Mind the Gap: Subspace based Hierarchical Domain Adaptation

Domain adaptation techniques aim at adapting a classifier learnt on a source domain to work on the target domain. Exploiting the subspaces spanned by features of the source and target domains respectively is one approach that has been investigated towards solving this problem. These techniques normally assume the existence of a single subspace for the entire source / target domain. In this work, we consider the hierarchical organization of the data and consider multiple subspaces for the source and target domain based on the hierarchy. We evaluate different subspace based domain adaptation techniques under this setting and observe that using different subspaces based on the hierarchy yields consistent improvement over a non-hierarchical baselineComment: 4 pages in Second Workshop on Transfer and Multi-Task Learning: Theory meets Practice in NIPS 201

Automatically extracting polarity-bearing topics for cross-domain sentiment classification

Joint sentiment-topic (JST) model was previously proposed to detect sentiment and topic simultaneously from text. The only supervision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by incorporating word polarity priors through modifying the topic-word Dirichlet priors. We study the polarity-bearing topics extracted by JST and show that by augmenting the original feature space with polarity-bearing topics, the in-domain supervised classifiers learned from augmented feature representation achieve the state-of-the-art performance of 95% on the movie review data and an average of 90% on the multi-domain sentiment dataset. Furthermore, using feature augmentation and selection according to the information gain criteria for cross-domain sentiment classification, our proposed approach performs either better or comparably compared to previous approaches. Nevertheless, our approach is much simpler and does not require difficult parameter tuning

Deep Memory Networks for Attitude Identification

We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral. Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.Comment: Accepted to WSDM'1

Deep Learning Approach For Sign Language Recognition

Sign language is a method of communication that uses hand movements between fellow people with hearing loss. Problems occur when communication between normal people with hearing disorders, because not everyone understands sign language, so the model is needed for sign language recognition. This study aims to make the model of the introduction of hand sign language using a deep learning approach. The model used is Convolutional Neural Network (CNN). This model is tested using the ASL alphabet database consisting of 27 categories, where each category consists of 3000 images or a total of 87,000 images of 200 x 200 pixels of hand signals. First is the process of resizing the image input to 32 x 32 pixels. Furthermore, separating the dataset for training and validation respectively 75% and 25%. The test results indicate that the proposed model has good performance with a value of 99% accuracy. Experiment results show that preprocessing images using background correction can improve model performance

Effects of Data Standardization on Hyperparameter Optimization with the Grid Search Algorithm Based on Deep Learning: A Case Study of Electric Load Forecasting

This study investigates data standardization methods based on the grid search (GS) algorithm for energy load forecasting, including zero-mean, min-max, max, decimal, sigmoid, softmax, median, and robust, to determine the hyperparameters of deep learning (DL) models. The considered DL models are the convolutional neural network (CNN) and long short-term memory network (LSTMN). The procedure is made over (i) setting the configuration for CNN and LSTMN, (ii) establishing the hyperparameter values of CNN and LSTMN models based on epoch, batch, optimizer, dropout, filters, and kernel, (iii) using eight data standardization methods to standardize the input data, and (iv) using the GS algorithm to search the optimal hyperparameters based on the mean absolute error (MAE) and mean absolute percent error (MAPE) indexes. The effectiveness of the proposed method is verified on the power load data of the Australian state of Queensland and Vietnamese Ho Chi Minh city. The simulation results show that the proposed data standardization methods are appropriate, except for the zero-mean and min-max methods

Distant Transfer Learning via Deep Random Walk

Transfer learning, which is to improve the learning performance in the target domain by leveraging useful knowledge from the source domain, often requires that those two domains are very close, which limits its application scope. Recently, distant transfer learning has been studied to transfer knowledge between two distant or even totally unrelated domains via auxiliary domains that are usually unlabeled as a bridge in the spirit of human transitive inference that it is possible to connect two completely unrelated concepts together through gradual knowledge transfer. In this paper, we study distant transfer learning by proposing a DeEp Random Walk basEd distaNt Transfer (DERWENT) method. Different from existing distant transfer learning models that implicitly identify the path of knowledge transfer between the source and target instances through auxiliary instances, the proposed DERWENT model can explicitly learn such paths via the deep random walk technique. Specifically, based on sequences identified by the random walk technique on a data graph where source and target data have no direct edges, the proposed DERWENT model enforces adjacent data points in a squence to be similar, makes the ending data point be represented by other data points in the same sequence, and considers weighted training losses of source data. Empirical studies on several benchmark datasets demonstrate that the proposed DERWENT algorithm yields the state-of-the-art performance

