Reviewerly/exHarmony huggingface dataset:
Effect of Gamification on Learner s Class Participation Motivation and PUBLIC VALUE

Purpose: The researcher tried to give fun and interest to class activities for learners who are familiar with the game environment. And I tried to actively utilize Gamification, which can help achieve educationally meaningful goals, in class education. A lot of research is needed so that learners can learn the learning elements that feel boring and difficult by applying the gamification element to education more fun and easily by themselves. So, The researcher applied edutech tools related to gamification to the beauty curriculum, and studied the effect of gamification on class participation motivation. Method: During the beauty and health class in the first semester of 2021, The researcher conducted an interactive class using a quiz-type gamification edutech tool such as Kahoot in subjects like wedding makeup, beauty hygiene laws, total coordination, and beauty English etc. In the 15th week of the final exam period, The researcher used a Google questionnaire to investigate the students responses to class activities during the first semester. In the first semester of 2021, The researcher conducted a survey of 100 people in the 1st&#8211;4th grades of the Department of Beauty and Health as research subjects. Results: More than 90% of the students who participated in the survey were women, and 88.5% of students in their 20s were surveyed. As a result of the survey, it was found that the students concentration aspect, learning experience, learning goal achievement, and lecture satisfaction were somewhat higher in the game class activities using the gamification tool. Most of the students responded positively to using gamification tools in class. Conclusion: ‘There were also opinions that the accessibility of the gamification app was somewhat difficult and difficult for students who experienced it for the first time. During subject orientation, and when students experience difficulties from time to time, prior guidance and steady guidance on how to approach the gamification app should be combined. In a future study, The researcher would like to study more deeply in which parts of the gamification tool the students are most interested in.
ICARE Model Learning Video For Economic Mathematics Subject

Learning videos are teaching materials that will help individual students understand the subject matter using interactive tools.It is quite important to use learning in the electronic-based learning paradigm to support students in the learning process.ICARE-based learning videos are able to evoke student behavior in the learning process, improve understanding skills, and independent learning abilities.The whole research attempts to develop an economic mathematics video-based ICARE learning that is valid, effective, and practical.This study is a method of research and development specific to the ADDIE model, involving analysis, design, development, implementation, and evaluation.Participants of this study include media specialists, content experts, and academics of economics.The methods used to determine the eligibility of the video include a feasibility assessment sheet by a content expert, media specialists, and a student answer survey for the use of the video.The results showed that the economic mathematics learning video developed based on ICARE was practical with an average score of 3.18 content experts, 3.20 media experts, and 3.14 academic lecturers, so the validity of the e-module developed was in the "Good" and valid range to use.The results of the student learning assessment have been achieved with a proportion of 85.71 percent in the category of strong performance criteria so that the video can be classified as effective and practical for use in learning.In this study, only a few videos were developed from some of the existing material.It is hoped that for further research to make videos with all materials and with models that are adapted to the material for which the video will be made.The video in this study uses an ICARE-based KKNI curriculum which is easy to understand for independent learning and is recommended for use by educators and students in mathematics economics courses.
Problems of Standard Model, Review

It is assumed that standard model is the most successful theory of particle physics but it is not perfect. In this paper, I am interested to flash the published results of various proposals of theoretical physicists in various modes of Standard Model and beyond. There are no specifications theory to declare new model to till date, although some ideas that would modify the standard model in many ways helps to understand the existing results.
A Practical Exploration of Blended Teaching in SPOC-Based “Circuit Analysis” Course

In view of the problems of the traditional teaching mode, the SPOC (small private online course)-based hybrid teaching mode and the teaching practice exploration in the circuit analysis course were carried out among the 19-level undergraduate students of electrical engineering and its automation in conjunction with the school's SPOC platform project of Circuit Analysis.The practice shows that the SPOC-based hybrid teaching mode has realized the transformation from teacher teaching-centered to student learning-centered, from classroom-based to combined classroom and extracurricular, from result-based to combined process evaluation and result, and more effectively cultivated students' independent learning ability and improved teaching quality and teaching efficiency.
Cablefree Acquisition Techologies - Progress and Ecology

