Reviewerly/exHarmony huggingface dataset:
Use of Rc4 Method In Android-Based Sms Security Application

. In maintaining the confidentiality of SMS, it takes a way to secure information that is important or confidential, namely by encrypting the SMS text, the level of information security of the message can be increased. One way to use for data and or information security is to use cryptographic system, this application uses RC4 algorithm which has the advantage of having a high level of security and speed process. This makes RC4 the best choice for the encryption process required by the information world towards the next century. The purpose ofbya is to prevent the occurrence of information / data taker without being known by the owner.
Synthesis of an ETL Icing Forecasting System

This paper considers one of the possible options for building an ETL icing forecasting system using a fuzzy identifier based on four basic icing parameters (air temperature, air humidity, wind direction, and icing intensity). This identifier is the sum of three fuzzy logic controllers with an individual weight factor each. Each controller has 3 inputs. This paper describes the setting of each fuzzy identifier and shows the distribution of the membership functions of the input and output variables of each fuzzy identifier. A rule base is given for each fuzzy identifier. Blocks are given to calculate the weight factors for each fuzzy identifier. The adequacy of the functioning and the forecasting structure has been estimated, and the variable weight factors have been compared with the constant ones. To estimate the system's response to additional (disturbing) factors, the system was simulated with the introduction of an input disturbance as a fuzzy identifier. This paper also shows the distribution of membership functions of the input and output variable of the controller and its rule base. A general description of the ETL icing forecasting system model is given.
Record Augmented Reality Experiences with ReplayKit

Great experiences are worth living and, perhaps even more important, sharing! Developers and designers sometimes forget that making an app conveniently shareable gives users the ability to spread words of mouth on your app. They can recommend your app and help your business grow. In augmented reality, you can record wonderful experiences using ReplayKit.
The Control Principle and Mathematical Model of Intelligent Solar Panels

Abstract Energy is an important material that promotes social development and maintains human life. Clean energy has become a scientific research theme of open world which appeal to many scientists to research it. This paper inspired by the experience of sunflower growth. So we proposed a mathematical model for automatic adjustment of the maximum luminous flux for a limited solar panel, which can help to adjust the best light position. The physical model added to the light detector in the practical application, real-time comparison of the difference in illumination at different positions to adjust the direction of solar panel, thereby enhancing the utilization of light energy. In addition, in order to save space and prevent severe weather attacks, we designed a autofolding circle disc and retractable rectangular solar panels. The intelligent system judgement and decision making based on the electrical signals fed back by the wind sensor or the vibration sensor. When the wind speed reaches the threshold, it can be folded or contracted automatically, reducing manual maintenance. The fully automatic solar panels are designed in this paper, the research results show that theis model has a high utilization, strong sensitivity, safety and durability, and easy control. This is also a process of theoretical exploration of interdisciplinary services for the energy system.
Multi-Class Segmentation of High Resolution Geophysical Data Using Deep Learning

Geohazard studies aid the identification of geological features on surface and subsurface that might be related to potential risks for the installation of subsea engineering projects in the oil and gas (O&G) industry.The detection and interpretation of these risk features are crucial tasks both in the exploration and development phases of oil production.Within this context, high resolution geophysical methods are used to manually identify and map the features of potential hazards to subsea installations.This work presents the application of computational techniques to raise the level of automation in geohazard studies, using models based on deep learning through U-Net, a type of Fully Convolutional Networks (FCNs).The result was the implementation of an intelligent system for automating the task of identifying and classifying geohazards, which makes it possible to mitigate the time spent in the search for occurrences of potential risks and provides greater attention to their analysis.
Blockchain versus IOTA Tangle for Internet of Things

Without the Internet of Things (IoT), there is no smart home, smart city or smart system. It is at the vanguard of the industrial revolution. Engineers need a proper understanding of its architecture to design an optimal system. This chapter presents a comparative analogy on the best architecture for IoT implementation. It seeks to review contemporary IoT architectures and analyse their drawbacks. A critical review of blockchain, IOTA and IoT architecture is done. The study reveals the underlying architecture of low-constrained IoT devices. The IoT devices require a specialized protocol for seamless communication within the network. A review of the consensus protocol and data structure of the two distributed ledger technologies (blockchain and IOTA) enlist IOTA as a relatively better alternative for the low-constrained IoT devices. On this premise, the chapter presents a promising IOTA Tangle as a possible better solution for IoT implementation.
SAR Image Compression Using Integer to Integer Transformations, Dimensionality Reduction, and High Correlation Modeling

