Reviewerly/exHarmony huggingface dataset:
Application of OLS Regression and VAR Models to Analyse the Economies of Varying Political Regimes

The GDP per capita is a popular method of measuring the economic success of a country. This paper uses regression analysis to predict the GDP per capita (GDPPC) of countries using different independent variables. We applied Ordinary Linear Squares Regression and Vector Autoregression to check for a correlation between the chosen independent variables (Corruption Perception Index, Political Rights score, Civil Liberties score, Gender Inequality Index, Consumer Price Index, Population Density, and the percentage of people using the Internet) and the GDPPC. Using empirical evidence, we determine which model might be more accurate to attain this goal. Four countries of varying political regimes are studied - USA and Canada are categorised as democratic countries and China and Russia are non-democratic countries. Our results show trends in the correlations between the independent and dependent variables, and we can draw a distinction between the political regimes. We found that Corruption Perception Index and Population Density negatively correlates with the GDPPC of all 4 countries. We also noticed that the percentage of people using the internet and Gender Inequality Index correlates negatively with the GDPPC for non-democratic countries and in democratic countries the Consumer Price Index negatively influences the economy.
Evaluation and estimation of discomfort during continuous work with mixed reality systems by deep learning

Mixed reality systems are often reported to cause user discomfort.Therefore, it is important to estimate the timing at which discomfort occurs and to consider ways to reduce or avoid it.The purpose of this study is to estimate the discomfort of the user while using the MR system.Psychological and physiological indicators during the task were measured using the MR system, and a deep learning model was constructed to estimate psychological indicators from physiological indicators.As a result of 4-fold cross-validation, the average F1-value of each discomfort score was 0.608 for 1 (Nothing at all), 0.555 for 2 (Slightly Discomfort), and 0.290 for 3 (Very Discomfort).This result suggests that mild discomfort can be detected with a certain degree of accuracy.
PENGEMBANGAN DAN IMPLEMENTASI PANDUAN MENDESAIN PEMBELAJARAN DARING DENGAN PLATFORM SEDERHANA UNTUK GURU-GURU

This research is a development research that aims to facilitate teachers to be able to design online learning at this time, by utilizing certain application platforms that suit their learning needs. Through this research, a Digital Guide is produced that can be used by teachers, so that in carrying out online learning or learning from home (BDR) for their students, they will be helped in using a platform that is simple and easy, but can create fun and meaningful learning. In this product development process, the Derek Rowntree development model is used, which consists of three main stages, namely 1) planning, 2) writing preparation, 3) writing and editing. At the writing and editing stage, after the product prototype is produced, expert review is carried out by material experts, media experts, and learning design experts. After experiencing improvement, the product is tested on users in a training which is designed for teachers at Tenjo Village, Bogor. The training is held in order to execute community service of Educational Technology Master Program UNJ. The test results indicated that the product is feasible to use and is very good at facilitating teachers who will design online learning, as well as selecting and using certain appropriate application platforms.
STUDENTS OF PROFESSIONAL TEACHER ON THE JOB EDUCATION PERCEPTIONS FOR E-LEARNING

The purpose of this study is to describe the perception of Teacher Professional Education students towards e-learning.The research method used is the mix method, which is a method that combines quantitative and qualitative research methods. The research was conducted in March 2020, at the Satya Wacana Christian University, Salatiga. The population of this study were 571 students of Satya Wacana Christian University Teacher Professional Education. Determination of the research sample random sampling technique as many as 230 people. Collecting qualitative and quantitative data using closed questionnaires and open questionnaires distributed using google form. The data collection instrument was arranged with 5 choices with a score range of 5 – 1. Quantitative data was analyzed descriptively quantitatively and qualitative data was analyzed using data condensation techniques, data display, and drawing conclusions.The results showed that based on data analyisis, student perception to display aspect,access, interaction, control facility, form and arrangement, and usefulness of e-earning is good. Based on qualitative data, it can be known that the e-learning has quite attractive interface but it still need some addition of pictures or another media; e-learning access is good but it can be a little difficult for people who lived far from the town due to the trouble of internet access; the interaction aspect is good but sometimes it needs a gap to get the feedback from both the lecturer and student; control facility for student who adept with computer is not a problem; the form and structure from e-learning was good, but the learning material still in full text form; learning through online is useful, the weakness is overcomed with face to face in college for material drilling, workshop, and teaching technique.
PREDIKSI POTENSI SISWA PUTUS SEKOLAH AKIBAT PANDEMI COVID-19 MENGGUNAKAN ALGORITME K-NEAREST NEIGHBOR

