Reviewerly/exHarmony huggingface dataset:
Distributed Policy Gradient with Heterogeneous Computations for Federated Reinforcement Learning

The rapid advances in federated learning (FL) in the past few years have recently inspired federated reinforcement learning (FRL), where multiple reinforcement learning (RL) agents collaboratively learn a common decision-making policy without exchanging their raw interaction data with their environments. In this paper, we consider a general FRL framework where agents interact with different environments with identical state and action spaces but different rewards and dynamics. Motivated by the fact that agents often have heterogeneous computation capabilities, we propose a Federated Heterogeneous Policy Gradient (FedHPG) algorithm for FRL, where agents can use different numbers of data trajectories (i.e., batch sizes) and different numbers of local computation iterations for their respective PG algorithms. We characterize performance bounds for the learning accuracy of FedHPG, which shows that it achieves a learning accuracy ∊ with sample complexity of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O$</tex> (1/∊ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), which matches the performance of existing RL algorithms. The results also show the impacts of local iteration numbers and batch sizes for iteration on the learning accuracy. We also extend FedHPG to heterogeneous policy gradient variance reduction (FedHPGVR) algorithm based on the variance reduction method, and analyze the convergence of this algorithm. The theoretical results are verified empirically for benchmark RL tasks.
Data-centric optimization method to imbalanced datasets

The imbalanced data sets is an important investigation in the data science and machine learning domain. This paper is focused on providing an effective algorithm for such cases. In these cases, the number of observations is not enough to construct a machine learning model with high values of the estimated magnitudes. For example, most of the observations are labeled as one class (majority class), and the rest as the other, commonly considered as the class of interest (minority class). We propose a methodology based on the properties of the data resampling approach to systematically enhance and optimize the training dataset. The paper examines numerical experiments applying the data centric optimization methodology and compares with previously obtained results by other authors.
Analysis of Art Education Application and Derivation Problems of VR(Tilt-Brush) Image Creation Media

Purpose: This study aimed to confirm the application of Art Education using the tilt brush programas part of future education incorporating virtual reality technology and analyze various problems derivedfrom the field. Methods: A practical study was conducted with participants in Art Education activities using virtualreality at B-Middle School of Arts, a specialized school, and a focus group interview was conductedwith professional lecturers who had experience in conducting the class were carried out. Results: Physical and non-physical problems were confirmed through conducting the class. First,educators and trainees felt ‘unfamiliar with the medium’ following the introduction of new technologiesand devices that were not in popular use. Second, the physical limitations according to the specialnature of school education environment are confirmed. Third, it was judged that there were variousproblems for the media to be actively introduced into Art Education, and that these environmentshinder development. This was interpreted as the ‘limitation point of base expansion’. Conclusion: In order to introduce future education practices that meets the 'digital-based education'of the 2022 revised curriculum, it is necessary to identify various problems derived from the educationalfield, as shown in the results of this study. In order to solve this problem, this research suggests thatchanges in the educational environment and system establishment should be made through continuousadministrative support at the national level, and social interest and efforts to rapidly apply variouspreceding studies are required.
Twitter Data Sentiment Analysis

Abstract: Twitter sentiment analysis has gained significant attention in recent years due to the vast amount of data generated on social media platforms and the potential applications of sentiment analysis in various domains. This paper presents an overview of Twitter sentiment analysis, including its applications, challenges, and state-of-the-art techniques. We discuss the various methods and algorithms used for sentiment analysis and highlight their advantages and limitations. We also examine the ethical considerations associated with sentiment analysis and the need for responsible and transparent use of this technique. Through a comprehensive analysis of the existing literature, we explore the potential of Twitter sentiment analysis for understanding public opinion and its implications for various domains, including marketing, politics, and social sciences. Finally, we present a case study to demonstrate the application of sentiment analysis in the context of real-world data. Our analysis shows that Twitter sentiment analysis has significant potential for understanding public opinion, tracking trends, and providing valuable insights into various domains. However, it also highlights the need for careful consideration of ethical concerns and the importance of responsible and transparent use of this technique.
Differences in Academic Resilience, Academic Engagement and Academic Achievement according to Academic Coping Strategies Profiles of Mongolian Students in Korea