In this document, we present new techniques for near-lossless and lossy compression of SAR imagery saved in PNG and binary formats of magnitude and phase data based on the application of transforms, dimensionality reduction methods, and lossless compression.In particular, we discuss the use of blockwise integer to integer transforms, subsequent application of a dimensionality reduction method, and Burrows-Wheeler based lossless compression for the PNG data and the use of high correlation based modeling of sorted transform coefficients for the raw floating point magnitude and phase data.The gains exhibited are substantial over the application of different lossless methods directly on the data and competitive with existing lossy approaches.The methods presented are effective for large scale processing of similar data formats as they are heavily based on techniques which scale well on parallel architectures.
Application of C4.5 Algorithm to Prediction Sales at PT. Sumber Sayur Segar

Fresh vegetables, fruits and fresh meat are one of the basic needs for human life. The need for fresh vegetables, fruits and meat is one of the most important factors for buyers before making a purchase transaction. Likewise with the needs of fresh vegetables, fruit and meat needed by restaurants, cafes, hospitals, hotels and so on. With the increasing number of requests from customers for the needs of fresh vegetables, fruit and meat, companies engaged in the supply and sale of these necessities need to record sales transactions so that there are no stock vacancies and excess stock of goods. Therefore, companies must be more careful in providing fresh vegetables, fruits and meat which are in great demand, so it needs a data processing in the form of data mining using the C4.5 algorithm. In this study, the predicted sales transactions are the last three months of January, February and March 2021. Then for the sales prediction criteria used are in the form of price, type of goods, type of unit and month of sale so that from these criteria can be obtained sales transactions that are selling or not selling. Data mining is a process of mining important information from a very large data. While the C4.5 algorithm is a data classification that has numeric and categorical attributes, where the results of the classification process in the form of rules can be used to predict the value of discrete type attributes from new records. The system was built using the PHP programming language and MySQL as the database. This study obtained predictive results which were implemented in the form of a decision tree, namely the category of types of vegetables belonging to the best-selling sales transactions.
Metode Live Memory Acquisition untuk Pencarian Artefak Digital Perangkat Memori Laptop Berdasarkan Simulasi Kasus Kejahatan Siber

Information technology's hardware and software are constantly evolving. The rise in cybercrime cases is consistent with this trend. While operating systems can be hacked, personal data can be stolen and encrypted, making it impossible for users to access the information. Cybercriminals are using social media data to target personal information currently. Users' usernames, passwords, and other personal information can be stored in a device's memory, as well as browser cookies. Pre-analysis, analysis, and post-analysis are the three stages of the research process. "Live Memory Acquisition" is the proper method for obtaining data from a device's memory. There is digital evidence in the form of an email address, a password, Facebook, and PayPal accounts as well as a link URL discovered in the analysis of the results of the acquisition of artifact one. The results of the artifact 2 acquisition did not reveal any passwords. There was no evidence of email or Facebook passwords in the analysis of artifact 3 acquisition results, but the overall data test results showed that the total number of test results on artifact 1 was 100%, 57.14% on artifact 2, and 71.42% on artifact 3.
Learning to Count Words in Fluent Speech enables Online Speech Recognition

Sequence to Sequence models, in particular the Transformer, achieve state of the art results in Automatic Speech Recognition. Practical usage is however limited to cases where full utterance latency is acceptable. In this work we introduce Taris, a Transformer-based online speech recognition system aided by an auxiliary task of incremental word counting. We use the cumulative word sum to dynamically segment speech and enable its eager decoding into words. Experiments performed on the LRS2, LibriSpeech, and Aishell-1 datasets of English and Mandarin speech show that the online system performs comparable with the offline one when having a dynamic algorithmic delay of 5 segments. Furthermore, we show that the estimated segment length distribution resembles the word length distribution obtained with forced alignment, although our system does not require an exact segment-to-word equivalence. Taris introduces a negligible overhead compared to a standard Transformer, while the local relationship modelling between inputs and outputs grants invariance to sequence length by design.
Learning Sparse Filters in Deep Convolutional Neural Networks with a l1/l2 Pseudo-Norm

