Reviewerly/exHarmony huggingface dataset:
ENGLISH ONLINE LEARNING AND CHALLENGES DURING THE COVID 19 PANDEMIC AT STMIK HANDAYANI MAKASSAR

The purpose of this study was to identify the problems experienced by students in the process of learning English during the pandemic and to describe the use of online learning media in the learning process. The formulation of the research problem is what problems are faced by STIMIK Handayani students in the online English learning process during the pandemic? And how is the application of online learning applications in the English learning process? This study uses a qualitative descriptive research approach. Respondents of this study were students who programmed English courses for the SK class with a total population of 12 people and were in the first semester. The total number of respondents is 6 students who are students who are actively participating in lectures. Questionnaires are used to explore information obtained from respondents later. The data analysis technique refers to Miles &amp; Huberman through the stages of data collection, data reduction, data presentation, and drawing conclusions or verification. From the findings and discussion, the researcher concludes that the main obstacles commonly faced by students when participating in online learning are difficulties in understanding the material and poor signals. Other obstacles are in the form of feeling bored, less interactive, and having difficulty focusing. With this finding, the researcher hopes that there will be findings related to increasing the effectiveness of learning through online learning. Next is the application of application-based online learning. Based on the data collected and presented descriptively, the researcher concluded that the application of online learning was less effective.
Independent component support vector regressive deep learning for sentiment classification

Sentiment analysis is the process used to extract the keywords from the user review and classify the text into positive, negative and neutral. Sentiment analysis and opinion mining is the field used to analyzes people opinion, sentiment, evaluation, attitude and emotion. Many researchers introduced opinion mining methods to improve the detection accuracy. But, the time consumption was not reduced and accuracy level was not improved. In order to address these problems, an Independent Component Support Vector Regressive Deep Learnt Sentiment Classification (ICSD) Method is designed. ICSD Method comprises five layers to perform sentiment classification. ICSD Method performs pre-processing, feature extraction and classification to enhance the classification accuracy with extracted words from user review comments. Initially in ICSD Method, the user review comments are collected as an input at the input layer. The user review comments are sent to the hidden layer 1. In that layer, ICSD Method pre-processes the use review comments for removing the stop words to reduce the file size. The pre-processed user review comments are sent to the hidden layer 2. In hidden layer 2, Independent Component Feature Extraction is carried out in ICSD Technique to extract the opinion word from review comments. The associated opinion words are arranged for the semantic similarity of sentiment based on the extracted word to minimize the time consumption. After that, the extracted words are transferred to the hidden layer 3. In that layer, Support Vector Regressive Sentiment Classification is performed in ICSD Method to examine the semantic opinion words for determining the sentiment class label. The sentiment class are categorized into positive, neutral and negative sentiments. Finally, the results are sent to the output layer. This in turn helps to minimize the time consumption and to progress the accuracy of opinion mining. The performance evaluation of ICSD Method is carried out with consumer product and services reviews extracted. The parameters used are evaluated with number of customer review words, accuracy, time complexity and false positive rate. Experimental evaluation of ICSD Method minimizes the time consumption and false positive rate during opinion extraction from reviewers.
Integrating individual preferences into collective argumentation

Abstract In the field of collective argumentation, multiple agents may have different knowledge representations and individual preferences. In order to obtain a reasonable collective outcome for the group, either individual frameworks should be merged or individual preferences should be aggregated. However, framework merging and preference aggregation are different procedures, leading to disagreements on collective outcomes. In this paper, we figure out a solution to combine framework merging, argumentative reasoning and incomplete preference aggregation together. Furthermore, a couple of rational postulates are proposed to be the criteria for the rationality of collective outcomes obtained based on our approach.
Trajectory Reconstruction of Launch Vehicle in Atmospheric Flight using the Unscented Kalman Filter

In this paper the performance of a smoothed Unscented Kalman filter is analyzed for the determination of the best-estimated trajectory of a launch vehicle in atmospheric flight. A kinematic formulation of the filter is considered, that makes use of vehicle and ground-based measurements, where acceleration and angular velocity measured by an Inertial Measurement Unit are treated as inputs in the filter model, and the states include uncertainty parameters such as IMU biases. Results for state estimation and related uncertainty are presented and discussed for a case study involving the first-stage flight of the ARES I launch vehicle, where synthetic measurement data are generated by a nonlinear high-fidelity dynamic model of the vehicle, and the estimates obtained by the proposed method are compared to those evaluated using the Extended Kalman filter algorithm.
Capabilities to Increase Access for Unmanned Surveillance Systems

