Reviewerly/exHarmony huggingface dataset:
Mandala in Operation of Web 3.0

Purpose: There is no other means of expressing emotions in the world that can be compared to mandalas, rather its use in our festivals, rituals, behavior, rituals or many activities from birth to death carries not only our emotions but also resolutions and visions, that is why mandalas are a mandatory use in every operation in modern times of web 3.0. If it can be done, its possibility of easy operability will increase. Design/Methodology/Approach: Secondary data as Case Projects followed by Primary data through consultation regarding use of Mandala and its outcome in response to Blockchain will be carried out systematically for assuring further application on the basis of features comparisons. Findings/Result: Mandala is an integral part of Buddhism, culture, and philosophy. In the development of civilization, the influence of mandalas can be seen on various topics from the origin of the universe to daily life. From ancient times to the present, it seems that the art and subject of the mandala have been equally reflected in the culture, customs, and manners, political and social fields. With the development of technology, the practice of using ancient knowledge, skills and methods in an age-old manner has started to develop. Especially in the fields of literature and language, if such a practice had been going on for a while and it is an opportunity of applying the same in the field of technology and development also as an advanced data management tool in the field of applied IT as wider utility without boundary under decentralized data mechanism for rapid development of supplying Society 5.0 demands of web 3.0. In this regard, Japan is benefiting a lot by using the structural method of mandala in educational activities, while Nepal and India as Akhanda Bharat, which has made a significant contribution to the origin and development of mandala, should also be able to benefit by using it in the educational and professional fields. Originality/Value: The study is have reinvented mandala as a new and innovative technology in the field of information technology. Paper Type: Research paper
Fractional order total variational infrared image denoising with fused flower pollination particle swarm optimization

The traditional fractional order total variational model has better results in denoising and maintaining texture details in infrared images. However, it is difficult to determine the order of fractional order differentiation in image processing so that the model has the best denoising effect. To solve this problem, a fractional order total variational infrared image denoising model incorporating a flower pollination particle swarm optimization (PSO) algorithm is proposed in this paper. The model combines the search advantages of the flower pollination optimization algorithm and the PSO algorithm. The maximization multiobjective equation is designed as the fitness function of the optimization algorithm. The optimal order of the fractional order total variational model is found adaptively according to different features in different regions of the infrared image. The experimental results show that the improved model not only achieves the adaptivity of the adaption of the fractional order of total variational model order but also effectively removes the noise and retains the texture structure of infrared images to the maximum extent.
Extracting Branch Factors of Scenarios from a Gaming Simulation Using Log-Cluster Analysis

This study proposes a method for analyzing gaming simulation results. The gaming simulation we focus on intends to be played by both human and computer agent players. To extract the knowledge of what and how they have played, we must determine what type of decision-making process leads to specific scenarios. Such simulation results, however, tend to have so many branch factors of scenarios that it is hard to understand by manual operations. To deal with the issues, we have developed a method for obtaining the branch factors of scenarios from gaming simulation results. We have demonstrated the effectiveness of the proposed method by identifying the branching factors of scenarios as follows. First, software agents were asked to play a gaming simulation for career education. Next, logs acquired through gaming were classified into multiple scenarios using machine learning techniques. Finally, decision-making factors separating the scenarios were identified using a decision tree.
Research on Image Inpainting Algorithms Based on Attention Guidance

In recent years, the use of deep learning in image inpainting has yielded positive results. However, existing image inpainting algorithms do not pay sufficient attention to the structural and textural features of the image when inpainting, which leads to issues in the inpainting results such as blurring and distortion. To solve the above problems, a channel attention mechanism was introduced to emphasize the importance of structure and texture after extraction by the convolutional network. A bidirectional gated feature fusion module was employed to exchange and fuse the structural and textural features, ensuring the overall consistency of the image. In addition, the features of the image were better captured by selecting a deformable convolution that can adapt the receptive field to replace the ordinary convolution in the contextual feature aggregation module. This resulted in highly vivid and realistic restoration results with more reasonable details. The experiments showed that, compared with the current mainstream network, the repair results of this algorithm were more realistic, and the superiority of this algorithm was proved by qualitative and quantitative experiments.
Trajectory tracking method based on Bayesian classifier for pulse array image sensor

