Reviewerly/exHarmony huggingface dataset:
Design and Development of Bungo Emergency Service (BES) System Information 119 Android-Based:

Emergency services are very important things to consider. One of the factors that influence the quality of pre-hospital emergency services is the response to the handling of victims. With the advancement of information technology that is developing at the time, especially applications in smartphones that are very advanced and can be synergized with the needs in the pre-hospital emergency service system. The problems that have occurred so far in emergency services are the length of response time of  finding the location of the victim and the destination location   referring to the nearest health facility. The purpose of this research in to make it easier for the public to request medical emergency assistance to Bungo Emergency Service (BES) 119. This type of research is qualitative and quantitative. by using the  the one group pre and post test research design, the method used in developing system is FAST (Framework for Application of System Engineering). Subjects in the study were 2 Village Midwives, 2 Ambulance Drivers, and 5 respondents who worked at Public Safety Center 119.The method of data collection in this study by means of observation and interviews, then analyzed by the method of content analysis and descriptive analysis using a weighted average. Descriptively, the results of the analysis are the weighted average value of overall information quality before system development 1.78 and after system development 3.65 with a difference of 1.87. Calculation of response time service to victims is 5.4 seconds faster.The results of this study concluded that there was an increase in the quality of information systems before and after the development of a new system, support and commitment are needed from the Public Safety Center 119 of the Bungo District Health Office to implement the Bungo Emergency Service 119 information system optimally. Periodic monitoring and evaluation of system users must be carried out by the 119 Public Safety Center  and Bungo District Health Office.
Design and Application of Process Object Intelligent Model

Multi-order inertial system is a common controlled object in industrial field. The conventional modeling method is least squares identification algorithm. However, it is necessary to determine the order of the object model before using the excitation data to identify it. This requires prior knowledge, and the identified model is prone to large deviations. This paper presents an intelligent modeling method for multi-order inertial objects. Firstly, the third-order inertial objects are discretized and the DNN deep learning network is embedded in the discrete structure to establish the intelligent model structure. Secondly, in order to ensure that the third-order inertial system can contain the object’s inertial time, three third-order inertial systems are set up in the intelligent model to estimate the inertial time of the controlled object, and the inertial time of the intermediate inertial system is set to be close to the object’s inertial time. At the same time, a margin of (± 50%) is given and set in the other two inertial systems. Finally, the pseudo-random excitation signal is added to the open-loop or closed-loop system of the object, and the input and output data are sent to the intelligent model for training, so the accurate identification model can be obtained. The validity of the intelligent model is verified by testing the simulation data. This method has great practical significance for the design and application of intelligent model.
Deep Learning Approaches for Photonic-Assisted Modulation Format Recognition

An experiment for modulation format recognition (MFR) is demonstrated to investigate the performances of 5 deep learning models. The results show that DNN has the best performance with >95.481% recognition accuracy at −10∼15 SNR.
Analisis Model Pengelolaan Platform New Media Oleh Klub Sepakbola di Indonesia

This study aims to determine what models are created from the management of the new media platform by football clubs in Indonesia. Furthermore, the results of this study will be compared with the new media management model conducted by European football clubs through the MOSC (Model for Online Sport Communication) approach. The benefit of this research is the creation of a model that can be used as a reference for other football clubs, in utilizing the new media platform. This study uses a qualitative approach in which data collection techniques will begin by using content analysis of all new media platforms for football clubs in Indonesia, which leads to the selection of ten clubs having the highest number of followers, where in the course of the journey there are only seven clubs namely Persib Persija, Arema, Persebaya Persela , PSIS, and PSS who are willing to be the main objects in this study. Furthermore, data collection techniques through in-depth interviews are also used by researchers to the media officer division to obtain research answers. This research produces findings in the form of the creation of three models in the management of new media platforms in Indonesian soccer clubs, namely: active interaction models, passive interactions, and non-interactions.
A New Approch to Design of an Email Classifier

Due to the increase in the number of spam emails recently, the need of anti-spam filters has increased. To filter the spam emails we generally use Machine Learning Technique now a days. Naive Bayes is an open source anti-spam email filters which is widely used. In this algorithm, probability of the words occurring in the ham and spam mails is calculated during the training phase. Based on these trained set a message is tested for spam or legitimate mails. In our work, we have designed two other algorithms whose initial part are same as that of the Naive Bayes algorithm, but these two algorithms do not use the probability measure. Instead we have derived the Effective Spam words and Effective Ham words from training set and got two set of trained word list-one consisting of spam words and other consisting of ham words. Based on these two sets the testing of mails is done. The test result is presented in the project.
Periodization of Children’s Imaging Activities

