Reviewerly/exHarmony huggingface dataset:
Augmented Reality Experiment on Mechanical Stress of Block on Arch with Simulation-based Asset

Physical experiments assisted by information and communication technologies (ICT) are becoming more popular because of their potentials to support teaching and learning. Although mechanical experiments have been widely investigated using ICT devices such as smartphones, the observation points are limited by the available types and numbers of mobile device sensors. Inspired by an arch bridge attracting kids, an experiment with daily life objects, magnet blocks on arch paper, is designed toward the inquiry-based learning on the von Mises stress distribution and spatial deformation. An application of the game engine Unity with assets created from the finite element analysis software COMSOL is developed. And the incompatible file format is transformed by the free visualization tools Paraview and Blender. Finally, a teaching practice base on the proposed augmented reality (AR) experiment is carried out and the results suggest that the AR application with simulation-based assets sheds light on the simple, accurate, and interesting exploration of physical phenomena for kids.
A new method for solving the linear recurrence relation with nonhomogeneous constant coefficients in some cases

Abstract Recursive relation mainly describes the unique law satisfied by a sequence, so it plays an important role in almost all branches of mathematics. It is also one of the main algorithms commonly used in computer programming. This paper first introduces the concept of recursive relation and two common basic forms, then starts with the solution of linear recursive relation with non-homogeneous constant coefficients, gives a new solution idea, and gives a general proof. Finally, through an example, the general method and the new method given in this paper are compared and verified.
An Enhanced Version of Cat Swarm Optimization Algorithm for Cluster Analysis

Clustering is an unsupervised machine learning technique that optimally organizes the data objects in a group of clusters. In present work, a meta-heuristic algorithm based on cat intelligence is adopted for optimizing clustering problems. Further, to make the cat swarm algorithm (CSO) more robust for partitional clustering, some modifications are incorporated in it. These modifications include an improved solution search equation for balancing global and local searches, accelerated velocity equation for addressing diversity, especially in tracing mode. Furthermore, a neighborhood-based search strategy is introduced to handle the local optima and premature convergence problems. The performance of enhanced cat swarm optimization (ECSO) algorithm is tested on eight real-life datasets and compared with the well-known clustering algorithms. The simulation results confirm that the proposed algorithm attains the optimal results than other clustering algorithms.
A Study on the Direction of AI Liberal Arts Education Based on the AI Perceptions of Freshmen in University

In the era of the 4th Industrial Revolution, AI competency is essential not only for computer majors but also for non-majors. Therefore, AI courses are being offered as liberal arts education for non-majors at universities. Compared to designing a curriculum for AI majors, when designing an AI curriculum for non-majors their experiences and perceptions of AI must be taken into account.The purpose of this study is to examine the direction that the design of a liberal arts AI curriculum needs to take by examining the experiences and perceptions of AI among freshmen at university. A survey was conducted on 286 freshmen enrolled in the Humanities, Social Studies, and Arts departments of P University at the beginning of the first semester of 2021. The questionnaire consisted of questions to determine the degree of experience the students had with AI, as well as questions to determine their overall perception of AI. Based on the results of the survey, the correlation between their AI experience and their perception of AI was also examined. Furthermore, we considered the direction that AI liberal arts education should take for non-majors in universities. I hope this study will help us better design an AI liberal arts curriculum.
From Classification to Visualization: A Two Way Trip

High Dimensional Data (HDD) is one of the biggest challenges in Data Science arising from Big Data. The application of dimensionality reduction techniques over HDD allows visualization and, thus, a better problem understanding. In addition, these techniques also can enhance the performance of Machine Learning (ML) algorithms while increasing the explanatory power. This paper presents an automatic method capable of obtaining an adequate representation of the data, given a previously trained ML model. Likewise, an automatic method is introduced to bring a Support Vector Machine (SVM) model based on an adequate representation of the data. Both methods provide an Explanaible Machine Learning procedure. The proposal is tested on several data sets providing promising results. It significantly eases the visualization and understanding task to the data scientist when a ML model has already been trained, as well as the ML selection parameters when a reduced representation of data has been achieved.
Geochemical evaluation of the <i>in situ</i> regolith at Madengi Hill, Dodoma, Tanzania: implications for bedrock mapping and delineating gold mineralization targets

