Reviewerly/exHarmony huggingface dataset:
Research on the Application of "Internet Plus" and Artificial Intelligence in Middle School Music Teaching

With the gradual development of "Internet plus" and artificial intelligence technology, educational and teaching means are becoming more and more abundant. The author conducts in-depth analysis by analyzing the current situation of music education in middle schools and the development of internet education technology, and proposes optimization suggestions. Through the research, the "Internet plus" technology has a positive role in promoting the innovation of teaching methods in music education in middle schools, improving students' interest in learning, and enriching teaching content.
A Comparative Study of Educational Informatization in the Post-Pandemic Era

Along with the rapid growth of technology, education has recently undergone steady change. Before China, of many developed countries started the process of incorporating digital resources and technology into education. The execution of a new education infrastructure program has been given priority by China's Ministry of Education at the moment. Comparatively speaking, the United States has made more advancements in the integration of instructional technology thanks to a comparatively developed sector. Despite being at a higher overall level than China, Japan's development in integrating educational technology is nevertheless lagging behind the U.S. and other quickly emerging nations. Through a study and comparison of the educational advancement in the United States, Japan, and China, this article evaluates the future opportunities and problems of China's education "New Infrastructure" business. The analysis includes market analysis in the post-pandemic era as well as the process of developing educational "New Infrastructure" and the educational environment during the COVID-19 period. Currently, the United States and Japan represent different phases in the integration of global educational technologies.
Role of project activities in the professional development of philologists – future native language and literature teachers (by the example of the Mordovian State Pedagogical University)

The aim of the study is to substantiate the need to organize project activities in the process of professional training of philology students – future native language and literature teachers. The paper considers the proven types and forms of project activities that play an important role in the formation of a teacher who meets the modern requirements of education standards. In addition, the study describes project work in students’ academic and extra academic activities, shows its importance in personality formation, professional self-determination. The study presents the best projects of philology students completed over the past three years. The scientific novelty of the study lies in determining the role of project technology in the educational space of higher education in order to increase competitiveness and mobility among future teachers. As a result of the study, the place of project work in students’ academic and extra academic activities, in the professional development of a future teacher, in the transition to independent teaching was identified.
CHUYỂN ĐỔI SỐ TRONG QUẢN LÝ HOẠT ĐỘNG ĐÀO TẠO Ở TRƯỜNG ĐẠI HỌC

Digital transformation in the management of training activities is an indispensable and mandatory requirement when universities undertake digital transformation. The digital transformation in training activities brings about changes in the modes of conducting educational procedures which also necessitate corresponding changes in management. This article presents the research findings on the digital transformation in the management of training activities at universities in the current context.
HỆ THỐNG QUẢN LÝ GIẤY ĐI ĐƯỜNG TRỰC TUYẾN: GIẢI PHÁP ỨNG DỤNG CÔNG NGHỆ SỐ CỦA TRƯỜNG ĐẠI HỌC VINH

Developing digital infrastructure and promoting the application of digital technology in the management of training institutions is currently an urgent issue for universities associated with the trend of digital transformation in the field of education and training towards building a smart university. Vinh University, with more than 1,000 officials, annually pays an average of more than 2,500 travel documents in many different fields of work. The manual issuance and approval process is a waste of time and resources, has posed the problem of applying digital technology in online travel document management, improving management efficiency. This article presents the results of research and application of the online travel document management system, one of the digital technology application solutions of Vinh University.
Cultural heritage on the Semantic Web: The Europeana Data Model

The Semantic Web allows data to be linked on the Web and structures information for use by humans and machines. Furthermore, it makes relationships between data explicit, enabling the creation of Linked Data. Based on a literature review, the principles and technologies underlying Linked Data are presented – namely, the Resource Description Framework and models developed for libraries, archives and museums. Europeana aggregates European institutions’ digital cultural heritage, having developed a model that follows linked-data principles. For a deeper understanding of this model, the Europeana Data Model is presented with examples of two representation approaches and the advantages of metadata enrichment in information discovery. The experience of the University of Coimbra with Europeana is briefly explained. Finally, the authors discuss the challenges that cultural heritage institutions face in adopting these models and freeing their information from the silos it is in, taking advantage of the potential that Linked Data provides.
Golden eagle based improved Att-BiLSTM model for big data classification with hybrid feature extraction and feature selection techniques

