Reviewerly/exHarmony huggingface dataset:
Optimization Path of Art Teaching Methods in Colleges Based on Multiuniverse Algorithm and IOT

The optimization path of art teaching methods in universities cannot balance the contradiction and conflict between cost and benefit, which affects the educational efficiency. This paper proposes an optimization path method for art teaching methods in universities based on a multiuniverse algorithm. According to the relationship between teachers, students, and teaching materials, build an efficient art teaching knowledge dissemination model, extract the characteristics of art teaching innovation path, and make decisions on costs and benefits. Establish the art teaching path selection model based on the multiuniverse algorithm, give full play to the leading role of the university organization system through path design, and meet the personalized teaching and learning needs of teachers and students by designing the cost and cost objective function under the optimal benefit of art teaching. Using dance as an example, the art teaching path optimization approach based on a multiuniverse algorithm has an education efficiency of 1.476, which is 0.593 and 0.607 greater than the methods based on association rule and clustering algorithms. As a result, this strategy can improve the quality of art instruction while also facilitating reform and innovation in the classroom.
Variational image motion estimation by preconditioned dual optimization

&lt;p style='text-indent:20px;'&gt;Estimating optical flows is one of the most interesting problems in computer vision, which estimates the essential information about pixel-wise displacements between two consecutive images. This work introduces an efficient dual optimization framework with accelerated preconditioners to the challenging nonsmooth optimization problem of total-variation regularized optical-flow estimation. In theory, the proposed dual optimization framework brings an elegant variational analysis to the given difficult optimization problem, while presenting an efficient algorithmic scheme without directly tackling the corresponding nonsmoothness in numeric. By introducing efficient preconditioners with a multi-scale implementation, the proposed preconditioned dual optimization approaches achieve competitive estimation results of image motion, compared to the state-of-the-art methods. Moreover, we show that the proposed preconditioners can guarantee convergence of the implemented numerical schemes with high efficiency.&lt;/p&gt;
The Effect of Teaching and Learning Method Using Mobile Augmented Reality(MAR) according to Scaffolding Types on Chemistry Academic Achievement, Learning Flow, and Learning Motivation

Objectives The purpose of this study was to find out the effect of the mobile augmented reality-based teaching and learning method according to scaffolding types on chemistry academic achievement, learning flow, and learning motivation.&#x0D; Methods 142 students in the second grade of coeducational general high school were randomly assigned to three groups: the group of supportive scaffolding POGIL using mobile augmented reality (SS), the group of reflective scaffolding POGIL using mobile augmented reality (RS), and the group of traditional lecture (TL). Students studied concepts related to chemical bonding and molecular structures throughout the 5 lessons. Two-way ANCOVA of 3 (teaching-learning method) × 2 (prior academic achievement level) factors was performed by conducting chemistry academic achievement, learning flow, and learning motivation tests as pre- and post-tests.&#x0D; Results The RS group had significant improvement in chemistry achievement than the SS group regardless of the prior academic achievement level. In learning flow, the RS group and the SS group had significant improvement, and in learning motivation, only the RS group had significant improvement. As the results of analyzing by level of prior academic achievement, in the high-level group, the RS group had significant improvement in chemistry academic achievement, and there was no difference between the groups in learning flow and learning motivation. In the low-level group, both the SS group and the RS group had significant improvement in chemistry academic achievement, learning flow, and learning motivation.&#x0D; Conclusions Based on these results, the educational implications of the teaching and learning methods using augmented reality in order to be applied to the school were discussed.
Deep learning-based image deconstruction method with maintained saliency

Visual properties that primarily attract bottom-up attention are collectively referred to as saliency. In this study, to understand the neural activity involved in top-down and bottom-up visual attention, we aim to prepare pairs of natural and unnatural images with common saliency. For this purpose, we propose an image transformation method based on deep neural networks that can generate new images while maintaining the consistent feature map, in particular the saliency map. This is an ill-posed problem because the transformation from an image to its corresponding feature map could be many-to-one, and in our particular case, the various images would share the same saliency map. Although stochastic image generation has the potential to solve such ill-posed problems, the most existing methods focus on adding diversity of the overall style/touch information while maintaining the naturalness of the generated images. To this end, we developed a new image transformation method that incorporates higher-dimensional latent variables so that the generated images appear unnatural with less context information but retain a high diversity of local image structures. Although such high-dimensional latent spaces are prone to collapse, we proposed a new regularization based on Kullback–Leibler divergence to avoid collapsing the latent distribution. We also conducted human experiments using our newly prepared natural and corresponding unnatural images to measure overt eye movements and functional magnetic resonance imaging, and found that those images induced distinctive neural activities related to top-down and bottom-up attentional processing.
On the Professional Development Environment of Young Teachers in the Series of Quality Education in Colleges and Universities

