Reviewerly/exHarmony huggingface dataset:
Utilization of RSSI on Visitor's Cellphones in Calculating Distance to Wifi Transmitter in the Reading Room of the State Polytechnic of Malang Library

The pandemic period caused by the coronavirus disease causes restrictions on the number of users in a room to minimize the spread, for example, libraries. The library is a room area that can cause virus transmission when crowds of visitors. A system is needed to provide information regarding the number of library visitors. Utilizing a network (wifi transmitter) available in the library can provide information on the number of visitors based on the number of network users in a room. Utilization of the network (wifi transmitter) can be in the form of a positioning system by utilizing the wifi signal beam received by the user in RSSI (Received Signal Strength Indicator). To calculate the estimated user position, a trilateration method is used based on the placement of three wifi transmitters. The library visitor positioning system consists of users as wifi transmitter users and admins. At the X-coordinate, the measuring point with a low deviation of the measurement value is indicated by points C and E with a value of 0.1. The large deviation of the measurement value is indicated by the P point of 4.5. At the Y coordinate, point O has a low deviation value of 1.25, and the largest deviation value is at point M of 5.6. The accuracy of the X coordinate shows a value of 97.647% and at the Y coordinate of 96.57%. Both coordinates have good accuracy for the points used as position measurements.
Online Child Predator Detection Using ML (Research Paper)

Abstract: Proficient analysts should all the more completely comprehend the risks of online sexual sales and manners by which to shield youth from sexual stalkers who utilize the Internet. Albeit the Internet has numerous positive angles, quite possibly the most malicious aspect is its expected use for online sexual predation. The Internet addresses a medium that permits sexual stalkers admittance to innumerable kids in a moderately mysterious climate. The primary goal of our task is to distinguish kid hunter base on remarks and post of web-based media account and send hunter record to digital cell admin. A late public review demonstrated that around one out of five youth are requested for sex over the Internet yearly (Finkelhor, Mitchell, and Wolak, 2000; Mitchell, Finkelhor, and Wolak, 2001). This task report presents our present advancement to empower the formation of the framework. Thus, with the created framework, youngster hunter accounts recognition any report to administrator for additional activity. Index Terms: ML, dataset, Training Module, Predator.
STUDYING THE EFFECT OF SOLAR ACTIVITIES ON THUNDERSTORMS IN IRAQ FOR SOLAR-CYCLES 23 &amp; 24

Studying the impact of solar activity on the Earth's climate is significant for predicting the change of its elements for long periods. Most studies have focused on the relationship between sunspots and changes in one of the climate elements. In this research, the effect of the solar activities that consist of coronal mass ejections and solar flare, and the sunspots number on thunderstorms that occur during the winter season in Iraq for the period from (1996-2019) representing the Solar-Cycles 23 &amp; 24, respectively, were investigated in this study. In general, the statistical results show that, the monthly average increase for each of the following: energies of coronal mass ejections, solar flares, and the sunspots number, the monthly frequency of thunderstorms decreases. The Solar-Cycle 24 is the weakest among the cycles of the last century; therefore, its results were not precise.
A Bimodal Model Based on Truncation Positive Normal with Application to Height Data

In this work, we propose a new bimodal distribution with support in the real line. We obtain some properties of the model, such as moments, quantiles, and mode, among others. The computational implementation of the model is presented in the tpn package of the software R. We perform a simulation study in order to assess the properties of the maximum likelihood estimators in finite samples. Finally, we present an application to a bimodal data set, where our proposal is compared with other models in the literature.
Design and Implementation of A Microcontroller-Based Automatication System for Salted Eggs Production

Duck eggs are an animal food that can be consumed. In community life, generally there are two ways of salting, namely the first with water, salt, and bricks. The process of mixing water and salt still uses the manual method, namely by hand. Salt is a major factor in the salting process of eggs which functions as a preservative to prevent spoilage of eggs, thereby increasing their shelf life. In order to become salted eggs in general, that is by preservation. The method used is experimental, the purpose of experimental research is to create or develop an existing research.The test results on this tool are to produce specification data or to get certainty whether a tool is functioning properly or not. So that if there is damage to the tool or equipment it cannot run properly, it can be analyzed precisely and easily, and we can conclude that the difference in the results obtained from one day to the next
Mobile Monetization Underpins Emerging Economies Through Disruptive Innovation

At a time when the uncertainty of the pandemic is looming large, the need for mobile connectivity has understandably gained significant prominence. People had to communicate, if not meet. Importantly, it is a given that businesses could also not come to a standstill The wheels of the economies worldwide had to keep moving. Humans are inherently resilient; they always rise to meet the need of the hour. The importance of communication was recognized as a huge factor to improve conditions, and consequently, MobileTechnology responded magnificently to this critical issue at hand.This is a comparative study of how mobile technologies changed the dynamics of emerging economies mainly.
Towards practical quantum repeaters using room-temperature vapor technology

