Reviewerly/exHarmony huggingface dataset:
Anomaly Detection in Spacecraft Telemetry Data using Graph Convolution Networks

Telemetry data anomaly detection is of great significance to guarantee the safe operation of spacecraft. However, the high dimensionality of telemetry variables and the strong correlation between variables pose a great challenge to multivariate anomaly detection. This paper proposes a Graph Convolution Network (GCN)-based anomaly detection method for telemetry data. In this method, GCN is proposed to extract correlation features between variables and learn an updateable correlation map. Then, Convolution Neural Network (CNN) extracts temporal information and combines correlation features for attention to obtain multivariate prediction results. In addition, a novel method of calculating single variable anomaly score and integrated anomaly score is proposed to locate anomalous variables. Finally, experiments on a real dataset are conducted, by which the proposed GCN-based approach is demonstrated to be effective and accurate in telemetry data anomaly detection. In addition, experiments also show our method preforms well in locating anomalous, providing interpretable for anomaly detection results.
Breast Cancer Diagnosis with Machine Learning Techniques

Cancer deaths are one of the highest rates of death. Although breast cancer is commonly associated with women, it is sometimes seen in men, and the mortality rate for men with breast cancer may be higher. The importance of early detection and treatment of breast cancer cannot be overstated. Cancer is diagnosed at an early stage thanks to expert systems, artificial intelligence, and machine learning approaches, and data analysis makes life easier for healthcare professionals. The nearest neighbor method, principal component analysis, neighborhood component method approaches were employed to detect breast cancer in this study. "Breast Cancer Wisconsin Diagnostic" database was used to create and test the approach. According to the results obtained, the highest success rate with 99.42% was obtained by using neighborhood component analysis and nearest neighbor classification algorithm method.
Introduction to AI in E-healthcare

Artificial intelligence includes the improvement of computer frameworks that are suitable for performing assignments that regularly require human knowledge, for example, object detection, solving complex problems and so on. Nowadays, humans encounter various novel diseases, with symptoms that may not be identifiable at an early stage, causing high loss of life. AI can improve healthcare and so extend the human life span. Specifically, with rapid enhancements in computer handling, these AI-based frameworks are currently improving the accuracy and productivity in identifying and treating diseases across different specializations. AI is evolving in the e-healthcare industry in a number of areas, including: collecting medical records, AI-based diagnostics, automatic follow-up, etc. Data science (AI, machine learning, deep learning) has been extensively developed for the e-healthcare sector to provide improved quality in decision-making, finding daily infection patterns, identifying early risks and prediction, and so saving lives and promoting good health. Electronic health records enable the provision of better data-driven decisions, building good machine learning models for prediction, greater operational speed and accuracy, and reducing the amount of valuable time spent by patients and healthcare workers. In this chapter we discuss the complete road map to construct powerful artificial intelligence (AI) in e-healthcare systems using tools and intelligent algorithms. AI e-healthcare applications can be used to manipulate, transmit, and keep patient information, using medical records to enable fast access anywhere in the world. This works on the Internet which is able to monitor payments and maintain records through e-healthcare areas.
Even-odd crossover: a new crossover operator for improving the accuracy of students’ performance prediction

Prediction using machine learning has evolved due to its impact on providing valuable and intuitive feedback. It has covered a wide range of areas for predicting student’ performance. Instructors can track student’s dropout in a particular course at an early stage and try to improve students’ performance. The problem of students’ future performance prediction using advanced statistics and machine learning is a hard problem due to the imbalanced nature of the student data where the number of students who passed the exam is generally much higher than the number of students who failed the exam. This paper proposes a new type of crossover operator called Even-Odd crossover to generate new instances into the minority class to handle the imbalanced data problem. The experiments are implemented using three machine learning (ML) algorithms: random forest (RF), support vector machines (SVM), and K-Nearest-Neighbor (KNN) to ensure the efficiency of the proposed technique. The performance of the classifiers is evaluated using several performance measures. The efficient ability of the proposed method on solving the imbalance problem is proved by performing the experiments on 22 real-world datasets from different fields and four students’ datasets. The proposed Even-Odd crossover shows superior performance compared to state-of-the-art resampling techniques.
A Query Optimization Method for Real-Time Embedded Database Based on Minimum Arborescence

