Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions

Reinforcement learning (RL) exhibits impressive performance when managing complicated control tasks for robots. However, its wide application to physical robots is limited by the absence of strong safety guarantees. To overcome this challenge, this paper explores the control Lyapunov barrier function (CLBF) to analyze the safety and reachability solely based on data without explicitly employing a dynamic model. We also proposed the Lyapunov barrier actor-critic (LBAC), a model-free RL algorithm, to search for a controller that satisfies the data-based approximation of the safety and reachability conditions. The proposed approach is demonstrated through simulation and real-world robot control experiments, i.e., a 2D quadrotor navigation task. The experimental findings reveal this approach's effectiveness in reachability and safety, surpassing other model-free RL methods.
Anomaly Detection For Robust Autonomous Navigation

Human drivers are remarkably robust against various unexpected occurring variations and corruptions by understanding temporal changes and traffic scenes. In contrast, the neural network based autonomous navigation system can be easily affected by sensor data anomaly, like occlusion, sensor noise, challenging weather and illumination conditions. Such external disturbances are inevitable in practical driving applications. In this paper, we develop a semi-supervised anomaly detection module to detect the corrupted data while extracting the traffic scenario features. We further introduce an end-to-end robust autonomous navigation framework based on the idea that the consecutive frames of clean data depict a similar traffic scenario and the differences among the sequential data imply the dynamic state changes. By taking into consideration both spatial traffic scenario and temporal environmental variation, the model is able to achieve robust navigation against sensor data corruptions. We conduct experiments in CARLA platform and the evaluation results show the effectiveness of the proposed method.
Construction of Smart Campus in Primary and Secondary Schools Based on Teachers’ Needs

Smart campus is a crucial task for the informatization and modernization of education.The construction of smart campus is supported by multiple intelligence technologies such as information technology and computer technology, and the media literacy of teachers is also the guarantee for the effective application of smart campus.To better realize "educational modernization" in Dongcheng District, Beijing, in order to better build a smart campus.Research use "Media Literacy Assessment Scale (Simplified Version)" developed by Li Jincheng.A survey was conducted on the status of media literacy among 1810 teachers.At the same time, the demand of teachers in the construction of smart campus is investigated.The survey consists of a self-compiled questionnaire with ten questions covering four dimensions: teaching, educational administration and logistics support, and intelligent office.KMO = 0.859, Cronbach's Alpha = 0.865.It shows that the scale has good reliability and validity.The study found that, an investigation of teachers' media literacy reveals that the overall level of teachers' media literacy is relatively high and that there are no significant differences between teachers of different genders, educational backgrounds, or ages.Teachers have a strong desire for smart campus buildings, according to an examination of their needs for smart campuses.Teaching, educational administration and logistical support, and intelligent office are the dimensions in which demand is greatest to least.
Media Technologies in the System of Training of the Students of Creative Specialties

The article discusses innovative technologies of teaching in the system of higher education in the field of art and culture based on the lists of specialties and areas of training. The article describes the experience of developing academic disciplines and modernizing the content of work programs in connection with the spread of new technologies in the field of art, as well as changes in cultural practice as a specific area of economic activity. Innovative methods and forms of scientific research, development of practical skills and control of students' knowledge are considered.
Asynchronous e-learning web prototyping for first-grade students learning (case study: SD Muhammadiyah Inovatif Pujotomo Magelang)

Distance learning (online) is currently a learning tool that plays an important role during this pandemic, one of which is affected, namely in the field of education at Muhammadiyah Innovative Elementary School Pujotomo Magelang. The difficulty experienced by teachers and students is in the online learning system which uses a lot of media platforms such as Quizziz, Google Classroom, Youtube etc. so that material data, assignments and grades for each subject will be easily mixed and lost. This makes it difficult for students to find material data and past assignments, while teachers find it difficult to find data on assignments that have been collected and quiz scores that have been done by students because there is no copy of the data. E-learning is a learning media that really helps teachers and students in carrying out the teaching and learning process. The asynchronous e-learning method is a means for teachers and students to interact such as giving materials, assignments, quizzes, attendance and giving grades so that teachers and students can easily find data according to their subjects. The results of the asynchronous e-learning test using blackbox testing went well, did not experience any errors. Subsequent testing using UAT with several users, namely admin with 97%, teachers 91%, and grade 1 students 93% it can be concluded that the user's needs have been met.
Democracy’s Unique Advantage in Promoting Development

