Reviewerly/exHarmony huggingface dataset:
Innovation Of Learning Methods In Improving Early Children's Language

The early childhood education learning model (PAUD) is a learning system that must be owned by teachers. The PAUD learning model also has characteristics in developing various aspects of child development. One of them is improving the language of early childhood. Namely through appropriate learning
PANDUAN PEMBELAJARAN MIKRO BAGI DOSEN DAN MAHASISWA

The aim of this research is to solve the problem of lecturers and students regarding the lack of microteaching guides at the Faculty of Art and Design, Makassar State University. Specifically, this research aims to develop a valid micro-learning guide for lecturers and students at the Faculty of Art and Design, Makassar State University. This type of research is research and development (R&amp;D), is to develop microteaching learning guidelines. The development research model developed was adopted from the model developed by Thiagarajan, is to the Four-D model (Define, Design, Develop, and Disseminate). The Define stage is analyzing the needs of lecturers and students for microteaching learning. The analysis is carried out by observing and reviewing the needs of lecturers and students in microteaching learning, as well as reviewing literature related to microteaching. Design stage (design), after analyzing needs, the next step is to design a form of guide by making a storyboard, arranging the material included in the guidebook, creating learning scenarios, and creating assessment guides in microteaching learning. Develop stage (development), this stage is the stage of validating the micro learning guide that has been developed. and the Disseminate stage, the stage where the guidelines that have been developed are distributed to lecturers who teach microteaching courses and to students who program the courses. The number of validators involved in this research was two validators. Each one material expert and one media expert. Data collection techniques were carried out using observation, interviews, questionnaires and documentation techniques. Data analysis techniques were carried out using qualitative and quantitative descriptive analysis techniques. The research results show that the development of the microteaching guide went through four stages and was carried out in accordance with the research and development model developed by Thiagarajan, namely Define, Design, Develop and Disseminate. The microteaching guide for lecturers and students has been declared suitable for use in microteaching at the Faculty of Art and Design, Makassar State University.
The Influence Of Providing Rewards And Reinforcement On Stuedent Learning Outcomes In Class IV Mathematics At State Primary School 106143 Sukamaju School Year 2022/2023

&#x0D; &#x0D; &#x0D; &#x0D; This study aims to determine the effect of giving rewards and reinforcement on student learning outcomes in mathematics in class IV at SD Negeri 106143 Sukamaju for the 2022/2023 academic year. In this study, researchers used quantitative methods. The population used was all students at SD Negeri 106143 Sukamaju with a sample of 31 students. Sampling using nonprobability sampling. The results of this study indicate that the results obtained from correlation test can be seen in the correlation coefficient of r (count) 0.689 ≥ r (table) 0.355, it can be concluded that the alternative hypothesis (Ha) is accepted, that is, there is a significant effect between reward and reinforcement on student learning outcomes at SD Negeri 106143 Sukamaju. It can also be seen from the results of the t-test where t (count)&gt; t (table) is t (count) of 5.113 and t (table) of 2.042, then the hypothesis (Ha) is accepted. This shows that there is a significant positive effect between the provision of rewards and reinforcement and mathematics learning outcomes in class IV of SD Negeri 106143 Sukamaju for the 2022/2023 academic year.&#x0D; &#x0D; &#x0D; &#x0D;
Targeted Display Advertising using Machine Learning

This paper delves into the intricate challenges of problem formulation and data representation in the context of a large-scale machine learning system for targeted display advertising. Unlike traditional models, this system is not just conceptual but has been operational for years across thousands of advertising campaigns. Since obtaining ideal training data is cost-prohibitive, data is sourced from related domains and tasks and then adapted for the target task. The paper outlines the architecture of this multi-stage transfer learning system, emphasizing the problem formulation aspects. Extensive experiments demonstrate the value of each transfer stage. Real-world results with diverse advertising clients from various industries showcase the system's performance. The paper concludes with valuable insights gained from over half a decade of work on this complex, widely deployed machine learning system.
HOLOGRAPHIC 3D WINDOWS AS A MEANS OF VISUALIZING SIMULATION MODELS

