Reviewerly/exHarmony huggingface dataset:
ONTOLOGIES - FACILITATORS FOR CURRICULUM DESIGN IN CENTERS OF EXCELLENCE FOR ENGINEERING EDUCATION

Ontologies are engineering artefacts which reduce conceptual and terminological confusion by providing a unified framework for describing the concepts of the world and their relations. Thus, they improve communication and cooperation between people and organizations. Also, ontologies are cross-platforms, cross-applications, thus they are facilitators for software interoperability, and they provide reusability, reliability, specification. In e-learning, ontologies are used to improve personalization, search and retrieval, learning domain description, learner profile modelling, feedback implementation, assessment, but also curriculum modelling and management. In this paper, we focus on this last-mentioned application of ontologies. In curriculum development, ontologies foster the transparency, collaboration, exchangeability and interoperability, support the alignment, classification, comparison, and matching between universities, educational systems or relevant disciplines, help in managing the educational offerings of an institution, revealing useful information like course overlapping, uncovered or less covered areas, possible synergies between courses, support course recommendation. Besides highlighting the best practices of ontologies in curriculum development (e.g. the Bowlogna Ontology, the BBC Curricula Ontology or the CCSO ontology), we propose an ontological model for curriculum design in centres of excellence in engineering education developed within the Erasmus+ Capacity Building EXTEND project and underline the possible advantages brought by them in the network of centres - knowledge transfer between centres in different countries and regions, exchange activities between those centres, double graduation certificates and so on. Also, our proposed ontology contains the competences which are considered necessary results for a teacher training program in engineering education within EXTEND framework and facilitates the interrogation of the curriculum via dedicated SPARQL queries.
Re-detection and distractor association from a global perspective: A long-term tracking system

Great success has been achieved in short-term visual object tracking in recent years. However, long-term tracking which is closer to practical applications still lacks enough attention, especially for the target disappearances and reappearances which are common phenomena and important properties in long-term tracking due to full occlusion and out-of-view. In this paper, we propose a long-term tracking framework based on the cooperation of a short-term local tracker and the high-quality global re-detection and distractor association mechanisms. The former mechanism provides target candidates for target relocation, and the latter utilizes the Kalman Filter and Hungarian algorithm with motion cues to build distractor tracklets which attempts to avoid incorrect target relocation after the target disappears. We apply the tracking framework on the state-of-the-art algorithm STARK and evaluate the performance on the popular long-term benchmarks VOTLT-2020 and TLP. The experimental results show the effectiveness of the proposed framework and our tracker performs well compared with other competing algorithms.
Privacy Security System for Video Data Transmission in Edge-Fog-cloud Environment

Fog computing is an efficient paradigm to diminish latency and congestion of networks, in which the applications and data are no longer deployed in the cloud alone.Alternatively, the cloud server is improved with devices working near the edge layer and end users such as mobile applications and sensors respectively in internet of things (IoT) scenarios.Video surveillance system is a critical issue needs considerable computing and network instruments to provide the substantial quality of data.Multimedia communication includes videos transferring between fog and cloud server via secure channel is considered a critical problem needs to resolve.This paper proposes a light-weight cryptographic scheme to provide the secrecy and privacy for the transferred video data within edge-fog-cloud platform.Specifically, the framework of the proposed cryptographic scheme involves three main phases; secret key (SK) generation algorithm and distribution processes, pre-processing of video frames based on frame region of interest extraction FROI approach, and video data encryption/decryption phase using 2D chaotic map.The major parameters that effect on video encryption performance within edge-fog-cloud infrastructure are latency, accuracy and practicability.The secret key generation process is implemented into central fog node (CFN), while keys distribution and management processes are implemented into cloud computing layer for higher security distribution procedure.Common evaluation benchmarks were utilized in the experimental results to analyse and evaluate the proposed cryptographic scheme.The performance evaluation of the proposed cryptographic scheme is carried out based on some of metrics which are memory consumption rate, encryption time, rate decryption time, key sensitive analysis and computation complexity via using urban surveillance video dataset (USVD).The results of these metrics prove that the proposed scheme has been very suitable for securing transmit video data for edge-fogcloud structure against intruders.Scientific comparisons against stat of art methods are conducted in our experiments to highlight the performance evaluation of the proposed scheme.Finally, the finding results based on scientific comparisons show that the proposed scheme is more appropriate for transfer encryption video frames from fog-layer to cloud-computing layer.
Canny-Net: Known Operator Learning for Edge Detection

