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In this pilot study, diachronic semantic analysis is employed to probe the origin and semantic evolution of the classifier 架 (jià). The study has three objectives. Firstly, it intends to probe the emergence and development of the Chinese classifier 架 (jià). Secondly, it seeks to attest to the perspective of the fundamental role of human cognition and perception in the classifier language system, as indicated by Tai and Wang (1990). Finally, it suggests pragmatic classifiers teaching approaches in alignment with cognitive linguistic perceptions. The preliminary analysis of this study signifies that the classifier 架 (jià) is not an arbitrary linguistic device. Instead, its utilization throughout history reflects human categorization based on the perceptual property of the supporting framework of the referents. To enhance the efficiency of teaching Chinese classifiers and provide learners with a more natural and comprehensive acquisition mode, future studies on classifier acquisition are expected to align with the conceptual structure of the classifiers’ domains and the cognitive linguistic approach.

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Research work, presented in this article, aims to create IT tools for the study and research of handwriting, transcription and writing in general. It provides tools for structuring, processing, managing, visualizing and analyzing this data, which greatly facilitates research and study processes. An online accessible digital repertorium (web-based software environment) has been developed, storing information about valuable manuscripts and copyists with significant contributions to the development of South Slavic languages, writing and culture. Each written resource is digitized and described according to a descriptive model based on the metadata available for it. Once entered into the digital repertorium, it can be discovered through a variety of context-based search and intelligent curation services. Tools are provided for detailed study and analysis of each letter and its element, detection of similarities between pairs of manuscripts - spelling of letters and their elements, tools to support the joint simultaneous work of researchers in the system and management of the process of description and structuring of data. The goal of the development is to reach and effectively use the vast amount of knowledge about the digitized written heritage by providing tools for higher adaptability and effective interaction between the researcher and the digital repertorium. [ABSTRACT FROM AUTHOR]

Objectives: Satellite mission planning is an important way to rationally allocate satellite resources and obtain effective data. In order to solve the problem of resource waste in the absence of user demand for Qimingxing-1(QMX-1), we proposed a semantic description driven autonomous mission planning method for QMX-1. Methods: By analyzing the characteristics of earth observation targets and their requirements for remote sensing data, we have construct a semantic description model for global tasks and form a global earth observation target database. Based on the description model, we design a satellite autonomous mission planning method. By building a mission planning model, we can achieve autonomous planning for high-value targets and maximize the value of satellite in orbit applications. Results and Conclusions: We verifyed the proposed model based on the QMX-1 and proved the feasibility of the method in this paper. The model is applicable to different types of remote sensing satellites and can play a certain value in the future remote sensing constellation real-time intelligent service applications. [ABSTRACT FROM AUTHOR]

Data-driven fault detection techniques have attracted extensive attention in engineering, industry and many other areas in recent years. In many real applications, the following situation often occurs: data for certain types of faults (unseen faults) are not available to train models that are used for fault detection. Such a scenario can occur when data collection becomes highly time-consuming or destructive. To address this challenging problem, a novel fault detection method using zero-shot learning (ZSL) is proposed in this paper, which contains three phases: feature extraction, label embedding, and feature embedding. The method first extracts features from raw signals by applying a one-dimensional convolutional neural network (1D CNN), then builds semantic descriptions (human-defined) as fault attributes shared between seen faults and unseen faults, and finally uses a bi-linear compatibility function to find the highest-ranking fault type. The proposed semantic space based zero-shot learning with 1D CNN is called SSB-ZSL-1DCNN. The cosine distance is used to measure the similarity between feature embeddings and fault attributes. An important characteristic of SSB-ZSL-1DCNN is that the model, trained using only samples of seen faults, can be used to detect unseen defects. To evaluate the proposed method, two case studies are designed based on two well-known benchmarks (the Tennessee-Eastman chemical control process and the rolling bearing experiments at the Case Western Reserve University, respectively). The results demonstrate that the proposed method shows remarkable performance in detecting unseen faults. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article explores the ways of making emotional lexemes semantic description consistent with interpretative intuition of the ordinary language speaker. The research novelty is determined by the fact that it is based on the data retrieved from the emotional assessment of 3920 internet-texts in Russian made by informants via using a specially designed computer interface. When applied this interface, we can aggregate the weight of 8 emotions (distress, enjoyment, anger, surprise, shame, excitement, disgust, fear) in text. Thus, the data we have used for this publication includes two sets of 150 internet-texts assessed by 2000 informants with the highest score of emotions of distress or anger. The scope of the study covers the semantics of two mentioned above lexemes (grust’ and gnev) analyzed through the prism of collective introspection of informants. The article purpose is to discuss the case when a semantic description of emotives is given by an expert, which largely uses “the best texts” of corresponding emotions, according to the collective opinion of informants. Our methods include psycholinguistic experiment, corpus and semantic analysis. The research led us to three main conclusions. Firstly, the semantic descriptions of emotives grust’ and gnev obtained in proposed way represent prototypical scenarios of living an emotion in social context and take into account not only the introspective sensations of an expert-linguist, but the interpretative strategies of language users. Secondly, such semantic explanation provides us with keys for explaining, why machine learning technologies are better at detecting anger than sadness in text. Finally, it creates a precedent in using new technologies for making an ecological semantic description of emotive vocabulary. The research results can find application in emotiology, lexicographic practice and didactics.

