Reviewerly/exHarmony huggingface dataset:
PENYULUHAN KESIAPSIAGAAN BENCANA KEBAKARAN DI DESA LERENG KECAMATAN KUOK

Fire is one type of disaster that is quite potential to leave large losses if it does not get sufficient attention and handling in disaster mitigation efforts. Many people still do not understand the concept of disaster preparedness in tackling the bad impacts caused by disasters. counseling on disaster preparedness to heads of families in Lereng village, district. Kuok. Which can be identified in several specific problems, namely whether there is an impact of disaster preparedness counseling on increasing the knowledge of family heads in Lereng village, Kec. Kuok. However, after participating in community outreach activities, they will be able to understand the meaning of disaster, factors causing fire disasters, disaster preparedness, activities in disaster preparedness and benefits in preparedness. This community service activity aims to grow and increase community knowledge, especially family heads related to fire disaster preparedness that is at risk of occurring in slope villages, Kec. Kuok. This activity will be carried out using several methods, namely lectures and questions and answers. Participants in this service activity are community heads of 35 people. Regarding the problems faced by partners, the lecturer as the executor of community service (PKM) together with 3 students will provide a solution to overcome the problem of high losses caused by the fire disaster.&#x0D; Keywords : fire ; education
A Novel Image Encryption using Improved Chaotic Maps and Multiple Encryption Tables

This paper proposes a new symmetric cryptographic system using chaotic systems. The basic idea is that a random strand is generated from chaotic functions. The developed random strand is merged with the strand obtained from the original image. The researcher used tables called multiple encryption tables in the composition of these strands. These tables are produced according to a specific pattern. The strand combination algorithm randomly selects a table from a set of tables and performs the combination operation. This table will be replaced with another table under certain conditions. The diversity of tables and their dynamic selection in the integration operation will increase the resistance to attacks and threats and increase the proposed encryption system's efficiency. To prove the proposed system's efficiency, the obtained results have been evaluated by various tests such as entropy, correlation coefficient, and pixel change rate. Our results show that the proposed cryptographic system is cost-efficient, practical, and resistant, yet further research is required to make a final statement about its competence.
THE EFFECT OF MULTIPLE-SLOT SUBSTITUTION DRILL IN TEACHING PRESENT CONTINUOUS TENSE TO IMPROVE STUDENTS’ SPEAKING ACHIEVEMENT AT THE EIGHTH GRADE OF SMP IT ASY-SYADZILI PAKIS-MALANG

This study investigated students’ speaking achievement on present continuous tense using multiple-slot substitution drill at the eighth grade students of SMP IT Asy-Syadzili Pakis-Malang. Designed used in this study was true experimental research conducted with one experimental group taught present continuous tense using multiple-slot substitution drill and control group without multiple-slot substitution drill. To collect the data, the researcher used speaking post-test in oral test form. In analyzing the data, the researcher used IBM spss statistic versions. 20 application of independent sample t.test to know the differences between experimental and control groups. After applying the statistical computation, it was found that the mean score of experimental group (X) in post-test 53.58 with standard deviation 3.951 was higher than the mean score of control group (Y) in post-test 41.57 with standard deviation 2.750 . So it can be concluded that students taught present continuous tense using multiple-slot substitution drill achieve better than taught without multiple-slot substitution drill at the eighth grade students of SMP IT Asy-Syadzili Pakis-Malang.&#x0D;
Black Boxes, White Noise: Similarity Detection for Neural Functions

Similarity, or clone, detection has important applications in copyright violation, software theft, code search, and the detection of malicious components. There is now a good number of open source and proprietary clone detectors for programs written in traditional programming languages. However, the increasing adoption of deep learning models in software poses a challenge to these tools: these models implement functions that are inscrutable black boxes. As more software includes these DNN functions, new techniques are needed in order to assess the similarity between deep learning components of software. Previous work has unveiled techniques for comparing the representations learned at various layers of deep neural network models by feeding canonical inputs to the models. Our goal is to be able to compare DNN functions when canonical inputs are not available -- because they may not be in many application scenarios. The challenge, then, is to generate appropriate inputs and to identify a metric that, for those inputs, is capable of representing the degree of functional similarity between two comparable DNN functions. Our approach uses random input with values between -1 and 1, in a shape that is compatible with what the DNN models expect. We then compare the outputs by performing correlation analysis. Our study shows how it is possible to perform similarity analysis even in the absence of meaningful canonical inputs. The response to random inputs of two comparable DNN functions exposes those functions' similarity, or lack thereof. Of all the metrics tried, we find that Spearman's rank correlation coefficient is the most powerful and versatile, although in special cases other methods and metrics are more expressive. We present a systematic empirical study comparing the effectiveness of several similarity metrics using a dataset of 56,355 classifiers collected from GitHub. This is accompanied by a sensitivity analysis that reveals how certain models' training related properties affect the effectiveness of the similarity metrics. To the best of our knowledge, this is the first work that shows how similarity of DNN functions can be detected by using random inputs. Our study of correlation metrics, and the identification of Spearman correlation coefficient as the most powerful among them for this purpose, establishes a complete and practical method for DNN clone detection that can be used in the design of new tools. It may also serve as inspiration for other program analysis tasks whose approaches break in the presence of DNN components.
LITERASI PUBLIKASI ILMIAH HASIL PENELITIAN TINDAKAN KELAS

