Attribute Extraction Study in the Field of Military Equipment Based on Distant Supervision

The effective organization and utilization of military equipment data is an important cornerstone for constructing knowledge system. Building a knowledge graph in the field of military equipment can effectively describe the relationship between entity and entity attribute information. Therefore, relevant personnel can obtain information quickly and accurately. Attribute extraction is an important part of building the knowledge graph. Given the lack of annotated data in the field of military equipment, we propose a new data annotation method, which adopts the idea of distant supervision to automatically build the attribute extraction dataset. We convert the attribute extraction task into a sequence annotation task. At the same time, we propose a RoBERTa-BiLSTM-CRF-SEL-based attribute extraction method. Firstly, a list of attribute name synonyms is constructed, then a corpus of military equipment attributes is obtained through automatic annotation of semistructured data in Baidu Encyclopedia. RoBERTa is used to obtain the vector encoding of the text. Then, input it into the entity boundary prediction layer to label the entity head and tail, and input the BiLSTM-CRF layer to predict the attribute label. The experimental results show that the proposed method can effectively perform attribute extraction in the military equipment domain. The F 1 value of the model reaches 77% on the constructed attribute extraction dataset, which outperforms the current state-of-art model.

Enhanced Handwritten Document Recognition Using Confusion Matrix Analysis

The handwritten Multilanguage phase is the preprocessing phase that improves the image quality for better identification in the system. The main goals of preprocessing are diodes, noise suppression and line cancellation. After word processing, various attribute extraction techniques are used to process attribute properties for the identification process. Smoothing plays an important role in character recognition. The partitioning process in the word distribution strategy can be divided into global and local texts. The writer does not use this header line to write the text which creates a problem for skew correction, classification and recognition. The dataset used are HWSC and TST1. The tensor flow method is used to estimate the consistency of confusion matrix for the enhancement of the text recognition .The accuracy of the proposed method is 98%.

AVALIAÇÃO DA QUALIDADE DE CLASSIFICADORES DE IMAGENS LANDSAT 8 EM AMBIENTE COMPUTACIONAL SAGA GIS PARA MAPEAMENTO DE COBERTURA DA TERRA NO BIOMA CERRADO

QUALITY ASSESSMENT OF LANDSAT 8 IMAGE CLASSIFIERS IN A SAGA GIS COMPUTER ENVIRONMENT FOR LAND COVERING MAPPING IN THE CERRADO BIOMEEVALUACIÓN DE LA CALIDAD DE LOS CLASIFICADORES DE IMAGEN LANDSAT 8 EN UN ENTORNO COMPUTACIONAL SAGA GIS PARA EL MAPEO DE COBERTURA DE TIERRAS EN EL BIOMA DE CERRADORESUMOUma das principais aplicações das imagens de satélites é a caracterização da cobertura terrestre, que a partir do uso de técnicas de classificação permite monitorar as transformações espaciais da superfície terrestre. O Sistema Automatizado de Análise Geociêntífica – Saga Gis apresenta um conjunto de ferramentas voltado à análise geográfica, incluindo pacotes de classificação de imagens digitais, onde se destacam os classificadores: Maxver, Mahalanobis, distância mínima, paralelepípedo. O objetivo deste artigo é avaliar o potencial dos classificadores de imagens do Saga Gis no bioma Cerrado, sendo objeto de estudo, o município de Brejo-MA. Foi utilizada uma imagem Landsat 8 de 2017, com resolução espacial de 30 metros. A metodologia consistiu na aplicação de um conjunto de técnicas de tratamento digital de imagens, segmentação, extração de atributos e classificação. A análise dos dados pautou-se na comparação visual e análise da exatidão global e de índice Kappa. O classificador Maxver apresentou os melhores resultados para o Kappa e exatidão global, já os piores valores foram associados ao classificador paralelepípedo.Palavras-chave: Geotecnologia; Processamento de Imagem; Acurácia, Mapeamento. ABSTRACTOne of the main applications of satellite images is the characterization of terrestrial coverage, which from the use of classification techniques allows to monitor the spatial transformations of the terrestrial surface. The System for Automated Geoscientific Analyzes-Saga Gis presents a set of tools aimed at geographic analysis, including digital image classification packages, in which the classifiers stand out: Maxver, Mahalanobis, minimum distance, parallelepiped. The objective of this article is to evaluate the potential of the Saga Gis image classifiers in the Cerrado biome, being the object of study, the municipality of Brejo-MA. It was to use a Landsat 8 image (2017), with a spatial resolution of 30 meters. The methodology consisted of applying a set of techniques for digital image processing, segmentation, attribute extraction and classification. Data analysis was based on visual comparison and analysis of global accuracy and Kappa index. The Maxver classifier presented the best results for Kappa and overall accuracy, whereas the worst values were associated with the parallelepiped classifier.Keywords: Geotechnology; Image Processing; Accuracy; Mapping.RESUMENUna de las principales aplicaciones de las imágenes de satélite es la caracterización de la cobertura terrestre, que, a partir del uso de técnicas de clasificación, permite el seguimiento de las transformaciones espaciales de la superficie terrestre. El Sistema de Análisis Geocientífico Automatizado (Saga Gis) presenta un conjunto de herramientas orientadas al análisis geográfico, que incluyen paquetes de clasificación de imágenes digitales, en los que destacan los clasificadores: Maxver, Mahalanobis, distancia mínima, paralelepípedo. El objetivo de este artículo es evaluar el potencial de los clasificadores de imágenes Saga Gis en el bioma del Cerrado, siendo objeto de estudio, el municipio de Brejo-MA. Se utilizó una imagen Landsat 8 de 2017 con una resolución espacial de 30 metros. La metodología consistió en aplicar un conjunto de técnicas de procesamiento, segmentación, extracción de atributos y clasificación de imágenes digitales. El análisis de los datos se basó en la comparación visual y el análisis de la precisión global y el índice Kappa. El clasificador Maxver presentó los mejores resultados para Kappa y precisión general, mientras que los peores valores se asociaron con el clasificador paralelepípedo.Palabras clave: Geotecnología; Procesamiento de imágenes; Precisión; Mapeo.