The remarkable development in technology has led to the increase of massive big data. Machine learning processes provide a way for investigators to examine and particularly classify big data. Besides, several machine learning models rely on powerful feature extraction and feature selection techniques for their success. In this paper, a big data classification approach is developed using an optimized deep learning classifier integrated with hybrid feature extraction and feature selection approaches. The proposed technique uses local linear embedding-based kernel principal component analysis and perturbation theory, respectively, to extract more representative data and select the appropriate features from the big data environment. In addition, the feature selection task is fine-tuned by using perturbation theory through heuristic search based on their output accuracy. This feature selection heuristic search method is analysed with five recent heuristic optimization algorithms for deciding the final feature subset. Finally, the data are categorized through an attention-based bidirectional long short-term memory classifier that is optimized with a golden eagle-inspired algorithm. The performance of the proposed model is experimentally verified on publicly accessible datasets. From the experimental outcomes, it is demonstrated that the proposed framework is capable of classifying large datasets with more than 90% accuracy.
Efficient Object Detection and Intelligent Information Display Using YOLOv4-Tiny

This study aims to develop an innovative image recognition and information display approach based on you only look once version 4 (YOLOv4)-tiny framework. The lightweight YOLOv4-tiny model is modified by replacing convolutional modules with Fire modules to further reduce its parameters. Performance reductions are offset by including spatial pyramid pooling, and they also improve the model’s detection ability for objects of various sizes. The pattern analysis, statistical modeling, and computational learning visual object classes (PASCAL VOC) 2012 dataset are used, the proposed modified YOLOv4-tiny architecture achieves a higher mean average precision (mAP) that is 1.59% higher than its unmodified counterpart. This study addresses the need for efficient object detection and recognition on resource-constrained devices by leveraging YOLOv4-tiny, Fire modules, and SPP to achieve accurate image recognition at a low computational cost.
The Prediction of Low-Rise Building Construction Cost Estimation Using Extreme Learning Machine

This study aims to predict the possibility of low-rise building construction costs by applying machine learning models, and the performance of each model is evaluated and compared with ensemble methods. The artificial neural network (ANN) emerges as the top-performing individual model, attaining an accuracy of 0.891, while multiple linear regression and decision trees follow closely with accuracies of 0.884 and 0.864 respectively. Ensemble methods like maximum voting ensemble (MVE) improve the accuracy beyond individual models with an impressive accuracy rate of 0.924. Meanwhile, the stacking ensemble and averaging ensemble also demonstrate competitive performance with accuracies of 0.883 and 0.871, respectively. These findings can result in more informed decision-making, which is valuable for the real estate industry.
AGNES: Abstraction-Guided Framework for Deep Neural Networks Security

Deep Neural Networks (DNNs) are becoming widespread, particularly in safety-critical areas. One prominent application is image recognition in autonomous driving, where the correct classification of objects, such as traffic signs, is essential for safe driving. Unfortunately, DNNs are prone to backdoors, meaning that they concentrate on attributes of the image that should be irrelevant for their correct classification. Backdoors are integrated into a DNN during training, either with malicious intent (such as a manipulated training process, because of which a yellow sticker always leads to a traffic sign being recognised as a stop sign) or unintentional (such as a rural background leading to any traffic sign being recognised as “animal crossing”, because of biased training data). In this paper, we introduce AGNES, a tool to detect backdoors in DNNs for image recognition. We discuss the principle approach on which AGNES is based. Afterwards, we show that our tool performs better than many state-of-the-art methods for multiple relevant case studies.
Language Taboos in Social Activities of the Acehnese Community

This study aims to describe language taboos in the context of social activities within the Acehnese community. The research design employed is field research, utilizing a qualitative approach. The method employed is descriptive-analytical. The data for this research focus on language taboos within the Acehnese community in the Pidie Regency. The data sources are informants, comprising Acehnese individuals residing in the Pidie Regency, spanning three sub-districts, with a total of 21 informants. Data collection involves structured and unstructured interview techniques, supported by the participant observation method with face-to-face conversations and the baiting technique. The data analysis technique used is qualitative analysis with stages such as selection, description, grouping, and data analysis. The research findings reveal language taboos in the social activities of the Acehnese community as follows: (a) language taboos in hunting activities; (b) language taboos in farming activities; (c) language taboos in carpentry activities; (d) language taboos in trading activities; and (e) language taboos in courtship activities.
Journalists’ Perception of the Adoption of New Communication Technologies in the UAE’s Media Organizations

The study investigates how journalists perceive the impact of the adoption of new communication technologies on the journalistic profession in terms of news production and the quality of news content and on their job stability and status in the United Arab Emirates’ (UAE) newspapers. The study applied technology acceptance model and relied on data collected from 63 journalists and editors in the major newspapers in UAE It provides much-needed insights into the implications of new digital technology for the news industry and the profession. The findings show that journalists strongly support the adoption of new technologies in newsrooms, but they are wary of the impact of these technologies on their performance and job stability.
Evaluation and performance comparison of different models for global solar radiation forecasting: a case study on five cities