Unmanned systems are proliferating into increasingly difficult operational environments, especially indoors, where navigation, locomotion, and communication are all challenged. With increasing capabilities, unmanned systems are being considered for even the most difficult missions, such as breaching and exploring subterranean spaces, which may encompass a range of challenging underground facilities. In this work, the development of an unmanned ground vehicle is described which includes a unique ability to launch small articles that can increase access into otherwise secured regions. The proposed system is equipped with Red-Green-Blue-Depth cameras for visual navigation and obstacle avoidance to explore underground facilities, and a spring-loaded cannon to identify and infiltrate personnel doors. An algorithm is included to identify doors, position the robot appropriately in front of the door, calculate a trajectory, and place the article at the desired location on the door. A prototype of the proposed system was developed and, in testing, demonstrated all of the critical functions.
Image blind deblurring networks with back-projection feature fusion

Aiming at the problem of image motion blur caused by handheld camera jitter and object motion in the process of collecting photos, a generative adversarial network (GAN) based on feature fusion of back projection is proposed for blind image deblurring. Firstly, the generator network is established by using U-Net structure, and a feature fusion residual block based on back projection is designed according to the error feedback principle, which solves the problem of saving spatial information in U-Net structure. Secondly, the self-attention module is introduced into the generator network to extract the feature map that pays more attention to detail. Finally, the combination of perceptual loss, mean square error loss and relative generative adversarial loss effectively alleviates the mode collapse problem of traditional GAN and improves the stability of model training. The experimental results show that the peak signal-to-noise ratio and structural similarity of this method on GoPro data set are 30.183 dB and 0.941, respectively, and 26.962 and 0.837 on the Kohler dataset, with the shortest running time, which are better than the existing state-of-the-art methods. The restored image is clearer good visual results and richer in texture details, which can effectively improve the image deblurring effect.
Cyberbullying : How To Stay Safe on Social Media?

Facebook, TikTok, Instagram, Twitter. There is rarely anyone without any of these social media accounts. According to 2020 Internet User Survey by Malaysian Communications and Multimedia Commission (MCMC), 88.7% of Malaysians are internet users. Out of that number, 93.3% of users use social media extensively. Social media has enabled users to connect with family and friends. It has done wonders to small businesses to prosper. It has also empowered communities to grow and prosper. However, there is one dark side to it; CYBERBULLYNG. According to the Merriam Webster dictionary, bullying is defined as “abuse and mistreatment of someone vulnerable by someone stronger, more powerful”. Most of the time, bullying is done when a bully is in the presence of his or her victim. However, in the cyber world, bullying extends beyond the physical presence. The act of bullying can be done 24 hours a day, by anyone at any time. A victim can be harassed with harsh comments via any social media accounts and even through text messages which sometimes invade the victim’s own privacy. There is no clear guidelines and policy regarding cyberbullying. Any form of enforcement is lax due to the nature of the internet being free for all. Thus, it is imperative to empower oneself to understand how to use social media effectively to prevent from being cyberbullied and also NOT to be a cyberbully. There are also tools that can be used in each social media platform to report such cyberbullying issues and also to report to the relevant agencies such as the Royal Malaysian Police and Malaysian Communications and Multimedia Commission. International Journal of Human and Health Sciences Supplementary Issue: 2023 Page: S14
THE CONSEQUENCES OF EL-SALVADOR'S ADOPTION OF CRYPTOCURRENCY AS LEGAL TENDER

The mini-project is an experimental one. In this research the data is utilized to analyze an Impact of bitcoin adoption and mining on its economy with lighting up of some major energy sources that El Salvador uses to generate cryptocurrencies and to study influence of mining and crypto-adoption on El-Salvador’s financial system and nature. Next point is to focus on quantitative research with some Technique for Collecting Secondary-Data – main methods with help of which the information was gathered and evaluate. In mini-project would be included: Adoption of crypto (Bitcoin) law in El-Salvador, Energy-matrix condition IN El-Salvador including country ability to mine bitcoin, Bitcoin mining and its implication regarding climate change in El-Salvador and some graph with related project to project data.
QUANTUM COMPRESSION OF BINARY MESSAGES

In the proposed work, the problem of compressing binary messages in one cycle is solved. A method for solving it in relation to Bernoulli sources of information is proposed, using tabular logic, according to which the original message is converted by the decoder of the compression system into a pre-recorded number in one of its memory cells together with a number representing the number of units in the original message. Restoration of compressed information also occurs according to tabular logic, in which the number of the compressed message excites the original message recorded in the corresponding memory cell, and it is transmitted to the system output. A significant advantage of the proposed method, in addition to speed, is that the compression effect approaches the entropy of the original messages and makes it possible to build quantum processors both on ordinary elements and on elements that use quantum effects.
ON THE NATURE OF INFORMATION

