Reviewerly/exHarmony huggingface dataset:
ANN-Based Model for Daily Solar Radiation Prediction with A Low Number of Hidden Neurons And Optimal Inputs

Solar radiation prediction has been the focus of many studies over the past years due to its usefulness for clean energy generation through photovoltaic (PV) systems. The prediction result is important for both standalone and grid-connected PV systems as it is used for the design of these systems, for making power dispatching plans in hybrid systems, as well as for potential future PV system feasibility. In this article, an Artificial Neural Network (ANN)-based model for daily global solar radiation prediction is developed. This model is trained with a back-propagation training algorithm and make prediction using meteorological variables as inputs. While keeping a good accuracy level, the model is built using a low number of neurons in the hidden layer of the ANN. Therefore, the proposed model is simpler as compared to many existing models. First, the minimum and the maximum numbers of hidden neurons are calculated (page 4). Second, simulations based on a trial-and-error method can show us the good number of hidden neurons. A simple model is preferred than a complex one when the performance is same or almost. In addition, a simple model is easy to understand and to implement and also allows quicker modifications when needed.
Exploring Convolutional Neural Networks for Top-Level Domain Name Recommendation

Domain names consist of a fixed choice of top level domain (TLD) names, such as.com,.net,.org, or.co, preceded by a second level domain (SLD) name, such as godaddy in godaddy.com. To provide an optimal domain name automatically, we examine the problem of predicting a TLD based on an often cryptic SLD. The task of assigning the best TLDs given an SLD raises several challenges. Namely, in our training data, there are over 400 TLDs to consider and thus a relatively large number of labels and there is a class-imbalance issue in our training data with 73% of domain names registered as.com. SLDs provide very short input that are restricted to under 64 characters that further complicates accurate prediction. Finally, SLDs can be registered under multiple TLDs. Hence, TLD recommendation is a multi-label, class-imbalanced text classification problem for very short text input. Here, we show that a convolutional neural network (CNN) based model provides an attractive solution and report the optimal hyperparameters. We believe the obtained results show that our model is a general framework for related problems, such as SMS message classification.
The Red Sea Metalliferous Sediments

&amp;lt;p&amp;gt;The demand for metals and raw materials continues to increase as onshore deposits become more depleted. Our oceans contain large unexplored areas that may contain new resources in the form of Mn-nodules, Co-rich crusts, and massive sulfides. A complete characterization and assessment of these deposits are fundamental for the evaluation of resource extraction, separation, and disposal processes.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The Red Sea holds unique examples of sediment accumulations formed under distinctive environmental conditions. The Atlantis II deep is located in the central part of the Red Sea at 2 km depth and on top of the spreading axis. This deep accumulates sediments that result predominantly from the discharge of hydrothermal fluids into hot and stratified brine pools. The changes in environmental conditions and the hydro-chemical conditions in the brine pool control sediment formation. The accumulations are enriched with metals, such as Ag, Au, Cu, Co, and Zn. The sediments in this deep hold a record of the formation history and their brine pools tell a story about on-going processes.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;On-going research at the Energy Geo-Engineering Laboratory EGEL, KAUST includes (1) Geotechnical index properties (liquid limit, grain size distribution, and specific surface) and consolidation tests to infer engineering properties, (2) Sediment classification based on the Revised Soil Classification System, (3) Geochemistry and mineralogy using XRD, ICP-OES and (4) Microstructure and texture with SEM imaging. An advanced sediment characterization of these fine-grained metalliferous deposits gives a comprehensive understanding of the soil behavior.&amp;lt;/p&amp;gt;
3.1운동과 구약성경의 구속사적 의의

The purpose of this essay is to explore the redemptive-historical significance of the Sam-il Movement on March 1, 1919, on its 100th anniversary. It argues that the Sam-il Movement is a part of God’s saving activities for the oppressed people, and that the anti- Japanese independence struggle that lasted for 27 years was a body prayer analogous to “the outcry of the Hebrew slaves” who longed for liberation from the Egyptian bondage. This paper summarizes the significance of the Sam-il Movement and its impact on the 27-year long resistances against Japan in three aspects. The greatest significance lies in its contribution to the emergence of ‘the Republic of Korea’ where the people replaced the king or emperor in ruling the country. In addition, the Sam-il Movement was the forefront of the anti-imperialist union that was first realized in anti-Japanese independence movements during the following 27 years. Finally, while the Sam-il Independence movement failed in the short term, it has became a ‘canon’ to secure the legitimacy and sustainability of the anti-Japanese movements that continued for 27 years. The reason why this paper regards the 36-year long anti-Japanese independence movements initiated by the Sam-il Movement as a redemptive history led by God’s saving will and interest, is that they were carried out in the same pattern of the saving activities of God who intervenes in human history (the situation of oppression → outcry for liberation → redemption → renewal of the covenant). In this respect, this paper contradicts the understanding of the modern history of Korea that has been espoused by the New Rightists, which undermines the 36- year long independence struggles.
Web-technologies in Knowledge Integration as a Means of Mathematical Literacy Forming of School Students

