Reviewerly/exHarmony huggingface dataset:
Immersive Learning for Medicine using Augmented Reality with iPads

The COVID-19 pandemic crisis has resulted in educational institutions across the world being pressured to revamp their teaching and learning practices, as well as to utilize the available technological tools to create content for remote learning. Medical education, on the other hand, requires a learning approach that is more appropriate with the program structure and technological advancements. In the medical domain, approaches as Augmented Reality (AR) could revolutionize the way to teach and train medical students. AR can be used in training for complex physiological systems to provide the medical students experience much closer to real-life, developing adaptive expertise and acquiring collaborative skills. AR technology could help physicians and surgeons-to-be learn about the latest methods and tools in their areas, as well as refining and encouraging their knowledge retention. In this capacity, AR is more than just a learning approach. Learning in the medical domain is usually situated in a real-life context. In other words, training in this real-life context is not always possible. AR learning environments potentially offer a meaningful situated learning experience that may enable the transfer of learning into the professional context. Previous studies were performed primarily into the development, usability, and initial implementation of AR learning environments. As such, this study was aimed to develop and evaluate an immersive learning environment for medical students using AR. The study applied the design and development research approach that involved three stages: (i) analysis; (ii) design and development; (iii) implementation and evaluation. This study described how AR enhanced the experiences of medical students, by improving understanding and knowledge, as well as practical and social skills. These elements were discussed within the context of AR in immersive medical education, which were action, performance, competency, and knowledge. Moreover, these elements were used to develop the final immersive learning environment for medical education using AR. Finally, this study also discussed the challenges of AR in teaching and learning, and proposed future directions for the use of this technology in revolutionizing medical education.
The Effect of Working Environment, Parent-Teacher Cooperation, and Director's Emotional Leadership on The Practice of Childcare Respecting Rights for Young Children

Objectives The purpose of this study was to investigate the effects of working environment, parent-teacher cooperation, and emotional leadership of directors on the practice of childcare respecting rights for young children.&#x0D; Methods The questionnaires were distributed to 234 childcare teachers in the cities of Seoul and the metropolitan area In order to examine the influence of working environment, parent-teacher cooperation, and director's emotional leadership on the practice of childcare respecting rights for young children, hierarchical regression analysis was conducted.&#x0D; Results The working environment, parent-teacher cooperation, and the director's emotional leadership were found to have an effect on the practice of childcare respecting rights for young children. The parent-teacher cooperation showed the most significant influence, and the work environment, physical environment, and emotional leadership of the director were similar among the work environments.&#x0D; Conclusions Therefore, it is important to cooperate with parents and teachers to raise the level of the practice of childcare respecting rights for young children. In addition, it is important to recognize the positive working environment and the importance of emotional leadership of the director. The results of this study are significant in that they provide basic data for the practice of childcare respecting rights for young children.
Research on the Application and Influence of Digital Technology in International Chinese Language Education

Digital technology provides an important opportunity for the modernization of internationalChinese language education.The technical logic followed by the practice of "digital technology+ international Chinese language education" has spawned new ideas, new systems, new forms and new methods.This paper analyses the application status of digital technology in international Chinese language education from three aspects, including application software in the process of Chinese learning, digital technology in the process of intelligent education evaluation, intelligent teacher assistant supported by digital technology, as well as the construction of comprehensive learning platform and intelligent teaching system of international Chinese language education.Then, it explores the multiple impacts of digital technology on international Chinese language education.The first is to promote the change of international Chinese language education form and the dissemination of Chinese knowledge, the second is to promote the supply and innovation of digital resources of international Chinese language education, and the third is to improve the perceptual shaping of "technology embodiment" of teaching subjects.
Dynamic Path Planning for Mobile Robots Based on the Improved A-Star Algorithm