Abstract Recently, solar energy has emerged as the most promising renewable energy source to meet the world’s energy demands. However, to harness the potential of solar energy, accurate data on solar radiation are crucial. It is considered the first step in assessing solar resources for various applications and achieving energy sustainability goals. Due to the unavailability of solar radiation measurements in many parts of the world, several models have been developed to predict global solar radiation (GSR) at these locations. Thus, this study aims to evaluate the proficiency of several GSR models at five new locations and determine the most suitable one for GSR prediction. The study has further developed solar radiation models for these new locations, as well as general ones for the entire region, which does not have any GSR models despite the existence of many planned solar energy facilities in this area. Additionally, the study investigates the effect of changing the length of the validation dataset on models’ performance and accuracy, as well as assesses the introduced models’ generalization capability. To achieve these objectives, the observed data of GSR for approximately 37 years at studied locations are used to develop and validate the proposed models. The study’s findings reveal that Model 1 provides the best performance at all locations, with accuracy, coefficient of determination ( $$R^{2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>R</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> ), ranging from 95 to 98%, except for the coastal location, where it is from 91 to 95%. The remaining performance indicators of the best models, such as RMSE, MABE, MAPE, and $$r$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>r</mml:mi> </mml:math> , are good, and their values range from 0.7863 to 1.9097 (MJ m −2 day −1 ), from 0.6430 to 1.7060 (MJ m −2 day −1 ), from 3.4319 to 10.0890 (%), and from 0.9914 to 0.9981, respectively. The length of the validation dataset has a slight effect on the models’ performance, ranging from about 1% to 2%. Therefore, Model 1 is the recommended solar radiation model, which can provide precise and rapid estimates of global solar radiation. This approach could be used in the design and performance evaluation of many solar applications. The primary benefit of this approach in the current investigation is that temperature data are continuously and effortlessly recorded for various purposes.
BCLM: a novel chaotic map for designing cryptography-based security mechanism for IEEE C37.118.2 PMU communication in smart grid

Abstract Today, creating a smart grid that is resistant to cyberattacks is a subject of utmost significance. One of the components of the smart grid that is most susceptible to a cyber-attack is the phasor measuring unit (PMU). The reason is that PMU employs IEEE C37.118.2 communication standards, which specify the structure and sequencing of data packets but offer no security measures. Users must implement the security techniques to ensure the protection of PMU data. Additionally, PMU communicates via a public wide-area network, raising the risk to security. In addition, PMU is a crucial component of the smart grid, enabling different crucial choices for the reliable functioning of the smart grid to be made using its data. This research suggests a chaos-based data encryption solution to close the knowledge gap and reduce the confidentiality assault on PMU data. For this, a brand-new boost converter logarithmic map (BCLM), a one-dimensional (1D) chaotic map, has been presented. The research demonstrates how the suggested chaotic map has better chaotic qualities than conventional chaotic maps. The pseudorandom generator is the chaotic BCLM system. The PMU data are encrypted using the random sequence produced by the BCLM chaotic system. The suggested chaotic map is not computationally demanding, making it simple to implement in a PMU device with limited resources.
Interpretable Machine Learning Using Partial Linear Models*

Abstract Despite their high predictive performance, random forest and gradient boosting are often considered as black boxes which has raised concerns from practitioners and regulators. As an alternative, we suggest using partial linear models that are inherently interpretable. Specifically, we propose to combine parametric and non‐parametric functions to accurately capture linearities and non‐linearities prevailing between dependent and explanatory variables, and a variable selection procedure to control for overfitting issues. Estimation relies on a two‐step procedure building upon the double residual method. We illustrate the predictive performance and interpretability of our approach on a regression problem.
Real-time vehicle detection system on the highway