This chapter elaborates on the notion of democracy and growth. It presents the quantitative evidence of a new institutional theory on democracy's indirect and conditional advantage for promoting economic growth, especially growth via innovation. The channel of liberty-to-innovation holds the most critical channel in which democracy holds a unique advantage over autocracy in promoting growth, especially during the stage of growth via innovation. Additionally, political regimes influence growth indirectly through the institutional foundations they lay down and the economic policies they make and implement. The chapter considers liberty as the best solution for knowledge, while liberal democracy is the best solution to social stability.
Peranan Literasi Digital Pada Teknik Penulisan Konten dengan Menggunakan Website Digital di Samura Kecamatan Kabanjahe Kabupaten Karo

Abstract: English learning activities for school-age children could not be separated by game methods that made a child learned to explore, discovered, expressed feelings, be creative, increased the activity of new thinking patterns, and provided opportunities to socialize with their environment. One example of a game that was starting to develop and be used in all areas of English language skills was bingo. The purpose of this community service was to describe the ability to improve the English language of special school students at the Markus Foundation through bingo games and as a guide for the implementation of English learning. The method used was the training in the use of vocabulary and speaking English through cards and powerpoint. The result of this community service activity was that the students were able to increase their English vocabulary through the bingo game because the researcher provided pictures and asked them to talk about the activities in the pictures. Bingo card pictures were able to provide clues to introduce the use of words and students practice a lot of pronunciation techniques with other students which made learning activities more challenging, motivated, and increased self-confidence. The existence of this training activity was expected to be able to develop the use of English as a foreign language in the context of the learning model of children with special needs in the form of interesting games and it required the responses of other students as a stimulation of the development of affective, cognitive, and psychomotor intelligence.Â Keywords: Bingo Games; English Speaking Ability; English VocabularyÂ Â Abstrak: Ide penyebaran konten positif dalam gerakan literasi digital belum dipahami dengan baik di kalangan anak muda. Hal ini diperkuat oleh fakta yang menyatakan bahwa tidak ada konsensus tentang definisi sebenarnya dari konten positif. Data menunjukkan bahwa konten positif lebih bermanfaat daripada konten negatif. Nilai konsumen yang informatif dan menginspirasi adalah contoh konten positif. Hasil observasi di lapangan menunjukkan bahwa literasi digital dan sosialisasi berbagai jenis konten positif masih diperlukan agar ide ini lebih diterima dan disebarluaskan. Tujuan penulisan artikel ini adalah untuk memberikan alternatif model pembelajaran yang dapat digunakan oleh para penggiat literasi untuk menumbuhkan dan mengembangkan budaya literasi digital dengan membuat konten digital sehingga generasi muda dapat membentuk etika digital dalam penggunaan media digital dan internet. Saat menulis artikel, metode studi literatur digunakan. Penelitian ini menggunakan metode deskriptif kualitatif. Sampel diambil dari 20 orang usia muda yang berumur 18-23 tahun di Samura, Kecamatan Kabanjahe, Kabupaten Karo.Â Kata kunci: Konten Â website digital; Media digital; Literasi menulis
An Analysis of the List of AI-Applied Services for Future Intelligent C4I System of Ground Forces