Successful gold exploration projects depend on a piece of clear information on the association between gold, trace elements, and mineralization controlling factors. The use of soil geochemistry has been an important tool in pinpointing exploration targets during the early stage of exploration. This study aimed to establish the gold distribution, the elemental association between gold and its pathfinder elements such as Cu, Zn, Ag, Ni, Co, Mn, Fe, Cd, V, Cr, Ti, Sc, In, and Se and identify lithologies contributing to the overlying residual soils. From cluster analysis, a high similarity level of 53.93% has been shown with Ag, Cd, and Se at a distance level of 0.92. Au and Se have a similarity level of 65.87% and a distance level of 0.68, hence is proposed to be the most promising pathfinder element. PCA, FA, and the Pearson's correlation matrix of transformed data of V, Cu, Ni, Fe, Mn, Cr, and Co and a stronger correlation between Pb and U, Th, Na, K, Sn, Y, Ta and Be shows that source gold mineralization might be associated with both hornblende gneisses interlayered with quartzite, tonalite, and tonalitic orthogneiss. From the contour map and gridded map of Au and its pathfinder elements, it has been noted that their anomalies and target generated are localized in the Northern part of the area. The targets trend ESE to WNW nearly parallel to the shear zones as a controlling factor of Au mineralization emplacement. Supplementary material: https://doi.org/10.6084/m9.figshare.c.5721965
Writaupair: Assistive Platform for Children with Writing Difficulties

The Zone of Proximal Development (ZPD) is the range of abilities that an individual can perform with assistance but cannot yet perform independently. These skills are called “proximal” because the individual is close to mastering them but needs more guidance and practice in order to perform these actions independently. Recently, there has been a noticeable increase in the number of children with learning difficulties. Children with cognitive and other learning difficulties often are unable to organize their thoughts and express their comprehension of a given topic through writing. Such children require comprehensive care and quick intervention, especially during the early childhood years. Based on the concept of the ZPD, we present our work on a contextualized assistive platform called Writaupair, wherein we diagnose the type and extent of learning difficulty faced by a child and accordingly provide contextualized assistance to help him/her with writing long text such as essays. Writaupair employs dependency parsing and state-of-art next sentence prediction model BERT to diagnose the type and extent of learning difficulty a learner has and accordingly prompts keywords, phrases or next sentences to stimulate a topical thinking in children.
Image Enhancer

Image processing is done using machine learning algorithms and tools like OpenCV (short for Open Computer Vision). This research paper shows an improved and convenient approach towards processing of images. Images that are highly distorted by any means or noise are too common in todays' era. This problem can rise by too many reasons be it transmitting from one source to another, by physical device damages, signals poor decoding from the source and even the noise from the sensor of the device they are taken. Prior to the invention of computers and applications such as Photoshop, these jobs were clearly performed only by experts with extensive knowledge in these areas. They mainly used to process physical copies of the images like enhancing the images by brushing techniques. For eliminating undesired noises and thus providing higher image quality, a new decision-based filtering strategy combining K-means and PCA is proposed. Now a days, there are various filters available in many free editing tools that surely do enhance the quality of the image the way you want them to be.
90th anniversary from the birth of academician A.P. Ershov

The article presents the main milestones in the life and scientific path of Andrei Petrovich Ershov (1931-1988), an outstanding scientist, one of the founders of informatics and programming in our country, whose name is closely connected with the development of the school informatics course. The main ideas of the first domestic textbook of informatics, created under the leadership of A.P. Ershov, are presented in the article. These ideas are an extraordinary phenomenon in the world school informatics, which largely determined the subsequent direction of the development of the subject. The quotes from several articles by A.P. Ershov are given, which are especially relevant at the present time.
Research on teaching mode of Japanese translation course based on computer aided translation

research-article Research on teaching mode of Japanese translation course based on computer aided translation Share on Author: Haiyan Wei Xi'an Fanyi University, China Xi'an Fanyi University, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 444–448https://doi.org/10.1145/3482632.3482726Online:22 November 2021Publication History 0citation5DownloadsMetricsTotal Citations0Total Downloads5Last 12 Months5Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Performance Analysis of Adaptive Unsharp Masking Filter Techniques for Image Contrast Enhancement

Image contrast enhancement is known as one of the important techniques applied in the field of image processing. In order to improve the contrast of the captured image, different adaptive Unsharp Masking Filter (UMF) techniques were proposed by the researchers. In this paper, the main contribution is the implementation of three algorithms namely adaptive gain adjustment approach using an UMF (ASAUMF), design of UMF kernel and gain using Particle Swarm Optimization (UMFKG) and lastly, intensity and edge-based adaptive UMF (IntEdgUMF) which is denoted as Algorithm 1, 2 and 3 respectively. These algorithms were tested on the standard and biometric images like face images. This is because these adaptive UMF were mainly applied to natural scenery, but the importance of high image quality is not limited to the environment but also to the other fields such as biometric identification. Based on the results, Algorithm 1 is able to achieve the highest average PSNR values of 31.6079 dB and 35.8052 dB when applied on Set14 and LFW databases respectively. Although Algorithm 1 needs a longer running time in producing the output images, this algorithm can emphasize the details or information from the input image by enhancing the contrast of the image. Thus, Algorithm 1 can be concluded as the best adaptive UMF techniques among the three algorithms tested. For future work, the use of these adaptive UMF can be implemented onto various images, for instance gray scale images or other biometric images in order to test the effectiveness of the algorithms in different applications.
Blended Learning of Higher Mathematics in the Century of the Digitalization of Education