Locating and classifying different types of vehicles is a vital element in numerous applications of automation and intelligent systems ranging from traffic surveillance to vehicle identification, with deep learning models now dominating the field of vehicle detection. However, vehicle detection in Bangladesh remains a relatively unexplored research lacuna. One of the main goals of vehicle detection is its real-time application, with “You Only Look Once” (YOLO) models proving to be the most effective. This paper compared real-time vehicle highway detection systems using YOLOv4, Faster R-CNN and SSD algorithms to determine the best performance. A vehicle detection and tracking system was also developed that improved highway safety. Vehicle trials compared the real-time performances of the YOLO, Faster R-CNN and SSD algorithms in detecting and tracking highway vehicles by measuring precision, recall, F1-score and operating speed. Models for each algorithm were constructed and each model was trained and tested, with performance measured using a confusion matrix. This statistical tool assessed the efficiency of the system using a prepared test dataset and evaluated the results using appropriate indicators such as real-time road lines, traffic signs and vehicle detection false positive rates. Results showed that the YOLOv4 algorithm outperformed Faster R-CNN and SSD in real-time vehicle detection and tracking on highways. YOLOv4 also processed the results more quickly and proved superior in detecting and tracking objects in real time. The Faster R-CNN algorithm gave high object detection, tracking accuracy and recall while reducing the number of locations needing detection, with the SSD algorithm providing high precision, recall and good image detection results.
An adaptive image compression algorithm based on joint clustering algorithm and deep learning

Abstract In recent years, deep artificial neural networks have attracted much attention and have been applied in various fields because they surpass the parameter fitting effect of traditional methods under the condition of data convergence. On the other hand, limited transmission bandwidth and storage capacity make image compression necessary in communication. Here, a compression algorithm that combines the K‐means clustering algorithm with the neural network algorithm is proposed. First, the pixel points of the image are clustered by K‐means algorithm in order to reduce the amount of data input to the neural network algorithm. Secondly, neural network is used to extract image features which realizes further compression. The experiment results show that the peak signal‐to‐noise ratio (PSNR) is 33.48 dB at most with compression ratio at 32:1. The ablation experiment shows that the run time speeds up 9.5% compared to the algorithm without K‐means clustering. Comprehensive comparison experiment shows that the average PSNR is 30.09 dB, which is larger than other baseline approaches. The proposed algorithm is an efficient solution for image compression.
An Efficient FPGA-Based Accelerator for Perceptual Weighting Filter in Speech Coding

In speech coding, denoising of the speech signal is essential as well as crucial. The filters for minimizing errors through denoising employ the autoregressive moving average (ARMA) approach, introducing higher computational complexity in speech coder design. This research work presents the design and implementation of an effective perceptual weighting filter (PWF) for speech coding. The high-level synthesis of the fixed-point PWF filter is optimized by multiple optimization techniques along with detailed design space exploration using the weighted sum (WS) method. To enhance the performance, an FPGA-based hardware accelerator is proposed using hardware/software (HW/SW) co-design in an embedded environment. Simulative analysis in Vivado HLS and final accelerator design in the Vitis IDE tool validate the proposed architecture by using real-time speech samples, demonstrating a 50% reduction in area and a 99% execution improvement. This makes it well-suited for use in modern speech codecs, enhancing the efficiency.
White Blood Cell Type Detection Using K-means Clustering and Multicolor Spaces

The white blood cell (WBC) plays an instrumental role in protecting the human body from different infectious diseases and unknown biological invaders. Their count and cellular morphology drastically change when the body suffers from any such disease or abnormal conditions. This paper demonstrates the detection of five types of WBC present in human blood smear images. Several authors proposed WBC detection using only one color space. But this paper addresses WBC-type detection using four popular color spaces – RGB (red, green, and blue), HSV (hue, saturation, and intensity), YCbCr (luminance, chroma blue, and chroma red), and L*a*b* (luminosity, chromaticity layer “a*”, and chromaticity layer “b*”). The proposed methodology involves color thresholding, K-means clustering, nuclear region analysis, and the implementation of a feedforward neural network. The unsupervised machine learning algorithm, K-means clustering is used to segment nuclei from the blood smear images. The detection of WBC-type using four color spaces is compared in terms of accuracy, sensitivity, specificity, and positive predictive value. It is observed that HSV-based WBC-type detection methodology outperforms methodologies based on RGB, YCbCr, and L*a*b* color spaces. The same methodology mentioned above is then compared with the works proposed by other authors.
Guaranteed Inference for Probabilistic Programs: A Parallelisable, Small-Step Operational Approach

We put forward an approach to the semantics of probabilistic programs centered on an action-based language equipped with a small-step operational semantics. This approach provides benefits in terms of both clarity and effective implementation. Discrete and continuous distributions can be freely mixed, unbounded loops are allowed. In measure-theoretic terms, a product of Markov kernels is used to formalize the small-step operational semantics. This approach directly leads to an exact sampling algorithm that can be efficiently SIMD-parallelized. An observational semantics is also introduced based on a probability space of infinite sequences, along with a finite approximation theorem. Preliminary experiments with a proof-of-concept implementation based on TensorFlow show that our approach compares favourably to state-of-the-art tools for probabilistic programming and inference.