Reviewerly/exHarmony huggingface dataset:
Construction of a Multimedia Education Resource Security Model Based on Multistage Integration

A security model of multimedia education resource fusion based on multistage integration of multimedia educational resources is constructed in this paper in the context of smart education. First, the teaching model of multimedia education resource fusion is analyzed and the IoT communication evolution model and functional architecture model are constructed accordingly, based on which the realization path of IoT intelligent sensing, intelligent management, emotional computing, device sharing, and vision simulation functions for smart education are discussed. The security model uses a decentralized cryptographic data security sharing method based on blockchain for protection and supports blockchain to record user attributes to ensure the confidentiality and integrity of data. The experimental and analytical results indicate that the scheme can effectively reduce the computational overhead of smart devices while ensuring the security of data sharing. Moreover, the proposed model in the paper achieves better performance than other methods in terms of security strength, encryption, and decryption time.
Design and Development of the Rapid Prototyping Techniques Ontology with the Appropriate Technique Selection Approach

This article aims to design and develop Rapid Prototyping Techniques Ontology based on the study of new generation web as the Semantic Web that is a method of encoding and retrieval of information will be able to understand and process the information. To create an ontology that makes up the backbone of the Semantic Web, first, the selective techniques of rapid prototyping systems were studied, and in the operating area of the appropriate technique, knowledge was extracted with the content analysis method. The output of this process is the ontology of rapid prototyping techniques that are fully covered knowledge in a given area with more than 600 axiom, 120 classes and sub-classes, and more than 60 features. In addition to a knowledge-based view in the field of selector systems of Rapid Prototyping, opens a new arena. In the end, domain knowledge using the owl language in the Protégé application is implemented as Rapid Prototyping Ontology.
FPB-UNet++: Semantic Segmentation for Remote Sensing Images of reservoir area via Improved UNet++ with FPN

In order to improve the accuracy of semantic segmentation of remote sensing images in the reservoir area, this paper improves UNet ++, and proposes a UNet ++ semantic segmentation network model fused with feature pyramid network, called FPB-UNet ++. First, in order to fully extract the semantic information of different scales and enhance the recovery ability of the spatial information of remote sensing images, this paper uses the improved feature pyramid structure as the basic unit of the UNet ++ coding structure. Then, the pooling of position information will be lost between each coding unit To remove the layer, use convolution instead. Finally, in order to make full use of multi-scale feature information in the multi-sided output part, all the side output feature maps are stitched and fused in the channel dimension. Through experiments on the open and self-built remote sensing image semantic segmentation data set of Xiaolangdi Reservoir area, the results show that the network model has a good segmentation effect on feature information.
Landscape Rippling: Context‐based water‐mediated interaction design

Abstract With a core purpose of helping users to understand the context, a water interface provides possibility for enhancing user experience in interaction process. Starting from analyzing existing water‐mediated interaction approaches, we proposed a water‐mediated interaction design model and a corresponding user experience model, aiming to eliminate the boundary between users and the context with water as the medium. According to the proposed model, we implemented a water‐mediated interaction system Landscape Rippling, with the painting “A Panorama of Rivers and Mountains” as its context. Ultimately, user experience tests of the interaction system demonstrate the effectiveness of this water‐mediated interaction design model.
Path of Preschool Education Personnel Training Under the Background of Big Data

<p style="text-align: justify;"><span style="font-family: "times new roman";">With the emergence of big data, the improvement of China's comprehensive national strength and the development of modern educational technology, the important position of preschool education in the education system has been widely valued by people, and our government has also incorporated the preschool education reform into the national medium and long-term education reform and development plan, which has promoted the rapid development of preschool education in China in recent years. Taking a Kindergarten Teachers College as a case, this paper designs the questionnaire and interview outline by using the methods of questionnaire and interview, and carries out the investigation and interview on the educational practice ability of the selected research objects. Aiming at the deficiency of the current students' educational practice ability, this paper analyzes the problems existing in the cultivation of the current preschool education majors' educational practice ability at the junior college level by combining with the survey data. In view of these problems, this paper puts forward some thoughts on the cultivation of the educational practice ability of the preschool education majors at the junior college level, including determining the scientific talent training objectives, optimizing the curriculum structure, and reforming the curriculum system Reform the teaching methods of teachers and improve the practice of education.</span>
VeriSIM: A model-based learning pedagogy for fostering software design evaluation skills in computer science undergraduates