Summary One would think that technology would have a limited impact on conservation and safety, but newer technologies have demonstrated that technology can go hand in hand with operational improvements. We are now starting to see small but confident technological steps to provide the following solutions: Environmental friendliness. Reduction of safety risk. No loss and in some cases, improvement of seismic data quality. Greater crew efficiencies at no increase in operational costs. In 2014 Gazprom Neft together with Wireless Seismic Incorporated, carried out the first successful test in Russia using a real-time cableless recording system. This project led to Gazprom Neft’s “Green Seismic” initiative. According to Gazprom Neft, more than 4.5 million trees have by the use of modern technology. In this presentation, we will look at various cableless technologies, including blind/semi-blind nodes and modern systems that provide real time cable free recording capabilities. As we will see, the ecological footprint can be reduced if more technologically advanced equipment is used on projects combines the advantages all cableless nodal technologies with full control over the receiver stations with real-time return of all seismic data as is currently enjoyed with cabled systems.
HLocalExp-CM: confidence map by hierarchical local expansion moves for accurate stereo matching

We present a stereo matching approach referred to as HLocalExp-CM by exploiting the hierarchical local contextual information and a confidence map based on a new grid structure. The proposed approach preserves fine depth edges and extracts accurate disparities in weak texture, textureless, and repeated texture regions. The proposed approach adopts a two-stage optimization strategy. In the framework of first stage, a multiresolution cost aggregation is minimized to reduce the search space of the disparity plane of each pixel. The second stage iteratively optimizes the confidence map and a global energy function to progressively improve the disparity accuracy for each pixel. The confidence map is estimated through classifying the pixels into distinctive and ambiguous ones by computing the decreasing rate of the multiresolution cost aggregation and then performs a spatial propagation and plane refinement for the update of the disparity of each pixel, thereby successfully eliminating the ambiguity of nondistinctive pixels. The global energy function based on a pairwise Markov random field uses cross-scale cost aggregation for taking advantage of context information of objects in different scenarios on local grid regions, which is different from the deep learning technique uses convolution layers extracting the context information. The proposed approach is evaluated on Middlebury benchmark V3, and is ranked first based on “bad 2.0 all metric,” a widely used criterion for the evaluation of stereo images, while the eighth place on “bad 2.0 nonocc metric” (recorded on July 24, 2021).
Improving the Security of Reversible Data Hiding Using Multiple Histogram Modification

Reversible data hiding (RDH) allows carrying secret information in cover media without introducing permanent distortion. For a RDH method, the important performance measurements are embedding capacity and image quality. Since embedding capacity is an important requirement in the field of data hiding, it is necessary to consider the security of data embedding in RDH applications. In general, RDH algorithms usually prefer data embedding in simple image regions with low local complexity. As a result, image degradation is alleviated at the cost of poor embedding security. In this study, a novel RDH method is proposed to embed data into complex image regions, wherein the data hiding becomes more secure in defending against modern steganalysis. To measure regional local complexity, the harmonic mean of directional local variances is employed to combine directional pixel differences. To embed data into complex regions instead of smooth regions, multiple histogram modification is adopted and updated for optimized data embedding with higher complexity. Experiment results show that embedding security is significantly improved with a considerable amount of payload and well-preserved image quality.
An incremental approach to gapping in Japanese

Gapping in Japanese, which is an SOV language, differs from gapping in SVO languages in that the conjuncts with the elided verbs appear in non-final position. In this paper I present an incremental approach to gapping in Japanese, where it is assumed that an argument structure type is constructed in the non-final clause(s) in the gapping construction. This type is unified with the construction type created by the final clause resulting in identical construction types for all conjuncts in the construction.
Teaching Transformation of Computer Application Technology Specialty Based on Artificial Intelligence

In today’s era of rapid development of artificial intelligence, with the continuous development of education, computer teaching has become an important part of quality education, and computer has formed a culture and integrated into the teaching process of various majors. The experimental results show that the professional teaching of computer application technology based on artificial intelligence explored in this article is more recognized and liked by students, and students’ love of class has increased by about 55%. The use of artificial intelligence algorithms makes computer teaching targeted to improve students’ Class concentration has played a significant role. It has greatly promoted the enthusiasm of college students to learn computers and is of great significance. This has an important relationship with the new teaching of artificial intelligence-based computer application technology professional teaching. Artificial intelligence has powerful data processing and analysis capabilities, which can arouse the enthusiasm of students. Therefore, the new teaching of computer application technology based on artificial intelligence plays an important role in teaching.
Transfiguring Handwritten Text and Typewritten Text