Quantum entanglement sources and memories are critical for quantum networking architecture. For high-rate networking, it is important that both technologies are compatible with each other and existing fiber infrastructure. We present our work on the development of a bichromatic photon source (one in the telecom band and one at near-IR) based on warm atomic vapors. We characterize the source, and show our progress towards interfacing it with atomic memories. This paves the way towards building a quantum repeater node based on room-temperature technologies. These narrow linewidth photons are natively well-suited for interfacing with many quantum communication, computation and sensing technologies.
Feature Selection for Analyzing Data Errors Toward Development of Household Big Data at the Sub-District Level Using Multi-Layer Perceptron Neural Network

This research aims to analyze the patterns of data errors in order to fulfill the data required for household big data development at the sub-district level in Thailand. Feature Selection and Multi-Layer Perceptron Neural Network were applied, while the data imbalance was solved by the SMOTE method and the comparison between the CFS feature selection method and Information Gain (IG) feature selection method. Afterward, the datasets were classified the data errors by the Multi-Layer Perceptron Neural Network. Each model’s effectiveness was measured by the 10-fold cross-validation method. The research results revealed that the suitable data size after being adjusted data imbalanced was 400%. Once the data had been processed for developing the model, it was found that after being adjusted data size towards the application of the SMOTE, CFS feature selection technique, and classified data errors by the Multi-Layer Perceptron Neural Network, the model provided the highest level of effectiveness in data errors classification with an accuracy of 98.29 %. Moreover, the application could effectively classify data errors and display the household big data at the highest level. The application evaluation results given by the experts and the users had an average mean of 4.69 and higher, a standard deviation of 0.47 and lower, which has the level of effectiveness of 93.78% and higher, while interquartile range values not over 1, a quartile deviation of no more than 0.5.
The Application of Building Information Modelling Method for Carbon Emission Analysis: A Case Study of Housing in Peat Lands

The construction sector is the largest contributor to carbon emission, especially from housing development activities, which has been on high demand due to the continuous increase in the population. This study aims to analyze the amount of carbon emission produced from house building materials on peat soil, using the Building Information Modeling method. The construction of houses on peat land is different from that of hard soil, because it requires to strengthen the foundation and to minimize the load on the superstructure of the building which was performed by choosing the type of material that is suitable for the location, and is also environmentally friendly. The carbon emission was calculated using a quantitative approach, where the volume of the material used was multiplied by its emission coefficient. The volume of material was obtained using the Building Information Modeling method, while the emission coefficients were acquired by utilizing the inventory data from ICE University of Bath and several literature studies. The results showed that the types of materials that produced the largest emissions were cement, mild steel, and wood, which had 14,051.90 KgCO<sub>2eq</sub>/house, 9,565.89 KgCO<sub>2eq</sub>/house, and 7,865.75 KgCO<sub>2eq</sub>/house, carbon value respectively. After redesigning the building and replacing light steel with wood, the emission reduction was 5.01% from a total of 42,523.33 KgCO<sub>2eq</sub>/house or 2,109 KgCO<sub>2eq</sub>/house with a saving of Rp. 154,397.04 or 10.87/house. Based on data from Central Bureau of Statistics of the City of Palangka Raya, the number of houses built in Palangka Raya City reached 918 units with an average building area of 45 - 70 m<sup>2</sup>, it is estimated to save Rp. 141,397,482.72 or $ 9,978.66. It shows the importance of environmentally friendly innovations in designs and the selection of low-emission materials, which has a positive impact on finance and climate sustainability. This finding is expected to provide insights for the government in the implementation of housing construction policies that prioritize environmental factors which reduce the level of carbon produced during the construction process and its operation.
The Applying of Cognitive Strategy Instruction in Writing (CSIW) To Increase the students’ Writing Abilities

This research is based on students' problems related to learning English, especially writing English. this is due to the very poor quality of their writing. therefore, the purpose of this study was to find out whether the use of strategy CSIW in learning English can help improve students' writing skills in class X TITL B SMK Negeri 2 Parepare.&#x0D; The researcher applied a pre-experimental method using a one-group pre-test and post-test design, using the sample X TITL B, which consisted of 36 students from SMK Negeri 2 Parepare by using cluster sampling of 72 students from the population.&#x0D; The results of the data analysis showed that the students' writing ability increased significantly. This is indicated by the average score of students from the pre-test results (48.77) and the average score of students from the post-test is 80.03. This indicated that the writing ability of students in class X TITL B at SMK Negeri 2 Parepare has progressed by the use of the strategy CSIW to improve students' descriptive writing skills. This is proved by the t-test value (3.11) which is higher than the t-table value (1.689).
Fire SM: new dataset for anomaly detection of fire in video surveillance

