Classification Theory Research Articles

Noncommutative aspects of Villadsen algebras

In this short article, we survey the importance of Villadsen algebras in the context of the classification theory, with an emphasis on its non-commutative aspects.

LSTM‐based deep learning framework for adaptive identifying eco‐driving on intelligent vehicle multivariate time‐series data

AbstractIn the context of automated driving, the connected and automated vehicles (CAVs) technology unlock the energy saving potential. This paper develops an LSTM‐based deep learning framework for eco‐driving adaptive identification on Intelligent vehicle multivariate time series data. The framework can be adapted for Driver Assistance Systems (DAS) to reduce fuel consumption. Specifically, considering overtaking on rural road is a critical maneuver for operation and has potential to reduce consumption, a simulated driving experiment with 30 participants was conducted to collect the multivariate time series data of the overtaking operation behaviors in conditional automation driving. Driving behaviors were classified into eco‐driving operation behaviors and high fuel consumption operation behaviors based on fuel consumption calculated by using vehicle specific power (VSP). Significance analysis based on linear regression was adopted to identify operation behaviors, and an eco‐driving behavior identification model was established with the use of long short‐term memory (LSTM) for multivariate classification theory. Meanwhile, the other four classification algorithms were used to establish identification models for comparison. The results indicated that the gear position, lane position, the acceleration pedal depth, the clutch pedal depth, and the brake pedal depth had a significant influence on fuel consumption. The eco‐driving behavior identification model of overtaking demonstrated a high classification power and robustness with a classification accuracy of 89.16%. According to the simulation results, the developed adaptive identification model is with promising performance. The conclusions provide theoretical support for developing an adaptive strategy for connected eco‐driving.

Undergraduate research and students’ learning outcomes: digging into different disciplinary contexts

ABSTRACT Despite the importance of undergraduate research for student learning and development, not enough work has been done to clarify its effects in different disciplinary contexts. Using data from the China College Student Survey, this research adopts Becher and Trowler’s theory of discipline classification to understand the relationship between undergraduate research and students’ learning outcomes in different disciplines. It indicates that first, contributing to journals/conferences correlates with students’ self-reported learning outcomes in Physics, History, and Engineering, but these relationships can be explained by process-centered activities, except in Physics. Second, early-year undergraduates’ learning outcomes are relatively weakly related to conducting research with faculty in pure disciplines. By contrast, such relationships are distinct in applied disciplines. Third, in soft disciplines, participating in competitions significantly correlates with more types of students’ learning outcomes than conducting research with faculty, while in hard disciplines, they both play quite important roles. It makes recommendations for future research and practice, which enrich insight into how universities can best engage undergraduates in research.

Verification of classification modernization solutions (the case study of Library Bibliographic Classification)

Efficiency of library classifications depends on their capacity to embrace current knowledge and social practices. Therefore, the verification (late Latin verificatio – evidence, confirmation, from the Latin verus – true and facio – to make) of upgrade solutions is an essential part of classification system operation and development. The problem of library classification verification is often out of researchers view. The verification mechanism (tools) for upgrade solutions has not been described yet. Therefore, the reliability and relevance of classification upgrade solutions is of scientific and practical value.The purpose of the paper is to summarize the related experience of Research LBC Center of the Russian State Library. Based on s tructured system analysis and the long practice of LBC development and application, the authors examine the mechanism for monitoring publications and identifying innovations, including new terms, concepts, social phenomena, etc., and for assessing and processing data for continuing update of the classification system. Using the example of LBC modernization solutions, the authors discuss advantages and disadvantages of individual methods. They conclude that the classification upgrade verification – essential to the nature of the library classification systems – has to become the key element of the classifying culture. The researchers review and suggest the ways to improve the verification mechanism adopted at the RSL LBC Research Center. The integrated approach to LBC verification is of applied relevance both for the theory of classifications and librarianship.

A note on mimicry with multiple dupes

Abstract This note explores the possibility of a mimetic relationship that requires multiple dupes (usually predators) with different preferences. Two prey species that have different defence mechanisms effective against complementary sets of predators could both benefit from mimicking each other, given that the resulting uncertainty could deter both types of predators from attacking either of them. A mimetic interaction with multiple dupes could thus give rise to a mutualistic, yet deceptive kind of mimicry. This kind of mimicry does not seem to fit easily into the established categories and might thus be interesting for the theory of mimicry classification. It could also provide a new potential mechanism for some interesting phenomena such as deceptive mimetic convergence and imperfect resemblance.

