Classification Theory Research Articles

Analysis of Multiple-Choice Questions in College Entrance Examination Mathematics Based on SOLO Classification Theory—Taking the 2020~2023 New College Entrance Examination I Paper as an Example

Analysis of Multiple-Choice Questions in College Entrance Examination Mathematics Based on SOLO Classification Theory—Taking the 2020~2023 New College Entrance Examination I Paper as an Example

Outcomes of VR, AR and MR Technologies in K-12 Language Education: A Review

Since the 21st century, Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) technologies have been increasingly used in the field of language education to promote student motivation, engagement, and so on. This paper reviews the results of 180 reviews of empirical studies on the use of VR, AR, and MR to enhance K-12 teaching or learning from 2012 to 2022 and summarizes the trends. Based on Bloom’s classification theory of instructional goals, namely Cognitive Goals, Affective Goals, and Behavioral Goals, the outcomes of 53 papers are summarized and analyzed from these three dimensions to find the function of VR, AR, and MR in K-12 Language Education, which can improve literacy, creative thinking, communication, collaboration, confidence, participation, and enjoyment in the digital era. And it also promotes self-learning, enables multisensory learning, and reduces cognitive load. Despite the technological complexity and problems associated with the use of AR in education, VR, AR, and MR applications can successfully improve language learning. Compared with previous research approaches, this study first constructs a VR, AR, and MR-supported learning model based on their characteristics respectively and the relevance of these technologies.

On distributions of countable models for constant expansions of the dense meet-tree theory. I

We study all possible constant expansions of the structure of the dense meet-tree ⟨М; <, П⟩ [3]. Here, a dense meet-tree is a lower semilattice without the least and greatest elements. An example of this structure with the constant expansion is a theory that has exactly three pairwise non-isomorphic countable models [6], which is a good example in the context of Ehrenfeucht theories. We study all possible constant expansions of the structure of the dense meet-tree by using a general theory of classification of countable models of complete theories [7], as well as the description of the specificity for the theory of a dense-meet tree, namely, some distributions of countable models of these theories in terms of Rudin– Keisler preorders and distribution functions of numbers of limit models. In this paper, we give a new proof of the theorem that the dense meet-tree theory is countable categorical and complete, which was originally proved by Peretyat’kin. Also, this theory admits quantifier elimination since complete types are forced by a set of quantifier-free formulas, and this leads to the fact that it is decidable

An insight into 110 years of influence of Principle of Literary Warrant on Dewey Decimal Classification system

The present article is an endeavour to understand the one hundred and ten years of influence of the Principle of Literary warrant as introduced by E. Wyndham Hulme on the revision of Dewey Decimal Classification (DDC) through different editions in the field of library classification theory and practice. During this study, a comparison with other warrants/consensus has been undertaken whenever and wherever deemed necessary. The literary exploration was based on all the editions of DDC which are available on Internet Archive platform. The paper divides different editions of DDC into two distinct groups: one in which only veiled references to literary warrant were found corroborating Dewey‟s thinking about applicability of different consensus angle and the other where declared references to the concept by Editorial Policy Committee were encountered. The article concludes that the principle has extended its influence on the web environment of DDC as well.

The Hermeneutics of Husein Muhammad and Nasaruddin Umar As A Reformulation of the Problems on The Surah An-Nisa in Indonesia

The unresolved gender problem is caused by a less critical interpretation. This article aims to find gender relations based on the thoughts of Husein Muhammad and Nasaruddin Umar. This study used a comparative method and a qualitative approach with three formulations of the problem; First, what are the views of Husein Muhammad and Nasaruddin Umar toward women? Second, what are the thoughts of the two figures toward the verse on the creation of women, women's social roles, and polygamy? Third, how is the actualization of the character's thoughts on women in Indonesia? This study found that women are human beings who are equal and balanced with men through their rights and obligations as human beings. On the other hand, the differences between men and women are biologically non-exchangeable, but these differences are not discriminatory, but a way to partner with each other, work together and complement each other in goodness. The most striking thing in the comparison of interpretations was in the concept of understanding the verse, and both of them make a classification and contextual theory to understand the gender verses. The significance of Husein Muhammad and Nasaruddin Umar's thoughts can be seen in the results of their interpretations that are friendly to women and gender so that they can form knowledge of gender interpretation and civilization that is more precise and equal.

