Reviewerly/exHarmony huggingface dataset:
Content analysis of music education studies related to augmented reality technology

This study aims to examine the studies scanned in Web of Science and Scopus databases between 2006-2020 on the use of augmented reality applications in music education in terms of their descriptive features, methodological features, and outcomes, and to reveal the trends in this field. The criterion sample approach was utilized in this qualitative study, and 35 selected studies were reviewed using the publication classification form and analyzed using the content analysis method. The descriptive and methodological feature data were translated into frequency values using the SPSS 22 program and then interpreted using tables and charts for easy comprehension. Furthermore, the advantages and limitations of AR applications utilized in music education, which were the research's outputs, were coded, organized into relevant categories, and interpreted. The results showed that most studies focused on piano and guitar education for beginners; no studies were undertaken on wind instruments or voice training. Studies, particularly in recent years, have focused on user experience research. Furthermore, it was found that AR applications have the advantages of increasing and facilitating student learning performance, making learning exciting and fun, and providing motivation. Some challenges during use, owing to technical issues and limited field content in the programs, resulted in limited improvements in music education.
The game model with multi-task for image denoising and edge extraction

Abstract Image denoising and edge extraction are two main tasks in image processing. In this paper, a game model is proposed to solve the image denoising and edge extraction, which combines an adaptive improved total variation (AdITV) model for image denoising and a global sparse gradient (GSG) model for edge extraction. The AdITV model is a forward-and-backward diffusion model. In fact, forward diffusion is applied to the homogeneous region to denoise, and backward diffusion is applied to the edge region to enhance the edge. A unified explicit discrete scheme is established in this paper to solve the AdITV model, which is compatible to forward diffusion and backward diffusion. The stability of the scheme is proved. On the other hand, GSG is a functional model based on sparse representation, which is robust to extract edges under the influence of noise. AdITV and GSG are chosen as two components of the game model. The alternate iteration method is used to solve the game problem. The convergence of the algorithm is proved and numerical experiments show the effectiveness of the model.
Image retrieval based on multimodality neural network and local sensitive hash

With the rapid development of deep convolutional neural networks, the use of deep convolutional neural networks to extract features instead of manual features has become one of the current research hotspots. However, deep convolutional neural network can not understand image features well, and there is a “semantic gap”. Contrastive Language-Image PreTraining (CLIP) model is a pre-training neural network model based on matching image and text. Use the pre-trained CLIP model to extract the high-dimensional feature vector of the image data set to be retrieved, and the Local Sensitive Hash (LSH) algorithm was used to extract the retrieval speed to complete the retrieval task based on the image content and text. Experimental results show that compared with other content-based image retrieval algorithms, the proposed algorithm can also understand the text information in the image to complete the retrieval task, and has a wider retrieval range.
A Study on the Instructional Design of Blended Learning to Improve Students' Critical Thinking

BackgroundNowadays, human beings are living in an era of great development, great change and great adjustment.People are surrounded by a large amount of information every day, and modern political, economic, cultural and social life has put forward increasingly high requirements on people's ability to distinguish, think and solve problems.Critical thinking has become one of the basic skills that people must have in the 21st century.Critical thinking is a purposeful and rule-based judgment process that produces the interpretation, analysis, evaluation, reasoning and explanation of evidence, ideas, methods, standards or contexts.Critical thinking has the characteristics of determining knowledge and action, rationality, reflexivity and constructiveness, and the qualities of thinking of clarity, relevance, consistency, rationality and predictability.Thinking determines behavior.Whether in life, study or work, people need critical thinking, because our thoughts are shaping our life.The change of society determines the change of talent demand, which also promotes the reform of education.Educators increasingly recognize that critical thinking has become a basic ability and should be the focus of education in today's society.Macpherson (1999) emphasizes that critical thinking is crucial for college graduates, because they will be faced with a very complex environment after graduation, in which students need to use their thinking ability to solve various problems and puzzles.The Skills for the 21st Century Partnership has identified critical thinking as one of several learning and innovation skills necessary to prepare students for higher education and employment.Critical thinking has also been mentioned repeatedly in China.As early as September 2012, Premier Wen Jiabao pointed out in his speech at Tsinghua University that independent thinking and critical thinking should be advocated.The Research Report on 21st Century Core Literacy 5C Model published by China Education Innovation Research Institute of Beijing Normal University puts forward the 21st century core literacy 5C model, which includes critical thinking.In the face of this situation, educators need to re-examine education, especially when setting learning goals for students, they need to set higher standards, including the cultivation of critical thinking, because critical thinking can not only enhance students' understanding ability, but also motivate students to learn and give them more opportunities and abilities.
Study on the Relationship between Supply and Demand of Musical Talents in the Middle and Western Regions of Zhejiang, China

