Reviewerly/exHarmony huggingface dataset:
Stravinsky Re-barred

In Emotion and Meaning in Music Leonard B. Meyer offered the view*—no doubt shared by many—that much contemporary music ‘looks more irregular than it actually sounds’. 1 As illustration he cited a passage from the ‘Soldier’s March’ of Stravinsky’s L’Histoire du soldat (1918). Reproduced here as Ex. 1a and b, the passage at bs 48-57 was re-barred by Meyer according to the steady https://www.w3.org/1998/Math/MathML"> 2 4 https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781003359166/5ed983df-2b0b-48f2-97f2-c12fc6ac03c7/content/math2phan4_B.tif" xmlns:xlink="https://www.w3.org/1999/xlink"/> periodicity of the basso ostinato. This analysis raises interesting questions about Stravinsky’s methods, especially with reference to the (A)-B-C♯-D-(E) upward sweep of the melody at bs 48-50, 51-3 and 55-7. Meyer’s point was that ‘the main beat of the melody’, which he identified as A at b.48, shifts in relation to the ostinato’s accentuation at b.51, but that at the end of the climactic stretch at b.55 the initial alignment is restored. 2 In other words, at the point marked ‘y’ in Ex. 1, A’s placement relative to the ostinato shifts, while further along at ‘z’ its original placement above the G of the ostinato is restored.
Reform and Innovation of College Music Teaching under Background of Multimedia and Network Technology

With the rapid development of information technology, China's higher education system is facing significant challenges.Modern multimedia technology is gradually being applied to various fields of study at universities.As a result, vigorously developing modern educational information technology is an unavoidable choice for the historical development of higher education.The Internet and multimedia technology's effects on music education reform in colleges and universities are examined in this essay.The study outlined the challenges faced by Chinese music education and suggests appropriate solutions through research on the viability of multimedia and network technology.In terms of classroom efficiency, student satisfaction, and teaching comprehensive score, the multimedia teaching mode were compared to the traditional mode.The results showed that the model's teaching efficiency in a multimedia environment improved by 0.63, the proportion of satisfaction increased by 25%, and the comprehensive score was 8.1 points higher than the traditional model.This also demonstrated that music education reform in colleges and universities was feasible in the context of multimedia and network technology.
Capital predestination and projects of rescue from the Russian "Rockefellers": The Cherry Orchard and the Moscow Art Theater

The article deals with the situation associated with the production of The Cherry Orchard by the Moscow Art Theater at the turn of the 19th-20th centuries. Although The Cherry Orchard is a fiction by A.P. Chekhov, a kind of virtual case, and the art theater is a real story, nevertheless, the article considers the staging of the play as a business project that requires an economic solution, and not just an artistic one. The article compares the images of Savva Morozov, a real capitalist and philanthropist, and Lopakhin, the hero of the play The Cherry Orchard. The article will be of interest as additional material within the framework of areas related both to culture and art, and economics.
Main organizational and managerial ways to improve educational activities in the context of destabilization of the political and socio-economic situation

In the context of destabilization of the political and socio-economic situation, the issue of changes in the forms of the educational process is becoming more relevant. Based on the analysis of the main shortcomings of the Bologna system, conclusions about the need to change it were drawn. Taking into account the negative trends that have developed in education, the author proposes the main directions and ways to improve the educational process.
Effect Evaluation and Trend of Higher Education Online Courses Based on Digital Artificial Intelligence Technology

At this stage, a new online course teaching mode has emerged in higher education, which has promoted the development of education and teaching to a certain extent.However, the existing online course teaching mode still has many problems.For example, the function of online course system is not perfect.Students' online course learning lacks a sense of on-site learning atmosphere.The students' self-discipline is not strong, which leads to the low completion rate of courses.The emergence of digital educational resources has provided teachers and students with rich educational resources, and has gradually become an important part of education and teaching.In view of the deficiency of online course teaching mode, this paper proposed that artificial intelligence (AI) technology was applied to online courses.Combined with feature weighted support vector machine algorithm, the online course learning behavior was analyzed experimentally.The experimental results showed that the average prediction accuracy of the four learning behaviors was 93.38%, and the average prediction error was 1.20.From the above data, it can seen that this algorithm could play a good optimization effect on the prediction of online course learning behavior.This paper also conducted a questionnaire survey on the use frequency of digital education resources in various disciplines.The results showed that English was the most frequently used discipline, accounting for 24.8%.Digital education resources have certain applications in various disciplines.
Multi-scale Residual Interaction for RGB-D Salient Object Detection