In recent decades, cultural heritage survey practices have significantly evolved due to the increasing use of digitization tools providing quick and easy access to faithful copies of study objects. While these digital data have clear advantages, especially in terms of geometric characterization, they also introduce a paradigm shift by outsourcing ex situ most of the analysis activities. This break between real and virtual working environments now raises new issues, both in terms of data dispersion and knowledge correlation in multidisciplinary teams. Benefiting from the fields of information systems and augmented reality, we proposed an integrated approach allowing the fusion of geometric, visual and semantic features in a single platform. Today, this proof of concept leads to new perspectives for the production of semantically enriched digital data. In this paper, we intend to explore the different possibilities in terms of implementation and their benefits for cultural heritage survey.DOI: https://doi.org/10.20365/disegnarecon.26.2021.13

Gas-water two-phase flow process widely exists in petroleum, chemical and other industrial processes, which is intricate, manifested by dynamic evolution of fluid structures and real-time fluctuation of flow parameters. Detailed description and real-time monitoring of flow status are of great significance to safe operation of industry processes. In this paper, a monitoring framework jointly mining the fluid knowledge and latent property behind multi-sensor data is proposed to address the issues of uncertainty and sample scarcity in transitional flow statuses, which enables real-time monitoring on structural evolution and parameter change of flow process. Firstly, the shared iterative algorithm with multi-task weight discovery is proposed to establish the relative semantic description system to characterize flow process knowledge by mining the relationship between multi-sensor data and different fluid semantic properties, which is helpful for interpretable monitoring of flow status structure and parameter information. Secondly, latent properties mined from multi-sensor data by sparse exponential discriminant analysis contribute to flow characterization completeness and discrimination. Thirdly, the process knowledge and latent information of flow status is transformed into different degrees of describable semantic and latent properties, which obtain relative representations of flow status in the joint description space realized by rank support vector machine. The results demonstrate that the method enables precise monitoring and detailed description of typical and uncertain flow statuses in the absence of transitional flow status modeling samples. [ABSTRACT FROM AUTHOR]

In view of the current demand of heterogeneous, cross domain, many service platforms, wide distribution and low-cost resource sharing platform construction of agricultural public welfare information service resources, this paper puts forward a set of standardized and orderly data acquisition and interpretation sharing methods. Methods combined with the characteristics of multi domain resource sharing, metadata semantic modeling based on cognition is proposed to establish the mapping between metadata cognition and metadata semantics to resource description model, build a basic framework of resource sharing, extract the minimum core metadata required for resource sharing, define the basic rules for the existence of resource sharing data, and give the resource sharing description model. At the same time, in order to clearly explain the semantic annotation information in the model, the construction method of shared resource semantics is introduced. In order to reduce the difficulty of resource description, sharing and publishing on heterogeneous platforms, a shared resource description tool based on "resource sharing semantic description model" is introduced. This method combines the advantages of existing methods. At the same time, through the production of description tools and the introduction of semantics, the development difficulty and development volume of each service platform are reduced, the development cost is saved, the data sharing efficiency is improved, and the requirements of low-cost, cross domain, light dependence, resource readability and machine solvability for the construction of agricultural public welfare information service resource sharing platform are better solved. [ABSTRACT FROM AUTHOR]

