Reviewerly/exHarmony huggingface dataset:
A Study on The Legal Systems of The Korean Local Autonomy Police Problems and Its Implications

Korea has maintained National Police Agency system from the beginning of the Korean national government in 1948. However, there have been lots of needs to adopt local autonomous police systems for local government autonomy and enhancing the local police responses against the crimes and police services for the people. Eventually, from the July of year 2021, Korea launched local autonomy of the police. This thesis analyzed the legal systems of the Korean Local Autonomy Police and abstracted several policy implications for the advanced local autonomous policing. For this purpose, this thesis focused on the legal provisions of the Police Law of Korea National Police and Local Autonomy Police, in short, the Police Law of Korea. The autonomy range of the Local Police is the most important aspect of the Autonomy of the Local Police. Decentralization of the highly centralized and authoritative Korean National Police was a big issue, because of the national police has been very reluctant to respond to the demands of the local people. The result of the analysis shows a very narrow range of autonomy of the Local Police. The legal powers of the local police’s investigations for the crime incidents are restricted. Local Police has powers to investigate only several small crimes occurred in the mid level province. The committee of the Local Autonomy Police should be established in the local great city and province. The selection of the members of the committee is important affairs, because the committee controls the Local Autonomy Police. In the Police law, status of members are ensured. However, the qualification of the members needs to be classified more highly to guarantee profession of the committee. The National Police’s administrative and investigation procedure controlling over the Local Autonomy Police, analyzed on the basis of provisions of the Police Law, is too severe to be called as autonomous police system. National police Agency Chief has the special power to direct the local autonomy police. As a result, the autonomy level of the Local Autonomy Police is assessed as poor as it could not be called as a really autonomous police system. The achievement of recent Police Law’s adoption of the Local autonomy Police is just a start to be a decentralizing process of Korean Police. If we desire a fully autonomous local police system and highly responsive local police services, our police law needs some revisions to permit more autonomy and police investigation powers on the local autonomous police.
Automatic Peak Frame Selection from Dynamic Facial Expressions

Studies on facial expression recognition have been active in recognizing human emotions by analyzing faces since humans express their emotions through facial expressions. In the real world, the time for expressing facial expressions is short and facial expressions are drastically changed over time. Capturing that brief moment is vital for recognizing emotions since it provides valuable and relevant information. Similarly, capturing the peak moment that provides a fully expressive facial expression for the respective emotion is essential for training the facial expressions recognition model. This paper proposes the methodology to automatically spot peak frames, which provide the most expressive facial expressions from image sequences. Our experiment will utilize the Multimedia Understanding Group (MUG) Facial Expression database to conduct our proposed methodology. Our method achieves 95% accuracy performance with more simplicity and time-efficient.
PERSONAL CHARACTERISTICS OF GIFTED STUDENTS WITH VARIOUS LEVELS OF PSYCHOLOGICAL WELL-BEING

Introduction. The paper presents the results of the comparative analysis of the personal characteristics of students with various levels of psychological well-being. Materials and Methods. The study relies on the following methods: Maddi’s Hardiness survey, V.V. Stolin and S.R. Panteleev’s Self-attitude test questionnaire, R. Lazarus and S. Folkman’s Test for coping with difficult situations, Kolb’s Learning style inventory, and H. Sievert’s Test to identify creative abilities. Results. Students with a high level of psychological well-being are characterized by constructive strategies for resolving problem situations, a high level of hardiness, and a positive self-attitude. Students with a low level of psychological well-being are characterized by a low degree of self-acceptance, a high degree of self-blame, a desire to avoid solving problems, and a lack of control over their own lives. Conclusion. The findings indicate that there are relationships between the psychological well-being of gifted students and indicators of their hardiness, coping strategies, self-attitude, learning style, and creativity. The study has revealed that students with different levels of psychological well-being differin some of the studied characteristics. Keywords: psychological well-being, giftedness, specialized schools, personal characteristics, hardiness, self-attitude, coping strategies, creativity
Item Analysis of the Rasch Model Items in the Final Semester Exam Indonesian Language Lesson

This study aims to analyze and describe the characteristics of the items for the Final Semester Examination for Indonesian Language courses at the PKN STAN official high school. The item analysis in this study was carried out with a modern approach (item response theory) using the Rasch model with Winstep software. This research is a quantitative research with descriptive method. The data was obtained through the documentation method, namely 25 multiple choice questions with 314 respondents. The results of the analysis show that the unidimensional requirements are not met. There are several items in the test that do not meet the local independence requirements, while the monotonicity requirements have been met. The ability of the subject on this test is greater than the level of difficulty of the questions indicated. The reliability of the test is generally satisfactory with the quality of the items and the consistency of the subject's answers are quite good. Persons or subjects who do not fit are 28 people, while items that are not fit are 5 items. There are 11 items indicated bias.
ІНФОРМАЦІЙНА СИСТЕМА ОЦІНКИ СТАНУ ІНТОКСИКАЦІЇ ОРГАНІЗМУ НА ОСНОВІ МЕРЕЖІ БАЙЄСА

