Reviewerly/exHarmony huggingface dataset:
Analysis of the implementation status of early childhood music activities recorded in the childcare journal: Focused on childcare journal of one public childcare center in Seoul

Objectives This study aims to provide data on the current status of music activities so that pre- and in-service early childhood teachers can improve the quality of musical activities by analyzing early childhood musical activities recorded in childcare journal at chldcare center. Methods After obtaining written consent for the reading of the childcare journal from the 3 to 5 year old teachers at a public childcare center located in Seoul, 3,529 musical activities recorded in the childcare journal were were extracted for 1,516 days and analyzed on the music activities analysis creteria(3 areas, 6 categories). For each musical activity, frequency and percentage were calculated according to the analysis creteria. Results First, the overall descriptive status of the content of early childhood musical activities was that 277 (7.8%) were describe musical elements, that 3,252 (92.2%) were not describe musical elements, 3,529 (100.0%) were describe the type of musical activity, 1,182(33.5) were describe the Nuri Curriculum area, and 2,347 (66.5%) were not describe the Nuri Curriculum area. Second, the overall descriptive status of teaching-learning methods of early childhood music activities was that 3,529 (100.0%) were describe the daily routine, 0(0.0%) was not describe the daily routine, that 2,774 (78.6%) were describe the music activity materials, and that 755 (21.4%) were not describe the daily routine. Third, the overall descriptive status of the evaluation of early childhood music activities was that 3,264(92.5%) were not describe the evaluation of music class activities, and that 265(7.5%) were describe the evaluation of music class activities. Conclusions It have suggested that early childhood music activities were found to be many cases in which the nursery journal does not describe content, teaching-learning methods, and evaluation of them, and that efficient methods of recording them should be sought to improve the quality of early childhood music activities.
To the 100th anniversary of the library of South Ukrainian National Pedagogical University named after K. D. Ushynsky: history and the present

In 2022, the library of South Ukrainian National Pedagogical University named after K. D. Ushynsky celebrates its 100th anniversary, which makes us return to the origins of higher pedagogical education in the South of the country. It is this fact that determines the relevance of our research. The article traces the history of the university and the library, starting from the 20s of the 20th century to the present day. The purpose of the article consists in the necessity to analyse the history of the creation and development of the university and the library in inseparable interaction. The historical method of research allowed us to systematise and summarise the available information on the history of the library of Ushynsky University in chronological order. The article examines the main stages of the existence of Ushynsky University, starting from its foundation in 1920, when it received the name of the Institute of National Education and the task of providing training for education workers in the South of Ukraine. It was also found that the library of this educational institution was created in 1922 to provide students with textbooks and fiction. It is shown how our higher educational institution survived the educational reforms implemented by the new, at that time, Soviet government. On the basis of archival documents and memories of employees stored in the library, the history of the evacuation of the university and the library during the Second World War alongside their post-war revival has been traced. The problems of the development of the university, the introduction of new educational directions, the opening of new faculties and the process of rebuilding the library in the second half of the 20th and the beginning of the 21st centuries have been revealed. Considerable attention is devoted to the current state of the library, its structure, aspects of activity and challenges. It was concluded that the library of Ushynsky University is an important structural unit that contributes to the teaching / learning activities, research-oriented, cultural and education-related activities of teachers and students. It has been proved that today the library is a powerful information institution, a scientific and cultural centre of the university.
Robust Lane Departure Warning System for ADAS on Highways

Lane detection in Advanced Driver Assistance System (ADAS) has a significant role and must be handled well. Lane detection is the core for Lane Departure Warning System (LDWS) that warns the driver upon unintendedly leaving the lane. Unfortunately, road type conditions such as straight and curved road, weather conditions such as fog, low illumination can increase the difficulty of finding the lane lines. In this paper, lane departure warning system that is based on image processing technique for detecting lane lines is implemented. Video frames are fed to the pre-processing phase where brightness and contrast of images are adjusted, then Red Green Blue (RGB) images are changed to Hue Saturation Lightness (HSL) colour space. Gaussian blur filter to remove noise. Sobel edge detection to detect and filter lane lines. Perspective warp to convert view to flat 2D surface. Sliding window technique to differentiate between the left and right lane boundaries. Finally, after fitting polynomial equations that represents lane boundaries, the vehicle offset from lane center can be calculated. The algorithm is tested using self-made dataset as well as on Raspberry pi 4 on highway in Egypt that includes daytime, nighttime, and foggy video frames, Results showed that it needs improvements.
Assessment of Innovation Readiness and Technology Acceptance Using Immersive Sci-Fi Prototyping