최근 4차 산업혁명 기술의 발달과 전쟁양상의 변화가 급격하게 이루어지고 있다. 이에 따라 한국군은 첨단과학 기술군 육성을 위해 다양한 노력을 기울이고 있다. 그 중에서도 지능형 강군을 육성하고 인명피해의 최소화를 위해 드론, 로봇 등 무인무기체계를 비롯하여 다양한 무기체계에 인공지능 기술을 적용하기 위한 연구를 추진하고 있다. 특히 신속하고 정확한 전장 상황인식을 통해 지휘관이 보다 빠르게 판단하고 결심할 수 있도록 AI 참모 등의 결심지원체계 뿐 아니라 지휘통제체계를 지능화하기 위한 연구 또한 진행하고 있다. 그러나 현재 진행되는 국방분야의 지능화 연구는 드론, 로봇 등 특정 무기체계에 대한 인공지능 기술의 적용방안 연구 또는 국방분야 전반에서 일반적인 인공지능 활용방안에 대한 연구가 대다수이며, 지휘통제체계의 사용자 입장에서 인공지능 기술의 적용이 필요한 서비스 목록 분석 연구는 이루어지지 않고 있다. 따라서 본 논문에서는 델파이 설문 분석을 통해 지휘통제체계의 사용자가 요구하는 인공지능 적용이 필요한 서비스 목록을 분석하고 OODA 모델을 바탕으로 분석한 국방 AI 기술 로드맵과의 연관성 분석 결과를 제시한다. The Korean military is striving to build an advanced and intelligent army by leveraging the rapid developments in technology related to the 4th Industrial Revolution, with a focus on minimizing human casualties. The military is researching the application of AI technology to various weapon systems to develop decision-making support systems, such as AI staff and C4I systems, to enable commanders to make quicker and more accurate judgments based on rapid and accurate battlefield situational awareness. However, the current research mainly focuses on specific weapon systems and general AI utilization, with little analysis of the services requiring AI technology application from the perspective of C4I system users. This paper aims to fill this gap by identifying the list of services that require AI technology application for C4I system users through Delphi survey analysis and association analysis using the OODA model.
Decomposed Guided Dynamic Filters for Efficient RGB-Guided Depth Completion

RGB-guided depth completion aims at predicting dense depth maps from sparse depth measurements and corresponding RGB images, where how to effectively and efficiently exploit the multi-modal information is a key issue. Guided dynamic filters, which generate spatially-variant depth-wise separable convolutional filters from RGB features to guide depth features, have been proven to be effective in this task. However, the dynamically generated filters require massive model parameters, computational costs and memory footprints when the number of feature channels is large. In this paper, we propose to decompose the guided dynamic filters into a spatially-shared component multiplied by content-adaptive adaptors at each spatial location. Based on the proposed idea, we introduce two decomposition schemes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</i> , which decompose the filters by splitting the filter structure and using spatial-wise attention, respectively. The decomposed filters not only maintain the favorable properties of guided dynamic filters as being content-dependent and spatially-variant, but also reduce model parameters and hardware costs, as the learned adaptors are decoupled with the number of feature channels. Extensive experimental results demonstrate that the methods using our schemes outperform state-of-the-art methods on the KITTI dataset, and rank 1st and 2nd on the KITTI benchmark at the time of submission. Meanwhile, they also achieve comparable performance on the NYUv2 dataset. In addition, our proposed methods are general and could be employed as plug-and-play feature fusion blocks in other multi-modal fusion tasks such as RGB-D salient object detection.
Unbalanced credit fraud modeling based on bagging and bayesian optimization

Credit fraud modeling is a crucial area of research that is highly relevant to the credit loan industry. Effective risk management is a key factor in providing quality credit services and directly impacts the profitability and bad debt ratio of leading organizations in this sector. However, when the distribution of credit fraud data is highly unbalanced, it can lead to noise errors caused by information distortion, periodic statistical errors, and model biases during training. This can cause unfair results for the minority class (target class) and increase the risk of overfitting. While traditional data balancing methods can reduce bias in models towards the majority class in relatively unbalanced data, they may not be effective in highly unbalanced scenarios. To address this challenge, this paper proposes using Bagging algorithms such as Random Forest and Bagging to model highly unbalanced credit fraud data. Bayesian optimization is utilized to find hyperparameters and determine the accuracy of the minority class as an optimization function for the model, which is tested with real European credit card fraud data. The results of the proposed packing algorithms are compared with traditional data balancing methods such as Balanced Bagging and Balanced Random Forest. The study found that traditional data balancing methods may not be compatible with excessively unbalanced data, whereas Bagging algorithms show promise as a solution for modeling such data. The proposed method for finding hyperparameters effectively deals with highly unbalanced data. It achieved precision, recall, and F1-score for the minority category of 0.94, 0.81, and 0.87, respectively. The study emphasizes the importance of addressing the challenges associated with unbalanced credit fraud data to improve the accuracy and fairness of credit fraud models
The Effectiveness of the Port Knocking Method in Computer Security

