Style Classification Research Articles

From Grasslands to Global Beats: A Study of Mongolian Ethnic Music Integration in Chinese Pop

This study goes into the integration of Mongolian Ethnic Music into Chinese pop culture, exploring its historical roots, musical characteristics, and contemporary manifestations. Mongolian Ethnic Music, rooted in millennia of tradition and nomadic heritage, reflects the spiritual essence and cultural richness of the Mongolian people. Through an examination of ancient origins, influences of shamanism and Buddhism, and modern developments, the evolution of Mongolian music is traced, showcasing its diverse melodic, rhythmic, and tonal qualities. The classification of Mongolian music genres and styles, including the Hulunbuir, Xilingol, Ordos, and Alxa styles, highlights regional distinctions and stylistic variations. Furthermore, the study examines the integration of Mongolian Ethnic Music into Chinese pop through the works of renowned musicians such as Tengger, Han Lei, and Dege Ma, exemplifying a fusion of traditional melodies with modern sensibilities. Ulan Tuya, Qi Feng, and Yang Weiling represent contemporary artists contributing to the diversification of Chinese pop music with their unique styles. This exploration underscores the cultural significance of Mongolian Ethnic Music as a bridge between traditions, enriching the global music landscape while preserving the heritage of the Mongolian people.

Stylistic classification of cuneiform signs using convolutional neural networks

Abstract The classification of cuneiform signs according to stylistic criteria is a difficult task, which often leaves experts in the field disagree. This study introduces a new publicly available dataset of cuneiform signs classified according to style and Convolutional Neural Network (CNN) approaches to differentiate between cuneiform signs of the two main styles of the first millennium bce, Neo-Assyrian and Neo-Babylonian. The CNN model reaches an accuracy of 83 % in style classification. This tool has potential implications for the recognition of individual scribes and the dating of undated cuneiform tablets.

Improving craft beer style classification through physicochemical determination and the application of deep learning techniques

The consumption of craft beer at fairs and festivals is a phenomenon that keeps growing in the world. For this reason, it is important to control the quality characteristics of the different styles. This study aimed to analyze the different styles of beer, classify them according to their physicochemical parameters, and propose a predictive pattern-based model known as deep learning that best defines the styles that are presented at festivals. Physicochemical analyses of final gravity, color, alcohol, bitterness, and α-acids were carried out on eight styles of beer. The first four parameters are those that characterize the styles according to the Beer Judge Certification Program style guide. The incorporation of the α-acid determination allowed a more realistic classification that considers the brewers' new tendencies. This study will lay the foundations to improve local recipes, implement standardization, and provide training to local brewers.

On Turbans, Abayas, and Colanders: The Scope of Religious Dress in a Pluralistic Society

Abstract This article scrutinizes the ongoing European debate surrounding religious attire, particularly in the context of legal restrictions and requests for accommodation. The core inquiry revolves around the ambiguous definition of ‘religious dress’, encompassing both prescribed attire and symbols with religious significance. Beyond conventional religious garments that are generally legally accepted, the article delves into the contentious classification of cultural styles, as seen recently with the ban on abayas and qamis in French public schools, and the exemptions for ‘religious dress’ claimed by adherents to mock religions such as Pastafarianism. Acknowledging the contested nature of defining religion, the article examines the potential necessity for legal definitions, emphasizing their role in determining both recognition and limitations. The study navigates the European legal framework, drawing insights from supranational sources and the case law of the European Court of Human Rights to unravel the intricacies, challenges, and implications of regulating religion and religious dress in Europe.

Design of Music Emotion Analysis and Creation Aid System Based on Machine Learning

