Reviewerly/exHarmony huggingface dataset:
Buddhism as an Educational Theory: Focusing on Fundamental Buddhism and Paññā Thought

본 연구의 목적은 불교수행이라는 종교활동이 그 자체로 교육활동이면서 동시에 교육이론이 될 수 있는지를 밝히는 데에 있다. 이 목적을 달성하기 위하여 본 연구에서는 근본불교와 교육의 관계, 그리고 반야사상과 교육의 관계를 고찰하였다. 연구 결과에 의하면, 근본불교에서 수행방법으로 제시한 사념처나 팔정도는 인간에게 사물이나 현상을 욕탐이 아니라 있는 그대로 볼 것을 주문한다. 인간이 사물이나 현상을 있는 그대로 보기 위해서는 지식이 필요하다. 이때 지식은 오늘날 학교에서 가르치는 이론적 지식과 다르지 않다고 말할 수 있다. 이 점에서 근본불교 수행은 지식을 가르쳐 사물이나 현상을 있는 그대로 보고자 하는 교육활동과 다르지 않다고 말할 수 있다. 반야사상에서 수행방법으로 제시한 무주상 6바라밀은 인간의 고정관념이나 선입견이 없는 상태에서 실천하는 6바라밀을 가리킨다. 그러나 인간은, 언어를 사용하는 한, 고정관념이나 선입견을 없애기 어렵다. 그리하여 반야사상에서는 인간의 고정관념을 없애기 위하여 즉비시명(卽非是名)의 논리를 제시한다. 즉비시명의 논리는 우리가 일상생활에서 옳다고 믿는 개념이나 명제, 또는 이론 등에 대하여, 그것이 과연 진리에 부합하는가를 반성하도록 요구한다. 이것은 학교에서 배우고 가르치는 지식이 현상을 있는 그대로 보도록 하기 위한 교육방법에 해당한다고 말할 수 있다. 이 방법으로 지식을 가르치고 배울 경우, 교사와 학생은 비록 불완전하나마 반야사상에서 추구하는 보살이라고 말할 수 있다.The purpose of this study is to examine whether the religious activity of Buddhist practice can be an educational activity and an educational idea at the same time. In order to achieve this purpose, this study examined the relationship between fundamental buddhism and education, and the relationship between paññā thought and education. According to the results of the study, cattāro satipatthāna practices and the noble eightfold path suggested as the method of performance in fundamental Buddhism require humans to see things or phenomena as they are, not by greed. In order for humans to see things or phenomena as they are, they need knowledge. In this respect, it can be said that fundamental buddhist practice is not different from educational activities that want to teach knowledge in schools and see objects or phenomena as they are. Muju-sang 6-paramita, suggested as a method of performance in paññā thought, refers to 6-paramita practiced without human stereotypes or prejudices. However, as long as humans use language, it is difficult to eliminate stereotypes or prejudices. Thus, in paññā thought, the logic of “A is not A” is presented to eliminate human stereotypes. In other words, the logic of “A is not A” requires us to reflect on the concepts, propositions, and theories that we believe are right in our daily lives. It can be said that this is an educational method to ensure that the knowledge learned and taught in school is seen as it is. When teaching and learning knowledge in this way, teachers and students can be said to be bodhisattva pursued by paññā thought, even if incomplete.
Definition of the subject of fundamental acmeology

The article notes the timeliness of the birth of acmeology, due to the search for patterns in the theory of the quality of education and methods for measuring it. The subject of acmeology is determined as a result of the generalization of the factors for the achievement by mature people of the heights of quality in various types of creative activity. The identified connections and dependencies have been verified and confirmed by candidate and doctoral theses (more than two hundred). Acmeology is presented as a new fundamental humanitarian science, and the acmeological approach to the study of the factors of reaching peaks in any kind of professional activity as a strategy and technology for self-promotion to the peaks of productivity and professionalism. Acmeology is considered as the foundation of modern pedagogical culture, the culture of understanding the quality of the results of ones own activity
Bahasa Indonesia bagi Penutur Asing (BIPA): Masalah Kefasihan, Kebakuan, Rujukan Belajar, dan Kesusastraan

Bahasa Indonesia is one of the languages with the most speakers in the world. However, Bahasa Indonesia still needs to be campaigned to become one of the most important languages in the international arena. This is the context of why the BIPA or Indonesian Language for Foreign Speakers program was born. It has been going on for a long time, the BIPA program continues to experience improvements. This article aims to provide some input in to improving Indonesian language learning through the BIPA program. Observation, survey, and interview through informal conversations were used in conducting qualitative investigations to describe BIPA learning related to fluency, standard language, learning tool or reference, and literature . These were conducted from around February to early August 2022. Th is research show s that active class participation and learning consistency of BIPA students have a major impact on the progression speed of Indonesian language mastery. This study also shows that the misuse of Google Translate has caused a negative impact on BIPA students and the urgency of using local folklore materials in BIPA learning in Indonesia.
Research on the influences of carrier deviation on digital moire interferometric technique