The article provides an overview of the possibilities of holographic 3D showcases as one of the methods of visualizing 3D content by recognizing individual objects in the image and transferring them as individual elements to a holographic 3D showcase. Depending on the types of simulation models, there is a need for visualization of the obtained results for their evaluation and testing, with the possibility of further improvement. The use of displays that transmit images on a plane does not make it possible to get a complete picture of the subject being studied. The use of holographic 3D showcases opens the possibility of working with simulated models even at the stage of their creation in software applications for 3D modeling or for creating simulated 3D models. This gives the developer the opportunity to speed up the adjustment of object parameters even at the stage of forming models according to the initial technical task. It should be noted that the holographic 3D showcase is most effective for working with objects that have a 1:1 ratio with a real industrial sample. This makes it possible to adjust the development and testing of the model, as well as to change or evaluate the effectiveness of the functionality and to change the operating parameters without creating a useful model. This technique significantly speeds up the time of model implementation from the design project to the realization of a physical object. The information technologies used in the work of holographic 3D showcases are based on the use of web technologies, which, due to the programming of js scripts and parameters of style tables, allows you to obtain optimal results with a minimum load on the working computer, which allows you to use the application on a computer in parallel with applications for developing simulation models. The ability to display elements of the work area on a holographic 3D showcase directly in the modeling process allows you to focus on details that are invisible on flat displays.
Image compression-encryption scheme using SPIHT and 2D-BCM

As network technology advances, data sharing over the internet has also increased significantly. Images are the most shared data over the internet. However, certain images are sensitive and require secure storage or transmission, especially when communicated through insecure channels. Digital images typically exhibit high data correlation where compression can be done to reduce the load on transmission channels and storage memory. To address this, a proposed algorithm combines image compression and encryption techniques. The algorithm employs the Set Partitioning in Hierarchical Trees (SPIHT) technique for image compression. After compression, the algorithm proceeds to encrypt the compressed image. It achieves this by employing chaotic sequences generated using the Two dimensional Beta Chaotic Map (2D-BCM) and a permutation-diffusion architecture. Based on the experimental results and security analysis conducted, the compression-encryption algorithm exhibits a high level of sensitivity and security. As a result, the algorithm demonstrates its capability to withstand chosen plaintext attacks.
Audio

Sound is a crucial part in almost any game today. Background music and when the player or enemy walks, dies, attacks, or is injured are scenarios in which a chord sound is commonly played to enhance the experience. In Flame, to use sounds, we must install an additional extension to the Flame package:
Game 4: Many Experiments

In this chapter, we will implement several extra developments that are not part of the basic gameplay of the game, such as a sun counter to add the plants based on a counter, generation of suns to register in the counter, pause, restart, and changing the size of the screen.
What factors influence Bitcoin’s daily price direction from the perspective of machine learning classifiers?

The paper examines the factors that influence Bitcoin price direction from the perspective of machine learning (ML) models. The observed factors cover Bitcoin market data, technical indicators, blockchain variables, sentiment analysis, and other macro-financial variables. Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) classifiers are employed. Three train-test ratios are considered. Grid search and blocking time series cross-validation are used to adjust the hyperparameters of the proposed ML algorithms resulting in the three most accurate models for each train-test ratio. Variables that affect the next-day price direction are ranked using LR and RF best models. For each method and train-test ratio, the smallest subsets of independent variables with the highest test set accuracy were chosen to reduce dimensionality. Models show that technical indicators influence daily Bitcoin price direction the most, followed by blockchain and Bitcoin market variables. Contrarily, models disagree on the importance of Tweets and macro-financial variables. Finally, SVM performed better on the test set when the LR optimal sets of independent variables were considered, indicating that the analysis of individual factors' influence on the Bitcoin price is not important only for corresponding model. Combining only influential independent variables and 90:10 train-test ratio yielded the greatest accuracy of 58.18 % achieved by RF model.
Security of an image utilizing modified use of DWT and SVD along with multiple watermarking

In the case of digital communication, the security of a signal is the most important factor. Security can be ensured by several techniques. In this chapter, the security of an image is developed by using digital watermarking. In the proposed technique, images as well as audio signals are used as watermarks which are the proofs of the true owner of the image being sent. To embed the watermarks into the host image, the total energy of both the host and the watermark signals are divided several times. These energy divisions are performed by using discrete wavelet transform (DWT) and singular value decomposition (SVD). The energy divisions are incorporated for utilizing the total energy distribution of the host signal for embedding the watermarks. For this, the developed technique ensures the insertion of a large piece of information into the host signal. Apart from that, the developed technique distributes the energy of the watermark signals into the entire region of the host signal which will create difficulty in removing or destroying the watermarks by the unwanted parties. The proposed technique offers a PSNR (Peak Signal to Noise Ratio) of 63.7525 dB and normalized cross-correlation (NC) close to 0.9. The robustness is tested against not only conventional attacks but also against real-life scenarios.
Privacy-preserving identification for monitoring images