In human-centric contexts, visual vision plays an increasingly important role in decision making, learning, communication, and situation awareness. Music is a language that conveys emotion to all creatures, including plants and animals. These emotional characteristics are included in the emotion found in music, along with other feelings conveyed via melodies, rhythms, and composer-highlighted moments. Human interaction, communication, decision-making, and cognitive processes are all heavily influenced by emotion. However, Subjective music emotion classification is made more difficult by pure machine classification due to its objectivity; additionally, emotion classification has not reached the same level of accuracy as style classification due to the complex and poorly understood mechanisms underlying how humans perceive music and generate emotions. This manuscript proposes a Multi-Scale Adaptive Graph Neural Network (MSAGNN)optimized with the Remora Optimization Algorithm (ROA) for predicting music emotion MEA-MSAGNN-ROA. Initially data is taken from Music Video Emotion (MVE) dataset Afterward the data is fed to Attribute-based Neural Collaborative Filtering (AbNCF) based pre-processing process. The outcome from the pre-processing data is transferred to the MSAGNN. The music emotions are successfully classified by using MSAGNN are exciting, fear, sad and relaxation. The ROA is used to optimize the weight parameter of MSAGNN. The proposed MEA-MSAGNN-ROA is applied in Python working platform. Performance parameters, like accuracy, precision, sensitivity, F1-score, ROC and recall analyzed to compute proposed approach. The proposed MEA-MSAGNN-ROA approach yields improved results in terms of accuracy (22.46%, 38.58%, 21.74%), sensitivity (21.97%, 33.88%, 25.52%), and precision. reduced computation times of 21.86%, 36.76%, 28.95%, and 81.46%, 95.97%, 86.77%.The proposed MEA-MSAGNN-ROA method is compared with the existing methods such as MEA-CNN, MEA-BPNN, and MEA-FNN, respectively. From the result it is concludes that the proposed MEA-MSAGNN-ROA method based accuracy is higher than the existing methods.

Weighted double Q-learning based eco-driving control for intelligent connected plug-in hybrid electric vehicle platoon with incorporation of driving style recognition

Driving style poses significant impacts on eco-driving performance of vehicles, especially for those with hybrid powertrains. By incorporating driving style recognition, an efficient velocity planning and energy management strategy is developed for a platoon of intelligent connected plug-in hybrid electric vehicles (PHEVs). Firstly, a high-fidelity driving style classification and recognition model is established based on the agglomerative hierarchical clustering algorithm, and then the support vector machine algorithm is employed to recognize the driving style. Next, a multi-objective velocity planning problem is formulated with the consideration of the fuel economy, driving adaptability and comfort, following performance, and driving safety optimization as the optimization objective, and is then solved based on orthogonal collocation direct transcription and the interior-point methods. Finally, an energy management strategy incorporating model predictive control and weighted double Q-learning algorithm is built. The simulation results demonstrated that the velocity planning algorithm incorporating driving styles achieved preferable adaptability and comfort. The proposed strategy can achieve up to most 98.88 % energy economy of the stochastic dynamic programming for the following PHEVs, and reduce the overall fuel consumption by 27.39 % for the platoon, comparing to that without driving style incorporation.

Learning Styles and Second Language Acquisition: A Review of Influences, Factors, and Educational Implication

Academia and the public have focused on studies of learning styles in second language acquisition in recent years. Students’ cultural backgrounds can reveal how education and culture affect learning. This paper summarizes the definition and the classification of learning styles and lists a brief review of influence, factors, and educational implications related to learning styles in the field of second language acquisition. The researchers analyzed learning styles are variable and numerous factors affect academic success. Studies show the beneficial correlation between learning styles and academic achievement within an appropriate teaching environment. A strong association exists between an effective learning style and favorable language learning results in students. Simultaneously, variables such as the learners' native language background and their personal traits will impact the selection of their specific learning style. There is a correlation between the variety of learning styles among students and the teaching techniques and aims of teachers in the field of education. An in-depth understanding of students' learning patterns will facilitate the advancement of the teaching profession. Since current evaluations and methods for evaluating students' learning types are questioned, more trustworthy learning style assessments must be developed in future studies.