Detecting and recognizing handwritten characters have become a challenging task nowadays. It comes under pattern recognition. The recognition of handwritten text automatically is widely used in many applications, where to process huge handwritten text. This application is useful for not only transforming handwritten text to editable text or typed text, but also for transfiguring the typewritten text to handwritten text. In transforming the typed text to handwritten text, also includes randomization of handwritten character styles to pragmatize the output. The output text file of both transfigurations is translated to the corresponding audio format for easy output validation by the user and automatically mailed to the user to avoid data loss. Here the whole input is taken by the user and driven via graphical user interface (GUI). This application reduces the time and manual work involved in processing and converting both handwritten and typewritten texts.
Product Review Analysis

Many websites sell products and customers buy these products. Customers give their rating to the products they bought from these websites which helps genuine rating of the products. But some websites for fame and profit give fake reviews so it is hard for a customer to know which product is genuine. So, because of this, we detect fake reviews and genuine reviews using sentimental analysis and here we calculate the sentimental score and remove negative words in order to detect fake and genuine reviews. After detecting fake and genuine reviews using with stop words and without stop words, we find the accurate algorithm to find these fake and genuine reviews and calculate the accuracy here. Later, we choose the best accurate algorithm. We plot the visualization graphs for fake and genuine reviews.
Manage your Finance with SAP S/4 HANA Finance Solution

SAP S/4 HANA inherits capabilities of SAP HANA. SAP HANA provides native in-memory engines that can process any types of data in real time. It can support both Online Transaction Processing and Online Analytical Processing requirements and is built to run on high-end hardware. The data model for Financial Accounting was redeveloped and the application code for Finance was completely rewritten on simplified data model. The rewritten application is called SAP S/4HANA Finance, formerly known as Simple Finance. Other applications like sales, procurement, and inventory management etc, are also simplified. This new solution also helps to extract meaningful reports across all lines of business (MIS Reports), provide greater opportunities for innovation, Predictive Analysis, Real time decision support, automatic reconciliation of Financial Accounting with Cost Accounting and Fixed Asset Accounting and Material Ledger, faster month end and year end closing, Group Reporting (Financial Consolidation), Transfer Prices, COGS and Variance Split, better costing integration with production, etc.
Optical neuromorphic processing with soliton crystal Kerr microcombs: Scaling the network in size and speed

Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy. They are of significant interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis [1-7]. Optical neural networks offer the promise of dramatically accelerating computing speed to overcome the inherent bandwidth bottleneck of electronics. Here, we demonstrate a universal optical vector convolutional accelerator operating beyond 10 Tera-OPS (TOPS - operations per second), generating convolutions of images of 250,000 pixels with 8-bit resolution for 10 kernels simultaneously — enough for facial image recognition. We then use the same hardware to sequentially form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 900 pixel handwritten digit images with 88% accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. We show that this approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicle and real-time video recognition.Keywords: Optical neural networks, neuromorphic processor, microcomb, convolutional accelerator
VISIOCONFERENSING TECHNOLOGY AND THE RIGHT TO A FAIR TRIAL

VISIOCONFERENSING TECHNOLOGY AND THE RIGHT TO A FAIR TRIAL
Information Teaching Model of Preschool Art Education in Colleges and Universities Based on Finite Element Higher-Order Fractional Differential Equation

Abstract In order to meet the social demand for preschool education professionals, based on the finite element higher-order fractional differential equation, this paper studies the information teaching model of preschool education of fine arts in colleges and universities. Through the scientific and effective fine arts education in the preschool education major in colleges and universities, the students’ fine arts ability can be effectively improved, and the application of information means can make this goal can be better realised so that the teaching needs can be better satisfied and they promote the better development of fine arts education in the preschool education major.
Addressing the Semantic Gap in the Consumer-to-Manufacturer Strategy Using Dual Convolutional Neural Network