Digital transformation is ubiquitous and is generating unprecedented forms of innovation. However, it brings new challenges due to its comprehensive nature and potentially profound impacts. Technology assessment examines the long-term impacts of technologies on society and the environment. One aspect of this is a broad societal discourse, which is considered indispensable in a functioning democracy. To this end, various perspectives are sought, especially from experts in science, business, and politics, but also the opinion of the public. In practice, however, this poses a challenge. How is the public supposed to be able to form an informed opinion about a technological change, an upcoming innovation, if this change is multi-layered and not easily transparent and comprehensible? In this paper, attitude formation and attitude change are examined in more detail using a science fiction prototype in the democratic field of action. The main focus lies on immersion. The effects of immersive scenarios on attitudes toward technological innovations have not yet been sufficiently studied. The authors believe that conventional methods appeal mainly to cognition, but people are often driven by emotions. The immersive sci-fi prototyping method is designed to allow technological innovations to be experienced with virtual reality and thus also appeals to emotions in the process of forming attitudes. The authors hypothesize that with the immersive sci-fi prototyping method, a better starting point is provided to evaluate technology acceptance and innovation readiness. For this purpose, a laboratory experiment is conducted with a pre- and post-survey. The results of the low-immersion group, who click through the sci-fi prototype as hypertext in the browser, are compared with the high-immersion group, who experience the sci-fi prototype in a VR environment. The results show that both sci-fi prototyping and immersive sci-fi prototyping are suitable for capturing attitude. In the immersive sci-fi prototyping method, subjects are better able to visualise the technology and, in general, VR has shown a stronger impact on attitude change. These results relate to the digital democratic assistant presented in the sci-fi prototype.
Reconstructing 3D Face of Infants in Social Interactions Using Morphable Models of Non-Infants.

3D morphable models (3DMMs) simultaneously reconstruct facial morphology, expression and pose from 2D images, and thus could be an invaluable tool for capturing and characterizing the face and facial behavior in early childhood. However, 3DMM fitting on infants is a largely unexplored problem. All publicly available 3DMMs are developed for adults, and it is unclear if and to what extent they can be used on videos of infants. In this paper, we compare five state-of-the-art 3DMM fitting methods on data from naturalistic infant-caregiver interactions. Results suggest that it is possible to produce consistent and subject-specific reconstructions of 3D shape identity from multiple frames, but not from a single frame. Qualitative evaluation highlights that facial regions with high texture variation, such as eyes, brows and mouth, are captured with higher accuracy compared to the rest of the face. Thus, even though a 3DMM developed for adults has significant limitations when reconstructing the morphology of the entire facial region of infants, applications that involve analysis of facial behavior can be feasible. Our encouraging results, combined with the unique ability of 3DMMs to disentangle two major sources of noise for expression analysis (i.e., identity bias and pose variations), motivate future research on using 3DMMs to measure the facial behavior of infants.
Market Basket Analysis Using FP-Growth Algorithm On Retail Sales Data

Technological developments make industrial competition increasingly fierce, especially in lifestyle-related industries. In the store where we do our case study, the buyer's interest in buying fashion products has decreased significantly. It has implemented many marketing strategies such as offering discounts, events, or product bundling, but sales still have not reached the target. Therefore, the store needs a system that can predict the pattern of habits and desires of buyers for an item. This system can be based on sales transaction data, which is processed using data mining techniques. In this case, the most suitable method is the association or commonly called market basket analysis. This method works by analyzing the behavior patterns of buyers when buying a product simultaneously at a given time. We choose the FP-Growth algorithm because the process is faster and more efficient. This research output is a system that can count the product association value in transactional data. When we tested 571 transaction data items with minimum support of 4% and a minimum of 50% confidence, four rules were obtained with a lift ratio test value above 1.
Cross-scale sampling transformer for semantic image segmentation

In increasingly complex scenes, multi-scale information fusion becomes more and more critical for semantic image segmentation. Various methods are proposed to model multi-scale information, such as local to global, but this is not enough for the scene changes more and more, and the image resolution becomes larger and larger. Cross-Scale Sampling Transformer is proposed in this paper. We first propose that each scale feature is sparsely sampled at one time, and all other features are fused, which is different from all previous methods. Specifically, the Channel Information Augmentation module is first proposed to enhance query feature features, highlight part of the response to sampling points and enhance image features. Next, the Multi-Scale Feature Enhancement module performs a one-time fusion of full-scale features, and each feature can obtain information about other scale features. In addition, the Cross-Scale Fusion module is used for cross-scale fusion of query feature and full-scale feature. Finally, the above three modules constitute our Cross-Scale Sampling Transformer(CSSFormer). We evaluate our CSSFormer on four challenging semantic segmentation benchmarks, including PASCAL Context, ADE20K, COCO-Stuff 10K, and Cityscapes, achieving 59.95%, 55.48%, 50.92%, and 84.72% mIoU, respectively, outperform the state-of-the-art.
Matrices and arrays