This study aims to examine the academic stress coping strategies profiles of Mongolian students in Korea and to explore the differences in academic resilience, academic engagement, and academic achievement according to academic stress coping strategies profiles. The study respondents were 211 students from the 5th grade of elementary school to the 3rd grade of high school at a Mongolian school in Korea. As a result of the study, five types of latent profiles were found to be suitable for the data. 'The lowest adaptive coping strategies+the and lowest maladaptive coping strategies group' (Group 1), 'The high adaptive coping strategies+low maladaptive coping strategies group' (Group 2), 'the low adaptive coping strategies+moderate maladaptive coping strategies group' (Group 3), 'adaptive coping strategies+high maladaptive coping strategies group' (Group 4), and 'medium adaptive coping strategies+high maladaptive coping strategies group' (Group 5). Examining the differences between profiles in academic resilience, academic engagement, and academic achievement, with the exception, academic resilience, academic engagement, and academic achievement of the 'high adaptation + low maladjustment academic stress coping strategies' group and 'high adaptation + high maladaptive academic stress coping strategies' group were higher than the other groups. Based on the research results, educational interventions to help Mongolian students in Korea were discussed.
A Study on Interest Factors of Kimwonjeon: Focused on the Characters and Establishing Their Worldview

Kimwonjeon is a story of a protagonist, Kim Won, who wins the confrontation with the evil character through his extraordinary ability and the help of a supporting character and ends up in an amicable relationship with the two female characters. This story organically structured the plot by arranging various types of characters rather than simply enumerating the motifs. It implies that the interest factors of tales familiar to the readership are developed into novelistic features. Firstly, the protagonist was embodied by inheriting the narrative tradition of heroic characters. The story structure of a hero's journey whose life undergoes trials at a young age but escapes from the crisis with helpers and achieves something great based on his outstanding abilities. Kim Won's oviparity and conquest were used to enhance his heroism. Secondly, the antagonist in the story was put into a process called characterization to create an adversary of the protagonist. What draws readers' attention is the enormous appearance and power of the monster and the detailedly described underworld while simultaneously arousing curiosity about how Kim Won would defeat such a strong presence. The stunning portrayal of the antagonist is to embody the protagonist's activities against it in a more exciting way. Thirdly, female characters in the story play a role in making the narrative more vivid. The princess contributes to exterminating the monster, devotes herself to Kim Won, and supports her parents-in-law. The princess of the Sea God even leaves her hometown at Kim Won's request and plays a significant role in reviving him. In this way, the two female characters, the princess and the princess of the Sea God, are actively involved in making the story more interesting. By embracing narrative elements into the classic novel, Kimwonjeon established a worldview that readers would be enthusiastic about. The protagonist who descended from the celestial realm defends the ideology of loyalty, filial piety, and fidelity in the process of performing heroic activities in unique spaces such as the underground world and the underwater world, and returning to the earthly world to enjoy wealth and glory. The Confucian perspective was set up so that the protagonist and female characters protect it, naturally pleasing readers' desires.
Improvement of Database Rights for the Development of Data Economy

In order to support the transition to a data economy where data functions as an important product and commodity, it is necessary to establish an institution to encourage producing and opening quality datasets. Database right under the Copyright Act and the prohibition of unauthorized use of other’s achievements and illegal use of data under the Unfair Competition Prevention Act serves as potential institution for protection of datasets. Each of these has its own strengths and weaknesses and can play a role, but the prospects of the database right stand out in particular. This is because it has excellent clarity and predictability, protects against infringement by non-competitors, and separates the domains of monopoly and sharing appropriately and clearly. However, database right is not without room for improvement. First, there are unstructured datasets without searchability among training data for AI, and it is necessary to remove the element of searchability from the concept of database in order to include them in the scope of protection. Second, in order to compensate for the uncertainty of protection requirements, it is necessary to introduce the provision for presuming the establishment of ‘substantial part’ of the database when structural information on the arrangement or composition of materials is included. Third, it is necessary to reexamine the boundary between monopoly and sharing. For example, if the database is the sole source for a certain data, it may be desirable to introduce a compulsory licensing regime according to the FRAND standard.
Data and Fair use