An expert system is a computer program that simulates the judgment and behavior of a human or an organization that has expert knowledge and experience in a particular field. It is a program that emulates the interaction a user might have with a human expert to solve a problem. The end user provides input by selecting one or more answers from a list or by entering data. An Expert System is a problem solving and decision making system based on knowledge of its task and logical rules or procedures for using knowledge. Both the knowledge and the logic are obtained from the experience of a specialist in the area. This paper considers approaches to building a knowledge base for medical systems. In developing the knowledge base of the information system, Bayesian networks were chosen as the basis for the decision-making model by type of patient pathology. This choice was due to the availability of these networks the ability to work with uncertain knowledge used in the diagnosis of diseases, in choosing the optimal course of treatment and subsequent prediction of patients. In addition, they offer the most adequate formal representation of inaccurate knowledge, as they are the result of a synthesis of statistical methods of data analysis and artificial intelligence. The presence of hydrosulfide ion intoxication (HS-intoxication), divalent iron ion intoxication (Fe-intoxication), the patient's absence of pathology and the value of Ag2S and Pt electrode potentials were selected as nodes of this network. Based on the accumulated experience of monitoring the condition of patients during their postoperative treatment (data obtained in collaboration with Ivano-Frankivsk National Medical University), as well as experimental data, conditional probabilities of values that can take the readings of the electrodes were established.Experimental testing of the adequacy of the proposed and implemented model was performed on an array of data from potentiometric measurements of patients' biomaterial. The prediction made by the network was taken as the node that had the highest probability of being in a state that indicates the presence of a pathology. Comparison of the results of the network with data obtained by other methods showed their convergence in 85% of cases.Thus, the developed network can be used to facilitate the process of diagnosing the presence and type of intoxication of the patient and is included in the information system for monitoring the patient's condition.
A Novel Negative Sampling Based on Frequency of Relational Association Entities for Knowledge Graph Embedding

Knowledge graph embedding improves the performance of relation extraction and knowledge reasoning by encoding entities and relationships in low-dimensional semantic space. During training, negative samples are usually constructed by replacing the head/tail entity. And the different replacing relationships lead to different accuracy of the prediction results. This paper develops a negative triplets construction framework according to the frequency of relational association entities. The proposed construction framework can fully consider the quantitative of relations and entities in the dataset to assign the proportion of relation and entity replacement and the frequency of the entities associated with each relationship to set reasonable proportions for different relations. To verify the validity of the proposed construction framework, it is integrated into the state-of-the-art knowledge graph embedding models, such as TransE, TransH, DistMult, ComplEx, and Analogy. And both the evaluation criteria of relation prediction and entity prediction are used to evaluate the performance of link prediction more comprehensively. The experimental results on two commonly used datasets, WN18 and FB15K, show that the proposed method improves entity link and triplet classification accuracy, especially the accuracy of relational link prediction.
End-to-end mapping of a spear-phishing attack on Higher Education Institution in EU

Spear-phishing is a growing threat to the education sector. This analysis maps a specific attacker and demonstrate a likelihood 15% to be attacked by this attacker. The analysis uses open source intelligence tools to reveal a continued pattern where the actor is reusing infrastructure and procedure against several HEI in Europe. For a spear-phising attack to become successful, it has to be able to lure the enduser. This study includes a user vulnerability assessment on the specific spear-phishing attacks used in two comparable studies consisting of 36,851 respondents from two educational institutions. The studies show that without prior training, the concrete spear-phishing attack will lure 20 to 49% of all users. To investigate the high risk of this attack to endusers an eye-tracking study was conducted. The study shows that respondents generally spend more time viewing phishing indicator than one expect by chance, but there seems to be no correlation between viewing indicators and lured to action. Endusers seems to rate the trustworthiness of mails by an overall reading. As a consequence endusers are easily lured by the attacker because of the trustworthiness of the specific spear-phishing mail.
Implementation of an end-to-end gradual verification system

