Reviewerly/exHarmony huggingface dataset:
The Impact of Data Injection on Predictive Algorithm Developed Within Electrical Manufacturing Engineering in the Context of Aerospace Cybersecurity

Cybersecurity plays a relevant role in the new digital age in aerospace industry. Predictive algorithms are necessary to interconnect complex systems within the cyberspace. In this context, where security protocols do not apply, challenges to maintain data privacy and security arise for the organizations. Thus, the need of cybersecurity is required. The four main categories to classify threats are interruption, fabrication, modification and interception. They all share a common thing, soften the three pillars which cybersecurity needs to guarantee. These pillars are confidentiality, availability and integrity of data (CIA). Data injection can contribute to this event by creation of false indicators which can lead to errors creation during the manufacturing engineering process. In this paper, the impact of data injection on existing dataset used on manufacturing process is shown. The design model synchronizes the following mechanisms developed within machine learning techniques which are, the risk matrix indicator to assess the probability of producing an error, the dendrogram to clusters the dataset in groups with similarities, the logistic regression to predict the potential outcomes and the confusion matrix to analyze the performance of the algorithm. The results presented in this study, which was carried out using a real dataset related to the electrical harnesses installed in a C295 military aircraft, estimate that injection of false data indicators increase the probability of errors creation in 24.22 % on the predicted outcomes required for the generation of the manufacturing process. Overall, implementing cybersecurity measures and advanced methodologies to detect and prevent cyberattacks are necessary.
The Enlightenment of Cai Yuanpei’s Aesthetic Education to Personality Education

With the development of human society and the progress of science and technology, people’s cultural cognition has constantly strengthened. With people’s research and thinking, all kinds of cultural education came into being, aesthetic education, moral education, and sports, all play a significant role in people’s physical and mental development, among which the aesthetic education thought advocated by Cai Yuanpei has a great value as guidelines for the significance of personality education. It is of great importance to people’s physical and mental development.
Analysis of the Performance of a Hybrid Thermal Power Plant Using Anfis-Based Approaches

The hybridization of conventional thermal power plants by incorporation of renewable energy systems has witnessed widespread adoption in recent years. This trend aims not only to mitigate carbon emissions but also to enhance the overall efficiency and performance of these power generation facilities. However, calculating the performance of such intricate systems using fundamental thermodynamic equations proves to be both laborious and time-intensive. Nevertheless, possessing accurate and real-time insights into their performance is of utmost significance to ensure optimal plant operation, facilitate decision-making, and streamline power production planning. In this paper, we delve into the application of machine learning techniques, particularly hybrid methodologies, to predict the performance of power plants. Specifically, we employ three approaches: the Adaptive Neuro-Fuzzy Inference System (ANFIS), the ANFIS optimized via Particle Swarm Optimization (ANFIS-PSO), and the ANFIS optimized through Genetic Algorithm (ANFIS-GA). These techniques are alternatively applied to a complex hybrid thermal power plant, namely an Integrated Solar Combined Cycle Power Plant (ISCCPP). This ISCCPP comprises a steam turbine section, a gas turbine section, and a concentrated solar power system utilizing troughs as collectors. The energy transfer fluid, in conjunction with exhaust from the gas turbine section, heats water within a vessel or steam generator, producing steam to propel the steam turbine&amp;#039;s rotor. Electricity generation is facilitated by both gas and steam turbines, which are coupled to a generator. The findings of this study underscore the accuracy and appropriateness of ANFIS-based models in evaluating and predicting the performance of intricate hybrid power plants. The ANFIS model exhibited an impressive overall coefficient of correlation of 0.9991. Notably, the integration of evolutionary algorithms (PSO and GA) into the ANFIS framework elevated model performance, yielding correlation coefficients of 0.9994 for ANFIS-PSO and 0.9997 for ANFIS-GA. It is noteworthy that ANFIS-GA demonstrated superior predictive capability. The modeling framework developed in this research offers valuable support to designers, energy managers, and decision-makers. It provides a robust and dependable tool to assess the performance of hybrid thermal power plants, which are poised for a global surge in numbers amid the ongoing energy transition.
Exploration and Application of a Blended English Teaching Mode in Military University Based on Production-Oriented Approach