The Search for Medieval Music in Africa and Germany, 1891–1961: Scholars, Singers, Missionaries, by Anna Maria Busse Berger

<i>The Search for Medieval Music in Africa and Germany, 1891–1961: Scholars, Singers, Missionaries</i>, by Anna Maria Busse Berger

On the Computation of Geometric Features of Spectra of Linear Operators on Hilbert Spaces

Computing spectra is a central problem in computational mathematics with an abundance of applications throughout the sciences. However, in many applications gaining an approximation of the spectrum is not enough. Often it is vital to determine geometric features of spectra such as Lebesgue measure, capacity or fractal dimensions, different types of spectral radii and numerical ranges, or to detect gaps in essential spectra and the corresponding failure of the finite section method. Despite new results on computing spectra and the substantial interest in these geometric problems, there remain no general methods able to compute such geometric features of spectra of infinite-dimensional operators. We provide the first algorithms for the computation of many of these long-standing problems (including the above). As demonstrated with computational examples, the new algorithms yield a library of new methods. Recent progress in computational spectral problems in infinite dimensions has led to the solvability complexity index (SCI) hierarchy, which classifies the difficulty of computational problems. These results reveal that infinite-dimensional spectral problems yield an intricate infinite classification theory determining which spectral problems can be solved and with which type of algorithm. This is very much related to S. Smale’s comprehensive program on the foundations of computational mathematics initiated in the 1980s. We classify the computation of geometric features of spectra in the SCI hierarchy, allowing us to precisely determine the boundaries of what computers can achieve (in any model of computation) and prove that our algorithms are optimal. We also provide a new universal technique for establishing lower bounds in the SCI hierarchy, which both greatly simplifies previous SCI arguments and allows new, formerly unattainable, classifications.

Improved Spatiotemporal Information Fusion Approach Based on Bayesian Decision Theory for Land Cover Classification

High-spatial-resolution (HSR) images and high-temporal-resolution (HTR) images have their unique advantages and can be replenished by each other effectively. For land cover classification, a series of spatiotemporal fusion algorithms were developed to acquire a high-resolution land cover map. The fusion processes focused on the single level, especially the pixel level, could ignore the different phenology changes and land cover changes. Based on Bayesian decision theory, this paper proposes a novel decision-level fusion for multisensor data to classify the land cover. The proposed Bayesian fusion (PBF) combines the classification accuracy of results and the class allocation uncertainty of classifiers in the estimation of conditional probability, which consider the detailed spectral information as well as the various phenology information. To deal with the scale inconsistency problem at the decision level, an object layer and an area factor are employed for unifying the spatial resolution of distinct images, which would be applied for evaluating the classification uncertainty related to the conditional probability inference. The approach was verified on two cases to obtain the HSR land cover maps, in comparison with the implementation of two single-source classification methods and the benchmark fusion methods. Analyses and comparisons of the different classification results showed that PBF outperformed the best performance. The overall accuracy of PBF for two cases rose by an average of 27.8% compared with two single-source classifications, and an average of 13.6% compared with two fusion classifications. This analysis indicated the validity of the proposed method for a large area of complex surfaces, demonstrating the high potential for land cover classification.

The foundations of spectral computations via the Solvability Complexity Index hierarchy

The problem of computing spectra of operators is arguably one of the most investigated areas of computational mathematics. However, the problem of computing spectra of general bounded infinite matrices has only recently been solved. We establish some of the foundations of computational spectral theory through the Solvability Complexity Index (SCI) hierarchy, an approach closely related to Smale’s program on the foundations of computational mathematics and McMullen’s results on polynomial root finding with rational maps. Infinite-dimensional problems yield an intricate infinite classification theory, determining which spectral problems can be solved and with what types of algorithms. We provide answers to many longstanding open questions on the existence of algorithms. For example, we show that spectra can be computed, with error control, from point sampling operator coefficients for large classes of partial differential operators on unbounded domains. Further results include: computing spectra of (possibly unbounded) operators on graphs and separable Hilbert spaces with error control; determining if the spectrum intersects a compact set; the computational spectral gap problem and computing spectral classifications at the bottom of the spectrum; and computing discrete spectra, multiplicities, eigenspaces and determining if the discrete spectrum is non-empty. Moreover, the positive results with error control can be used in computer-assisted proofs. In contrast, the negative results preclude computer-assisted proofs for classes of operators as a whole. Our proofs are constructive, yielding a library of new algorithms and techniques that handle problems that before were out of reach. We demonstrate these algorithms on challenging problems, giving concrete examples of the failure of traditional approaches (e.g., “spectral pollution”) compared to the introduced techniques.

