Reviewerly/exHarmony huggingface dataset:
METBAG – A Web Based Business Application

Digitization has made the online business in the domain of core sector of metals, minerals and ores more competitive. A lot of web and mobile applications are available to make the process smooth. However, a user has to follow the price trend and analyse it in order to make the right choice. This and possible repetition of the process leads to its slowing down. Also, from the company perspective the operations and transactions involved makes the process complex. So, automation in generating the Irrevocable cooperate purchase order (ICPO) and Sales and Purchase Agreement (SPA), invoices and Non Circumvention and Non-Disclosure Agreement (NCNDA) forms is highly recommended. For increment in business accurate data analytics plays an important role. It is our aim in this paper to focus on developing an automated web application to frame a transaction integrated with all necessary features which is user-friendly from the letter of intent till the generation of invoice for the user and an auto generated dashboard for the company. The process includes the price prediction for the products and also takes care of the security of user credentials in view using hashing and multi-lingual jumbled salting.
Algorithm of Basketball Posture Motion Feature Extraction Based on Image Processing Technology

In the field of basketball, the existing training concept is based on the simulated observation and personal experience of the coaches, and it is subject to subjective judgment of inadequacies. Using image technology to train athletes is mainly to help coaches make decisions and improve the strength of athletes through the identification and recognition of athletes' states and transfer characteristics. The purpose of this paper is to study the basketball pose motion feature extraction algorithm based on image processing technology. This paper firstly builds the basketball posture model, introduces the feature extraction and selection in basketball posture, analyzes the application of image processing algorithm in basketball posture movement feature extraction, and mainly uses filtering algorithm to denoise the moving image. The algorithm in this paper mainly selects the normalized data processing algorithm to perform data induction processing on the feature data extracted in this paper. Experiments show that after image processing technology, the recognition rate of motion features by BP neural network is the highest, and the average recognition rate reaches 96%, which can effectively recognize the motion features of basketball posture.
A Study on the Diversified Development of English Education and Teaching

English is an indispensable foreign exchange language in China at this stage, and the diversified development path of English education and teaching is studied.This paper discusses the importance of diversified development of English education and teaching, including four aspects: teaching objectives, teaching principles, teaching methods and teaching evaluation.It designs diversified development strategies for english teaching, and studies the application strategies of diversified teaching objectives, diversified teaching principles, diversified teaching methods and diversified teaching evaluation.
Multi-objective Evolutionary Instance Selection for Multi-label Classification

Multi-label classification is an important topic in machine learning, where each instance can be classified into more than one category, i.e., have a subset of labels instead of only one. Among existing methods, ML-kNN [25], the direct extension of k-nearest neighbors algorithm to the multi-label scenario, has received much attention due to its conciseness, great interpretability, and good performance. However, ML-kNN usually suffers from a terrible storage cost since all training instances need to be saved in the memory. To address this issue, a natural way is instance selection, intending to save the important instances while deleting the redundant ones. However, previous instance selection methods mainly focus on the single-label scenario, which may have a poor performance when adapted to the multi-label scenario. Recently, few works begin to consider the multi-label scenario, but their performance is limited due to the inapposite modeling. In this paper, we propose to formulate the instance selection problem for ML-kNN as a natural bi-objective optimization problem that considers the accuracy and the number of retained instances simultaneously, and adapt NSGA-II to solve it. Experiments on six real-world data sets show that our proposed method can achieve both not worse prediction accuracy and significantly better compression ratio, compared with state-of-the-art methods.
Covid-19 Smart Contact Tracing Solution: A Process Methodology on Attribute Selection

Contact tracing technique have been very useful in determining proximity that can be used to successfully curb or prevent further spread of disease during a pandemic. In this paper, we investigated how contact tracing will perform better if proper attribute selection is determined beforehand for Covid-19 pandemic. In accordance to the smart contact tracing concept, the more accurate the attributes, the better the contact tracing solution. The process methodology that we conducted are of two parts: (1) Systematic Literature Review (SLR) and (2) Delphi methodology utilizing Problem, Intervention, Comparison and Outcome (PICO) framework. Upon integrating the views of the experts and review of literature, the output for a comprehensive smart contact tracing Index has been introduced as our contribution.
Design Science Research Methodology Concept and Its Application in the Development of Online Community Communication System for Selotapak Village