The implementation of the PSBB has an impact on all sectors, one of which is education, namely the threat of children dropping out of school. Dropouts explain that every student or student who leaves school or other educational institutions for any reason before finishing school without moving to another school. Early prediction must be done, to prevent many students dropping out of school. The dataset used in this study was taken from students in Junior High School (SMP) in Banyumas Regency. The method used in this study is the confusion matrix and 10-fold cross validation on the K-Nearest Neighbors (KNN) algorithm. The results obtained on the KNN algorithm in predicting the potential for dropout students are 87.4214%, with a precision value of 88.2%, recall 87.4% and F-Measure 87%. Then the results of the accuracy value on the KNN algorithm are categorized as Good Classification
ReCapture: A VR interactive narrative experience concerning perspectives and self-reflection

This project presents a virtual reality (VR) Interactive Narrative aiming to leave users reflecting on the perspectives one chooses to view life through.The narrative is driven by interactions designed using the concept of procedural rhetoric, which explores how rules and mechanics in games can persuade people about an idea, and Shin's cognitive model, which presents a dynamic view of immersion in VR.The persuasive nature of procedural rhetoric in combination with immersion techniques such as tangible interfaces and first-person elements of VR can effectively work together to immerse users into a compelling narrative experience with an intended emotional response output.The narrative is experienced through a young woman in a state between life and death, who wakes up as her subconscious-self in a limbolike world consisting of core memories from her life, where the user is tasked with taking photos of the protagonist's memories for her to come back to life.Users primarily interact with and are integrated into the narrative through a photography mechanic, as they have the agency to select "perspective" filters to apply to the woman's camera from which to view a core memory through, ultimately choosing which perspectives of her memories become permanent when she comes back to life.
Incomplete Feature Recovery of Cloud Network System Based on Artificial Intelligence

There are a lot of incomplete multi-modal data in cloud network data, which need to be processed by intelligent analysis method. Recently, there have been a lot of incomplete multi-view studies. Combined with previous studies, most of them are based on multimedia videos, natural images and other aspects to study random missing or view missing. Based on matrix decomposition, non-negative matrix decomposition, Laplacian matrix and other methods, good results have been achieved. Feature extraction can not only rely on general models, but also process data under professional advice. Traditional methods lack pertinence for specific data recovery problems, and are difficult to obtain optimal optimization due to too flexible constraints, so the application effect is not good. By analyzing the data of the complete view of the sample, analyzing the connections within the view and the differences between the views, this paper tries to analyze or learn the structure and probability distribution of the data of multiple views, and then establishes a common subspace or uses GAN to complete the missing values based on the existing data, which proves the effectiveness of the method in the data set. We apply the method to the analysis of cloud network data, and the experiment proves the effectiveness of the method.
Comparison of Activation Function for Offline Handwritten Kanji Document Detection Using Convolutional Neural Network

In an offline kanji handwriting detection and recognition system, the ability of the neural network to correctly recognise each handwritten character within a document tends to be a significant problem. However, the present state-of-the-art neural network adopted for the object detection task settle for the object location principle but cannot achieve complete detection and lacks the proper use of an activation function. Also, there appears to be a lack of research focusing on developing an activation function that can perfectly enhance the learning ability of an artificial neuron used in a deep neural network model. Therefore, this research paper presents a visual evaluation between monotonic and non-monotonic activation function performance effect on a neural network. The results obtained show that the non-monotonic activation functions outperformed the monotonic activation function by achieving a fast speed for detection and recognition of the kanji handwritten characters
Virtual Environments in Learning the Biology of High School Students

This work analyzed the potential impact of virtual environments on learning biology at a high school. This case study was done at a secondary institution in Ecuador. A quantitative and descriptive study was carried out and two measuring instruments were applied to different populations. This work con-cluded that the educational community has favorable views about the use of technology as a learning strategy. Virtual environments were identified to positively affect meeting the class objectives and improve learning condi-tions such as interest, attention, and student motivation.
A Post-Processing Algorithm for Boosting Contrast of MRI Images