The article discusses the changes that occur in the drawings of children as they grow up. Based on the analysis of studies of children’s drawings and taking into account their own pedagogical experience, the author’s periodization of children’s visual activity was compiled. Its age stages and stages of artistic development of children aged from 1 year to 12 years are reflected in it. The characteristics of children’s drawings at each stage are given, the corresponding features of the children’s pictorial activity, their relation to the drawing process and their artwork are analyzed. Brief methodological recommendations are given on the organization of the pictorial activity of children at each of the distinguished stages.
Algorithm and Model for a Fixed Sun Position Detection System

A sun position detection system that is not based on movements made by mechanical components is presented. The proposed solution uses a low-resolution optical sensor as a detection element and a number of optical fibers placed with one end on the surface of a hemisphere and the other in the proximity of the photo sensor serving as solar light guides. The structural arrangement of the fibers maps points on the surface of the sensor hemisphere to points on the optical sensor. Having a well-established correlation between the insertion position of each of the optical fibers on the surface of the dome exposed to sunlight and the area of the photo sensor that each of the optical fibers illuminates, it is possible to determine the position of the Sun on the celestial vault by processing the photo sensor output (by identifying the maximum brightness spots in the resulting grayscale image).
Illustrative Electrical Engineering in the Primary School.

Electro technical kits have many positive effects in school education, suitably supplement as well as support it, and are also one of its subject matters. Considering the fast, global development information technology is experiencing, there is a growing need to combine computer systems and electro technical kits in education. This assists in the process of digitization, too, which asserts itself in the conditions set for educating children as early as in nursery schools. The constructivist concept of education is based on the activities of students which lead to the development of their cognitive skills, thinking, and creativity; the issues of motivation, activity, independence, creativity, and humanizing education are appreciated; respect is given to the notion that students interpret new facts on the basis of understanding what they learned previously; of their existing knowledge and experiences. These mental structures constitute patterns which serve as a foundation for new, constructed knowledge.When a teacher assumes the constructivist approach to education, they assess and diagnose the students’ dispositions and attitudes to the expected content and to the manner of its processing. Subsequently, the teacher adapts their approach to these results. It is the teacher’s responsibility to establish suitable conditions and materials for education to take place, to make it easier for students to construct new knowledge as a part of their education, to respect their individual traits and pace, ensure that the students are active, and combine their knowledge with activities and skills – all of this can be ideally combined by using of electro technical kits.The paper presents the results of past research surveys which were conducted in relation to incorporating electrical engineering into education, and of the effectiveness of the implemented education in the practical reality of teaching which includes using electro technical kits as one of the basic didactic training tools in this sphere of technics. One subject of discussion also includes the condition / the current state of implementing education in the primary school environment.Keywords: Education; Technical Education; Electrotechnical kits; Illustrative Electrical Engineering; Primary School
PENGARUH KUALITAS PELAYANAN, BIAYA PENDIDIKAN, DAN KELOMPOK REFERENSI TERHADAP KEPUTUSAN MAHASISWA PENDIDIKAN EKONOMI MEMILIH PADA PROGRAM STUDI PENDIDIKAN EKONOMI

This research is a quantitative approach. With explanatory type research. The study population was students of economic education class of 2015-2018 Faculty of Economics and Business, University of Kanjruhan Malang. Sampling using proportional random sampling technique with a sample of 62 students. Data collection techniques using questionnaires and documentation. Analysis of the data used is multiple linear analysis. The results showed that: (1) the quality of service, the cost of education, and the reference group jointly had a positive effect on student decisions, with an F value of 14,623 and a significance level of 0,000. (2) the quality of service has a positive effect on student decisions, with a t value of 6,039, and a significance level of 0,000. (3) the cost of education has an effect on student decisions, with a tcount of -2.093 and a significance level of 0.041. (4) the reference group has a positive effect on student decisions, with a t value of 2.172 and a significance level of 0.034. R2 Test The value of the determinant coefficient (R Square) shows R2 of 43.1%, while the remaining 56.9% is affected by other factors outside the variables of this study
Soluções numéricas para o problema de principal-agente.