Natural science is a collection of global validated knowledge on objects to understand the characteristics and properties of natural phenomena. The natural science field uses the scientific method as its research method. In further development, humans use this scientific field to meet their daily needs, leading to the emergence of an artificial terminology known as design science. Many design science researchers create Design Science Research Method (DSRM) as an experimental method used in design science research, which came from modification of the scientific research method. Based on the broad utilization of design science to determine the solutions to human problems, many scientific fields have adopted the DSRM approach, such as medical science, pharmacy, socio-technical, management, engineering, education, psychology, etc. This article aims to discuss the concept of DSRM and to illustrate this method's application in developing the Online Community Communication System for Selotapak Village as the research case. Each DSRM phase run in this research was modified to iterate in 2 rounds. The first round aimed to capture problems generally; meanwhile, comprehensive result validation and identification were observed in the second round. Semi-structured interviews, questionnaires distribution, and observation were used in the DSRM iteration phase as research strategies. Several design models were used to depict data, user interface, and processes required, such as Entity Relationship Diagram and Business Process Modeling Notation. The evaluation result shows that all users were satisfied with the application.
Blind Quality Assessment of Tone-Mapped Images Based on Visual-Processing Features

Albeit high dynamic range (HDR) images contain way richer information than low dynamic range (LDR) images, they need to be tone mapped for the visualization on traditional display devices. The community has developed diverse tone-mapping operators (TMOs), and they produce images with varying qualities. Therefore, developing effective evaluation methods consistent with human visual perception is an urgent necessity for determining the best TMO in specific scenarios, or accordingly optimizing the parameters of a certain TMO. Towards this end, we propose a new blind quality assessment model by simulating the mechanism of human visual processing (HVS), which can adaptively adjust the sensitivity according to the chromatic properties of scenes at the beginning and well represent the perception process. Specifically, we first obtain all the retinal response maps of four visual sensitivities from three opponent color channels. After that, gradient similarities in each color channel are calculated as global features to represent the procedure of visual experience, and local mean values and standard deviations are computed in the brightest and darkest regions of the maps to measure the distortions caused by over- and under-exposure. Meanwhile, the maps’ natural scene statistics (NSS) are utilized as the third set of features. Finally, all these features are blended for quality assessment by support vector regression (SVR). Extensive experiments on two public benchmark databases show our method correlates highly with subjective perception and outperforms other state-of-the-art quality assessment methods.
PETROLOGY, GEOCHEMISTRY AND U-PB GEOCHRONOLOGY OF MAFIC AND ULTRAMAFIC ROCKS OF NW GUYANA, GUIANA SHIELD, SOUTH AMERICA

The Guiana Shield covers 900,000 km² and is one of the least explored areas in the world [1].We are studying mafic and ultramafic rocks of NW Guyana (Fig. 1), part of the Guiana Shield, to better characterize them including determining the number of distinct events, their mantle sources, differentiation histories and to assess which belong to LIPs, and which can be linked to coeval units in the formerly adjacent West African craton.One magmatic group (consisting mostly of fresh unmetamorphosed dolerite) is linked to the 1790 Ma Avanavero LIP.The remaining samples, named Matthews Ridge (MR) group, have been metamorphosed to a low-grade, greenschist facies.The geochemical results show that the samples concentrate in the fields of andesitic basalt and basaltic compositions, and have a tholeiitic affinity.Their SiO 2 content ranges from 39 to 54 wt% which support the presence of mafic and ultramafic rocks in the area.For the Avanavero group, the chondrite-normalized diagram for the Rare Earth Elements (REE) displays a slight enrichment in the light-REE relative to the heavy-REE; the MR samples, have a flat pattern, meaning that there is almost no difference in the concentration of light and heavy REE.However, two main groups are formed when binary diagrams are used, Matthews Ridge unit 1A (low-MgO) and Matthews Ridge unit 1B (high-MgO), with the possibility of subgroups within them.Analysis of zircon and baddeleyite grains from 4 samples collected by FQM (First Quantum Minerals) have ages of 2097±39 Ma. (MSWD=1.7);2125±25 Ma. (MSWD=0.14);2157±40 Ma. (MSWD=1.3) and 2238±81 Ma. (MSWD=0.13).The ages show that these samples are not part of the Avanavero LIP [2]. This project will contribute to the global understanding of LIP events, and benefit future studies involving paleogeographic reconstructions as more information about the LIP events in the Guiana Shield are added to the world LIP database.
ABOUT ETNOPEDAGOGICAL DIALOGUES IN THE SCIENTIFIC HERITAGE OF ACADEMICIAN OF THE RAE G.N. VOLKOV (FOR THE TEACHER’S ANNIVERSARY)