Based on the path planning and obstacle avoidance problems of mobile robots in dynamic environments, a planning algorithm combining global path planning and local path planning is proposed.Firstly, four different path planning algorithms for robots in static environments are compared.On the basis of the A_star algorithm, an improved A_Star algorithm is proposed.The search direction is reduced from eight to five to improve the search efficiency; a path smoothing optimization algorithm is designed to improve the smoothness of the path; and the adaptive function is optimized to speed up the convergence speed of the algorithm.Secondly, the path planning problem of robots in unknown dynamic environments is studied, and an optimization algorithm combining the improved A_Star algorithm with the rolling window algorithm is proposed.The algorithm can plan a global optimal path and obtain map information through the scrolling window of Dynamic Window Approach (DWA) to calculate a suitable obstacle avoidance strategy in real time for the obstacles that appear, so as to plan the optimal path after avoiding the obstacles.Finally, the simulation platform is used to verify whether the fusion algorithm has better path planning performance through comparative experiments in a randomly changing environment.
DMvLNet: deep multiview learning network for blindly accessing image quality

In image quality assessment (IQA), the quality of an image is essentially dependent on both semantic contents and image distortions. Conventional nonreference IQA (NR-IQA) methods either encode the semantic contents only or combine them in a straightforward manner without exploring their intrinsic correlation. A unified deep multiview learning network (DMvLNet) is proposed to combine both semantic contents and image distortions for NR-IQA. Using ResNet50 as the backbone, we design IQA-aware semantic features instead of directly using a recognition-specific CNN model for IQA, as in many existing methods. To this end, a high-level feature refining module built on deep layers is developed for generating refined high-level feature maps. Meanwhile, shallow layers are employed for low-level descriptions. Thus, both the refined high-level and low-level features are used for characterizing semantic contents and image distortions, respectively. Furthermore, the refined semantic features and distortion features are simultaneously integrated into DMvLNet by exploring their mutual correlation, leading to the projected multiview embedding passing through the subsequent transformer network for image quality prediction. Extensive experiments conducted on both our assembled large-scale dataset and seven public benchmark datasets demonstrate the superiority of DMvLNet over the state-of-the-art methods.
Neural Network Analysis of Evacuation Flows According to Video Surveillance Cameras

The paper is dedicated to establishing the relationship between the speed of evacuation participants and their density on the basis of neural network analysis of their movement using video surveillance systems. An analytical review of modern methods for studying the evacuation flows movement parameters war carried out. Despite the availability of modern software-simulating complexes, the movement parameters of the evacuation participants for these complexes are determined by establishing relationships between the flow density and the speed of the evacuation participants. These dependencies can be identified mainly by the results of field observations and processing of video recordings of these observations, which takes a lot of time and requires a lot of effort and indicates the need for automation and optimization of the video processing process. The tools for analyzing and classifying images, as well as detecting and classifying moving objects in a video stream deserve special attention, as they can be successfully used to study the problems of evacuation in case of fire. It was concluded that the tools of the OpenCV library are the most convenient in use. This library was applied for recognizing evacuation participants of various mobility groups. Apart from this the convolutional neural network was used for processing movement parameters. The neural network demonstrated best accuracy (81%) in the closest to the camera part of the evacuation route up to 5 m at a human flow density of up to 3 person per square meter.
Customer Service Assist System based on Natural Language Processing

This paper proposes a novel assist system for customer service representatives based on natural language processing (NLP). In the proposed system, an NLP model calculates the relationships between a question from a customer and all the questions in a given FAQ list. Based on the model’s calculation, the system will recommend several FAQs that are more similar to the customer’s question than the others in the FAQ list and then the representative responses, whether the recommended questions are actually similar to the customer’s question or not. Since these responses become the data for the NLP model’s next training, the NLP model’s accuracy can be incrementally enhanced by repetitive fine-tuning with the accumulated data. The experimental result shows that the proposed system can effectively help customer service representatives as well as incrementally improve via the automatically accumulated data.
Longitudinal Gradient and Historical Trends of Arsenic and Metals in a Tropical Reservoir

This study aimed to establish the longitudinal gradient and historical trends of arsenic and metals in a tropical reservoir. Water and sediment samples were collected along the Rio Bonito reservoir (RBR) (ES, Brazil) and a sediment core was collected in the lacustrine zone. The determination of As, Cd, Co, Cu, Fe, Mn, Ni, and Pb was performed using plasma spectrometric techniques. Higher concentrations of these elements were observed in surface water samples from the riverine zone and in bottom water samples from the transition zone. Regarding surface sediment samples, higher concentrations were observed in the transition and lacustrine zones, where the deposition process is more intense. The sediment core was dated 1940, a period before the construction of the reservoir dam, using 210Pb. Some elements presented increasing concentration over time. The enrichment factor and the pollution load and ecological risk indexes show that the RBR requires periodic monitoring from now on.
Philanthropic conference-based requirements engineering in time of pandemic and beyond

