Reviewerly/exHarmony huggingface dataset:
노인문화예술교육 프로그램 기획과정 사례연구 - 구미지역 노인연구모임 [노인을 위한 나라는 없다] 사례를 중심으로 -

The research looks into the definition of arts education for elderly people in a light of raising civil artists. Under the purpose of this it selects retirees from old people who are driving forces of our societies and at the same time main agents building local communities. The main unit of this research's qualitative case study method is the "senior citizen" and the smallest unit is the "senior citizen interested in arts education." In addition to these two units, civic surveys, interviews were combined to produce results as intermediate units. It attempts to build a program enabling them not only to start their second lives but also to connect them to young people and the next generation. To improve qualities of old people’s lives. attentions from local societies are needed to pay and it is important for them to benefits from participating the program. It also matters that arts education for elderly people becomes popular and that other communities for them can be formed. It investigates effects given by a desirable arts education for elderly people. It hopes that it can be a benchmark for other communities as retiree program, and to contribute to increase awareness from societies of their wellbeing in the age of population aging. I hope that it helps retirees to live healthy lives.
Industry 4.0

The video file is important for quickly analyzing the facts of the data or knowledge or information. To extract the specific file from the data is a time-consuming task. Managing these files is easy if they are organized in the correct format. To achieve this in video data, an indexing mechanism plays a significant role in accessing the various content in the database. Video indexing is a technique for providing an index related to video content for easy access from the frames of interest. For video indexing, machine learning and data mining approaches are used to provide better video indexing mechanisms and better searchable results for the users. Due to the huge size of the video files and the complexity involved in retrieving the video files, content-based indexing and automatic retrieval method with low human resources are proposed. Video metadata, audio data, records, and visual data are taken into account for indexing. The various machine learning concepts for video indexing are analyzed. Applications of machine learning concepts for video indexing and case studies are detailed.
Mitochondrial DNA suggests the existence of two distinct species in Moluccas and New Guinea within Nyctimystes infrafrenatus (Günther, 1867)

Abstract. Sulaeman TN, Hamidy A, Farajallah A, Fouquet A, Riyanto A, Arida E, Mulyadi, Trilaksono W, Munir M. 2021. Mitochondrial DNA suggests the existence of two distinct species in Moluccas and New Guinea within Nyctimystes infrafrenatus (Günther, 1867). Biodiversitas 22: 3287-3297. Nyctimystes infrafrenatus is a widespread frog species distributed across northern Australia, New Guinea, Bismarck archipelago and in the eastern part of Indonesian archipelago. The species thus encompasses two biogeographic regions, Papua and Wallacea. We evaluated the phylogenetic relationships among the populations from Papua and Moluccas based on two mitochondrial loci (16S rRNA and Cytochrome Oxidase Subunit I). Two major subclades are recovered within Nyctimystes infrafrenatus with subclade A being represented by populations from New Guinea and northern Australia and subclade B by Moluccan populations (Halmahera and Tidore). Genetic distances (2.3-4.2% in 16S rRNA) between these subclades suggest they could belong to distinct species. Since New Guinea populations correspond to the nominal species and that Nyctimystes tennuigranulatus (Boettger 1895), currently considered as a junior synonym of Nyctimystes infrafrenatus, is available for the northern Moluccan populations, we proposed to remove this taxon from synonymy. However, samples used in this study come from northern Moluccas, further studies including samples from Ambon (where another synonym was described i.e. Calamita dolichopsis) is needed to determine the taxonomic status of the southern Moluccan population whether they are conspecific with northern Moluccan population or not.
Using Deep Learning Neural Networks to Predict the Knowledge Economy Index for Developing and Emerging Economies

Missing values and the inconsistency of the measures of the knowledge economy remain vexing problems that hamper policy-making and future research in developing and emerging economies. This paper contributes to the new and evolving literature that seeks to advance better understanding of the importance of the knowledge economy for policy and further research in developing and emerging economies. In this paper we use a supervised machine deep learning neural network (DLNN) approach to predict the knowledge economy index of 71 developing and emerging economies during the 1995-2017 period. Applied in combination with a data imputation procedure based on the K-closest neighbor algorithm, DLNN is capable of handling missing data problems better than alternative methods. A 10-fold validation of the DLNN yielded low quadratic and absolute error (0,382 +- 0,065). The results are robust and efficient, and the model’s predictive power is high. There is a difference in the predictive power when we disaggregate countries in all emerging economies versus emerging Central European countries. We explain this result and leave the rest to future endeavors. Overall, this research has filled in gaps due to missing data thereby allowing for effective policy strategies. At the aggregate level development agencies, including the World Bank that originated the KEI, would benefit from our approach until substitutes come along.
A Study of Meaning and Messages With Reference to the Novel Rage of Angel