Learning resources are massive, heterogeneous, and constantly changing. How to find the required resources quickly and accurately has become a very challenging work in the management and sharing of learning resources. According to the characteristics of learning resources, this paper proposes a progressive learning resource description model, which can describe dynamic heterogeneous resource information on a fine-grained level by using information extraction technology, then a semantic annotation algorithm is defined to calculate the semantic of learning resource and add these semantic to the description model. Moreover, a semantic search method is proposed to find the required resources, which calculate the content with the highest similarity to the user query, and then return the results in descending order of similarity. The simulation results show that the method is feasible and effective. [ABSTRACT FROM AUTHOR]

This paper outlines the development of machine-readable semantic structures, supporting the research and analysis of the manuscript works of the South Slavic scribes from the fourteenth century. Our hope is to reach and effectively exploit the vast amounts of knowledge about the digitized written heritage by providing means to semantically describe, structure, process, manage, visualize and analyze the data, greatly facilitating the research and study processes. [ABSTRACT FROM AUTHOR]

Cloud computing has emerged as a tremendous opportunity for both industry and academia as it introduces a fundamental shift in Cloud service delivery. This situation has led to more competitive Cloud providers giving end-users greater freedom to choose the best Cloud service. However, each Cloud provider uses its techniques to describe its Cloud services. As a result, it is increasingly difficult for users to find and access Cloud services with similar functionality. The variety of these techniques is due to the lack of Cloud services description standardization. To deal with such issues and due to the vast search space, we propose in this paper a Cloud services description ontology, "CSDO," that assists the Cloud service publication, discovery, and selection processes. The proposed description is based on the Linked-USDL language to describe Cloud services thanks to its expressiveness. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a new data analysis technology based on human‐oriented analysis. This analysis covers semantic methods of data description and interpretation referring to marketing preferences of system users. The proposed methods of cognitive marketing – in order to interpret fully all possible preferences which can occur – are a subject to an analysis focusing on their meaning and usefulness at the stage of product evaluation, promotion, but also regarding product distribution and price. All these processes will constitute marketing analysis due to the possibilities to assess some preferences of the analyzed data. The essence of this paper is a possibility to present the methodology of cognitive marketing based on the application of the meaning analysis which reaches the human brain and the attention attractors registered by the brain, which give rise to some interest or, quite the contrary, which remain unnoticed. A new solution is dedicated to deep semantic analysis based on the registration, processing, and analysis of attention attractors and their perception by individual person. Such attractors can be detected on the basis of observation of how attention is focused on some specific features of the examined information groups. The variety of the examined information and data can enable a wide‐ranging analysis of the issue discussed here; as a result, can assess the degree to which human attention focuses on the process of meaning interpretation of various cognitive features and observations. [ABSTRACT FROM AUTHOR]

To facilitate the management of 3D content in applications, some researchers add semantics to the geometric description of 3D models. However, the insurmountable semantic gap between 3D model and semantic description is the biggest obstacle to the matching of them. This paper proposes a novel network framework named Multi-modal Auxiliary Classifier Generative Adversarial Network with autoencoder (MACGAN-AE) for the matching of 3D model and its semantic description. Firstly, the Multi-modal Auxiliary Classifier Generative Adversarial Network is presented to solve the multi-modal classification. It captures the latent correlated representation between multi-modes and bridges the semantic gap of them. Then, the autoencoder is introduced to construct MACGAN-AE to further enhance the correlation between 3D model and its semantic description. The framework is expected to minimize the semantic gap between 3D model and its corresponding semantic description. In addition, to preserve the relationships between data after feature projection, this paper also defines a structure-preserving loss to reduce the intra-class distance and increase the inter-class distance. Experimental results on XMediaNet dataset demonstrate that our method significantly outperforms other methods.

