Reviewerly/exHarmony huggingface dataset:
Design of Teaching System of Physical Yoga Course in Colleges and Universities Based on Computer Network

The new generation of computer network technology has driven the development of the whole society and brought about earth-shaking changes. In the context of “Internet +”, the combination of the Internet and education has created today’s diversified online course model. Fitness yoga can stretch our limbs well. The teaching of physical education courses of fitness yoga in colleges and universities can alleviate the students’ long-term tension for learning and play a role in exercising and cultivating all-round development talents. This article takes the current status of online sports yoga courses in ordinary universities as the research object and uses a series of research methods to organize and summarize the teaching team, number of class selections, course evaluations, learning resources, etc. of online yoga courses in ordinary universities. Further, this paper designed a set of course teaching system based on computer network. The system is embedded with multimodal yoga action posture detection, which can solve related problems in yoga online teaching. The realization result proves that the system effectively improves the learning effect and comprehensive quality of students and has strong operability and feasibility.
Collaborative Filtering Recommendation Algorithm Based on User Attributes and Item Score

To solve the problems of cold start and data sparseness existing in traditional collaborative filtering recommendation algorithm, a collaborative filtering recommendation algorithm based on user attributes and item scoring is proposed. Firstly, we improve the credibility of user similarity and explore the potential interests of users, a new user rating similarity calculation method is constructed by introducing confidence, item popularity, and Pearson weighting. Secondly, we construct a user attribute similarity measurement method by introducing cultural distance, age attribute similarity, and user label similarity. Finally, user rating similarity and user attribute similarity are weighted to form a new similarity measurement model. Through simulation comparison between the collaborative filtering recommendation algorithm and the traditional recommendation algorithm, our results show that the collaborative filtering recommendation algorithm can effectively improve the accuracy of recommendations and the diversity of results and effectively alleviate the problem of data sparseness.
PERSEPSI SISWA TERHADAP PENGGUNAAN GAME EDUKASI DIGITAL PADA PEMBELAJARAN MATEMATIKA

This paper examined the students’ view on advantages of using digital educational game in learning mathematics. The background of this paper is the appearance of people perception phenomenon about the negative effect of using games for learning. The method of this paper conducted descriptive qualitative design for the questionnaire and interview as the instruments. The data analysis technique used is the Miles and Huberman model (Analysis Interactive Model). This paper was conducted at Vocational High School for 21 students. The result of data analysis indicated that almost all of the students (86%) agree for using educational game can facilitate the students to learn mathematic. On the other hand, there were only 3 students claimed that the they were not sure or even do not know of using educational game can facilitate the students to learn mathematic. And there were no students who disagree if using educational games can not facilitate the students to learn mathematic.
An Overview of the Digital Identity Lifecycle (v2)

A digital identity goes through several stages during its existence, from creation, through various modifications in response to different events, to inactivation or deletion. This article walks through the types of digital identities that must be managed, along with the various stages of a digital identity, describing the typical beginning-to-end lifecycle within or across multiple systems. The lifecycles outlined in this document are not meant to be comprehensive but should be applicable over most B2B, B2C, and B2E use cases.
Optimal Allocation of IaaS Cloud Resources through Enhanced Moth Flame Optimization (EMFO) Algorithm

A new generation of computing resources is available to customers via IaaS, PaaS, and SaaS administrations, making cloud computing the most significant innovation in recent history for the general public. A virtual machine (VM) is configured, started, and maintained across numerous physical hosts using IaaS. In many cases, cloud providers (CPs) charge utility customers who have registered their premises with the utility registration authorities. Given the opposing aims of increasing customer demand fulfillment while decreasing costs and optimizing asset efficiency, efficient VM allocation is generally considered as one of the most difficult tasks for CPs to overcome. This paper proposes the Enhanced Moth Flame Optimization (EMFO) algorithm to provide a unique strategy for assigning virtual machines to suit customer requirements. The recommended approach is applied on Amazon’s EC2 after three distinct experiments are assumed. The utility of the proposed method is further shown by the use of well-known optimization techniques for effective VM allocation. The app was created using a Java-based programming language and then run on the Netbeans IDE 12.4 platform.
Formal Analysis of Boomerang Probabilities