In-season crop type identification using optimal feature knowledge graph

Early or in-season crop type mapping using remote sensing data is important for crop managements to maximize crop yield. Existing remote sensing-based approaches in the literature rely mainly on the timing and temporal frequency of satellite data acquisition during a growing season to obtain optimal identification feature (OIF) to complete crop mapping. At present, the optimal features for mapping crop types are commonly generated by using different feature selection methods, and thereby are not generic in nature. In this paper, a novel approach based on the OIF knowledge graph is proposed for identification of crop types within the crop growing season. The concept of OIF was proposed based on the one-class classification and optimization theory, which relates a specific crop type to its OIF. The OIF knowledge graph can be created to describe the seasonal trend of OIF in a season. Eventually, the OIF knowledge graphs can be incorporated into the moment-preserving segmentation method to automatically identify crop types within the growing season. To evaluate the performance of the new method, crop mapping was conducted using 2019 Sentinel-2 data in Heilongjiang province, China, and Illinois, USA, respectively. Results demonstrate that integrating the OIF knowledge graph and moment-preserving thresholding method is effective in identifying crop types without ground-truth data. The overall accuracy higher than 90 % (Kappa coefficient greater than 0.90), and the producer’s accuracies for corn, soybean, and rice are generally greater than 93 % in Heilongjiang province. The proposed approach has the advantage of simplifying crop identification process while maintaining a satisfactory identification accuracy in shorter time than conventional methods.

Relationship between creative thinking and experimental design thinking in science education: Independent or related

Experimental design thinking and creative thinking have been increasingly recognized as the crucial basic thinking to promote scientific and technological innovation. And developing students’ experimental design thinking and creative thinking is considered as the key point to cultivate innovative talents. But the relationship between experimental design thinking and creative thinking remains contradictory. In this study, we construct a structural equation model to verify the hypothesis of experimental design thinking and creative thinking based on the thinking classification theory and creative problem-solving model. The results show that there is a positive correlation between fluency in creative thinking and experimental design thinking, but variable thinking and reflective thinking in experimental design thinking are negatively related to creative thinking. That means, experimental design thinking and creative thinking are neither completely separated, nor undifferentiated. Instead, they are different from each other but complement each other, constituting a unified whole of innovative experimental design thinking. These findings fill in the theory of experimental design thinking and creative thinking, and provide important theoretical support for training innovative scientific and technological talents.

Evaluation of the safety and efficacy of a novel Anatomical classification and dUal anchoRing theory to Optimize the tavR strategy for pure severe Aortic regurgitation (AURORA): a prospective cohort study

BackgroundSuccess rate of transcatheter aortic valve replacement (TAVR) in aortic regurgitation (AR) patients is relatively low on account of the absence of calcified anchoring structures. Morphological classification and corresponding TAVR strategies for AR are lacking yet.MethodsThe AURORA study is a prospective, multicenter, single-arm cohort study to evaluate the safety and efficacy of transfemoral TAVR for severe AR in patients with high or prohibitive risk for surgery. Patients who are ≥ 65 years and diagnosed with severe pure AR as defined by the Echocardiographic Core Laboratory will be consecutively enrolled for further multidetector computed tomography (MDCT) scanning and multiplanar analyses. Based on a new anatomical classification and dual anchoring theory, patients will be classified into 4 types according to the level of the anchoring area. Types 1, 2 and 3 (at least 2 anchoring areas) will undergo the TAVR procedure with a domestic Chinese self-expanding valve (VitaFlow Valve, MicroPort, Shanghai, China), whereas type 4 (0 or 1 anchoring area) patients will be considered unsuitable for TAVR and will receive medical treatment. Our goal is to recruit 100 patients to account for 10% missing data or loss of patients to follow-up. Procedural, 30-day, 6-month and 12-month outcomes will be assessed according to Valve Academic Research Consortium-3 criteria.DiscussionThe AURORA study will establish a new AR anatomical classification based on dual anchoring theory through MDCT multiplanar measurement and assess the safety and efficacy of TAVR guided by this new classification and strategy in AR patients.Trial registration: This Study was registered at Chinses Clinical Trial Registry. The registration number: ChiCTR2200055415; The date of registration: 9, January 2022; The URL of the registration: http://www.chictr.org.cn/showproj.aspx?proj=141209.