With the changes of the times, China has made remarkable achievements in economic, political, social, cultural, and education can improve the comprehensive quality of the people, enhancing the country’s comprehensive national strength and international competitiveness effect. China has importance to education, and in recent years has increased its investment in education. Higher education undertakes important functions such as cultivating talents, scientific research, and serving the society. It is an indispensable part of the education system, and these are closely related to the teaching staff of colleges. The development of quality education is an inevitable choice to promote it, an important way to implement it with morality, and promoting it of national governance capabilities. At present, there are four major problems in quality education in colleges: the marginalization of status, fuzzy goals, segmentation of content, and isolation of development paths. It is important to study and explore new ideas and strategies for promoting quality education. Nowadays, colleges are no longer the “ivory towers” of the past, and the construction of college teachers has also undergone many changes compared with the past. The topics and discussions about college teachers are increasing day by day, and the problems and confusion exposed to them have also aroused the attention of the society. Young teachers are the fresh blood of college teachers and the future of our country. It is particularly meaningful to study and understand the current professional development of these groups. The research object is the young teachers in colleges. Through the literature research method, it analyzes the status of the professional quality education in colleges, understands the problems existing in their professional development, analyzes the reasons, and puts forward corresponding countermeasures. It helps for the career development of the whole group of young teachers in colleges in order to improve the current situation, seek better development for young teachers in colleges, and also provide a reference for promoting quality education in colleges.
Region-adaptive, Error-controlled Scientific Data Compression using Multilevel Decomposition

The increase of computer processing speed is significantly outpacing improvements in network and storage bandwidth, leading to the big data challenge in modern science, where scientific applications can quickly generate much more data than that can be transferred and stored. As a result, big scientific data must be reduced by a few orders of magnitude while the accuracy of the reduced data needs to be guaranteed for further scientific explorations. Moreover, scientists are often interested in some specific spatial/temporal regions in their data, where higher accuracy is required. The locations of the regions requiring high accuracy can sometimes be prescribed based on application knowledge, while other times they must be estimated based on general spatial/temporal variation. In this paper, we develop a novel multilevel approach which allows users to impose region-wise compression error bounds. Our method utilizes the byproduct of a multilevel compressor to detect regions where details are rich and we provide the theoretical underpinning for region-wise error control. With spatially varying precision preservation, our approach can achieve significantly higher compression ratios than single-error bounded compression approaches and control errors in the regions of interest.
Improve the automatic transliteration from Nôm scripts into Vietnamese National scripts by integrating Sino – Vietnamese knowledge into Statistical Machine Translation

Nôm scripts (chữ Nôm) are Vietnamese ancient scripts that were popularly used in Vietnam from the 10th century to the early 20th century. Nowadays, some automatic transliteration from Nôm scripts (NS) into Vietnamese National scripts (chữ Quốc ngữ - QN) systems were developed to help modern Vietnamese people acquire many valuable lessons and knowledge from previous generations through preserving the Sino-Nom heritage. However, these systems have still not performed well in many domains, except for Literature. Our research continues to employ Statistical Machine Translation (SMT) but expands the dataset up to 10 domains. Furthermore, we also focus on analyzing the impact of Chinese scripts with Sino-Vietnamese readings on Nôm script – National script and then integrating this knowledge into our transliteration model. Our experimental results show that our approach helps the model reach 94.04 BLEU score, dramatically increasing by 8.63 BLEU score in the genealogical domain and 0.31 BLEU score in the general model.
A Technical Review on UAV for Object Detection

