Reviewerly/exHarmony huggingface dataset:
Reading movement in community: A study of Gerakan Buru Membaca

&lt;span&gt;The Buru Reading Movement &lt;/span&gt;&lt;em&gt;&lt;/em&gt;&lt;span&gt;(&lt;/span&gt;&lt;em&gt;&lt;span&gt;Gerakan Buru &lt;/span&gt;&lt;/em&gt;&lt;em&gt;&lt;span&gt;Membaca&lt;/span&gt;&lt;/em&gt;&lt;span&gt;) was an initiative of Iqra Buru University community service and developed later into one of the Buru District Government programs, which have been implemented since 2017. The program was designed to improve and promote the community’s reading habits in Buru Regency, Maluku, Indonesia. This was a qualitative study aimed at describing the implementation of &lt;/span&gt;&lt;em&gt;&lt;span&gt;Gerakan Buru Membaca&lt;/span&gt;&lt;/em&gt;&lt;span&gt; in Buru Regency. The research located in Namlea District because of its relatively high population density and heterogeneity. There were forty people interviewed, and this number was obtained through purposive sampling. We employed an analysis technique proposed by Miles and Huberman. The results revealed that the reading movement received a positive response from the community and the village government. The active role is implemented through the provision of village libraries, study rooms, and study houses of learning available. Furthermore, a mobile library, which was only available in the Namlea Village area, prioritized its services in open or public spaces. In other villages, however, the movement was still in the form of reading and learning spaces provided by the village government, with the participation of the community. Despite the positive response from the community, the movement still experienced several constraints. &lt;/span&gt;
Professional Orientation Formation of Undergraduate Students Based on Interdepartmental Interaction and Social Partnership with Employers

The study aims to substantiate the need and possibility of forming professional orientation of undergraduate students in economic training programmes at university, taking into account the requirements of higher education professional standards in terms of implementation of elective disciplines, while using the opportunities of the municipality based on interdepartmental interaction and social partnership with employers. The paper presents the experience of forming a professionally oriented open and integrated educational environment with integrity and continuity, which ensures efficiency, balance of quality and quantity of training of professional personnel to meet the needs of the municipality. Scientific novelty of the study lies in developing and implementing pedagogical conditions for professional orientation formation of undergraduate students in economic training programmes at university. As a result, the researcher has clarified the notion and has identified the components of professional orientation of undergraduate students in economic training programmes, has analysed the level of professional orientation in graduates of modern universities of economics. In addition, the subjects of the model of professional orientation formation of undergraduate students in economic training programmes based on interdepartmental interaction with the municipality and social partnership with employers have been characterised and the pedagogical conditions necessary for the effective implementation of this model have been presented.
Integrating singular spectrum analysis and nonlinear autoregressive neural network for stock price forecasting

&lt;span&gt;The main objective of stock market investors is to maximize their gains. As a result, stock price forecasting has not lost interest in recent decades. Nevertheless, stock prices are influenced by news, rumor, and various economic factors. Moreover, the characteristics of specific stock markets can differ significantly between countries and regions, based on size, liquidity, and regulations. Accordingly, it is difficult to predict stock prices that are volatile and noisy. This paper presents a hybrid model combining singular spectrum analysis (SSA) and nonlinear autoregressive neural network (NARNN) to forecast close prices of stocks. The model starts by applying the SSA to decompose the price series into various components. Each component is then used to train a NARNN for future price forecasting. In comparison to the autoregressive integrated moving average (ARIMA) and NARNN models, the SSA-NARNN model performs better, demonstrating the effectiveness of SSA in extracting hidden information and reducing the noise of price series.&lt;/span&gt;
A new approach to solve the of maximum constraint satisfaction problem

&lt;span lang="EN-US"&gt;The premature convergence of the simulated annealing algorithm, to solve many complex problems of artificial intelligence, refers to a failure mode where the process stops at a stable point that does not represent to an overall solution. Accelerating the speed of convergence and avoiding local solutions is the concern of this work. To overcome this weakness in order to improve the performance of the solution, a new hybrid approach is proposed. The new approach is able to take into consideration the state of the system during convergence via the use of Hopfield neural networks. To implement the proposed approach, the problem of maximum constraint satisfaction is modeled as a quadratic programming. This problem is solved via the use of the new approach. The approach is compared with other methods to show the effectiveness of the proposed approach.&lt;/span&gt;
Implementasi Perkuliahan Keterampilan Berbahasa secara Daring