The Analysis of Ellipsis Found in Gary Dauberman’s Annabelle Comes Home

Communication is an activity that is always carried out by humans. In communicating, many people use ellipsis, where they omit part of the sentences they use. This research aims (1) to reveal the types of ellipsis found in Gary Dauberman's Annabelle Comes Home and (2) to reveal the classifications of sentences containing ellipsis in Gary Dauberman's Annabelle Comes Home. The research method used by researchers in this study was a qualitative method. The data source of this research is the Annabelle Comes Home movie. The research was conducted using the ellipsis theory proposed by Halliday and Hasan (1976) and the sentence classification theory by Frank (1972). The results showed that there were 81 uses of ellipsis, which included 7 nominal ellipsis, 12 verbal ellipsis and 62 clause ellipsis. The ellipsis were found in four sentence classifications. 56 uses of ellipsis were found in declarative sentences. In addition, in the interrogative sentence, 19 uses of ellipsis were found. Not only that, 5 uses of ellipsis were found in imperative sentences. Finally, in the exclamative sentence, only 1 use of ellipsis was found.

OPERATIONALIZATION OF IMPORTANCE-PERFORMANCE ANALYSIS WITH NINE CATEGORIES AND TESTED FOR GREEN PRACTICES AND FINANCIAL EVALUATION

The Importance-Performance Analysis is a widely used diagnostic tool in many fields of economic activity, such as: transport, health, construction, public food industry, finance, banking, sustainable activities, etc. Despite its use for over 40 years in many economic and social fields, this tool has some important drawbacks that affect the accuracy of managerial decisions. Over time, many variants for improving the standard Importance-performance matrix have been proposed. The aim of our research is to propose a method to solve one of the biggest problems of the standard Importance-Performance Analysis, respectively a method of boosting confidence in the positioning of attributes in the matrix. We use a mathematical method, inspired by classification theory tools, to apply the nine categories division of attributes in the importance-performance plane. Moreover, we introduce a level of confidence in a nine categories Importance-performance matrix, which helps the practitioners to prioritize a decision on attributes, according to the resources, managerial plan, competition, etc. We test and discuss the effectiveness of the new method on two studies: on the green practices in educational restaurant operations and on the financial performance evaluation.

Analysis of Logical Fallacy on the Arguments Made by Student from 2019 Indonesian Protests and Riots in Television Debates

AbstractThis study sought to identify the many sorts of logical fallacies used in Indonesian television debates, notably those made by students who participated in the 2019 protests and rioting there. This is a descriptive study that takes a qualitative approach. In this study, the five categories and sixty-two types of fallacies introduced by Damer's (2009) theory of fallacy classification were used. There were eighteen of the sixty different types identified. First, the results revealed that students consistently violated the acceptability and rebuttal criteria in their arguments, using five different fallacies in each category. Second, with a frequency of 27.98%, poisoning the well was the type of fallacy that students used the most frequently, followed by the two-wrong fallacy with a frequency of 10.29% and manipulation of emotion with a frequency of 8.82%.

Information Theory for Biological Sequence Classification: A Novel Feature Extraction Technique Based on Tsallis Entropy

In recent years, there has been an exponential growth in sequencing projects due to accelerated technological advances, leading to a significant increase in the amount of data and resulting in new challenges for biological sequence analysis. Consequently, the use of techniques capable of analyzing large amounts of data has been explored, such as machine learning (ML) algorithms. ML algorithms are being used to analyze and classify biological sequences, despite the intrinsic difficulty in extracting and finding representative biological sequence methods suitable for them. Thereby, extracting numerical features to represent sequences makes it statistically feasible to use universal concepts from Information Theory, such as Tsallis and Shannon entropy. In this study, we propose a novel Tsallis entropy-based feature extractor to provide useful information to classify biological sequences. To assess its relevance, we prepared five case studies: (1) an analysis of the entropic index q; (2) performance testing of the best entropic indices on new datasets; (3) a comparison made with Shannon entropy and (4) generalized entropies; (5) an investigation of the Tsallis entropy in the context of dimensionality reduction. As a result, our proposal proved to be effective, being superior to Shannon entropy and robust in terms of generalization, and also potentially representative for collecting information in fewer dimensions compared with methods such as Singular Value Decomposition and Uniform Manifold Approximation and Projection.

Automatic classification of document resources based on Naive Bayesian classification algorithm

World Wide Web has become big as the amount of documents collection is increasing rapidly. The automatic classification of document resources based on Naive Bayesian classification algorithm is detailed in this paper. Firstly, this paper introduces the relevant theories of naive Bayes classification and the automatic document classification system. Then, a massive network academic document automatic classification system is designed and implemented. The system uses modular design, including academic document automatic capture module, academic document word document matrix processing module, ontology integration module and semantic driven classification module. Finally, based on the Naive Bayesian classification algorithm, the training set of 12 categories preset is utilized in the professional classification directory of the Ministry of education.. Experiments show that the naive Bayesian classification algorithm can effectively complete the automatic capture, processing and classification of massive academic documents, which can not only improve the classification accuracy, but also reduce the running time of automatic classification. It solves the problems of the integration of two heterogeneous ontology libraries and also the problem that the traditional word vector space cannot meet people's needs for semantic classification.

