Sentiment analysis is a field within machine learning that focus on determine the contextual polarity of subjective information. It is a technique that can be used to analyze the "voice of the customer" and has been applied with success for the English language for opinionated information such as customer reviews, political opinions and social media data. A major problem regarding machine learning models is that they are domain dependent and will therefore not perform well for other domains. Transfer learning or domain adaption is a research field that study a model's ability of transferring knowledge across domains. In the extreme case a model will train on data from one domain, the source domain, and try to make accurate predictions on data from another domain, the target domain. The deep machine learning model Convolutional Neural Network (CNN) has in recent years gained much attention due to its performance in computer vision both for in-domain classification and transfer learning. It has also performed well for natural language processing problems but has not been investigated to the same extent for transfer learning within this area. The purpose of this thesis has been to investigate how well suited the CNN is for cross-domain sentiment analysis of Swedish reviews. The research has been conducted by investigating how the model perform when trained with data from different domains with varying amount of source and target data. Additionally, the impact on the model’s transferability when using different text representation has also been studied. This study has shown that a CNN without pre-trained word embedding is not that well suited for transfer learning since it performs worse than a traditional logistic regression model. Substituting 20% of source training data with target data can in many of the test cases boost the performance with 7-8% both for the logistic regression and the CNN model. Using pre-trained word embedding produced by a word2vec model increases the CNN's transferability as well as the in-domain performance and outperform the logistic regression model and the CNN model without pre-trained word embedding in the majority of test cases.

La question des liens entre forme urbaine et transport se trouve depuis une vingtaine d'années au cœur des réflexions sur la mise en place de politiques d'aménagement durable. L'essor de la diffusion de données sur grille régulière constitue dans ce cadre une nouvelle perspective pour la modélisation de structures urbaines à partir de mesures de densités affranchies de toutes les contraintes des maillages administratifs. A partir de données de densité de population et de surface bâtie disponibles à l'échelle de la France sur des grilles à mailles de 200 mètres de côté, nous proposons deux types de classifications adaptées à l'étude des pratiques de déplacement et du développement urbain : des classifications des tissus urbains et des classifications des morphotypes de développement urbain. La construction de telles images classées se base sur une démarche de modélisation théorique et expérimentale soulevant de forts enjeux méthodologiques quant à la classification d'espaces urbains statistiquement variés. Pour nous adapter au traitement exhaustif de ces espaces, nous avons proposé une méthode de classification des tissus urbains par transfert d'apprentissage supervisé. Cette méthode utilise le formalisme des champs de Markov cachés pour prendre en compte les dépendances présentes dans ces données spatialisées. Les classifications en morphotypes sont ensuite obtenus par un enrichissement de ces premières images classées, formalisé à partir de modèles chorématiques et mis à œuvre par raisonnement spatial qualitatif. L'analyse de ces images classées par des méthodes de raisonnement spatial quantitatif et d'analyses factorielles nous a permis de révéler la diversité morphologique de 50 aires urbaines françaises. Elle nous a permis de mettre en avant la pertinence de ces classifications pour caractériser les espaces urbains en accord avec différents enjeux d'aménagement relatifs à la densité ou à la multipolarité / Since a couple of decades the relationships between urban form and travel patterns are central to reflection on sustainable urban planning and transport policy. The increasing distribution of regular grid data is in this context a new perspective for modeling urban structures from measurements of density freed from the constraints of administrative division. Population density data are now available on 200 meters grids covering France. We complete these data with built area densities in order to propose two types of classified images adapted to the study of travel patterns and urban development: classifications of urban fabrics and classifications of morphotypes of urban development. The construction of such classified images is based on theoretical and experimental which raise methodological issues regarding the classification of a statistically various urban spaces. To proceed exhaustively those spaces, we proposed a per-pixel classification method of urban fabrics by supervised transfer learning. Hidden Markov random fields are used to take into account the dependencies in the spatial data. The classifications of morphotypes are then obtained by broadening the knowledge of urban fabrics. These classifications are formalized from chorematique theoretical models and implemented by qualitative spatial reasoning. The analysis of these classifications by methods of quantitative spatial reasoning and factor analysis allowed us to reveal the morphological diversity of 50 metropolitan areas. It highlights the relevance of these classifications to characterize urban areas in accordance with various development issues related to the density or multipolar development

