Reviewerly/exHarmony huggingface dataset:
The Application of Wireless Sensor Technology of Internet of Things in Korean Language Teaching

The Internet of Things sensor technology network can be regarded as consisting of three parts: data acquisition network, data distribution network, and control management center. Sensor technology has the comprehensive processing ability to obtain various information signals, and the technology is integrated into the sensor equipment, so that the sensor technology is connected to form a sensor network with comprehensive information processing capability, which has been widely used in foreign language teaching in recent years. This paper analyzes the shortcomings of traditional Korean language teaching methods in the new situation in detail. It starts from the actual situation and focuses on stimulating students’ interest and proposes a Korean language teaching method based on the Internet of Things wireless sensor technology. This article focuses on analyzing the application of the wireless sensor technology of the Internet of Things in Korean language teaching and applies this method in practical teaching. It verifies the feasibility and effectiveness of the technology for Korean language teaching through the analysis and comparison of the collected data. In this paper, the experiment shows that the application of the wireless sensor technology of the Internet of Things to the Korean language teaching can improve the students’ grades by about 30%. This stimulates students’ interest in learning very well and is of great help to improve students’ learning ability, oral communication ability, and Korean thinking ability, which stimulates students’ interest in learning and improves students’ Korean listening, speaking, reading, writing, and comprehensive learning abilities.
Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

Sentiment analysis is based on the orientation of user attitudes and satisfaction towards services and subjects.Different methods and techniques have been introduced to analyze sentiments for obtaining high accuracy.The sentiment analysis accuracy depends mainly on supervised and unsupervised mechanisms.Supervised mechanisms are based on machine learning algorithms that achieve moderate or high accuracy but the manual annotation of data is considered a time-consuming process.In unsupervised mechanisms, a lexicon is constructed for storing polarity terms.The accuracy of analyzing data is considered moderate or low if the lexicon contains small terms.In addition, most research methodologies analyze datasets using only 3-weight polarity that can mainly affect the performance of the analysis process.Applying both methods for obtaining high accuracy and efficiency with low user intervention during the analysis process is considered a challenging process.This paper provides a comprehensive evaluation of polarity weights and mechanisms for recent sentiment analysis research.A semi-supervised framework is applied for processing data using both lexicon and machine learning algorithms.An interactive sentiment analysis algorithm is proposed for distributing multi-weight polarities on Arabic lexicons that contain high morphological and linguistic terms.An enhanced scaling algorithm is embedded in the multi-weight algorithm to assign recommended weight polarities automatically.The experimental results are conducted on two datasets to measure the overall accuracy of proposed algorithms that achieved high results when compared to machine learning algorithms.
Gaussian Pyramid for Nonlinear Support Vector Machine

Support vector machine (SVM) is one of the most efficient machine learning tools, and it is fast, simple to use, reliable, and provides accurate classification results. Despite its generalization capability, SVM is usually posed as a quadratic programming (QP) problem to find a separation hyperplane in nonlinear cases. This needs huge quantities of computational time and memory for large datasets, even for moderately sized ones. SVM could be used for classification tasks whose number of samples is limited but does not scale well to large datasets. The idea is to solve this problem by a smoothing technique to get a new smaller dataset representing the original one. This paper proposes a fast and less time and memory-consuming algorithm to solve the problems represented by a nonlinear support vector machine tool, based on generating a Gaussian pyramid to minimize the size of the dataset. The reduce operation between dataset points and the Gaussian pyramid is reformulated to get a smoothed copy of the original dataset. The new dataset points after passing the Gaussian pyramid will be closed to each other, and this will minimize the degree of nonlinearity in the dataset, and it will be 1/4 of the size of the original large dataset. The experiments demonstrate that our proposed techniques can reduce the classical SVM tool complexity, more accurately, and are applicable in real time.
SCA-MMA: Spatial and Channel-Aware Multi-Modal Adaptation for Robust RGB-T Object Tracking

The RGB and thermal (RGB-T) object tracking task is challenging, especially with various target changes caused by deformation, abrupt motion, background clutter and occlusion. It is critical to employ the complementary nature between visual RGB and thermal infrared data. In this work, we address the RGB-T object tracking task with a novel spatial- and channel-aware multi-modal adaptation (SCA-MMA) framework, which builds an adaptive feature learning process for better mining this object-aware information in a unified network. For each type of modality information, the spatial-aware adaptation mechanism is introduced to dynamically learn the location-based characteristics of specific tracking objects at multiple convolution layers. Further, the channel-aware multi-modal adaptation mechanism is proposed to adaptively learn the feature fusion/aggregation of different modalities. In order to perform object tracking, we employ a binary classification module with two fully connected layers to predict the bounding boxes of specific targets. Comprehensive evaluations on GTOT and RGBT234 datasets demonstrate the significant superiority of our proposed SCA-MMA for robust RGB-T object tracking tasks. In particular, the precision rate (PR) and success rate (SR) on GTOT and RGBT234 datasets can reach 90.5%/73.2% and 80.2%/56.9%, significantly higher than the state-of-the-art algorithms.
Research on Digital Teaching of Creative Writing in the Context of Computer Big Data

This paper is aimed at examining the effects of digital teaching on reconstructing creative writing skills and to judge the bettering of creativity and writing skills after accepting digital technology. Based on this paper, creativity and writing skills are the most required elements of creative writing. Fluency, flexibility, originality, and elaboration are the major constructs of creativity. From the contribution of Torrance, we find these constructs of creativity. Narrative and linguistics are the major components in writing skills to evaluate the pupils. Concept and background come under the narrative components as subscales. Likewise, content, the structure of grammar, punctuation, and spelling are considered as subscales in linguistic components. Digital teaching methods and traditional teaching methods are used for this research. We are using a nonequivalent comparison-group design for this research. The rubric is a scoring guide used to evaluate performance. We used this scoring guide to access the improvement of pupils. In total, 58 participants were selected from a primary school. 28 participants were selected from the section A class and 30 pupils from the section B class. In creativity, after the digital teaching, the experimental group is greater than the control group; therefore, the digital teaching method improves the pupils’ creativity level. In writing skill, after digital teaching, the experimental group is better than the control group; therefore, the digital teaching method increases the pupils’ writing skill. We proposed a SVM-based Chaotic Grasshopper Optimization algorithm for digital teaching of creative writing in the context of big data. The comparative analysis result shows that the SVM-Chaotic Grasshopper Optimization method accuracy rate is higher than the other three methods’ accuracy rate. The SVM-CGO method execution time (second) speed is fast when compared with that of the other methods. Thus, we have analyzed the effects of digital teaching and evaluated the improvement in creativity and writing skills after utilizing digital tools.
Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning

Image Captioning is an emergent topic of research in the domain of artificial intelligence (AI).It utilizes an integration of Computer Vision (CV) and Natural Language Processing (NLP) for generating the image descriptions.It finds use in several application areas namely recommendation in editing applications, utilization in virtual assistance, etc.The development of NLP and deep learning (DL) models find useful to derive a bridge among the visual details and textual semantics.In this view, this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning (OHHO-DLIC) technique.The OHHO-DLIC technique involves the design of distinct levels of pre-processing.Moreover, the feature extraction of the images is carried out by the use of EfficientNet model.Furthermore, the image captioning is performed by bidirectional long short term memory (BiLSTM) model, comprising encoder as well as decoder.At last, the oppositional Harris Hawks optimization (OHHO) based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM models.The experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.
Big Data Statistics Analysis of Computer Intelligence and Machine Learning Technology

In the recent technological world, Computer technology and internet technology plays a vital role. These technologies are deployed in many industries. In this research, we are going to study the correlation between the complexities of financial reporting and the value of economic statistics in the context of Computer intelligence and information technology. Financial reporting is referred to as an accounting practice that involves processes like documentation & communication of financial activities. The performance over time of the companies is derived from the financial report. Financial reports provide vital information about the company’s financial status and it also helps the company in decision making. Balance sheets, income statements, statements on shareholder equity, cash flow statements, are some of the types of financial reports. It facilitates tracking the cash flow, analyzing shareholder equity, evaluating the assets and liabilities, checking the profitability etc. Economic statistics are referred to us as the branch of applied statistics that deals with the collection and processing of economic data. It also compiles and analyzes the economic data. Financial reports check the money flow for businesses. Economic Statistics checks the larger trends that decide the money & resources flow. Even though, both help in making financial decisions there is some complexity between the two correlated fields. With the help of computer intelligence and internet technology, the complexity is reduced considerably.
Research on Optimization and Allocation of English Teaching Resources

To rationally allocate teaching resources in English teaching, a teaching resource optimization and allocation management method is proposed based on a convolutional neural network (CNN) and Arduino device. By constructing a 9-layer CNN classification and recognition model and the English education resource library of the Arduino device and applying them to the recognition program design of the Arduino device, the rational optimization and allocation of teaching resources are realized. Simulation results show that the recognition accuracy of the proposed method is over 90% for Arduino devices, and the recognition accuracy is over 80% for real English teaching scenarios, which means that the proposed method has a certain practical application value. Moreover, the interaction mode between English learners and English teaching resources is innovated, which contributes to the optimization and allocation of English teaching resources. Thus a new idea is generated to integrate the English teaching resources.
The Effects of Digital Empowerment Teacher Training Program Applying Virtual Reality on Digital Media Literacy, Teacher Efficacy, and Problem-solving Ability of School Nurse

Objectives: This study was conducted to examine the effects of digital empowerment teacher training program applying virtual reality on digital media literacy, teacher efficacy, and problem-solving ability of school nurse.Methods: A quasi -experimental study of pre- and post-experimental design was performed on 47 school nurse located in K city and the developed digital empowerment teacher training program was implemented. The data collection period is July 27, 2021, and the collected data is collected using the SPSS 18.0 program. Paired t-test were analyzed.Results: According to the results of the effect, it was found that there was a significant difference in digital media literacy and problem-solving ability.Conclusions: The digital empowerment teacher training program developed in this study can be used as an effective program to enhance the digital media literacy and problem-solving ability of school nurse.
Game-based Learning of SQL Injections

SQL injections are one of the most widely used techniques to attack data-driven applications. Thus, the potential for cyber-attacks increases consistently. To create awareness of hacker attacks, we demonstrate a web-based educational game. The game shows how SQL injections work and which kind of attacks they enable, e.g., logging in without knowing a user’s password, deleting data or retrieving secret or sensitive data. At each level, the players have to fill out login or search forms or use URL parameters to attack an example retail website. While solving these tasks, players can look at the SQL query and the application code. The players learn how SQL injections work, how dangerous they are, and how to make their own applications more robust against SQL injections, for example by using prepared statements.
Self-Supervised Sentiment Analysis in Spanish to Understand the University Narrative of the Colombian Conflict

Sentiment analysis is a relevant area in the natural language processing context–(NLP) that allows extracting opinions about different topics such as customer service and political elections. Sentiment analysis is usually carried out through supervised learning approaches and using labeled data. However, obtaining such labels is generally expensive or even infeasible. The above problems can be faced by using models based on self-supervised learning, which aims to deal with various machine learning paradigms in the absence of labels. Accordingly, we propose a self-supervised approach for sentiment analysis in Spanish that comprises a lexicon-based method and a supervised classifier. We test our proposal over three corpora; the first two are labeled datasets, namely, CorpusCine and PaperReviews. Further, we use an unlabeled corpus conformed by news related to the Colombian conflict to understand the university journalistic narrative of the war in Colombia. Obtained results demonstrate that our proposal can deal with sentiment analysis settings in scenarios with unlabeled corpus; in fact, it acquires competitive performance compared with state-of-the-art techniques in partially-labeled datasets.
Image Classification of Missing Insulators Based on EfficientNet

In this paper, for the detection of missing images of power insulators, the production of image datasets and image classification methods are discussed. Using the drone aerial insulator images from the power grid company, images containing insulators in different scenarios such as high-voltage transmission lines and substations were collected, 2000 insulator images were extracted, and a power insulator database was constructed. Insulator-missing image classification model to verify the robustness of the EfficientNet algorithm. Use EfficientNet to build a transfer learning network, train it, and compare it with the commonly used classifier ResNet-50. By introducing classification evaluation indicators and class activation maps, the experimental results show that EfficientNet-b0 has good transfer ability and can significantly improve the model. Efficiency, better than ResNet-50 for insulator-missing image classification.
A New Impedance Sensor Based on Electronically Implemented Chaotic Coupled van der Pol and Damped Duffing Oscillators