While deep neural networks (DNNs) have proven to be efficient for numerous tasks, they come at a high memory and computation cost, thus making them impractical on resource-limited devices. However, these networks are known to contain a large number of parameters. Recent research has shown that their structure can be more compact without compromising their performance. In this paper, we present a sparsity-inducing regularization term based on the ratio l1/l2 pseudo-norm defined on the filter coefficients. By defining this pseudo-norm appropriately for the different filter kernels, and removing irrelevant filters, the number of kernels in each layer can be drastically reduced leading to very compact Deep Convolutional Neural Networks (DCNN) structures. Unlike numerous existing methods, our approach does not require an iterative retraining process and, using this regularization term, directly produces a sparse model during the training process. Furthermore, our approach is also much easier and simpler to implement than existing methods. Experimental results on MNIST and CIFAR-10 show that our approach significantly reduces the number of filters of classical models such as LeNet and VGG while reaching the same or even better accuracy than the baseline models. Moreover, the trade-off between the sparsity and the accuracy is compared to other loss regularization terms based on the l1 or l2 norm as well as the SSL, NISP and GAL methods and shows that our approach is outperforming them.
Scientific work in the Far Eastern State Scientific Library: content aspect

Scientific work is one of the most important factors affecting the efficiency of individual libraries and the library industry as a whole. This is one of the main areas of regional universal scientific libraries’ work.The purpose of the article is to trace how this type of libraries’ scientific work – of the Far Eastern State Scientific Library – evolved in 1930-2020: from implementation of scientific and informational activities to scientific-research ones, through participation in centralized and collective scientific studies to initiating their own investigations.This work’ content aspects: priority topics of research and bibliographic products are in the center of consideration. The article also contains information about this library activity in terms of organizing and maintaining scientific communications.Document analysis and bibliographic are the methods used in the work.
Development of Chatbots to Support Web Site Content Search Based on Thematic and Genre Characteristics

The paper considers an approach to creating intelligent assistants in the form of chatbots that support information search based on preliminary genre and thematic clustering of website content. The tasks of finding the necessary in-formation and providing information support to the user, organizing feedback to improve the quality of the search are being solved. A feature of the approach is the use of genre models developed for a given type of resource (educational, informational, etc.), on the basis of which genre structuring of the content of a particular site is carried out. The resulting genre structures allow you to more accurately determine the boundaries of thematic clusters related to the topic of the user's search query. To provide feedback to the user, a simple script has been developed that allows not only to clarify the request, but also to implicitly get information about what exactly did not suit the user in the resulting out-put. An experimental study was conducted on the Telegram platform, the results were compared with the Yandex search engine.
Accessibility Then and Now: History, Myths, Considerations, and Tools

Accessibility is a critical aspect of game development. It is important for many reasons, some of which will be touched upon in this chapter, together with an overview of principles, techniques, and some of the history of how it has developed.
Improved SMOTE and Optimized Siamese Neural Networks for Class Imbalanced Heterogeneous Cross Project Defect Prediction

Heterogeneous Cross-Project Defect Prediction (HCPDP) predicts the defects by incorporating the project datasets with different distribution and metrics.However, the class imbalance problem and shallow feature learning of classifiers reduce the overall performance.This paper presents an HCPDP using Improved Synthetic Minority Oversampling Technique (ISMOTE) and hybrid deep learning classifier of Golden Eagle Optimized Siamese Neural Networks (GEO-SNN).Initially, Peters filter removes irrelevant instances in source-target dataset based on heterogeneous metrics.ISMOTE, enhanced through fuzzy mutual information based KNN attribute weights assignment, is applied solve the class imbalance problem.Features are scaled using Z-Score standardization and metric matching by Spearman's Rank Correlation (SRC).Finally, GEO-SNN, developed by SNN parameter optimization using GEO, performs deep semantic feature learning for defect prediction.Experiments using benchmark datasets showed that the proposed ISMOTE and GEO-SNN based HCPDP technique has provided 99% defect prediction accuracy and reduced processing time by 20%than the state-of-the-art methods.
The authorship of <i>Wily Beguiled</i>

Abstract The authorship of the anonymous Elizabethan comedy Wily Beguiled could be established with the combined methodologies of the R Stylo program suite on the basis of 110 well-attributed and single-authored reference texts (see Appendix A) and was compared with the findings of the relevant secondary literature using a set of distinctive parameters. The greatest plausibility applied to Rhodes’s and Maxwell’s assumption that an earlier text like that of 1601/02 was reworked by John Marston and Thomas Dekker in the War of the Theatres. Rolling Delta, Rolling Classify, and the General Imposters method were able to find convincing evidence that the earlier text must have been composed by John Lyly who had probably drawn on the early university play Wylie Beguylie (1566/7).
Pelatihan Peningkatan Kemampuan Pengunaan Microsoft Office Bagi Mahasiswa Sekolah Tinggi Teologi Baptis Medan