Research on Visual Teaching Strategy of Chemical Declarative Knowledge in Middle School

The rapid development of information technology has a strong impact on the traditional chemistry teaching mode. According to the teaching status of Chinese middle school chemistry classroom, the thesis combines declarative knowledge and visual teaching in junior high school chemistry textbooks as a subject for in-depth research. By elaborating the concepts of declarative knowledge, knowledge visualization and visual teaching, taking knowledge classification theory, visual thinking theory, double coding theory and information processing theory as the research basis, this paper deeply analyzes and summarizes the chemical declarative knowledge and visual teaching methods, and puts forward feasible visual teaching strategies to promote students to master knowledge and develop ability. According to the research and design of the teaching case of the section "Solubility" in the junior high school chemistry textbook, the purpose of this teaching case is to improve the teaching mode, help students understand and build a new system of knowledge, and cultivate students' innovation ability and core accomplishment of the subject.

A Conceptual Model Study of Tourism Resource Sharing in the Digital Economy

The concept of “sharing” and the development of the digital economy have driven the transformation of resource allocation in the tourism industry to resource sharing. The article defines resource sharing in the tourism industry from a comprehensive perspective according to the systematic analysis of the concept of resource sharing and the background of the digital economy, which is an economic behavior of integrating, opening and sharing resources among members under the guidance of the government and relying on modern information technology such as the internet, with the sharing of usage rights as the main feature. Based on the dual inspiration of MICK resource classification theory and digital economy theory, the article constructs a conceptual model and measurement scale of resource sharing in the tourism industry from four dimensions: knowledge resource sharing, material resource sharing, information resource sharing and capital resource sharing. The panel data of 31 provinces (municipalities and autonomous regions) from 2016 to 2020 were analyzed mathematically and statistically. The analysis results showed that the weight of knowledge resources is the highest. Moreover, the eastern region had the highest level of resource sharing and the western region had the lowest level of resource sharing. Furthermore, the level of resource sharing in the past five years showed a trend of year-on-year improvement. Based on the scores, countermeasures are proposed in terms of enhancing the importance of information technology and strengthening basic support according to local conditions, which provide a reference for cities to improve the level of resource sharing.

Elite Crimes as a Destructive Social Phenomenon: Methodological-Criminalistic Aspects of Research

The article draws attention to the destructive phenomenon of elite crime understood as the aggregate of actions of different categories committed by representatives of the elite (administrative-managerial, political, intellectual, etc.). The author, recognizing that the concept and content of the “elite” have not been fully formed yet and are still being debated in the research community, thought it necessary to present arguments in support of his own position on this issue and then focus on researching elite crime. It is stressed that although the concept of elite crime is lacking in criminal law, the research instruments of criminalistics, specifically, the theory of the criminalistic classification of crime, makes it possible to single out typical categories of crime committed by representatives of the elite together with specific features characteristic of the corresponding types of the elite. In its turn, it provides necessary support for developing recommendations on improving the effectiveness of investigating and solving elite crimes.

Feature selection for binary classification based on class labeling, SOM, and hierarchical clustering

Feature selection plays an important role in algorithms for processing high-dimensional data. Traditional pattern classification and information theory methods are widely applied to feature selection methods. However, traditional pattern classification methods such as Fisher Score, Laplacian Score, and relief use class labels inadequately. Previous information theory based feature selection methods such as MIFS ignore the intra-class to tight inter-class to sparse property of the samples. To address these problems, a feature selection algorithm for the binary classification problem is proposed, which is based on class label transformation using self-organizing mapping neural network (SOM) and cohesive hierarchical clustering. The algorithm first converts class labels without numerical meaning into numerical values that can participate in operations and retain classification information through class label mapping, and constitutes a two-dimensional vector from it and the attribute values to be judged. Then, these two-dimensional vectors are clustered by using SOM neural network and hierarchical clustering. Finally, evaluation function value is calculated, that is closely related to intra-cluster to tightness, inter-cluster separation, and division accuracy after clustering, and is used to evaluate the ability of alternative attributes to distinguish between classes. It is experimentally verified that the algorithm is robust and can effectively screen attributes with strong classification ability and improve the prediction performance of the classifier.