RGB-D salient object detection (SOD) is used to detect the most attractive object in the scene. There is a problem in front of the existing RGB-D SOD task: how to integrate the different context information between the RGB and depth map effectively. In this work, we propose the Siamese Residual Interactive Refinement Network (SiamRIR) equipped with the encoder and decoder to handle the above problem. Concretely, we adopt the Siamese Network shared parameters to encode two modalities and fuse them during decoding phase. Then, we design the Multi-scale Residual Interactive Refinement Block (RIRB) which contains Residual Interactive Module (RIM) and Residual Refinement Module (RRM). This block utilizes the multi-type cues to fuse and refine features, where RIM takes interaction between modalities to integrate the complementary regions with residual manner, and RRM refines features during fusion phase by incorporating spatial detail context with multi-scale manner. Extensive experiments on five benchmarks demonstrate that our method outperforms the state-of-the-art RGB-D SOD methods both quantitatively and qualitatively.
Multi-modal Characteristic Guided Depth Completion Network

Depth completion techniques fuse sparse depth map from LiDAR with color image to generate accurate dense depth map. Typically, multi-modal techniques utilize complementary characteristics of each modality, overcoming the limited information from a single modality. Especially in the depth completion, LiDAR data has relatively dense depth information for objects in the near distance but lacks the information of distant object and its boundary. On the other hand, color image has dense information for objects even in the far distance including the object boundary. Thus, the complementary characteristics of the two modalities are well suited for fusion, and many depth completion studies have proposed fusion networks to address the sparsity of LiDAR data. However, the previous fusion networks tend to simply concatenate the two-modality data and rely on deep neural network to extract useful features, not considering the inherited characteristics of each modality. To enable the effective modality-aware fusion, we propose a confidence guidance module (CGM) that estimates confidence maps which emphasizes salient region for each modality. In experiment, we showed that the confidence map for LiDAR data focused on near area and object surface, while those for color image focused on distant area and object boundary. Also, we propose a shallow feature fusion module (SFFM) to combine two types of input modality. Furthermore, a parallel refinement stage for each modality is proposed to reduce the computation time. Our results showed that the proposed model showed much faster computation time and competitive performance compared to the top-ranked models on the KITTI depth completion online leaderboard.
SG-Net: Semantic Guided Network for Image Dehazing

From traditional handcrafted priors to learning-based neural networks, image dehazing technique has gone through great development. In this paper, we propose an end-to-end Semantic Guided Network (SG-Net (Codebase page: https://github.com/PaulTHong/Dehaze-SG-Net )) for directly restoring the haze-free images. Inspired by the high similarity (mapping relationship) between the transmission maps and the segmentation results of hazy images, we found that the semantic information of the scene provides a strong natural prior for image restoration. To guide the dehazing more effectively and systematically, we utilize the information of semantic segmentation with three easily portable modes: Semantic Fusion (SF), Semantic Attention (SA), and Semantic Loss (SL), which compose our Semantic Guided (SG) mechanisms. By embedding these SG mechanisms into existing dehazing networks, we construct the SG-Net series: SG-AOD, SG-GCA, SG-FFA, and SG-AECR. The outperformance on image dehazing of these SG networks is demonstrated by the experiments in terms of both quantity and quality. It is worth mentioning that SG-FFA achieves the state-of-the-art performance.
Self-Supervised Dehazing Network Using Physical Priors

In this paper, we propose a lightweight self-supervised dehazing network with the help of physical priors, called Self-Supervised Dehazing Network (SSDN). SSDN is a modified U-Net that estimates a clear image, transmission map, and atmospheric airlight out of the input hazy image based on the Atmospheric Scattering Model (ASM). It is trained in a self-supervised manner, utilizing recent self-supervised training methods and physical prior knowledge for obtaining realistic outputs. Thanks to the training objectives based on ASM, SSDN learns physically meaningful features. As a result, SSDN learns to estimate clear images that satisfy physical priors, instead of simply following data distribution, and it becomes generalized well over the data domain. With the self-supervision of SSDN, the dehazing performance can be easily finetuned with an additional dataset that can be built by simply collecting hazy images. Experimental results show that our proposed SSDN is lightweight and shows competitive dehazing performance with strong generalization capability over various data domains.
Light Attenuation and Color Fluctuation for Underwater Image Restoration

Underwater images with low contrast and color distortion of different scenarios often pose a significant challenge in improving underwater image quality. Therefore, it is beneficial to restore the real restoration effect of the scene by estimating the degradation parameters related to their changes, and these parameters should be updated with the degradation scene to obtain the best visual effect. We propose a robust underwater image restoration method. Specifically, we adjust the color of the input image according to a background light (BL) estimation strategy guided by depth, chromatic aberration considering hue, and information entropy. At the same time, we adjust the depth map of the input image by computing color fluctuation and attenuation. According to qualitative and quantitative evaluation, the proposed method generates vivid results with a more natural appearance and more valuable information.
Threshold Text Classification with Kullback–Leibler Divergence Approach