This study was aimed to identify and analyze the meaning and message in several sentences found in the novel Rage of Angels by Sidney Sheldon published in 1980. The selected sentences in the novel, which are the data of this study, were collected by applying the documentation method and note-taking technique which then were analyzed using the descriptive qualitative method. The data were presented informally in descriptive form. This study analyzes the types of meaning based on the theory proposed by Leech (1974) and the messages of the novel based on the biographical approach theory by Wellek and Warren (1962). As a result, all the seven types of meaning, namely connotative meaning, conceptual meaning, affective meaning, stylistic meaning, reflected meaning, collocative meaning, and thematic meaning, were found in the novel. In relation to the message of the novel, based on the biographical approach, there were five messages that the author delivered.
Human Activity Recognition Technology Based on Sliding Window and Convolutional Neural Network

Due to the lack of unified human activity model and related specifications, the existing wearable human activity recognition technology uses different types, numbers and deployment locations of sensors, and affects its promotion and application. Based on the analysis of human activity skeleton characteristics and human activity mechanics, a human activity model based on Cartesian coordinates is established and the normalization method of activity sensor deployment location and activity data in the model is standardized; Secondly, a sliding window technique is introduced to establish a mapping method to convert human activity data into RGB bitmap, and a Convolutional Neural Network is designed for Human Activity Recognition (HAR-CNN); Finally, a HAR-CNN instance is created and experimentally tested based on the public human activity dataset Opportunity. The experimental results show that HAR-CNN achieves the F1 values of 90% and 92% for periodic repetitive activity and discrete human activity recognition, respectively, while the algorithm has good operational efficiency.
Feeding Smart Contract Legal Requirements with Semantic and Event Detection Logic Structures from Modern Service Oriented Supply Chains

This chapter investigates the broad range of real-world applications of smart contracts and relevant rules associated with them by examining the requirements coming from supply chain management and the current technical landscape. Issues such as copyright protection, product delivery etc., have been enforced through various technological means. However, more dynamic means are now needed. The modern supply chain is a complex network of suppliers and creators and it has become increasingly difficult to control and track abnormal conditions occurring during manufacturing, delivery, sale, and management. This chapter will use the AffectUs:TagItSmart semantic and event detection framework as a use case example to demonstrate how the aforementioned concepts could be implemented with relevant pseudo-code that can be used as triggering logic or core rules in potential smart contracts for supply chains. The transformation of these rules and use of semantics will provide a more effective and multi-layered end-to-end supply chain solution that can aid in the integration with a smart contract rationale.
Pendekatan Scientific Berbantuan Media Pembelajaran Puzzle dalam Meningkatkan Curiosity dan Kemampuan Analogi Mahasiswa pada Pembelajaran Ekonomi

This research is based on real conditions in the field, namely the low curiosity and analogy ability of students at the University of Muhammadiyah Bone, Campus 3 Kahu, through economics learning, especially in the subject of Introduction to Economics II, Taxation on the Basics of Social Sciences which has not used a scientifically assisted approach. puzzle learning media in the learning process. This type of research uses quantitative research with the type of research design, namely Quasi Experimental Design with the type of Nonequivalent Control Group Design. Based on the results of the recapitulation of student scores in the experimental class, the average score was 76.92 and the control class was 57.41, which means that the value of the experimental class (with a scientific approach assisted by puzzle learning media) is higher than the control class (direct approach). Meanwhile, based on the SPSS output from the Independent Samples Test obtained by the researcher, it can be concluded that the statistical test in the t-test above uses two-tailed testing which can be concluded that the value of Sig. of 0.000 < 0.05 based on the decision rule of the t-test, H1 is accepted and H0 is rejected. Thus, it can be concluded that there is an Influence of Scientific Approach on Curiosity and Analytical Ability of Students in Economic Learning at Muhammadiyah University Bone, Campus 3 Kahu, Bone Regency in economic learning
Gender Detection Based on Machine Learning Using Convolutional Neural Networks