Enhancing Learning Object Analysis through Fuzzy C-Means Clustering and Web Mining Methods

The development of learning objects (LO) and e-pedagogical practices has significantly influenced and changed the performance of e-learning systems. This development promotes a genuine sharing of resources and creates new opportunities for learners to explore them easily. Therefore, the need for a system of categorization for these objects becomes mandatory. In this vein, classification theories combined with web mining techniques can highlight the performance of these LOs and make them very useful for learners. This study consists of two main phases. First, we extract metadata from learning objects, using the algorithm of Web exploration techniques such as feature selection techniques, which are mainly implemented to find the best set of features that allow us to build useful models. The key role of feature selection in learning object classification is to identify pertinent features and eliminate redundant features from an excessively dimensional dataset. Second, we identify learning objects according to a particular form of similarity using Multi-Label Classification (MLC) based on Fuzzy C-Means (FCM) algorithms. As a clustering algorithm, Fuzzy C-Means is used to perform classification accuracy according to Euclidean distance metrics as similarity measurement. Finally, to assess the effectiveness of LOs with FCM, a series of experimental studies using a real-world dataset were conducted. The findings of this study indicate that the proposed approach exceeds the traditional approach and leads to viable results. Doi: 10.28991/ESJ-2023-07-03-010 Full Text: PDF

Prediction of the geological indicators in TBM tunnel based on optimized proportion of surrounding rock grades

With tunnel boring machine being used in underground engineering, accurate geological indicators have been the important basis for tunnel boring machine (TBM) construction. Back propagation neural network (BPNN) has been used to predict the geological indicators of tunnels in previous studies. Nevertheless, these studies ignored the imbalance proportion of surrounding rock grades, leading to the indiscriminate use of data, thus affecting the predictive effect of BPNN. In order to prove the importance of the proportion of surrounding rock grade in geological prediction, we mainly attempt to utilize particle swarm optimization (PSO) to optimize the proportion of sample data, and integrate with BPNN to establish a PSO-BPNN theoretical model to predict geological indicators. At the same time, combined with the actual engineering data, 5 tunneling indicators were selected as input and 4 geological indicators were selected as output by a variety of dimensionality reduction methods. The geological indicators are density, uniaxial compressive strength, internal friction angle (φ) and Poisson's ratio (ε). On this basis, the PSO-BPNN prediction model was established in detail. By comparing the prediction of traditional BPNN, PSO-BPNN and other optimization-integrated models, the result shows that optimized proportion of surrounding rock grades reduces the prediction error and improves the interpretability of the prediction model. Meanwhile, we combined the theory of surrounding rock partition to illustrate the rationality of surrounding rock proportion in PSO result, that is, the proportion of complex surrounding rock should be increased appropriately to improve the prediction result. Ultimately, based on the optimization-integrated models with engineering data and the surrounding rock classification theory, the importance of proportion of surrounding rock grades for tunnel geological prediction is confirmed.

Development and validation of a deep learning-based model to distinguish acetabular fractures on pelvic anteroposterior radiographs.

Objective: To develop and test a deep learning (DL) model to distinguish acetabular fractures (AFs) on pelvic anteroposterior radiographs (PARs) and compare its performance to that of clinicians. Materials and methods: A total of 1,120 patients from a big level-I trauma center were enrolled and allocated at a 3:1 ratio for the DL model's development and internal test. Another 86 patients from two independent hospitals were collected for external validation. A DL model for identifying AFs was constructed based on DenseNet. AFs were classified into types A, B, and C according to the three-column classification theory. Ten clinicians were recruited for AF detection. A potential misdiagnosed case (PMC) was defined based on clinicians' detection results. The detection performance of the clinicians and DL model were evaluated and compared. The detection performance of different subtypes using DL was assessed using the area under the receiver operating characteristic curve (AUC). Results: The means of 10 clinicians' sensitivity, specificity, and accuracy to identify AFs were 0.750/0.735, 0.909/0.909, and 0.829/0.822, in the internal test/external validation set, respectively. The sensitivity, specificity, and accuracy of the DL detection model were 0.926/0.872, 0.978/0.988, and 0.952/0.930, respectively. The DL model identified type A fractures with an AUC of 0.963 [95% confidence interval (CI): 0.927-0.985]/0.950 (95% CI: 0.867-0.989); type B fractures with an AUC of 0.991 (95% CI: 0.967-0.999)/0.989 (95% CI: 0.930-1.000); and type C fractures with an AUC of 1.000 (95% CI: 0.975-1.000)/1.000 (95% CI: 0.897-1.000) in the test/validation set. The DL model correctly recognized 56.5% (26/46) of PMCs. Conclusion: A DL model for distinguishing AFs on PARs is feasible. In this study, the DL model achieved a diagnostic performance comparable to or even superior to that of clinicians.