In the past 20 years since their conception, boomerang attacks have become an important tool in the cryptanalysis of block ciphers. In the classical estimate of their success probability, assumptions are made about the independence of the underlying differential trails that are not well-founded. We underline the problems inherent in these independence assumptions by using them to prove that for any boomerang there exists a differential trail over the entire cipher with a higher probability than the boomerang.While cryptanalysts today have a clear understanding that the trails can be dependent, the focus of previous research has mostly gone into using these dependencies to improve attacks but little effort has been put into giving boomerangs and their success probabilities a stronger theoretical underpinning. With this publication, we provide such a formalization.We provide a framework which allows us to formulate and prove rigorous statements about the probabilities involved in boomerang attacks without relying on independence assumptions of the trails. Among these statements is a proof that two-round boomerangs on SPNs with differentially 4-uniform S-boxes always deviate from the classical probability estimate to the largest degree possible.We applied the results of this formalization to analyze the validity of some of the first boomerang attacks. We show that the boomerang constructed in the amplified boomerang attack on Serpent by Kelsey, Kohno, and Schneier has probability zero. For the rectangle attack on Serpent by Dunkelman, Biham, and Keller, we demonstrate that a minuscule fraction of only 2−43.4 of all differential trail combinations used in the original attack have a non-zero probability. In spite of this, the probability of the boomerang is in fact a little higher than the original estimate suggests as the non-zero trails have a vastly higher probability than the classical estimate predicts.
Effectiveness of Augmented Reality-Based Learning Media for Engineering-Physics Teaching

This study aimed to determine the effect of augmented reality-based learning media and cognitive ability as well as their interaction on student learning outcomes in engineering-physics teaching. The results of the study revealed that augmented reality-based learning media improved student learning outcomes for activity-centered models and exploratory tutorial models. Furthermore, cognitive ability had a positive effect on learning outcomes, and the learning outcomes of students with high cognitive abilities were better than those of students with low cognitive abilities. Augmented reality-based learning media developed by an exploratory tutorial model proved effective for all students, while those developed by an activity-centered model was effective only for students who had high cognitive abilities.
kNN Algoritmasının Optimum k Değerini Kullanarak İris Veri Seti SınıflandırmaTahmin Başarısının İyileştirilmesi

Machine learning methods are widely used in automated technologies. Classification prediction is a machine learning based on data mining. Today, many technological devices can make new predictions by gaining experience from past data with machine learning methods. Machine learning is widely studied in two types, supervised and unsupervised. The limits of the objectives in supervised learning are predetermined. In unsupervised learning, there are no predetermined targets. In this learning, the machines are required to determine the targets automatically. Prediction process is one of the basic components of machine learning. Machines need to use some algorithms in order to perform the prediction process on the basis of data mining. k nearest neighbor (kNN), Naive Bayes (NB), Decision Tree (DT) and Support Vector Machine (SVM) algorithms are used mostly. k nearest neighbor (kNN), Naive Bayes (NB), Decision Tree (DT) and Support Vector Machine (SVM) algorithms are used mostly. These algorithms can be applied with the help of some tools on data sets. In this study, kNN algorithm was used to estimate Iris data set classification using Orange tool. The success of the KNN algorithm depends on using the correct attribute and changing the optimum k value. As a result of the tests, it was determined that when the k neighbor value was selected as 15, it was the most suitable k neighbor value, providing 98.67% classification prediction success in the Iris dataset.
Photo-Realistic Continuous Image Super-Resolution with Implicit Neural Networks and Generative Adversarial Networks

The implicit neural networks (INNs) can represent images in the continuous domain. They consume raw (X, Y) coordinates and output a color value. Therefore they can represent and generate images at arbitrarily high resolutions in contrast to convolutional neural networks (CNNs) that output a constant-sized array of pixels. In this work, we show how to super-resolve a single image using an INN to produce sharp and photo-realistic images. We employ a random patch-based coordinate sampling method to obtain patches with context and structure; we use these patches to train the INN in an adversarial setting. We demonstrate that the trained network retains the desirable properties of INNs while the output is sharper compared to previous work. We also show qualitative and quantitative comparisons with INN and CNN baselines on benchmark datasets of DIV2K, Set5, Set14, Urban100, and B100. Our code will be made public.
Enhanced Black Widow Algorithm for Numerical Functions Optimization

Black widow optimization algorithm is a recently evolutionary metaheuristic that imitates the unique mating behaviour of the black widow spiders in the real life. The trend of published papers utilizing the BWO algorithm is growing rapidly due to its efficiency in solving various engineering optimization problems. However, the BWO does not always perform as well as it should, and this is due to the random initialization of the spiders also the loss of good candidate solutions during the search. To remedy these problems, we propose in this paper a modified black widow optimization algorithm (MBWO) based on three ideas. First, an efficient initialization technique is adopted, which can guarantee starting the search with finest quality spiders and plays a significant role in determining an optimal or near-optimal solution. Second, the sexual cannibalism phase is modified to avoid the loss of high-quality solutions. Finally, an adaptive adjustment of crossover and mutation probabilities is presented to achieve a compromise between the diversification and intensification. Experiments are carried out on nineteen standard benchmark functions with different dimensions. The simulation results reveal that MBWO algorithm outperforms the original one also other metaheuristic algorithms in term of solution accuracy, global optimality, and the convergence speed.
The importance of empathy in the development of creativity by design thinking