업무상 재해의 요건과 인과관계의 증명책임

The recent Supreme Court en banc decision (2017du45933) maintained the legal principle of the existing precedents of the Supreme Court as they were. That is, the legal principle that workers must prove a proximate causal relation between their duty and the accident in order for an accident to be recognized as an accident on duty in cases related to industrial accident compensation insurance was reconfirmed. However, it is difficult to agree with the conclusion as such. As mentioned by the majority opinion in the judgment above, the main purpose of the enactment of the proviso to Article 37 Paragraph 1 of the Industrial Accident Compensation Insurance Act was to make it clear that a proximate causal relation between the duty and accident is necessary for all types of accidents but since the reason for the revision in the report on the review of the system and wordings of the National Assembly Legislation and Judiciary Committee at the time had the explicit written intent that, in cases where the Corporation proves that there is no proximate causal relation between the duty and the accident, the accident cannot be regarded as an accident on duty in the criteria for recognition of accident on duty, it is clear that the intention of the legislator is to have the Korea Workers' Compensation &amp; welfare Service, not the employee, prove that there is no proximate causal relation between the duty and the accident. This is also the case even when the proviso is analyzed from the position of the legal requirement classification theory for the distribution of burden of proof. In addition, when the purpose of the Industrial Accident Compensation Insurance Act is examined, it can be seen that it is a law to protect people who are engaged in work at the industrial site. Therefore, unilaterally imposing the burden of proof, which is a core requirement for the recognition of accident on duty, does not fit the intent of the industrial accident compensation insurance as a social security system and the purpose of granting the authority to investigate accidents to the Corporation. Therefore, as the four Supreme Court judges argued in this judgment, it is reasonable to have the Korea Workers' Compensation &amp; welfare Service prove the proximate causal relation between the duty and the accident, not the workers.

AN ANALYSIS OF FIGURATIVE LANGUAGE ON EFEK RUMAH KACA’S SONG LYRICS: SINESTESIA 2015

Figurative language is one kind of the language styles to make the expression and the message of the speaker or writer is strengthen. Figurative language usually used in song lyrics. Sometimes, figurative languages existed in song lyrics can’t be understandable. One of the song lyrics that has a lot of figurative languages is songs from Sinestesia album by Efek Rumah Kaca. Therefore, this research is trying to analyze figurative languages existed in song lyrics on Efek Rumah Kaca’s album titled Sinestesia. The research is focused on analyzing type of figurative languages with its meaning by using Kennedy’s theory of classification of figurative language (1983) and Ogden and Richard’s theory of meaning (1923). Qualitative descriptive is the method that used for this research. The result of this research is 4 type of figurative languages is found, which is 25 personifications, 18 metaphors, 15 overstatements, and 2 apostrophes with each of figurative language’s meaning is elaborated . Besides of it, the writer found that figurative languages existed in the Sinestesia album’s song lyrics has a connection to the song itself.

A Novel Multi-Source Domain Adaptation Method with Dempster–Shafer Evidence Theory for Cross-Domain Classification

In this era of big data, Multi-source Domain Adaptation (MDA) becomes more and more popular and is employed to make full use of available source data collected from several different, but related domains. Although multiple source domains provide much information, the processing of domain shifts becomes more challenging, especially in learning a common domain-invariant representation for all domains. Moreover, it is counter-intuitive to treat multiple source domains equally as most existing MDA algorithms do. Therefore, the domain-specific distribution for each source–target domain pair is aligned, respectively. Nevertheless, it is hard to combine adaptation outputs from different domain-specific classifiers effectively, because of ambiguity on the category boundary. Subjective Logic (SL) is introduced to measure the uncertainty (credibility) of each domain-specific classifier, so that MDA could be bridged with DST. Due to the advantage of information fusion, Dempster–Shafer evidence Theory (DST) is utilized to reduce the category boundary ambiguity and output reasonable decisions by combining adaptation outputs based on uncertainty. Finally, extensive comparative experiments on three popular benchmark datasets for cross-domain image classification are conducted to evaluate the performance of the proposed method via various aspects.