Extending user control for image stylization using hierarchical style transfer networks

The field of neural style transfer refers to the re-rendering of content image while fusing the features of a style image. The recent studies either focus on multiple style transfer or arbitrary style transfer while using perceptual and fixpoint content losses in their respective network architectures. The aforementioned losses provide notable stylization results but lack the liberty of style control to the user. Consequently, the stylization results also compromise the preservation of details with respect to the content image. This work proposes the hierarchical style transfer network (HSTN) for the image stylization task that could provide the user with the liberty to control the degree of incurred style via denoising parameter. The HSTN incorporates the proposed fixpoint control loss that preserves details from the content image and the addition of denoising CNN network (DnCNN) and denoising loss for allowing the user to control the level of stylization. The encoder-decoder block, the DnCNN block, and the loss network block make the basic building blocks of HSTN. Extensive experiments have been carried out, and the results are compared with existing works to demonstrate the effectiveness of HSTN. The subjective user evaluation shows that the HSTN's stylization represents the best fusion of style and generates unique stylization results while preserving the content image details, which is evident by acquiring 12% better results than the second-best performing method. It has also been observed that the proposed work is amongst the studies that achieve the best trade-off regarding content and style classification scores, i.e. 37.64% and 60.27%, respectively.

Clothing Style Similarity Matching and Classification Recognition Algorithm Based on Double-Layer Model in the Context of the Internet of Things

Abstract The identification of different clothing style attributes is helpful for designers to grasp the clothing style, and it is also helpful for consumers to find clothes with the same style attributes according to their own preferences. This is becoming more important in the clothing design, Internet, and e-commerce industries. It is of great significance to carry out clothing style similarity matching and classification recognition. For the problem of clothing style similarity matching and classification recognition, the traditional algorithm stays in the stage of qualitative analysis and subjective evaluation, unable to quantitatively and objectively determine clothing style, resulting in the decline of clothing style similarity matching effect and classification recognition accuracy. Therefore, a similarity matching, classification, and recognition algorithm of clothing style based on the double-layer model in the context of the Internet of Things is designed. The double-layer model is constructed through the target detection layer and target segmentation layer. The double-layer model is used to realize the segmentation of clothing image. The overall similarity evaluation index of image style is obtained according to the image gradient to complete the similarity matching of clothing style. On this basis, the clothing image features are extracted and input into the support vector machine classifier to complete the clothing classification and recognition. The experimental results show that the proposed garment style similarity matching and classification recognition algorithm based on the two-layer model has high image segmentation accuracy, good style similarity matching effect, high classification recognition accuracy, and high efficiency, which proves that the algorithm is feasible for garment style similarity matching and classification recognition using the two-layer model and can be further applied in the field of garment design.

Classification of Styles and Expressive Characteristics in Higher Vocals Teaching - Based on Emotional Calculation Approach

Abstract Higher vocational vocal music teaching not only lies in enhancing students’ grasp of notes but also in deep emotional expression and emotional transmission. How to realize the precise classification of emotions expressed by musical style and features is an urgent problem to be solved. In this paper, starting from the classification of acoustic features and music styles, the STFT algorithm is used to extract the music features, and the music emotion model is constructed by combining the two-dimensional emotional psychological model. Based on the Turkish music emotion dataset, the acoustic data are preprocessed by using methods such as frame splitting, pre-emphasis, windowing, and mute frames, a deep confidence network extracts the music features, and the SVM model is introduced to establish the emotion classification model of music style and expression features. To verify the effectiveness of the model, this paper uses the dataset to analyze the music style and emotion classification dimensions, respectively. It is found that the classification accuracy of the Renaissance music category in classical music reaches 97.94%, and the accuracy of the music emotion classification of the model when the music data preprocessing slice granularity is 5 s is 0.838. The DBN-SVM model can realize the emotion classification of the music style and expression characteristics, which can provide a reference for the teachers to carry out targeted vocal music teaching and for the students to realize the release of emotion and emotional resonance when singing in vocal music. It provides a reference for teachers to carry out targeted vocal music teaching and for students to realize emotional release and resonance during vocal singing.

Intelligent Recognition and Classification of 3D Animation Styles - Based on AIGC Technology Application

Abstract The study of style identification in 3D animation not only enhances the artistry of animation scenes but also has a profound significance on the development of 3D animation works. In this paper, we first sorted out the art style expressions of 3D animation and explored the innovation path of 3D animation art style and related influencing factors. Based on the generative adversarial network in AIGC technology, the discriminator and generator are trained. The 3D animation style intelligent recognition and classification model is established by combining the GAN network and SoftMax classifier, and the loss function of the model is optimized. Using crawler technology to obtain 3D animation-related images and crop them to 128*128 size for dataset construction, 3D animation style recognition and classification experiments were designed to verify the application effectiveness of the 3D-GAN model. It is found that the 3D-GAN model reaches the peak of IS index 9.222 after 60*103 iterations, which is 12.85% higher than the ALGAN model. The average classification accuracy of the 3D-GAN model is 91.42%, which is 6.01 percentage points higher than that of the sub-optimal CycleGAN model. Conducting intelligent recognition and classification of 3D animation styles based on AIGC technology can provide a new direction for innovating 3D animation styles and optimizing scene design.