Data collection and use are the beginning and end of machine learning. Looking at ChatGPT, data is making machines comparable to human capabilities. Commercial purposes are not naturally rejected in the judgment of fair use of the process of producing or securing data for system learning. The UK, Germany, and the EU are also introducing copyright restrictions for data mining for non-profit purposes such as research studies, and Japan is more active. Japan’s active legislation is the reason why there are no comprehensive fair use regulations like Korea and the United States, but it shows its willingness to lead the artificial intelligence industry. In 2020, a revision to the Copyright Act was proposed in Korea to introduce restrictions for information analysis. It will be able to increase the predictability for operators. However, the legislation of the amendment is expected to be opposed by the right holder and may take time. Therefore, it was examined whether machine learning such as data crawling and TDM corresponds to fair use through fair use under the current copyright law. In conclusion, it was considered that it may correspond to fair use, citing that it is different from human use behavior. However, it is questionable whether it is reasonable to attribute all exclusive negligence to the business operator by using the works of others according to fair use. The reason why the compensation system for profits earned by operators through the use of machine works generated by TDM or machine learning cannot be excluded from the possibility of serious consequences for a fair competitive environment.
Repetitive Description Techniques and Classical Novel Teaching Methods of Sukhyangjeon

This study examined the repetitive description parts of Sukhyang's hardships in Sukhyangjeon, and considered why these repetitive descriptions appear in Sukhyangjeon. In addition, I considered ways to use these repetitive description parts in classical novel education. In Sukhyangjeon, the journey of Sukhyang's hardship is repeatedly presented through the utterances of various characters including Sukhyang. This repetitive description can be seen as the author's technique to assist readers' memory, who may forget the plot or confuse the sequence of the journey due to the nature of the long novel and many hard journeys experienced by Sukhyang. Also, I think that the author aimed at the effect of emphasizing the tragic situation of Sukhyang more by continuously presenting the hardships experienced by Sukhyang. The repetitive description parts appearing in Sukhyangjeon can be used for Classical novel education. I summarized these into three categories. First, the importance of repetitive learning can be emphasized. In the process of reading the repetitive description parts, Learners can learn the details of Sukhyang's journey contents naturally due to the effect of repetition, without intentionally memorizing the contents of Sukhyangjeon. By mentioning this point, the importance of repeated learning can be emphasized. Second, it can help to understand the narrative structure of the novel. The repetitive description part is the Sukhyang’s life story and the plot of the novel. The learner can easily understand the narrative structure of the novel by naturally organizing the contents of the novel in the process of reading the repetitive description parts. Third, it can help to understand the world view of the work. The learner can easily understand the world view of Sukhyangjeon through the Taoist characteristics of the characters appearing in the repetitive description parts.
Assessment of a Consolidated Algorithm for Constrained Engineering Design Optimization and Unconstrained Function Optimization

For real-life optimization problems, methods with adequate capability in exploring the search space are crucial especially when having in mind the perpetual complexity of the problems. Consequently, presenting an effective algorithm to address these problems becomes imperative. The major objective of this work is to assess the application of a consolidated algorithm in addressing constrained and unconstrained function optimization problems. Though the flower pollinated algorithm (FPA) is commonly used, it does have its limitations, including being stuck at local minima, causing premature convergence, and creating imbalances between intensification and diversification. As the FPA operates, the solution to the optimization problem relies on communication with pollen individuals. Consequently, instead of leading pollens randomly, the FPA’s exploratory skills are boosted by employing the pathfinder algorithm’s (PFA) components to route them to much better locations in order to avoid local optima. For that reason, the PFA has been incorporated into the FPA in order to increase its performance. The efficacy of the proposed algorithm is tested using conventional mathematical optimization functions as well as two well-known constrained engineering design optimization problems. Experimental results showed that the suggested algorithm outscored its counterparts for both constrained and unconstrained optimization problems.
Image Recognition and Enhancement Using Multi-Scale Retinex and Histogram Equalization

Nowadays, the need for a safe and at ease device is favoured by using every character in society. A fee-effective system is needed for Aerial surveillance systems capable of enhancing situational focus in the course of a couple of scales of place and time. With the assistance of an aerial surveillance machine, we are capable of accumulating a device that could report unmanned vehicles and humans. With improvements in technology, Unmanned motors and optical sensors are being fast produced with powerful usage of resources, therefore, it has ended up being less complicated to get images from the UAV at a low price. But the selection of those pixy received is reduced because of climatic conditions like fog, rain, harsh sunlight, etc. So, we advocate a photograph processing technique as a manner to use algorithms that include single-scale retinex, Multiscale retinex, histogram equalization, and colour recuperation to grow the fine of photographs. These select images have a higher evaluation, and subsequently can be used for better item detection. 1. The program can determine the objects without getting observed.2. The program can find multiple items. Three. MSRP for similar processing like histogram equalization and item detection to get the exceptional possible output
Modulation Format Recognition Based on Coordinate Transformation and Combination in VLC System