Writing scientific papers and their publication is a must for teachers today but many teachers have problems related to it. Seminars and community service training conducted by researchers revealed this problem. This research is a qualitative type with a descriptive approach. The data is in the form of questionnaire results and the data source is 44 teachers of SMP Negeri 17 Surabaya. The data collection technique is a closed questionnaire with the aim that researchers get absolute data. This closed questionnaire contains 10 questions related to publications. The closed questionnaire contains 4 answer options with a positive and negative scale. The data collection steps conform to community service procedures. The data analysis technique in this study is content analysis with the aim of exploring the answers to each question. The content analysis steps in this research are formulating research problems, library research, determining samples and variables, data categorization, and data processing. The results of this study indicate that seminars and training for teachers regarding writing and publication of scientific papers is something that is very important and is their need. But they have many problems and their problems are related to time, lack of motivation, lack of information, and quite a heavy workload.
Using VGG Models with Intermediate Layer Feature Maps for Static Hand Gesture Recognition

A hand gesture recognition system provides a robust and innovative solution to nonverbal communication through human–computer interaction. Deep learning models have excellent potential for usage in recognition applications. To overcome related issues, most previous studies have proposed new model architectures or have fine-tuned pre-trained models. Furthermore, these studies relied on one standard dataset for both training and testing. Thus, the accuracy of these studies is reasonable. Unlike these works, the current study investigates two deep learning models with intermediate layers to recognize static hand gesture images. Both models were tested on different datasets, adjusted to suit the dataset, and then trained under different methods. First, the models were initialized with random weights and trained from scratch. Afterward, the pre-trained models were examined as feature extractors. Finally, the pre-trained models were fine-tuned with intermediate layers. Fine-tuning was conducted on three levels: the fifth, fourth, and third blocks, respectively. The models were evaluated through recognition experiments using hand gesture images in the Arabic sign language acquired under different conditions. This study also provides a new hand gesture image dataset used in these experiments, plus two other datasets. The experimental results indicated that the proposed models can be used with intermediate layers to recognize hand gesture images. Furthermore, the analysis of the results showed that fine-tuning the fifth and fourth blocks of these two models achieved the best accuracy results. In particular, the testing accuracies on the three datasets were 96.51%, 72.65%, and 55.62% when fine-tuning the fourth block and 96.50%, 67.03%, and 61.09% when fine-tuning the fifth block for the first model. The testing accuracy for the second model showed approximately similar results.
CONCEPTUAL FOUNDATIONS FOR PREPARING MATHEMATICS AND COMPUTER SCIENCE TEACHERS FOR THE USE OF VIRTUAL CLARITY MEANS

In the article, the authors touch on the problem of professional training of teachers of mathematics and computer science in the context of the development of digital technologies and the demand of society for visual support of training. The generalization of scientific and pedagogical research on the training of teachers of mathematics and informatics is presented. The conceptual contradictions of the educational sector, which are proposed to be resolved through the implementation of the author's concept of preparing future teachers of mathematics and computer science for the use of virtual clarity in professional activities, are singled out. Based on the results of the analysis and systematization of scientific sources and own experience of teaching, the requirements for the training of teachers of mathematics and computer science were clarified, taking into account the requests of stakeholders for the effective implementation of professional pedagogical activity. The leading ideas of the concept of professional training of future teachers of mathematics and computer science for the use of virtual clarity in professional activities are revealed. The implementation of the concept is described on the methodological (the provisions of philosophy regarding the development, self-realization, self-improvement of the individual and the unity of the theory and practice of conscious human activity as a subject of cognitive activity, as well as systemic, acmeological, cognitive-visual, synergistic, reflexive-activity, BYOD-approach, made-self-approach), theoretical (based on the formation of teachers' readiness to use virtual clarity as a complex personal education) and practical (characterizes the practical aspects of the implementation of the concept) levels. It is expected that the developed concept will determine the fundamental positions of designing a pedagogical system of professional training of future teachers of mathematics and computer science for the use of virtual clarity tools in professional activities at the level of higher education institutions.
ADAPTIVE NEURO-FUZZY MODEL FOR THE CONTROL SYSTEM OF THE CLINKER GRINDING PROCESS IN BALL MILLS IN CEMENT FACTORIES