An approach to a real-time gender classification using a deep convolutional neural network (CNN) is proposed based on face images. Automatic recognition of gender is now important for expanding its use in various software and hardware, especially as social networking websites and social media grow. However, the performance of the current system in the physical world is not excellent, particularly compared to the result of tasks related to face recognition. Using a CNN, which is used to remove different facial features, the proposed framework explains a gender recognition computer vision-based approach to machine learning. Facial extraction is tested first, and the best features are introduced, which will be beneficial for the dataset training and testing. We suggest that by looking at the results of our study, not only a single facial appearance but also multiple facial detections can also be performed with enhanced efficiency using CNN techniques.
Improved Model Configuration Strategyusing Particle Swarm Optimization for Kannada Handwritten Numeral Recognition

Handwritten numeral recognition has been an important area in the domain of pattern classification. The task becomes even more daunting when working with non-Roman numerals. While convolutional neural networks are the preferred choice for modeling the image data, the conception of techniques to obtain faster convergence and accurate results still poses an enigma to the researchers. In this paper, we present a new method for the optimization of the traditional convolutional neural network architecture to obtain better results for Kannada numeral images. Specifically, a new strategy for choosing the CNN configuration is presented, which is based on particle swarm optimization. The optimization strategy is helpful to reduce the time required for the manual iterative approach of architecture selection. The proposed setup is trained on varying handwritten Kannada numerals. The new method is assessed on multiple standard datasets: the standard dataset of Kannada Dig-MNIST and our own custom created dataset. Significant improvements across multiple performance metrics are observed in our proposed system over the traditional CNN training setup. The improvement in results makes a strong case for relying on such method for faster and more accurate training and inference of digit classification, especially when working in the absence of transfer learning.
Research on visualization method of educational resources based on intelligent classroom

In order to solve the problems of the long existence of traditional visualization methods of educational resources and low user satisfaction, this paper studies a visualization method of educational resources based on intelligent classroom. The framework of intelligent classroom data analysis is designed, the data mining model of intelligent classroom education resources is constructed, and the mined resource data is integrated and processed. Based on the integration results of intelligent classroom education resource data, the data set is processed hierarchically according to the length of data encoding, and the visualization of education resource is realized by combining the time dimension of parallel coordinate system. The experimental results show that the maximum visualization time of educational resources in this method is only 3.56s, and the average user satisfaction score is 9.6. The visualization efficiency and user satisfaction of educational resources are both high.
Dynamic Texture Feature Extraction Based on multi-scale Convolutional Autoencoder

Aiming at the difficult problem of dynamic texture feature extraction in complex scenes, this paper proposes a dynamic texture modeling method based on multi-scale convolutional autoencoder. It merges information on multiple scales of convolutional neural networks and uses an attention mechanism to increase the weight of the main channel. Finally, the loss function is optimized by calculating the errors of multiple network levels. We have conducted experiments on the DynTex database. Compared with several other typical dynamic texture feature extraction methods, the dynamic texture reconstructed by this model has the best comprehensive effect. It solves the problems of blur, noise, and residual image in the reconstruction of dynamic texture features. At the same time, the effectiveness of the modeling method is verified.
Should U Rly Be Txtng Ur S/O:

This paper examines the impact of phone-based communication and the language of texting on romantic relationships in the United States. Texting has become an integral aspect of romantic relationships for many young adults (Luo 2014), but expert opinion is divided on the subject. Certain studies have shown that texting may lead to “disconnect” and mixed signals, further amplified by the lack of “standardization in [emoji] deployment. Conversely, others have found that the similarity of mobile communication between partners may lead to “higher understanding” and greater relationship satisfaction, and emoji usage can be effective in cross-cultural engagement and as a signal of conversational and relational exclusivity. Through an analysis of research in media and relationships, the paper argues that the ambiguity presented by texting and phone-based communication can be dangerous, but can also contribute to powerful in-group and pro-social activity. If misused, the effect on romantic relationships can be adverse, so it is crucial that both partners are aware of the possible pitfalls to be able to navigate them with care.
Solution of Multilevel Image Thresholding Using Rao Algorithms