With the development of modern communication technology, the new generation of communication network should have the characteristics of faster speed, higher bandwidth utilization, and wider coverage of users and business needs. At the same time, the intelligent optical network is also facing the challenges from the rapid growth of bandwidth demand and the rapid increase of different users services. Therefore, it is necessary to mix different modulation formats in the communication network to meet these requirements. Modulation format recognition (MFR) technology is therefore widely used at the receiving end to improve the performance of subsequent digital signal processing (DSP) algorithms. Compared with traditional machine learning (ML), deep learning (DL) has achieved amazing achievements in classification tasks in recent years. Therefore, we propose a coordinate merging algorithm (CMA) to process the data for DL. Compared with the traditional method of directly processing the in-phase and quadrature (IQ) components of the received signal, and another method we tried to fuse the weights of classification results of signals with different coordinates, the CMA method achieved higher accuracy and better performance against low signal-to-noise (SNR) ratios in visible light communication (VLC) system.
An Adaptive Differential Evolution Algorithm with Hierarchical Mutation Strategy and Opposition Learning

In recent years, differential evolution algorithm (DE) has shown excellent performance in solving various optimization problems, therefore it has been extensively applied in many research and scientific fields. However,it's easy for DE to obtain the local optimal solution. In the cause of improving the convergence performance and global search ability of DE,an improved differential evolution algorithm is proposed in this paper. In this algorithm, the initial population is generated by the Halton sequence, and in the process of mutation and crossover, adaptive mutation operator and crossover operator are applied. Then on the basis of different adaptive values, the population is layered to two parts, the two parts take different mutation strategy. The proposed algorithm is compared with DE and other variants of DE in 10, 30, and 50 dimensions respectively by using a set of twenty-six benchmark functions. The experimental results indicate that the proposed algorithm can significantly improve global optimization performance.
Axon and Myelin Sheath Segmentation in Electron Microscopy Images using Meta Learning

Various neurological diseases affect the morphology of myelinated axons. Quantitative analysis of these structures and changes occurring due to neurodegeneration or neuroregeneration is of great importance for characterization of disease state and treatment response. This paper proposes a robust, meta-learning based pipeline for segmentation of axons and surrounding myelin sheaths in electron microscopy images. This is the first step towards computation of electron microscopy related bio-markers of hypoglossal nerve degeneration/regeneration. This segmentation task is challenging due to large variations in morphology and texture of myelinated axons at different levels of degeneration and very limited availability of annotated data. To overcome these difficulties, the proposed pipeline uses a meta learning-based training strategy and a U-net like encoder decoder deep neural network. Experiments on unseen test data collected at different magnification levels (i.e, trained on 500X and 1200X images, and tested on 250X and 2500X images) showed improved segmentation performance by 5% to 7% compared to a regularly trained, comparable deep learning network.
Towards Interpretable Probabilistic Classification Models for Knowledge Graphs

Tackling the problem of learning probabilistic classifiers that can be used the context of knowledge graphs, we describe an inductive approach based on learning networks of Bernoulli variables. Namely, we consider the application of multivariate Bernoulli models, a simple one and a two-levels mixture model. In addition, we also consider a hierarchical model combining the multivariate Bernoulli model with a restricted Boltzmann machine as the first level. We show how such models can be converted into probabilistic rule bases ensuring more understandability. A preliminary empirical evaluation is presented to test the effectiveness of these models on a number of classification problems with different knowledge graphs.
Gravitational Discriminative Optimization for Multiview Reconstruction of Free-Form Surface

Automatic free-form surface reconstruction enables flexible and efficient industrial manufacturing. However, due to the performance of the 3-D measurement system, accurate and robust reconstruction is quite difficult due to corrupted data, discontinuous scanning, etc. To address these issues, in this article, we propose a novel gravitational discriminative optimization (GDO) method based on a multiview reconstruction framework for free-form blades. Our method consists of a training phase and a reconstruction phase. In the training phase, we introduce a novel gravitational distribution feature that is robust against the perturbations caused by corrupted data to instruct the sequence of update maps (SUM) learning process of GDO. To deal with the low overlap caused by the limited and discontinuous scanning, we propose a sequential scenes-to-model framework. Equipped with the trained SUM, we design the reconstruction phase to perform the blade model reconstruction accurately and robustly by partitioning it into inference with a gravitation map, an adaptive maps scheme to boost convergence, and an iterative closest point refinement. Experiments on both limited scanning-based synthetic data and real scene reconstruction of four blade models with different profiles show that GDO outperforms five other methods in terms of both accuracy and robustness.