&lt;p class="Abstract"&gt;Tiny datasets of restricted range operations, as well as flawed assessment criteria, are currently stifling progress in video anomaly detection science. This paper aims at assisting the progress of this research topic, incorporating a wide and diverse new dataset known as Fire SM. Further, additional information can be derived by a precise estimation in early fire detection using an indicator, Average Precision. In addition to the proposed dataset, the investigations under anomaly situations have been supported by results. In this paper different anomaly detection methods that offer efficient way to detect Fire incidences have been compared with two existing popular techniques. The findings were analysed using Average Precision (AP) as a performance measure. It indicates about 78 % accuracy on the proposed dataset, compared to 71 % and 61 % on Foggia dataset, for InceptionNet and FireNet algorithm, respectively. The proposed dataset can be useful in a variety of cases. Findings show that the crucial advantage is its diversity.&lt;/p&gt;
IMPROVING OF ARTISTIC TALENTS ON STUDENTS WITH THE USE OF PROJECTIVE-RESEARCH KNOWLEDGE, SKILLS AND ABILITIES

In this article, the advantages of fulfills the projective-researching performance while arranging creational upbringing in a comprehensive-secondary institution.
The Prosodic Aspects of Politeness in Instructional Interactions of Elementary School Teachers in Surabaya

&#x0D; &#x0D; &#x0D; This study aimed to describe the prosodic aspects of politeness in the instructional interactions of elementary school teachers in Surabaya. These prosodic aspects describe the sound intensity, duration, speed and tone. The data were collected using observations at four elementary schools in Surabaya. Data analysis was performed by using Praat acoustic analysis devices and a computer software package for the scientific analysis of speech in phonetics. Based on the results, it can be concluded that the intensity, the speed of sound, and the tone of the prosodic aspects of politeness in the declarative mode of speech had lower averages than the prosodic aspects of politeness in the interrogative and imperative modes of speech. This may be because the declarative mode does not contain the illocutionary directive, while the interrogative and imperative modes do, which affects their use in response to threatening acts.&#x0D; Keywords: prosody characteristics, politeness, teacher instructional interactions&#x0D; &#x0D; &#x0D;
Spatial Analysis of Crime in East Java Province in 2019

Crime is one of the consequences of fluctuations in the economic condition of a country. Crime incidents harm many parties. The number of criminal acts increased in 2019, especially in Sumatra and Java Island. Most provinces experienced an increasing number of criminal acts, one of them was East Java. East Java contributed more than a quarter of the number of crimes throughout Java Island. The number of criminal acts is count data with overdispersion because its variance is higher than its average. This study aims to analyze the number of criminal acts by applying Geographically Weighted Negative Binomial Regression (GWNBR). The result shows that GWNBR formed two regional groups based on significant variables. The four independent variables namely the unemployment rate, the number of poor people, the Gini ratio, and the police population ratio have a significant effect on all districts/cities. However, the mean year of schooling shows a significant effect only in certain districts/cities. The GWNBR is the best model in modelling the number of criminal acts in East Java.
The Model Development for Early Lung Cancer Analysis by Using Image Processing and Neural Network

Lung cancer is the leading mortality disease for men and women compared to other organ cancers. The leading cause is smoking until the patient begins to show symptoms. Therefore, they see a doctor determine that the disease has spread in the last stage, causing cumbersome and complex treatment. Therefore, early screening of patients is essential to allow patients to receive treatment promptly and have a chance to recover from the disease. This research has developed a model for early lung cancer analysis by using a CXR image that can screen many patients when they are asymptomatic. Improve the image enhancement to reduce the noise with median filter and then go into image processing by image segmentation with Active contour algorithm, image edge detection with Laplacian of Gaussian (LoG) algorithm, and image extraction with Shape and GLCM in combine with data classification with a neural network using MLP compared against SVM classifiers. Training and testing the model's performance by the result of MLP provides a better time and up to 99% accuracy.
Innovative Teaching Technologies in Postmodern Education: Foreign and Domestic Experience