The paper deals with the organization of a remote and blended format of teaching higher mathematics during the period of quarantine measures. It is told about the digital tools and services already available and their further development, as well as the possibilities and prospects of using digital technologies in the learning process. The question of the rationality of a complete transition to digital services and the organization of blended learning, as the most acceptable in the current conditions, is discussed. The results of intermediate students’ knowledge assessment in higher mathematics are given.
Settlement Prediction Method of Bridge Pile Foundation on Soft Soil Foundation Based on Hyperbolic Method

In order to improve the fitting degree between the predicted value and the measured value of pile foundation settlement of bridge on soft soil foundation, a method of pile foundation settlement prediction of bridge on soft soil foundation based on hyperbola method is proposed. According to the deformation mechanism of soft soil foundation, the deformation process of soft soil foundation is analyzed from three stages: plastic elastic deformation, uneven deformation and creep; According to the settlement deformation of soft soil foundation, the settlement development process of soft soil foundation under the action of bridge pile is studied from three stages of instantaneous settlement, primary consolidation settlement and secondary consolidation settlement, and the final settlement of foundation is calculated; In terms of time, the soft soil foundation is divided into two parts: construction period settlement and post construction settlement, and the development time of foundation settlement is calculated; The settlement of bridge pile foundation on soft soil foundation is predicted by hyperbolic prediction equation and hyperbolic fitting method. Experimental results: the fitting degree of the settlement time curve between the predicted settlement value and the measured settlement value of the bridge pile foundation is very high, and the error is mostly within 1%, the maximum is not more than 3%, which has high applicability and reliability.
Usability Assessment of the GoPro Hero 7 Black for Chinese Users

In today’s Internet age, video recordings of life called vlogs are popular. Most of the vlogs were shot with a motion camera, and it is necessary to improve some specific requirements of sports cameras. This study chooses GoPro Hero 7 Black and explores the usability assessment of this product for Chinese users. The research also aims to measure usability through a series of questionnaires. Analytical methods are used to determine its advantages and any existing functional problems. Based on the results of the usability assessment, some suggestions were given.
A real-time local path planning method in complex scenes

Aiming at complex military application scenarios, this paper proposes a vision and lidar fusion scheme, and designs an algorithm for real-time construction of local scenes and real-time local path planning. It satisfies that the vehicle can avoid ground obstacles and aerial obstacles in real time under the state of no navigation and no map, and realize autonomous navigation. Experiments have verified that the algorithm has high execution efficiency and can meet the application requirements of autonomous driving.
Local, Interactive, and Actionable: a Pandemic Behavioral Nudge

The informational environment surrounding the Covid-19 pandemic has been widely recognized as fragmented, politicized, and complex [1]. This has resulted in polarized public views regarding the veracity of scientific communication, the severity of the threat posed by the virus, and the necessity of nonpharmaceutical interventions (NPIs) which can slow the spread of infections [2]. This paper describes CovidCommitment.org, an effort toward enhancing NPI adoption through the combination of a social behavioral commitment device and interactive map-based visualizations of localized infection data as tabulated via a 1-hour-drive-time isochrone. This paper describes the system design and presents a preliminary analysis of user behavior within the system.
Study of Kinematic Chains of Group IV-D, E, and F: Part-Distinct Mechanisms

The author's basic aim is to prepare all the information at one place of all similar, dissimilar bars, and the similar as well as different mechanisms of 10 bars kinematic chains, 13 kinematic pairs, single degree of freedom closed chains of Group IV-D, E, and F using a simple, reliable, and efficient [A] matrix method. Two identification numbers namely ‘absolute sum’ [AS] and ‘absolute maximum’ [AM] of polynomials are determined for every matrix knowing similar or dissimilar. If both identification numbers have same numerical values, then both the matrices are similar and therefore, both the mechanisms are considered similar, and is counted as one mechanism. But, if the numerical values of both the identification numbers are different then, both the mechanisms are different and called distinct mechanisms. The study is extremely useful for new researchers/design engineers and scientist for their research work in the beginning sate of any project. The study is shown step by step with case study.
The Search for the Best Real- Time Face Recognition Method for Finding Potential COVID Patients

