Reviewerly/exHarmony huggingface dataset:
Research and Practice of Case Teaching in Network Engineering Course Teaching Under OBE Concept

In view of the traditional teaching methods of network engineering major core courses are difficult to tap students' potential motivation and learning enthusiasm, in order to further improve the teaching quality of network engineering major courses, mobilize students' enthusiasm and initiative in learning, and improve the teaching effect of professional core courses, based on the OBE education concept, this paper proposes to introduce case teaching method into the teaching process from the aspects of teaching method, teaching content and teaching mode, further consolidate the concept of "student-centered, output-oriented and continuous improvement", and finally realize the goal of cultivating applicationoriented talents for the country and society.Case teaching method is an open and shared teaching mode with students as the main body and teachers as the leading part through "teaching and learning".Case teaching method is introduced in the teaching process of professional courses, and appropriate cases are selected to blend into knowledge points combined with the course content.According to the steps of case explanation, question raising, group discussion, summary and promotion, effect evaluation, and problem solving, relying on the industry-education integration platform of school-enterprise cooperation, the case library is formed, and the application of cases in professional courses is analyzed and discussed.
Prediksi Kunjungan Wisatawan Ke Indonesia Dengan Reduksi Noise Pada Mesin Pencari Menggunakan Metode Hilbert Huang Transform

&#x0D; In many studies, Google Trends Data is efficient to analyze and estimate as explanatory variables, including tourism predictions. However, data retrieval and tourism are always plagued by noise. Without noise processing, the predictive ability of search engine data may be weak, even invalid. As a noise processing method, Hilbert-Huang Transform (HHT) can reduce or clean noise. Forecasting is the art and science of predicting future events. LSTM is able to overcome long-term dependence. This study tries to provide predictions of tourist visits by processing noise in search engines using the Hilbert-Huang Transform method. The forecasting architecture that is built is composed of 3 hidden LSTM layers with 100 units of neurons or nerves that function to process information, which in the LSTM layer also becomes the input layer. Prediction test results on a dataset of 156 rows, resulting in RMSE values in 2019 getting RMSE LSTM 129249 results, and RMSE HHT + LSTM 653058. so that the resulting RMSE is closer to remembering 0.
Development of Learning Media Portable Wind Direction Indicators as Visual Aids for Flight Navigation

In this study, we designed a wind direction indicator learning media to help cadets understand how the equipment works. Wind direction indicator is used as a navigation tool for pilots to determine the direction of landing on the airport runway. This research was conducted at the Palembang Civil Aviation Polytechnic with research and development methods. The object of research is the cadets of the airport engineering study program. The results showed that the cadets were very enthusiastic in learning these tools, and increased their knowledge and competence in the field of visual landing aids navigation at airports. With the increase in the competence of cadets, it is hoped that it will support flight safety in Indonesia.
New Student Interest: Price, Promotion, and School Image in Vocational High School

This quantitative study aims to examine the impact of price, promotion, and school image on the interest of new students at a private vocational high school in Sidoarjo, East Java. The research utilized a saturated sampling technique, encompassing all 42 new students who registered at the school. IBM SPSS V. 25 software was employed for data analysis, including classical assumption tests, multiple linear regression analysis, t-tests, multiple correlation coefficient tests, and the coefficient of determination. The findings reveal that price, promotion, and school image all significantly influence the interest of new students at the vocational high school. This study provides valuable insights for educational institutions in understanding the factors that affect new student interest, guiding their efforts to attract and retain students.&#x0D; Highlights:&#x0D; &#x0D; Factors influencing new student interest: Examining the impact of price, promotion, and school image on the interest of new students.&#x0D; School choice determinants: Analyzing the significance of these factors in shaping the decision-making process of new students.&#x0D; Implications for educational institutions: Providing insights for private vocational high schools in attracting and retaining new students through effective pricing strategies, promotional activities, and cultivating a positive school image.&#x0D; &#x0D; Keywords: price, promotion, school image, new student interest, private vocational high school
Relationship of Secure Attachment to Fathers and Mothers with Emotional Intelligence in Junior High School Adolescents

