Reviewerly/exHarmony huggingface dataset:
Application of Virtual Reality Technology in Art Design: A Systematic Approach

The 21st century witnesses innovative concepts in the field of technology and multimedia integrated with networking. The new concept is referred to as Virtual Reality, which is a combination of multimedia and networking. Virtual Reality is wholly different from other advanced technologies that are visually appealing. The main features of virtual reality are identified as spatial conception, perception comprehensiveness, and information interaction. Apart from the above, other specialties of virtual reality are the generation of 3D images and presenting information in a unique way. These features are generally applied across different fields, such as environmental design, architecture, public art design, and other industries. Virtual Reality processes the precise data calculation and then optimises the display experience of the users/customer. It also fulfills customers’ needs and enhances user satisfaction by exchanging valuable information. The entire gaming industry applies Virtual Reality, and thus, the world looks more mature. Even the tourism industry has started using Virtual Reality which has the potential to bring novel experiences and vitality to the audience. Therefore, it can be confirmed that the application of Virtual Reality will undergo continuous development and advancement, adjusting to the needs of the industries. The primary aim of this study is to gain an in-depth understanding of the Virtual Reality concept and the application of Virtual Reality in art design and to examine the impact of Virtual Reality on public art design. This article also reviews the features of Virtual Reality and its application in art designs. The advantages of Virtual Reality will also be discussed broadly. At last, this study proposes a feasibility model of Virtual Reality that can be applied in public art design.
Evaluating Image Quality Estimators for Face Matching

Understanding the quality of a face image can be useful for improving the performance of automated face matching systems. With the increasing number of face quality estimators (QEs) being proposed recently, it is important to have systematic methods to evaluate and compare the performance of these QEs. In this paper, we describe two existing strategies for evaluating face QEs, and propose several new approaches. Our new approaches focus on targeted QE evaluation using carefully constructed image datasets. We show that these strategies lead to important insights about the effectiveness of existing face QEs.
Analysis of Teaching Strategies of College English Speculative Reading Based on Big Data Analysis of Student Behavior in Cross-Cultural Education Environment

Both dynamic and interactive, cross-cultural education emphasizes respect, interaction, and understanding between various cultural groups. Its fundamental principle is to acknowledge and value cultural diversity while opposing attempts to artificially unite cultures in order to recognise the equality and common heritage of all cultures. The goal of intercultural education, from an individual’s perspective, is to support those who are educated in understanding, accepting, and respecting other foreign cultures in order to better adapt to and thrive in a multicultural society. To develop students’ intercultural communication skills is the goal of intercultural education. Based on the associations between intercultural education and speculative reading, this essay analyzes the crucial role that intercultural English teaching and speculative reading instruction play in colleges and universities before going over the situation and approaches that English speculative reading instruction is currently facing. This paper primarily discusses the data on students from the student work management system, the relationship between grades and actual behavior, and the rules that are mined to provide a scientific basis for decision-making for the relevant units in colleges and universities. According to experimental analysis, the data mining algorithm in this paper performs better than the other two in terms of predicting students’ English achievement, with an average accuracy of 0.86 compared to 0.71, 0.075, and 0.73 for the other three algorithms. The decision tree created using this methodology can quantitatively reflect the impact of different teaching factors on learning quality and offers a clear and reliable foundation for formulating targeted improvement strategies.
Combining Machine Learning &amp; Reasoning for Biodiversity Data Intelligence

The current crisis in global natural resource management makes it imperative that we better leverage the vast data sources associated with taxonomic entities (such as recognized species of plants and animals), which are known collectively as biodiversity data. However, these data pose considerable challenges for artificial intelligence: while growing rapidly in volume, they remain highly incomplete for many taxonomic groups, often show conflicting signals from different sources, and are multi-modal and therefore constantly changing in structure. In this paper, we motivate, describe, and present a novel workflow combining machine learning and automated reasoning, to discover patterns of taxonomic identity and change - i.e. “taxonomic intelligence” - leading to scalable and broadly impactful AI solutions within the bio-data realm.
Lecture demonstrations of experiments in physics at the Novosibirsk State University

The article tells about the 60-year history of the creation of a set of basic lecture demonstrations at the Physical Department of the Novosibirsk State University and about the subdivision for demonstrations support – from the moment the university was founded. The evolution of a set of demonstration installations, methodological descriptions andtechniques for using equipment in the process of teaching physics in various situations is considered. It tells about the people who made a great contribution at different stages of the transformation of DKF – a demonstration room in physics and MLDKS – an interfaculty laboratory of demonstrations and computer support. Bibliographic references to catalogs with methodological descriptions of lecture demonstrations developed and created at NSU are given.
Deep Discriminative Feature Models (DDFMs) for Set Based Face Recognition and Distance Metric Learning

