Reviewerly/exHarmony huggingface dataset:
A Study on the Reference Texts and the Compilation Methods of the SohakJegajibju小學諸家集註 by Yulgok栗谷 Yi Yi李珥

This article examines the reference texts of the SohakJegajibju小學諸家集註 by Yulgok栗谷 Yi Yi李珥, who compiled the commentary on the Sohak小學(Xiaoxue) for the first time in Joseon, and its compilation methods and characteristics. According to the results of research so far, the SohakJegajibju has been compiled from the XiaoxueJijie小學集解 by Wu Ne吳訥, the XiaoxueJijieZhengwu小學集解正誤 by Chen Zuo陳祚, and the XiaoxueZengzhu小學增註 by Chen Xuan陳選, and the XiaoxueJishuol小學集說 by Cheng Yu程愈, including XiaoxueJicheng小學集成 by He Shih-xin何士信. However, through this study, it was confirmed that SohakJegajibju is basically based on Wang Ao王鏊's XiaoxueJijieDaquan小學集註大全. Although it’s system follows the six-volume system of XiaoxueJishuo, but in terms of content, all quotations from Jijie, Zhengwu, and XiaoxueZengzhu are all based on Wang Ao's XiaoxueJijieDaquan, and in the form of commentary also, it does not follow that of XiaoxueJishuo, which is in the form of ‘吳氏曰’, ‘陳氏曰’, etc., but follows the form of XiaoxueJijieDaquan, which signed the titles of the reference texts, such as Jijie, Zengzhu, Jicheng, JIshuo, Zhengwu, etc. These shows how much weight Wang Ao's XiaoxueJijieDaquan occupies in the compilation of the SohakJegajibju, and it can also be said to show that the XiaoxueJijieDaquan was already introduced into Joseon. In addition, it was confirmed that Yulgok Yi Yi minimized his self-report, and briefly annotated only 10 places, and if the content was the same, he preferred to select the commentary with the commenter's name, such as ‘吳氏曰’ or ‘陳氏曰’. This means that he focused on accepting the commentaries of the scholars rather than presenting his own views, and on the one hand, it is due to the nature of ‘Sohak(Xiaoxue)’, which is a practical book rather than an academic book, and on the other hand it shows that he maintained the position of the editor who said to convey the previous opinions faithfully about the target text.
Students’ ability in writing annotated bibliography: Teaching critical writing

Annotated bibliographies are used in various situations. Annotated bibliography is a short-annotated list of sources that summarizes, evaluates, and states source relevance. The ability to write annotated bibliography shows one’s ability to find the right information for writing research article. This paper aims to see how the ability of second year students majoring in English is in making annotated bibliography. This research is qualitative research using data obtained from 42 students who assigned on writing annotated bibliography. Data were explained descriptively from the score mean and percentage using Turnitin then scaled them using 4-scale Linkert Scale. Results showed that of five variables, quality of sources, annotations content, overall quality were good, while accuracy and annotations structure variables were adequate. For Turnitin check, 69.05% showed good and very good quality, while only 9.52% them showed poor quality. Finally, it isindicated that most of the second-year subjects consider as having good standard abilities categorized in writing annotated bibliography. DOI: 10.26905/enjourme.v7i2.8966
High Path Converging Reward Space: Imitating Human Behavior in Robot Arm Control

In this paper, we suggest High Path Converging Reward Space (HPCR) as a reinforcement learning reward shaping method. HPCR helps robot arms learn quickly and stably in certain environments that require specific actions and are difficult to receive rewards. In environments where a person reaches a goal at a distance, he or she tends to reach the goal by first determining the direction to throw and then increasing the speed or strength. To imitate this way of human behavior, the initial reward space is defined as the space between the robot arm and the goal point. It allows the robot arm to learn along the path in that direction. Then, when the robot arm proceeds with a certain number of successes or episodes, the reward space range gradually converges toward the goal point, eventually becoming the size of the actual goal size. In addition, HPCR can minimize the range in the z-axis direction of the reward space by considering only the value at the maximum height of the goal, even if the environment is complicated. Using HPCR with SAC+HER and TQC+HER algorithm, it is possible to stably reach a position that cannot be reached using the existing reward space.
Design and Development of a Medicine Box Recognition System Based on Machine Vision