Now it is possible to speak about a number of basic principles of the description of semantics of language units during expert research of the disputable text which should underlie any expert linguistic research. The major of them, relevant for expert estimation of semantics of a word in disputable text, are the following: 1. The account of polysemy of words and expressions. 2. The account of context of the use of a disputable word or expression. 3. The account of situation of the use of a disputable word or expression. 4. The account of real language consciousness of the ordinary native speaker. The article features the substantiation of each of these principles and presents examples of realization of each of the principles in the practical analysis of the text.

Evolved living beings can anticipate the consequences of their actions in complex multilevel dynamic situations. This ability relies on abstracting the meaning of an action. The underlying brain mechanisms of such semantic processing of information are poorly understood. Here we show how our novel concept, known as time compaction, provides a natural way of representing semantic knowledge of actions in time-changing situations. As a testbed, we model a fencing scenario with a subject deciding between attack and defense strategies. The semantic content of each action in terms of lethality, versatility, and imminence is then structured as a spatial (static) map representing a particular fencing (dynamic) situation. The model allows deploying a variety of cognitive strategies in a fast and reliable way. We validate the approach in virtual reality and by using a real humanoid robot.

Evolved living beings can anticipate the consequences of their actions in complex multilevel dynamic situations. This ability relies on abstracting the meaning of an action. The underlying brain mechanisms of such semantic processing of information are poorly understood. Here we show how our novel concept, known as time compaction, provides a natural way of representing semantic knowledge of actions in time-changing situations. As a testbed, we model a fencing scenario with a subject deciding between attack and defense strategies. The semantic content of each action in terms of lethality, versatility, and imminence is then structured as a spatial (static) map representing a particular fencing (dynamic) situation. The model allows deploying a variety of cognitive strategies in a fast and reliable way. We validate the approach in virtual reality and by using a real humanoid robot. [ABSTRACT FROM AUTHOR]

The study focuses on the ability of a computer to interpret natural human language, to process, retrieve and analyze data obtained from a user's natural language request. The Bulgarian language presents serious challenges to developers and researchers in the field of natural language processing. The purpose of this article is to describe some of these challenges and to present some solutions that would target current and future natural language professionals. The article focuses on the problem of classification and annotation of logical predicates in order to obtain a modern semantic analyzer in natural language processing in Bulgarian. Conceptualization involves the description of many objects and concepts, knowledge about them, hierarchies of types, specifying classes, and the relationships between them. Each sentence, process, or phenomenon in the world is characterized by welldefined relationships between participants in it. In the paper, we present the semantic relations that participants engage in situations described by predicate - argument structures. The development of linguistic analysis aims to determine semantically and syntactically the type of linguistic units and to build upon it by adding an additional level of abstraction over syntactic analysis. The study set up the base of upgrading the existing semantic syntactic resource FrameNet by developing structural accumulative frame models (AFM) for compatibility of knowledge bases, by modeling as accurately as possible to recognize predicate arguments in a predicate-argument frame structure. [ABSTRACT FROM AUTHOR]

Smart manufacturing refers to integrating new generation information technologies and advanced manufacturing technologies to improve enterprise efficiency, increase customer satisfaction and enhance market competitiveness. Cloud manufacturing (CMfg) is a typical paradigm of smart manufacturing. With the rapid deployment and application of various CMfg systems, it is extremely urgent and significant to implement the standardized semantic description of manufacturing resources (MRs) characterized by geographical dispersion, high autonomy, and heterogeneity. In this paper, a metadata based manufacturing resource ontology modeling method is proposed to solve the consistency description problem of MRs in cloud. Firstly, the characteristic attributes of MRs in CMfg systems are analyzed, and a metadata-based manufacturing resource ontology-modeling framework is constructed. Then, a manufacturing resource domain ontology (MR_Onto) and a metadata model (MR_Meta) are designed, the mapping rules between them are discussed. Finally, a prototype tool describing manufacturing resources is developed, the feasibility and effectiveness of the proposed method and the developed tool are verified in an application case. [ABSTRACT FROM AUTHOR]

The article deals with methodology for description of scientifi c and educational services in education and information on the basis of interoperability stack EIF (European Interoperability Framework). The passage describes operation factors to depict services on every level of the methodology, tools used to describe the services and the content. We also provide the link between methodology of description with the life span of the service. The article presents an example of service description according to the methodology considering the current education and professional standards, ITIL recommendations, ontology on the OWL basis and WSDL-description.