abstract: Robotic lower limb prostheses provide new opportunities to help transfemoral amputees regain mobility. However, their application is impeded by that the impedance control parameters need to be tuned and optimized manually by prosthetists for each individual user in different task environments. Reinforcement learning (RL) is capable of automatically learning from interacting with the environment. It becomes a natural candidate to replace human prosthetists to customize the control parameters. However, neither traditional RL approaches nor the popular deep RL approaches are readily suitable for learning with limited number of samples and samples with large variations. This dissertation aims to explore new RL based adaptive solutions that are data-efficient for controlling robotic prostheses. This dissertation begins by proposing a new flexible policy iteration (FPI) framework. To improve sample efficiency, FPI can utilize either on-policy or off-policy learning strategy, can learn from either online or offline data, and can even adopt exiting knowledge of an external critic. Approximate convergence to Bellman optimal solutions are guaranteed under mild conditions. Simulation studies validated that FPI was data efficient compared to several established RL methods. Furthermore, a simplified version of FPI was implemented to learn from offline data, and then the learned policy was successfully tested for tuning the control parameters online on a human subject. Next, the dissertation discusses RL control with information transfer (RL-IT), or knowledge-guided RL (KG-RL), which is motivated to benefit from transferring knowledge acquired from one subject to another. To explore its feasibility, knowledge was extracted from data measurements of able-bodied (AB) subjects, and transferred to guide Q-learning control for an amputee in OpenSim simulations. This result again demonstrated that data and time efficiency were improved using previous knowledge. While the present study is new and promising, there are still many open questions to be addressed in future research. To account for human adaption, the learning control objective function may be designed to incorporate human-prosthesis performance feedback such as symmetry, user comfort level and satisfaction, and user energy consumption. To make the RL based control parameter tuning practical in real life, it should be further developed and tested in different use environments, such as from level ground walking to stair ascending or descending, and from walking to running. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2020

Ces dernières années, l'apprentissage profond a complètement changé le domaine de vision par ordinateur. Plus rapide, donnant de meilleurs résultats, et nécessitant une expertise moindre pour être utilisé que les méthodes classiques de vision par ordinateur, l'apprentissage profond est devenu omniprésent dans tous les problèmes d'imagerie, y compris l'imagerie médicale.Au début de cette thèse, la construction de réseaux de neurones adaptés à des tâches spécifiques ne bénéficiait pas encore de suffisamment d'outils ni d'une compréhension approfondie. Afin de trouver automatiquement des réseaux de neurones adaptés à des tâches spécifiques, nous avons ainsi apporté des contributions à l’optimisation d’hyper-paramètres de réseaux de neurones. Cette thèse propose une comparaison de certaines méthodes d'optimisation, une amélioration en performance d'une de ces méthodes, l'optimisation bayésienne, et une nouvelle méthode d'optimisation d'hyper-paramètres basé sur la combinaison de deux méthodes existantes : l'optimisation bayésienne et hyperband.Une fois équipés de ces outils, nous les avons utilisés pour des problèmes d'imagerie médicale : la classification de champs de vue en IRM, et la segmentation du rein en échographie 3D pour deux groupes de patients. Cette dernière tâche a nécessité le développement d'une nouvelle méthode d'apprentissage par transfert reposant sur la modification du réseau de neurones source par l'ajout de nouvelles couches de transformations géométrique et d'intensité.En dernière partie, cette thèse revient vers les méthodes classiques de vision par ordinateur, et nous proposons un nouvel algorithme de segmentation qui combine les méthodes de déformations de modèles et l'apprentissage profond. Nous montrons comment utiliser un réseau de neurones pour prédire des transformations globales et locales sans accès aux vérités-terrains de ces transformations. Cette méthode est validé sur la tâche de la segmentation du rein en échographie 3D. / In the last few years, deep learning has changed irrevocably the field of computer vision. Faster, giving better results, and requiring a lower degree of expertise to use than traditional computer vision methods, deep learning has become ubiquitous in every imaging application. This includes medical imaging applications. At the beginning of this thesis, there was still a strong lack of tools and understanding of how to build efficient neural networks for specific tasks. Thus this thesis first focused on the topic of hyper-parameter optimization for deep neural networks, i.e. methods for automatically finding efficient neural networks on specific tasks. The thesis includes a comparison of different methods, a performance improvement of one of these methods, Bayesian optimization, and the proposal of a new method of hyper-parameter optimization by combining two existing methods: Bayesian optimization and Hyperband.From there, we used these methods for medical imaging applications such as the classification of field-of-view in MRI, and the segmentation of the kidney in 3D ultrasound images across two populations of patients. This last task required the development of a new transfer learning method based on the modification of the source network by adding new geometric and intensity transformation layers.Finally this thesis loops back to older computer vision methods, and we propose a new segmentation algorithm combining template deformation and deep learning. We show how to use a neural network to predict global and local transformations without requiring the ground-truth of these transformations. The method is validated on the task of kidney segmentation in 3D US images.