Aiming at solving the problems of outdated mode and single method of English teaching for sergeant students, as well as the separation of learning and applying in traditional English classes, this paper proposes a blended teaching mode guided by the production-oriented approach, carries out feasibility analysis, designs teaching activities according to the output-driven hypothesis, and tests the effectiveness of this teaching mode in Sergeant English class through practice. It has proved that this teaching mode can effectively stimulate students’ motivation and interest in learning English, improve their English output and enhance their learning confidence, and significantly improve the teaching effectiveness.
Analysis of Electric Guitar Improvisation Techniques

As an important form of modern fusion music performance, electric guitar improvisation is widely praised and welcomed for its unique performance style. The main points and difficulties of its performance lie in tone switching, speed playing skills, and grasping the title, mood, and style of the music. Therefore, it is necessary to integrate the conventional techniques of string sweeping, pizzicato, and dotted strings with improvisation, applying differentiated rhythmic rhythms, timbre modulation, and special playing techniques to deepen the distinctive art-rock music characteristics.
The influence of brain gym on changes in the numerical ability of grade I elementary students

Introduction: The child is one of the important human resources in building a nation with improved quality and quality of life. One of which is the issue of education. Education is a process of self-development potential to increase optimal knowledge. Therefore, using a brain gym to improve children's learning abilities is more optimal. This study is to determine the effect of brain gym on the change of numeracy skills in grade 1 elementary school children.&#x0D; Methods: This research was pre-experimental with a one-group pretest-posttest approach with purposive sampling. The respondents were 19 samples that fulfilled the inclusion and exclusion criteria in public elementary school 1 Jatiwarna, Bekasi, Indonesia. Data were taken before and after the three weeks of brain training exercises using the 1st-grade end-of-year skills test. Analysis of univariate data used Shapiro Wilk and bivariate using paired sample t-test.&#x0D; Results: From 20 respondents, the average counting ability before intervention was 65.20, and after intervention was 80.60. The results showed an effect of the Brain Gym intervention on the counting abilities of children with p=0.000 &lt;α (0.05).&#x0D; Conclusion: Based on these results, it can be concluded that exercise brain gym influenced the increase in children's numeracy skills at Public Elementary School 1 Jatiwarna Bekasi in 2018.
Proactive complaint management with ClientID

E-REDES is the largest Portuguese Distribution System Operator with more than 6 million customers and must deal with millions of interactions yearly. To improve their involvement E-REDES wants to implement new tools for better engagement that are agile, efficient, personalized and convenient. With the development of every means of communication and easier ways to communicate, clients are becoming more demanding and aware of the information they can access. Registering and analyzing every contact is essential to monitor and manage the client experience and the quality of the service provided. The end-to-end process of receiving and answering a client complaint includes its classification by an operator, its treatment and the support and preparation of the final answer, which must be done in less than 15 working days. These involve possible classification errors and difficulty obtaining root causes for the claim. In this context, it was developed a solution to work on the client&apos;s claims, including (i) the anonymization of sensitive data and preparing its processing; (ii) the development of a text reading algorithm and consequent classification of complaints and suggestion of root causes; (iii) the automatization of the process and integration on E-REDES infrastructure; (iv) the creation of a platform for the management of complaints. This project, developed in partnership with Smartwatt, helped to reach a robust solution with an algorithm that has a good performance and a platform that fits the needs of the client management team, ensuring the increase of operational efficiency and the decrease of time to answer a complaint.
Towards the digital transformation of distribution system operators using knowledge graphs and conversational AI

As part of the digital transformation of large companies, such as Distribution System Operators (DSO), the communication between companies and their customers is moving more and more in the direction of Conversational AI. Two big challenges need to be faced in this context, namely: 1) how to efficiently transfer the partially fragmented company knowledge into a Knowledge Graph (KG) that is used as background knowledge for conversational AI and 2) how can the partially manual setup process of a Conversational AI Platform be fully automated using the semantic descriptions in the KG? This paper outlines a solution developed to address these challenges, as part of the WisNat project.
The effectiveness of using the QR Code in learning the skill of draping the pleats on the dress

