A Survey on Formal Specification and Verification of Smart Mass Transit Railway Interlocking System

Nowadays interest in Smart Mass Transit Rail has grown-up to a large extent in a metropolitan area as the need for urban mobility has increased steadily. The reliability of software being used in such mass transit rail is crucial for us, specifically when software crashes may lead to catastrophic loss of human life and assets. For example, when we travel by metro it is essential for us that the interlocking system software controlling the metros are accurate so collisions and derailment are prevented. The reliability and safety of such interlocking systems are made on the precise functional requirements specification and verification respectively. Therefore, the precise functional requirements specification and verification of such interlocking systems represent a challenge in an active research area, so in this paper, we survey various articles in this field and discuss their consequences.

Global-local attention for emotion recognition

Human emotion recognition is an active research area in artificial intelligence and has made substantial progress over the past few years. Many recent works mainly focus on facial regions to infer human affection, while the surrounding context information is not effectively utilized. In this paper, we proposed a new deep network to effectively recognize human emotions using a novel global-local attention mechanism. Our network is designed to extract features from both facial and context regions independently, then learn them together using the attention module. In this way, both the facial and contextual information is used to infer human emotions, therefore enhancing the discrimination of the classifier. The intensive experiments show that our method surpasses the current state-of-the-art methods on recent emotion datasets by a fair margin. Qualitatively, our global-local attention module can extract more meaningful attention maps than previous methods. The source code and trained model of our network are available at https://github.com/minhnhatvt/glamor-net.

Crowd Counting Using End-to-End Semantic Image Segmentation

Crowd counting is an active research area within scene analysis. Over the last 20 years, researchers proposed various algorithms for crowd counting in real-time scenarios due to many applications in disaster management systems, public events, safety monitoring, and so on. In our paper, we proposed an end-to-end semantic segmentation framework for crowd counting in a dense crowded image. Our proposed framework was based on semantic scene segmentation using an optimized convolutional neural network. The framework successfully highlighted the foreground and suppressed the background part. The framework encoded the high-density maps through a guided attention mechanism system. We obtained crowd counting through integrating the density maps. Our proposed algorithm classified the crowd counting in each image into groups to adapt the variations occurring in crowd counting. Our algorithm overcame the scale variations of a crowded image through multi-scale features extracted from the images. We conducted experiments with four standard crowd-counting datasets, reporting better results as compared to previous results.

Which OCR toolset is good and why? A comparative study

Optical Character Recognition (OCR) is a very active research area in many challenging fields like pattern recognition, natural language processing (NLP), computer vision, biomedical informatics, machine learning (ML), and artificial intelligence (AI). This computational technology extracts the text in an editable format (MS Word/Excel, text files, etc.) from PDF files, scanned or hand-written documents, images (photographs, advertisements, and alike), etc. for further processing and has been utilized in many real-world applications including banking, education, insurance, finance, healthcare and keyword-based search in documents, etc. Many OCR toolsets are available under various categories, including open-source, proprietary, and online services. This research paper provides a comparative study of various OCR toolsets considering a variety of parameters.

Leaf Features Extraction for Plant Classification using CNN

Deep learning is now an active research area. Deep learning has done a success in computer vision and image recognition. It is a subset of the Machine Learning. In Deep learning, Convolutional Neural Network (CNN) is popular deep neural network approach. In this paper, we have addressed that how to extract useful leaf features automatically from the leaf dataset through Convolutional Neural Networks (CNN) using Deep Learning. In this paper, we have shown that the accuracy obtained by CNN approach is efficient when compared to accuracy obtained by the traditional neural network.

Supramolecular Polymerization of BODIPY Dyes Extended with Rationally Designed Pyrazole-based Motif

Controlled supramolecular polymerization has emerged as an active research area in the last decade. Recent contributions have revealed that their dynamic self-assembly into different molecular packings is strongly influenced by...

Unprecedented coupling of natural rubber and ELP: synthesis, characterization and self-assembly properties

Developing new biomaterials is an active research area owing to their applications in regenerative medicine, tissue engineering and drug delivery.

A Mini-Review: Quinones and their Derivatives for Selective and Specific Detection of Specific Cations

The development of fluorescence and colorimetric sensors has been an active research area in the last few decades due to their wide range of environmental, agricultural, and medicinal chemistry applications. This review provides an overview of quinones' recent contribution and their derivatives to fluorescence and colorimetric sensors. It discusses their sensing properties with promising features.

Bio-Catalysis in Multicomponent Reactions

Enzyme catalysis is a very active research area in organic chemistry, because biocatalysts are compatible with and can be adjusted to many reaction conditions, as well as substrates. Their integration in multicomponent reactions (MCRs) allows for simple protocols to be implemented in the diversity-oriented synthesis of complex molecules in chemo-, regio-, stereoselective or even specific modes without the need for the protection/deprotection of functional groups. The application of bio-catalysis in MCRs is therefore a welcome and logical development and is emerging as a unique tool in drug development and discovery, as well as in combinatorial chemistry and related areas of research.

Semantic Segmentation of Medical Images with Deep Learning: Overview

Semantic segmentation is one of the biggest challenging tasks in computer vision, especially in medical image analysis, it helps to locate and identify pathological structures automatically. It is an active research area. Continuously different techniques are proposed. Recently Deep Learning is the latest technique used intensively to improve the performance in medical image segmentation. For this reason, we present in this non-systematic review a preliminary description about semantic segmentation with deep learning and the most important steps to build a model that deal with this problem.