The Two-Way Drive in Modern Informational Music Teaching and Traditional Music Culture Inheritance

Abstract This article explores the application of digital information technology in music education, especially in teaching traditional music. Taking the intersection of conventional music culture and applied teaching practice, the article utilizes advanced algorithmic techniques - Support Vector Machines and Apriori Association Rule Algorithm - to construct an innovative model of emotional cognition and stylistic categorization of traditional music. This model integrates standard music emotion cognition and style classification into modern informationized music teaching. To verify the model’s effectiveness, the researchers selected 500 pieces of traditional Chinese music as samples, trained and tested using the constructed model, and compared it with other algorithmic models. The test results show that the model outperforms BP neural networks and Linear neural networks regarding emotion perception. Specifically, the model shows efficient performance with mean square error (MSE) and correlation coefficient (CC) averages of 0.0099 and 90.50% on the test set, respectively. In addition, the model has the shortest processing time and is lower than the IM-Apriori algorithm regarding space consumption, showing its efficiency and resource-saving advantages. The model’s accuracy ranges from 75.23% to 90.06% in classifying traditional music. In summary, the model proposed in this paper improves the accuracy and efficiency of emotional cognition and style classification of conventional music and provides essential technical support for effectively integrating traditional music into modern informative music teaching.

Classification of frost formation style interacting with mist formation on a flat plate with temperatures ranging from general-low to cryogenic using optical measurements

Classification of frost formation style interacting with mist formation on a flat plate with temperatures ranging from general-low to cryogenic using optical measurements

ВИЗУАЛЬНЫЕ МЕТАМОРФОЗЫ: КУЛЬТУРОЛОГИЧЕСКИЙ АНАЛИЗ ЦИФРОВОЙ КИБЕРЭСТЕТИКИ

This article focuses on the emerging styles of visual digital aesthetics that have become relevant in light of technological advancements and the introduction of innovative tools for creating and processing digital images. The analysis is based on a cultural approach and aims to identify the influence of contemporary digital technologies on the shaping of cultural benchmarks and aesthetic preferences in art and design. The primary focus is on scrutinizing the defining attributes of modern styles that have taken shape within the digital sphere at the onset of the 21st century (such as vaporwave, seapunk, webpunk, psychedelia, pixel art, voxel art, and others) and delving into their artistic autonomy and cultural nuances. Style is defined here as both a guiding principle of artistic thought and a distinctive mode of communication among cultural agents. An integral component of this study involves comprehending how digital technologies impact the interpretation of contemporary culture and society at large. The study aims to uncover the peculiarities of modern transnational culture, as a mode of consciousness and worldview, through the lens of the artistic culture of the early 21st century. The research identifies overarching tendencies characteristic of contemporary network art, outlining the interrelations and differentiations among new digital styles. It meticulously examines distinct style clusters based on various criteria. The classification of contemporary visual styles is predicated on methods and techniques of creation and imitation, usage contexts, presentation specifics, and design elements. The results of the study can enrich the exploration of contemporary culture and visual phenomena, aiding in forecasting future trajectories within the realm of innovative visual expressions.

A Study on Learning Strategies and Styles Segmentation of Learner Groups in Online Education Platforms

Abstract Online education is becoming a popular form of education day by day. In order to provide corresponding learning programs according to the types of learning styles and learning strategies of different learners, this paper proposes a learning style recognition algorithm based on the Felder-Silverman model. The Bayesian discriminant analysis algorithm is used to calculate the probability estimation of samples belonging to one category, and then the clustering center is calculated by k-means clustering analysis so as to complete the classification of learning styles. According to the results of learning style identification, the perceptual learning group has the highest level of cooperative communication strategies in the information perception style dimension, with a mean score of 3.575, the linguistic group is the least adept at all strategies in the information input dimension, with a mean score of only 3.55, the sequential group excels in a variety of strategies for information comprehension, and the active group is the most adept at performing information processing. The personalized learning experiment was conducted according to the characteristics of different style groups. It was found that students who received personalized teaching based on their learning styles improved their performance more significantly, with their average scores in each subject increasing from 73.174 to 82.247 in the pre-test. Their scores in the post-test were 5.729 points ahead of those of the control class. The application effects are significant when learning strategies and styles are classified, and teaching programs are improved accordingly.