As software engineering (SE) practitioners, we can help society by using our communities of experts to address a software need of a socially conscious organization. Doing so can benefit society in the locale of a SE conference and provide access to international experts for local organizations. Furthermore, established SE researchers as well as practitioners and students have the opportunity for a unique learning experience. While the SE community has already realized the importance of addressing human values and promoting social good objectives in software development, we are unaware of previous attempts to leverage SE conferences for this activity. Conferences present an opportunity to enjoy the assembly of SE practitioners, researchers, and students for the purpose of a philanthropic endeavor. Over the past four years of running a “Requirements Engineering for Social Good” event called RE Cares, co-located with the International Conference on Requirements Engineering, we worked with the stakeholders local to the conference venue. We selected stakeholders who would not necessarily have ready access to requirements engineering, software design, and development expertise otherwise, to build software targeting “good causes.” In the last two years, this event was altered to adapt to the constraints induced by COVID-19, moving to a hybrid mode and changing many of its practices accordingly. This paper summarizes and generalizes our experiences, discussing our lessons learned in the context of the pandemic and beyond and providing a framework for conducting similar social contribution in any SE conferences in general.
Distributed Mobile Cooperative Control of Complex Power System Based on Wireless Sensor Network

Stable power supply is the guarantee for social and economic development and people's normal life.Under the social background of pursuing sustainable development, the realization of energy diversity, resource optimization deployment, power storage and user demand response has become the new development direction of the power system.In order to promote the effective connection between complex power systems and smart grids, it is necessary to use wireless sensor networks to implement distributed mobile coordinated control of the system.In this paper, the application of wireless sensor networks in complex power systems has been studied, and many issues such as distributed control, distribution routes, fault identification, information monitoring and user services in smart grids have been discussed.To ensure that the data transmission rate in the sensor network is maintained at a high level, the number of sensor nodes can be controlled between 50 and 55.Experimental data shows that the data transmission rate of the number of nodes in this interval is 95.37%.If the number of nodes is low, the problem of insufficient alternative paths is likely to occur.If the number of nodes is too high, the data transmission rate will also decrease due to location distance and power consumption.In order to ensure that the power system can realize the comprehensive management of power transmission and distribution under various complicated conditions, distributed control has stronger competitiveness than traditional centralized control.
Performance skills and artistic conception of Chinese Zither music "Mooring by Maple Bridge At Night"

The characteristic tunes of Kunqu Opera, Silk bamboo
The internet gang of violence: Trend of cyberbullying on the internet

In the era of the internet, which is increasingly fast and growing today, the delivery of information and feedback on that information is very fast. Every second the posts, both statuses, stories and news spread in cyberspace continue to grow. This is none other than because of the very large number of social media support and various content sharing models. Cyberbullying is someone using information and communication technology such as, internet, cellphone, computer, camera, audio/video recording to share SMS, pictures or videos with threats, hoaxes and terror. This activity makes the victim feel embarrassed, stressed or depressed because all their disgrace is published on social media, it is difficult to delete something that has already been posted. This crime continues to grow along with the rapid development of the use of information technology and the internet. This study aims to analyze the phenomenon of cyberbullying among teenagers in Indonesia.
Construction of accounting information distortion evaluation model based on artificial intelligence technology

In order to overcome the problems of low accuracy of the current accounting information distortion evaluation methods, this paper designs an accounting information distortion evaluation model based on artificial intelligence technology. This paper analyses the causes, types, specific characteristics and performance of accounting information distortion, focuses on the statistics of the characteristics of accounting information distortion, constructs the initial evaluation index system according to the statistical analysis results, and selects the principal component indicators by principal component analysis. Through the establishment of classification and regression tree pruning, combined with artificial intelligence technology to build accounting information distortion evaluation model, completes the accurate evaluation of accounting information distortion. The simulation results show that the evaluation error of the model is between 0%-1.1%, and the economic loss to the enterprise is about 15,100 yuan, which can reduce the evaluation error and the economic loss of the enterprise.
The Current Situation, Problems and Countermeasures of Online Teaching in the Post-Epidemic Era—Based on the Perspective of Educational Ecology