Central and western Zhejiang of China mainly includes Jinhua City, Lishui City and
Analysis on the Consistency between Language Literacy of English Teachers and Students’ Learning Effect

In English classroom, the discourse is the language used in order to complete the teaching task and plan, which is the important language input for the students.Teacher's discourse is not only an important tool for English teaching, but also a source of language learning for students, which has a positive guiding significance for students' English learning effect.What's more, the quantity and quality of teacher's talk have a deep influence on the teaching quality, so we must attach great importance to the teacher's talk in English teaching in senior high school.
Research on the Implementation Strategy of Industry-education Integration in Local Application-oriented Universities

With the transformation of social and economic development mode and the transformation and upgrading of industrial structure, society and enterprises are in urgent need of high-level and compound professionals with professional quality and application skills. As a new talent training mode, "integration of industry and education, school-enterprise cooperation" is the only way for the development of local application-oriented undergraduate colleges and universities., but also promote talent training in line with industrial needs, better serve local economic development and industrial upgrading and transformation, and realize the seamless connection from education to employment, which is of great strategic significance to local economy.This paper mainly analyzes and studies the important role, existing problems and specific strategies of the implementation of industry-education integration in local application-oriented universities.
Research Hotspots and Development Trends of Education Digital Transformation: A Bibliometric Study

Education digital transformation provides direction and guidance for education reform in the new era, making it extremely important to understand the main trends in the current development of education digitalization. This study uses Vosviewer and Citespace software to conduct quantitative statistics and analysis of relevant literature from the WoS database from 2001 to 2022, including authorship, country of origin, and publication journals. Visual analysis is conducted on high-frequency keyword networks, keyword timeline maps, and visual maps of journals and authors' countries. This study introduces the research hotspots, evolutionary paths, and development trends in the field of education digital transformation. By analyzing highly cited and representative literature in different periods and summarizing the mainstream research hotspots in the field, the study aims to grasp the development trends in the field and present the macroscopic context of education digital transformation since the beginning of the new century.
Study and application of geometric concepts in the movement of a vehicle using cartesian coordinates

Autonomous vehicles are already a reality in many contexts. Most of them need sensors to move around their environment, which increases the complexity and cost of the project. This study uses the coordinates of the Cartesian plane to orient a prototype vehicle in relation to its geographic space, allowing it to move to predefined coordinates without the use of any sensors. For the steering system of the prototype, concepts of point, line, and vectors were used. The initial position of the vehicle is represented by a vector, which informs its direction in the plane, having as final destination any point in the Cartesian system. From this information, a displacement vector is created, which leads the prototype to the reference point, and indicates the angle it must rotate. Regarding the direction of rotation, the concept of parallel lines between the prototype's vector and the destination point was used, where the analysis of their y-intercept determines if the vehicle rotates to the left or right. So, with the use of vectors and the Cartesian plane, it was possible to determine fundamental information for the locomotion of the prototype, such as angle and direction of rotation, although there was some difficulty in determining the graphic location of the vector.
Standard sine motion video-based virtual traceability method for monocular vision low-frequency vibration measurements

The low-frequency vibration exists in building structures, mechanical devices, instrument manufacturing, and other fields, and is the key to modal analysis, steady-state control, and precision machining. At present, the monocular vision (MV) method has gradually become the primary choice to measure the low-frequency vibration because of its distinctive advantages in efficiency, non-contact, simplicity, flexibility, low cost, etc. Although many literature reports have demonstrated that this method has the capability to reach high measurement repeatability and resolution, its metrological traceability and uncertainty evaluation are difficult to be unified. In this study, a novel, to the best of our knowledge, virtual traceability method is presented to evaluate the measurement performance of the MV method for the low-frequency vibration. This presented method achieves traceability by adopting the standard sine motion videos and the precise position error correction model. Simulations and experiments confirm that the presented method can evaluate the amplitude and phase measurement accuracy of the MV-based low-frequency vibration in the frequency range from 0.01 to 20 Hz.
MAP3D: An explorative approach for automatic mapping of real-world eye-tracking data on a virtual 3D model