Abstract Evaluating a software design is an important practice of expert software designers. They spend significant time evaluating their solution, by developing an integrated mental model of the software design and the requirements. However, sufficient emphasis has not been given on teaching and learning of evaluation practices in software design courses, and hence, graduating students find it difficult to critically analyse an existing design and improve upon it. In this paper, we describe a model-based learning pedagogy for teaching–learning of software design evaluation. Model-based learning has been extensively used in science education and entails helping students construct, refine, revise, evaluate, and validate scientific models. We argue that modelling practices in software design evaluation are analogous to these practices. We adapted the model-based learning paradigm and operationalised it into a technology-enhanced learning environment (TELE) for fostering software design evaluation skills in computer science undergraduates. We conducted a research study with 22 undergraduate students to explore how the TELE and its features help students effectively evaluate a given software design. Students attempted a pre-test and post-test which asked them to identify defects in the design. We used the content analysis method to identify categories of defects from student responses in the pre-test and post-test. We also analysed student interaction logs and conducted focus group interviews to identify how features in the TELE contributed towards student learning. Findings from the study showed that students’ understanding of evaluation improved, from merely adding new functionalities and requirements, to a process which involved identifying alternate scenarios in the design which violate the given requirements. Students perceived that pedagogical features of the TELE were useful in helping them effectively evaluate software designs. Findings from the study provide evidence for the model-based learning paradigm as an appropriate pedagogy for software design and also opens the space for researchers to investigate model-based learning in other aspects of software design, such as designs of different types and varying complexities.
MOOC Teaching Model of Basic Education Based on Fuzzy Decision Tree Algorithm

In recent years, the development of science and technology in China has greatly affected people's ways of entertainment. In the traditional industrial model, new industries and Internet industries represented by the Internet have emerged, and the Internet video business is an emerging business that has been gradually emerging in the Internet industry in recent years. Moreover, this new teaching method has been gradually noticed in simple education, such as MOOC, I want to self-study network, and Smart Tree, and other online learning websites have sprung up. At present, the epidemic environment makes people pay more attention to this convenient and wide range of online video education. Therefore, we need to evaluate this kind of online video teaching model from the effectiveness of this kind of method and the quality of user experience. This paper takes this as the starting point and chooses the earliest online video platform, MOOC, as the model to establish a set of perfect user experience quality evaluation methods suitable for domestic online video education mode. Considering the data source, the accuracy of the results, and other factors, we chose the industry-leading platform MOOC network as an example. Through the exploration of the MOOC teaching mode in basic education, a member experience evaluation model is established based on fuzzy decision tree algorithm. The experimental results show that the model has high accuracy and high reliability.
Path Planning of Intelligent UAVs using Computational Geometric Techniques

In recent years, Unmanned Aerial Vehicles, commonly referred to as UAVs, are heavily used in various real-time scenarios without involving human interaction. Also, they are among the most extensively researched topics. The profit of using UAVs has also come up with different challenges. One of the most important among them is the Coverage Path Planning problem, generally abbreviated as CPP. Computation of path also involves the consideration of various static and dynamic obstacles. While considering geographic areas as polygons, path planning of UAVs becomes difficult for concave polygons. To obtain an optimal path in a concave polygon, we have proposed the Field of View-based Sweep-Line method(Fo VSL) technique. This proposed work has enhanced the existing Sweep-Line method for finding the optimal path in a polygonal area. As the solution can be obtained with ease when the polygons are convex, the use of the convex polygonal decomposition technique is adapted with the proposed method. The inclusion of the Boustrophedon method reduced the total number of turns taken by the UAV. Comparative analysis with the existing technique proves that the proposed method minimizes the total path to be traveled with a reduction in the number of turns taken by the UA V resulting in overall minimization of the time consumed for the entire traversal.
An arithmetic scaler circuit design for the expanded four-moduli set <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1216" altimg="si391.svg"><mml:mrow><mml:mo>{</mml:mo><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msup><mml:mo linebreak="goodbreak" linebreakstyle="after">−</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msup…

This paper suggests a numerical scaler for the moduli set {2k−1,2k+1,22k+1,22k}, k∈Z+,k>1. The scaling value used is the power-of-two modulus 22k. The suggested scaler, which operates in four parallel scaling channels, is built using carry-save and modular adders. The newly introduced structure is compared with a similar scaler for the four-moduli set {2k−1,2k+1,22k+1−1,22k}, which operates in a cascaded manner. The comparison is held using the technology-independent gate model and VLSI realization tools. The gate model showed that the proposed scaler reduces area by 18% and time by 46%. The VLSI synthesis showed also that the new design has reduced area and time by 22% and 51%, respectively.
Effect of Improved Association Algorithm on Mining and Recognition of Audit Data