This paper introduces two methods that find compact deep feature models for approximating images in set based face recognition problems. The proposed method treats each image set as a nonlinear face manifold that is composed of linear components. To find linear components of the face manifold, we first split image sets into subsets containing face images which share similar appearances. Then, our first proposed method approximates each subset by using the center of the deep feature representations of images in those subsets. Centers modeling the subsets are learned by using distance metric learning. The second proposed method uses discriminative common vectors to represent image features in the subsets, and entire subset is approximated with an affine hull in this approach. Discriminative common vectors are subset centers that are projected onto a new feature space where the combined within-class variances coming from all subsets are removed. Our proposed methods can also be considered as distance metric learning methods using triplet loss function where the learned subcluster centers are the selected anchors. This procedure yields to applying distance metric learning to quantized data and brings many advantages over using classical distance metric learning methods. We tested proposed methods on various face recognition problems using image sets and some visual object classification problems. Experimental results show that the proposed methods achieve the state-of-the-art accuracies on the most of the tested image datasets.
The Development of CEFR-Based Nahwu And Shorof Learning Evaluation in MA An-Nur Jagasatru Cirebon

This study aimed to determine the implementation and development of CEFR-based nahwu and shorof learning evaluations at MA An-Nur Jagasatru Cirebon. The research methodology used is the research and development method. In the development of this research, we used the Rowntree model, which was consisted of three stages, namely the planning stage, the development stage, and the evaluation stage. The trial phase began with the validation of material experts and linguists. Then, after being revised, the product was tested on class X students, namely 8 students for testing prototype 1 and 21 students for testing prototype 2. The results showed that the average score of material experts was 85%, which meant the product was considered very good, and linguists 88%, which meant the product was considered very good. Then at the testing stage of prototype 1, an average score of 64.37, which meant the product was considered quite good, and at the testing stage of prototype 2, an average score of 74.71, which meant it was considered very good. Based on interviews with teachers, they obtained very good results. Based on the results of the study, it can be concluded that the evaluation of nahwu and shorof learning based on CEFR is feasible and effective as an evaluation tool in nahwu and shorof learning, especially in MA An-Nur Jagasatru Cirebon.
Relaxation of the parameter independence assumption in the bootComb R package

Abstract Background The bootComb R package allows researchers to derive confidence intervals with correct target coverage for arbitrary combinations of arbitrary numbers of independently estimated parameters. Previous versions (&lt;1.1.0) of bootComb used independent bootstrap sampling and required that the parameters themselves are independent—an unrealistic assumption in some real-world applications. Findings Using Gaussian copulas to define the dependence between parameters, the bootComb package has been extended to allow for dependent parameters. Implications The updated bootComb package can now handle cases of dependent parameters, with users specifying a correlation matrix defining the dependence structure. While in practice it may be difficult to know the exact dependence structure between parameters, bootComb allows running sensitivity analyses to assess the impact of parameter dependence on the resulting confidence interval for the combined parameter. Availability bootComb is available from the Comprehensive R Archive Network ( https://CRAN.R-project.org/package=bootComb ).
Intuitionistic Epistemic Logic with Distributed Knowledge

We develop intuitionistic epistemic logicswith distributed knowledge, which is more general thana logic proposed by (J¨ager & Marti 2016) in thata distributed knowledge operator is parameterized bya group of agents.Specifically, we present Hilbertsystems of intuitionistic K, KT, KD, K4, K4D, and S4 withdistributed knowledge. The semantic completeness ofthe logics with regard to suitable Kripke frames is shownby modifying the standard argument of the semanticcompleteness of classical distributed knowledge logicsvia the concept of pseudo-model.We also presentcut-free sequent calculi for the logics, based on which weestablish Craig interpolation theorem and decidability.
Ensemble Recurrent Neural Network using Genetic Algorithm applied in Times Series Prediction

This paper shows a new method of ensemble recurrent neural networks for time series prediction. The proposed method we seek to find the structure of ensemble recurrent neural network and its optimization with Genetic Algorithms applied to the Prediction of time series. For this method, two systems are proposed to integrate responses ensemble recurrent neural network that are type-1 and Interval type-2 Fuzzy System. The optimization consists of the modules, hidden layer, neurons of the ensemble neural network. The fuzzy system used is of type Mamdani, which has five inputs variables and one output variable, the number of inputs of the fuzzy system is according to the outputs of Ensemble Recurrent Neural network. Test are performed with Mackey Glass benchmark, Mexican Stock Exchange, Dow Jones and Exchange Rate of US Dollar/Mexican Pesos. tis in this was show that the method is effective for time series Prediction.
Research on UAV Task Assignment Based on Fireworks Algorithm