African-American Vernacular English as Hip-Hop Artist Identity in Indonesian Rapper Ramengvrl's Songs

This study aimed to investigate and identify the grammatical features of African American Vernacular English (AAVE) used in the song lyrics of the Indonesian Rapper Ramengvrl’s album, Can’t Speak English, and understand the underlying reasons for this AAVE use by applying the language and society of sociolinguistics approach. The data was analyzed using Wolfram’s theory of classification of grammatical features of AAVE in Ramengvrl’s songs to elaborate on the purpose of its use. The data analyzed were qualitatively collected by closely and thoroughly listening to the songs and reading the transcribed lyrics. The results are as follows: Ramengvrl employs 8 out of 13 grammatical features of AAVE. Those are copula/auxiliary absence, specialized auxiliary, subject-verb agreement, negation, remote “been,” nominals, non-standard pronouns, and question formation. Furthermore, this AAVE use by Ramengvrl is because of (1) the influences of Hip-hop artists, (2) the authenticity of Hip-Hop culture, and (3) as an anti-language to represent herself.
 Keywords: African-American Vernacular English, AAVE, hip-hop, identity

Comparison of Machine Learning Based on Category Theory

In recent years, machine learning has been widely used in data analysis of network engineering. The increasing types of model and data enhance the complexity of machine learning. In this paper, we propose a mathematical structure based on category theory as a combination of machine learning that combines multiple theories of data mining. We aim to study machine learning from the perspective of classification theory. Category theory utilizes mathematical language to connect the various structures of machine learning. We implement the representation of machine learning with category theory. In the experimental section, slice categories and functors are introduced in detail to model the data preprocessing. We use functors to preprocess the benchmark dataset and evaluate the accuracy of nine machine learning models. A key contribution is the representation of slice categories. This study provides a structural perspective of machine learning and a general method for the combination of category theory and machine learning.

Anatomical variability of kidney arterial vasculature based on zonal and segmental topography

IntroductionTo date, there is no unified approach to the lobar, zonal, and segmental structure of the kidney vasculature. There is no recognizable approach to define basic characteristics in regard to the lobes and segments identifying of the kidney. The branching of the renal artery has often been the subject of scientific research. This study aimed to analyze the arterial anatomy on the basis of zonal and segmental topography. Materials and methodsThis study is a prospective cadaver study on autopsy material using corrosion casting and CT imaging techniques. The arterial vasculature was visualized using corrosive casting. In this study, 116 vascular casts were included. We identified the number of arteries in the kidney hilum, their topography, branching variations of the renal artery, and local blood supply zones of renal masses considering second- and third-order renal artery branches. We used a micro-CT BRUKER SkyScan 1178, digital camera, Mimics-8.1, and R. ResultsThis study has shown that RA divides into two or three zonal arteries, forming a two- or three-zonal vascular supply system. In the case of the two-zonal system, 54.3% of cases accounted for RA branching into ventral and dorsal arteries, whereas 15.5% of cases referred to superior polar and inferior polar zonal arteries. The three-zonal system implies 4 types of RA branching: 1) superior polar, ventral, and dorsal zonal branches (12.9%); 2) ventral, dorsal, and inferior polar zonal branches (9.5%); 3) two ventral and one dorsal zonal branches (5.2%), and 4) superior polar, central, and inferior polar zonal branches (2.5%). ConclusionsThe results of this research make us reconsider Grave's classification theory.

The Influence of The Quantitative Relationship Between Bad and Positive Online Reviews on Consumers’ Willingness to Shop Online

Online shopping evaluation is one of the important decisions for consumers to do online shopping. The negative comments in the evaluation have an important impact on consumers in shopping. Based on the product classification theory, this paper classifies the products consumers mainly buy on online shopping platforms into five major categories: daily necessities, educational and sporting goods, home appliances and digital products, food, and clothing accessories, and serves as a moderating factor. The hypothesis is that the number of negative reviews about logistics and transportation would negatively affect consumers’ willingness to purchase daily necessities and food. The number of bad reviews about merchants’ services would negatively affect consumers’ purchase intention for apparel accessories. The number of bad reviews about products that would negatively affect consumers’ willingness to purchase digital home appliances and educational and sporting goods online was proposed. A questionnaire was used to obtain the sample data of consumers’ online shopping intention, and SPSS was used to test its reliability with different analyses and correlation analyses. Based on the correlation between the content and the number of negative reviews, it showed that six or more negative reviews for food products significantly affect consumers’ willingness to purchase. The number of negative reviews about merchant services for daily necessities and the product for clothing accessories significantly affects consumers’ willingness to purchase. Furthermore, for all types of products, when the number of negative reviews increases, consumers need to increase their willingness to purchase with more positive reviews. Among them, consumers’ occupation, average monthly consumption level, and length of exposure to online shopping moderate the credibility of consumers’ judgment of negative reviews. In conclusion, it is concluded that Taobao merchants should pay attention to the content and quantity in negative reviews, improve the quality of products, services, logistics, and transportation from the consumers’ point of view, and improve the competitiveness of their products for better online marketing.