The article presents theoretical considerations on the role of empathy in the development of creativity. The analysis of the scientific literature on this topic allows us to conclude that empathy plays an important role in the development of creativity. The issues of learning empathy in early adulthood and methods of enhancing empathic abilities in relation to the process of education, among others, by the method of design thinking, are discussed.
Smart Pandemic Surveillance?: A Neo-Materialist Analysis of the “Monitora Covid-19” Application in Brazil

This article discusses smart surveillance based on the particular case of the Brazilian mobile app Monitora Covid-19 from the perspective of issues related to personal-data protection. Brazil is today one of the epicenters of the pandemic. The application under analysis is the tip of a wide network of data monitoring and medical assistance formed by public and private institutions. Based on a neo-materialist analysis of this network, this article discusses the use of surveillance technologies and data during the period of the pandemic; describes and comments on visible, discursive, and hidden materialities; and indicates the main issues of the application in the use and protection of users’ personal data. In conclusion, it indicates some of the application’s flaws in relation to personal-data protection. More broadly, it reinforces the need for the creation of publicly controlled regulatory bodies for smart surveillance systems that are able to oversee the application of (public and private) technologies with ethical assurances and public control.
Pareto-efficient strategies in 2-person games in staircase-function continuous and finite spaces

A tractable method of solving noncooperative 2-person games in which strategies are staircase functions is suggested. The solution is meant to be Pareto-efficient. The method considers any 2-person staircase-function game as a succession of 2-person games in which strategies are constants. For a finite staircase-function game, each constant-strategy game is a bimatrix game whose size is sufficiently small to solve it in a reasonable time. It is proved that any staircase-function game has a single Pareto-efficient situation if every constant-strategy game has a single Pareto-efficient situation, and vice versa. Besides, it is proved that, whichever the staircase-function game continuity is, any Pareto-efficient situation of staircase function-strategies is a stack of successive Pareto-efficient situations in the constant-strategy games. If a staircase-function game has two or more Pareto-efficient situations, the best efficient situation is found by holding it the farthest from the pair of the most unprofitable payoffs.
Design of the detection system for the position of the crankshaft flange hole group based on vision measurement technology

This paper studies a method of detecting the position of crankshaft flange hole group based on vision measurement, and sets up the position detection system of crankshaft flange hole group. The relative measurement model of hole group position is established by using the standard crankshaft information, and the system calibration method is studied. In this paper, a multi-camera calibration method based on polynomial fitting two-dimensional image mapping model is adopted. In addition, the image processing technology of hole group is studied. The improved Canny edge detection method is used to extract the contour of the hole group. Redundant edge filtering algorithm is used to eliminate unreliable edges. Then use the gradient interpolation method to extract sub-pixel edges. The measurement results show that the single-direction measurement error of the central coordinate of the crankshaft flange hole group is less than 0.07mm, and the repeatability error is less than 0.009mm, which provides a basis for the realization of industrial online efficient detection.
A Novel Technique of Mixed Reality Systems in the Treatment of Spinal Cord Tumors

Several reports have compared spinal cord tumor removal techniques but none have clearly described the appropriate site and level of indication for laminectomy or laminoplasty. The approach method for tumor removal depends on the type and localization of the tumor and the surgeon's skill. Therefore, a system that can suggest various surgical techniques is useful for spinal cord tumor surgery. The mixed reality system introduced in this paper is an excellent system that can suggest various surgical procedures. Using this system for spinal cord tumor removal, we made the surgery less invasive; therefore, we introduced this system and demonstrated its usefulness. Stereoscopic data of the patients with spinal cord tumors were obtained from preoperative myelogram-CT data. Stereoscopic laminectomy models including tumors were created using Blender, a free three-dimensional (3D) image editing software. We observed these data as 3D object images using a head-mounted display (HMD). This HMD is commercially available and relatively inexpensive. The surgical procedure is determined by considering those 3D images, radiological diagnosis, and the skill of surgeons. Intraoperative confirmation of the laminectomy site could be performed using the HMD. The 3D visualization of pathological conditions resulted in correct preoperative surgical planning and less invasive surgery in all five cases. Stereoscopic images using HMDs allow us a more intuitive understanding of the positional relationship between the tumor and spinal structure. These 3D object images can bring us more accurate preoperative planning and proper determination of surgical methods.
Finding Influential Users in Social Networking using Sentiment Analysis