Computer network is a group of autonomous computers that are interconnected with each other using communication protocols through communication media so that they can share information, programs, share hardware devices such as printers, hard disks, and so on. One of the most important network components is network security, computer security involves various aspects, including protection against unauthorized access, data theft, system tampering, and operational disruptions. This research method uses descriptive method. At this stage specifications are determined regarding the system that will be designed to meet the objectives of this study, the Port Knocking method to secure the Router can be applied to Mikrotik routers by utilizing a firewall which functions to guard against illegal access and overcome problems caused by Attackers
A Study on the Convergence Education of Korean Language Education and Local Culture

The purpose of this study is to explore new ways of convergence education, emphasizing the need for convergence education with the field of Korean language education and local culture. Based on literature research, the concept, current status, and implications of convergence education were analyzed by reviewing regional cultural studies and discussions on convergence education conducted in subject education. Based on this, convergence education was proposed for multicultural family youth who need to understand the region as a major resource to be nurtured as local talent. As a representative local culture in Gwangju, a class model applicable in the actual classroom was presented. In the future, it is necessary not only to teach multicultural family youth knowledge of Korean culture, but also to actively try convergence education so that learners can enjoy the uniqueness and individuality of local culture compared to their own culture through cultural experiences. In other words, true cultural learning should be explored by linking educational contents and methods with the culture of the region where they actually live so that learners can gain insight through the process of comparing each other's cultures in different cultural frameworks.
Waveform Classification Applied on a Stack of Horizons: a Larger-Scale Comparison

Waveform classification is a classic technique for interpreting seismic consistent facies by comparing portions of seismic traces along a single horizon. In order to help the interpretation, two innovative techniques are proposed to apply the classification not only on one, but on several horizons simultaneously. The first approach involves assembling traditional classification maps in a new stack to quickly identify horizons heterogeneities. Yet, every classification map is based on a unique set of templates making the identification and extraction of geological characteristics that are apparent on different horizons very challenging. A second strategy based on dependent templates shared by all classification maps is presented to solve this issue. A stack of horizons created from the interpretation of the Maui cube in New Zealand (Durot et al., 2017) has been classified from envelope waveforms permitting the automatic extraction of a turbidite system geobody. A stack of probability maps based on the channel class has been computed to check the quality of results. Multiple horizons created by a discretisation of the geobody have been classified with the dependent method to analyse thin variations of envelope waveforms within the channel.
AUTOMATION OF DAILY LANDSLIDE POTENTIAL INFORMATION BASED ON REMOTE SENSING SATELLITE IMAGERY USING OPEN-SOURCE SOFTWARE TECHNOLOGY

This automation system automatically generated landslide potential information based on daily precipitation data. This system is essential to replace the previous manual processing system with an automated and integrated system. The results of the developed system are the distribution of areas with landslide potential based on daily precipitation data. The system was built using geographic information systems and web service techniques. This allows the automation process to be performed quickly and accurately. The landslide susceptibility map used is from the National Disaster Management Agency, so the information is more reliable. Himawari-8 is used to determine the potential for extreme precipitation in 10 minutes because this satellite has a very high temporal resolution. The system is already in use and has proven to replace manual processing and is faster. Further development will be more challenging if the system can be connected to the sensors installed on site so that the sensors on site can issue a landslide warning in case of extreme precipitation so that the surrounding communities can respond immediately. Opportunities for future development of the system may also be incorporated into landslide potential prediction based on the precipitation forecast model
The Changes in the 2022 National Curriculum: Focusing on Democratic citizenship Education and Ecoliteracy Education in Korean Language and Social Studies