Systematic Knowledge Management of Construction Safety Standards Based on Knowledge Graphs: A Case Study in China

Construction safety standards (CSS) have knowledge characteristics, but few studies have introduced knowledge graphs (KG) as a tool into CSS management. In order to improve CSS knowledge management, this paper first analyzed the knowledge structure of 218 standards and obtained three knowledge levels of CSS. Second, a concept layer was designed which consisted of five levels of concepts and eight types of relationships. Third, an entity layer containing 147 entities was constructed via entity identification, attribute extraction and entity extraction. Finally, 177 nodes and 11 types of attributes were collected and the construction of a knowledge graph of construction safety standard (KGCSS) was completed using knowledge storage. Furthermore, we implemented knowledge inference and obtained CSS planning, i.e., the list of standard work plans used to guide the development and revision of CSS. In addition, we conducted CSS knowledge retrieval; a process which supports interrogative input. The construction of KGCSS thus facilitates the analysis, querying, and sharing of safety standards knowledge.

Artificial intelligence and infrared thermography as auxiliary tools in the diagnosis of temporomandibular disorder

Objective: To assess three machine learning (ML) attribute extraction methods: radiomic, semantic and radiomic-semantic association on temporomandibular disorder (TMD) detection using infrared thermography (IT); and to determine which ML classifier, KNN, SVM and MLP, is the most efficient for this purpose. Methods and materials: 78 patients were selected by applying the Fonseca questionnaire and RDC/TMD to categorize control patients (37) and TMD patients (41). IT lateral projections of each patient were acquired. The masseter and temporal muscles were selected as regions of interest (ROI) for attribute extraction. Three methods of extracting attributes were assessed: radiomic, semantic and radiomic-semantic association. For radiomic attribute extraction, 20 texture attributes were assessed using co-occurrence matrix in a standardized angulation of 0°. The semantic features were the ROI mean temperature and pain intensity data. For radiomic-semantic association, a single dataset composed of 28 features was assessed. The classification algorithms assessed were KNN, SVM and MLP. Hopkins’s statistic, Shapiro–Wilk, ANOVA and Tukey tests were used to assess data. The significance level was set at 5% (p < 0.05). Results: Training and testing accuracy values differed statistically for the radiomic-semantic association (p = 0.003). MLP differed from the other classifiers for the radiomic-semantic association (p = 0.004). Accuracy, precision and sensitivity values of semantic and radiomic-semantic association differed statistically from radiomic features (p = 0.008, p = 0.016 and p = 0.013). Conclusion: Semantic and radiomic-semantic-associated ML feature extraction methods and MLP classifier should be chosen for TMD detection using IT images and pain scale data. IT associated with ML presents promising results for TMD detection.

On augmenting database schemas by latent visual attributes

Decision-making in our everyday lives is surrounded by visually important information. Fashion, housing, dating, food or travel are just a few examples. At the same time, most commonly used tools for information retrieval operate on relational and text-based search models which are well understood by end users, but unable to directly cover visual information contained in images or videos. Researcher communities have been trying to reveal the semantics of multimedia in the last decades with ever-improving results, dominated by the success of deep learning. However, this does not close the gap to relational retrieval model on its own and often rather solves a very specialized task like assigning one of pre-defined classes to each object within a closed application ecosystem. Retrieval models based on these novel techniques are difficult to integrate in existing application-agnostic environments built around relational databases, and therefore, they are not so widely used in the industry. In this paper, we address the problem of closing the gap between visual information retrieval and relational database model. We propose and formalize a model for discovering candidates for new relational attributes by analysis of available visual content. We design and implement a system architecture supporting the attribute extraction, suggestion and acceptance processes. We apply the solution in the context of e-commerce and show how it can be seamlessly integrated with SQL environments widely used in the industry. At last, we evaluate the system in a user study and discuss the obtained results.

Cup-to-Disc Ratio Based Efficient Glaucoma Prediction on Fundus Images Using Tripartite Tier Convolutional Neural Network

Glaucoma is a condition that causes permanent damage to the optic nerves, resulting in partial or total vision loss. In this paper, a deep learning model using Tripartite Tier Convolutional Neural Network (TTCNN) structure is proposed to detect the glaucomatous images from the normal images. The proposed system includes different steps such as preprocessing, attribute extraction, and glaucoma evaluation. Preprocessing discusses how to convert RGB fundus images to grayscale and how to improve fundus feature contrast. Then, the optic cup (OC) and optic disc (OD) boundaries are fragmented during the attribute extraction using TTCNN. Finally, the Cup-to-Disc Ratio (CDR) has been determined to diagnose glaucoma in the image. This system has been verified on two different publicly available datasets DRIVE and RIM-ONE, yielding an average sensitivity, specificity, accuracy, and precision in glaucoma diagnosis of 84.50%, 98.01%, 99%, and 84% respectively. The obtained results show that the proposed recognition system is suitable for detecting glaucoma with higher precision.