The scientific legacy of the outstanding teacher of our time, Academician of the Russian Academy of Education G.N. Volkov is extensive and multifaceted; a special place in it is occupied by the consideration of folk traditions of personality formation and various aspects of the traditional culture of education in the aspect of systematization of the basic concepts of ethnopedagogy in their correlation with the postulates of professional pedagogy, as well as as- pects of the ethnopedagogical dialogue of cultures of different peoples. Using a large amount of factual material, the scientist in his more than seven hundred publications (of which more than fifty monographs) convincingly proves the typological relationship, the similarity of the ethnopedagogical heritage of different peoples, which, in the current conditions of the education crisis, with a methodologically and methodologically competent approach, can become an essential part of the content base of the modern education.
Showing Your Spring Application on the Web

In the last two chapters, we took a look at persistence in Spring, through the use of JDBCTemplate (when we built our own DAO) and Spring Data (where we let Spring build out all of the mechanisms by which we interacted with a data store). In this chapter, we’re going to switch gears and take a look at exposing our application over the World Wide Web, using a templating engine known as Thymeleaf, using Spring Boot’s web starter mechanism.
Vulnerability Detection Using Deep Learning Based Function Classification

Software vulnerabilities are becoming increasingly severe problems, which can pose great risks of information leakage, denial of service, or even system crashes. However, their detection is still formidable, due to the diverse forms of software development and the diverse programming styles of software developers. In this paper, we propose a vulnerability detection tool to explore security issues in source code using deep learning-based function classification. Specifically, we first extract and parse function prototypes in the source code. Then, with the help of the pre-built corpus, we split the function prototypes into segmentations with semantic meanings. By utilizing deep learning-based classifiers, the segmentations are further classified into seven categories. Finally, we use static scanning analyzers to separately detect vulnerabilities of different types of functions. Additionally, the experimental results show that the proposed method can effectively and efficiently distinguish vulnerabilities in the benchmark source code (5 of 7 memory corruptions, 13 of 18 cryptography vulnerabilities, 5 of 6 data processing errors, and 13 of 18 random number issues).
The Distributions of Vocational High School Teachers’ Advanced Digital Competence (ADC)

Digital competence is one of the significant skills to be possessed by the teachers as the impact of rapid technology development in many aspects such as economy, education, health, and environment. Various digital competence measurements and frameworks had been developed to ensure that someone has an advance digital skill and one of those is DigComp 2.1 by European Commission. Many previous studies had been studied about teachers’ digital competence levels influenced by some factors such as age, gender, working period, and educational background. However, it is infrequently analysed that the advance digital competence (ADC) of Vocational High School (VHS) teachers is differentiated by engineering field and non-engineering field, area or region and its school quality. Moreover, this study aimed: (a) to show the ADC level analysis between VHS teachers in engineering and non-engineering school, (b) to delineate the ADC level of VHS teachers based on the islands where the school is located, and (c) to represent the impact of school quality towards the VHS teachers’ ADC level. The data was analysed using a quantitative approach with a descriptive statistics method. Additionally, the instruments of the survey to collect the data were adapted from the DigCompEdu framework in part of advanced level and measured using Likert scale. The participants of this study were 392 respondents but, it was classified and divided into some fields based on VHS fields, islands and school accreditation. The result of this study expounded that VHS teachers in the engineering field have higher scores at the ADC level even though there are some ADC components that should be upgraded by the VHS teachers in both fields. Meanwhile, islands and school accreditation had no significant impact yet showed the opposite result.
Construction of Business English Translation Teaching Mode in Higher Vocational Colleges Based on Online Learning Platform

The Internet era not only provides abudant teaching resources for higher vocational education, but also puts forward higher requirements for teachers.Based on constructivism learning theory and online learning platform, this paper discusses establishment of a student-centered teaching mode in Business English Translation teaching by building a "scaffold" and constructing learning scenarios, so as to continuously promote teaching reform and improve teaching quality.
Learner Model of Technology Education for AI Convergence Individualized Learning

The purpose of this study was to derive a learner model from technological education for AI convergence individualized learning.The learning model devised in this study reflects the characteristics of technological problem-solving as it considers all areas of cognition, definition, and psychodynamics.Among the dimensions of recognition, judgment, creativity, execution, attitude, and interaction of technological problem-solving learning styles, the dimensions that acted mainly according to the content of the problem and the learning type could be selected and constructed as a model.In this study, a creativity-execution model was devised in consideration of the 'adaptationinnovation type' of the creativity level and the 'reflection-action type' of the execution level.In addition, an interactive-attitude model was devised in which the 'independent-cooperative type' in the interaction dimension and the 'avoidance-participation type' in the attitude dimension act dynamically.Furthermore, a three-factor learner model was developed considering the dimensions of cognitive judgment, creativity, and execution.Also, a five-factor learner model for technological problem-solving was devised and presented, with all five dimensions of cognition-judgment, creativity, execution, interaction, and attitude of the technological problem-solving learning style as factors.The learner characteristics defined through the model design suggested the direction of AI convergence education considering individual characteristics of learners and provided basic data for it.In the future, it is suggested that specific individualized teaching and learning development research be conducted in AI convergence education, considering the characteristics of learners according to the learning style of technological problem-solving.
A New Foreground and Background Image Segmentation Method Based on a Convex Shape Prior and a Nonconvex Regularizer