이 연구는 2022 개정 교육과정 총론의 개정 중점 사항 가운데에서 향후 우리 교육의 중요한 내용이 될 것으로 예상되는 ‘민주시민 교육’과 ‘생태소양 교육’이 국어과와 사회과 교육과정에 구현된 양상을 살펴보고, 그러한 변화가 구체적으로 어떻게 실현될 수 있을지 그 가능성을 탐색해 보고자 하였다. 이를 위해 먼저 2022 개정 교육과정 총론이 추구한 개정 중점들을 검토하고, 이러한 총론의 변화가 국어과와 사회과에 어떻게 반영되었는지 살펴보았다. 그리고 이 과정에서 ‘민주시민 교육’과 ‘생태소양 교육’의 두 부분에서 국어과와 사회과의 연계 가능성을 모색하였다. 향후 다양한 방식의 협업, 팀티칭, 융합수업 등의 방법을 통해 국어과와 사회과의 연계 교육이 활성화된다면, 범교과학습의 주제들이 교육 현장에 활발하게 다루어질 수 있을 것이며, 궁극적으로 총론에서 지향하고 있는 핵심 역량 및 2022 개정 교육과정의 중요 지향점들이 보다 수월하게 성취될 수 있을 것이다.This study examines the implementation of ‘Democratic Citizenship Education’ and ‘Ecoliteracy Education’, which are key focuses of the General guidelines of the 2022 National Curriculum, in Korean language and Social studies curricula. It aims to explore the possibilities of how such changes can be realized in a concrete manner. To achieve this, the study examines the revised key focuses and core competencies presented in the General guidelines and how these changes have been reflected in Korean language and Social studies curricula. Additionally, it explores the possibility and methods of integrating or combining the contents of ‘democratic citizenship education’ or ‘ecoliteracy education’ between Korean language and Social studies subjects. If collaborative approaches, team teaching, and convergence class are actively employed to promote interdisciplinary education between Korean language and Social studies, it is anticipated that cross-curricular themes will be extensively addressed in educational settings. Furthermore, such lessons will ultimately align with the emphasized aspects in the general guidelines of the 2022 National Curriculum, leading to positive effects on learners’ development of key competencies.
Detection of Software Security Weaknesses Using Cross-Language Source Code Representation (CLaSCoRe)

The research presented in the paper aims at increasing the capacity to identify security weaknesses in programming languages that are less supported by specialized security analysis tools, based on the knowledge gathered from securing the popular ones, for which security experts, scanners, and labeled datasets are, in general, available. This goal is vital in reducing the overall exposure of software applications. We propose a solution to expand the capabilities of security gaps detection to downstream languages, influenced by their more popular “ancestors” from the programming languages’ evolutionary tree, using language keyword tokenization and clustering based on word embedding techniques. We show that after training a machine learning algorithm on C, C++, and Java applications developed by a community of programmers with similar behavior of writing code, we can detect, with acceptable accuracy, similar vulnerabilities in C# source code written by the same community. To achieve this, we propose a core cross-language representation of source code, optimized for security weaknesses classifiers, named CLaSCoRe. Using this method, we can achieve zero-shot vulnerability detection—in our case, without using any training data with C# source code.
Advanced Machine Learning Applications in Big Data Analytics

We are currently living in the era of big data. [...]
MANUAL SKILLS AS AN IMPORTANT ELEMENT OF THE DEVELOPMENT OF PROFESSIONAL QUALITIES OF STUDENTS

The problem of reducing the cognitive functions of students is considered. It is indicated that this problem is systemic in nature. The influence of information flows on the formation of clip thinking and immersion of young people in the virtual environment is shown. The problem of the existing system of assessment of students'