Text classification based on thresholds belongs to the supervised learning method which assigns text material to predefined classes or categories based on different thresholds with divergence approach. These categories are identified by a set of documents trained by an automated algorithm. This work presents an approach of text classification using an automatic keyword extraction algorithm based on the Kullback–Leibler divergence approach. The proposed method is evaluated on 2000 documents in Vietnamese, covering ten topics, collected from various e-journals and news portal Web sites including vietnamnet.vn, vnexpress.net, and so on to generate a completely new set of keywords. Such keywords, then, are leveraged to categorize the topic of new text documents. The obtained results verifying the practicality of our approach are feasible as well as outperform the state-of-the-art method.
An Automatic Method for Clay Minerals Extraction from Landsat 8 OLI Data. A Case Study in Chi Linh City, Hai Duong Province

Landsat satellite data have been effectively used for mineral resources extraction. This paper presents an automatic method for clay minerals extraction from Landsat 8 OLI image based on band rationing and principal component analysis methods. Firstly, the Landsat 8 data is used to calculate the NIR/RED (band5/band4) and SWIR1/SWIR2 (band6/band7) band rationing images. To highlight the distribution of clay minerals, these band rationing images are further used to multiply the digital number values of NIR and SWIR2 bands, then obtaining (band5/4) × band5 and (band6/7) × band7 images. Finally, the principal component analysis (PCA) method is used to calculate the principal components, then select the 2nd principal component (PC2) to detect clay minerals by the automatic thresholding method. The results in this research can be used to provide input information for mineral exploration.
Randomness Testing on Strict Key Avalanche Data Category on Confusion Properties of 3D-AES Block Cipher Cryptography Algorithm

In data encryption of block cipher cryptographic algorithm, the security of the algorithm is measured based on Shannon’s confusion and diffusion properties from randomness tests. The 3D-AES block cipher was developed from selected and inspired by antigen-antibody interaction, somatic hyper mutation and protein structural features in immune systems. However, these computation elements from immune systems have not proved yet whether it can be successfully applied and satisfies with Shannon’s diffusion property in designing a new block cipher algorithm. Based on the results obtained from the analysis done in previous research, from seven categories except Strict Key Avalanche (SKA), the results on randomness test is passed. Even though the 3D-AES block cipher algorithm is random, based on 0.1% significance level and for 1,000 key stream generated. Hence, in this research paper, we present and compare the statistical analysis conducted towards 3D-AES and its enhanced version namely Enhanced 3D-AES block cipher. To ensure adequate high security of the systems in the world of information technology, the laboratory experiment results are presented and analyzed. They show that the randomness and non-linearity on SKA of the output in the enhanced 3D-AES symmetric encryption block cipher are comparable to the 3D-AES symmetric encryption block cipher.
English-Filipino Speech Topic Tagger Using Automatic Speech Recognition Modeling and Topic Modeling

We present an English-Filipino Speech Topic Tagger that transcribes English-Filipino speech audio into text and produces relevant keywords from such audio. The tagger was implemented in two parts by transcribing speech data to text using a Filipino fine-tuned English XLSR-Wav2Vec2 Automatic Speech Recognition (ASR) model then extracting context from the transcription using a generative statistical model used for Topic Modeling, Latent Dirichlet Allocation (LDA). The trained English-Filipino ASR model shows a 26.8% Word Error Rate in the validation set. The Speech Topic Tagger was evaluated through an observation-based approach using different YouTube videos as input and achieved the highest evaluation of 46.15% accuracy and lowest evaluation of 14.28% accuracy in producing relevant top-weighted words.
Predictive Analysis of Solar Energy Production Using Neural Networks

The energy industry is always looking at smart ways of conserving and managing the growing demand of consumable and renewable energy. One of the main challenges with modern electric grids systems is to develop specialized models to predict various factors, such as optimal power flow, load generation, fault detection, condition-based maintenance on assets, performance characteristics of assets, and anomalies. When it comes to energy prediction, the data that is associated with it is diverse. Accurate analysis and predictions of such data cannot be done manually and hence needs a powerful computing tool that is capable of reading such diverse data, converting them to a form that can be used for analysis and knowledge discovery. In this research effort, data collected from a local utility company over a period of three years from 2019 to 2021 were used to perform the analysis. Machine learning and neural network algorithms were used to build the predictive model. Different levels of data abstraction along with optimization of model parameters were done for better results. A comparative study was done at the end on the various algorithms used to determine the model performance.
Comparison of Various CNN Models for Image Classification