This study was conducted with the aim of revealing whether or not there is a relationship between secure attachment to fathers and mothers with emotional intelligence in adolescents at SMP Negeri 2 Sedati. The research method used is correlational quantitative. The variables in this study are secure attachment to the father and secure attachment to the mother as the independent variable and emotional intelligence in adolescents as the dependent or dependent variable. This research was conducted at SMP Negeri 2 Sedati with 198 students as the sample using proportionate stratified random sampling technique. Data analysis was performed by multiple regression correlation analysis using the JASP 0.14.1.0 program. based on the results of data analysis showed a positive relationship between secure attachment to father and mother with emotional intelligence with an F value of 73,958 and a significance of p &lt; 0.001 which means that the hypothesis in this study can be accepted with an effective contribution of 43.1% which indicates that secure attachment to father and mother effect on adolescent emotional intelligence.
BLENDED LEARNING FOR SPEAKING ABILITY: STUDENTS’ MOTIVATION IN EFL CLASSROOM

The purpose of this study is to explore whether blended learning might encourage students in the English-speaking second-grade class at SMPN 9 Kendari. This study is designed precisely to identify these issues. The focus of the research in this study is a second grade Junior High School class of 36 students. A qualitative research design was used for this study. Data is gathered through classroom observation and documentation. The findings show that blended learning is unsuccessfully implemented to encourage students' motivation in speaking class because there is an imbalance in the results between online and offline learning. The study also exposes the difficulties that students experience when studying online, such as their inability to use smartphone mobile functions, poor internet connections, and a lack of engagement as well as the teachers' insufficient supervision of such learning to prevent students from concentrating on the learning process.
A Mine Target Detection Technology Based on Artificial Intelligence Algorithm

Aiming at the low accuracy and reliability of traditional mine target detection methods, a mine target detection technology based on artificial intelligence algorithm is proposed. Through dataset construction, target detection model Yolo design, small target optimization, model compression, and other technical means, mine targets can be accurately recognized in visible light images. Experimental results show that the final index AP@0.5 is 0.929, AP@0.5:0.95 is 0.703. The detection results of the test set are good, which verifies the effectiveness of the proposed method.
Aggregate-and-Excite Network for Image Super-Resolution

Recently, convolution neural network (CNN)-based image super-resolution (SR) has achieved excellent performance. However, most existing works have ignored the importance of accurately utilizing contextual information. To solve this problem, we propose Aggregate-and-Excite block (AEB) with integration into our proposed Aggregate-and-Excite Network (AENet). AEB is functionally equivalent to feature extraction block. Each contains a global contextual information unit (GCIU) and a gate unit mechanism (GU), capturing the enriched contextual information for more powerful feature representations and emphasizing informative features according to the global contextual information. Compared with other methods, our AENet has achieved good performance through extensive experiments.
LSTM-Based Deep Learning Architecture for Recognition of Human Activities

This paper uses raw sensor data to develop and train the networks for human activity recognition in a variety of applications, ranging from tracking their fitness and the safety monitoring. The models may simply be expanded to be trained with additional data sources for greater accuracy or classification extension for different prediction classes. This research delves into WISDM’s accessible dataset as well as the distinct characteristics of each class for various axes. Furthermore, for the application of human activity identification, the design of a long short-term memory (LSTM) architectural model is defined. In the first 500 epochs of training, an accuracy of over 96 percent and a loss of less than 30% have been achieved.
Federated Learning Ciphertext Retrieval Based on Hybrid Cloud Architecture

Aiming at the traditional ciphertext retrieval scheme in cloud environment, which is based on TF-IDF statistical model and does not consider the deep semantic information of files, this paper proposes a deep semantic ciphertext retrieval model based on hybrid Cloud Architecture. Through federated learning, data owners use private cloud to jointly train deep-seated semantic neural network model. According to the model, file vector and index vector can be generated. In addition, the encrypted inverted index table is used to improve the efficiency of retrieval without disclosing the retrieval information. The analysis and experiments on real data sets show that our scheme is better than the current ciphertext retrieval schemes of the same type in terms of security and search efficiency, and the retrieval accuracy remains at about 95% with the increase of the number of files.
Analysis and Research of Digital Image Processing Technology Based on Fuzzy Algorithm

As the main content of digital image processing technology, image segmentation plays a positive role in robot vision, imaging target recognition and tracking, image understanding, and so on. Image filtering recovery technology is the main link of digital image preprocessing, and the quality of actual operation determines the accuracy of filtering segmentation. In the process of image formation, it is difficult for people to quickly understand the main situation of information loss during imaging, resulting in ambiguity and similarity in image processing. Fuzzy information processing technology based on fuzzy set theory shows unique advantages in dealing with imprecise knowledge description and fuzzy uncertain events, so this paper mainly discusses how to use fuzzy information processing technology to improve image filtering algorithm and segmentation algorithm. According to the histogram exponential smoothing principle and peak point detection principle, a new image segmentation algorithm is constructed, and the final practice results show that this content can obtain the best segmentation threshold value in meticulous segmentation.
Application of 3D Reconstruction and Leap Motion Gesture Interaction in Virtual Assembly System