A pulse array image sensor (PAIS) is a bionic image sensor that converts light intensity into a pulse sequence to reduce the data volume. The traditional tracking method is not suitable for the sparse data form because of the absence of grayscale information. A trajectory tracking method based on a Bayesian classifier is proposed to maximize the property of the pulse data. First, candidate points with the smallest interval distances are selected in the area of interest. Then the total pulse numbers in a specified period at different positions are gathered to compose the positive and negative feature vectors, which are used to train a naive Bayesian classifier. The classifier can exactly obtain positions from the candidate points, and features in the new tracking positions train and update the classifier. The two-step filtering target point tracking algorithm using the interval distance and Bayesian classifier requires only the raw pulse data without processing, which can maximize the advantages of the special data format and improve computing efficiency. In this way, the position information can be directly obtained from pulse data, and the problem of wasting computing resources on reconstructing grayscale images and treating them in the traditional way is avoided. Experiments were performed on both real filmed data and the public event camera datasets. Our method can obtain trajectories with a high accuracy and long tracking time. The tracking errors are in single digits. In the comparison experiments, although our method has a smaller data volume than other models that obtain both frame and event data, the results still show that it has a comparable performance to the state-of-the-art methods.
Real-time intelligent system for wind turbine monitoring using fuzzy system

In these last ten years, the use of wind energy has become a strategic alternative in several countries; the increase in reliability of these systems has become a crucial issue. This is explained by the economic, human or environmental) losses that could generate even if only for a moment, their malfunctioning or their shutdown. In this context, many research methods have been developed to monitor these machines during their operation time. But the problem with these methods was that they did not take into account the criticality, the type of fault, or the degradation degree of the components to trigger an alert, so that the real-time monitoring and the decisions taken were distorted. In this work, four types of defects encountered in wind turbines are studied. Three models of the fuzzy logic system are compared on the severity of the faults in order to know which one is the most efficient. However, an estimation of the fault parameters by the Fast-Estimation of Signal Parameters via Rotational Invariant Techniques (Fast-ESPRIT) algorithm followed by an identification of each fault type by the Classification Algorithm of Fault Harmonics (CAFH) algorithm are first performed. The obtained results show the possibility of monitoring the severity of faults in electric induction machines using the Tsukamoto model in real time. The simulations are performed by using MATLAB software and the obtained results demonstrate the feasibility of such a system.
Effect of School-Based Feeding Program Implementation of Elementary Schools on Learners’ Academic Performance

A school feeding program is a thoroughly thought-out strategy for feeding students to support academic achievement and extracurricular activities.This study aimed to determine the level of school-based feeding program management and learners' performance at Esperanza District III.The researcher employed a descriptive correlational research design to attain its objectives.A total one hundred (100) teachers and 100 learner beneficiaries was included as respondents.It utilized the descriptive and inferential statistics like mean, frequency and percentage and Pearson r moment correlation.The findings revealed that behaviour of beneficiaries was described as always observed.School feeding program was described as frequently observed.The strategy implementation was always observed in every school and food safety was implemented to keep the learners safe.Majority of the learners obtained an outstanding performance ranging from grades of 90-100.The academic performance of SBFP beneficiaries is dependent to the level of the school-based feeding program management.The effective strategy creates good communication and information dissemination to parents as well as interactive way to establish linkages in gaining support of the program.The food safety is always observed by the school implementer of the feeding program.The implementation of feeding program is effective especially to the poor and deserving learners.When feeding program is manage properly it give san impact to the learners by giving staple and enough food to eat.
Examination of Distance Learning Assignments in Mechanical Design Drafting and 3D-CAD by using Onshape

The selection criteria and the selection results of a distance learning assignments on “mechanical design and drafting” considering the “Corona disaster” and assignments by using Onshape, such as the Shako vise and manual winch are studied, were examined through the actual confirmation work by the students in the author’ s laboratory. It was found that Onshape was effective for distance lectures on mechanical design and drafting, and for providing an environment for conducting collaborative assignments. However, it also became clear that there are technical issues to consider when using Onshape to perform the drafting of a classic manual winch. The actual lecture management was conducted in collaboration with the university’s technical staff. The effectiveness of the collaborative lecture by the teaching staff and technical staff is described.
Thinking About and Conceptualizing Management by Oneself: Implementation and Consideration of SBL-type Lecture