Students at almost all levels of education are required to be technology literate, especially information technology. In addition to supporting educational activities, it also supports students in solving administrative problems. One of the software that can be used to support education and administrative issues is Microsoft Office. The application has many features that are still not familiar to some users, especially students. This was experienced by STT Baptist Medan who was not familiar with the many tools provided in Microsoft Word, Microsoft Excel, and Microsoft PowerPoint software. Some of the obstacles that are often encountered are the less than optimal use of the merge function in Word, the function of conditional formulas in Excel and animation, and multimedia in PowerPoint. During the implementation of service activities, it was concluded that the importance of direct Microsoft office training to students, especially study programs outside of computers. In the implementation of the training, students are able to use Microsoft Excel to manage numerical data. Microsoft word in managing data. Also the use of Microsoft PowerPoint in making presentations.
Animated Pedagogy in Primary School: Ways to Meet Modern Educational Objectives

The paper aims to identify the objectives of primary school and to reveal the potential of animated pedagogy to meet them based on the analysis of modern social problems. The article identifies the main modern problems: rapidity of events change, large amount of information, communication problems, absence of a life plan among the young generation. All the above mentioned problems can be solved by using the means of animated pedagogy. The scientific originality of the research lies in the fact that it analyzes the potential of animated pedagogy and identifies the main ways to use this potential to meet the modern educational objectives of primary school. As a result, it has been proved that animated pedagogy has a great potential to meet the educational objectives of the modern primary school.
PROJECT BASED LEARNING THROUGH "SEVIMA EDLINK APPS TO IMPROVE STUDENTS' ACADEMIC WRITING OF EDUCATION PROGRAM AT STKIP KUSUMANEGARA

Distance Learning is a big challenge for the world of education because not all have experienced it before. Project Based Learning (PBL) through Edlink Sevima Application is one alternative that can be applied by lecturers in Distance Learning activities. The purpose of this study was to determine whether the Project Based Learning (PBL) as Learning Model through the Edlink Sevima Application could improve the academic writing skills of students of the English Education Study Program at STKIP Kusumanegara. This research was conducted with a Classroom Action Research (CAR) research approach which consisted of two learning cycles with 18 students. The data collection technique used is through observation, interviews, and student academic writing tests. The results of this study can be seen by using the Project Based Learning learning model through edlink sevima which can improve students' academic writing test results from two learning cycles given a minimum completeness score of 75 and a success target of 85%. In the first cycle students were able to achieve the target of 6i%, and the second cycle 90%. Based on the results of the study, it can be said that the Project Based Learning as a learning model through the Sevima edlink application is effective in improving students' academic writing skills in distance learning.&#x0D; Key words: Project Based Learning, Edlink Sevima, Academic Writing.&#x0D;
Identification of Ancient Ceramics Based on Support Vector Machine

Ancient ceramics have condensed the wisdom and strength of several generations of working people in China, and have both artistic value, aesthetic value, and commercial value. However, ancient ceramics have a long history. How to accurately dating is a key problem and difficult problem. In this paper, we select ceramics from the three dynasties of Yuan, Qing and Ming as three classes, and extract the percentage of 17 materials as features. First, in order to solve the possible outliers in the features, the isolation forest is used to clearly isolate the abnormal samples of the dataset without considering the labels. Next, the classification experiment is based on the SVM model, and four kernel functions processing samples are employed, and further introduced the PSO algorithm to optimize the parameter combination of SVM, and achieved better performance.
Reviewer Acknowledgements for Journal of Education and Training, Vol. 9, No. 1

Journal of Education and Training (JET) would like to acknowledge the following reviewers for their assistance with peer review of manuscripts for this issue. Many authors, regardless of whether JET publishes their work, appreciate the helpful feedback provided by the reviewers. Their comments and suggestions were of great help to the authors in improving the quality of their papers. Each of the reviewers listed below returned at least one review for this issue.Reviewers for Volume 9, Number 1 A. F. Elgamal, Computer Science Dep.Mansoura University, EgyptBryan Spiegelberg, Rider University, United StatesCarolyn Stevenson, Kaplan University, United StatesJohn Cowan, Edinburgh Napier University, ScotlandLazarus Ndiku Makewa, University of Eastern AfricaBaraton, KenyaMontasser M. A. W. Mahmoud, Imam Abdulrahman bin Faisal Univ., Saudi ArabiaSofia Freire, Institute of Education of University of Lisbon, Portugal Dora WangEditorial AssistantJournal of Education and TrainingMacrothink Institute--------------------------5348 Vegas Dr.#825Las VegasNevada 89108United StatesPhone: 1-702-953-1852 ext.534E-mail 1: jet@macrothink.orgE-mail 2: jet@macrothink.comWebsite: http://jet.macrothink.org