It is difficult to grasp and assemble multi-part small objects in virtual assembly. How to grasp and assemble parts quickly and accurately is an important part of virtual reality assembly. In this paper, a high-precision object model is obtained by using linear laser scanning technology. Leap Motion controller and mesh collision detection technology are used to improve the accuracy of collision detection. Finally, a virtual assembly system of a pop gun is developed to achieve a strong immersive virtual assembly effect.
Software Mechanisms of the Content Kinds

The semantics of agent-based DBS is ‘grounded’ in that the Content kinds concept, indexical, and name have their foundation in the agent’s recognition and action. Each Content kind has its own computational Mechanism. For a concept it is computational pattern matching between the type provided by memory and raw data provided by the interface component. For an indexical it is pointing at a STAR value of the on-board orientation system (OBOS). For a name it is the address of a ‘named referent,’ which an implicit or explicit act of baptism inserts into a lexical name proplet as the core value.
A Modified Dehazing Method for Single Infrared Image of Disc Suspension Porcelain Insulators

In this paper, a modified geometric averaging filter (MGAF) is presented for removing haze from single hazy infrared image of disc suspension porcelain insulators, which further contributes to improvement on image enhancement. The proposed MGAF algorithm is capable of overcoming zero forcing problem that occurs in conventional geometric averaging filter, revealing better results in regards to subjective evaluation as well as outperforming peer filtering techniques in terms of PSNR and SSIM indicators.
Compact non-volatile ferroelectric electrostatic doping optical memory based on the epsilon-near-zero effect

With the booming development of optoelectronic hybrid integrated circuits, the footprint and power consumption of photonic devices have become the most constraining factors for development. To solve these problems, this paper proposes a compact, extremely low-energy and non-volatile optical readout memory based on ferroelectric electrostatic doping and the epsilon-near-zero (ENZ) effect. The writing/erasing state of an optical circuit is controlled by electrical pulses and can remain non-volatile. The device works on the principle that residual polarization charges of ferroelectric film, which is compatible with CMOS processes, are utilized to electrostatically dope indium tin oxide to achieve the ENZ state. Simulation results show that a significant modulation depth of 10.4 dB can be achieved for a device length of 60 µm with an energy consumption below 1 pJ.
Might Digital Revolution be a threat for Employability?

The present study aimed to discuss the 21st Century digital revolution and its impact on the employability of individuals, and how they can be prepared to reinvent themselves to face the new demands of the labor market. Recent developments in computing, robotics and artificial intelligence have developed even more powerful systems, showing to be capable of performing complex tasks, previously performed only by humans. In this sense, when analyzing societies connected by a bunch of technological resources integrated into a networked system leads us to think about the employment scenario in near future, considering the importance of preparing ourselves to guarantee our existence in a highly challenging and, yet unknown context. The likely outcomes of this revolutionary transformation depend on how the gains are distributed as it is already perceived a deterioration of inequalities in the labour market. What is expected is that impacts generated by the digital revolution on employability might be more socially fair and economically prosperous for all parts involved.
Feasibility Assessment of Interactive Multimedia as an Innovation for Mathematics Learning in Elementary Schools

Mathematics is a subject that is full of challenges and practical tasks that students must be able to do, this of course must be in line with the availability of various appropriate and innovative learning tools and resources which can then be used by students to deepen the material. Through research that aims to develop an interactive multimedia, it is hoped that it can become one of the appropriate learning media for students to use. This type of research is a Research and Development (RnD) method that adopts a 4D development model (Define, Design, Develop, Disseminate), with a focus on extracting data on Development (D) which contains an assessment of the feasibility of media products, with data collection techniques, namely non- test through a questionnaire, accompanied by a questionnaire instrument validation sheet. The data will be analyzed descriptively using percentage conversion in order to describe the feasibility of multimedia products. The results show that interactive multimedia that has gone through an assessment process from each evaluator with details, namely the assessment of media experts 90.1%, material expert assessments 83.3%, linguistic experts assessment 98%, media developer assessments 89.9% and assessment of elementary school teachers 97%. Through the results of the assessment, it can be concluded that the product of innovation in the form of interactive multimedia for learning in elementary schools is included in the category suitable for use as learning media
Development of Character-Based Student Worksheets in Online Learning in Geography Subjects

