation attempts to effectively use a machine translation system to leverage two relevant multilingual aspects of the semantics of text. First, the various senses of a target word may be translated into different words, which constitute unique, yet highly salient signal that effectively expand the target word’s feature space. Second, the translated context words themselves embed co-occurrence information that a translation engine gathers from very large parallel corpora. The second approach for multlingual word sense disambiguation attempts to reduce the reliance on the machine translation system during training by using the multilingual knowledge available in Wikipedia through its interlingual links. Finally, the experiments on a lexical sample from four different languages confirm that the multilingual systems perform better than the monolingual system and significantly improve the disambiguation accuracy.

Deep neural networks (DNNs) have achieved remarkable success in data-intense applications, while such success relies heavily on massive and carefully labeled data. In practice, obtaining large-scale datasets with correct labels is often expensive, time-consuming, and sometimes even impossible. Common approaches of constructing datasets involve some degree of error-prone processes, such as automatic labeling or crowdsourcing, which inherently introduce noisy labels. It has been observed that noisy labels severely degrade the generalization performance of classifiers, especially the overparameterized (deep) neural networks. Therefore, studying noisy labels and developing techniques for training accurate classifiers in the presence of noisy labels is of great practical significance. In this thesis, we conduct a thorough study to fully understand LNL and provide a comprehensive error decomposition to reveal the core issue of LNL. We then point out that the core issue in LNL is that the empirical risk minimizer is unreliable, i.e., the DNNs are prone to overfitting noisy labels during training. To reduce the learning errors, we propose five different methods, 1) Co-matching: a framework consists of two networks to prevent the model from memorizing noisy labels; 2) SELC: a simple method to progressively correct noisy labels and refine the model; 3) NAL: a regularization method that automatically distinguishes the mislabeled samples and prevents the model from memorizing them; 4) EM-enhanced loss: a family of robust loss functions that not only mitigates the influence of noisy labels, but also avoids underfitting problem; 5) MixNN: a framework that trains the model with new synthetic samples to mitigate the impact of noisy labels. Our experimental results demonstrate that the proposed approaches achieve comparable or better performance than the state-of-the-art approaches on benchmark datasets with simulated label noise and large-scale datasets with real-world label noise. / Dissertation / Doctor of Philosophy (PhD) / Machine Learning has been highly successful in data-intensive applications but is often hampered when datasets contain noisy labels. Recently, Learning with Noisy Labels (LNL) is proposed to tackle this problem. By using techniques from LNL, the models can still generalize well even when trained on the data containing noisy supervised information. In this thesis, we study this crucial problem and provide a comprehensive analysis to reveal the core issue of LNL. We then propose five different methods to effectively reduce the learning errors in LNL. We show that our approaches achieve comparable or better performance compared to the state-of-the-art approaches on benchmark datasets with simulated label noise and real-world noisy datasets.