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Text classification is a central task in Natural Language Processing (NLP) that aims at categorizing text documents into predefined classes or categories. It requires appropriate features to describe the contents and meaning of text documents, and map them with their target categories. Existing text feature representations rely on a weighted representation of the document terms. Hence, choosing a suitable method for term weighting is of major importance and can help increase the effectiveness of the classification task. In this study, we provide a novel text classification framework for Category-based Feature Engineering titled CFE. It consists of a supervised weighting scheme defined based on a variant of the TF-ICF (Term Frequency-Inverse Category Frequency) model, embedded into three new lean classification approaches: (i) IterativeAdditive (flat), (ii) GradientDescentANN (1-layered), and (iii) FeedForwardANN (2-layered). The IterativeAdditive approach augments each document representation with a set of synthetic features inferred from TF-ICF category representations. It builds a term-category TF-ICF matrix using an iterative and additive algorithm that produces category vector representations and updates until reaching convergence. GradientDescentANN replaces the iterative additive process mentioned previously by computing the term-category matrix using a gradient descent ANN model. Training the ANN using the gradient descent algorithm allows updating the term-category matrix until reaching convergence. FeedForwardANN uses a feed-forward ANN model to transform document representations into the category vector space. The transformed document vectors are then compared with the target category vectors, and are associated with the most similar categories. We have implemented CFE including its three classification approaches, and we have conducted a large battery of tests to evaluate their performance. Experimental results on five benchmark datasets show that our lean approaches mostly improve text classification accuracy while requiring significantly less computation time compared with their deep model alternatives. • Provides a supervised weighting scheme based on Term Frequency-Inverse Category Frequency model. • Introduces three lean-architecture classifiers compared with more complex deep learning models. • Iterative-additive classifier builds term-category matrix producing category vector features. • Gradient descent classifier computes term-category matrix using gradient descent ANN model. • ANN classifier trains feed-forward ANN to transform documents to the category vector space. [ABSTRACT FROM AUTHOR]

Low-light image (LLI) enhancement is an important image processing task that aims at improving the illumination of images taken under low-light conditions. Recently, a remarkable progress has been made in utilizing deep learning (DL) approaches for LLI enhancement. This paper provides a concise and comprehensive review and comparative study of the most recent DL models used for LLI enhancement. To our knowledge, this is the first comparative study dedicated to DL-based models for LLI enhancement. We address LLI enhancement in two ways: (i) standalone, as a separate task, and (ii) end-to-end, as a pre-processing stage embedded within another high-level computer vision task, namely object detection and classification. The paper consists of six logical parts. First, we provide an overview of the background and literature in LLI enhancement. Second, we describe the test data and experimental setup of the study. Third, we present a quantitative and qualitative comparison of the visual and perceptual quality achieved by 10 of the most recent DL-based LLI enhancement models. Fourth, we present a comparative analysis for object detection and classification performance achieved by 4 different object detection models applied on LLIs and their enhanced counterparts. Fifth, we perform a feature analysis of DL feature maps extracted from normal, low-light, and enhanced images, and perform the occlusion experiment to better understand the effect of LLI enhancement on the object detection and classification task. Finally, we provide our conclusions and highlight future steps and potential directions. • Evaluates Deep Learning (DL) models for Low-light Image (LLI) enhancement. • Compares 10 LLI enhancement models and 4 object detection and classification models. • Provides a quantitative and qualitative comparison of visual and perceptual quality. • Evaluates impact of LLI enhancement on object detecting and classification quality. • Performs occlusion experiment to study LLI enhancement's effect on object detection. [ABSTRACT FROM AUTHOR]

XML document comparison is becoming an ever more popular research issue due to the increasingly abundant use of XML. Likewise, a growing interest fosters the development of XML grammar matching and comparison, due to the proliferation of heterogeneous XML data sources, particularly on the Web. Nonetheless, the process of comparing XML documents with XML grammars, i.e., XML document and grammar similarity evaluation, has not yet received the attention it deserves. In this paper, we provide an overview on existing research related to XML document/grammar comparison, presenting the background and discussing the various techniques related to the problem. We also discuss some prominent application domains, ranging over document classification and clustering, document transformation, grammar evolution, selective dissemination of XML information, XML querying, as well as alert filtering in intrusion detection systems and Web Services matching and communications. [ABSTRACT FROM AUTHOR]

Low-light image (LLI) enhancement techniques have recently demonstrated remarkable progress especially with the use of deep learning approaches. However, most existing techniques are developed as standalone solutions and do not take into account the impact of LLI enhancement on high-level computer vision tasks like object classification. In this paper, we propose a new LLI enhancement model titled LLHFNet (Low-light Homomorphic Filtering Network) which performs image-to-frequency filter learning and is designed for seamless integration into classification models. Through this integration, the classification model is embedded with an internal enhancement capability and is jointly trained to optimize both image enhancement and classification performance. We have conducted a large battery of experiments using SICE, Pascal VOC, and ExDark datasets, to quantitatively and qualitatively evaluate our approach's enhancement quality and classification performance. When evaluated as a standalone enhancement model, our solution consistently ranks among the best existing image enhancement techniques. When embedded with a classification model, our solution achieves an average 5.5% improvement in classification accuracy, compared with the traditional pipeline of separate enhancement followed by classification. Results produce robust classification quality on both LLIs and normal-light images (NLIs), and highlight a clear improvement to the literature. • Introduces Low-Light Image (LLI) Enhancement model for Object Classification task. • Introduces LLHFNet deep learning model for image-to-frequency filter learning. • Introduces LLI enhancer–classifier to integrate LLHFNet in object classification. • Performs joint training to optimize both enhancement and classification models. • Evaluates impact of LLI enhancement on object detecting and classification quality. • Produces 5.5% improvement in classification accuracy on Pascal VOC & ExDark LLIs. [ABSTRACT FROM AUTHOR]