With a rapid expansion of image segmentation over the decades, the growth of the mathematical optimization in the form of image thresholding is enormous on the segmentation. A need to organize the image thresholding arises to help medi-cal imaging, detection, and recognition in making an informed decision about the image. The proposed Rao algorithms are relied upon to quickly get the top-notch optimal thresholds are controlled by maximizing the Kapur entropy of various classes. Different from previous optimization techniques, Rao algorithms have been utilized as a prime optimization method as it has been ended up being a suc-cessful optimization when applied to different down to earth optimization issues and its execution is straightforward including less computational exertion. The technique has been tried on standard benchmark test images and the examination of the numerical outcome shows that this method is a promising option for the multilevel image thresholding issue
Efficient Communication in Multi-Agent Distributed Reinforcement Learning

We present in this work an approach to reduce the communication of information needed on a multi-agent learning system inspired by Event Triggered Control (ETC) techniques. We consider a baseline scenario of a distributed Q-learning problem on a Markov Decision Process (MDP). Following an event-based approach, N agents explore the MDP and communicate experiences to a central learner only when necessary, which performs updates of the actor Q functions. We analyse the convergence guarantees retained with respect to a regular Q-learning algorithm, and present experimental results showing that event-based communication results in a substantial reduction of data transmission rates in such distributed systems. Additionally, we discuss what effects (desired and undesired) these event-based approaches have on the learning processes studied, and how they can be applied to more complex multi-agent learning systems.
Gamification of Decentralized Ledger for Credit Card Application using Blockchain

We propose a credit card application using blockchain to improve the approval, transparency and security process. Some studies explain the use of blockchain technology in credit card applications but have not described the concept and architecture. Therefore, this study describes and describes the architecture of using credit cards for credit card applications. The motivation of this research is to explain the implementation of blockchain technology, especially the decentralization process of ledgers in credit card applications. Next, we propose architecture and algorithms for credit card applications using blockchain. The conclusion in this study is a decentralized ledger for credit card applications that allows peer-to-peer transactions, decentralized credit card applications that are not bound by geographical boundaries, have two advantages for debtors and the banking industry. In addition, the architecture is made using a credit card for application credit cards and an explanation of how the architecture works.
Financial Development, Economic Growth and Welfare: Evidence From Emerging Countries

Purpose- This study investigates how financial development has affected the economic growth and welfare for four emerging economies, namely Brazil, Turkey, Hungary, and, Poland over the period 2000-2013.Methodology- In this research, we employed a panel dataset for the four countries using five different indicators for financial development separately. Which are (financial development index, domestic credit to the private sector, stock exchange market capitalization, and Lerner Index).Findings- The results confirmed that financial development has a positive impact on economic growth, and there is a difference between Turkey, Brazil, Poland, and Hungary in terms of the impact size of financial development on economic growth. The highest and most energetic effect was on Turkey then on Poland, then on Hungary, and finally on BrazilConclusion- This study found that GDP growth is highly correlated with financial development. Thus, countries may undertake different steps to increase the efficiency of their financial sectors and therefore enhance their economic growth.
Development of professional competence of teachers in the system of continuing education

The article discusses the concept of the creative nature of pedagogical activity, which forms teachers' attitude to expectation and introducing new things into the pedagogical process, and a creative attitude to professional activity ensures the maximum completeness of the teacher's personal self-realization. Thus, pedagogical activity, pedagogical creativity and self-realization of a higher schoolteacher are interconnected, complement and develop each other. To solve the set tasks, the following research methods were used: theoretical analysis of psychological and pedagogical literature, study and analysis of normative documents, pedagogical observation, university teachers, methods of diagnosis and self-study. study. All research methods, complementing each other, ensured the reliability of the information received. The results of the study showed that the development of professional competencies occurs at each of the identified stages. One of the important areas of research has become the establishment of the relationship between key competencies and knowledge, skills, abilities, personal qualities.
Learning Curricula for Multilingual Neural Machine Translation Training.

Low-resource Multilingual Neural Machine Translation (MNMT) is typically tasked with improving the translation performance on one or more language pairs with the aid of high-resource language pairs. In this paper and we propose two simple search based curricula – orderings of the multilingual training data – which help improve translation performance in conjunction with existing techniques such as fine-tuning. Additionally and we attempt to learn a curriculum for MNMT from scratch jointly with the training of the translation system using contextual multi-arm bandits. We show on the FLORES low-resource translation dataset that these learned curricula can provide better starting points for fine tuning and improve overall performance of the translation system.