Static verification is used to ensure the correctness of programs. While useful in critical applications, the high overhead associated with writing specifications limits its general applicability. Similarly, the run-time costs introduced by dynamic verification limit its practicality. Gradual verification validates partially specified code statically where possible and dynamically where necessary. As a result, software developers gain granular control over the trade-offs between static and dynamic verification. This paper contains an end-to-end presentation of gradual verification in action, with a focus on applying it to C0 (a safe subset of C) and implementing the required dynamic verification.
Thick Cloud Removal for Sentinel-2 Time-Series Images via Combining Deep Prior and Low-Rank Tensor Completion

In this study, we combine both the deep prior with low-rank tensor completion (DP-LRTC) for thick cloud removal in Sentinel-2 time-series images. On the one hand, DP-LRTC utilizes the low-rank property of multitemporal images via 3-order tensor completion. On the other hand, DP-LRTC employs the 3D spatiotemporal feature expression ability by deep learning. Through integrating both model-driven with data-driven strategy, the proposed method can effectively removal thick cloud in Sentinel-2 time-series images.
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS ICT DI KELAS IV SDN 34 SEBERANG PALINGGAM KOTA PADANG

This research is motivated by the importance of developing learning media used by teachers in the learning process. The main objective of this research is to produce an ICT-based learning media product in the form of Macromedia Flash 8 which refers to the 2013 Curriculum and to describe the quality of ICT-based learning media products referring to the 2013 SD Curriculum.This type of research is development research. The development procedure used in this study consists of 4 steps, namely: 1) Define, 2) Design, 3) Development, 4) Dissemination (Dissemination). The instrument used in this study was the learning media validation instrument sheet. The validation instrument was used to determine the validity of the designed Macromedia Flash 8 media. Validation is carried out by design experts and material experts.The results showed that ICT-based learning media in Integrated Thematic learning in grade IV SD is when used in learning. This is evidenced by (1) the results of expert validation Y in ICT-based learning media are included in the criteria of very valid with a score of 95.83%. (2) the results of expert validation FOF ICT-based learning media are included in the very valid criteria with a score of 95.83%. (3) the results of expert validation A ICT-based learning media are included in the criteria very valid with a score of 93,75%. Thus, the developed ICT-based learning media is if used as a medium in the learning process.
Bank Abnormal Behaviour Recognition Technology Based on Deep Learning

To resolve the problem of low recognition accuracy of existing bank abnormal behavior recognition methods, a bank abnormal behavior recognition method based on deep learning is proposed. The static background of a bank surveillance video image is acquired by the Mixture-of-Gaussians (MoG) model. The static background is extracted by background subtraction, and the foreground image is filtered to make the moving human target as clear as possible. The video frame image is divided into blocks to obtain the motion effect map of the foreground area, and the motion effect map features of each space-time block are extracted. After obtaining the depth features of moving objects, the sparse reconstruction method is used to introducea coefficient learning dictionary and a sparse coding vector to judge whether the behavior is normal or not according to the sparse reconstruction cost. The experimental results show that compared with traditional methods, the proposed method based on deep learning is more accurate and has a higher application value.Keywords: MoG model, Background subtraction, Feature extraction, Coefficient reconstruction, Abnormal behavior recognitionCite AsY. Du, Bank Abnormal Behaviour Recognition Technology Based on Deep Learning, Engineering Intelligent Systems, vol. 29 no. 4, pp. 235-239, 2021.
Learning-based Co-Design of Distributed Edge Sensing and Transmission for Industrial Cyber-Physical Systems

Industrial cyber-physical systems (ICPS) refer to an emerging generation of intelligent systems, where distributed data acquisition is of great importance and is influenced by data transmission. In the improvement of the overall performance of sensing accuracy and energy efficiency, sensing and transmission are tightly coupled. Due to the unknown transmission channel states in the harsh industrial field environment, intelligently performing sensor scheduling for distributed sensing is challenging. In this paper, edge computing technology is utilized to enhance the level of intelligence at the edge side and deploy advanced scheduling algorithms. We propose a learning-based distributed edge sensing-transmission co-design (LEST) algorithm under the coordination of the sensors and the edge computing unit (ECU). Deep reinforcement learning is applied to perform real-time sensor scheduling under unknown channel states. The conditions for the existence of feasible scheduling policies are analyzed. The proposed algorithm is applied to estimate the slab temperature in the hot rolling process, which is a typical ICPS. The simulation results demonstrate that the overall performance of LEST is better than other suboptimal algorithms.
Robot Motion Control with Compressive Feedback