Existing aspheric testing methods are sensitive to environmental effects. The digital moire interferometric technique (DMIT) demodulates surface errors from a single interferogram by building a virtual interferometer to avoid environmental effects. This method requires the actual carrier and the virtual carrier to be the same. In this paper, the effect of carrier deviation on DMIT is investigated. It was found that the carrier deviation would extend the spectrum of low-frequency information. Spectrum expansion can deteriorate the noise immunity of DMIT. Spectrum expansion may also lead to spectrum overlap. Method for determining the actual carrier from frequency domain is analyzed. A carrier alignment algorithm is proposed, and the simulation results deviate from the true value by only 6%.
Needs Analysis of Adult Learner's Instructional Method at Lifelong Education System of University

The purpose of this study was to explore ways to support customized instructional methods for adult learners by variable through the analysis of the needs of instructional methods in adult learners in lifelong education institutions. To this end, a survey was conducted for demand analysis, and the results of analyzing the collected data through the t-test and The Locus for Focus model are as follows. Among the instructional method items, statistically significant differences were found in experiment and practice, discussion and discussion, flipped learning, problem-based learning, and blended learning. Second, by age of adult learners, experimentation and practice were the top priorities for those in their 20s, 30s, and 40s, while flipped learning was the top priority for those in their 50s and project learning methods for those in their 60s. Third, experiment and practice in the construction environment convergence engineering department, flipped learning in the welfare and management department, lecture method in the sports welfare department, and action learning in the smart electric energy engineering department were the top priorities. The results of this study are expected to be used as evidence for supporting adult learners' customized instructional methods at lifelong education system of university.
Prediction of Dangerous Pedestrians using Depth and Stance Estimation

According to the Tokyo Fire Department, from 2015 to 2019, there were 211 people who were taken to the hospital due to accidents involving pedestrians using their smartphones while walking. Around 40% of these accidents were due to pedestrians running into bicycles, people, and other objects. Local ordinances have been passed to reduce the danger posed by pedestrians engaging in this unsafe behavior, but this has not significantly reduced the use of smartphones while walking. In this paper, we propose a metric for evaluating the level of danger posed by pedestrians captured on video. In our proposed method, we extract the skeletons of pedestrians using MediaPipe, then predict the danger level by estimating the pedestrian stance and depth within the image. Then, we evaluate the accuracy of our model by calculating the accuracy of the depth and stance estimators on a video taken by a car-mounted camera. We estimated danger levels on a local road and obtained an accuracy of 0.459.
Analyzing Angle detection in aerial image

We explore a method to extract the rotated angle of an object from the aerial image extracted from DOTA Dataset. The recently proposed methods of Object Detection are in forms of fine regression after find most appropriate box from set anchor boxes of various angles. These models showed excellent detection performances about rotated object in aerial image. However, Those are a lot of added calculations for rotated anchor boxes. In order to approach this problem in a different way than the rotated anchor box. We analyse a different type of model that directly estimate the angle of an object like Image Tilt correction. Directly applying this method also have a own problem, which occurs in angle detection in aerial image. we modify the method that using logical operation for angle loss to separate Direction from angle and improve the model performance in Angular Error Accuracy by section. All experiment in this paper using resnet50 based model and ablation studies on model depth were conducted.
Multi-hop Knowledge Base Q&amp;A in Integrated Energy Services Based on Intermediate Reasoning Attention

Knowledge base with multiple hops quizzing aims to discover the subject entity in a question at a distance from the knowledge base’s answer entity for multiple hops. The lack of supervised signals for the intermediate phases of multi-hop inference, which leaves a model only able to get input on the final output, is a significant difficulty for the study, where the inference instructions for the intermediate steps cannot be effectively optimized and the forward propagation of inference states is weakened. Most of the existing research approaches use global attention to motivate the model to learn the inference instructions of each hop, which has been shown to fail to achieve effective performance in weakly supervised tasks. To address this challenge, this paper proposes an intermediate inference attention mechanism to handle multi-hop knowledge base quizzing tasks. Inspired by the human execution of multi-hop quizzing where each hop question is influenced by the previous hop answer, in this approach, the model pays more attention to the inference state generated by the previous hop inference instruction when generating each hop inference instruction, prompting a close interaction between the inference state of the intermediate step and the inference instruction, and providing effective attentional feedback for the optimization of the intermediate step inference instruction. On the KBQA dataset in the integrated energy service domain, which is self-constructed in this research, we conduct comprehensive comparison experiments. The findings suggest that the technique we provided achieves optimum performance in this study.
Multi-channel Contrastive Learning for Sequential Recommendation