Foreground and background image segmentation plays an important role in the field of image processing. Convex shape priors are introduced in some image segmentation methods to estimate those implicit boundaries of image objects when these convex objects are partly obscured by unrelated surroundings that appeared in images. Classical convex shape priors based image segmentation methods use binary label functions to distinguish different image regions, which are sensitive to model parameter choices and often show poor performance in segmentation results. To solve these problems, in this paper fuzzy membership functions, which can be seen as relaxed forms of typical binary label functions, are chosen to represent different image parts, and a new variational model with respect to fuzzy membership functions is proposed in which a convex shape prior on fuzzy membership functions is designed to guarantee estimated image foregrounds having convex shapes in geometry, while to avoid the indicator function from being over-smoothed, a minimax concave regularization term is introduced to measure the smoothness of fuzzy membership functions. In order to construct an efficient algorithm to obtain desired solutions, the original model we proposed is transformed into an equivalent constrained minimization problem using the variable splitting technology, and then numerical solutions for the equivalent constrained model can be solved by an iteration algorithm which integrates the alternating direction method of multipliers, the fast Fourier transform, and the soft thresholding method. Numerical results demonstrate our proposed image segmentation method can keep estimated foreground regions convex in geometry. Compared with classical image segmentation methods, our proposed method shows more robust in model parameter choices and can obtain better convex shape in image segmentation results.
Design of Lightweight and Miniaturized Motor Control System Based on Anti-Integral Saturation

Lightweight and miniaturized hollow motors have the advantages of high power density, light weight and small volume, and are important functional components for today’s aerospace and specific businesses. In order to solve the problem of a given large step, the traditional controller is unable to achieve the rapid response of the system while satisfying the small overshoot or even no overshoot of the system due to integral saturation. A three-loop control strategy is proposed to optimize the traditional control technology, combined with the system Technical index requirements, design a new type of anti-integral saturation PI controller, and use Simulink to build a controller model for simulation analysis. The simulation analysis results show that the control system meets the load rotation 180° with a transition time of not more than 0. 2s, and the positioning accuracy of less or equal to The index requirement of 10’, the whole motor control system has good rapidity and accurate positioning function.
A note on the assessment of a fuzzy derivatives’ time-series predictor

This note presents the validation of a new methodology for the prediction of complex discrete-time systems or time-series based on a disturbed fuzzy system. This is a generalisation of the traditional fuzzy system that can incorporate in their linguistic fuzzy relationships of the series and is combined with the ODE Taylor method. Disturbed fuzzy systems are capable of approximating regular functions, as well as their derivatives up to a given order, on compact domains. This results into a new algorithm to solve forecasting problems. The validation of this new time-series forecasting model is done in this brief article using two iconic examples, the Box-Jenkins furnace and a nonlinear dynamic system. The application of the algorithm to both examples, and also a comparative study with other fuzzy and neural network predictors, permits us to conclude that this approach can offer competitive performance. Its virtue lies in the fact that efficiency in accurate and robust forecasting cannot rest solely on a good algorithm but instead should capture and make explicit use of the derivatives of the time-series.
Detection of Hands for Hand-Controlled Skyfall Game in Real Time Using CNN

With human-computer interaction technology evolving, direct use of the hand as an input device is of wide attraction. Recently, object detection methods using CNN models have significantly improved the accuracy of hand detection. This paper focuses on creating a hand-controlled web-based skyfall game by building a real time hand detection using CNN-based technique. A CNN network, which uses a MobileNet as the feature extractor along with the single shot detector framework, is used to achieve a robust and fast detection of hand location and tracking. Along with detection and tracking of hand, skyfall game has been designed to play using hand in real time with tensor flow framework. This way of designing the game where hand is used as input to control the paddle of skyfall game improved the player interaction and interest towards playing the game. This model of CNN network used egohands dataset for detecting and tracking the hands in real time and produced an average accuracy of 0.9 for open hands and 0.6 for closed hands which in turn improved player and game interactions.
Organizers

List of organizers of the Intelligent Narrative Technologies workshop held at the Ninth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment.