Reviewerly/exHarmony huggingface dataset:
Algorithm Design in Programming Language Education

Nowadays, when algorithms are combined with programming languages, great technological works are emerging. Examples of these works continue to differentiate and increase in military and police systems, agricultural applications, image processing applications, data engineering field, language processing works, and cyber security applications. With the differentiation of the areas of use of algorithms, the programming languages used on these platforms are also changing.&#x0D; Python is an object-oriented and functional modern programming language. It is ideal for beginners due to its readability and ease of use. C# is a simple, modern, object-oriented, and type-safe programming language that combines the high productivity of application development languages with the raw power of C and C++. The Java programming language, on the other hand, shares many features that are common to most programming languages used today. Since it is designed with the structures of C and C++, where their languages are similar, the language is familiar to C and C++ programmers (Lerdorf, 2002; Hejlsberg, 2003; Arnold, 2005; Deitel, 2004; Kelly, 2016; Gavrilović, 2018; Pala, 2019; Chollet, 2021; Chen, 2023).
Generation of a time–bin Greenberger–Horne–Zeilinger state with an optical switch

Abstract Multipartite entanglement is a critical resource in quantum information processing that exhibits much richer phenomenon and stronger correlations than in bipartite systems. This advantage is also reflected in its multi-user applications. Although many demonstrations have used photonic polarization qubits, polarization-mode dispersion confines the transmission of photonic polarization qubits through an optical fiber. Consequently, time–bin qubits have a particularly important role to play in quantum communication systems. Here, we generate a three-photon time–bin Greenberger–Horne–Zeilinger (GHZ) state using a 2 × 2 optical switch as a time-dependent beam splitter to entangle time–bin Bell states from a spontaneous parametric down-conversion source and a weak coherent pulse. To characterize the three-photon time–bin GHZ state, we performed measurement estimation, showed a violation of the Mermin inequality, and used quantum state tomography to fully reconstruct a density matrix, which shows a state fidelity exceeding 70%. We expect that our three-photon time–bin GHZ state can be used for long-distance multi-user quantum communication.
Systematic Review on on-Air Hand Doodle System for the Purpose of Authentication

Air-drawing for authentication or gesture recognition is vastly studied such that new methods for the character drawn can be identified with better accuracy and be independent of any hardware components such as gloves to detect the fingertip movement or any wearable sensor or using any external pen. This article surveys how air-writing is done. Some of them include using fingertips as a pen and having the trained CNN model against it using the techniques DNST and Bi-LSTM for the regression and hand gesture identification and classification. This system had an overall accuracy of 88 percentage. Another system solves the air-writing issue by using deep learning architecture, by executing it on 3D space where the next evolution is numerical digits structured into multiple dimensions time-series extracted from a sensor called LMC. In another system, wi-write recognizes hand writing mainly to overcome blurred vision and neurological diseases in people, this will use COTS WiFi but is a device-free handwriting recognition system. In many other systems deep learning models are used and those parameters can be used to achieve higher recognition rate for example 98 percentage above.
Research on Modulation Recognition Method of Single Carrier Signal Based on Lightweight Network

Aiming at the problems of network complexity, large computation amount and high hardware platform requirements in the current process of applying deep learning to realize digital signal modulation recognition, this paper proposes a method of using signal constellation diagram modulation recognition in the improved MobileNetV3 lightweight neural network, and introduces the cross-layer structure of Resnet into the MobileNetV3 network. The phenomenon of vanishing gradient caused by decreasing weight with increasing network layers is solved. Firstly, the received MPSK and MQAM signals are converted into constellation diagrams, which are extracted from gray images and enhanced. The image data set of constellation diagram is constructed, which is used to train the lightweight neural network weight of MobileNetV3, and then the constellation diagram is recognized. MobileNetV3 is based on deep convolution separable and Network Architecture Search (NAS) technology, which greatly reduces the number of parameters and training time on the premise of ensuring the recognition accuracy. For the modulation recognition of simple signals, the lightweight neural Network can effectively simplify the Network structure caused to reduce hardware requirements. The simulation results show that the modulated signals (BPSK, QPSK, 8PSK, 16QAM, 64QAM) can achieve the recognition rate of 99.76%. Compared with the traditional network using deep learning to realize the modulation recognition, the number of network parameters and the computational cost are significantly reduced.
Sample Reduction-Based Pairwise Linear Regression Classification for IoT Monitoring Systems

At present, the development of the Internet of Things (IoT) has become a significant symbol of the information age. As an important research branch of it, IoT-based video monitoring systems have achieved rapid developments in recent years. However, the mode of front-end data collection, back-end data storage and analysis adopted by traditional monitoring systems cannot meet the requirements of real-time security. The currently widely used edge computing-based monitoring system can effectively solve the above problems, but it has high requirements for the intelligent algorithms that will be deployed at the edge end (front-end). To meet the requirements, that is, to obtain a lightweight, fast and accurate video face-recognition method, this paper proposes a novel, set-based, video face-recognition framework, called sample reduction-based pairwise linear regression classification (SRbPLRC), which contains divide SRbPLRC (DSRbPLRC), anchor point SRbPLRC (APSRbPLRC), and attention anchor point SRbPLRC (AAPSRbPLRC) methods. Extensive experiments on some popular video face-recognition databases demonstrate that the performance of proposed algorithms is better than that of several state-of-the-art classifiers. Therefore, our proposed methods can effectively meet the real-time and security requirements of IoT monitoring systems.
USING THE BUSINESS MODEL CANVAS AS A METHODOLOGICAL APPROACH PROMOTING INNOVATIVE AND ENTREPRENEURIAL SPIRIT AMONG THE STUDENTS OF THE CHEMISTRY DEGREE

USING THE BUSINESS MODEL CANVAS AS A METHODOLOGICAL APPROACH PROMOTING INNOVATIVE AND ENTREPRENEURIAL SPIRIT AMONG THE STUDENTS OF THE CHEMISTRY DEGREE
INTERNET OF THINGS AS A PEDAGOGICAL SUPPORT TOOL IN ARTIFICIAL INTELLIGENCE LEARNING IN HIGH SCHOOL

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 4007-4013ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.1064Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
INFORMATION LITERACY: ANALYSIS OF INSTITUTIONAL STRATEGIES TO DEVELOP INFORMATION LITERACY SKILLS FOR UNDERGRADUATE STUDENTS AT SELECTION COLLEGES AND UNIVERSITIES

INFORMATION LITERACY: ANALYSIS OF INSTITUTIONAL STRATEGIES TO DEVELOP INFORMATION LITERACY SKILLS FOR UNDERGRADUATE STUDENTS AT SELECTION COLLEGES AND UNIVERSITIES
USING ORACLE QUERY PLAN FOR AUTOMATED ASSESSMENT OF SQL

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 4978-4982ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.1296Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
TALKYTUTOR: AN INNOVATIVE APPROACH FOR CHATBOTS FOR EDUCATION

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 3088-3093ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.0857Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
A COMPARATIVE TO UNDERSTAND E-LEARNING IN PANDEMIC BETWEEN SECONDARY AND UNIVERSITY STUDENTS

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 7049-7055ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.1927Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
Vehicle Detection from Aerial Imagery Using Principal Component Analysis and Deep Learning

The main goal of object detection is to identify and find one or more effective targets in still or video data. It covers a wide range of techniques, including image processing, pattern recognition, and machine learning. The scope of this study is to detect small vehicles in an uncontrolled environment from aerial photographs using effective pre-processing and deep learning algorithms. The model architecture is divided into two phases: Training and Detection. Cropping and extraction of the training samples, feature representation, and classification are all part of the training step. The detection phase comprises of extracting the regions of interest, feature extraction, and classification. The tests are carried out using the Vehicle Detection in Aerial Imagery (VEDAI) dataset. In this paper, we explore the feasibility of dimensionality reduction as pre-processing through Principal Component Analysis (PCA) for effective detection of vehicles in aerial imagery; used to remove unwanted features to remove the common misclassifications caused due to ambiguity of small objects. A comparative study between Deep Learning Models such as ResNet50 and MobileNetv1 based on the proficiency to detect small objects, were coupled with PCA pre-processing to provide the observations. ResNet50 gave a classification accuracy of 85%, whereas MobileNetv1 gave a classification accuracy of 76.25%. From the experimental results, when PCA pre-processing is coupled with architectures comprising skip connections like ResNet50, the misclassification rate of vehicles in aerial imagery was brought down drastically, providing a better detection rate comparable to existing benchmark.
Adversarial attacks on cross-resolution person re-identification

Person Re-Identification (re-ID) is a task that involves matching individuals captured by various cameras and in various poses. And because of the uncertainty of input image resolution in practice, traditional Person Re-Identification tends to have poor performance. Cross-Resolution Person re-ID is a hot branch of Person re-ID that aims to address the image with a different resolution. Besides, Person re-ID has been applied in lots of fields because of its practicality, including public security systems. Therefore, this poses a challenge to the security of the re-ID model. This paper discusses the results of attacks on PS-HRNet, VGG16, and ResNet networks using the Fast Gradient Sign Method. The experiment result of this paper proves the effectiveness of the Fast Gradient Sign Method on Cross-Resolution Person Re-Identification. It tests the transferable of the adversarial samples generated by the Fast Gradient Sign Method.
Activity recognition based on adaptive window and broad learning

With the widespread use of sensing elements in commercial equipment, action recognition technology is required to be more practical in people's life, especially the stable and accurate recognition. Among them, using sliding window for motion perception is an effective recognition method. However, most of the current recognition models are designed for a single action, which not only has poor recognition stability, but also cannot effectively recognize the action. This paper presents a method of action recognition based on adaptive window and broad learning, and designs an action recognition system EVM, the system effectively preprocesses the action data and realizes the accurate recognition of actions. Firstly, EVM smooth the source action data. Then, this paper proposes an extreme value filtering method to avoid the interference of peak/valley extreme points and ensures the effectiveness of action division through the adaptive window. Finally, a recognition model based on broad learning is used to classify action behaviors. According to the comparison and verification of a large number of experiments, the EVM system has a recognition accuracy as high as 97.91%, which is much better and faster than the CNN model.
Research on AOD furnace end-point temperature prediction based on sparrow search algorithm optimization ELM

In order to make the endpoint temperature prediction hit rate and velocity meet the requirements, in this paper, the Extreme Learning Machine (ELM) is used to build a model which predicts the endpoint temperature of the AOD refining furnace, and the model is trained with the real production data to search the nonlinear variation law between the input quantity and the endpoint temperature. The Sparrow Search Algorithm (SSA) is used to adjust the parameters of model to improve the hit rate of ELM. By comparing with other prediction models, found that the model is better than the pre-optimized temperature model in prediction hit rate and better than Support Vector Regression (SVR) in prediction velocity, which is more suitable for guiding actual production.
A Study on the PEST-SWOT Analysis on Sustainable Coastal Fishing Village In the post COVID-19 outbreak: Focusingonthesurvey was centered on Daehang fishing village in Busan

As a country surrounded by the sea, Korea is necessary for the development of fisheries, and considering that we enjoy a form of tourism consumption with social value in fishing villages, the decline of fishing village tourism would have a significant impact on our society. Since the post COVID-19 in 2019, all industries have been affected to varying degrees, especially the tourism industry, which is a contact business activity. PEST represents the development environment and SWOT represents the evaluation criteria. Based on the current situation of fishery development, this study uses PEST-SWOT analysis method, Find out the 16 keywords are P-S, P-W, P-O, P-T, E-S, E-W, E-O, E-T, S-S, S-W, S-O, S-T, S-T, T-S, T-W, T-O, T-T, which represent the advantages, disadvantages, opportunities and threats of politics, economy, society and technology, and design the questionnaire model based on them. Put together 24 questionnaire options. According to the survey data, among politics, economy, society and technology, the importance of society(S) is considered the most important by the people, while in the SWOT of social factors, the opportunity to develop social factors(S-O) should be seized under the premise of the advantages of the current situation(S-S). On the other hand, there is a lack of technical factors, which is reflected in the great threat of fishing villages, aging facilities, technical limitations and other problems(T-T) that constantly affect the economic development of fishing villages. To seize the opportunity of fishing villages' development(T-O), the COVID-19 pandemic can be used to update the outdated technology of fishing villages.
The prevention and control of online consumer fraud

Online shopping has been rapidly increasing. Aggravated by Covid-19, the risks inherent to purchasing online are multiple. One of them is the online consumer fraud, which occurs when the products are bought, but are not delivered, and situations when the products are falsified, counterfeit and/or are not the same as they were announced. The scientific community has been trying to understand what the risk factors are associated with great likelihood of being victimized of online consumer fraud. Moreover, even if individuals are victimized, research has been found that most of them do not report. The present chapter has three main goals: firstly, to review the main determinants of victimization (both individual and contextual); secondly, to understand why people do (not) report this type of crime to the police or other entities and, finally, to discuss the most important prevention strategies to decrease online consumer fraud.
Counterfeit Merchandise Identification Using Blockchain

As a result of a lack of openness, supply chain management frequently confronted issues such as service duplication, poor collaboration between divisions, and a lack of uniformity. Item duplicating is something which is extremely normal now-a-days and its exceptionally difficult to distinguish a fake item by simply checking it out forgers cause critical difficulties for authentic firms yet unreasonably many individuals have no clue about the whole measure of fake things effect on brands there are a few strategies concocted in the past to pull off this issue of item falsifying the most well-known strategies are utilizing radio frequency identification (RFID) labels, computerized reasoning QR code based frameworks and so forth however every one of them had many weaknesses for example the QR code can be duplicated from a certified item and put on a fake item, computerized reasoning purposes CNN and AI which needs high computational power etc. This undertaking is to further develop identification of fake items by following its store network history this is accomplished with block chain innovation which guarantees the recognizable proof and detectability of genuine items all through the store network block chain based framework makes all that decentralized that might be gotten to by a few gatherings simultaneously one of its fundamental benefits is that the recorded information is challenging changing without the assent of every interested individual which makes the information incredibly secure and safeguard from all weaknesses. This paper present’s framework planned utilizing blockchain innovation for discovery of fake items. Keywords: Blockchain, Counterfeit (Fake) product, QR code, Supply Chain, Transaction history.
FALL DETECTION AND ALERTS IN HEARING AIDS FOR DEAF AND HEARING IMPAIRMENT

According to research, a considerable percentage of individuals over the age of 65 living at home fall at least once a year, which can have serious consequences for their independence and overall quality of life. To mitigate this risk, a fall detection and alerting system can be highly beneficial for elderly individuals with hearing difficulties. In this paper, we propose the development of such a system using a tilt sensor, ESP8266 microcontroller, MIT App Inventor, and Firebase database. The system is designed to be integrated into a hearing aid device to detect falls and promptly alert caregivers or emergency services. The tilt sensor is utilized to detect changes in the hearing aid's orientation, indicating a fall has occurred. The ESP8266 microcontroller is responsible for connecting the device to the internet, enabling real-time alerts to be sent via the MIT App Inventor app. The app is user-friendly and can be easily installed on a caregiver's smartphone. The Firebase database stores user data, including fall detection logs and emergency contact information. Our tests show that the system can accurately detect falls and send timely alerts via SMS and calls to designated emergency contacts, providing an additional layer of safety and security for hearing aid users. KEYWORDS- IoT, Fall detection, alert SMS, Hearing aid.
Data from Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression–Morphology Analysis in Breast Cancer

&lt;div&gt;Abstract&lt;p&gt;Molecular profiling is central in cancer precision medicine but remains costly and is based on tumor average profiles. Morphologic patterns observable in histopathology sections from tumors are determined by the underlying molecular phenotype and therefore have the potential to be exploited for prediction of molecular phenotypes. We report here the first transcriptome-wide expression–morphology (EMO) analysis in breast cancer, where individual deep convolutional neural networks were optimized and validated for prediction of mRNA expression in 17,695 genes from hematoxylin and eosin–stained whole slide images. Predicted expressions in 9,334 (52.75%) genes were significantly associated with RNA sequencing estimates. We also demonstrated successful prediction of an mRNA-based proliferation score with established clinical value. The results were validated in independent internal and external test datasets. Predicted spatial intratumor variabilities in expression were validated through spatial transcriptomics profiling. These results suggest that EMO provides a cost-efficient and scalable approach to predict both tumor average and intratumor spatial expression from histopathology images.&lt;/p&gt;Significance:&lt;p&gt;Transcriptome-wide expression morphology deep learning analysis enables prediction of mRNA expression and proliferation markers from routine histopathology whole slide images in breast cancer.&lt;/p&gt;&lt;/div&gt;
Investigation of Influencing Factors on Elementary School Teachers’ Efficacy Using Random Forest

The purpose of this study is to identify the influencing factors on teacher efficacy using「Korean Teacher Longitudinal Study-Elementary School Teacher」data from a more comprehensive perspective by analyzing all available items, which is the strength of the random forest technique. The data from 2,794 elementary school teachers were used, and teacher efficacy, which is the response variable of this study, was divided into three sub-domains (instruction/assessment efficacy, student guidance/class management efficacy, and future education efficacy). A random forest regression was conducted for each sub-domain. The main results of this study are as follows. First, the influencing factor with the highest importance index in all teacher efficacy was job satisfaction. Second, among the top 25 factors (approximately 10% of the total explanatory variables), 15 items, which account for 60%, were commonly found to be major influencing factors in all teacher efficacy. Third, among the top 25 factors, there were 4 to 7 major influencing factors unique to each teacher efficacy, and these factors appeared to represent the unique characteristics of each teacher efficacy. Fourth, the variables that appeared to be mainly related to teacher efficacy were all individual teacher-level factors, and school-level factors did not appear. In this study, it was able to examine the relationship between individual teacher efficacy and influencing factors by dividing teacher efficacy into three sub-domains (instruction/assessment efficacy, student guidance/class management efficacy, and future education efficacy) due to the strength of data. Also, this study examined the relationship between teachers’ practical teaching/learning activities and teacher efficacy, which was difficult to examine in previous studies.
Robot operating system based autonomous navigation platform with human robot interaction

In emerging technologies, indoor service robots are playing a vital role for people who are physically challenged and visually impaired. The service robots are efficient and beneficial for people to overcome the challenges faced during their regular chores. This paper proposes the implementation of autonomous navigation platforms with human-robot interaction which can be used in service robots to avoid the difficulties faced in daily activities. We used the robot operating system (ROS) framework for the implementation of algorithms used in auto navigation, speech processing and recognition, and object detection and recognition. A suitable robot model was designed and tested in the Gazebo environment to evaluate the algorithms. The confusion matrix that was created from 125 different cases points to the decent correctness of the model.