This research is a Research and Development which aims to develop and determine the feasibility of character-based student worksheets in the subject of Geography. The development model applied is a 4-D model which is modified into 3-D consisting of define, design, and develop. Data collection techniques were carried out using observation, interviews, documentation, and questionnaires. The results of research and development show that first, character-based student worksheets have been able to be developed and declared very valid based on the results of the questionnaire obtained from the assessment and responses by validators, namely material experts at 90.52 %, linguistic experts at 95.00 %, and teaching materials experts at 84.00 %. Second, character-based worksheets are considered very practical based on the results of the teacher's response questionnaire of 91.30 % and students of 86.60 %. The character-based student worksheets that have been produced have only been tested on a limited group, so it is necessary to conduct extensive trials in order to further confirm the effectiveness of the student worksheets made. In addition, the character-based student worksheets produced is only limited to one Basic Competence, therefore further development can be carried out with other Basic Competencies so that the process of strengthening character values can be carried out more deeply.
Uncertain Multiobjective COTS Product Selection Problems for Modular Software System and Their Solutions by Genetic Algorithm

This chapter focuses on a genetic algorithm (GA)-based approach to find the solution of uncertain multiobjective commercial-off-the-shelf (COTS) product selection problem with triangular and/or trapezoidal numbers in objective functions subject to several pragmatic constraints. In this chapter, α—level set is employed to categorize fuzzy decision for the decision maker (DM) to optimize the various scenario of fuzzy objective functions and a fuzzy technique is used to tackle multiobjective optimization problem wherein the DM must specify different aspiration levels (ALs) according to his/her preferences and different shape parameters in the exponential membership function to show the effect of integration on the effective allocation plans of COTS production selection problems. An existent scenario is proposed to represent the significance of the proposed 54approach with data set from realistic circumstances. This research turned out to be a GA-based approach that provides effective output based on analysis to take decision regarding the situation.
TreeOptimizer: A classifier-based task scheduling framework

Distributed Computing (DC) involves a collection of tasks (or modules) executed in parallel on different compute nodes connected through a network. Cloud Service providers (CSP) such as Azure[1], Amazon[2], and Google[3] are providing DC platforms as PaaS (Platform As A Service) offerings. These cloud platforms reduce implementation costs but have a significant drawback as these services can be configured to spawn only a single type of compute node for executing all the tasks in the DC environment. These drawback lead to inefficiency in execution cost and time as each task will have specific compute node requirements. This paper presents a novel framework called TreeOptimizer(TO) to resolve these shortcomings. TO uses a classifier-based dynamic task scheduling to determine the best available node to perform the task. The framework has been tested in Azure Batch[1] for an Oil Industry use case for extracting data from scanned images. Experimental results indicate that TO significantly reduces the overall execution cost by 68% and processing time by 8%. Although this paper uses Batch Service to explain the proposed framework, it can be applied to other PaaS DC platforms.
Cryptocurrencies

This chapter introduces the concept of cryptocurrencies such as bitcoin, ether, or litecoin. The chapter describes the history of cryptocurrency, blockchain technology, and the quest for secure digital money, followed by a discussion of cryptocurrency as a phenomenon. Next, it discusses individual cryptocurrencies, including an overview of bitcoin and relevant subgroups, such as so-called forks or privacy coins. It also explains developments such as stablecoins or central bank digital currencies, which are potentially much more in line with bitcoin’s original idea of digital cash. Overall, the chapter provides a basic understanding of cryptocurrencies, their defining characteristics, challenges, and markets.
Applying Effective Software for Controlling Computers Remotely

Today, many educational institutes, labs, houses or organisations have their private Local Area Network (LAN), so they need to monitor employees, students in exams or machines which are working on the network. This paper aims to design and implement efficient application that can be used to track everyone who is working between 1-10 users (operating systems) on LAN network remotely; so access PC, Mac and Linux. In other words, it can monitor screens remotely, send a warning message and shutdown system. Including, it operates at low-cost, enables daily using in the services of all scales; and provide the network performance simultaneously. The implementation has achieved using Front end- JDK, JAVA language, Windows 7, 8, and10, Visio, Wireshark, Task manager, chart expert, Microsoft excel, ten computers, Ethernet &amp; Wireless of LAN network, etc. This software has applied in real-world in students examinations at three schools in Iraq. As a result, this software eligible to allocate the task to clients to restrict them from misusing the resources. As well as, automates lab and monitor attendance with performance analysis.&#x0D;