Audit evidence is the proof material on which the auditors issue audit opinions and make relevant audit conclusions, and audit evidence in the era of big data presents new characteristics in terms of adequacy, relevance, and reliability. This paper combines the improved association algorithm to construct the audit data mining system to improve the data processing effect of the audit process. Moreover, this paper proposes a new dynamic threshold method, gives the calculation method of some important parameters in the algorithm, and presents the improved cell-based association algorithm flow. In addition, this paper discusses how the outlier algorithm is applied to the acquisition of audit evidence and the application scenarios in the audit system. The experimental research results show that the audit data mining system based on the improved association algorithm proposed in this paper has a good effect in the audit of accounting and financial data.
Septic-convolution Kernel - Comparative Analysis of the Interpolation Error

The first part of the paper describes the septic-convolution (QC) interpolation kernel. The SC kernel consists of seventh-order polynomials and approximates the ideal sinc function in the interval [−4; 4]. The second part of the paper presents the results of the Experiment, which was realized with the aim of determining the interpolation error, MSE, when interpolating with the interpolation SC kernel. In addition, a comparative analysis with interpolation error, which occurred during interpolation with an interpolation fifth-order polynomial kernel (QC kernel), was performed. After that, the analysis of the values of the kernel parameters, α, determined theoretically and experimentally, is presented. Deviations of the theoretical values, in relation to experimental ones, were analyzed over statistical parameters (µ, σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). Finally, the efficiency of the algorithm for estimating the theoretical values of the kernel parameters, using parameters Accuracy and Precision, was determined. Experimental results are shown in tables and by graphs.
Real-time Smile Detection using Integrated ML Model

This study investigates the ways to provide unhindered entertainment solutions to artists and common people. The research has led to the conclusion that a new social media platform can be built for artists around the globe. The aim of the study is to merge video conferencing features with face detection and smile detection machine learning algorithms to provide an interactive environment to audience virtually over Internet. The findings of the study suggest that people from different age groups and places can find the comedy shows of common interests for enjoyment. The project can be launched as application to build a worldwide community of comedians. The paper is intended to provide a fast and efficient Machine learning model to detect smiling faces in real time environment with low latency and high accuracy. The comparison between some smile detection models is done among which our model has proven to be more effective than 2 pre-existing models with accuracy score of 91.97%. Although the accuracy of this model is less than other one having 94.87% accuracy.
Evaluation of Naïve Bayes and Voting Classifier Algorithm for Credit Card Fraud Detection

In this new generation, each and everything is done online and most of the time the payment is performed via the internet using net banking or a credit card. Debit card and credit card plays a major part in day-to- day life. The total amount of money transfers through online has a great amount of growth. Fraudulent transactions have escalated as E-commerce continues to expand at a rapid pace. Therefore banks, financial institutions and many other companies offer credit card fraud detection applications with more demand, and it adds more value to the applications. To reduce the transactions that are fraud, Credit Card Fraud Detection that employ Machine Learning Techniques comes to the rescue, which is a data investigation procedure carried out by a Data Science team, with the model generated providing the greatest outcomes in stopping fraudulent transactions.
To Develop Multi-Object Detection and Recognition Using Improved GP-FRCNN Method

The Region-based Convolutional Neural Network (R-CNN), which is more rapid, has been recognized as the best detection algorithm. With our design basis for Faster RCNN (FRCNN), the designers reclassify the proposed object recognition changes with just a rough spatial estimate of the incident. The researchers solve problems such as delicate to complex environments and its principles are derived reductions each time it is -actually confronted with noisier initiatives. Through which designers categorize proposed changes to object recognition using a rough geometric estimate of the scene. This simple expansion requires complex changes of scale, which the authors describe in their entirely throughout this study. Moreover, Geometric Proposals with FRCNN (GP-FRCNN) performed relatively well on small and large things, which has always been a problem for computer vision algorithms. Any use of GP-FRCNN for monitoring film analysis is simple and requires no specific geometric description. We tested the algorithm on the UA-DETRAC database, finding that GP-FRCNN beat the FRCNN by 21&#x0025;.
Reliability of references to scientific publications: Where myths and inaccuracies originate