Robot motion control aims to generate control inputs for a robotic system to track a planned trajectory. Feedback provided by sensors plays an essential role in motion control by improving system performance when external disturbances and/or initial errors exist. However, feedback signals, such as images are often of a large size, which imposes a heavy computational burden on the system. In this paper, a new robot motion control scheme is proposed based on compressive feedback to improve feedback rate. The controller is designed in non-vector space using compressive feedback. As an application, visual servoing is formulated under the proposed framework by considering a feedback image as a set, instead of a traditional feature vector. Experiments are conducted to validate the proposed scheme.
GENERTATION OF SYNTHETIC EEG SIGNAL; EVALUATION OF MUTUAL INFORMATION AND CORRELATION COEFFICIENT

When it comes to the diagnosis and treatment of epilepsy, as well as the general quality of life of the patient, the electroencephalogram (EEG) is an often utilised as auxiliary test to aid in the process. It is the primary diagnostic test for epilepsy because it gives a continuous assessment of brain function with great temporal resolution over a long period of time, making it an excellent tool for early detection of epilepsy. Specifically, in this paper, we propose the creation of two Simulink models that can generate synthetic EEG data while maintaining the statistical characteristics of the EEG. In addition, we present the evaluation of two characteristics such as mutual information and correlation coefficient, in order to test the characteristics of any such synthetic generated data. The characteristics proposed here are tested with standard data available on online repository. Apart from using these characteristics for testing the validity of synthetic data, we may use these characteristics as features for machine learning applications.
Secure and Consistent Job Administration Using Encrypted Data Access Policies in Cloud Systems

• In this paper, efficient fast processing Advanced Encryption Standard (AES), Secure Hashing Algorithm (SHA-3) and other session key distribution mechanisms are used. • The proposed SEDAP helps to understand the cloud services and accesses in different perspectives. • The results of SEDAP are compared with the other existing cloud security schemes, which shows that SEDAP has 20% more stability and ensures multilevel security in cloud job administration process. Cloud services are provided to individuals or particular communities and groups. The data produced or communicated are expected to be more secure in the cloud environment, which requires the continuous services of Cloud Service Provider (CSP). In this article, Securely Encrypted Data Access Policies (SEDAP) are configured in the cloud platform. The proposed SEDAP helps to understand the cloud services and accesses in different perspectives such as user actions, jobs initiated from users and the batches evolved from the dependent jobs. The boundary between these perspectives is used in SEDAP for providing encrypted and authenticated job administration in cloud services. In this work, efficient fast processing Advanced Encryption Standard (AES), Secure Hashing Algorithm (SHA-3) and other session key distribution mechanisms are used. The results of SEDAP are compared with the other existing cloud security schemes, which shows that SEDAP has 20% more stability and ensures multilevel security in cloud job administration process. Jobs Arrays and Flow of Data.
Pengembangan E-Module Berbasis Problem Based Learning Berbantuan Android Untuk Meningkatkan Kemampuan Komunikasi Matematis Peserta Didik SMP Kelas VIII

This research is motivated by the low mathematical communication skills of junior high school students. This study aims to produce an Android-assisted problem-based learning E-module that is valid, practical, and effective to facilitate mathematical communication skills. This research is a development research carried out with the Plomp development model. The Plomp development model consists of three stages, namely preliminary investigation, prototyping stage, and assessment phase. The research was conducted at SMPN 3 Sungai Rumbai with the research subjects being material experts, linguists and educational technology experts as well as class VIII students at  SMPN 3 Sungai Rumbai. Data collection instruments in the form of interviews, documentation, questionnaires and test questions. The data obtained were analyzed using qualitative and quantitative data analysis techniques. The results showed that the problem-based learning-assisted mathematics E-module developed was classified as very valid (88.9% for lesson plans and 89.1% for e-module) and very practical, respectively 86.3% and 86, 5% (for small group lesson plans and e-modules) 85.9% and 85.5% (for large group lesson plans and e-modules). Meanwhile, based on the test results of the mathematical communication ability test, 76.2% of students met the criteria for the success of the mathematical communication ability test > 65%, meaning that the problem-based learning-assisted e-module was effective on mathematical communication skills.
Self-Control and Self-Regulated Learning on Students

Students who are active in non-academic activities can get different academic scores from students who do not participate in student organizations. This study examines the relationship between self-control and self-regulated learning of students who are members of the Student Organization of the University of Muhammadiyah Purwokerto-Indonesia. This study uses quantitative methods. The sample of this research is 88 students who follow the Student Organization. Sample selection using probability sampling technique by taking 14% of the total population. Data were collected using an online questionnaire, and namely, google Forms. The instrument used is a scale of self-control and self-regulated learning. The data analysis technique uses the product-moment method from Karl Pearson. This study indicates a positive and significant relationship between self-control and self-regulated learning in students participating in Student Organizations; the correlation value of r is 0.689 at p = 0.000 (p &lt; 0.01).