Customer-to-manufacturer (C2M) is an emerging smart manufacturing strategy. In C2M, customers are directly connected with manufacturers for tailor-made product development and manufacturing. Thus, the R&D and marketing-driven process in traditional manufacturing evolves into a customer-driven product development process. Product sales using the C2M mode have been one of the highest growth sectors in Chinese ecommerce platforms. However, customers usually lack the necessary domain knowledge. They cannot communicate directly with engineers by indicating the desired technical specifications of the product. A semantic gap exists. This paper presents a dual convolutional neural network-(CNN)-based structure, to automatically address this semantic gap. To mitigate the data sparsity issue in the customer needs domain, we use a massive amount of product review texts which were crawled from ecommerce websites to build a source mapping from reviews to product technical specifications. A small amount of customer needs text was deployed to adapt the source mapping to the target customer needs-product specifications mapping and thus close the semantic gap. Promising experiment results were obtained to show the effectiveness of the method.
Towards Formalisation of Concept Descriptions and Constraints

The integrated management of industrial systems in future environments like Industry 4.0 requires the effective management of information throughout the engineering life cycle. As systems pass through phases of design, construction, operation, maintenance, renewal or replacement, they will be administered via different information ecosystems, requiring changing perspectives on their descriptive information. A central role in the interplay of software and hardware artefacts, functions, documentation and managing software is played by the descriptions of concepts (i.e. formalised definitions of concepts within the domain of quantification). In this paper we propose a unified formalisation of descriptions that permits consistent analysis of the relationships between the designs, types, products, and concrete artefacts that can be found in the industrial engineering life-cycle. The approach is consistent with our earlier framework that describes artefacts, requirements and functional roles in the context of the DOLCE foundational ontology.
Development of Arduino Basic Learning Kit in Robotics for Beginner Level Learning

This Basic Arduino Learning Kit in Robotics is developed to help someone who wants to learn the basic concepts of Arduino and its operation in the field of robotics which is becoming increasingly important in our daily lives. In this learning kit, it provides three practical works that allow students to deepen their knowledge of the Arduino Uno microcontroller in robotics step by step. In Practical Work 1, students are exposed to the experiment of lighting three LED lights with pattern of running lights and flashing lights. In Practical Work 2, students are exposed to measurement work using ultrasonic detector and displaying its reading on a computer screen and on the screen of a mobile phone with the Android operating system via a Bluetooth module. In Practical Work 3, students are shown how to build a three -wheeled prototype robot and how to control the robot using a mobile phone with the Android operating system. The effectiveness of this learning kit has been tested in a learning workshop on 21 Pre-Diploma students of the Session of June 2020 Polytechnic of Sultan Mizan Zainal Abidin and has shown very encouraging feedback.
COVID Safety Measures Alert System

The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection method is wearing a face mask and maintaining social distance in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. Reports Indicate that wearing facemasks and maintaining social distance while at work clearly reduces the risk of transmission. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. So we are doing a Project on detecting whether a person wears a mask or not, also giving an alert message to the person to wear a mask, and maintain social distance or not. A hybrid model using deep and classical machine learning for face mask detection will be presented for face mask detection, a face recognition model is used to identify faces and an object detection algorithm is used to identify persons and also calculate social distance between each other. We collected face mask detection dataset consisting of mask and without mask images, and person photos to identify the person. We are going to use OpenCV to do real-time face detection from a live stream via our webcam. We will use the dataset to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and TensorFlow and Keras, use a face recognition module to identify faces and a YOLO algorithm to detect objects and calculate social distance. Our goal is to identify whether the person on a video stream is wearing a face mask or not, if not give an alert message to wear a mask and check for social distance between each other with the help of computer vision and deep learning.
Breast Cancer Prediction Models: A Comparative Study and Analysis

Breast cancer is a vital cancer disease among women. The death rate increases predominantly due to the breast cancer tumor in such a way that one out of ten ladies are detected having breast malignancy. It is also second most cause for death of women in the USA. Thus, it is an important public health problem. Breast cancer is mostly categorized into two parts: benign and malignant. The early detection or prediction of this cancerous cell helps in preventing from higher death rates. In this paper, our main focus is to discuss and analyse different prediction models. The objective of the work is to design a classification model to predict the cancerous cells. In addition, a comparative analysis is to be performed among the classification techniques to yield an accurate classification result in the aim to finding out the classifier that works best in predicting the class with least error.