Towards a moderate realist foundation for ontological knowledge organization systems: The question of the naturalness of classifications

Several authors emphasise the need for a change in classification theory due to the influence of a dogmatic and monistic ontology supported by an outdated essentialism. These claims tend to focus on the fallibility of knowledge, the need for a pluralistic view, and the theoretical burden of observations. Regardless of the legitimacy of these concerns, there is the risk, when not moderate, to fall into the opposite relativistic extreme. Based on a narrative review of the literature, we aim to reflectively discuss the theoretical foundations that can serve as a basis for a realist position supporting pluralistic ontological classifications. The goal is to show that, against rather conventional solutions, objective scientific-based approaches to natural classifications are presented to be viable, allowing a proper distinction between ontological and taxonomic questions. Supported by critical scientific realism, we consider that such an approach is suitable for the development of ontological Knowledge Organisation Systems (KOSs). We believe that ontological perspectivism can provide the necessary adaptation to the different granularities of reality.

Which neural networks can be computed by an algorithm? – Generalised hardness of approximation meets Deep Learning

AbstractClassical hardness of approximation (HA) is the phenomenon that, assuming P ≠ NP, one can easily compute an ϵ‐approximation to the solution of a discrete computational problem for ϵ > ϵ0 > 0, but for ϵ < ϵ0 – where ϵ0 is the approximation threshold – it becomes intractable. Recently, a similar yet more general phenomenon has been documented in AI: Generalised hardness of approximation (GHA). This phenomenon includes the following occurrence: For any approximation threshold ϵ1 > 0, there are AI problems for which provably there exist stable neural networks (NNs) that solve the problem, but no algorithm can compute any NN that approximates the AI problem to ϵ1‐accuracy. Moreover, this issue is independent of the P vs NP question and thus is a rather different mathematical phenomenon than HA. GHA implies that the universal approximation theorem for NNs only provides a partial understanding of the power of NNs in AI. Thus, a classification theory describing which NNs can be computed by algorithms to particular accuracies is needed to fill this gap. We initiate such a theory by showing the correspondence between the functions that can be computed to ϵ‐accuracy by an algorithm and those functions that can be approximated by NNs which can be computed to ϵ̂‐accuracy by an algorithm. In particular, the approximation thresholds ϵ and ϵ̂ cannot differ by more than a factor of 12. This means that computing function approximations through NNs will be optimal – in the sense of best approximation accuracy achievable by an algorithm – up to a small constant, compared to any other computational technique.

The Mechanism of Interaction of Natural Physical Factors and the Integral Organism Forms the Basis of the General Theory of Physiotherapy

Optimal combination of drug therapy and physical factors increases the efficacy of treatment and medical rehabilitation of patients. It is regrettable that the search for methods to improve the individual dosing of the applied energies with an approach to the cellular level of dynamicity (constant correction of parameters of the applied physical factors during the whole period of the treatment) and comprehensive (in the presence of several diseases) treatment are clearly insufficient for the present time. Perhaps such a search is difficult due to the current lack of a general theory of physiotherapy and imperfect classification of physical factors. Thus, it became necessary to develop principles and methods for a comprehensive study of the effects produced by the physical methods in a clinical setting, and the requirements for testing new devices for physiotherapy sharply increased. The author of this doctrine was an outstanding scientist Vladimir Sergeyevich Ulashchik. In his opinion, the creation of a general theory of physiotherapy is one of the promising tasks of the scientific physiotherapy. It is known that since the end of the XIX century, the principles of nervism and reflex theory have been the theoretical basis of domestic physiotherapy. Influence of humoral and endocrine changes was further added to the reflex theory of the mechanism of the therapeutic effect of the external environment (natural and preformed physical factors), but still with the leading principle of nervism. It should be noted that there have always been doubts about the exhaustive explanation of the reflex theory of all causes and consequences of the interaction of external physical factors with the human body.

Figurative Language Analysis on Efek Rumah Kaca’s Song Lyrics at Sinestesia 2015 Album

Figurative language is one kind of the language styles to make the expression and the message of the speaker or writer is strengthened. 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.