Face detection and face recognition has been a part of our daily life for a while now. They usually help us speeding up the time of any identification process in many places. In early 2020, COVID-19 has become a worldwide pandemic and drives many people to find many solutions to this problem. Our contribution to the case is finding the best method to find potential COVID patients before they infect more people in public places by using facial detection and recognition. Using the PRISMA Flowchart methodology, which helps authors systematically analyze relevant publications and improve the quality of reports and meta-analyses The first matter to be solved is to find the most used algorithms used in facial detection and recognition, then followed by finding out which is the best one to be implemented for our case study. Our findings suggests that algorithms that can detect and recognize faces under occluded conditions works best for this case.
Insulators Recognition Based on Enhancement of High-Level Feature with Improved Attention Method

The integrity of the insulator has a great impact on the reliability and safety of the overhead transmission line. In order to inspect the defects, image segmentation is an important step. The performance of segmentation network will have large influence on the insulator defect inspection strategies. In this case, inspired by the classical Fully Convolutional Networks (FCNs) and Attention mechanism, this paper explores the direction of improving network’s performance by enhancing the encoding quality of high-level features to contrive an optimal way of insulator image segmentation. We introduce a cascaded high-level feature enhancement network, which uses cascaded contracting path of U-Net as encoder and cascaded High-level Feature Enhancement Module (HFE) as decoder. The HFE is committed to selectively aggregating the optimal feature information of high- and low-level features, so as to enhance the ability of decoder to reconstruct high-resolution details. In addition, the HFE can provide independent attention to each value of input features, so as to comprehensively optimize the target related feature. We validated the effectiveness of our method in the insulator segmentation task. The experimental results show that our method achieved considerable improvement compared with the state-of-the-art FCNs including U-Net and attention-based U-Net.
Instant SNR Estimation on Digital Symbols

Signal-to-noise ratio is a very important parameter that must be known in order to make many communication systems to work efficiently.The determination of Signal-to-noise ratio may cause extra cost, complexity or can cause a decrease in the efficiency of resource allocation depending on the method used.The machine learning methods offer a practical solution while eliminating these negative aspects with supervised learning and offline training.Deep learning stands out with its success as a type of machine learning.In this study, the estimation of the instantaneous value of the signal to noise ratio in digital symbols was investigated using the deep learning technique.
SeqVectorizer: Sequence Representation in Vector Space

The latest strategies for learning vector space portrayals of words have prevailed with regard to catching fine-grained semantic and syntactic consistencies utilizing vector arithmetic. However, the sequence representation is not present in these methods. As a result, to consider the sequence, we are utilizing the sequence neural networks like RNN or statistical techniques like HMM. To represent the sequence through every state vector, we propose a new term or word representation technique called SeqVectorizer, which stands for sequence vectorizer. In SeqVectorizer every state represents a combined vector of two separate joined states, and these are the previous sequence state and the current state probability. Comparing with other representation systems, it shows a state-of-the-art performance on some testing data-sets.
Illinois Express Quantum Network for Distributing and Controlling Entanglement on Metro-Scale

We describe an implementation of a quantum network over installed fiber in the Chicago area. We present network topology and control architecture of this network and illustrate preliminary results for quantum teleportation and coexistence of quantum and classical data on the same fiber link.
Real-time profilometry by bicolor grating video projection

A novel real-time 2+1 three-dimensional(3D) measuring method based on bicolor grating video projection is proposed. Firstly, only two frames of bicolor gratings, in which the red channels are two sinusoidal fringes with a shifting phase of π/2 and the blue channels are the same background light equivalent to the DC component of the two sinusoidal fringes are encoded and arranged alternatively to synthesize into a repetitive bicolor grating video, While this video is projected onto the measured object, the real-time bicolor deformed pattern video can be recorded by using a color CMOS camera, and the bicolor deformed pattern sequence at different moments can be extracted by computer processing, so that the 2+1 algorithm can be used to accomplish real-time 3D measurement of moving object. Before measuring, we used the same method to design two sinusoidal fringes with a difference of π in their red channels, respectively, to calibrate the sensitivity ratio between the red and blue channels of the CMOS camera, which can effectively eliminate the chromaticity imbalance between R and B channels and reduce the color crosstalk. Experimental results and analysis confirm the feasibility and effectiveness of the proposed method. Because the proposed method needs a repetitive bicolor grating video synthesized with only two-frame bicolor gratings to be projected, the 3D measurement acquisition speed and real-time accuracy will be improved compared with the traditional 2+1 3D measuring method.