Matrix-Qubit Algorithm for Semantic Analysis of Probabilistic Data

The paper reports a method for compressed representation of matrix data on the principles of quantum theory. The method is formalized as complex-valued matrix factorization based on standard singular value decomposition. The developed approach establishes a bridge between standard methods of semantic data analysis and quantum models of cognition and decision. According to the quantum theory, real-valued observable quantities are generated by wavefunctions being complex-valued vectors in multidimensional Hilbert-space. Wavefunctions are defined as superpositions of basis vectors encoding composition of semantic factors. Basis vectors are found by singular value decomposition of the initial data matrix transformed to a real-valued amplitude form. Phase-dependent superposition amplitudes are found to optimize approximation of the source data. The resulting model represents the observed real-valued data as generated from a small number of basis wavefunctions superposed with complex-valued coefficients. The method is tested for random matrices of sizes from 3 × 3 to 12 × 12 and dimensionality of latent Hilbert-space from 2 to 4. The best approximation is achieved by encoding latent factors in normalized complex-valued amplitude vectors interpreted as wavefunctions generating the data. In terms of approximation fitness, the developed method surpasses standard truncated SVD of the same dimensionality. The mean advantage over the considered range of parameters is 22 %. The method permits cognitive interpretation in accord with the existing quantum models of cognition and decision. The method can be integrated in the algorithms of semantic data analysis including natural language processing. In these tasks, the obtained improvement of approximation translates to the increased precision of similarity measures, principal component analysis, advantage in classification, and document ranking methods. Integration with quantum models of cognition and decision is expected to boost methods of artificial intelligence and machine learning improving imitation of natural thinking.

This paper describes application of the basic methods of semantic analysis of text data – Porter stemming, frequency semantic analysis, latent semantic analysis and syntactic semantic analysis using an automated system. The system allows analyzing the text using these methods. The characteristics and features of the methods’ implementation as well as the obtained results of their applying to texts of small complexity are considered. The research allows to reveal features of usage of the methods according to the text analysis purposes.

In order to solve the problem that the image encryption algorithm cannot eliminate the strong correlation between adjacent pixels in the image, with poor ability to resist attack and low efficiency, an Image encryption algorithm for torsional components of generators based on complex chaotic model is proposed. Extracting the RGB torsional vibration component of the image for discrete cosine transform transformation, it is then rotated and fused to complete the initial encryption of the image information. In order to further enhance the security of image information, 2-D discrete cosine transform and 2-D compressed sensing measurement are applied to the initial encrypted image information. The real number matrix of measurement is merged into the complex value matrix, and the adaptive random phase coding is applied. The image information complex chaotic encryption model is constructed according to the coding result, so as to eliminate the strong correlation between the adjacent pixels. Particle swarm optimization is used to co-ordinate and optimize the parameters of the compound chaotic encryption model to improve the encryption performance of the model. Experimental results show that the pixel has low correlation, high security and strong ability to resist attacks after encrypting with this algorithm.

Condition monitoring of complex systems is usually based on various sensors to gain information on the system status and identify potential problems at an early stage. However, evaluation and interpretation of multiple sensor data is often a major problem due to complex interdependencies between measured sensor data and actual system condition. We have addressed this problem for the example of a fluid condition monitoring system in wind turbines, i.e. an oil filter and cooling subsystem equipped with various sensors to monitor relevant temperatures, differential and absolute pressure drop across the filter as well as specific oil conditions such as dielectric constant and viscosity to monitor chemical oil degradation. Data were recorded from two wind turbines in a field test over more than two years. Data evaluation was performed with both statistical as well as semantic analysis methods with the goal of predicting the remaining filter lifetime and identifying unusual data patterns indicating potential problems for the overall system. Results indicate a large potential for the hybrid combination of statistical and semantic sensor data analysis for condition monitoring and have especially indicated the potential for learning data patterns with one specific system and transferring the results to other systems. This would allow using operational experience in larger networks such as offshore wind parks to improve the performance and reliability of intelligent condition monitoring systems.