Since the outbreak of the Covid-19 pandemic, schools of all levels and types have carefully organized and carried out online teaching in accordance with the spirit of the Ministry of Education's instructions, and achieved remarkable results.This paper analyzes the current situation and problems of online teaching for teachers and students during the epidemic, and how to solve these problems in the post-epidemic era, explores new teaching models to make reasonable educational decisions, implements effective educational response measures, and improves teaching quality.
On Director Jia Zhangke's Style from the Perspective of Cinemetrics

Driven by the trend of big data technology and digital humanities, quantitative research methods combining metrology, statistics and theory of film study have emerged in the world.The emergence of cinemetrics not only breaks the problem of narrow research horizon, but also provides a more accurate, more systematic, and more objective film analysis model, which brings a new paradigm to the traditional film and television research.With the help of "ysjl", a film and television measurement and analysis tool developed by the team of film and television quantitative analysis of the School of Humanities of Tiangong University, the author visualized the data of recent film works directed by Jia Zhangke, and analyzed their style characteristics, rhythm changes and spatial feature.combined with relevant film theories.
Research on the Teaching Mode of Social Training for Agricultural Aid Talents in Vocational Colleges

In recent years, the state has actively encouraged the talent training to help poverty alleviation and rural revitalization, and has issued a series of supporting policies for this purpose, which has attracted wide attention from all walks of life.Among them, vocational colleges are the key units to carry out talent training.However, from the current situation of talent training in most vocational colleges, there are generally some problems such as poor training effect and low willingness of migrant workers to participate.Based on various difficulties in talent training, the author and the team organized many research activities, analyzed the problems in talent training deeply, and finally summarized five training modes of talents in vocational colleges according to the research results.During the period of exploring the talent training mode, the author summed up the outstanding problems from many practical cases and put forward the solutions, in order to help vocational colleges optimize the talent training mode and help rural revitalization.
Real Time Social Distance Monitoring with Alarm System

World Health Organization (WHO) recommends social distancing as one of the most effective measures to prevent the spread of COVID-19 infection. Ensuring strict adherence to the norms is challenging especially in public places and work environments. Hence, we propose a system using the existing CCTV cameras or an IP camera and develop a Computer Vision and YOLOv3-based Deep Neural Network (DNN) model for automatic people detection. The model works in both in both indoor and outdoor environments. The model is trained using Microsoft Common Objects (COCO dataset). The model developed can alternatively be deployed for other applications including detection of autonomous cars, anomaly detection, crowd analysis etc.
Optical Character Reader

We present an instance of the available OCR tools and teach the Tesseract tool in English which is transcribed in Latin male or female. OCR accessibility for the blind is a splendid development, where customers can experiment with books, magazines, plugins or other programs. It can also be mixed with sound utilities or sound synthesizers, which read textual content optically recognized by the software. OCR additionally has its own boundaries. This can no longer understand complicated text, mathematical variables and handwriting scripts. The upscale OCR utility is used inside the vicinity needed, but it comes with additional computing fees. This paper targets to offer the OCR software that recognizes text characters, using the device getting to know model.
Simulation and Optimization Model of Bicycle Time Trial Based on Genetic Algorithm

We have established a simulation optimization model of bicycle time trial based on genetic algorithm.By using the algorithm, we can not only get the best force of different sections, but also get personalized analysis of different players.These two sets of data can directly help us find the shortest time for a particular rider to finish a particular race.In this paper, we expanded the model biased towards the six-person team time trial, and successfully combined the genetic algorithm to find out the best time duration and the best order for everyone in the team to break the wind.First of all, we built a basic physical simulation model of bicycle individual time trial.The two resistances can be obtained by the basic laws of physics.Then we consider different terrain factors.Based on this, we also built the corresponding topographic map in combination with the actual road conditions.Finally, we consider the physical strength change of the players, and establish a dynamic physical strength change model combined with related papers.After building the basic physical model, we first need to consider the riding ability of a rider.In order to quantitatively describe how much power players should use in different places, we introduce the road cognition coefficient and terrain difficulty degree.Therefore, an optimal power table of a specific rider for a specific track is obtained.