Recommender systems are widely used in many domains. In this work, the importance of a recommender system in an online learning platform is discussed. After explaining the concept of adding an intelligent agent to online education systems, some features of the Course Networking (CN) website are demonstrated. Finally, the relation between CN, the intelligent agent (Rumi), and the recommender system is presented. Along with the argument of three different approaches for building a community recommendation system. The result shows that the Neighboring Collaborative Filtering (NCF) outperforms both the transfer learning method and the Continuous bag-of-words approach. The NCF algorithm has a general format with two various implementations that can be used for other recommendations, such as course, skill, major, and book recommendations.

abstract: In recent years, Convolutional Neural Networks (CNNs) have been widely used in not only the computer vision community but also within the medical imaging community. Specifically, the use of pre-trained CNNs on large-scale datasets (e.g., ImageNet) via transfer learning for a variety of medical imaging applications, has become the de facto standard within both communities. However, to fit the current paradigm, 3D imaging tasks have to be reformulated and solved in 2D, losing rich 3D contextual information. Moreover, pre-trained models on natural images never see any biomedical images and do not have knowledge about anatomical structures present in medical images. To overcome the above limitations, this thesis proposes an image out-painting self-supervised proxy task to develop pre-trained models directly from medical images without utilizing systematic annotations. The idea is to randomly mask an image and train the model to predict the missing region. It is demonstrated that by predicting missing anatomical structures when seeing only parts of the image, the model will learn generic representation yielding better performance on various medical imaging applications via transfer learning. The extensive experiments demonstrate that the proposed proxy task outperforms training from scratch in six out of seven medical imaging applications covering 2D and 3D classification and segmentation. Moreover, image out-painting proxy task offers competitive performance to state-of-the-art models pre-trained on ImageNet and other self-supervised baselines such as in-painting. Owing to its outstanding performance, out-painting is utilized as one of the self-supervised proxy tasks to provide generic 3D pre-trained models for medical image analysis. / Dissertation/Thesis / Masters Thesis Computer Science 2020

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In this thesis, we proposed a deep learning based emotion recognition system in order to improve the successive classification rate. We first use transfer learning to extract visual features and use Mel frequency Cepstral Coefficients(MFCC) to extract audio features, and then apply the recurrent neural networks(RNN) with attention mechanism to process the sequential inputs. After that, the outputs of both channels are fused into a concatenate layer, which is processed using batch normalization, to reduce internal covariate shift. Finally, the classification result is obtained by the softmax layer. From our experiments, the video and audio subsystem achieve 78% and 77% respectively, and the feature fusion system with video and audio achieves 92% accuracy based on the RAVDESS dataset for eight emotion classes. Our proposed feature fusion system outperforms conventional methods in terms of classification prediction.