Mobile eye tracking helps to investigate real-world settings, in which participants can move freely. This enhances the studies’ ecological validity but poses challenges for the analysis. Often, the 3D stimulus is reduced to a 2D image (reference view) and the fixations are manually mapped to this 2D image. This leads to a loss of information about the three-dimensionality of the stimulus. Using several reference images, from different perspectives, poses new problems, in particular concerning the mapping of fixations in the transition areas between two reference views. A newly developed approach (MAP3D) is presented that enables generating a 3D model and automatic mapping of fixations to this virtual 3D model of the stimulus. This avoids problems with the reduction to a 2D reference image and with transitions between images. The x, y and z coordinates of the fixations are available as a point cloud and as .csv output. First exploratory application and evaluation tests are promising: MAP3D offers innovative ways of post-hoc mapping fixation data on 3D stimuli with open-source software and thus provides cost-efficient new avenues for research.
An Explorative Study on Extractive Text Summarization through k-means, LSA, and TextRank

Notably the difficult and exciting issue in the field of Natural Language Processing (NLP) is summarizing the text. Understanding the main objective of any type of document is crucial. Some of the applications of text summarization are media monitoring, social media, marketing, health care, literature, and books. Text summarization techniques are implemented using extractive summarization techniques in the health care domain in which it considers patient health history. To visualize a lengthy patient health history document quickly we use machine learning techniques like k-means, Text Rank, and Latent Semantic Analysis to comprehend and identify the sections that communicate important information to produce the summarized texts. These methods are evaluated using ROUGE-1, ROUGE-2, and ROUGE-N metrics to obtain the highest similarity of extracted text. k-means outperformed the considered approaches compared to Text Rank and Latent Semantic Analysis in summarizing the documents. k-Means was more efficient, where it achieved an average of 94.52% precision, 90.98% recall, and 91.25% F1-score.
A Survey on Currency Recognition Method

Currency is an important part of trading in our everyday life. Although humans can easily identify and recognize currency used in everyday life, the problem of currency recognition arises when automated machines have to identify currencies for different tasks. The need for an efficient currency recognition system occurs due to the mainstream use of vending machines, currency counters and in several banking areas. With new technology, machines make tedious and monotonous jobs easy for us. Smart trading is impossible without an accurate and efficient currency recognition method. It is widely used in archaeology to identify and study the ancient coins from different eras and places and know more about the period it is from. It also possesses an excellent scope for an intelligent future. Over the years, various number of currency recognition techniques has been developed. Most of them (mechanical and electromagnetic methods) depend on the physical parameters of the currency while image processing methods depend on the features like shape, colour, and edge. The later includes several steps like image acquisition, pre-processing, feature extraction and classification of the given currency. In this paper, authors have briefly discussed the various methods or techniques used for both coin and paper currency recognition. All these methods use variety of techniques and tools to increase the efficiency and accuracy of recognition. The method to be used for currency recognition should provide the maximum accuracy for a variety of coins, but it should also be efficient in terms of cost, time, space, and more. By summarizing all the works till date on currency recognition, the authors have compared all the methods to look for a suitable currency recognition method and the areas that need to be worked upon to produce an even better currency recognition system.
Online Doctoral Faculty Engagement: Building Connections Through Authenticity

Adult learners enrolled in online doctoral programs face nuanced challenges unique to them. Overwhelmingly, students enrolled in Bradley University’s (BU) online Doctor of Education (Ed.D.) in Educational Leadership, with a concentration in Higher Education Administration and Leadership (HEAL) are fulltime working professionals, with focused time to commit toward a terminal degree. In the fall of 2022, program administration surveyed both active BU HEAL students and program alumni regarding the overall student experience. This survey, guided by the Community of Inquiry (CoI) framework, provided important feedback for individual and programmatic student successes but also noted important barriers that exist in the virtual environment. This paper will discuss student observations related to faculty’s use of technology to provide a more holistic learning environment that provides rigor, curiosity, compassion, and authenticity.
An Enhanced Optimize Outlier Detection Using Different Machine Learning Classifier

Data mining (DM) is an efficient tool used to mine hidden information from databases enriched with historical data. The mined information provides useful knowledge for decision makers to make suitable decisions. Based on the applications, the knowledge required by the decision makers will differ and thus need different mining techniques. Hence, an ample set of mining techniques like classification, clustering, association mining, regression analysis, outlier analysis, etc. are used in practice for knowledge discovery. These mining techniques utilize various Machine Learning (ML) algorithms. ML algorithms assume the normal objects as highly probable and the outliers as low probable. The global outliers which occur very rarely will deviate totally from the normal objects and can be easily distinguished by unsupervised ML algorithms. Whereas, the collective outliers which occur rarely as groups will deviate from the normal objects and can be distinguished by ML algorithms. This paper analyzes the outliers and class imbalance for diabetes prediction for different ML algorithms, i.e. logistic regression (LR), decision tree (DT), random forest (RF), K-neighbors (K-NN), and XG-Boosting (XGB).
Online Ed.D. Program Development: A Program Level Perspective