The article deals with issues related to the essence of information, its definition and forms of existence. The relationship between message and information is described. Theoretically and practically shows the difference between the concepts of information and knowledge and their mutual transitions into each other. Two forms of existence of information in messages in the form of free and structural information are considered. Equations are given for the amount of structural information in messages and their arrays. The optimal ratio for the amount of structural information in message arrays is found, at which they contain the greatest amount of knowledge. On this basis, the information conservation law is proposed, which is widely used in the problems of the theory and practice of information transformations. Its mathematical formulation is proposed. The questions of the complexity of messages and the trees modeling them are studied. The definition of information is given, taking into account its ideality and limitations in information tasks. The issues of optimal coding of messages and the introduction of redundancy into them are considered.
SYMMETRY AND ASYMETRY IN CHILDREN’S POETIC DISCOURSE

The concept “symmetry” is one of the most important in every branch of science. This concept is associated with “balance”, “order”, “harmony of form’s proportions”, “correspondence” whereas “asymmetry” is related to “imbalance”, “disorder”, “disharmony of forms”, “difference” or even “conflict”. In linguistics, this concept is determined as the correspondent relation between speaker / writer / addresser and listener / reader / addressee as the result of social, institutional and other factors.
The communicative competence of the future teacher in communicating with the parents of students

This article helps teachers identify strategies for initiating and maintaining effective communication with parents of students.Parents are the first educators and teachers of the child, so their role in the formation of his personality is huge. The task of the teaching staff is to educate a harmonious, comprehensively developed personality. Therefore, we need to establish close contact with parents in order to achieve this task, and for this, teachers must be professionally competent in the field of communication.
Residual Quantum Effects beyond Mean‐Field Treatment in Quantum Optics Systems

Abstract Mean‐field treatment (MFT) is frequently applied to approximately predict the dynamics of quantum optics systems. It simplifies the system Hamiltonian by neglecting the quantum statistics of certain modes that are driven strongly by lasers or couple weakly with other modes. However, the neglected quantum correlations between different modes result in unanticipated quantum effects and might lead to significantly distinct system dynamics. Here, a general and systematic theoretical framework based on perturbation theory in company with MFT is provided to capture these quantum effects. The form of nonlinear dissipation and parasitic Hamiltonian as well as their relationship to the nonlinear coupling rate are predicted. Furthermore, the indicator is also proposed as a measure of the accuracy of mean‐field treatment. As an example, this theory is applied to quantum frequency conversion, in which mean‐field treatment is commonly applied, to test its limitation under strong pump and large coupling strength. The analytical results show excellent agreement with the numerical simulations. This work clearly reveals the residual quantum effects neglected by MFT and provides a more precise theoretical framework for nonlinear optics and quantum optics.
Optimization of photovoltaic panel deployment in centralized photovoltaic power plant under multiple factors

Solar energy is one of the main renewable energy sources and has rapidly developed in many countries. However, the photovoltaic (PV) output power will be different under various meteorological and geographical conditions. Therefore, this paper presents an optimization method for the deployment of PV panels in a centralized PV power plant considering multiple factors. Firstly, the whole planning area is divided into a certain amount of sub-areas according to a given area, and fuzzy C-means algorithm is used for terrain clustering according to the geographical characteristics of the sub-areas. Secondly, the correlation analysis between each meteorological factor and PV output power is carried out separately to select the main factors affecting PV output power, and then the expected annual PV output power under the joint action of several main meteorological factors in each terrain is calculated by dual-stage attention mechanism based long short-term memory algorithm. Finally, according to the expected annual PV output of each terrain, considering the constraints including cost, area and so on, the deployment optimization of PV panels is obtained to maximize the annual PV output of the whole PV power plant and minimize the construction cost. The results of case studies show that the proposed methods effectively improve the expected PV output power of the PV power plant and reduce the construction cost.
Analyzing All the Instances of a Chaotic Map to Generate Random Numbers

All possible configurations of a chaotic map without fixed points, called &ldquo;nfp1&rdquo;, in its implementation in fixed-point arithmetic are analyzed. As the multiplication on the computer does not follow the associative property, we analyze the number of forms in which the multiplications can be performed in this chaotic map. As chaos enhanced the small perturbations produced in the multiplications, it is possible to built different pseudorandom number generators using the same chaotic map.
Wavelet based ensemble models for early mortality prediction using imbalance ICU big data