Camera sensors embedded in monitor units or mobile phones make it easy to capture various personal images in daily life. Machine learning especially deep learning provides an elegant way to identify images (e.g., person re-identification, face recognition, facial expression recognition). However, a personal image usually involves an amount of sensitive data, such as identity, face, and facial expression. Accordingly, image identification poses severe challenges of privacy leakage for persons&apos; identities, face data, facial expressions, etc. Either GDPR (General Data Protection Regulation) or EDPS (European Data Protection Supervisor) stipulates that monitoring images involve private data and are easy to intrude on the fundamental right to privacy. In this chapter, we first sort out the privacy concerns in monitoring image identification and then formalize privacy-preserving identification for monitoring images. Next, we give a general framework to achieve privacy-preserving monitoring image identification and discuss privacy-preserving person re-identification based on the proposed framework. Finally, we conclude the research challenges and attempt to foresee some new research directions in privacy-preserving monitoring image identification.
Conclusion

The digitization of data brings increasing importance to multimedia digital security How, current technology faces challenges such as increased data volume and resource demands Future research should focus on intelligent and adaptable solutions, as well as protecting privacy Practical and comprehensive approaches are needed to address these challenges and ensure the reliability of multimedia security technology.
NOVEL TRANSFORMER-BASED APPROACH ENHANCED BY REINFORCEMENT LEARNING AND ATTENTION MECHANISMS

In natural language processing (NLP), crafting succinct and precise text document summaries is a formidable challenge. Abstract text summarization (ATS) aims to extract the essence of the source text while preserving its main content and purpose. With the manual summarization of extensive text volumes posing a laborious task, the need for automated summarization techniques becomes increasingly evident. This research aims to pioneer an ATS model grounded in the transformative capabilities of the Transformer framework, enriched by a self-attention mechanism designed to tackle conference complexities, thus bolstering textual comprehension. As our journey reaches its zenith, we harness the empowering forces of reinforcement learning to elevate and refine the quality of our generated summaries. The resultant model exhibits remarkable advancements in text summarization performance. Trained and rigorously evaluated on a consolidated dataset comprising Inshorts News, DUC-2004, and CNN/Daily Mail shared tasks data, our model undergoes scrutiny using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, offering compelling evidence of its superiority over state-of-the-art baseline models. Impressively, our model attains formidable model accuracy, boasting a 49.10% F1-Score on both the Inshorts and CNN/Daily Mail datasets, along with a commendable 39.98% F1-Score on the DUC-2004 news dataset. Keywords: Natural Language Processing, Transformer Model, Self-Attention Mechanism, Abstract Text Summarization, Recall-Oriented Understudy for Gisting Evaluation Metrics DOI： https://doi.org/10.35741/issn.0258-2724.58.6.5
Filtering of Complex Signals Based on a Two-Level Fuzzy-Logic Model

Purpose of research. Development of a method and algorithm of complex analog radio signals filtering and binarization, such as the signal of Automatic dependent surveillance-broadcast (ADS-B), which allows to increase the sensitivity of the receiver of the AZN-B signal and increase the number of correctly detected received messages. Methods. To solve this problem, the basics of the theory of signal filtering and the theory of fuzzy sets were applied in the work. The proposed method is based on combining signal filtering by known filters and a two-level fuzzy model. The first and second levels of the fuzzy model contain three operations: automatic formation of membership functions, compositional output and defuzzification. Input variables of both levels are given by trapezoidal membership functions. At the first level, they are formed automatically depending on the characteristics of the complex signal. The output function at the first level is given by a singleton function, and defuzzification is carried out using a simplified center of gravity model. Results. The proposed algorithm was implemented in the developed device based on a programmable logic integrated circuit (FPGA). In addition to filtering, the developed device implements all signal processing functions, such as: receiving input data, decoding, checking the correctness of decoded data, storing them, transmitting ADS-B messages for further processing. A distinctive feature of the device is its small size and low power consumption, which allows use it in small spacecraft and unmanned aerial vehicles. Conclusion. A method of filtering complex signals based on a fuzzy logic model is considered, which can be used to filter complex signals, such as ADS-B messages in small spacecraft modules. The proposed implementation of the filtering method makes it possible to increase the sensitivity of the AZN-B signal receiver by 20% and correctly decode the received signal. The method was implemented by an FPGA-based device, which made it possible to reduce the size and power consumption compared to analogues.
Factors Affecting Job Satisfaction of Employees in Government Sector Organizations