The main purpose of this study consisted in the realization of the development model of a decision support system for the cement grinding process in ball mills, including the acquisition, processing and analysis subsystems of data regarding the progress of the technological process, based on hardware technologies and intelligent software. To create the decision-making models, the graphic environment for the development of adaptive neuro-fuzzy systems from the Matlab program package was used. The paper presents a model based on the techniques proposed and developed with the application of fuzzy logic and artificial neural networks. The input/output variables, the linguistic qualifiers and the membership functions specific to each shredding batch were identified. The inference rules were defined. A standard defuzzification method was applied to defuzzify the results obtained from the inference process. At the same time, the results of the simulations of the proposed models in the Matlab environment were also presented. The testing and verification of the data obtained with the proposed inference model was carried out by comparison with the experimental data.
КОМП’ЮТЕРНІ ТЕХНОЛОГІЇ В РОБОТІ ВИХОВАТЕЛІВ ЗАКЛАДІВ ДОШКІЛЬНОЇ ОСВІТИ

The study is devoted to the problems of using modern computer technologies in the work of preschool teachers. The author carried out an extensive analysis of the source base, including foreign ones, regarding the problems of using computer technologies in
Key Issues and Future Trends on Solar Power Forecasting

Owing to the effect of cloud, solar irradiance is intermittent and causes uncertainty in power system operations. Thus, accurate solar power forecasting helps system operators reduce the risk and cost of generator scheduling. Compared to wind power forecasting, solar power forecasting has a higher challenge because the prediction of solar irradiance is difficult. However, the curve of solar irradiance includes a daily pattern. Thus, statistical methods or deep learning models capture the important characteristics of solar power generation, which improves forecasting accuracy. This study summarizes essential issues about solar power forecasting, including potential training models, data processing, efficient weather classification, and other corresponding technologies. In addition, possible trends for solar power forecasts are also addressed.
Comparative Analysis of Naïve Bayes and Decision Tree Algorithms in Data Mining Classification to Predict Weckerle Machine Productivity

The level of data accuracy in everyday life is necessary because it is reflected in the ever-advancing development of information technology. Analysis of data processing in information that can provide knowledge with the help of data mining systems. Algorithms commonly used for prediction are Naive Bayes and Decision Trees. The purpose of this study is to compare the Nave-Bayes algorithm and the decision tree algorithm in terms of the accuracy of predicting the productivity of the Weckerle machine at PT XYZ. The method used is a literature study from various related sources and understanding of the data in the source related to the subject of the classification method of the Naive Bayes algorithm and the decision tree into the data mining system. The results of this study are a classification using the Nave-Bayes algorithm with a higher level of confidence than the decision tree algorithm.
What To Say When You Don't Know What to Say

Each "Communication Corner" essay is self-contained; however, they build on each other. For best results, before reading this essay and doing the exercise, go to the first essay "How an Ugly Duckling Became a Swan," then read each succeeding essay. Formal public speaking is when people are invited to come to hear what you have to say and for which you usually have plenty of time to prepare. Impromptu public speaking is when you are asked to say something of consequence at a moment's notice. For example, as a student during a class, as an employee during a company meeting, as a participant in a public forum, etc. On the internet, you can find loads of tips and tactics for successfully handling impromptu speaking. Here we present a tactic you won't find on the internet because it was invented (or rather discovered) by Philip Yaffe, Ubiquity's very own communication consultant, and has never before been published.
Pathologies Classification in Voice Signal Using Deep Learning Technique

Many references support the non-invasive detection of aberrant speech using machine learning function descriptors and classifiers.Deep learning with feature descriptors and time frequency images is a better option.The majority of deep learning frameworks for speech-language pathology use a binary classification model.A network that can identify accurate medical conditions is required to construct a hardware system.It is essential to do a serious examination of time-frequency analysis using advanced deep learning algorithms.Current research is focused on creating a non-invasive, dependable, and computationally expensive architecture for detecting multiclass laryngeal lesions.In a realistic scenario application, compare the performance of a fully linked network versus a completely collapsed deep learning voice denoiser network.Three alternative time-frequency picture corpora are generated in the noise reduction training example.For applying in a realistic environment, the capability of a fully-connected network and a fully convolutional deep-learning voice denoiser network is initially investigated.Denoised training samples are used to create three different time-frequency image corpuses.These multivariate image datasets are used to train three upgraded forms of the state-of-the-art convolution neuron network model using a 3D convolution kernel.
ELECTRONIC VOTING OR E-VOTING SYSTEM USING BLOCKCHAIN AND DEEPLEARNING