The purpose of Sequential Recommendation (SR) is to recommend the next commodities that a user wants to buy based on their historical interaction sequence. The current approach for SR focuses only on mining the user’s interest preferences, while they all fail to consider the influence of item prices on users’ purchase decisions and suffer from the data sparsity problem. In this paper, a Multi-channel Contrastive Learning method for SR (MCLSR) is proposed, which can effectively extract users’ interest preferences and price preferences and alleviate the sparsity issues. Specifically, first, a heterogeneous knowledge graph is constructed from all interaction sequence and the item attribute (i.e., item price and category) by us. Then, we leverage a heterogeneous graph neural network mechanism to learn user, item, and price node embeddings. Next, users’ price preferences and interest preferences are extracted by an attention network. Finally, a multi-channel contrastive learning mechanism is employed to build price and interest preferences’ relations and generate high-quality recommendation results. Experiments on both real datasets show that MCLSR obtains more sophisticated performance than the existing baseline.
Cleaning Up Mislabeled Data via Image Plotting Method and Self-Attention Module

This paper proposes the method for cleaning up label noise in multivariate time-series outlier data. An image plotting method is proposed to reflect the tendency of original time-series data. The image data generated from the plotting method shows effectiveness, since the data can be smoothly utilized in label noise cleaning process and easy to analyze. To verify the availability of plotted data, label noise cleasing algorithm is combined to backbone network, which can estimate original noise ratio and select the data evaluated as true outlier. In addition, self-attention module is also combined to backbone network, to induce backbone network to concentrate on outlier data in plotted data. Finally, the performance of overall algorithm is evaluated through experiment based on open dataset. Using generated data through proposed plotting method, noise ratio estimation accuracy and label noise cleaning performance are evaluated. Additionally, the comparison between the proposed algorithm and the algorithm without self-attention module is also attended.
Classification of Security Checklist Items based on Machine Learning to Manage Security Checklists Efficiently

NIST in the United States has developed SCAP, a protocol that enables automated inspection and management of security vulnerability using existing standards such as CVE and CPE. SCAP operates by creating a checklist using the XCCDF and OVAL languages and running the prepared checklist with the SCAP tool such as the SCAP Workbench made by OpenSCAP to return the check result. SCAP checklist files for various operating systems are shared through the NCP community, and the checklist files include ID, title, description, and inspection method for each item. However, since the inspection items are simply listed in the order in which they are written, so it is necessary to classify and manage the items by type so that the security manager can systematically manage them using the SCAP checklist file. In this study, we propose a method of extracting the description of each inspection item from the SCAP checklist file written in OVAL language, classifying the categories through a machine learning model, and outputting the SCAP check results for each classified item.
Handling overdispersion in poisson regression using negative binomial regression for poverty case in west java

Poverty is one of government's problems in West Java Province that should be suppressed even abolished considering its violates human rights to live in prosperity. In September 2019, number of Poor People reaches 3.38 million people (6.82 percent). Thus, the results of this paper expected could help government in overcome poverty problem in West Java Province by finding the best regression method and model to predict number of poor people and find the most influential predictor that affects the number of poor people in West Java Province. We used secondary datas in 2019 from BPS Jawa Barat with four predictor: Province Minimum Wage (UMP), Human Development Index (IPM), Open Unemployment Rate (TPT), and Number of House Hold (RumahTangga) and one respon variable: Number of Poor People in West Java. The conclusion of this paper are: overdispersion could be overcome and modelled better using Negative Binomial, and the significant variables (which means have impact for response variable) in this case are only two predictor: Human Development Index (IPM) and Number of House Hold (RumahTangga). Human Development Index (IPM) is the most impactable predictor that government should build up to decrease Number of Poor People, while Number of House Hold (RumahTangga) does not have a really big impact but if it get depress would decrease Number of Poor People in West Java.
ChatLinguist: web-based youtube live stream automatic chat message translator

Live streaming has become a very popular activity that Indonesians with access to the Internet participate in everyday, there are already many live streaming platforms available on the market, such as YouTube and Twitch, however these platforms are not region-locked, which means that viewers are able to watch streamers from other regions of the world. This means that a streamer can reach a wider audience, but it also means that language barriers are more apparent than ever. This issue can be seen when an Indonesian viewer watches a streamer who can only speak in English, as English is not the viewers mother tongue, the viewer will not feel as interactive as possible, this is especially true because the viewer is not able to communicate and interact with other viewers through the live stream chat box, as the chat box is probably utilizing English as the main language. Therefore, the author wanted to create a web application that can help solve this issue by translating YouTube Live chat box messages from English to Indonesian while also being able to translate Indonesian messages inputted by the user into English before sending it into the livestream chat box. This web application implements Django as its main framework and depends on Python libraries and YouTube API calls for its main functionality, while the translation process depends on Artificial Intelligence.
Chinese Biomedical Entity Relation Extraction Based On Directed Graph Convolutional Network