Online lectures demand creativity in all respects for both the teachers and the learners. In lecturing, Indonesian language skills for the collegians in Teacher Education at the Islamic State Islamic School (PGMI) major UIN Sayid Ali Rahmatullah Tulungagung (UIN SATU), especially during the Final Semester Examination (UAS), certain methods are needed so that the main objectives of the lecture can be achieved. At UAS Even of 2020/2021, collegians practice the results of attending Indonesian Language Skills lectures for one semester by teaching the Language Skills to elementary (MI/SD) students who live around their homes. The results of practicing these skills are made in the form of a report. Furthermore, the results of the report are used as a source of data in the form of documents and will be analyzed through the Miles and Huberman model (Sugiyono, 2016). The type of research used in this research is literature study by tracing several references in the library. The data findings will be discussed in accordance with online learning innovations (Harmanto, 2020). The results showed a positive response from parents and students. In addition, there are also several obstacles, including students who do not focus on learning, are not serious, are anxious, and so on.
Analisis Kepuasan Mahasiswa Terhadap Perkuliahan Online

This study aims to determine the level of student satisfaction with online lectures during the Covid-19 pandemic which is still ongoing today. Variables measuring student satisfaction with online lectures are limited to aspects of learning including lecture facilities, learning media, and the implementation of online lectures. This research method uses descriptive analysis techniques. Data was collected using a questionnaire, then the data was analyzed using statistical tests to determine the validity and reliability of the instrument, multicollinearity test, and ordinal logistic regression analysis and regression parameter tests at the 95% confidence level. Based on the test results the instrument is declared valid and reliable, and the obtained regression model is feasible to use. The results of the interpretation of the model lead to conclusions, (a) the probability of students who are satisfied with online lectures in the aspect of lecture facilities is 2.21 times compared to students who are not satisfied; (b) the probability of students being satisfied with online lectures in the aspect of learning media is 1.53 times compared to students who are dissatisfied; and (c) the probability of students being satisfied with online lectures in the aspect of lecture implementation is 2.28 times compared to students who are not satisfied.
EFFECT OF ONLINE GAME ADDICTION ON CHILDREN'S BEHAVIOR

The advancement of Information and Communication Technology in today's era is growing rapidly following the times. The development of communication and information technology brings changes and has a major impact on people's social life. One form of advances in communication and information technology is online games, nowadays children tend to spend a lot of time playing online games. Online games make children lose track of time and become addicted, they should use their time for more positive things. This study aims to describe and describe the reasons why children like online games, as well as nd the impact of playing online games and describe how to deal with game addiction. This research uses a qualitative approach and a qualitative descriptive method, the center of the research is to provide education and guidance to play online games wisely. The results of the research found in this study are the cause of online games being liked by children, online games are not boring like when studying, playing online games can have positive and negative impacts, how to overcome online game addiction is to make a play schedule and parental supervision is the most important. main.
Sentiment Analysis on Twitter Using Maximum Entropy : a Case Study on Indosat Ooredoo

The result of the current technological developments makes increasingly tight telecommunication provider competition. Various opinions expressed by customers about telecommunication providers are found in social media. Twitter is widely used by the public to share information and socialize; also, to share opinions, express opinions of a product or service, and provide reviews on communication providers. Many reviews are provided by users on Twitter, making it hard to classify manually. Therefore, to make it easy to classify the tweets, an automation system is needed to determine that a comment is positive or negative. Maximum Entropy can be used for sentiment analysis of Indosat Ooredoo tweets. On these grounds, this study explains how to define tweets into positive and negative classes with applications created using Java. Based on the research and after testing, the Maximum Entropy obtained an accuracy value of 86.21% and an AUC value of 0.968.
Clustering Algorithm for the Connected Model of Repeated measurements and Survival Data: Application to HIV Study

Abstract The cluster analysis is dividing the individuals into clusters or groups. This job is valuable and helpful to provide facts and information about individuals. Here in this paper we proposed the cluster analysis for special model. It is the connected model for repeated measures called longitudinal and survival for individuals or patients. The statistical analysis of the connected model from the longitudinal and survival datasets becoming popular recently because it comes together in many medical applications. Here in this study, we utilized two statistical methods, cluster analysis and, connect the two models from longitudinal and survival data. It is benecial since it gather the information from the repeated measurements, and survival responses. The shared random eect term is used joint multivariate Gaussian distribution (longitudinal) and Cox proportional hazard model (survival)for the same patients. Then, the pseudo-liklihood algorithm (clustering methodology) is performed for the joint model to distinguish the clusters or groups of patents. The application is HIV patients dataset with CD4+ counts responses and time to death (survival data) with some independent variables as gender and drug treatments. We conducted the clustering for S=2, and estimate the parameters from the longitudinal and survival models with and without clustering, and compare the estimations. Our results showed the generated clusters are dierent from each other, the estimation parameters be located around the original estimations (without clustering). It is helpful methodology to identify distinct groups or clusters from population. Finally, There is a big need for this type of application in medical fields.
SparkGrid: Blockchain Assisted Secure Query Scheduling and Dynamic Risk Assessment for Live Migration of Services in Apache Spark based Grid Environment