Electronic voting or e-voting, has been used in various forms from the 90s.Increase efficiency and reduce errors.However, wide adoption of such systems remains a challenge, especially in terms of improving resilience to potential failures.Blockchain is today's disruptive technology and promises to improve universal flexibility of electronic polling systems.It represents an attempt to exploit the advantages of blockchain for security.The proposed system fulfills the basic requirements of electronic voting systems and achieves persistent verifiability.This wrapper describes the details about proposed electronic voting system and its fulfilment using a multi-chain manifesto.This paper contains a detailed estimation of the system and it demonstrates successfully about the effectiveness in achieving an ongoing auditable electronic voting system.
A Secure and Efficient Cryptography System Based on Chaotic Maps for Securing Data Image in Fog Computing

The huge availability and prosperity of net technology results in raised on-line media sharing over the cloud platform which has become one of the important resources and tools for development in our societies. So, in the epoch of enormous data great amount of sensitive information and transmission of different media transmitted over the net for communication. And recently, fog computing has captured the world's attention due to their inherent features relevant compared to the cloud domain, But this push to head for many issues related to data security and privacy in fog computing which it's still under studied in their initial juncture. Therefore, in this paper, we will review a security system that relies on encryption as a kind of effective solution to secure image data. We use an approach of using chaotic map plus space curve techniques moreover the confusion and diffusion strategies are carried out utilizing Hilbert curvature and chaotic map such as two-dimensional Henon map (2D-HM) to assert image confusion with pixel level permutation .Also we relied in our system the way of shuffling the image with blocks and use a key for each block, which is chooses randomly to have a high degree of security. The efficiency of the proposed technique has been tested utilizing different investigations like analysis of entropy [7.9993], NPCR [99.6908%] and finally UACI [33.6247%]. Analysis of results revealed that the proposed system of image encryption technique has favorable effects, and can achieve a good results moreover it fights different attacks and by comparing with another techniques denote that our proposed fulfills high security level with high quality.
Persepsi Siswa Vokasi terhadap Sarana dan Prasarana Bengkel Instalasi Motor Listrik di Sekolah

Facilities and infrastructure in practical activities become one of the factors in the learning process in order to achieve learning goals in schools. The purpose of this study was to determine students' perceptions of the facilities and infrastructure of the Electric Motor Power Installation Workshop. This study used population and sample, the population in this study were students of class XII TITL SMK Negeri 5 Padang while the sample used Nonprobability sampling with a total of 88 students. The results of the study used the overall percentage value regarding students' perceptions of the facilities and infrastructure of the electric motor installation workshop in SMK Negeri 5 Padang is included in the good category. The percentage is obtained from the calculation results of the three existing indicators. Indicators of students' knowledge of electrical installation workshop facilities and infrastructure are in the good category, indicators of student experience in carrying out electrical installation practices are in the good category, and indicators of electrical workshop facilities and infrastructure are also in the good category.
Grouping The Districts in Sumatera Region Based on Economic Development Indicators Using K-Medoids and CLARA Methods

Inequality in economic development is an economic problem that is often felt by developing countries. In Indonesia, one of the regional areas that has not yet experienced equal distribution of economic development is the regencies/cities of the Sumatera Region. This study aims to determine regional groups and compare the results of grouping with the K-Medoids and CLARA methods. The K-Medoids and CLARA methods are non-hierarchical methods that are strong against outliers. While the best selection method is done by comparing the silhouette coefficient. The results obtained in this study using the K-Medoids and CLARA methods with 2 groups being better than forming 3 groups. The K-Medoids method resulted in cluster 1 as many as 59 districts/cities and cluster 2 as many as 95 districts/cities. Meanwhile, the grouping of districts/cities using the CLARA method with 2 groups resulted in cluster 1 as many as 74 districts/cities and cluster 2 as many as 80 districts/cities. From the comparison of the two methods, the silhouette coefficient values using the K-Medoids and CLARA methods are 0.13 and 0.15 respectively. Therefore, the CLARA method with 2 groups gave better cluster results