Deep Tree Transductions - A Short Survey

The paper surveys recent extensions of the Long-Short Term Memory networks to handle tree structures from the perspective of learning non-trivial forms of isomorph structured transductions. It provides a discussion of modern TreeLSTM models, showing the effect of the bias induced by the direction of tree processing. An empirical analysis is performed on real-world benchmarks, highlighting how there is no single model adequate to effectively approach all transduction problems.Comment: To appear in the Proceedings of the 2019 INNS Big Data and Deep Learning (INNSBDDL 2019). arXiv admin note: text overlap with arXiv:1809.0909

Learning Tree Distributions by Hidden Markov Models

Hidden tree Markov models allow learning distributions for tree structured data while being interpretable as nondeterministic automata. We provide a concise summary of the main approaches in literature, focusing in particular on the causality assumptions introduced by the choice of a specific tree visit direction. We will then sketch a novel non-parametric generalization of the bottom-up hidden tree Markov model with its interpretation as a nondeterministic tree automaton with infinite states.Comment: Accepted in LearnAut2018 worksho

Illustrations Segmentation in Digitized Documents Using Local Correlation Features

In this paper we propose an approach for Document Layout Analysis based on local correlation features. We identify and extract illustrations in digitized documents by learning the discriminative patterns of textual and pictorial regions. The proposal has been demonstrated to be effective on historical datasets and to outperform the state-of-the-art in presence of challenging documents with a large variety of pictorial elements

MULTI-MODEL BIOMETRICS AUTHENTICATION FRAMEWORK

Authentication is the process to conform the truth of an attribute claimed by real entity. Biometric technology is widely useful for the process of authentication. Today, biometric is becoming a key aspect in a multitude of applications. So this paper proposed the applications of such a multimodal biometric authentication system. Proposed system establishes a real time authentication framework using multi-model biometrics which consists of the embedded system verify the signatures, fingerprint and key pattern to authenticate the user. This is one of the most reliable, fast and cost effective tool for the user authentication

On classifying digital accounting documents

Advances in computing and multimedia technologies allow many accounting documents to be digitized within little cost for effective storage and access. Moreover, the amount of accounting documents is increasing rapidly, this leads to the need of developing some mechanisms to effectively manage those (semi-structured) digital accounting documents for future accounting information systems (AIS). In general, accounting documents contains such as invoices, purchase orders, checks, photographs, charts, diagrams, etc. As a result, the major functionality of future AIS is to automatically classify digital accounting documents into different categories in an effective manner. The aim of this paper is to examine flat nonhierarchical and hierarchical classification schemes for automatic classification of different types of digital accounting documents. The experimental results show that non-hierarchical classification of digital accounting documents performs better than hierarchically classifying digital accounting documents.Los avances en informática y en las tecnologías multimedia permiten, que muchos documentos de contabilidad sean digitalizados por poco dinero para un almacenamiento y acceso efectivos. Además, la cantidad de documentos de contabilidad está incrementando de forma rápida, lo que lleva a la necesidad por desarrollar algunos mecanismos para dirigir efectivamente aquellos (semi-estructurados) documentos de contabilidad digitales para los futuros sistemas de información de contabilidad (AIS en inglés). En general, dichos documentos contienen por ejemplo, facturas, órdenes de compra, comprobaciones, fotografías, gráficos, diagramas, etc. Como resultado, la mayor funcionalidad de los futuros AIS es para clasificar automáticamente los documentos digitales de contabilidad en diferentes categorías de una forma efectiva. El objetivo de este artículo es el de examinar los esquemas de clasificación jerárquica y no jerárquica sin cambios para la clasificación automática de los diferentes tipos de dichos documentos. Los resultados experimentales demuestran que la clasificación no jerárquica de estos documentos tiene más éxito que la jerárquica

Hybrid classification approach hdlmm for learning disability prediction in school going children using data mining technique

Learning Disability is a disorder of neurological condition which causes deficiency in child�s brain activities such as reading, speaking and many other tasks. According to the World Health Organization (WHO), 15 of the children get affected by the learning disability. Efficient prediction and accurate classification is the crucial task for researchers for early detection of learning disability. In this work, our main aim to develop a model for learning disability prediction and classification with the help of soft computing technique. To improve the performance of the prediction and classification we propose a hybrid approach for feature reduction and classification. Proposed approach is divided into three main stages: (i) data pre-processing (ii) feature selection and reduction and (iii) Classification. In this approach, preprocessing, feature selection and reduction is carried out by measuring of confidence with adaptive genetic algorithm. Prediction and classification is carried out by using Deep Learner Neural network and Markov Model. Genetic algorithm is used for data preprocessing to achieve the feature reduction and confidence measurement. The system is implemented using MatLab 2013b. Result analysis shows that the proposed approach is capable to predict the learning disability effectively. © 2005 � ongoing JATIT & LLS

Improving accuracy and speeding up Document Image Classification through parallel systems

This paper presents a study showing the benefits of the EfficientNet models compared with heavier Convolutional Neural Networks (CNNs) in the Document Classification task, essential problem in the digitalization process of institutions. We show in the RVL-CDIP dataset that we can improve previous results with a much lighter model and present its transfer learning capabilities on a smaller in-domain dataset such as Tobacco3482. Moreover, we present an ensemble pipeline which is able to boost solely image input by combining image model predictions with the ones generated by BERT model on extracted text by OCR. We also show that the batch size can be effectively increased without hindering its accuracy so that the training process can be sped up by parallelizing throughout multiple GPUs, decreasing the computational time needed. Lastly, we expose the training performance differences between PyTorch and Tensorflow Deep Learning frameworks

Tree Echo State Networks

In this paper we present the Tree Echo State Network (TreeESN) model, generalizing the paradigm of Reservoir Computing to tree structured data. TreeESNs exploit an untrained generalized recursive reservoir, exhibiting extreme efficiency for learning in structured domains. In addition, we highlight through the paper other characteristics of the approach: First, we discuss the Markovian characterization of reservoir dynamics, extended to the case of tree domains, that is implied by the contractive setting of the TreeESN state transition function. Second, we study two types of state mapping functions to map the tree structured state of TreeESN into a fixed-size feature representation for classification or regression tasks. The critical role of the relation between the choice of the state mapping function and the Markovian characterization of the task is analyzed and experimentally investigated on both artificial and real-world tasks. Finally, experimental results on benchmark and real-world tasks show that the TreeESN approach, in spite of its efficiency, can achieve comparable results with state-of-the-art, although more complex, neural and kernel based models for tree structured data