From military, industry, agriculture to artificial intelligence and other fields, unmanned aerial vehicles (UAVs) have been widely used.Task allocation and path planning are the two most basic technologies in all the application research of UAV.As the core of multi-aircraft tactical deployment and cooperative control, the task allocation scheme largely determines the operational efficiency and execution effect of multi-UAVs.Due to the influence of the task environment and its own resources, the multi-machine cooperative task allocation problem is a multi-objective optimization and decision-making problem limited by many constraints.In recent years, many methods of task assignment and path planning have been put forward, and some progress has been made.According to the overall control of the application scenario, the task planning makes a reasonable task allocation scheme for the UAV to help it complete the task better.Multi-UAV task assignment is a complex multiconstraint multi-objective nonlinear optimization problem.Aiming at the respective defects of common intelligent algorithms, this paper proposes to use fireworks algorithm to deal with this problem.
P-IOC-FPMine: an association rule mining algorithm for item constraints based on the MapReduce framework

In the application of big data, one of the most challenging problems is how to consider the requirements of users. To avoid this problem, we proposed IOC-FP-growth. This added user-defined pre-term or post-item constraints into the classic FP-growth algorithm. What's more, a parallel data mining algorithm based on MapReduce, namely P-IOC-FPMine was proposed, which was a low-memory fast association rule mining algorithm. Finally, by evaluating the effectiveness of the method in the public data. The results showed that the IOC-FP-growth method can consider the user's needs for association rules easily. Compared with the FP-growth, Recorder and PNARCMC, we only need to meet the requirements of users to extract accurate rules, which will bring huge advantages for data mining. After parallelisation, the performance of the P-IOC-FPMine was better than FP-growth on the data set. The results showed that the P-IOC-FPMine was more appropriate for handling large-scale data sets.
Object coding in peripersonal space depends on object ownership

Previous studies have shown that objects located in the peripersonal space (PPS) receive enhanced attention, as compared with extrapersonal space (EPS), However, most objects in the environment belong to someone in particular and how object ownership influences object coding in relation to PPS representation is still unclear. In the present study, after having chosen their own mug, participants performed a reachability judgement task of self-owned and other-owned mugs presented at different distances while facing a virtual character. This task was followed, on each trial, by a localisation task in which participants had to indicate where the mug, removed from view, was previously located. The two tasks were separated by a 900-ms visual mask during which the virtual character was unnoticeably shifted by 3° to evaluate the spatial frame-of-reference used. The results showed that self-owned mugs were processed faster than other-owned mugs, but only when located in the PPS. Furthermore, reachability judgements were biased for self-owned mugs, leading to an extension of the PPS representation, especially for participants with a high score on the fantasy scale of Interpersonal Reactivity Index (IRI). Finally, the virtual character shift altered the localisation performance but only for the distant mugs, suggesting a progressive shift from egocentric to allocentric frame-of-reference when moving from the PPS to EPS, irrespective of object ownership. Overall, our data reveal that the representations of ownership and PPS interact to facilitate the processing of manipulable objects, to an extent that depends on individual sensitivity to the social presence of others.
Application of the Whole Language Teaching Method in the Teaching of English Vocabulary Based on the Multimedia Environment of the Internet of Things

Multimedia technology has also been cited and developed in the field of education.Its intervention has had a profound impact on education, triggering the renewal of the concept of educators, the renewal of educational methods and the renewal of educational forms.Actively exploring, optimizing teaching modes, innovating teaching methods, and updating teaching concepts are the teaching reform direction of colleges and universities.How to take a scientific and rational mode of teaching based on different disciplines, to students to maximize guidance and inspiration, it is essential.As a teaching theory, the overall language teaching method has a great influence on language teaching.In college English learning, the correct way to master the vocabulary is to learn by use.Only through multiple learning and applications, the combination of language input and output can ultimately improve the language application ability.This coincides with the idea of an educational philosophy, the overall language teaching method.Today, multimedia has become an important teaching device in schools.Therefore, this paper combines the unique advantages of multimedia technology with the overall language teaching method and empirically compares it with the traditional vocabulary teaching method, trying to find an effective teaching method that can promote students' vocabulary acquisition.
Label Oriented Hierarchical Attention Neural Network for Short Text Classification