The influence of cognitive style differences on foreign language learning strategies

Finding out how different cognitive styles affect the strategies chosen for learning a foreign language was the driving force behind this research. The rapid pace of globalization has made fluency in more than one language an essential component of every well-rounded person’s toolbox. However, in the actual learning process, there are significant achievement differences between students, which is not only related to language talent, but also influenced by individual cognitive style. By reviewing the relevant literature, this paper gives a clear definition and classification of cognitive style, and gives an overview of foreign language learning strategies. On this basis, 150 foreign language learners from different schools were surveyed by a combination of questionnaire and interview. Students’ use of various tactics for learning a foreign language is influenced by their cognitive styles, according to the results. Metacognitive methods are more commonly used by students who are independent in their subject, whereas field-dependent students depend more on tactics that involve social and emotional support. Furthermore, the study concluded that in order to maximize the effectiveness of instruction, educators should use more individualized approaches that take into account individuals’ unique cognitive styles. In addition to offering concrete recommendations for enhancing the current state of foreign language instruction, this study offers a fresh viewpoint on the unique characteristics of language learners.

Attach-SwiNet: Multimodal Attachment Style Classification Model Based on Non-Verbal Signals

Attach-SwiNet: Multimodal Attachment Style Classification Model Based on Non-Verbal Signals

Exploring the Teaching Reform Path of Ideological and Political Education in Colleges and Universities in the Context of Deep Learning

Abstract Deep learning significantly enhances the comprehension of ideological and political education (IPE) concepts and facilitates the advancement of IPE teaching in colleges and universities through bilinear blended teaching reforms. This innovative teaching approach employs association rules from big data and an enhanced Apriori algorithm to analyze students’ online learning data. An analytical model of student learning behaviors in IPE, utilizing a Support Vector Machine (SVM) for learner classification, forms the basis for developing an online learner profile. This profile integrates assessments of learning outcomes, group identification, and style classification to tailor learning activities to individual needs. The efficacy of this reform was tested over one year, comparing the impacts of IPE across four different regions. Results indicate that students in the experimental group significantly outperformed their counterparts in the control group. For instance, in Shanghai, scores from the experimental group averaged 89.63 compared to 83.25 in the control group, yielding a significant difference of 6.38 points. Statistical analysis showed a t-value of 6.21 and a p-value of 0.000, affirming the positive impact of the bilinear blended teaching reform on enhancing student ideological engagement and promoting educational reform in colleges.

Design of automatic style classification system of visual art works based on image processing

Abstract The emergence of a vast array of visual artworks meets the growing public demand for cultural literacy while simultaneously presenting challenges in the understanding and selection of appropriate artistic styles. Leveraging advancements in image processing technology, this study successfully automates the classification of 158,652 visual art images by categorizing them into distinct artistic styles. A novel approach combining the Scale-Invariant Feature Transform (SIFT) feature extraction algorithm with the spatial pyramid matching technique is introduced. This paper details the development and evaluation of a classification system tailored for the automatic categorization of visual artworks. Comparative testing results demonstrate that this innovative combination surpasses the traditional SIFT algorithm in both execution speed and classification accuracy. Specifically, the Support Vector Machine (SVM) based classification system, utilizing the enhanced SIFT method, achieves an accuracy rate exceeding 90% in identifying five distinct styles, including PHPS, NOS, IS, TF, and AAS. The implementation of this automatic classification system proves to be a practical tool in aiding individuals to select the visual artwork style that best suits their preferences.

A Unified Model for Style Classification and Emotional Response Analysis

A Unified Model for Style Classification and Emotional Response Analysis