In the field of biomedical text mining, biomedical entity relation extraction is the core task to assist researchers in completing the text mining. However, the special characteristics of medical literature, such as long and complex syntax of medical text and a large number of overlapping relations, pose a challenge for biomedical entity relation extraction. In this paper, we propose and apply a Directed Graph Convolutional Network (D-GCN) to encode syntactic information based on the existing neural model as a backbone, thus enhancing the representational ability of the input sequence. Experiments on the evaluation data set showed that the framework could effectively enhance the baseline models based on LSTM, Transformer and pre-trained model BERT at three different levels. Compared with the baseline model PRGC, our model improved F1-score by 1.4% on average.
Improving the performance of speech-gesture multimodal interface in non-ideal environments

Multimodal interfaces have enhanced human-computer interaction by enabling users to interact with computers using a combination of multiple input modes, providing increased accessibility to a wider range of users in various situations. The multimodal system's ability to process multiple input modes allows it to rely on one input modal given that the second modal is unable to function due to exposure to extreme environments. This study will analyse a speech-gesture multimodal interface framework and the prototype that was initially developed by Sindy Dewanti and have been improved upon by Regita Isada. To further improve the framework and prototype's performance, this study will evaluate and resolve the issues encountered in the previous study regarding the configuration of each modal's confidence levels, environment detection, weight calculation, and how the unification process selects a final semantic. Upon implementing the changes, the prototype was tested under three environmental conditions: normal, moderate, and extreme in both unimodal and multimodal mode. The test results show that the prototype was able to deliver the expected results with improved accuracy in multimodal mode as compared to the previous study. Nonetheless, the way that the modals perform, and the unification process can still be further improved
High-resolution white light scanning stitching measurement of microstructure

With the development of ultra-precision machining technology, parts with microstructures on the surface are playing an increasingly important role in engineering practice. The scanning interference microscope measurement method is widely used in the measurement of microscopic surface topography. This paper proposes a method of interference microscopy combined with subaperture stitching. The first step is to divide and measure the sub-aperture. There is a partial overlap area between the divided adjacent subapertures. The white light interferometer measures each sub-aperture in turn. The second step is edge feature detection. There are highly steep edge features between the bottom of the groove and the surface of the surface microgroove. The stitching algorithm detects edge features by detecting the slope threshold of the sub-aperture. The third step is to correct the abscissa. Project the detected edge feature data onto the OXY plane to generate a 2D data set. The multi-dimensional motion scanning platform inevitably has motion errors in the measurement process, so the edge characteristic curves of adjacent sub-apertures have obvious lateral displacement in the overlapping area. The lateral displacement and angle of the lateral coordinate of each sub-aperture are corrected by the least square method. The fourth step is the height coordinate correction. The stitching algorithm minimizes the height difference between the corresponding points in the overlapping area, and solves it with a linear least squares problem model. We have completed the measurement of the fringe pattern on the computer-generated hologram (CGH) substrate and the complete topography of the long groove on the 10mm hemisphere. After the sub-aperture global iterative stitching algorithm, there is no stitching trace in the simulation result. For the long groove on the hemisphere, we designed a four-dimensional scanning motion platform and a special fixture to ensure that the long groove on the hemisphere rotates around an axis perpendicular to the geodesic line. The center of the white light interference fringe is always at the center of the long slot aperture.
Problems of professional self-realization of a young scientist in the conditions of transformation of institutes of educational and scientific institutions

Introduction . The article presents the problem of professional self-realization and self-determination of a young scientist in the conditions of transformation of educational and scientific institutions. The sharpness of it is added by the diversification of professional guidelines, supported by the vagueness of state policy in the field of fundamental science development, as well as its orientation to the use of quantitative assessment parameters. Purpose setting . The problem of professional self-determination and self-realization of a young scientist is considered not through the prism of postgraduate training and quantitative criteria for evaluating its effectiveness, but through the analysis and comparison of the dominant motives for carrying out research activities by young scientists, both in Russia and abroad, as well as the factors that determine its formation. This angle of the study represents the novelty of the work.The research methodology is based on the application of structural-functional and institutional approaches, as well as theories that reveal the process of self-determination, self-realization and motivation. Results . The author provides a wide range of examples of studying the phenomenon of self-realization in the scientific environment, both by Russian and foreign researchers. It shows the general and particular that distinguishes the process in Russia and other countries. Conclusions . The main problem of self-determination and self-realization is not so much the satisfaction of material needs as the strengthening of the meaning- forming component of research activity. It should be supported by transparency of criteria focused on the evaluation of the process, the development of the professional environment of interaction, including through improving the social status of scientists representing the middle age group. In this case, it will create prerequisites for young researchers to be confident in the future in science and education.