Classification is one of the core tasks of computer vision. In image classification, the system trained using one of the classification algorithms receives the input image, analyzes the image, and assigns the label based on its training. Image classification provides a vital role in various fields like medical imaging, automated vehicle, social media and many more. This paper compares convolutional neural network (CNN) models like AlexNet, VGG16, InceptionV3, Deep residual networks (ResNet), and DenseNet using the accuracy. Here, the CIFAR-10 dataset is used for comparison.
SGAS-es: Avoiding Performance Collapse by Sequential Greedy Architecture Search with the Early Stopping Indicator

Sequential Greedy Architecture Search (SGAS) reduces the discretization loss of Differentiable Architecture Search (DARTS). However, we observed that SGAS may lead to unstable searched results as DARTS. We referred to this problem as the cascade performance collapse issue. Therefore, we proposed Sequential Greedy Architecture Search with the Early Stopping Indicator (SGAS-es). We adopted the early stopping mechanism in each phase of SGAS to stabilize searched results and further improve the searching ability. The early stopping mechanism is based on the relation among Flat Minima, the largest eigenvalue of the Hessian matrix of the loss function, and performance collapse. We devised a mathematical derivation to show the relation between Flat Minima and the largest eigenvalue. The moving averaged largest eigenvalue is used as an early stopping indicator. Finally, we used NAS-Bench-201 and Fashion-MNIST to confirm the performance and stability of SGAS-es. Moreover, we used EMNIST-Balanced to verify the transferability of searched results. These experiments show that SGAS-es is a robust method and can derive the architecture with good performance and transferability.
Impact of Industrial Revolution 4.0 on Vietnamese Ethnic Minority Human Resources

Industry 4.0 is upon us with the dynamic developments in science and technology that are creating significant changes in global production. As a result, Vietnam is experiencing profound changes, requiring its labor force to meet the demands of a modern, digital world. To meet that requirement, Vietnam must solve new problems, including training and retraining its workforce to build a contingent of cadres and civil servants to develop economic opportunities in ethnic minority areas. This study examines 464 questionnaires from managers in nine provinces representing disparate ethnic minority regions of Vietnam. Our results indicate a positive effect of Industry 4.0 awareness on physical and mental strength and managerial acumen. Furthermore, it positively impacts the development of ethnic minority human resources. Based on the influence level of factors, the authors propose several solutions to promote positive impacts and mitigate the adverse effects of Industry 4.0 for developing Vietnamese ethnic minority human resources.
Predicting Corporate Failure Using Ensemble Extreme Learning Machine

Corporate failure prediction has become a major topic in the accounting and finance literature. Effective prediction models are essential for banks and financial institutions to solve financial decision-making problems. In general, artificial intelligence and machine learning techniques have been mainly employed to develop corporate failure models due to their prediction superiority in comparison to the traditional statistical method. Extreme learning machine is a newly developed artificial intelligence technique with an extremely fast learning speed. Nonetheless, its performance instability may be a major constraint for its practical application. The literature documents that the ensemble is one of the widely used methods to improve the generalization performance of weak classifiers. Therefore, we propose in this study an ensemble of extreme learning machine for improving the prediction performance on corporate failure task. In particular, we compare four benchmark ensemble methods (multiple classifiers, bagging, boosting, and random subspace) to evaluate which is best suited for extreme learning machine. Experimental results on French firms indicated that bagged and boosted extreme learning machine showed the best-improved performance.
LSTM Deep Learning Method for Radiation Short and Long-Term Prediction

The renewable energy market, like conventional energy, is often unstable. Solar energy in particular presents disturbances linked to the spatiotemporal fluctuations that solar radiation undergoes. To remedy this problem, it is essential to predict its fluctuations that have exhausting consequences for integration into the electricity network and reduce the dependence on fossil fuels essential for socio-economic development. The main objective of this study is to propose a prognostication model based on artificial intelligence to predict with great accuracy the fluctuations of global solar radiation (GSR). Indeed, we adopted a deep dynamic recurrent memory artificial neural network (ANN) model which can manipulate the dynamics of atmospheric factors and is capable to save the history of these characteristics. For this purpose, we developed two long-short term memory (LSTM) models for short and long term forecasting of global solar radiation time series. The first model is a monovariate LSTM, the second one is a multivariate LSTM, after selecting the input variables (predictors) by correlation study, the models are trained by dividing the data set into two subsections, the first subsection is used for model training and the second subsection is reserved for testing. The indices and results established in this work demonstrate the robustness attributed to our method which can provide power grid managers at Er-Rachidia, MOROCCO (Latitude: 31°55′53″N; Longitude: 4°25 ′35″ W; Altitude: 1039 m) credible predictions to ensure better supervision of the energy system and to adapt solar energy to the electricity production system, especially multivariate LSTM model which proved more accuracy compared to monovariate LSTM.