With the aging Chinese population and the increasing awareness of caring for vulnerable groups such as the blind, how to ensure the safety of medication in the state of living alone has attracted increasing attention. This paper proposes an intelligent recognition method for home medicine boxes. The drug name position is accurately determined after we preprocess the image photographed by the camera. Then the algorithm recognizes the character and puts it into a database for retrieval. According to the information from the retrieved speech, the algorithm tells the user the name of the drug and how to use it. The experimental results show that the method adopted in this paper has a high recognition rate for the information of common drug boxes in the market and a good promotional value.
Scalable Containerized Pipeline for Real-time Big Data Analytics

With the widespread usage of IoT, processing data streams in real-time have become very important. The traditional data-stream processing systems are inefficient in processing big data for detecting anomalies, classifications, clustering, and prediction in real-time using minimal resources. In this paper, we address this limitation by proposing a scalable pipeline for real-time processing of big data streams. Our proposed solution is capable of dynamically managing resources for different components of the pipeline using automatic scaling. The pipeline is containerized and deployed on a Kubernetes cluster. The proposed scalable pipeline is evaluated using a case study of anomaly detection in IoT data. The proposed solution yields a $\times 1.31$ to $\times 2.4$ increase in throughput, and $\times 32$ to $\times 80$ decreased latency compared to the commonly used static resource allocation strategy for data pipelines.
Private Web Search Using Proxy-Query Based Query Obfuscation Scheme

People use web search engines to retrieve information from the world wide web.Search engines maintain query logs to refine retrieved information for personalized web search.The query log includes information of users, both sensitive and non-sensitive.If utilized against an individual, the query log may pose privacy concerns and reveal a lot of information about them.In recent years many private web search (PWS) schemes have been proposed to realize privacy-preserving web search.Although each PWS scheme claims to have unique features for attaining web search privacy (WSP), no study explains which private web search characteristics should be considered when building and utilizing a PWS scheme.There are two objectives of this article.In the first part of the article, we present a novel PWS scheme that uses a proxyquery-based query obfuscation approach.Proxy-query-based query obfuscation is a new study topic in PWS research.It provides an IR facility for retrieving information from web search engines via proxy queries.One clear advantage of the proposed scheme is that users do not issue true queries to search engines for retrieving information.The article's second objective is to define the characteristics of PWS and analyze modern PWS schemes on these characteristics.We analyzed proposed and modern PWS schemes on the PWS characteristics.The analysis demonstrated only the proposed PWS scheme achieves all characteristics.Existing PWS systems have been discovered vulnerable to WSP attacks since they do not meet all PWS characteristics.
Continuous Monitoring for Noisy Intermediate-Scale Quantum Processors

We present a continuous monitoring system for intermediate-scale quantum processors that allows extracting estimates of noisy native gate and read-out measurements based on the set of executed quantum circuits and resulting measurement outcomes. In contrast to standard approaches for calibration and benchmarking quantum processors, the executed circuits, which are input to the monitoring system, are assumed to be out of any control. We provide the results of applying our system to the synthetically generated data obtained from a quantum emulator, as well as to the experimental data collected from a publicly accessible cloud-based quantum processor. In both cases, we demonstrate that the developed approach provides valuable results about inherent noises of emulators and processors. Considering that our approach uses only already accessible data from implemented circuits without the need to run additional algorithms, the monitoring system can complement existing approaches. We expect that our monitoring system can become a useful tool for various quantum computers in the near-term horizon, including publicly accessible cloud-based platforms, and reduce resources that are required for their benchmarking and calibration.
AI Based Identification of Gender from Images Based on Facial Features using CNN and OPENCV

The main objective of this paper is to classify the gender based on different facial features such as eyes, nose, mouth, overall features such as face contour, head shape, hair line etc. The gender classification algorithm uses machine learning technique (supervised learning). In this case the algorithm is trained on a set of male and female faces and then used to classify new data. In this paper, face detection and gender classification methods are combined. The face detection acts as a pre- processing operation to the gender classifier that determines the gender. There are multiple methods in which facial recognition systems work, but in general, they work by comparing selected facial features from a given image with faces within a database. It is also described as a Biometric Artificial Intelligence based application that can uniquely identify a person by analyzing patterns based on the person's facial textures and shape. Automated gender recognition plays an important role in many application areas such as human computer interaction, biometric, surveillance, demographic statistics etc. Existing systems has a disadvantage in accuracy. Though there are many algorithms in Present system are being developed and implemented to achieve accuracy in identifying gender the results arestill unsatisfactory. Proposed system has an advantage of accuracy. The accuracy achieved in this system is impressive compared to the existing system. CNN algorithm gives better accuracy compared to other algorithms
Stochastic Study on CNN Approach for Classifying Images