Digitalizing of social relations and new dominants of the younger generation entails a change in the requirements for mathematics teacher’s professional activity. A modern teacher should not only have the fundamental knowledge concerning with mathematics, but also possess the modern Web-technologies as a means and component of information and educational environment organizing and managing of functional literacy development of school students. The article deals with the components and techniques of mathematical literacy integration and information competence and procedures development in teaching mathematics on the basis of complex knowledge mastering in network educational community; the content and mechanisms for such integration, based on modern achievements in science adapting (fractal geometry, fuzzy sets and fuzzy logic, theory of coding and data encryption, etc.) to school mathematics is also revealed. Problems solving of universal educational actions and mathematical literacy development of school students as a structural phenomenon of an innovative educational environment organizing for managing the school student's cognitive activity in the network educational community is consistently reflected. The cognitive and activity-based aspects of the introduction of Web-technologies and information environments in the integration of mathematical literacy and information competence as well as school student’s cognitive activities are identified.
Designing Energy Management Aware Task Scheduling Algorithm And Model For Cloud Data Centers

In cloud computing many tasks come to servers from different sites for computation and storage which makes data centers consume large energy and become less responsive. Task scheduling is one of the best ways to utilize cloud data center resources efficiently, lower power consumption and latency, and empowers available bandwidth use. Task scheduling algorithms and data center network models employed previously are not much efficient from energy consumption perspective due to the features that they haven’t included. To solve this problem an appropriate and efficient proximity based task scheduling technique and model are designed and implemented for cloud computing data centers.This research work proposes and implements a new proximity based task scheduling algorithm and scalable data center model that can minimize power consumption and increase scalability of cloud computing data centers. The evaluation results, found from the experiment are presented and showed more efficient outcome in terms of energy efficiency for both the proposed scheduling algorithm and data center network model.
A Text classifying Method Based on Text Feature Cluster

With the rapid development of internet and computer application technology, tens of billions of unstructured data have been generated. In addition to the data of images, audio, video, etc., the most common is the text data. A variety of text data can be seen everywhere on the network, such as news, reports, and micro blog posts, in order to analyze the text file, it must be processed by text feature recognition. To this end, many scholars have conducted research on text feature recognition. In this article, author adds the word frequency measured by the value of the TF-IDF formula as a weight on the feature recognition algorithm CHI to improve the accuracy of text feature recognition. In order to further improve the classification of text anomalies, this paper proposes to change the single feature recognition method of text, combine different text features, and form abnormal text feature clusters to improve the classification accuracy. To this end, the TF-IDF formula is improved. This paper conducted experiments based on public bidding data. The experimental results show that the method is feasible and accurate.
Analysis of blind and non-blind de-convolud tion methods for de-blurring of ancient manuscript

Image restoration is the technique of enhancement of an original image which was degraded due to various factors like noise or other physical parameters in the environment.The process of image restoration involves the use of de-convolution of the degraded or blurred image. The de-convolution of the blurred image is taken with the PSF (point spread function) i.e the degradation function. There are two methods of de-blurring i.e. non-blind and blind de-convolution. In the restoration process of any image, if blur function is random, then the estimation of the PSF is done and this is how the ideal image is generated by using the original image as the input. In case of non blind method iterative method is used, which gives better results as number of iterations can be increased. Blind de-convolution is proposed to increase the quality of the deblurred image, to estimate the proper or true size of the PSF and also to remove the ringing effects in the output a specific weighing function is also proposed. In non-blind deconvolution technique, Lucy Richardson (LR) technique is presented in the paper.
Waze Female Gps Voice [English] V 2.0 1.35.X

This mod adds add the Waze voice for multiple language [changelog] Changelogs : 1.0.0 : Initial release 1.0.1 : Fix for the Keep Left and Keep Right ...
Deploying Your Chatbot to Web and Social Media Channels

There are lots of out-of-the-box integrations available in Dialogflow ES. You can enable these to bring your agents to (multiple) channels. This chapter will highlight the following (text-based) integrations.
Methods and tools for modeling the quality of education (sociological analysis)