This study examined Self-Objective Based Learning (SBL) -type lecture in the field of business administration. First, practical SBL was introduced in business administration course in collaboration with company and then questionnaire survey was administered to students after the course. The free-response statements in the questionnaire were analyzed in conjunction with the students′ grade data. The results of the survey suggested that students with lower grades set themes without much thought and conducted research on themes by simply searching the Internet, whereas students with higher grades set themes after careful consideration and referred to a variety of sources including academic papers and securities reports in their research methods.
Active Learning Materials Development with Applying to VR Technology

Authors have developed learning materials for active learning that attract students’ interest. Students could directly touch and handle these learning materials and understand its mechanism. However, the cost and time were required to produce these materials, it was difficult to disseminate them widely. Therefore, we built a virtual training facility where students can view and experience the learning materials in a virtual space using VR technology and a VR Head Mounted Display. The virtual training factory, built on a platform with real images captured by a 360-degree camera, is suitable for safety education because students can view the images with a sense of reality by use of mobile phone. In future education, it is necessary to construct a learning environment in which students can learn freely without discrimination.
Investigation and Analysis of the Acquisition of Scientific Concepts by First-Year Students at an Institute of Technology

In this paper, we analyzed the survey and results on basic knowledge and elementary concepts of chemistry in an institute of technology and clarified the actual condition of students’ misconception. The difference of the mastery situation of the basic knowledge may prevent the right mastery of the basic knowledge by the accumulation of scientifically wrong recognition. Such scientifically wrong recognition is called “Naive Concept” , and it may become a hindrance in learning the advanced special subject. Therefore, we investigated the status of possession of simple concepts and acquisition of basic knowledge on chemistry by first and third-year students in an institute of technology. As a result, the following conclusions were obtained:(i) In both the first and third-year students, many incorrect answers suggesting the possession of naive concepts are found in the low percentage of correct answers to the survey questions. (ii) The percentage of correct answers in the third-year students is almost the same as in the first-year students. Furthermore, the tendency of errors in the free response columns is also similar. This indicates that the naive concepts of third-year students have not been corrected despite having taken chemistry in the first and second-year students.
Analysis of Human Resource Development Issues in the Machining Industry

In recent years, the difficulty of systematic human resource development in the machining industry has become an important management issue for small and medium-sized machining companies. To address these issues, this paper interviewed managers and factory managers of 11 small and medium-sized machining companies, as well as teachers at technical colleges. Furthermore, a qualitative analysis was conducted to identify issues in the industry that are closer to the actual situation, and to compare issues between companies and school education. As a result, it was suggested that there were three major problems, lack of educational resources, lack of motivation, mismatch with existing educational methods.
Challenges of COVID‐19 Case Investigation and Contact Tracing in School Settings: An Initial Investigation

Abstract Background Case investigation and contact tracing (CI/CT) are important public health tools to interrupt COVID‐19 transmission. Our study aims to understand how parents and school staff perceive COVID‐19 CI/CT. Methods Using a mixed methods approach, we distributed a community survey and conducted 15 focus group discussions (FGDs) in English and Spanish between December 2020 and March 2021 with 20 parents and 22 staff from schools in San Diego County ZIP Codes with COVID‐19 rates in the top quintile as of August 2020. Results One in 4 survey respondents reported that they would be reluctant to participate in CI/CT. FGDs revealed themes of mistrust in government authorities, overburdened institutions, unfamiliarity with CI/CT, and uncertainty about its reliability. School community members emphasized that parents trust schools to be involved in CI/CT efforts, but schools are overwhelmed with this added responsibility. Conclusions Investing in schools as community hubs is necessary so they can become important partners in prevention and mitigation in public health.
FAIR Island: real-world examples of place-based open science

The relationship between people, place, and data presents challenges and opportunities for science and society. While there has been general enthusiasm for and work toward Findable, Accessible, Interoperable, and Reusable (FAIR) data for open science, only more recently have these data-centric principles been extended into dimensions important to people and place-notably, the CARE Principles for Indigenous Data Governance, which affect collective benefit, authority to control, responsibility, and ethics. The FAIR Island project seeks to translate these ideals into practice, leveraging the institutional infrastructure provided by scientific field stations. Starting with field stations in French Polynesia as key use cases that are exceptionally well connected to international research networks, FAIR Island builds interoperability between different components of critical research infrastructure, helping connect these to societal benefit areas. The goal is not only to increase reuse of scientific data and the awareness of work happening at the field stations but more generally to accelerate place-based research for sustainable development. FAIR Island works reflexively, aiming to scale horizontally through networks of field stations and to serve as a model for other sites of intensive long-term scientific study.
Barriers to COVID‐19 contact tracing: View from frontline healthcare students in Vietnam