DOTS - detection of covid-19 contagion symptoms and self-diagnosis in social networks

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáSocial media present ways for people to share emotions, feelings, ideas, and even symptoms of disease, and is a great source of data for a variety of analyses. At the end of 2019, an alert was raised for a global pandemic of a virus that has a very high contamination rate and can cause respiratory complications in the contaminated people. To help identify those who may have the symptoms of this disease or to control who are already infected, this paper analyzed the performance of KNN, Naive Bayes, Decision Tree, Random Forest, SVM, simple Multilayer Perceptron, Convolutional Neural Networks and BERT algorithms to classify tweets that contained reports of Covid-19 symptoms or selfreports of infection. The dataset was labeled using a set of disease symptom keywords taken from a list provided by the World Health Organization. The tests on these models showed that the Random Forest algorithm performed best when classifying the tweets in a small dataset. This work demonstrated a superior performance of the Random Forest algorithm over other more robust algorithms for this type of classification and dataset.As redes sociais apresentam meios para as pessoas compartilharem emoções, sentimentos, ideias e até sintomas de doenças, e são uma ótima fonte de dados para as mais diversas análises. No final do ano de 2019, um alerta foi levantado para uma pandemia global de um vírus que tem uma taxa de contaminação muito elevada e que pode causar complicações respiratórias nas pessoas contaminadas. Para o auxilio na identificação de pessoas que possam ter os sintomas desssa doença ou o controle das que já estão infectadas, neste trabalho foram analisados os desempenhos dos algoritmos KNN, Naive Bayes, Decision Tree, Random Forest, SVM, Multilayer Perceptron simples, Redes neurais Convolucionais e BERT para classificação de tweets que continham relatos de sintomas do Covid-19 ou auto-declaração de contaminação. O conjunto de dados foi rotulado utilizando um conjunto de palavras chaves dos sintomas da doença retirada de uma lista disponibilizada pela Organização Mundial da Saúde. Os testes nesses modelos mostraram que o algoritmo Random Forest foi o que obteve melhor resultado ao classificar os tweets em uma base de dados pequena. Este trabalho demonstrou o desempenho superior do algoritmo RandomForest sobre outros mais robustos para este tipo de classificação e conjunto de dados

Exploring Deep Neural Networks for Plant Image Classification

Automatically distinguishing different types of plant images is a challenging problem relevant to both Botany and Computer Science disciplines. Plant identification at the species level is a computer vision task called fine-grained categorization, which focuses on differentiating between hard-to-distinguish object classes. This classification problem is complicated and challenging because of the lack of annotated data, inter-species similarity, the large-scale features in appearance, and a large number of plant species. A plant classification system capable of addressing the complexity of this computer vision problem has important implications for society at large, not only in public computer science education but also in numerous agricultural activities such as automatic detection of cash crops and non-crop plants (called weeds). Furthermore, successful automation of crop and weed identification will lead to the reduction of chemical compounds currently used to eliminate weeds [15]. Deep Convolutional Neural Networks (CNN) can be a solution to perform this computer vision task. In this thesis, seven different CNN models are deployed to classify 1 million images - from the TerraByte dataset - of eleven very similar plant species [13]. This robust approach divides the problem into two main steps: the first step, called the generalist, identifies similar plants and separates them into different groups that contain indistinguishable plant species. The second step, called specialist, is used to classify plants within the groups of indistinguishable plants, including five weed and seven crop species, with high accuracy. The generalist-specialist CNN network shows that the hierarchical network outperforms simple CNN models in terms of accuracy and classifying similar plant images. The contributions of this thesis are the explored different CNN models and improved performance of those models by designing and implementing the generalist-specialist CNN models for classifying similar plant images."I would ... like to thank Mitacs, George Weston Ltd, the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Faculty of Graduate Studies for their financial support of this work."Master of Science in Applied Computer Scienc

第5回 コーパス日本語学ワークショップ予稿集

National Institute for Japanese Language and Linguistics会議名: 第5回コーパス日本語学ワークショップ, 開催地: 国立国語研究所, 会期: 2014年3月6-7日, 主催: 国立国語研究所 言語資源研究系・コーパス開発センタ