We present Canny-Net, a modification of the 2D Canny edge detector into a pre-weighted neural network. CT scans of objects that contain metal components are characterized by artifacts like beam hardening, total absorption and scatter. Edge detection with classical methods, e.g. the Canny edge detector, is therefore prone to error. Using known operator learning, Canny-Net reduces maximum error bounds for edge detection in such CT scans. We show that Canny-Net is a light-weight neural network with a small number of trainable parameters. After training, we observe an increase of 11% in F1 score on 2304 test images when compared to the Canny edge detector. Due to its adaptability and low computational cost, Canny-Net can be considered for a wide range of different applications.
Layout-Guided Indoor Panorama Inpainting with Plane-Aware Normalization

We present an end-to-end deep learning framework for indoor panoramic image inpainting. Although previous inpainting methods have shown impressive performance on natural perspective images, most fail to handle panoramic images, particularly indoor scenes, which usually contain complex structure and texture content. To achieve better inpainting quality, we propose to exploit both the global and local context of indoor panorama during the inpainting process. Specifically, we take the low-level layout edges estimated from the input panorama as a prior to guide the inpainting model for recovering the global indoor structure. A plane-aware normalization module is employed to embed plane-wise style features derived from the layout into the generator, encouraging local texture restoration from adjacent room structures (i.e., ceiling, floor, and walls). Experimental results show that our work outperforms the current state-of-the-art methods on a public panoramic dataset in both qualitative and quantitative evaluations. Our code is available online ( https://ericsujw.github.io/LGPN-net/ ).
PREDICTION AND MAPPING RAINFALL CLASSIFICATION USING NAIVE BAYES AND SIMPLE KRIGING

This study discusses the development of a prediction model for the classification of rainfall based on time in Java. The method used in this research is naive Bayes and simple kriging. Naive Bayes is used for classification prediction, while simple kriging is an interpolation method used for mapping. There are two scenarios used, that is building a prediction model for daily and monthly rainfall classification, with data taken from 27 weather stations on the island of Java from 2010 to 2021. The results obtained in the classification process are an accuracy value of 67% for the daily model and 88% for the monthly model. The daily model data uses a spherical semivariogram with an average RMSE of 1,021. For the monthly model data using a Gaussian semivariogram with an average RMSE of 0,34. Then interpolation using simple kriging for mapping rainfall. The results of this study are predictions for the classification and mapping of daily rainfall models from April 1 to April 7 2022 and monthly models from April to September 2022. The contribution of this research is to provide predictive information and mapping of future rainfall so that public people can anticipate more.
Exploring the Community Involvement in Smart City through a Co-creation Approach in Indonesia

Smart City is a model for developing a city to create a better quality of life using Information and Communication Technology to increase awareness, intelligence, welfare, and citizen participation. Currently, many cities declare themselves as smart cities. Therefore, they invest a lot in information technology infrastructure to provide website and application-based services. However, this has not been balanced with community development by fostering participation in public life processes and outputs. This study aims to explore community involvement in the context of a smart city through a co-creation approach. This study uses a qualitative approach with library research techniques sourced from journals, proceedings, books, and official government websites that provide information relevant to the research focus. This study reveals that community participation is not difficult to find because Indonesia has social capital and is practised until now in the public life of urban communities, for example, social awareness, deliberation, and mutual cooperation. However, community participation is still limited to input, not yet reaching the process and output, whereas Information Technology is still limited to replacing the offline system with online. Based on the results and discussion, this study suggests the need to expand and increase community involvement in resolving public affairs and combining forms of community involvement in both online and offline systems.
The long journey to the training of a deep neural network for segmenting pores and fibers

Even though it is a crucial step for achieving suitable results, the preprocessing of data before it is used as input to deep neural networks is often only described as a side note. This work elaborates on the required steps in this preprocessing procedure. Specifically, we provide insights into the selection of appropriate segmentation algorithms to generate reference volumes from X-ray computed tomography (XCT) scans as training data. Furthermore, this work evaluates the criteria for the selection of an appropriate deep learning network architecture, and a quantitative comparison between networks based on U-Net and V-Net.
A Study on the Experience of Child Care Teachers' Stress and Psychological Exhaustion