With the development of the current era, technology has become an integral part of our daily practices, and has led to noticeable changes in our lives, In view of the great facilities and services provided by technology to humanity, It has been employed in many institutions, including education, The integration of technology in the field of education has a positive impact on the educational process, such as raising motivation, and the development of thinking skills, diversification of teaching methods, QR Code technology is one of the modern technologies that have been used in education, The draping on the dress is one of the precise specialized areas that depend on the performance of the individual as a procedural behavior for some special work that needs manual skills, It is a process that requires combining ability and skill, and because learning the skills of draping on the dress requires a long time, The importance of research lies in the use of technology in the educational process to meet the strategic objectives in light of the Digital Transformation Program 2030,The research followed the semi-experimental approach in the procedures and steps of applying the QR Code technology, the research included a sample of fourth-year students and the research sample was randomly selected from the fourth year students, clothing and textile department, and the strength of the research sample reached (46) students, It was divided into two groups: a control group taught in the traditional style and numbered (23) students and an experimental group taught using the QR code technology and numbered (23) students, and (23) students were used as an exploratory sample, In order to calculate the scientific coefficients of the tests used in research and cognitive testing, The research required building a questionnaire for cognitive and skill tests, a questionnaire for students' opinions, The results of the research included that there are statistically significant differences between the average scores of the pre- and post-cognitive achievement test of the experimental sample in favor of the post-test. There are statistically significant differences between the average scores of the pre- and post-skill test of the experimental sample in favor of the post-test, Students' opinions are positive towards the use of the QR code in learning to draping pleats on the dress. The most important recommendations of the research are: the need to integrate compatible educational strategies that raise the efficiency of achievement and skill and achieve learning outcomes, Adding the proposed educational program using QR Code technology to the educational activities for self-learning in the courses of university students majoring in clothing and textile
A Survey on Collaborative and Effective Implementation of Augmented Reality (AR) Interactive Learning Application

As schools seek interactive learning methods to make complex concepts easily graspable, it is observed that technology can be leveraged toward the same. In particular, augmented reality has the potential to make demanding topics not seem like rocket science. With real-time visualization from day-to-day devices like smartphones and tablets, regular pedagogical strategies can be modified for the betterment of both students and teachers. This AR interactive learning application is specially designed for the NCERT science textbook of X standard CBSE board. Diagrams from the book can be scanned and visualized in 3D. The Unity game engine and Vuforia AR SDK are used for the development of the app. These models can be zoomed in and out, rotated, and rigged for an immersive experience. Along with the 3D model, a dialog box consisting of a brief description, additional resources, and aided text-to-speech audio functionality is available. The models and other assets are downloaded from TurboSquid. Additionally, to test their knowledge, students can participate in timed quizzes which have questions from the NCERT textbook for the particular topic.
Cyclostationary Feature based Modulation Classification with Convolutional Neural Network in Multipath Fading Channels

Modulation classification has been widely studied in recent years. However, few studies focus on the performance degradation in multipath fading channels, whose impact is non-negligible. In this paper, a convolutional neural network (CNN) employing cyclostationary (CS) feature, which maintain the essential characteristics in fading channels, is proposed for robust modulation classification. Our method can be implemented in two approaches, referred as CASE1 and CASE2. In CASE1, a single-structured CNN is designed for learning hybrid CS features to perform classification. And in CASE2, we present a CNN model based on a hierarchical structure to perform two-stage classification. Specifically, the coarse classification is performed by learning the second-order CS features with the first-level CNN. Next, another CNN can be selectively activated to learn from high-order CS features for fine classification within the subclass. In this way, our method uses CS features to provide favorable guidance for the learning process of CNN, thus improving the classification performance in fading channels. The experimental results demonstrate the advantages of the proposed method in terms of classification accuracy and computational complexity.
CHarity and Donation Tracking System Using Queue Structure

With the advent of new technology and online payment systems, acquiring donors for charitable causes has become easier. However, the donation process often ends for donors once they have made their contribution, leaving the remaining work to the charity. This lack of visibility and involvement can lead to donor disengagement and hinder future contributions. To address this problem, we propose implementing a comprehensive donor tracking system that uses the queue which operates on a first-in, first-out (FIFO) basis. This system ensures that the first donor's donation is prioritized and fulfilled before the second donor can utilize and benefit from their donation. This system aims to provide real-time updates on the progress and impact of donations, bridging the gap between donors and beneficiaries. Existing online donation platforms often focus solely on facilitating payments, neglecting post-donation tracking. This lack of transparency leaves donors unaware of how their contributions are utilized and whether they reach the intended beneficiaries, potentially affecting donor trust and confidence. By implementing the queue method, charities can streamline donation workflows, track, and acknowledge donations, and provide transparency to donors. When we examine the current tracking methods used in the donation field, it becomes evident that only a limited number of methods, and only blockchain-based systems are in use. Unfortunately, these systems tend to escalate costs significantly. This paper highlights the how and what are the benefits of adopting the queue method for donor tracking, including improved donor engagement and trust. Ultimately, the proposed system enhances donor satisfaction and maximizes the impact of charitable giving.
Personalized Learner Assistance Through Dynamic Adaptation of Chatbot Using Fuzzy Logic Knowledge Modeling