Abstract Grid computing is an emerging technology that enabled the heterogeneous collection of data and provisioning of services to the users. Due to the high amount of incoming heterogeneous request, grid computing needs an efficient scheduling to reduce execution time and satisfy Service Level Agreement (SLA) and Quality of Service (QoS) requirements. For that purpose, we proposed SprakGrid method to reduce execution time and satisfying SLA, QoS requirements. The proposed work includes four consecutive phases which are explained as follows, in first we perform user authentication in order to ensure the legitimacy of the users using Elliptic Curve based Chaos Theory (ECCT) algorithm which generate secret key and stored it into the blockchain. In second we perform query scheduling for resource discovery using Soft Actor Critic (SAC) algorithm by considering 3P’s parameters which is performed by spark environment that schedules optimal resources based on the service request. In third, we perform risk assessment and request dropping, in which the risk nodes of workers are evaluated by master node. To address the resource wastage by attacker, this research evaluates the risk value in a dynamic manner using Shannon entropy. Based on the risk assessment the requestsare classified into two classes such as normal and malicious. In fourth we perform service live migration, in which the malicious requests are dropped and normal request are migrated from source node to target node using Multi-Constraints based Emperor Penguin Optimization (MC-EPO). Finally, simulation is performed by GridSim and the simulation results demonstrate that the proposed SparkGrid method achieves superior performance compared to other state-of-the art methods.
Mineralogy and Origin of the Manganese Deposit in the Sulaimani Province, Kurdistan Region of Iraq: Insight to Serpentinization-Induced Manganese Production Scenario

The Manganese deposits in Sulaimani metallogenic province mainly occur within the top Qulqula Radiolarite chert unit of the Penjween ophiolite complex as strata bound type deposits near Kani Saif village, 15 km south of the Penjween district within the Imbricated zone, Kurdistan Region of Iraq. A restricted occurrence of massive manganese boulders was identified, 1 km north to the Mawat District, associated with the Oligocene Merga Red beds Group. The Qulqula Radiolarite represents the Middle Jurassic-Early Cretaceous sedimentary cover of Neo-Tethys oceanic crust deposits throughout the early to mature stages of the Neo-Tethys ocean opening along the northern and eastern margins of the Afro-Arabian plate. The field and petrographical data indicated that the nature of manganese mineralization in the area is of various forms including veins, brecciated, micronodules, banded and massive types. Reflected light microscopy, high resolution scanning electron microscope and X-ray diffraction reveal that the mineral assemblages are dominated by barunite, rhodinite, hematite, quartz and carbonate for the massive metamorphosed type deposit in the Mawat area. Meanwhile, the mineralogy of the vein, micronodule and banded types are simple and dominated by pyrolusite, hollandite minerals close to Kain Saif village. Combinations of the modal manganese minerals, filed and textural observations, a paragenetic-time model suggests that the manganese deposit in the area formed via a sequence of multiple processes (1) serpentinization of the upper mantle at mid oceanic ridge during the Cretaceous period, (2) deposition from diluted hydrothermal fluids in the Neo-Tethys mature oceanic basin, (3) the diagenetic modification of the manganese bearing sediment and (4) metamorphism of sediment a combing Zagros orogeny in Paleogene. Overall data suggest that the manganese deposits in the Kurdistan region of Iraq are cogenetic and spatially related to those which occur along the entire margin of the Arabian plates from Oman to Turkey associated with the radiolarite facies.
RSA cryptosystem for rings with commuting ideals

This article presents a generalization of the RSA cryptosystem for rings with commuting ideals. An analogue of the Euler function for ideals and the concept of an RSA-ideal are defined. An analog of a cryptosystem for the ring with commuting ideals is formulated and a description of the RSA-ideals for which this is possible is obtained.
The Analysis of Bullying Experienced By Eleanor in Rainbow Rowell's Eleanor &amp; Park.