Low contrast of Magnetic Resonance (MR) images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis.State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images.Drastic changes in brightness features, induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings.To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well.This method termed as Power-law and Logarithmic Modification-based Histogram Equalization (PLMHE) partitions the histogram of the image into two sub histograms after a power-law transformation and a log compression.After a modification intended for improving the dispersion of the sub-histograms and subsequent normalization, cumulative histograms are computed.Enhanced grey level values are computed from the resultant cumulative histograms.The performance of the PLMHE algorithm is compared with traditional histogram equalization based algorithms and it has been observed from the results that PLMHE can boost the image contrast without causing dynamic range compression, a significant change in mean brightness, and contrast-overshoot.
Logic Apps

Today many businesses use software as a service (SaaS) applications are used. There are so many vendors that you can find an SaaS application for nearly every thinkable task a company might come up with. From ticket systems to full-fledged development systems, everything is available out there on the internet. Applications that are used in a commercial context do not exist in an isolated environment. Often, they are part of a much larger picture and only fulfill one task of many. This leads to a spread of data and tasks over separate systems.
Phenotypic Parameters Prediction of Lettuce based on Computer Vision and Regression

To obtain phenotypic parameters by means of lossy measurement, we proposed a comprehensive and integrated approach to predict different parameters of four varieties of lettuces.By building different prediction models, we required predicted value of five phenotypic parameters of lettuce.Test results indicate that prediction models we have constructed are reliable and feasible.In addition,our methods can be better transferred to the research of other crops, and producers can adjust the growing environment of crops in time, so as to obtain higher yield.
Research on the Defogging Algorithm Based on Image Enhancement

Haze weather will degrade the quality of outdoor images. The processing of foggy images is to meet special needs and objectives. Through the processing of such images, the details in the images are more suitable for people to identify. Its purpose is to eliminate the effect of fog on the image, and finally to obtain a clearer image. The main content of this paper is to summarize the image defogging algorithms from the aspect of image enhancement. Finally, the existing problems and future prospects of these image enhancement defogging algorithms are pointed out.
Characteristics of Data in the Traditional Publishing Industry and Emerging Industries Including the Internet Industry

The long-standing traditional publishing industry has accumulated a large amount of content data. These contents are strictly examined before published and to a great extent knowledge products. Most publishing firms in China have transformed their contents to digital, fragmented forms and data, which will be the foundation for publishing organizations to develop big data and knowledge services.
The Multi-access Edge Computing (MEC)-Based Bit Rate Adaptive Multicast SVC Streaming Using the Adaptive FEC Mechanism

This work proposed the SVC streaming method for n users based on the Multi-access Edge Computing (MEC) architecture. In the proposed method, a group member’s handheld device is selected as the Header Handheld Device (H-HD) to be in charge of downloading video contents from the MEC Server and then multicasting downloaded video contents to the other group members’ Receiver Handheld Devices (R-HDs). The video bit rate that can be adopted in the next downloading cycle is derived by combining the proposed adaptive Forward Error Correction (FEC) scheme with the estimated bandwidth. To have the smooth streaming, the Currently Downloading Segment Section (CDSS) mechanism, for which the segments inside CDSS can be downloaded at the same time in a downloading cycle, was proposed. The performance evaluation results shown that the proposed method can utilize the network bandwidth more effectively to improve video playback quality in the multicast environment.
OldVSR: A model for the video super-resolution and restoration of old real-world TV series

With the recent advance in video super-resolution (VSR) techniques, there have been many requests for super-resolve realworld old analog TV series into high-definition digital content.As excellent classical TV series may receive little to no attention due to their poor video quality, restoring them would open new business opportunities for reusing old TV contents.A problem with restoring real-world old TV series is in the complex artifacts introduced by the old interlaced scanning and compression artifacts during the digitization of old analog videos.Though recent DNN-based VSR models perform nicely on clean videos, due to the artificial nature of interlacing and compression artifacts, they fail to restore old videos into a high-definition counterpart free from noticeable artifacts.In this work, we propose OldVSR for restoring old real-world TV series with artifacts of artificial nature.The proposed model implements a bidirectional recurrent structure with first and second-order propagation where each recurrent layer implements two main functions, i.e., Feature alignment (FA) and Pyramid feature aggregation (PFA).The outputs of the forward and backward layers are merged and upsampled to produce a High-Definition (HD) frame of the input standarddefinition (SD) frame.We demonstrate through experiments that our proposed OldVSR can effectively remove artifacts of artificial nature from old videos and successfully restores old TV series.
Trusted Management and Traceability Technology of Power Data