Based on conceptualizing Chinese online populism as a grassroots discourse and operationalizing it in terms of people-centrist and anti-elitist dimensions, we developed an automated semantic text analysis method for analyzing Chinese online populist discourse both quantitatively and qualitatively. First, we introduced semantic role labeling to reformulate the original text into semantic triplets. Second, we created a Chinese populism dictionary containing terms signifying “the people,” “elites,” and “popular sovereignty.” Third, we developed a rule-based method to recognize populist discourse automatically. The automated semantic text analysis method can capture a great deal of qualitative information throughout this process for further analysis. The validation test showed that the automated semantic text analysis method performed satisfactorily (accuracy > 0.9). This method not only creates a broadly applicable and reliable tool for studying Chinese online populism, but it also contributes to the literature on how populism manifests in various cultural and political contexts, expanding the possibilities of populism research globally.

Review for "A semantic big data analysis method based on enhanced neural networks in IoT"

In order to improve the effect of the mutual translation of Chinese and Korean literature, this article combines the semantic analysis technology to analyze the mutual translation of Chinese and Korean literature. Moreover, this article explores the analysis of Chinese and Korean literature mutual translation through intelligent semantic analysis methods and analyzes the theoretical basis of densely connected networks based on the semantic analysis and verification methods of densely connected networks and multilayer attention. Finally, this article implements semantic matching verification through the densely connected BiLSTM structure and adds a multilayer attention mechanism to the network to better interact with sentences to obtain the semantic relationship of sentences. The simulation analysis shows that the Chinese and Korean literature mutual translation system based on semantic analysis proposed in this article has a good translation effect and can effectively promote Sino-Korean literature exchanges.

Eigenvalue decomposition (EVD) of the sample data covariance matrix is, typically, used for calculating the whitening matrix and prewhitening the noisy signals. An important problem here is to reduce the computational complexity of the EVD of the complex-valued sample data covariance matrix. In this paper, we show that the complexity of the prewhitening step for complex-valued signals can be reduced approximately by a factor of four when the real-valued EVD is used instead of the complex-valued. Such complexity reduction can be achieved for any axis-symmetric array. For such class of arrays it enables real-time implementation of the prewhitening step for complex-valued signals. The performance of the proposed procedure is shown in application to a blind source separation (BSS) problem

In this paper, we deal with the multiple carrier frequency offsets (CFOs) estimation issues for orthogonal frequency-division multiple access (OFDMA) based uplink system. In such system, the interleaved carrier assignment scheme (CAS) has gained much attention for it brings maximum frequency diversity though it undergoes severe CFO problem than other schemes. The multiple-CFO estimation problem in this scheme uses a similar signal structure as that of Direction of Arrival (DOA) estimation problem in array signal processing, so subspace based multiple signal classification (MUSIC) algorithm can be exploited to estimate multiple CFOs. Compared with the conventional MUSIC algorithm utilizing eigenvalue decomposition of complex-value covariance matrix, the proposed unitary MUSIC and unitary root-MUSIC CFO estimation algorithms have comparable performance but lower computational complexity because of real-value covariance matrix used, which is verified by computer simulation.

The reliability of quality management in industrial processes mainly depends on information about the traceability, precision and accuracy of a measurement system as well as on its systematic bias. The progressive development of networking and sensing in industrial machinery facilitates a quality-related process monitoring regarding information of measurement systems and singular sensor nodes. Hereby, integration on the information level is mandatory. Furthermore, information from the shop floor and from enterprise applications is needed to provide a consistent and integrated quality analysis. Thereby, these systems use different standards and technologies for exportation and propagation of data. Besides, integrative quality management and data analysis require enriched data that does not only comprise, for example, the measured value and its standard-dependent unit on the sensor level; rather, additional information is needed (e.g. the production process or the time and place of measurement). In this paper, a framework is presented that facilitates the semantic integration and analysis of measurement and enterprise data according to real-time requirements. Semantic technologies are used to encode the meaning of the data from the application code. Herewith, the data is automatically annotated using terms and concepts taken from the application domain. Furthermore, a semantic integration and transformation process is facilitated. Thus, subsequent integration and, most importantly, analysis processes can take advantage of these terms and concepts using specialized analysis algorithms. Besides, the conceptual application of the presented framework and processes in a high-pressure-die-casting scenario is presented.