The aim of this research is to comprehend bullying types and its effects experienced by the main character of Eleanor in Rainbow Rowell’s novel, Eleanor &amp; Park. The writer utilized the psychological approach that applied theories in psychology to analyze, examine and understand bullying experienced of the main character. The method of this analysis was descriptive qualitative method, which tried to analyze bullying experienced by the main character. The writer discovered that the types of bullying experienced by Eleanor are physical bullying, verbal bullying, and sexual bullying. Meanwhile, the effects of bullying experienced by the main character are such as feeling of shame, low self-esteem, symptoms of depression, and suicidal thought.
Comparison of Cochrane-Orcutt and Hildreth-Lu Methods to Overcome Autocorrelation in Time Series Regression (Case Study of Gorontalo Province HDI 2010-2021)

Time series data is data that is prone to autocorrelation. Autocorrelation is a violation of assumptions in Ordinary Least Square regression. The presence of autocorrelation can make parameter estimates, not BLUE (Best, Linear, Unbiased Estimator). Several methods to overcome autocorrelation include Cochrane-Orcutt and Hildreth-Lu methods. Therefore, this study aimed to compare the Cochrane-Orcutt and Hildreth-Lu methods to deal with autocorrelation in the time series regression of the Gorontalo Human Development Index case in 2010 2021. We used HDI data for Gorontalo Province from 2010-to 2021, taken from the BPS-Statistics Indonesia Gorontalo Province. The method we used was Cochrane-Orcutt and Hildreth-Lu in the case of regression using Ordinary Least Squares (OLS) parameter estimation. The results obtained are that the Cochrane-Orcutt and Hildreth-Lu could overcome autocorrelation. The results of the Durbin Watson test after using both methods show no autocorrelation. However, the Hildreth-Lu method resulted in a lower Root Mean Square Error (RMSE) of 0.147 compared to the RMSE of the OLS model of 0.165 and the RMSE of the Cochrane-Orcutt model of 0.196. Therefore, the Hildreth-Lu method was the best method to overcame autocorrelation in this case.
Evenness Detection in Pavement of Roads &amp; Highway with Transfer Learning

This study presents a system using an image processing technique that evaluates the pavement condition from an image. Pavement condition evaluation is an integral part of roads and highway maintenance works, which mostly depends on human inspection. Although recently some researches have been conducted on road condition detection with image processing, these researches used huge databases and deep CNNs that require expansive computer and longer training time, which limits the use of deep CNN in practical problems where huge database collection is not possible always. To solve this problem, in this study, transfer learning in deep CNN is applied and with only 195 images in each category, pre-trained VGG-16 and Inception-ResNet v2 models are used for pavement condition evaluation. VGG-16 achieved more than 90% prediction accuracy, while Inception-ResNet v2 achieved more than 85% prediction accuracy. Moreover, to validate the performance, both models have been tested with random images collected from Google. Evaluating pavement conditions this way would reduce the need for human inspection. Finally, the outcome of the study shows that the transfer learning approach could be useful in research areas, especially in civil engineering, where image data is insufficient.
A Hardware Efficient Reservoir Computing System Using Cellular Automata and Ensemble Bloom Filter

Reservoir computing (RC) is an attractive alternative to machine learning models owing to its computationally inexpensive training process and simplicity. In this work, we propose EnsembleBloomCA, which utilizes cellular automata (CA) and an ensemble Bloom filter to organize an RC system. In contrast to most existing RC systems, EnsembleBloomCA eliminates all floating-point calculation and integer multiplication. EnsembleBloomCA adopts CA as the reservoir in the RC system because it can be implemented using only binary operations and is thus energy efficient. The rich pattern dynamics created by CA can map the original input into a high-dimensional space and provide more features for the classifier. Utilizing an ensemble Bloom filter as the classifier, the features provided by the reservoir can be effectively memorized. Our experiment revealed that applying the ensemble mechanism to the Bloom filter resulted in a significant reduction in memory cost during the inference phase. In comparison with Bloom WiSARD, one of the state-of-the-art reference work, the EnsembleBloomCA model achieves a 43× reduction in memory cost while maintaining the same accuracy. Our hardware implementation also demonstrated that EnsembleBloomCA achieved over 23× and 8.5× reductions in area and power, respectively.
Some New Mathematical Models of Synthesized Sound Signals

Modern sound synthesis systems make it possible to implement various signal generation algorithms of higher complexity. The theory of sound synthesis actively uses the mathematical apparatus of analog and digital radio engineering and signal processing, however, it should be noted that the classical signal models used in acoustics are not adequate to real-world synthesized signals, mainly due to the significant complexity of the latter. This article presents some models of synthesized signals typical for practical use.