Text of the abstract:Social Networking platforms facilitate the sharing of information, ideas, and thoughts through constructing the virtual communities. Therefore, finding people in given social networking who are really have the power to influence others is more critical. For example, finding the right person who has an impact to support or contradict any opinion or bring more profits when he/she publishes an advertisement for a certain business or product. This problem can be modelled as finding influential users in social networking. There are different ways to find the influential users in these platforms and there are some criteria that should be considered. In this article, we propose a solution named Muatheer which help to determine the influential users in Instagram by scraping data using Instagram API and applying the sentiment analysis algorithm to classify the comments whether it is positive or negative, as well as using the unigram as a feature extraction method. Then, the sentiment analysis result combines with other factors to calculate the “Influence Ratio” that proposes to determine the actual influencer in a specific domain. The experiments were conducted using set of training datasets and the proposed algorithm gives a high accurate result using some metrices.
Visual Sensor Networks for Indoor Real-Time Surveillance and Tracking of Multiple Targets

The recent trend toward the development of IoT architectures has entailed the transformation of the standard camera networks into smart multi-device systems capable of acquiring, elaborating, and exchanging data and, often, dynamically adapting to the environment. Along this line, this work proposes a novel distributed solution that guarantees the real-time monitoring of 3D indoor structured areas and also the tracking of multiple targets, by employing a heterogeneous visual sensor network composed of both fixed and Pan-Tilt-Zoom (PTZ) cameras. The fulfillment of the twofold mentioned goal was ensured through the implementation of a distributed game-theory-based algorithm, aiming at optimizing the controllable parameters of the PTZ devices. The proposed solution is able to deal with the possible conflicting requirements of high tracking precision and maximum coverage of the surveilled area. Extensive numerical simulations in realistic scenarios validated the effectiveness of the outlined strategy.
Internet of things based fall detection and heart rate monitoring system for senior citizens

&lt;span&gt;Falls cause the maximum number of injuries, deaths, and hospitalizations due to injury for senior citizens worldwide. So, fall detection is essential in the health care of senior citizens. Present methods lack either accuracy or comfortability. The design of fall detection and heart rate monitoring system for senior citizens has been presented in this paper. The hardware interface includes wearable monitoring devices based on a tri-axial accelerometer and Bluetooth module that makes a wireless connection by software interface (mobile application) to the caregiver. Global positioning system (GPS) can also track the location of the elder. For detecting falls accurately, an effective fall detection algorithm is developed and used. The performance parameters of the fall detection system are accuracy (97.6%), sensitivity (92.8%), and specificity (100%). A pulse sensor is used for monitoring the heart rate of the elder. The device is put on the hips to increase comfortability. Whenever the elder's fall is detected, the device can send information on fall data and heart rate with location to the respective caregiver successfully. So, this device can minimize the injury and health cost of a fallen person as a victim can get help within a short time.&lt;/span&gt;
Research on the Prediction of Popularity of News Dissemination Public Opinion Based on Data Mining

The development of news public opinion presents the characteristics of dynamic changes, and its life cycle is generally relatively short. For news public opinion to be welcomed by everyone, that is, to become hot news, it must be able to spread to a large number of readers in a short time. And, some of its characteristic attributes must satisfy the interests of most users and arouse users' desire to read. Therefore, it is particularly important to extract and study these characteristic attributes that determine the popularity of news public opinion and finally establish a model to describe the relationship between the popularity of news public opinion and these characteristic attributes. Based on data mining, this article mainly studies the popularity of news public opinion from two aspects. First is the sampling and attribute feature extraction of news. Then, considering the nonlinear relationship between the features, an improved principal component analysis method is proposed to analyze the correlation of the features. This can select important features from many irrelevant features and effectively reduce the original high-dimensional features. Second, the application of neural network in the prediction of news public opinion is studied. BP is an efficient data mining method. Considering BP network has some shortcomings. This work uses an improved particle swarm optimization to optimize the initial parameters for BP network, which can compensate for the defects of BP network. After that, the BP network with optimized parameters is used to establish a prediction model for the popularity of news public opinion. The experimental results prove that the neural network model proposed can accurately predict the popularity of news public opinion.