In this paper, a query optimization method based on minimum arborescence for real-time embedded database is proposed to solve the problems of memory limitation and real-time access performance limitation of spaceborne computer system. Based on the characteristics of SQL statements, this method applies the minimum arborescence algorithm to the real-time embedded query processing by relying on the relationship between the attributes of each table during the construction of SQL query. This method can improve the processing speed of multi-table complex query statements, reduce the system memory usage, and optimize query processing in embedded database. The verification on SQLite database shows that this method is an effective query optimization method for embedded database.
Comparative Study of Machine Learning Algorithms for Fraud Detection in Blockchain

Fraudulent transactions have a huge impact on the economy and trust of a block chain network. Consensus algorithms like proof of work or proof of stake can verify the validity of the transaction but not the nature of the users involved in the transactions or those who verify the transactions. This makes a block chain network still vulnerable to fraudulent activities. One of the ways to eliminate fraud is by using machine learning techniques. Machine learning can be of supervised or unsupervised nature. In this paper, we use various supervised machine learning techniques to check for fraudulent and legitimate transactions. We also provide an extensive comparative study of various supervised machine learning techniques.
Application of Computer Trajectory Planning Algorithm in UAVs Power Line Patrolling System

In order to complete the transmission circuit inorganic and automatic search, shrink deviation hand control, this paper is devoted to the complete type of uav autonomous cruise system based on laser point precise positioning, through the high precision 3 d laser spot time data to complete the course of independent planning, automatically generated, and then complete the inorganic and the whole flow of automatic cruise work. Experiment results shows that, given the precise positioning of the laser point cloud drones in autonomous cruise phase, with space collision testing and automatic blocking function, high efficiency to ensure the safety of the unmanned aerial vehicle (uav) navigation, reduce the latent threat to power grid, improve power transmission cable inspection results and the safety of the operation, provide strategies for the development of power transmission cable inspection to explore in the late.
The Development of Android Applications in Junior High Schools as Learning Media in Astronomy Course in SMP Cendekia Insani

This Research have a purpose to exploring teachers’ preference on development of learning media integrating assessment with android application as online assessment in science learning in SMP Cendekia Insani. Research and development use an ADDIE Model. Steps of ADDIE Model is Analysis, Design, Development, Implementation and Evaluation. Analysis is the steps description process of what is going to be taught and forms the basis of all other steps. Design is determine the process of have an information and development a strategy of analysis the problem. Development the component of multimedia are prepared during this phase. Implementation is development result is applied in learning process to know it’s the quality. Evaluation is the last step to evaluation and have a data to improve a research. This research have a result of most of the teachers prefers the online assessment that integrated with android application rather than the conventional one especially in the online learning environment nowadayas.
Language Learning through Social Media: Leaning English as a second Language (ESL) through YouTube in Saudi Arabia

This study is aimed to investigate the impact of social media platforms on Saudi students This chapter presents the details of research philosophy, research approach and research design of this study.It also highlights who will make the sample population, how it will be selected and which tools will be employed to collect and analyze data from them.In the later part, it discusses the significance of following an ethical code of conduct in order to establish validity and reliability of research process.
The Mediating Effect of Knowledge Sharing on Emotional Intelligence and Team Performance: An Empirical Study in Vietnam

This article presents the findings of a study which tested the relationship between emotional intelligence, knowledge sharing, and team performance.Further, the study determined the influence of emotional intelligence and knowledge sharing on team performance.Although these interrelations are very important for enhancing team performance, few empirical studies analyze these relationships together.This paper examined those relationships using SEM with data from 357 employees of SMEs located in Vietnam.The results indicated that emotional intelligence indirectly enhances team performance through knowledge sharing.Besides, knowledge sharing effects directly on team performance.On the other hand, there is a positive association exists between emotional intelligence and knowledge sharing.Findings, implications and recommendation for future research from this study are discussed.
AI Assisted Data Labeling with Interactive Auto Label

We demonstrate an AI assisted data labeling system which applies unsupervised and semi-supervised machine learning to facilitate accurate and efficient labeling of large data sets. Our system (1) applies representative data sampling and active learning in order to seed and maintain a semi-supervised learner that assists the human labeler (2) provides visual labeling assistance and optimizes labeling mechanics using predicted labels (3) seamlessly updates and learns from ongoing human labeling activity (4) captures and presents metrics that indicate the quality of labeling assistance, and (5) provides an interactive auto labeling interface to group, review and apply predicted labels in a scalable manner.
Use of network technologies in the professional activities of adult education teachers in Ukraine