In this chapter we introduce methods to create and manipulate matrices. The difference between matrix operations and array operations are described to illustrate the fact that you can do matrix algebra or operate on a matrix of data as an array (or list) of individual numbers. We also introduce character strings and the facilities for handling them. Examples of some uses of matrices are provided. They are population dynamics, Markov processes, and solving systems of linear equations. The analysis of sparse matrices to save space in the computer is also discussed.
The implementation of the enhanced quantum floating-point adder

The quantum adder is a vital arithmetic operation for quantum algorithms. However, the existing quantum floating-point adders only considered the case of two normal numbers. In this paper, we propose an enhanced quantum floating-point adder based on IEEE 754 standard. First, we divide quantum floating-point adders into four cases: the normal case, subnormal case, mixed case, and special case. Then, we propose a result-sign and mantissa-sign determining unit to avoid conversion from sign-magnitude to two complements. Moreover, we design a new quantum leading zero detector with the optimized T-depth and T-count. Comparison results reveal that the proposed floating-point adder has lower circuit width, T-depth, and T-count than the current works.
Semi‐MoreGAN: Semi‐supervised Generative Adversarial Network for Mixture of Rain Removal

Abstract Real‐world rain is a mixture of rain streaks and rainy haze. However, current efforts formulate image rain streaks removal and rainy haze removal as separated models, worsening the loss of image details. This paper attempts to solve the mixture of rain removal problem in a single model by estimating the scene depths of images. To this end, we propose a novel SEMI‐ supervised M ixture O f rain RE moval G enerative A dversarial N etwork (Semi‐MoreGAN). Unlike most of existing methods, Semi‐MoreGAN is a joint learning paradigm of mixture of rain removal and depth estimation; and it effectively integrates the image features with the depth information for better rain removal. Furthermore, it leverages unpaired real‐world rainy and clean images to bridge the gap between synthetic and real‐world rain. Extensive experiments show clear improvements of our approach over twenty representative state‐of‐the‐arts on both synthetic and real‐world rainy images. Source code is available at https://github.com/syy-whu/Semi-MoreGAN .
Observation of Human Entrainment with Robot Assistance Towards Education

At present, the world faces the challenge of making education available to all the people of the world, especially in under-developed areas, where there is a lack of human resources. To improve educational standards and address this lack of human resources, we believe that robots can be a good teaching aid, to assist both the teacher and the student. This study investigated the entrainment phenomenon between human interactions with a robot as a third-party influence to provide learning packages that will be suited for students and will reduce the strain on teachers. We used dynamic time warping (DTW) algorithm in our time series analysis to effectively calculate the similarity of the brainwaves of the subjects. Then, we defined the entrainment phenomenon between the subjects. To ensure the complexity of this experiment, crosswords were used for this task, which subjects were required to complete in cooperation with each another. At different points of time, the robot would use different expressions, actions, and words as reminders or hints, aiding communication and better cooperation between subjects. The timing of the robotic reminders, synchronization, conversations between the subjects in the crossword tasks, and the completion rate of the crossword puzzles were recorded. The average completion rate improved by 10% with the aid of the robot, as opposed to not using the robot. The results of this study prove that robotic participation in human interaction is beneficial, and that implementing robotic assistance will improve education.
Multimodal Analogy-Based Image Retrieval by Improving Semantic Embeddings

In this work, we study the application of multimodal analogical reasoning to image retrieval. Multimodal analogy questions are given in a form of tuples of words and images, e.g., “cat”:“dog”::[an image of a cat sitting on a bench]:?, to search for an image of a dog sitting on a bench. Retrieving desired images given these tuples can be seen as a task of finding images whose relation between the query image is close to that of query words. One way to achieve the task is building a common vector space that exhibits analogical regularities. To learn such an embedding, we propose a quadruple neural network called multimodal siamese network. The network consists of recurrent neural networks and convolutional neural networks based on the siamese architecture. We also introduce an effective procedure to generate analogy examples from an image-caption dataset for training of our network. In our experiments, we test our model on analogy-based image retrieval tasks. The results show that our method outperforms the previous work in qualitative evaluation.
Where does “Mining” go from here in smart cities? – Value endogenous and civilizational incentives for blockchain currencies