Recent years have seen a surge in interest in artificial intelligence and computer vision applications, which are essential for many real-time tasks like video summarization, picture retrieval, and image categorization. Convolutional neural networks, which are employed in numerous image processing and computer vision applications, are one of the most popular deep learning techniques. In this study, a CNN model for classifying colour images is proposed. The proposed model was developed using deep learning tools in MATLAB. Additionally, three separate datasets of varying sizes were used to test the suggested model. With the largest dataset, the suggested model produced the highest accuracy, precision, and sensitivity results, which are as follows: accuracy is 0.9924, precision is 0.9947, and sensitivity is 0.9931 when compared to other models
An Image Performance Against Normal, Grayscale and Color Spaced Images

Generally, an image matching belongs to comparing the two images, with the simple concept i.e. When the two images match or comparable and how can this similarity be measured? Fast and robust feature detection and image matching have always been a very major and challenging task in itself along with the applications. In this paper, we are using normal, grayscale and LAB color spaced images and measure the recital of contrasting approaches for image corresponding, i.e., SIFT, SURF, and ORB. For this purpose, we manually transform original images into grayscale and LAB color spaced images and compute all the parameters on the basis of which evaluation is done such as the total of distinct points in images, the match-up percentage. By this, we will show that which algorithm works best and more robust against each kind of image.
The one step fixed-lag particle smoother as a strategy to improve the prediction step of particle filtering

Sequential Monte Carlo methods have been a major breakthrough in the field of numerical signal processing for stochastic dynamical state-space systems with partial and noisy observations. However, these methods still present certain weaknesses. One of the most fundamental is the degeneracy of the filter due to the impoverishment of the particles: the prediction step allows the particles to explore the state-space and can lead to the impoverishment of the particles if this exploration is poorly conducted or when it conflicts with the following observation that will be used in the evaluation of the likelihood of each particle. In this article, in order to improve this last step within the framework of the classic bootstrap particle filter, we propose a simple approximation of the one step fixed-lag smoother. At each time iteration, we propose to perform additional simulations during the prediction step in order to improve the likelihood of the selected particles.
Multimodal Image Matching using Phase Congruency-based Self-Similarity Structural Features

Due to the significant differences in geometric and nonlinear intensity, multimodal image matching is still a challenging problem. To address this issue, this paper proposes a novel matching method using phase congruency (PC)-based self-similarity structural features for multimodal images. Firstly, the feature points are extracted from the PC maps of the original images by the Harris detector. Then, combined with the theory of the self-similarity, a PC-based self-similarity structural (PCSS) descriptor is designed for multimodal images. Finally, the Euclidean distance is used as the matching measure for the corresponding point recognition. Experimental results conducted on various real multimodal image pairs demonstrate that the proposed method can achieve better matching performance in terms of the number of correct matches and the registration precision in comparison with the traditional methods.
A selective approach to stemming for minimizing the risk of failure in information retrieval systems

Stemming is supposed to improve the average performance of an information retrieval system, but in practice, past experimental results show that this is not always the case. In this article, we propose a selective approach to stemming that decides whether stemming should be applied or not on a query basis. Our method aims at minimizing the risk of failure caused by stemming in retrieving semantically-related documents. The proposed work mainly contributes to the IR literature by proposing an application of selective stemming and a set of new features that derived from the term frequency distributions of the systems in selection. The method based on the approach leverages both some of the query performance predictors and the derived features and a machine learning technique. It is comprehensively evaluated using three rule-based stemmers and eight query sets corresponding to four document collections from the standard TREC and NTCIR datasets. The document collections, except for one, include Web documents ranging from 25 million to 733 million. The results of the experiments show that the method is capable of making accurate selections that increase the robustness of the system and minimize the risk of failure (i.e., per query performance losses) across queries. The results also show that the method attains a systematically higher average retrieval performance than the single systems for most query sets.
The Utilization of Augmented Reality Technology for the Development of Tourism Information Media