The use of network technologies is fundamental to all professional activities in the field of education in the society of the 21st century. The field of adult education has also been influenced by this trend and network technologies have been acknowledged as basic tools for the improvement of learning opportunities and for the development of the teaching-learning activities themselves. Hence, this paper aims to know about the use of network technologies in professional activities of teachers in the field of adult education in four Ukrainian pedagogical institutions. For this purpose, a questionnaire was passed on about the use of network technologies in the professional activities of teachers in these institutions. Among the main results, it is worth noting that the use of network technologies is relevant in the professional activity of teachers regardless of their age, as well as the widespread use of the computer and the Windows operating system for professional activity. Among the Microsoft applications, Word stands out, used by the entire sample, followed by Power Point and Excel, both of which received more than two thirds of the responses. Google Drive stands out for cloud storage, Viber and Facebook Messenger predominate in messaging services and gmail.com and ukr.net are mostly used for email services, the latter being a specific Ukrainian email service. In conclusion, it is suggested that teachers be trained in a wider range of network technologies to improve their professional activity.
Causal inference

"Causal inference." Journal of Statistical Computation and Simulation, ahead-of-print(ahead-of-print), p. 1
Science Teachers' Perception on Teaching Competency to Promote Students' Future Competency

Objectives The purpose of this study is to investigate the perception of science teachers on the teaching competency to promote students' future competency.&#x0D; Methods In order to achieve the purpose of the study, previous studies related to teacher competency were analyzed, and data were collected through e-mail and in-depth interviews with 13 science teachers.&#x0D; Results The teaching competency required for science teachers in the future educational environment was found to be the teaching competency to promote students' future competency, the ability to use edutech, the individually tailored class competency, and the ability to respond to changes in the educational environment. First, in order to cultivate scientific thinking, scientific inquiry, communication and collaboration skills among students' future competencies, research participants provided opportunities to think and discussed problems together through self-directed inquiry activities. instructional strategies were used to encourage divergent thinking by asking questions, exploring and thinking with students, and encouraging students to seek knowledge. Second, the participants' view of science class influenced not only the selection of teaching/learning strategies, but also the selection and implementation of evaluation methods. Research participants were aware of the practical difficulties of meeting both student-led inquiry activities and knowledge-oriented learning needs, and were proceeding with various solutions through trial and error. Third, teachers felt difficulties in teaching &lt;Project research&gt; because they did not experience project research while attending the college of education. In order to understand the changes in the educational environment, class competency, and learner characteristics, it is necessary to provide sufficient opportunities for class demonstrations to pre-service teachers by significantly extending the educational practice period.&#x0D; Conclusions The study participants emphasized the importance of an open mind to accept changes because the future educational environment is unpredictable. So, subjects for cultivating attitudes to respond to changes in the future educational environment were needed.&#x0D; &#x0D;
Internet of Things Security: A Blockchain Perspective

Since the arrival of computers in 1950s, technology has leaped over many generations at a rapid pace. Computers have found their use in every human activity, namely scientific experiments, industries, transportation, medicine, healthcare, home management, etc. Internet of things (IoT) is one such computing paradigm that aims to assist and automate various trivial and non-trivial aspects of these activities. IoT generates a huge amount of data at a fast rate. The data can be trivial, personal, or sensitive to someone or an organization. Hence, securing IoT data and its aggregation is of utmost importance. The paper gives a brief introduction of IoT in terms of its security aspects to understand the various facets of malicious attacks possible on it. Then, how blockchain, an ever-popular security suite, can be used to provide security to IoT in its various integrations is presented. Finally, some future research issues are discussed towards blockchain-IoT future.
Exploring the articulation between the social relations domain of the 2019 revised Nuri curriculum and the integrated curriculum for the elementary education