Dynamics of national and world publication activity demonstrate that the number of publications on reliability of scientific information in various fields of knowledge has increased in recent years. In the Scopus database, almost three quarters of publications related to the reliability of scientific information belong to the field of medicine, engineering and computer science; in the Web of Science the distribution across thematic areas proves to be relatively even, while in the RSCI the largest number of publications is concentrated in the area of economics. The author provides evidence that multiple references to a publication cannot guarantee its quality, moreover they even do not constitute a conclusive evidenceon the publication existence. The author refers to the situation around the monograph by Carl Martin Wiig that has been repeatedly cited in the RSCI. The monograph was allegedly translated into Russian and published in Russia in 1986. However, after searching for information in various e-catalogs, databases and clarification received from Professor K. Wiig, the existence of this monograph was rebutted. The information on the monograph was published by Russian Wikipedia in 2011, which gave the occasion for referring to the monograph in various articles, textbooks, etc. The author emphasizes that the overtrust in information loaded to Wikipedia gives rise to various myths and inaccuracies.
Heart Outer Surface Segmentation from Computed Tomography Images

Volumetric heart segmentation is an essential operation in cardiology; it makes it possible to locate the cardiac lesions, quantify them, and follow their evolution for treatment. From the study of heart outer surface segmentation, several characteristics can be extrapolated and interpreted as coronary arteries calcifications (CAC) lesions. Image segmentation is the appropriate tool for such localization. In this work, we have realized an automatic system, which segments, the heart outer surface using only native computer tomography acquisitions (CCT). A multi-level threshold, mathematical morphology, and 3D connected component labeling are performed for this purpose. In the end, the 3D view is realized. We used 32 patients from the OrCascore dataset. We compared our segmentation with ground truth volumes, plotted by two experienced cardiologists. Our approach achieved an accuracy, F1score, DICE, and a JACCARD coefficient of 96.99%, 92.01%, 91.81%, and 85.23% respectively in a short computation time.
The diagram as a mediator in collaborative learning: A conceptual review

In the current outlook of education, collaborative learning has been introduced as a strategy to build knowledge, in a multimodal culture where information is expressed, produced and distributed through new formats and representational languages. Regarding the latter, the diagram has been used as a way to represent and organize information in learning processes, making its comprehension necessary, since there are different interpretations from the theory. That is why, through this study of theoretical design, a systematic review of the literature (SRL) was carried out, which it allowed to get information about the concepts of diagram and its connotation in collaborative learning, taking into account the definitions stated about it. The results show that the diagram is a representational instrument and not only a graphic or textual organizer, but also, a semiotic-social mental artifact, a semiotic-social mental artifact that mediates meanings and regulates their construction in subjects' collaborative learning interactions. Finally, the role of the diagram as a potentiator in the development of metacognitive skills is highlighted.
World-class interpretable poker

Abstract We address the problem of interpretability in iterative game solving for imperfect-information games such as poker. This lack of interpretability has two main sources: first, the use of an uninterpretable feature representation, and second, the use of black box methods such as neural networks, for the fitting procedure. In this paper, we present advances on both fronts. Namely, first we propose a novel, compact, and easy-to-understand game-state feature representation for Heads-up No-limit (HUNL) Poker. Second, we make use of globally optimal decision trees, paired with a counterfactual regret minimization (CFR) self-play algorithm, to train our poker bot which produces an entirely interpretable agent. Through experiments against Slumbot, the winner of the most recent Annual Computer Poker Competition, we demonstrate that our approach yields a HUNL Poker agent that is capable of beating the Slumbot. Most exciting of all, the resulting poker bot is highly interpretable, allowing humans to learn from the novel strategies it discovers.
Optimization of Artificial Intelligence Model for Badminton Teaching and Training with Wireless Network Support

With the rapid development of the Internet of Things and artificial intelligence, the society gradually moves into the era of intelligence, and the research results and intelligent products based on wireless networks come into being. Machine learning algorithms are used to classify and recognize badminton strokes in this research, and a badminton technical feature statistics and pace training system are built on this foundation. By exploring the model characteristics and algorithm training method of the Hidden Markov Model (HMM), this paper proposes a model algorithm with an improved HMM training method for recognizing ten common badminton strokes, including serve, forehand rub, backhand rub, and forehand lunge. Serve, forehand rub, backhand rub, forehand flutter, forehand push, backhand push, forehand pick, backhand pick, and forehand loft are among the 10 typical badminton strokes identified by the algorithm. Our technique can distinguish ten common ball-striking movements in real-time, according to the testing.