The industrial revolution 4.0 has brought tourism trends using enriched interactive technology services. Technology in the world of tourism has helped the expansion of the industry through the promotion and mediation of independent tourists to carry out all services and transactions easily, including obtaining tourist information. This study aims to develop promotion media for geography education tourism in Soppeng Regency and test the effectiveness of the media. This study adopted the Alessi &amp; Trollip learning multimedia development model. The developed media was tested on 20 respondents with 10-item usability scale statements and 5 response options ranging from agreeing to strongly disagree. The level of effectiveness of the media developed after being tested using the usability scale (SUS) system is included in the Good category (good) with an average score of 76.5. This level of effectiveness is included in the acceptable to users or acceptable category, with a grade scale of C and an adjective rating in the Good or good category. The results of this study also indicate that users are satisfied with the developed media but are not enthusiastic so that users can switch to other, more interesting media at any time. In the end, based on the final score obtained, it was concluded that the promotion media for geography education tourism in Soppeng Regency (battle city application) was effective and acceptable to users.Â
Performance Efficient Layer-aware DNN Inference Task Scheduling in GPU Cluster

GPU has been widely applied to accelerate the DNN based applications. However, single GPU is overwhelmed by the increasing computation requirement of large-scale DNN inference task. Although GPUs cluster alleviates the pressure of massive inference task, it still traps into low efficiency owing to the limited computing power and network bandwidth. Besides, the exclusiveness of GPU device may cause the straggler problem and hence long inference time. Reinforcement Learning (RL) has been widely used in such task scheduling problems. But the delayed reward during the training may slow the convergence speed or even result in non-convergence. To this end, in this paper, we design an improved reinforcement learning based algorithm, called DRM-DQL, to achieve a layer-aware DNN inference task scheduling. We first analyze and model the layer-wise inference task scheduling problem by deep Q-learning. Then, a delayed reward matching strategy is proposed for matching the global reward value to the immediate reward value, which help the algorithm to get the right experience in DNN layer scheduling. The experiment results demonstrate that our algorithm performs better than both heuristic algorithm and the vanilla DQL algorithm, and show the robustness in various network bandwidths, computing power, and DNN model structures.
Visual Research Discovery Using Connected Papers: A Use Case of Blockchain in Libraries

The present study aims to highlight the features of Connected Papers, a visual tool that can help researchers find and explore academic papers in their literature search. The authors describe how Connected Papers allow to create a network of all the literature available related to a chosen paper. One can use Connected Papers to pull together related papers on a chosen topic and see prior and derivative works based on that topic. Using the Connected Papers tool authors attempt to visualize the key literature on the topic “Blockchain in libraries.” To date, most Blockchain technology applications in libraries are still in the conceptual stage. However, sooner or later, the development and implementation of the technology are expected to facilitate the transformation of how libraries provide services and organize information. This study contributes to examining the research being done to explore the potential of Blockchain technology in libraries through mapping and visualization. The paper is original in terms of the Visual tool used “Connected Papers.”
Analysis and Prediction of CET4 Based on Data Mining

CET4 analysis and prediction based on data mining is a technology that uses data mining technology to analyze system performance. It can be used as an effective tool to predict the future performance of any system or application. The main goal of this technology is to identify the weaknesses in the system in order to improve it, so as to obtain better results and efficiency. This technology helps improve the productivity of employees and improves their efficiency by identifying areas where they need more training or improvement. Based on the analysis and prediction of CET-4 scores based on data mining, this paper analyzes many factors affecting CET-4 based on multiple linear regression algorithm. According to the idea of data mining, historical data are collected and transformed appropriately, and many factors affecting CET-4 are analyzed by using statistical analysis technology. It is concluded that the linear regression relationship between CET-4 and its influencing factors has a high degree of fitting, which can be used to predict CET-4, and has a great guiding role and use value for students’ learning and school teaching management.
Research on the Evaluation of Constructive English Teaching Model Based on RBF Algorithm

The evaluation of constructive English teaching model based on RBF algorithm is a new method to evaluate the effectiveness of different types of corrective feedback to improve learners’ English level. The purpose of this study is to determine whether there are differences between students who receive corrective feedback and students who do not receive corrective feedback, and its effectiveness in improving their language skills. In this paper, we will focus on its application in English Teaching (ELT). In ELT, RBF algorithm can be used to evaluate students’ writing quality and measure their progress in learning new vocabulary or grammatical structures. You can also use this method to evaluate content based on specific criteria, such as lexical density (the number of unique words). The results showed that students who received corrective feedback scored higher than those who did not receive any correction at all, but they also differed from each other according to the type of correction they received. This finding shows that teachers should choose the appropriate type of feedback for students,the English learning platform plays a good and positive auxiliary role for students to learn English. As a better means of English education, it has certain research value and positive significance for the research of English teaching.