The article presents a sociological analysis of methods and tools for modeling the quality of Education.In this article,the problems of improving the quality of education are considered, the need to conduct a practical study of the social dimension of the management of higher educational institutions and through it to analyze the theoretical aspects of improving the quality of Education, determine the laws of development and create new socio-mathematical models of the management of educational processes is The article also analyzed the fact that the quality of education at the moment is one of the main components of human quality and is the main mechanism for the formation of public consciousness,the development of Science and culture.
Key Generation for Internet of Things

Key generation is a promising technique to bootstrap secure communications for the Internet of Things devices that have no prior knowledge between each other. In the past few years, a variety of key generation protocols and systems have been proposed. In this survey, we review and categorise recent key generation systems based on a novel taxonomy. Then, we provide both quantitative and qualitative comparisons of existing approaches. We also discuss the security vulnerabilities of key generation schemes and possible countermeasures. Finally, we discuss the current challenges and point out several potential research directions.
MMIM: An Interpretable Regularization Method for Neural Networks (Student Abstract)

In deep learning models, most of network architectures are designed artificially and empirically. Although adding new structures such as convolution kernels in CNN is widely used, there are few methods to design new structures and mathematical tools to evaluate feature representation capabilities of new structures. Inspired by ensemble learning, we propose an interpretable regularization method named Minimize Mutual Information Method(MMIM), which minimize the generalization error by minimizing the mutual information of hidden neurons. The experimental results also verify the effectiveness of our proposed MMIM.
몽골어-한국어 번역 오류 분석 및 교육적 개선 방안: 한국인 학부생을 중심으로

The present study attempts to analyze and classify errors in translations of Korean Mongolian language learners and to suggest educational implications based on the error analysis. Korea and Mongolia have had an established relationship since 1990 and held exchanges in various fields for a short period of time, and as a result the demand for professional translation is increasing. To meet this demand, undergraduate schools also require educational programs for professional translators, yet it is difficult to find any research or studies about the issue. In this study, Mongolian texts translated by Korean Mongolian learners were collected and analyzed, and the errors were classified into 1) grammar related errors, 2) translation skill related errors, and 3) meaning related errors. Based on the analysis, the following implications can be presented. First, it is necessary for students to recognize the differences between the use of punctuation marks in the two languages and to learn how those them appropriately in the target language. Second, there is the issue of transcribing Mongolian into Korean. Although precise common rules for transcribing have not yet been established, teachers should suggest a consistent transcription guide. Third, since many errors related to the source language appear, it is necessary to avoid literal translations and complete the translation suitable for the target language. Finally, students need to be exposed to various types of texts and practice the style of writing that fits to the text.
Teaching the Old Dog New Tricks: Supervised Learning with Constraints

Adding constraint support in Machine Learning has the potential to address outstanding issues in data-driven AI systems, such as safety and fairness. Existing approaches typically apply constrained optimization techniques to ML training, enforce constraint satisfaction by adjusting the model design, or use constraints to correct the output. Here, we investigate a different, complementary, strategy based on teaching constraint satisfaction to a supervised ML method via the direct use of a state-of-the-art constraint solver: this enables taking advantage of decades of research on constrained optimization with limited effort. In practice, we use a decomposition scheme alternating master steps (in charge of enforcing the constraints) and learner steps (where any supervised ML model and training algorithm can be employed). The process leads to approximate constraint satisfaction in general, and convergence properties are difficult to establish; despite this fact, we found empirically that even a naive setup of our approach performs well on ML tasks with fairness constraints, and on classical datasets with synthetic constraints.
소규모학교 과학실험 보조교사의 경험이 초등 예비교사의 과학 수업 불안 및 과학 교수 효능감에 미치는 효과

The purpose of this study was to investigate the effects of experience as a science experiment assistant teacher on the science instruction anxiety and the science teaching efficacy beliefs of elementary pre-service teachers in a small scale school. For this study, 21 elementary pre-service teachers were selected and then they worked as science experiment assistant teachers in small scale schools for about 10 months. They were tested the science instruction anxiety and the science teaching efficacy beliefs in before and after. After analyzing the tests results, some pre-service teachers were interviewed. The results of this study are as follows. The experiences as science experiment assistant teachers positively influenced on reducing the science instruction anxiety to elementary pre-service teachers. The experiences also had positive effects on reducing both state anxiety and trait anxiety, which are the sub-factors of science instruction anxiety. In addition, the science teaching efficacy beliefs and its sub-factor, the self-efficacy in science teaching, had positive impacts, but the experiences did not have a positive impact on the outcome expectancy. The effects of the experience as a science experiment assistant teacher were different according to the personal experiences and cognitions of pre-service teachers.
A Study of the Quality of Scholastic Aptitude Test by Applying Modern Test Theories