Job may be defined as a specific task done as part of the routine of one's occupation or for an agreed price. Job is a social expectation and social reality to which people seem to confirm for maintaining their livelihood. It not only provides status and psychological satisfaction to the individuals but also binds them to the society. Job satisfaction is a person’s attitude towards the job. Job satisfaction means measurement of excellence to which an employee feels contended and happy about his work and the conditions in which it has to be done.&#x0D; The main purpose of the study is to investigate the determinants like encouragement, management, working condition and training as the various facets of employee job satisfaction which plays a major role for employee performance. A quantitative approach is used in this study. A Pearson correlation research design and survey method is used to collect data. A research model and four hypotheses were developed. Regression analysis was used to test the hypotheses.&#x0D; The study found the implementation of independent variables like encouragement, management, working condition and training by public sectors enterprises. for maintaining the job satisfaction of employees in the organization. All of the predictor variables were significantly related to the success of employee performance.
DESIGN OF A MOBILE AND WEB-BASED ACADEMIC INFORMATION SYSTEM (Case Study: SD Negeri 02 Pegiringan)

SD Negeri 02 Pegiringan is one of the elementary schools in Pemalang. Currently, SD Negeri 02 Pegiringan is still using the 2013 elementary school curriculum, and has earned an A accreditation rating in 2022, the problem faced by SD Negeri 02 Pegiringan at this time is that parents cannot always monitor their children's grades, so they are less aware of their development. In addition, the report card data processing process still relies on the use of Excel manually. Based on these problems, this research builds a mobile web-based academic information system application. With this academic information system, it can simplify the process of checking, recording, and reporting student grade data in a computerized manner. The system design in the application utilizes the UML (Unified Modeling Language) methodology, and will be implemented with PHP (Pear Hypertext Preprocessor) as a programming language for web and Dart for Android, MySQL as a database server, with the data processed is student data, teacher data, grade data, class data, grade data, and subject data.
PENERAPAN PELAYANAN PRIMA PADA DONGENG ANAK REMAJA KELILING (DARLING) DINAS PERPUSTAKAAN DAN KEARSIPAN PROVINSI JAWA TIMUR

This research aims to determine the application of excellent service in the Fairy Tales of Traveling Teenagers (Darling) at the East Java Province Library and Archives Service. DARLING (Fairy Tales for Traveling Teenagers) is innovative and provides excellent service for the East Java Province Library and Archives Service. The DARLING program promotes reading and introduces children and teenagers to moving fairy cars. Qualitative descriptive methods were used in this research. The data collection technique uses data triangulation, combining interviews, literature study and documentation. The research results show that DARLING has successfully implemented the A6 principle of excellent service.
An artificial neural network approach for the language learning model

Abstract The current study provides the numerical solutions of the language-based model through the artificial intelligence (AI) procedure based on the scale conjugate gradient neural network (SCJGNN). The mathematical learning language differential model is characterized into three classes, named as unknown, familiar, and mastered. A dataset is generalized by using the performance of the Adam scheme, which is used to reduce to mean square error. The AI based SCJGNN procedure works by taking the data with the ratio of testing (12%), validation (13%), and training (75%). An activation log-sigmoid function, twelve numbers of neurons, SCJG optimization, hidden and output layers are presented in this stochastic computing work for solving the learning language model. The correctness of AI based SCJGNN is noted through the overlapping of the results along with the small calculated absolute error that are around 10 –06 to 10 –08 for each class of the model. Moreover, the regression performances for each case of the model is performed as one that shows the perfect model. Additionally, the dependability of AI based SCJGNN is approved using the histogram, and function fitness.
Blockchain-envisioned arbitrable multimedia data auditing based on post quantum computing paradigm in IoT

Data auditing is becoming increasingly important as numerous users want to outsource large multimedia data from the Internet of Things (IoT) devices to the cloud. This is because it allows users to confirm the accuracy of the data they are outsourcing. However, most existing data auditing techniques fail to ensure comprehensive data integrity, a vital prerequisite for meeting the security demands of valuable multimedia services. Moreover, when the cloud service provider (CSP) tampers with the outsourced data, users are not promptly compensated due to the lack of fair arbitration. This chapter introduces a blockchain-driven approach to enable arbitrable data auditing. In the proposed system, users often only need to perform private audits, and a smart contract is only triggered to perform a public audit when the private audit&apos;s verification is unsuccessful. The proposed hybrid auditing approach prevents users from paying audit charges and receiving immediate and rapid recovery when the CSP corrupts the outsourced data. Additionally, the presented system can ensure 100% data integrity by utilizing the deterministic checking technique based on an Number Theory Research Unit (NTRU) lattice accumulator. Furthermore, when users want compensation from the CSP, the proposed scheme can stop forged claims. We completed the prototype&apos;s implementation after assessing the security benefits. The testing outcomes of the suggested data auditing system based on blockchain demonstrate its security, efficiency, and advantageous characteristics.