In game theory, principal-agent problems happen when one of the participants of a contractholds relevant information to its realization. This class of games is well defined analytically,although, it has been observed the lack of content related to numerical solutions. Through theuse of penalty functions and optimization algorithms implemented in R, adverse selection andmoral hazard problems were evaluated and compared to its theoretical implications. The resultswere positive and the methods allow that other problems with similar structure be evaluated bythe proposed algorithms.
Cascaded reasoning neural networks for artificial general intelligence

This paper introduced a new approach to computational reasoning using an LSTM that has four inputs representing four different sources including one from a previous network and another from a knowledge base, called CRN. The network is able to extract the relevant information from the inputs and compress them to a concise, symbolic output that can become an input for the next network. This network thereby makes successive inferences in a cascaded manner, achieving a chain of reasoning with the end result as the answer to the main question, similar to how humans perform step by step reasoning to solve complex problems. CRN is also able to command the computer to compute a formula or provide more information. The study demonstrated the efficacy of CRN by solving a modified version of the bAbI QA database. After the augmentation of the dataset with labels for intermediate reasoning, CRN achieved the score of 20/20 on all tasks in the bAbI, being first to do so.Keywords: Artificia, Intelligence, Language, Reasoning, Neural, Networks, LSTM
Sentiment Analysis of Twitter Posts using Machine Learning Algorithms

Sentiment Analysis refers to the use of Natural Language Processing to determine the attitude of a speaker, writer,or other subject with respect to some topic or the overall contextual polarity to a document, interaction, or event. Sentiment Analysis can be applied to domains such as marketing, crowd surveillance, customer service and psychology. In recent years, there has been a tremendous increase in the rate at which data is generated by users on social media platforms such as Twitter. This user-generated data is a valuable source for mining public’s opinion and hence opens up several opportunities for Sentiment Analysis. This paper focuses on using machine learning techniques for sentiment analysis of the posts on the micro blogging website - Twitter. Four major machine learning techniques have been used, namely-Decision Tree Classifier, Logistic Regression, Support Vector Machine and Neural Network. The results are analyzed and the advantages of different techniques in comparison to others have been discussed.
Web-Based Economic Activity Classification: Comparing semi-supervised text classification methods to deal with noisy labels

In order to provide accurate statistics for industries, the classification of enterprises by economic activity is an important task for national statistical institutes. The economic activity codes in the Dutch business register are less accurate for small enterprises since small enterprises are not labelled manually. To increase the quality of the register, automatic classification of enterprises based on their websites has been tried with supervised text mining techniques. The performance of current supervised text mining techniques is limited by the available accurately labelled training data. Since inaccurate labels are available for all enterprises, the current study investigates how to leverage the noisy labelled data to improve the economic activity classification of small enterprises based on their webpage texts. The current study compares the performance of various semi-supervised methods that enlarge the training data by leveraging the abundance of noisy labelled data. The methods are compared against a supervised baseline, which uses all noisy data as is. The proposed proportional weakly self-training method queries noisy labelled instances through high probability sampling and filters mispredicted instances. Results showed that proportional weakly self-training improves upon the supervised baseline while requiring far less training instances. From qualitative analyses, we conclude that the filter of proportional weakly self-training reduces error propagation compared to classic self-training. Additional experimental results showed that large enterprises are less suitable as training data for prediction of small enterprises and that top-k performance scores improve results but are not yet sufficient for semi-automatic classification. Further examination of error detection methods is recommended to improve web-based economic activity classification.
Performance Analysis of Symmetric Encryption Algorithms for their Encryption and Decryption Time

In today’s Internet, use of multimedia data has been increased like anything. Our data is important so need to encrypt our data. For this we need various mechanisms for the same. Several symmetric and asymmetric algorithms exist for this purpose. In the era of wireless technology, each day is a new day for cryptography. In this paper we are have compared various types of riles i.e. Image, Audio and Video with various types of asymmetric algorithms by using Java cryptography library. Files with various sizes are compared using AES, DES, DESede, Blowfish, Twofish, etc. We have used various padding techniques of these like CBS, CBC, PKCS5Padding, etc. Various 48 types of comparisons are done on various types of files and at the last it was found that AES and Blowfish algorithms are better in symmetric algorithms as far as encryption and decryption time is concerned.
Descriptor Self-tuning Incremental Kalman Estimator

Under certain environmental conditions, the unknown system errors often occur and yield to larger filtering errors when the unverified or uncalibrated measurement equation is used. Incremental equation can be introduced, which will effectively solve the problem of state estimation for the systems with unknown system errors. For the linear discrete descriptor systems under poor observation condition, a descriptor incremental Kalman estimation algorithm is firstly presented under the canonical form. The state estimation problem for the descriptor systems with unknown measurement errors is solved. Furthermore, a descriptor selft-uning incremental Kalman estimator is proposed when the model parameters and noise statistics both are unknown. Based on dynamic error system analysis method, the convergence analysis is also carried out. The simulation example shows the effectiveness and feasibility.
Synthesis of highly nonlinear S-boxes satisfying higher order propagation criterion