A Study on the Experience of Child Care Teachers' Stress and Psychological Exhaustion.Childcare staff spend all day with children.Child abuse by childcare teachers is increasing every year.The teacher's job stress increases the expression of negative emotions such as anxiety, tension, anger, and irritability in the classroom, and these negative emotions of the teacher worsen the student-teacher relationship.Child abuse may occur due to intensified job stress.In order to solve this problem, it is necessary to view childcare as a national task and to make efforts to improve the treatment of childcare teachers and enhance professionalism at the national level.The subjects of this study were randomly sampled six incumbent nursery school staff working at kindergartens located in K and I cities and selected them as research subjects.Therefore, this study intends to conduct a childcare experience study on job stress reduction and psychological exhaustion targeting early childhood teachers.In order to improve the childcare staff's emotional expression and the quality of childcare services, it is necessary to improve the social perspective and continuous work environment of childcare staff.This suggests significance in providing evidence for the need for continuous teacher break time and teacher education and exchanges with fellow teachers to understand emotional expression and methods so that they can respond sensitively to the emotions of infants and toddlers and have a considerate perspective do.
Determinants of Job Satisfaction of Public Sector Organizations’ Employees in Six Continents: A Systematic Review

This article aims to systematically review the empirical literature on the determinants of employees’ job satisfaction in public sector organizations worldwide. This article conducted a systematic review referring to the protocol proposed by Lee &amp; Cummings (2008). Data sources used electronic databases, including Scopus Search, PubMed Search, and Google Scholar Search from 2008 to February 2022. Review methods included articles that examine the determinants of employee job satisfaction in public sector organizations. Team members independently reviewed and determined each paper that met the inclusion criteria. Then we assessed the quality of the articles using a predetermined quality assessment instrument. Next, we perform data extraction. Finally, we conducted a comprehensive content analysis to categorize the factors related to employee job satisfaction. The results concluded that implementing appropriate leadership styles, prosocial behavior, rewards, organizational commitment, organizational justice, empowerment, emotional intelligence, and organizational communication can increase job satisfaction in public sector organizations. Toxic leadership style and job stress can reduce employee job satisfaction. In addition, we also revealed some inconsistencies in research results, which have provided research gaps for other researchers to follow up on the future.
The pragmatic functions of emojis in Arabic tweets

Emojis have been used on different platforms and in different languages to express feelings and emotions in online communication, which has led to their widespread familiarity among social media users worldwide. The current study is concerned with the pragmatic functions (speaker and contextual meanings) of emojis in Arabic tweets. The study used mixed methods to analyze the use of emojis and emoji position to identify their functions and possible gender similarities and differences. The dataset of this study consists of 421 Arabic tweets by Arab users at the beginning of 2022 (149 by male users and 272 by females). This study addressed three questions: (1) What are the common emojis used in Arabic tweets and their position in the tweet? (2) What are the pragmatic functions of these emojis? (3) What are the possible differences and similarities between male and female users in the use of emojis? The findings revealed that Loudly Crying Face , Red Heart , Face with Tears of Joy , Broken Heart , Smiling Face with Heart-Eyes , Pleading Face , Slightly Smiling Face , Pensive Face , and Weary Face were preponderant in Arabic tweets. Also, this study found seven pragmatic functions ranging from the most to the least frequent: Multiple functions , Reaction , Action , Decoration , Physical action , Softening , and Tone modification . Regarding the role of gender, there were both similarities and differences in terms of the frequency of emoji use, emoji position, and function. Moreover, the findings showed the importance of context to interpreting the functions of emojis. Finally, the findings have implications for emoji designers and Natural Language Processing (NLP).
USING TINKERCAD TO SUPPORT ONLINE THE LABORATORY WORK ON THE DESIGN OF MICROPROCESSOR SYSTEMS AT TECHNICAL UNIVERSITY