According to a Forbes report published in 2018, the US drone industry has witnessed a dramatic commercial growth from merely $40 million in 2012 to a billion in 2017, as documented in the study conducted by McKinsey and Company in December 2017. McKinsey estimates that by 2026 the commercial drones will annually impact $31 billion to $46 billion on the country’s gross domestic product. Drones have been widely adopted for data capturing that can be used in defense, agriculture, emergency response, disaster management, conservation of endangered species, healthcare, etc. For most of the applications of UAVs, Real-time object detection is exceptionally crucial. In the last few years, considering the growth of the drone industry and the interest of around 300 companies making substantial investments of time and resources in drones, many technologies have emerged for advancements in the field of UAV, focusing on object detection and recognition for UAV. This paper summarizes a number of object detection techniques proposed to date by researchers. It reports the characteristics and requirements of UAVs from an object detection viewpoint. The objective of our research is to understand different architectures that are capable of detecting objects from aerial images. The main goal of this survey is to create an insight into an architecture that is accurate, fast, robust, and utilizes low computation power.
Networked Systems: The Role of Coordination of Formal and Nonformal Agents for Improving Education — Findings From a Mixed-Method Multiperspectives Longitudinal Study (2013–2017/Phase 1 With Nine Networks and Three Measurement Points)

This paper reports on final findings from phase 1 of a national program of 22 regional networks.This paper presents the overall period for nine networks with three measurements in time.The analysis in this paper draws on quantitative as well as qualitative data.The analysis of the qualitative data show that exchanges of knowledge between different agents and institutions have intensified over time.The various agents and institutions of formal and nonformal education are more aware of each other and better informed about the functions and services of others.Low fluctuation and increased intensity of collaboration is highly correlated with good network coordination.Differences of network features over time, can be interpretated by using mixed-method approaches.
Software Services Engineering Manifesto – Revisited

Industry observers with keen eyes have found that services computing and software engineering are becoming more and more intertwined. That is, services computing professionals and software engineers have been finding ways to intersect theories and practices that used to be more of a concern for the other camp. This emerging interdisciplinary field of study is hereby termed Software Services Engineering (SSE) [1].
The reduction of polynomial degrees using moving average filter and derivative approach to decrease the computational load in polynomial classifiers

Carbon monoxide is a type of pollutant that is harmful to human health and the environment. On the other hand, carbon monoxide also has benefits for industrial matter. Since the benefits and disadvantages of carbon monoxide, the measurement of carbon monoxide concentration is required. The measurement of carbon monoxide level is not easy moreover with low-cost sensors. The usage of 4 sensors namely TGS2611, TGS2612, TGS2610 and TGS2602 has been used along with feature extractor. The polynomial classifier is required to interpret the feature vector into the amount of substance concentration. The common classifier methods suffer fatal limitations. The polynomial classifiers method offers lower complexity in solution and lower computational effort. Since the involvement of a huge number of data points in the modelling process leads to high degree in the polynomial model. The occurrence of Runge's phenomenon is highly possible in this condition. This phenomenon affects the accuracy level of the generated model. The degree reduction algorithm is required to prevent the occurrence of Runge’s phenomenon. The combination of MAF (Mean Average Filter) and derivative approach as degree reductor algorithm has succeeded in reducing the polynomial model degree. The greater the number degree in the model means the greater the computational load. The model degree reductor algorithm has been succeeded to reduce computational load by 96.6%.
A Worker Selection Scheme for Vehicle Crowdsourcing Blockchain

With the improvement of the computing power and storage capacity of vehicular equipment, as well as the growing privacy concerns over sharing sensitive raw data, federated learning can be a promising solution for realizing distributed vehicular crowdsourcing services. The global learning model can be used as crowdsourcing tasks and assigned to vehicles that utilize their local training models. In such a distributed scenario, it is essential to ensure the completion of high-quality training tasks and the security of sensitive data. We propose a vehicular crowdsourcing blockchain to achieve secure reputation management of vehicles in a distributed manner, providing a safe and trusted solution for vehicle crowdsourcing services. We design a worker selection scheme, which combines the reputation of workers with the amount of data as an indicator of worker reliability. We further propose an effective incentive machine to enable more workers with high reputations to participate in crowdsourcing tasks and to contribute higher quality local data. Simulation results show that the worker selection scheme improves the matching rate by 10% and significantly improves the total utility. The proposed scheme is deployed on the IBM Hyperledger Fabric platform to observe its real-world running time and overall performance.
Digital Sovereignty and Software Engineering for the IoT-laden, AI/ML-driven Era

Today’s software engineering already needs to deal with challenges originating from the multidisciplinarity that is required to realize IoT products. Many variants consist of sensor/actuator-powered systems that nowadays use AI/ML systems to better cope with the unstructuredness of their intended operational design domain (ODD). At the same time, though, such systems need to be monitored, diagnosed, maintained, and evolved using cloud-powered dashboards and data analytics pipelines that process, aggregate, and analyze countless data points–preferably in real-time. This position paper discusses selected aspects related to Digital Sovereignty from a software engineering’s perspective for the IoT-laden, AI/ML-driven era. While we can undeniably expect more and more benefits from such solutions, light shall be shed in particular on challenges and responsibilities at design-and operation-time that, at minimum, prepare for and enable or, even better, preserve and extend digital sovereignty from a software engineering’s perspective.
Comparison of Different Machine Learning Models in Breast Cancer