Personalized approaches and tailored support have become increasingly significant in the field of online education, aiming to enhance the overall learning experiences of learners. This paper introduces a novel approach for addressing challenges in providing tailored support by utilizing chatbot technology and the flexibility of fuzzy logic. The chatbot is responsible for delivering precise and tailored responses to learners, considering their input, typically in text form. This is accomplished through the utilization of a rule-based system that is capable of generating accurate answers according to predefined criteria. To augment this support, fuzzy logic is employed for modeling the learners' knowledge, thereby enhancing the chatbot's proficiency in accurately evaluating and responding to inquiries. Consequently, the provision of assistance can be tailored to the specific knowledge level of learners, aiding them in achieving their educational goals. This methodology is incorporated in an intelligent tutoring system designed to provide tutoring for the programming language Java. The evaluation findings demonstrated the effectiveness of our approach in delivering personalized assistance through a chatbot. The results indicated that the chatbot's responses were highly rated in terms of clarity, relevance, and usefulness. Additionally, the system was found to effectively address learners' needs with quality and adequacy.
A Comparative Analysis of Tweet Analysis Algorithms Using Natural Language Processing and Machine Learning Models

Online Social Networks (OSNs) have become integral platforms for information sharing, attracting both legitimate users and spammers. Detecting and mitigating spam within these networks pose significant challenges due to the dynamic nature of content and user behavior. In this paper, we present a comprehensive comparative analysis of algorithms for tweet analysis, focusing on Natural Language Processing (NLP) and Machine Learning (ML) models. We evaluate these algorithms through sentiment analysis and multiple attribute analysis, utilizing diverse methodologies and datasets. Our study explores feed-forward neural networks, Bayesian classifiers, and transformer-based models for NLP tasks, achieving high prediction accuracy and insightful metrics such as precision, recall, and F1 score. Furthermore, we delve into multiple attribute analysis using Random Forest, Logistic Regression, and Gradient Boosting algorithms. Through a systematic exploration of various approaches, this work contributes to a deeper understanding of spam detection and sentiment analysis within the context of OSNs, paving the way for enhanced social network security and content analysis.
Busy Book for Improving Students' Language Skills

Education for early childhood is a very important educational stage for a child because various developments are taking place, including language development. To optimize children's language development, appropriate learning media is needed, one of which is the busy book. This is a qualitative descriptive research doing library research which aims to describe busy books as learning media to stimulate early childhood language skills. In this study, writer collected data from various research journals and books related to busy books and early childhood education. Research results from various journals show that busy books can stimulate children to recognize symbols and letter sounds, assemble letters into words, and assemble words into meaningful sentences; activities in the busy book can improve speaking skills by mentioning and telling the pictures in the busy book. This study shows that busy books can be used as learning media to stimulate early childhood language skills.
Methodology

The analysis of discourse confronts the linguistic world with some challenges. One of the reasons is the omnipresence and everyone’s participation in discourse, making it a mutual effort.
Deep Semantic Understanding and Sequence Relevance Learning for Question Routing in Community Question Answering

Question routing (QR) aims to route newly submitted questions to the potential experts most likely to provide answers. Many previous works formalize the question routing task as a text matching and ranking problem between questions and user profiles, focusing on text representation and semantic similarity computation. However, these works often fail to extract matching features efficiently and lack deep contextual textual understanding. Moreover, we argue that in addition to the semantic similarity between terms, the interactive relationship between question sequences and user profile sequences also plays an important role in matching. In this paper, we proposed two BERT-based models called QR-BERTrep and QR-tBERTint to address these issues from different perspectives. QR-BERTrep is a representation-based feature ensemble model in which we integrated a weighted sum of BERT layer outputs as an extra feature into a Siamese deep matching network, aiming to address the non-context-aware word embedding and limited semantic understanding. QR-tBERTint is an interaction-based model that explores the interactive relationships between sequences as well as the semantic similarity of terms through a topic-enhanced BERT model. Specifically, it fuses a short-text-friendly topic model to capture corpus-level semantic information. Experimental results on real-world data demonstrate that QR-BERTrep significantly outperforms other traditional representation-based models. Meanwhile, QR-tBERTint exceeds QR-BERTrep and QR-BERTint with a maximum increase of 17.26% and 11.52% in MAP, respectively, showing that combining global topic information and exploring interactive relationships between sequences is quite effective for question routing tasks.