With the rapid advancement of market economy and the rapid change of science and technology, big data related technologies have been rapidly developed, and the application scope of power big data has been continuously expanded, which has put forward higher requirements for the security, availability and integrity of enterprise power big data application. Consider various trust demand in this paper, we study the data management model and data in use process traceability method, is put forward based on the digital fingerprint technology of cryptography algorithm, using the block chain decentralized, not tampered with, the characteristics of open and transparent, solve the power data in multidimensional body circulation between availability and completeness of the requirements of the data analysis of the whole process of mutual trust, At the same time, the power data traceability mechanism should be established to solve the possible data tampering problems from the source. For the malicious tampering and misoperation of operation data, the accident source and responsibility can be traced.
STUDENTS’ VOCABULARY MASTERY BEFORE AND AFTER TAUGHT USING TALKING STICK STRATEGY

This exploration was expected to uncover that: to discover the understudies' vocabulary dominance previously, after educate through talking stick strategy, additionally the huge distinctive the understudies' vocabulary authority at the long term understudies of SMP Negeri 5 Pinrang. This examination utilized semi test strategy, with two gathering pre-test and post-test plan. The general population was the drawn out understudies of SMP Negeri 5 Pinrang. The example is picking by random sampling technic. Where, the specialist accept one class as trial class and one class as control class. The outcome of the data assessment showed that the understudies' jargon predominance improved through and through when given treatment. It was showed up by the mean score of the pre-test was (67.96) and the post-test was (78.70). It showed that talking stick strategy can improve the jargon authority of the drawn out understudies of SMP Negeri 5 Pinrang out and out. Resulting to inspecting the data by using t-test formula, the eventual outcome of t-test regard (4.58) and t-table (2.021), Those showed that H1 was recognized and H0 was excused.
Evolving Adaptive Neural Network Optimizers for Image Classification

The evolution of hardware has enabled Artificial Neural Networks to become a staple solution to many modern Artificial Intelligence problems such as natural language processing and computer vision. The neural network’s effectiveness is highly dependent on the optimizer used during training, which motivated significant research into the design of neural network optimizers. Current research focuses on creating optimizers that perform well across different topologies and network types. While there is evidence that it is desirable to fine-tune optimizer parameters for specific networks, the benefits of designing optimizers specialized for single networks remain mostly unexplored. In this paper, we propose an evolutionary framework called Adaptive AutoLR (ALR) to evolve adaptive optimizers for specific neural networks in an image classification task. The evolved optimizers are then compared with state-of-the-art, human-made optimizers on two popular image classification problems. The results show that some evolved optimizers perform competitively in both tasks, even achieving the best average test accuracy in one dataset. An analysis of the best evolved optimizer also reveals that it functions differently from human-made approaches. The results suggest ALR can evolve novel, high-quality optimizers motivating further research and applications of the framework.
A Non-Autoregressive Neural Machine Translation Model With Iterative Length Update of Target Sentence

The non-autoregressive decoders in neural machine translation are paid increasing attention due to their faster decoding than autoregressive decoders. However, their apparent problem is a low performance which is mainly originated from wrong prediction about the target sentence length. To attack this problem, this paper proposes a novel machine translation model with a new non-autoregressive decoder named Iterative and Length-Adjustive Non-Autoregressive Decoder (ILAND). This decoder adopts a masked language model to avoid generation of low-confident tokens and changes the length of a target sentence iteratively to an optimal length. To complete these goals, ILAND consists of three complementary sub-modules of a token masker, a length adjuster, and a token generator. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">token masker</i> and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">token generator</i> take charge of the masked language model, and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">length adjuster</i> optimizes the target sentence length. The sequence-to-sequence training of the translation model is also introduced. In this training, the length adjuster and the token generator are jointly trained since they share a similar structure. The effectiveness of the translation model is proven by showing empirically that the model outperforms other models with various non-autoregressive decoders. The thorough analysis suggests that the performance gain of the translation model comes from target sentence length adaptation and the joint learning. In addition, ILAND is also shown to be faster than other iterative non-autoregressive decoders while it is still robust against the multi-modality problem.
REAL-TIME ARTIFICIAL INTELLIGENCE (AI) AUTOMATIC DIAGNOSTIC SYSTEM FOR COLORECTAL POLYPS USING RESIDUAL NETWORK (RESNET)

Aims Convolutional neural networks (CNNs) are widely used for artificial intelligence (AI)-based image classification. Residual network (ResNet) is a new technology that facilitates the accuracy of image classification by CNN-based AI.
A seismic mystery

Deep inside Earth are two vast geological anomalies of unknown origin. Planetary scientists are on a mission to explain these mysterious blobs, as Robin George Andrews discovers