In the context of the complicated continuous waves of the COVID-19 pandemic in Vietnam, contact tracing, along with social distancing and lock-down, proved its crucial role in the suppression of epidemic spreading and management. With the high demand for responsiveness, healthcare students were constantly involved in the process, and challenges have emerged along the way. This study aims to identify the barriers faced by healthcare students while performing the contact tracing task at the frontline of the pandemic. A qualitative study was conducted in July 2021 in a health center in Binh Duong province, Vietnam. A total of 20 healthcare students were invited through random recruitment of participants and interviewed until the principle of saturation was reached. Three main barriers were identified including ineffective work management of local managers, lack of capability of human resources and facilities, and uncooperative attitude of local residents. Given the existing barriers, cooperative, innovative strategies, shared data systems, and timely public awareness campaigns, especially among primary health centers of the healthcare system, are imperative to reduce the workload and optimize the efficacy of healthcare students' support.
Deep learning machine based ship parametric rolling simulation and recognition algorithms

In this work, we propose a “grey box” algorithm consisting of a deep learning network and parametric roll equation to provide the parametric roll simulations. A basic unsupervised method——principal component analysis is introduced into this algorithm to improve efficiency and accuracy. Besides, it has been proved that different wave time histories generated by the same wave spectrum will induce very different parametric rolling behaviors. Thus, we also proposed an algorithm that combines the deep learning method with spectral analysis to recognize the wave time histories that induce large amplitude rolling. A convolution layer and a pooling layer are added to the recognition network to reduce the input dimension. Besides, according to our study of training methods, we have found that extreme learning machine performs much better than back propagation network. Using the principal component analysis technique and the “grey box” based extreme learning machine, we can obtain very accurate simulations of the parametric roll of the C11 container ship in a short time. Besides, by using the spectral analysis and the extreme learning machine with convolution and pooling layer, we can accurately recognize the time histories induce large-amplitude parametric roll of the C11 container ship.
Generative adversarial networks method of polarization 3D reconstruction based on meta-transfer learning

This paper makes a first attempt to combines meta-transfer learning and generative adversarial networks (GAN) technology to solve the Shape from Polarization (SfP) problem. To solve this physics-based ill-posed problem, some researchers choose to blend these physical models as priors into a neural network architecture however cannot meet the requirements of cross-domain and few-shot polarization data. This proposed approach put forward two innovative points. First, we design the meta-transfer method so adapt GAN for few-shot learning tasks. Second, we introduce physical priors between monocular polarization sequence and 3D normal vector into the generative loss term. We report to exceed the previous state-of-the-art on deepsfp dataset, showing the potential of meta-transfer learning in few-shot generative tasks.
Fold3D: Rethinking and Parallelizing Computational and Communicational Tasks in the Training of Large DNN Models

Training a large DNN (e.g., GPT3) efficiently on commodity clouds is challenging even with the latest 3D parallel training systems (e.g., Megatron v3.0). In particular, along the pipeline parallelism dimension, computational tasks that produce a whole DNN's gradients with multiple input batches should be concurrently activated; along the data parallelism dimension, a set of heavy-weight communications (for aggregating the accumulated outputs of computational tasks) is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inevitably serialized</i> after the pipelined tasks, undermining the training performance (e.g., in Megatron, data parallelism caused all GPUs idle for over 44% of the training time) over commodity cloud networks. To deserialize these communicational and computational tasks, we propose the AIAO scheduling (for 3D parallelism) which slices a DNN into multiple segments, so that the computational tasks processing the same DNN segment can be scheduled together, and the communicational tasks that synchronize this segment can be launched and overlapped (deserialized) with other segments’ computational tasks. We realized this idea in our <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fold3D</small> training system. Extensive evaluation shows <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fold3D</small> eliminated most of the all-GPU 44% idle time in Megatron (caused by data parallelism), leading to 25.2%–42.1% training throughput improvement compared to four notable baselines over various settings; <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fold3D</small> 's high performance scaled to many GPUs.