The article provides a theoretical analysis of the study of the issue of introducing innovations into educational activities on the basis of foreign and domestic experience of postmodern education. The essence of the problem of introducing innovative technologies in the system of postmodern education in the countries of the world and in Ukraine is revealed. The role of the teacher's professional competence in the application of innovative techniques for organizing the educational process was emphasized. The essential features of postmodern tendencies in foreign and domestic education and teaching practice are combined. In the context of professional postmodern education and to optimize innovative teaching with postmodern tendencies, pedagogical recommendations were submitted on the new conditions and requirements of innovative teaching. The goals of the article, as well as its scientific novelty, theoretical and practical significance for postmodern or innovative pedagogy are determined. It is substantiated that the development of innovative technologies in the field of training future specialists is carried out on the basis of a postmodern approach to the analysis of vocational education, which was integrated by means of personality-oriented, activity-based, professional-creative and psychological-pedagogical approaches at the systemic universal level. Considered professional requirements for educators-innovators.
Time-Lag Aware Latent Variable Model for Prediction of Important Scenes Using Baseball Videos and Tweets

In this study, a novel prediction method for predicting important scenes in baseball videos using a time-lag aware latent variable model (Tl-LVM) is proposed. Tl-LVM adopts a multimodal variational autoencoder using tweets and videos as the latent variable model. It calculates the latent features from these tweets and videos and predicts important scenes using these latent features. Since time lags exist between posted tweets and events, Tl-LVM introduces the loss considering time lags by correlating the feature into the loss function of the multimodal variational autoencoder. Furthermore, Tl-LVM can train the encoder, decoder, and important scene predictor, simultaneously, using this loss function. This is the novelty of Tl-LVM, and this work is the first end-to-end prediction model of important scenes that considers time lags to the best of our knowledge. It is the contribution of Tl-LVM to realize high-quality prediction using latent features that consider time lags between tweets and multiple corresponding previous events. Experimental results using actual tweets and baseball videos show the effectiveness of Tl-LVM.
Analysis and Application of Multiplicative Stochastic Process with a Sample-Dependent Lower Bound

A multiplicative stochastic process with the lower bound lognormally distributed is investigated. For the process, the model is constructed, and its distribution function (involving four parameters) and the related statistical properties are derived. By adjusting the parameters, it is confirmed that the theoretical distribution is consistent with empirical distributions of some real data.
Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators

Recently, there has been a rise in the development of powerful pre-trained natural language models, including GPT-2, Grover, and XLM. These models have shown state-of-the-art capabilities towards a variety of different NLP tasks, including question answering, content summarisation, and text generation. Alongside this, there have been many studies focused on online authorship attribution (AA). That is, the use of trained models to identify the authors of online texts. Given the power of natural language models in generating convincing texts, this paper examines the degree to which these language models can generate texts capable of deceiving online AA models. Experimenting with both blog and Twitter data, we utilise GPT-2 language models to generate texts using the existing posts of online users. We then examine whether GPT-2-based text generators are capable of mimicking authorial style to such a degree that they can deceive typical AA models. From this, we find that current AI-based text generators are able to successfully mimic authorship, showing capabilities towards this on both datasets. Our findings, in turn, highlight the current capacity of powerful natural language models to generate original online posts capable of mimicking authorial style sufficiently to deceive popular AA methods. This is a key finding given the proposed role of AA in real-world applications such as spam-detection and the investigation of criminal activity online -- where deceptive texts could be automatically generated to mimic authorship in order to mislead these critical AA systems.
Domain Representative Keywords Selection: A Probabilistic Approach

We propose a probabilistic approach to select a subset of a target domain representative keywords from a candidate set, contrasting with a context domain.Such a task is crucial for many downstream tasks in natural language processing.To contrast the target domain and the context domain, we adapt the two-component mixture model concept to generate a distribution of candidate keywords.It provides more importance to the distinctive keywords of the target domain than common keywords contrasting with the context domain.To support the representativeness of the selected keywords towards the target domain, we introduce an optimization algorithm for selecting the subset from the generated candidate distribution.We have shown that the optimization algorithm can be efficiently implemented with a near-optimal approximation guarantee.Finally, extensive experiments on multiple domains demonstrate the superiority of our approach over other baselines for the tasks of keyword summary generation and trending keywords selection.
Fast and Data Efficient Reinforcement Learning from Pixels via Non-parametric Value Approximation

We present Nonparametric Approximation of Inter-Trace returns (NAIT), a Reinforcement Learning algorithm for discrete action, pixel-based environments that is both highly sample and computation efficient. NAIT is a lazy-learning approach with an update that is equivalent to episodic Monte-Carlo on episode completion, but that allows the stable incorporation of rewards while an episode is ongoing. We make use of a fixed domain-agnostic representation, simple distance based exploration and a proximity graph-based lookup to facilitate extremely fast execution. We empirically evaluate NAIT on both the 26 and 57 game variants of ATARI100k where, despite its simplicity, it achieves competitive performance in the online setting with greater than 100x speedup in wall-time.