The online Doctor of Education (Ed.D.) in Educational Leadership at Bradley University launched in the fall semester 2019 with a concentration in Higher Education Administration and Leadership (HEAL). In the fall of 2022, program administration surveyed both active students and program alumni regarding the overall student experience. Using questions guided by the Community of Inquiry (CoI) framework, students provided feedback as it relates to their individual social, cognitive, and teaching/learning needs. This paper will discuss considerations for online graduate program administrators interested in how technology applies to programmatic decisions, access, institutional relationships, communication, and overall program advocacy.
CryptoLight: An Electro-Optical Accelerator for Fully Homomorphic Encryption

Fully homomorphic encryption (FHE) protects data privacy in cloud computing by enabling computations to directly occur on cipher-texts. To improve the time-consuming FHE operations, we present an electro-optical (EO) FHE accelerator, CryptoLight. Compared to prior FHE accelerators, on average, CryptoLight reduces the latency of various FHE applications by >94.4% and the energy by >95%.
Sentiment Analysis of Tweets on Online Education during COVID-19

The global coronavirus disease (COVID-19) pandemic has devastated public health, education, and the economy worldwide. As of December 2022, more than 524 million individuals have been diagnosed with the new coronavirus, and nearly 6 million people have perished as a result of this deadly sickness, according to the World Health Organization. Universities, colleges, and schools are closed to prevent the coronavirus from spreading. Therefore, distance learning became a required method of advancing the educational system in contemporary society. Adjusting to the new educational system was challenging for both students and instructors, which resulted in a variety of complications. People began to spend more time at home; thus, social media usage rose globally throughout the epidemic. On social media channels such as Twitter, people discussed online schooling. Some individuals viewed online schooling as superior, while others viewed it as a failure. This study analyzes the attitudes of individuals toward distance education during the pandemic. Sentiment analysis was performed using natural language processing (NLP) and deep learning methods. Recurrent neural network (RNN) and one-dimensional convolutional neural network (1DCNN)-based network models were used during the experiments to classify neutral, positive, and negative contents.
A Novel Disease Prediction Model Based on Blockchain and Federated Learning

Abstract With the advent of the era of big data, the model architecture of disease prediction has been transformed from cloud computing architecture to edge computing architecture to alleviate problems, such as single point of failure, huge burden of computing and long communication delays.However, due to the lack of trust among edge computing participants (herein referred to as medical institutions with data resources or data storage nodes), the patients’ personal privacy and security issues are still challenging, and there is an urgent need to propose new solutions with higher security. In this paper, we proposed a new disease prediction model with the edge computing framework based on blockchain and federated learning technology to realise full mining of data value, as well as protecting local data sharing security and privacy. In the process of federated learning with blockchain as the underlying platform working environment, we propose the concept of model accuracy as the selection indicator of data nodes in federated learning, which improves the accuracy of the federated learning model and optimizes the training delay.
The Employment Situation and Countermeasures of Colleges and Universities in Heilongjiang Province

Based on the micro-data of employment of college graduates in Heilongjiang Province in 2020, this paper analyzes the overall employment situation, employment gender, educational structure and other aspects of the data statistics, and defines the characteristics of the total employment volume and structural characteristics of college graduates in Heilongjiang Province in 2020.Finally, the paper puts forward countermeasures and suggestions to solve the "difficult employment" from the three aspects of the government, colleges and universities and students themselves, so as to provide certain references for improving the employment rate and employment quality of college graduates in Heilongjiang Province.
Salient Object Detection From Unlabeled Images

Abstract Salient object detection via deep neural networks usually needs a large amount of images with human annotation. To avoid laborious and consuming annotation, we propose a robust unsupervised salient object detection method with three stages in this work. Our method first uses unlabeled data to generate an activation map which indicates the coarse location of object. Then one scribble generation method based on the activation map is proposed, which provides foreground scribble and background scribble with high confidence. Finally, a salient object detection model is trained with the supervision of the generated scribble. Performance comparison is carried on four public datasets, showing that our method significantly outperforms state-of-the-art unsupervised methods, and also achieves better performance than some weakly supervised methods.