<p>Deep learning solutions to computer vision tasks have revolutionized many industries in recent years, but embedded systems have too many restrictions to take advantage of current state-of-the-art configurations. Typical embedded processor hardware configurations must meet very low power and memory constraints to maintain small and lightweight packaging, and the architectures of the current best deep learning models are too computationally intensive for these hardware configurations. Current research shows that convolutional neural networks (CNNs) can be deployed with a few architectural modifications on Field-Programmable Gate Arrays (FPGAs) resulting in minimal loss of accuracy, similar or decreased processing speeds, and lower power consumption when compared to general-purpose Central Processing Units (CPUs) and Graphics Processing Units (GPUs). This research contributes further to these findings with the FPGA implementation of a YOLOv4 object detection model that was developed with the use of transfer learning. The transfer-learned model uses the weights of a model pre-trained on the MS-COCO dataset as a starting point then fine-tunes only the output layers for detection on more specific objects of five classes. The model architecture was then modified slightly for compatibility with the FPGA hardware using techniques such as weight quantization and replacing unsupported activation layer types. The model was deployed on three different hardware setups (CPU, GPU, FPGA) for inference on a test set of images. It was found that the FPGA was able to achieve real-time inference speeds of 33.77 frames-per-second, a speedup of 7.74 frames-per-second when compared to GPU deployment. The model also consumed 96% less power than a GPU configuration with only approximately 4% average loss in accuracy across all 5 classes. The results are even more striking when compared to CPU deployment, with 131.7-times speedup in inference throughput. CPUs have long since been outperformed by GPUs for deep learning applications but are used in most embedded systems. These results further illustrate the advantages of FPGAs for deep learning inference on embedded systems even when transfer learning is used for an efficient end-to-end deployment process. This work advances current state-of-the-art with the implementation of a YOLOv4 object detection model developed with transfer learning for FPGA deployment.</p>

Quality control is an important part of any production line. It can be done manually but is most efficient if automated. Inspecting qualitycan include many different processes but this thesisisfocusedon the visual inspection for cracks and scratches. The best way of doingthis at the time of writing is with the help of Artificial Intelligence (AI), more specifically Deep Learning (DL).However, these need a training datasetbeforehand to train on and for some smaller companies, this mightnotbean option. This study triesto find an alternative visual inspection method,that does notrelyon atrained deep learning modelfor when trainingdata is severely limited. Our method is to use edge detection algorithmsin combination with a template to find any edge that doesn’t belong. These include scratches, cracks, or misaligned stickers. These anomalies arethen highlighted in the original picture to show where the defect is. Since deep learningis stateof the art ofvisual inspection, it is expected to outperform template matching when sufficiently trained.To find where this occurs,the accuracy of template matching iscompared to the accuracy of adeep learning modelat different training levels. The deep learning modelisto be trained onimage augmenteddatasets of size: 6, 12, 24, 48, 84, 126, 180, 210, 315, and 423. Both template matching and the deep learning modelwas tested on the samebalanceddataset of size 216. Half of the dataset was images of scratched units,and the other half was of unscratched units. This gave a baseline of 50% where anything under would be worse thanjust guessing. Template matching achieved an accuracy of 88%, and the deep learning modelaccuracyrose from 51% to 100%as the training setincreased. This makes template matching have better accuracy then AI trained on dataset of 84imagesor smaller. But a deep learning modeltrained on 126 images doesstart to outperform template matching. Template matching did perform well where no data was available and training adeep learning modelis no option. But unlike a deep learning model, template matching would not need retraining to find other kinds of surface defects. Template matching could also be used to find for example, misplaced stickers. Due to the use of a template, any edge that doesnot match isdetected.  The ways to train deep learning modelis highly customizable to the users need. Due to resourceand knowledge restrictions, a deep dive into this subject was not conducted.For template matching, only Canny edge detection was used whenmeasuringaccuracy. Other edge detection methodssuch as, Sobel, and Prewitt was ruledoutearlier in this study.