The global New Crown Pneumonia epidemic has led to introducing monetary easing in various countries, but the resulting financial instability has also become apparent. Blockchain currencies are attracting attention to their natural ability to withstand inflation and trade barriers. However, traditional ‘mining’ has problems with energy consumption, data risk, disconnection from the real economy and capital manipulation. We combine “mining” with smart big data, introduce the framework of endogenous value blockchain currency, and link the efficiency of “mining and distribution” with smart big data on health, knowledge, environmental protection and many other aspects, so that the value of “mining” is endogenous, avoiding the criticism of the old blockchain currency and making it an important tool to promote civilization.
Introduction to Recommendation Systems

In today’s world, every customer is faced with multiple choices for every decision. Let’s assume that a person is looking for a book to read without any specific idea of what he wants. There’s a wide range of possibilities for how his search might pan out. He might waste a lot of time browsing the Internet and trawling through various sites hoping to strike gold. He might look for recommendations from other people.
Graph-Based Recommender Systems

The previous chapter covered deep learning-based recommender systems and explained how to implement end-to-end neural collaborative filtering. This chapter explores another recent advanced method: graph-based recommendation systems powered by knowledge graphs.
Market Basket Analysis (Association Rule Mining)

Market basket analysis (MBA) is a technique used in data mining by retail companies to increase sales by better understanding customer buying patterns. It involves analyzing large datasets, such as customer purchase history, to uncover item groupings and products that are likely to be frequently purchased together.
Hybrid Recommender Systems

The previous chapters implemented recommendation engines using content-based and collaborative-based filtering methods. Each method has its pros and cons. Collaborative filtering suffers from cold-start, which means when there is a new customer or item in the data, recommendation won’t be possible.
Content-Based Recommender Systems

Content-based filtering is used in recommending products or items very similar to those being clicked or liked. User recommendations are based on the description of an item and a profile of the user’s interest. Content-based recommender systems are widely used in e-commerce platforms. It is one of the basic algorithms in the recommendation engine. Content-based filtering can be triggered for any event; for example, on click, on purchase, or add to cart.
Design and application of digital seawall management platform

Intelligent water conservation is an overall need of the Ministry of Water Resources' 14th Five-Year Plan, as well as an essential component of Zhejiang Provincial government's digital reform. Based on the Provincial “four horizontal, four vertical, and two ends” general framework, this paper constructs the general framework of a digital seawall management platform, designs a specific functional structure of the platform to achieve “accurate monitoring, dynamic research and judgment, automatic warning” of seawall, providing an important support for seawall management departments to prevent and resolve major risks and hidden dangers, and improve monitoring.
Research on comprehensive evaluation system of new campus pre-planning work of colleges and universities based on the analytic hierarchy process

This paper analyzes the content and main features of the pre-planning of the new campus of colleges and universities, sums up 20 work plans, and builds a comprehensive evaluation system for the pre-planning of the new campus in colleges and universities. The evaluation system uses the Analytic Hierarchy Process (AHP) to establish a pre-planning hierarchy model, determine the weight of each work, and obtain the evaluation membership matrix by means of questionnaires. Finally, the final evaluation result is obtained through the matrix operation. This paper takes a new campus of a university in Beijing as the research object and conducts a comprehensive evaluation of its pre-planning work. The resulting conclusions are consistent with the satisfaction of teachers and students, which verifies the reliability of the evaluation system.
VLDB Scalable Data Science Category

As part of the International Conference on Very Large Data Bases (VLDB) 2021 / Proceedings of the VLDB Endowment Volume 14, a new Research Track category named Scalable Data Science (SDS) was launched [2, 6]. The goal of SDS is to attract cutting-edge and impactful real-world work in the scalable data science arena to enhance the impact and visibility of the VLDB community on data science practice, spur new technical connections, and inspire new follow-on research. The inaugural year proved to be successful, with numerous interesting papers from a wide cross section of both industry and academia, spanning several data science topics, and originating from several countries around the world. In this report, we reflect on the inaugural year of SDS with some statistics on both submissions and accepted papers, SDS invited talks, and our observations, lessons, and tips as inaugural Associate Editors for SDS. We hope this article is helpful to future authors, reviewers, and organizers of SDS, as well as other interested members of the wider database / data management community and beyond.