The S-box is the main component of modern block cryptographic algorithms and hash functions. One of the main criteria for the cryptographic quality of an S-boxes is the criterion of a high nonlinearity distance, as well as a propagation criterion of order m. One of the effective methods for constructing S-boxes is the method of their construction on the basis of a set of Boolean functions with a given level of cryptographic quality. This paper discusses the spectral classes (obtained on the basis of the classification of the Walsh-Hadamard transform coefficient vectors) of Boolean functions of 1 … 5 variables, for each of which the numbers of Boolean functions that satisfy the propagation criterion of order m were found. Using the constructive method among the full set of Boolean functions of 5 variables, a subclass of 12 balanced maximally nonlinear Boolean functions satisfying the propagation criterion PC(4) were found. It is shown that on the basis of this set of Boolean functions, bijective cryptographic S-boxes can be synthesized, which are the best among the entire set of S-boxes of length N = 32, from the point of view of the criterion of maximum nonlinearity distance and propagation criterion.
Using Semi-supervised Classifier to Forecast Extreme CPU Utilization

A semi-supervised classifier is used in this paper is to investigate a model for forecasting unpredictable load on the IT systems and to predict extreme CPU utilization in a complex enterprise environment with large number of applications running concurrently.This proposed model forecasts the likelihood of a scenario where extreme load of web traffic impacts the IT systems and this model predicts the CPU utilization under extreme stress conditions.The enterprise IT environment consists of a large number of applications running in a real time system.Load features are extracted while analysing an envelope of the patterns of work-load traffic which are hidden in the transactional data of these applications.This method simulates and generates synthetic workload demand patterns, run use-case high priority scenarios in a test environment and use our model to predict the excessive CPU utilization under peak load conditions for validation.Expectation Maximization classifier with forced-learning, attempts to extract and analyse the parameters that can maximize the chances of the model after subsiding the unknown labels.As a result of this model, likelihood of an excessive CPU utilization can be predicted in short duration as compared to few days in a complex enterprise environment.Workload demand prediction and profiling has enormous potential in optimizing usages of IT resources with minimal risk.
Visual Analysis for Messages in Social Media

The VAST 2019 Mini Challenge 3 (MC3) shows current challenges in analyzing and finding useful information from social media text datasets. We develop tools to process and visualize data including heat map, stacked area chart and message tables. It helps us identifying situations and making suggestions.
High-Utility Itemset Mining in Big Dataset

High-utility mining (HUIM) is an extended concept from frequent itemset mining (FIM). It emphasizes the more important factors, such as profits or the weight of an itemset in commercial applications. In this paper, we assume a dataset is too big to be loaded in the memory, then propose a MapReduce framework to handle this kind of situation, and try to reduce the times of scanning dataset as possible and maximize parallelization of the process.
Fuzzy Set Similarity Between Fuzzy Words.

Fuzzy set similarity measures determine the similarity between two fuzzy sets. Semantic similarity measures determine the similarity between concepts within an ontology. Research in determining sentence similarity has used semantic similarity between fuzzy words that have been structured in an ontology based on data provided by human experts to create fuzzy sets for these fuzzy words. The research uses four different kinds of fuzzy set similarity measures, three that are standard ones for type-1 fuzzy sets and another one based on the distance between defuzzified and normalized COGs for type-2 fuzzy sets and examines both the Pearson and Spearman correlations among these different fuzzy set similarity measures on fuzzy words from four of the six categories established in past sentence similarity research. The results show that all standard fuzzy set measures are highly correlated though some categories of fuzzy words result in higher correlations than others. The standard fuzzy set similarity measures have lower correlation with the one developed for similarity between type-2 fuzzy sets using their defuzzified and normalized COGs.
Comparative Study of Masking and Mapping Based on Hierarchical Extreme Learning Machine for Speech Enhancement

In this study, we compare the speech enhancement performance based on hierarchical extreme learning machine (HELM) with two distinct strategies: masking and mapping. Experimental results of the perceptual evaluation of speech quality (PESQ) show that for both of limited and sufficient amounts of training data, mapping-based HELM tends to be more effective to improve the performance of speech enhancement.