The importance of teaching the nature of science (NOS) has been emphasized in the science curriculum, especially in the science curriculum for science-gifted students. Nevertheless, few studies concerning the structure and formation of students` mental model on NOS have been carried out. This study aimed to explore science-gifted students` understanding of `fact`, `hypothesis`, `theory`, `law`, and `scientificness` by utilizing semantic network analysis. One hundred ten science-gifted middle school students who were selected by a national university participated in this study. We collected students` written responses of five items and analyzed them by the semantic network analysis(SNA) method. As a result, the core ideas of students` understanding of `fact` were proof and reality, of `hypothesis` were tentativeness and uncertainty, of `theory` was proven hypothesis by experimentation, of `law` were absoluteness and authority, and of `scientificness` were factual evidence, verifiability, accurate and logical theoretical framework. The result of integrated semantic network illustrated that the viewpoint of science-gifted students were similar to absolutism and logical positivism (empiricism). Methodologically, this study showed that the semantic network analysis method was an useful tool for visualization of students` mental model of scientific conceptions including NOS.

This publication presents cognitive information systems dedicated to analysing and interpreting economic data. The systems for semantic data analysis presented here are exemplify UBMSS systems (Understanding-Based Management Support Systems). UBMSS systems are presented here using the example of two applications for assessing the liquidity and analysing the cash flow of a company.

This paper introduces a novel method for semantic annotation of Web page. We perform semantic analysis with regard to unwritten and empirically proven agreement between users and Web designers. This method is based on patterns in selected domains. Pattern provides formalization of the agreement and allows assignment of semantics to parts of Web pages. We will introduce experiments with this new method for semantic analysis.

Statement of the problem. This research work is focused on the analysis of the linguistic image of a female communicative type of politician, which has become one of the most attractive objects of study in recent decades. The term “linguistic image” is quite universal. The interest of modern linguistic science in the subject is explained not only by the leading anthropocentric paradigm, but also by the global process of human activities mediatization. The purpose of the article is to reconstruct the subjective component of the linguistic image of a female politician in the context of temporal actualization. Review of scientific literature on the problem. The wide range of works on the linguistic personality in general and the communicative component in particular reveals that the scope of researches related to the study of the time actualization component in the context of the modus categories of the female communicative type politician image is not sufficiently represented. The methodology (materials and methods). The research work is based on the texts of public speeches by Maria Zakharova, a representative of the Ministry of Foreign Affairs, for the period of 2016-2018. The methods of linguistic description (descriptive-analytical method), relative-comparative method, as well as the method of semantic analysis are used in the work. Research results. The semantic analysis of the texts of M. Zakharova’s public speeches revealed that one of the modus markers of the speaker’s public language images is the actualization of the temporality of a political event. It is interesting that for M.Zakharova the actual time periods in the structure of the traditional three-part time model are present and future time models. Conclusion. The analysis of the modus meanings of M. Zakharova’s public texts showed that one of the temporal fields is the field of the present tense. It seems logical since the subject of the power sphere identifies with the situation of speaking at present and the powers he or she is actively executing at the moment. However, it is interesting that M. Zakharova chooses futural forms as the second temporal component in the temporal speech paradigm. In this form the speaker appears in the speech position of the “forecaster” as she acts as the authority responsible for the political present and future.

This article demonstrates the application of semantic field analysis to the interpretation of interview data, with a particular focus on the integration of quantitative methods. The aim of the study was to determine how students participating in academic tutoring understand the term "academic tutoring" and what meanings they attach to it. The study formulates the following research question: "What images of tutoring in terms of its contexts and meanings do student participants in it, have?" The research base consisted of transcripts of two focus interviews conducted with 11 tutees. A qualitative and quantitative approach was used to analyse the research results - the method of semantic field analysis. The background of the analysis was the constructivist concept, which is rooted in the creative role of language. The conclusions of the analysis indicate that thanks to the reconstruction of the network of meanings, it is possible to define the term tutoring from the perspective of students participating in the program in a more accurate way. The results of these studies contribute to research in the field of social sciences related to the development of humans, while at the same time being a source of recommendations for a wider inclusion of personalised education strategies in higher education.