The objective of this research was to study the quality of the scholastic aptitude test by applying modern test theories through Item Response Theory, test parameters, reliability, and differential item functioning. The secondary  data were collected from the SWUSAT Test Development Project 2014, which consists of a test for all 4 subjects: Verbal Factor, Number Factor, Reasoning Factor and Spatial Factor. The data analysis was divided into 3 steps as follows: Step 1: Data preparation according to the studied factors, the researcher performed the analysis of the basic statistics of the data by analyzing the preliminary data with descriptive statistics, i.e. frequency, percentage. Step 2: Analyze test parameters using a 2-parameter test response model based on parameters, discriminant power (a), difficulty (b) parameter, and reliability. Step 3: Analysis of data with differential item functioning.The results of the research were as follows:The results of the analysis of the individual parameters of the 4-subject aptitude test with the 2-parameter test response analysis model were determined from the discriminant power parameter (a) and the difficulty parameter. (b) When considering the results of the course analysis, it was found that all subjects had a predominantly number of tests with a parameter that met the quality criteria, including a relatively high reliability level.The results of analysis of the differential item functioning and the SWUSAT test, which measures Verbal Factor, Number Factor, Reasoning Factor and Spatial Factor. The difference of the test was in the range of 6.67% - 43.33, with two tests having a number of questions that performed differently, with males being more likely to get the correct answers than females, while the other two had the same number of questions. And all tests performed at a low level of different functions
Knocking in the Game of Gin Rummy

We perform an empirical study of Gin Rummy knocking strategies, drawing insight from a population of AI players that vary in both discarding and knocking strategies. For our best performing player, simple linear regression yielded a knocking strategy that both affirmed the features expert players give attention to in making knock decisions, and yet called into question the way such features are conventionally used.
A Survey on Object Detection from Scientific Plots

Charts have always been the most effective way to provide a concise and comprehensive method to represent statistical data. Several studies have been undertaken in the field of object detection from scientific plots, which concentrate on the process of plot element detection, extraction of the value depicted by the element, and analysis of the extracted values, to reconstruct the dataset table that was used to generate the plot. Major computer vision problems may be solved using the high accuracy object detection procedures attempted while performing data extraction from chart plots. Applications that benefit from chart mining include tabular data recreation, text to speech solutions for the visually impaired, as well as digitization of print research. This paper aims to survey the most effective existing methodologies for data extraction from charts, while attempting to develop a pipeline that encompasses key modules from the analyzed documents to provide a higher accuracy during object detection.
Academics’ Self-Assessment Regarding the Mastery of the Academic Profession Competencies

The academic profession is faced with many changes and challenges in the social context, which are related to the requirements for (re)defining the existing and introducing new competencies. The main aim of the research presented in this paper is to examine the participants’ self-assessments regarding the mastery of the academic profession competencies at the beginning of senior academic career. The research method applied in this research was an online survey, and the research instrument used was a survey questionnaire. The data was analysed with the use of the Statistical Package IBM SPSS Statistics 20.0. The research was conducted on a sample of 1130 research participants (682 senior academics and 448 junior academics) from seven public universities in Croatia. The evaluation scale contained 45 items - academic profession competencies. Research results have shown that four of the main five competencies in terms of the self-assessment regarding the mastery level are from the group of general academic competencies which are not related to the traditional academic activities - teaching or research. Research participants assessed that the competencies they have mastered the least, are the ones related to universities’ third mission and leadership activities.
SLA-aware Workload Scheduling Using Hybrid Cloud Services.

Cloud services have an auto-scaling feature for load balancing to meet the performance requirements of an application. Existing auto-scaling techniques are based on upscaling and downscaling cloud resources to distribute the dynamically varying workloads. However, bursty workloads pose many challenges for auto-scaling and sometimes result in Service Level Agreement (SLA) violations. Furthermore, over-provisioning or under-provisioning cloud resources to address dynamically evolving workloads results in performance degradation and cost escalation. In this work, we present a workload characterization based approach for scheduling the bursty workload on a highly scalable serverless architecture in conjunction with a machine learning (ML) platform. We present the use of Amazon Web Services (AWS) ML platform SageMaker and serverless computing platform Lambda for load balancing the inference workload to avoid SLA violations. We evaluate our approach using a recommender system that is based on a deep learning model for inference.