Text classification task is a very common task in natural language processing.The conventional text classification task model often uses word bag model or representation model, but the existing model usually deals with long text classification task, which is not suitable for short text classification.Short text features are relatively fewer and often need more sophisticated feature extraction, and the role of keywords play great importance roles in the classification of short text.In this paper, we propose a label-oriented hierarchical attention mechanism network for short text classification.The model achieves better results on public data sets of Tiao and Weibo, compared with convolutional neural network CNN, GATE control unit neural network GRU, gate control unit neural network fusion convolutional neural network GRU-CNN and translation model Transfomer.It is proved that the model has good performance.Our model has two significant advantages: (1) it is a hierarchical structure consisting of two levels of attention mechanisms, which facilitates interpretation and analysis; (2) Compared with other architectures, this model can be used to extend multi-label text classification tasks.In addition, it can be used for long-term importance analysis in many industrial scenarios.
Design of English Teaching Postcompetency Evaluation System Based on Data Mining and IoT

Considering the unfortunate skill of English teaching posts, this paper advances the plan strategy for English teaching postcapability assessment framework in light of data mining, further develops the framework equipment arrangement, upgrades the activity cycle and capability of the framework programming, and builds the English teaching postability assessment framework. At long last, it is affirmed by tests. The English teaching postability assessment framework in view of data mining has high practicability during the time spent down to earth application and completely meets the examination necessities.
Motion Video Image Based on Sensor and Multiprocessing Technology in Track and Field Sports

In order to use motion video image processing technology to better guide and evaluate athletes’ technical and tactical ability, this paper first constructed a motion video image acquisition system. Secondly, a fuzzy kernel estimation method for uniform linear motion based on cepstrum property is proposed. Then, the motion blur areas to be processed are selected, and only these areas are deblurred. Finally, the effective removal of local motion blur is realized, and a clear scene image is obtained. Then, the maximum value of the total entropy of the image is calculated by using the information entropy theory, and the particle swarm optimization algorithm is introduced to find the maximum threshold of image segmentation. Finally, small wave optical flow estimation algorithm and rectangular window scanning algorithm across scales of motion image target detection algorithm are proposed, to not only solve the traditional optical flow estimation for fast moving object detection accuracy but also improve the efficiency of the optical flow computation. Compared to many kinds of algorithms, this paper proposed an algorithm that can improve the accuracy of moving target detection and measurement accuracy. And, the detection accuracy of the proposed algorithm is up to 86.5%. The estimated accuracy was as high as 65%. The segmentation accuracy is up to 95%.
Design and Construction of Intelligent Corridor of Tourist Attractions Based on Sensor Network

This paper presents an in-depth analysis and study of traditional corridor construction and tourism system planning using intelligent sensing networks. This paper proposes a node deployment and coverage optimization method for HWSNs based on particle swarm optimization emperor butterfly algorithm and extreme learning machine, with the objectives of reducing node deployment cost, reducing coverage redundancy and voids, improving monitoring area coverage, and increasing the agility of data processing by 15.03%, efficiency by 12.15%, and stability by 11.31%. In the paper, the static parameter table of the sensor is stored inside the smart sensor or stored on the IoT cloud platform to achieve automatic registration of the static parameters of the smart sensor. According to the characteristics of dynamic parameters of smart sensors, an autonegotiation algorithm is designed to realize the dynamic parameters of smart sensors to be determined by autonegotiation and complete registration without human intervention, which includes four parts: utility function, concussion protocol, proposal generation strategy, and response mechanism. The intelligent sensing system is composed of three parts: smart sensor, IoT gateway, and IoT cloud platform; the functions of each part in the system are analyzed, and MAR and STM32F4 are selected as the core board of smart sensor and IoT gateway. And for the problems of time-consuming and tedious access to smart sensors in the system, we propose to study the adaptive sensor UART serial port parameters and automatic registration of sensor parameters. Based on the analysis of the current environment and resource characteristics of the traditional corridor, the current situation of ecotourism resources of the traditional corridor is sorted out, an evaluation index system is established to evaluate ecotourism resources, and the current development basis is analyzed from four aspects: natural environment, tourism resources, product development, and infrastructure. Finally, to promote the sustainable development of the ecotourism corridor in the Chengdu section of the southern foot of the Qinling Mountains, the planning and development strategies to match the ecotourism corridor in the Chengdu section of the southern foot of the Qinling Mountains are proposed in four aspects: transportation organization optimization, ecological landscape enhancement, support system construction, and regional tourism development.