The chaotic behaviour of a system depends on the parameter values, and when the system exhibits such a chaotic behaviour, it is sensitive to the initial conditions. This feature can be used for manufacturing high sensitivity sensors. Sensors based on chaotic circuits have already been used for measuring water salinity, inductive effects, and both noise and weak signals. This article investigates an impedance sensor based on the van der Pol and Duffing damped oscillators. The calibration process is a key point and therefore the folding behaviour of signal periods was also explored. A sensitivity of 0.15 kΩ/Period was estimated over a range from 89.5 to 91.6 kΩ. This range can be adjusted according to the application by varying the gain of the operational amplifier used in this implementation. The development of this type of sensor might be used in medical and biological engineering for skin impedance measurements, for example. This type of chaotic impedance sensor has the advantage of sensing small disturbances and then rapidly detecting small impedance changes within biological materials.
Secure MRI Brain Image Transmission Using IOT Devices Based on Hybrid Autoencoder and Restricted Boltzmann Approach

In recent times, the medical image processing solves several clinical issues by inspecting the visual images, which are generated in the clinical health care units. The main objective of the research is to gain valuable information from the images for better clinical diagnosis. In the biomedical engineering domain, the medical image analysis is an emerging research topic. In the recent decades, the use of medical images is highly growing, which are acquired from different image modalities; so, there is a necessity for data compression for transmission, storage, and management of digital medical image datasets. Hence, the machine learning methods are effective for medical image analysis, where the deep learning models are used in the machine learning tools for automatically learning the feature vectors from the huge medical datasets. The automated deep learning models are effective compared to the conventional handcrafted features. In addition to this, a wireless sensor network (WSN) is used to create a primary health care scheme, which brings patient data together and expands the medical conveniences, whereas the WSN design must comprise of sensor nodes, because it consumes less power and resources at a relatively low cost; so, it is essential for implementing the Raspberry Pi-based WSN nodes. The sensor nodes are important for limited battery capacity and to transmit the vast amount of medical data. The proposed work is broadly classified into two categories such as (i) the medical image compression algorithm is developed using the deep learning model based on autoencoders and restricted Boltzmann machines (RBM) and (ii) implementation of the WSN sensors nodes with Raspberry Pi and Messaging Queue Telemetry Transport (MQTT) Internet of Things (IoT) protocol for secure transmission of the medical images. The experimental results are evaluated using the standard performance metrics like peak signal to noise ratio (PSNR) and presented a Real-Time Linux (RTL) implementation of the design. The proposed model showed 10 dB to 15 dB improvement in the PSNR value, while transmitting the medical images, which is better compared to the existing model.
A Digital Random Number Generator Based on Four Regional Examination of Double Scroll Chaos

This paper presents the design of a novel random number generator (RNG) based on a new method. Digital Double scroll chaos is used as an RNG production source. Design is performed based on the regional examination of regular samples from a double scroll chaos system. The digital double scroll chaos used in this study is implemented by exploiting the nonlinearity of the sawtooth wave in a 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> order ordinary differential equation (ODE). By analyzing the distributions of the chaotic state variables <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x, y, z$</tex> in 3-dimensions, it has been observed that they have an underlying distribution. The double scroll chaos is divided into four regions by preserving the characteristics of the underlying distributions, and binary data is obtained using each one of these four regions. These binary data are combined through a xor operation to generate the RNG output and a robust RNG is obtained. The proposed RNG is implemented using the Verilog hardware description language and is prototyped on the KCU105 Evaluation Board Xilinx FPGA. The proposed RNG is demonstrated to provide data throughput up to 13.25 Mbps. The RNG output satisfies all statistical randomness tests in the NIST 800-22 and TestU01 packages without post-processing.
Knowledge Spillover through Blockchain Network in Tourism: Development and Validation of Tblock Questionnaire

With the advent of disruptive technologies, blockchain is considered to be the most prominent technology that has the potential to have a significant influence on the knowledge management field, particularly knowledge sharing, knowledge transfer and knowledge spillover. This paper outlines the development and validation of the Knowledge Spillover through Tourism Blockchain Questionnaire, the TBlockQ. The purpose of this questionnaire is to acknowledge the key factors aligned with the level of knowledge spillover generated within a blockchain network. The TBlockQ was a synthesis of 29 5-point Likert scaled questions. A total of 422 correspondents participated in this study. The results of this study outline the reliability and validation of this questionnaire. The reliability statistics of all the items are high. Three factors, revealed from the factor analysis, identifying the knowledge spillover from a blockchain network in the tourism industry were: (a) networking expansion, improvement and spillover intention, (b) entrepreneurial and future prospects and (c) trust and security.