Breast Cancer is mainly found in women and is the main cause of increased mortality among women. Breast cancer diagnosis is time-consuming, and due to the low availability of the system, it is necessary to develop a system that can automatically diagnose breast cancer at an early stage. Various machine learning and Deep Learning Algorithms have been used to classify benign and malignant tumors. This paper focuses on the implementation of various models, such as Logistic regression, random forest and naive Bayes. Each algorithm has measured and compared the accuracy and obtained accuracy. This paper aims to compare the advantages and disadvantages of different regression models in breast cancer prediction. The method proposed in this paper can promote the integration of machine learning and medicine, and improve clinical diagnostic accuracy.
Design of Association Application System of Face Recognition and Test-Tube Barcode Based on CNN

In order to improve the standardization and accuracy of business process management of laboratory department in hospitals, combined with convolutional neural networks (CNN) and face recognition technology, an association application system of laboratory face recognition and test-tube barcode is designed by inputting patient’s face and blood test-tube barcode into the system for storage. When the patient logs into the system again, the system uses the patient’s face to automatically search for a matching test-tube barcode to obtain the test results. The simulation results show that the system can accurately recognize the face and match the corresponding test-tube barcode, and the accuracy and ROC of face recognition are 0.85 and 0.94, respectively. In addition, when the patient’s face is within 5 m from the system camera, the accuracy of face recognition can reach 100%. It can be seen that the system designed in this paper shows good performance.
Zero-trust architecture is creating a passwordless society

Entering traditional passwords on a website is not uncommon and most people have at least one. But as our digital reliance increases and cyber criminals become more tech savvy, passwords may not be suitable as our primary form of authentication in the future.
Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique

Since radiology reports needed for clinical practice and research are written and stored in free-text narrations, extraction of relative information for further analysis is difficult. In these circumstances, natural language processing (NLP) techniques can facilitate automatic information extraction and transformation of free-text formats to structured data. In recent years, deep learning (DL)-based models have been adapted for NLP experiments with promising results. Despite the significant potential of DL models based on artificial neural networks (ANN) and convolutional neural networks (CNN), the models face some limitations to implement in clinical practice. Transformers, another new DL architecture, have been increasingly applied to improve the process. Therefore, in this study, we propose a transformer-based fine-grained named entity recognition (NER) architecture for clinical information extraction. We collected 88 abdominopelvic sonography reports in free-text formats and annotated them based on our developed information schema. The text-to-text transfer transformer model (T5) and Scifive, a pre-trained domain-specific adaptation of the T5 model, were applied for fine-tuning to extract entities and relations and transform the input into a structured format. Our transformer-based model in this study outperformed previously applied approaches such as ANN and CNN models based on ROUGE-1, ROUGE-2, ROUGE-L, and BLEU scores of 0.816, 0.668, 0.528, and 0.743, respectively, while providing an interpretable structured report.
Semantic Image Segmentation using CNN (Convolutional Neural Network) based Technique

Segmenting images makes computer vision applications more manageable. By breaking an image into smaller groups called “image segments,” an image segmentation approach simplifies the complexity of an image for later processing or analysis. Individual pixels are labeled as part of the process known as segmentation. Many computational research, modeling, and forecasts require a sufficient microstructure sample. An alternate method is to extrapolate a 2D image into a virtual 3D sample, which saves time and money compared to collecting 3D microstructural images. A CNN Architecture-based U-NET technique was used to find extrapolation-based reconstructions for alloys, porous media, and sandstones. Using a deep convolutional neural network, VGG16, the objective function in this optimization is built using microstructural properties. We use a permutation operator to create multiclass feature maps from these stacks. Sandstone datasets, which contain a number of unique materials, are used to demonstrate the actual application of our technique. Pre-annotated data was used to guide our analysis. Image reconstruction is a commonly used approach in various fields for deciphering the many objects in a picture. Using U-NET, we first segment the photographs and then reassemble the image slices to be shown. The overall IoU and accuracy rates for this suggested design were both high, at 85% and 96%, respectively.