ICU mortality prediction is critical for board-certified physician to make efficient decision for seriously sick patients who stay in the ICU. Various techniques have been developed to overcome this problem based on the medical records. However, some physiological tests such as laboratory test, vital test are time consuming and need to handle by developing various machine learning (ML) models. In this paper, twelve different ML based classifiers are developed and from these classifiers, best three classifiers are selected and an ensemble learning classifier is proposed to predict the ICU patient's mortality. Three standard datasets Physionet challenge 2012, WiDS Datathon, 2020 and MIMIC-III are used for simulation and analysis as they are challenging. These datasets contain patient's data collected during the first 48 h, 24 h and 48 h respectively of patient's admission to ICU. The datasets have many features such as high dimension, huge missing values and highly imbalance which degrades the performance of the models. To handle these problems, k-NN imputation or mean imputation method is applied to fill the missing values and five variants of synthetic minority oversampling technique (SMOTE) such as BD-SMOTE, SVM-SMOTE, ADASYN, ENN-SMOTE and TL-SMOTE, are applied for resampling the data. To circumvent the curse of dimensionality problem, transform domain features are extracted using discrete wavelet transform (DWT). Then twelve different ML based classifiers are trained using wavelet coefficients as inputs and 5-fold cross validation over 10 independent experiments. Out of these twelve, (ENN-SMOTE + LGB) is performing best followed by (ENN-SMOTE + CatBoost) and (BD-SMOTE + XGB) in physionet challenge 2012 dataset. ENN-SMOTE + CatBoost is performing best followed by ENN-SMOTE + XGB and BD-SMOTE + RF models in WiDS Datathon, 2020. Similarly for MIMIC-III ENN-SMOTE + CatBoost is best performed model followed by ENN-SMOTE + XGB and ENN-SMOTE + RF. Finally, ensemble classifiers are proposed using these best performed models which resulted AUC of 92.66%, 94.44% and 98.06% for Physionet challenge 2012, WiDS Datathon, 2020 and MIMIC-III datasets respectively during testing phase. Further, the results of the proposed models are compared with the other reported models and seems to be outperformed all in terms of AUC score.
Halo-free image enhancement through multi-scale detail sharpening and single-scale contrast stretching

Conventional image enhancement methods manipulate images in just one scale. This is usually not enough in practice since details like edges live across scales. So, enhancing images through multiscale manipulations is necessary to achieve satisfying results. One drawback of conventional multiscale enhancement methods is that halo artifacts are easy to be introduced. In this paper, we propose a novel efficient multi-scale halo-free image enhancement method, which is based on the observation that image enhancement can be decomposed into two layers: detail sharpening and contrast stretching, and multiscale operations need to be employed to process just the detail layer. We then propose to decompose the multiscale detail manipulations into one-dimensional edge-preserving operations such that halos are completely avoided. For the contrast stretching layer, a modified histogram equalization method is proposed, which achieves state-of-the-art global contrast enhancing effect. Numerical experiments show that, compared to existing popular approaches, the proposed method produces quite competitive results for a variety of image types.
Research on Fuzzy Temporal Event Association Mining Model and Algorithm

As traditional models and algorithms are less effective in dealing with complex and irregular temporal data streams, this work proposed a fuzzy temporal association model as well as an algorithm. The core idea is to granulate and fuzzify information from both the attribute state dimension and the temporal dimension. After restructuring temporal data and extracting fuzzy features out of information, a fuzzy temporal event association rule mining model as well as an algorithm was constructed. The proposed algorithm can fully extract the data features at each granularity level while preserving the original information and reducing the amount of computation. Furthermore, it is capable of efficiently mining the possible rules underlying different temporal data streams. In experiments, by comparing and analyzing stock trading data in different temporal granularities, the model and algorithm identify association events in disorder trading. This not only is valuable in identifying stock anomalies, but also provides a new theoretical tool for dealing with complex irregular temporal data.
Improved measurement of software development effort estimation bias

While prior software development effort estimation research has examined the properties of estimation error measures, there has not been much research on the properties of measures of estimation bias. Improved measurement of software development effort estimation bias. Analysis of the extent to which measures of estimation bias meet the criterion that perfect estimates should result in zero bias. Recommendations for measurement of estimation bias for estimates of the mean, median, and mode software development effort. The results include the recommendation to avoid a commonly used measure of effort estimation bias. Proper evaluation of estimation bias requires knowledge about the type of estimates evaluated, together with the selection of a measure of estimation bias that gives zero bias for perfect estimates of that type.