This article reviews the possibility of using the Tinkercad Internet service for online study of the subjects connected with microprocessor systems. During the global coronavirus pandemic, online classes based on the Internet technologies have become virtually the only effective method of learning. However, while lectures can be taught successfully with the help of video conferences such as Zoom, Skype, Microsoft Teams, Google Meet etc., it is almost impossible to perform laboratory and practical work in the field of designing microprocessor systems with the help of these tools. Therefore, the authors suggest a new approach to practicums on the subjects related to microprocessor systems using tools of the Tinkercad Internet service. The existing methods and means of remote work with breadboard Arduino have been analyzed. The advantages of the Tinkercad Internet service in comparison with other tools of remote work have been disclosed. The service capability for microprocessor systems design has been considered. The article provides examples of elaborated lab works by means of the Tinkercad Internet service. Available capabilities and instruments in the system have been detailed. The process of learning based on the Tinkercad Internet service has been developed. Consequently, the use of the Tinkercad Internet service during e-learning gives the possibility to do lab works and microprocessor system design practicums without actual equipment, and the methods suggested by the authors assure the quality of students’ awareness in this direction. The prospective direction of this Internet service application can be the design of 3D printing models and their use during e-learning of other subjects, in particular Physics (Electrics section), Theoretical Basics of Electrical Engineering (DC section), as well as other sciences related to electronic circuit design.
Analisis Media Pembelajaran Berbasis Power Point terhadap Motivasi Belajar pada Mata Pelajaran PAI di SD Negeri 16 Air Tawar Timur

This study aims to analyze power point-based learning media as a learning medium used by teachers, in Q.S At-tin material. The approach used was through a qualitative method with a descriptive type. The data sources in this study were obtained from informants, namely teachers and students. Data collection techniques were obtained by means of observation, interviews and documentation. Data analysis techniques in this study used the Miles and Hubermen models with three techniques namely: data reduction, data presentation and conclusions. The results of this study are: (1), how is the use of power point-based learning media on learning motivation in PAI subjects in Class V. (2) what are the advantages and disadvantages of using power point media on learning motivation in PAI subjects in Class V. (3) how is the learning motivation from using power point-based learning media in class V. in research the need for learning media to support the learning process, so that students become interested and motivated, researchers can conclude that this power point media is suitable for use in learning and makes it easier for teachers to delivering material in class V Elementary School.
Risk likelihood of planned and unplanned cyber-attacks in small business sectors: A cybersecurity concern

Human factors such as planned and unplanned cyber-attacks are a serious threat to any institution. The presence of planned and unplanned actions exposes the state of cybersecurity within the small business sector – leaving them vulnerable to a range of cyber-risks. This study used the AgenaRisk package with Bayesian Network (BN) tools to illustrate the likelihood of risk in planned and unplanned attacks. Adopting the package demonstrates the dependent and independent variables of the human factors, which are planned and unplanned, with their relationships resulting in the ultimate data breach. The work also combined qualitative research with quantitative risk analysis techniques to determine the risk likelihood of planned activities and unplanned employee actions and their behaviors influencing data breaches.&#x0D; The work used the judgemental sampling method to select twenty-five (25) research participants who are business owners and Information Technology (IT) managers. An online survey was used to collect data from the selected research participants. Results were analysed using content analysis and interpreted using the package with BN tools, and risk analysis techniques. The results were further discussed, and the study concluded with remarks and future developments.
Social Media Manipulation Deep Learning based Disinformation Detection

The rapid grow and use of different social platforms enhanced communication between different entities and their audiences plus the transformation through digitalization of existing, e.g., ideas and businesses, or the creation of new ones fully existing or depending on this digital environment. Nevertheless, next to these promising aspects, social media is a vulnerable digital environment where a diverse plethora of cyber incidents are planned and executed engaging a diverse range of targets. Among these, social media manipulation through threats like disinformation and misinformation produce a broad span of effects that cross digital borders into the human realm by influencing and altering human believes, behaviour, and attitudes towards specific ideas, institutions, or people. To tackle these issues, existing academic, social platforms, dedicated organizations, and institutions efforts exist for building specific advanced and intelligent solutions for detecting and preventing them. Regardless, these efforts embed defender’s perspective and are focused locally, at target level, without being designed to fit a broader agenda of producing and/or strengthening social media security awareness. On this behalf, this research proposes a deep learning-based disinformation detection solution for facilitating and/or enhancing social media security awareness in respect to offender’s perspective. To achieve this objective, a Data Science approach is taken based on the Design Science Research methodology, and the results obtained are discussed with a keen on further field developments regarding intelligent, transparent, and responsible solutions countering social manipulation through realistic participation and contribution of different stakeholders from different disciplines.