Objectives The purpose of this study is to provide implications regards the articulation of curriculum to be revised between each level of school by analyzing the articulation between the social relations domain of the 2019 revised Nuri curriculum and the 2015 integrated curriculum for elementary education.&#x0D; Methods The data analyzed were the 2019 revised Nuri curriculum and the 2015 elementary education integrated curriculum: Right Life, Wise Life, and Joyful Life. Tyler's organization principle for curriculum content was used as the criteria for analysis, and the articulation form was analyzed in terms of continuity and sequence highly related to articulation. Articulation that correspond to sequence were analyzed as deepening/expansion and retrogression. Expanding the scope of content or increasing the level of action was analyzed as ‘deepening/expansion’, and reducing the scope of content or lowering the level of action was analyzed as ‘retrogression’. Repeating at the same level without changing the scope of content or level of behavior was considered ‘repetition’, and this was considered as articulation in terms of continuity.&#x0D; Results First, the social relations domain of the 2019 revised Nuri course is articulated to the ‘Right life’ and ‘Wise life’ of the integrated curriculum. Second, the field of social relations has articulation to school, family, village, and country, which are the spatial themes being part of the major themes of the integrated curriculum. Third, the range of content and sub-contents of the social relations domain are similar to the expandability shown by the spatial themes of the integrated curriculum, ‘school-family-village-country’. Fourth, it hard to say that the social relations domain is not related to the ‘Joyful life’ of the integrated curriculum, and it can be said that there is articulation with the ‘Joyful life’ that includes an expressive function. Fifth, the sub-contents of the social relations domain are intersected in a complex way to articulation and non-articulation, and sequence and continuity in the aspect of articulation.&#x0D; Conclusions It is not easy to match the articulation in a consistent and logical way because the composition systems of the two curricula are different. Educational contents related to social studies with continuously changing characteristics should be evaluated in the long term, and efforts to understand the mutual educational process for the articulation between early childhood education and elementary education are necessary.&#x0D; &#x0D;
Kajian Novel Mata Penakluk Karya Abdullah Wong dalam Perspektif Psikologi Sastra

This study analyzes the inner conflicts experienced by the characters in the novel Mata Penakluk. The theory used is Sigmund Freud's theory to examine the psychological condition of the characters described in the novel Mata Penakluk. In addition, it also examines educational values. The aims of this study are to (1) describe the mental turmoil experienced by Abdurrahman Wahid's character in the novel Mata Penakluk by Abdullah Wong, a review of literary psychology; (2) describe and imitate the values ??of character education contained in the novel Mata Penakluk by Abdullah Wong. The type of research used in this research is descriptive qualitative research. The object of this research is the novel Mata Penakluk by Abdullah Wong published by PT Mizan Publica. Data collection techniques in this study used library, listen, and note-taking techniques. Data validity uses data triangulation. The data analysis technique uses flow analysis techniques, namely data reduction, data presentation, and drawing conclusions. The results of the research are (1) the psychological condition experienced by the character KH. Abdurrahman Wahid (Gus Dur) in the novel Mata Penakluk is influenced by three elements including id, ego, superego; (2) the character values ??contained in the novel Mata Penakluk have 9 values ??including monotheism, humanity, justice, equality, liberation, brotherhood, simplicity, chivalry and local wisdom/tradition. Keywords: Character education,literary psychology, novel
Perceptions of Professional and Non-Professional Millennial Men Towards the Street Harassment of Women in San Pedro, San José, Costa Rica

Street harassment refers to the unwanted comments, gestures, and actions that strangers address to people in public places without the consent of the affected individuals. Although all people regardless of their gender can experience street harassment, this issue has a major incidence on women. This research aimed to examine the perceptions of professional and non-professional millennial men towards the street harassment of women in San Pedro, San José, Costa Rica. The researcher surveyed fifty-two men with the purpose of proving the study’s hypothesis: professional men harass women less frequently than non-professional men. The results revealed the opposite as both male groups harass women almost evenly, and many of them ignore the definition of street harassment and their types.
Understanding Physicians’ Perceptions of Patient-Identified Barriers to Osteoporosis Medication Initiation: A Cognitive Mapping Approach

Objective: Interventions to initiate medication and increase adherence for postmenopausal women who have had a fragility fracture were not always successful. The purpose of this study was to derive an empirical framework for patient-identified barriers to osteoporosis medication initiation and adherence from physician experts. Methods: A cognitive mapping approach involving nominal group technique (NGT) meetings and a card sorting and rating task were used to obtain formative data. We first conducted four NGT meetings with 18 women patients who were not on osteoporosis treatment to identify barriers to osteoporosis medication, then invited 27 osteoporosis physicians to sort and rate 25 patients identified barriers. Descriptive analysis, multidimensional scaling analysis, and hierarchical cluster analysis were applied for data analysis. Results: A two-dimensional five-cluster cognitive map was derived to provide an organizational framework for understanding patients perceived barriers to medication initiation and adherence. The five clusters were concerns about side effects, experience of side effects, lifestyle changes, medication access and complexity, and patient uncertainty about treatment and trust in the provider. The two dimensions were interpreted as internal to patients (X-axis) and external to patients (Y-axis). Conclusions/Implications: Views of patients solicited in a structured format provided directions to help in designing interventions to improve osteoporosis medication initiation and adherence. Keywords: nominal group technique, cognitive mapping, patient barriers, osteoporosis treatment, medication initiation, medication adherence