Reviewerly/exHarmony huggingface dataset:
Engagement in Online Learning Through the Lens of Adults' Learning Experience

The article aims to critically examine the role of engagement in the study of the educational experiences of adult learners in online learning. The authors highlight the existence of a gap between the nature of adult educational experiences and the lens through which these experiences are studied and evaluated. This lens is the learners' engagement. The authors highlight the complexity of the construct of learner engagement and point out the inadequacy of assessing it solely through behavioral markers.&#x0D; The theoretical framework of the article is the Community of Inquiry (CoI) model for studying the educational experiences of learners in the digital environment, based on the notion of the socially constructed nature of learning. The article shows how the idea of lifelong learning as the formation of a new professional identity of adults is consistent with the idea of an ontological turn in education. By conceptualizing the educational experiences of adults as active participants in the learning process, the authors propose to consider engagement as a characteristic of these experiences. The authors identify external and internal factors of learner engagement. Based on previous research, internal factors are proposed, namely learners’ prior experience, future self-image, and personal goals. External factors are based on the self-determination theory, where the needs for competence and belongingness become particularly salient for engagement in online learning.
TRAINING OF THEORETICAL AND PRACTICAL SKILLS FOR THE ORGANIZATION OF THEMATIC EXHIBITIONS IN TEACHERS OF LABOR EDUCATION AND TECHNOLOGY

Today, exhibition activity is a relevant area of modern culture and development of society. Exhibitions have an important aesthetic, cultural, scientific, educational and philosophical mission. Organization of exhibitions is an important element of the activity of a teacher of labor education and technology. The article presents the characteristics of one of the most common forms of exhibitions - thematic.Exhibitions on military topics are now relevant and necessary, they contribute to the formation of national self-awareness, the formation of the worldview and value system of students, and carry a patriotic meaning.The generalized and systematized experience of building the thematic exhibition «Life will conquer death» in the Poltava Local History Museum named after Vasyl Krychevsky, which was housed in a military tent, consisted of artifacts of the war, volunteer aid given to the front, and personal belongings of the defenders, is presented.
ANN AND EDUCATIONAL INTEGRITY: EXAMPLE OF SOLOMON’S POLYFUNCTIONAL

The analysis of the relationship between the growing interest in artificial neural net- work and it’s negligent exploitation by students of secondary and higher schools in order to minimize personal intellectual effort is conducted in work. The author has proposed the biographical and literary pattern from the ancient history - Solomon’s polyfunctional as one of the self stimulation tools of participation in educational process. That is con- sidered as a universal analogue of comprehensive use of personal potential. The personal development path is confirmed by biological data.
Practical Deep Neural Network Protection for Unmodified Applications in Intel Software Guard Extension Environments

Trusted computing, often referred to as confidential computing, is an attempt to enhance the trust of modern computer systems through a combination of software and hardware mechanisms. The area increased in popularity after the release of the Intel Software Guard Extensions software development kit, enabling industry actors to create applications compatible with the interfaces required to leverage secure enclaves. However, the prime choices of users are still libraries and solutions that facilitate code portability to Software Guard Extension environments without any modifications to native applications. While these have proved effective at eliminating additional development costs, they inherit all the security concerns for which Software Guard Extensions has been criticized. This chapter proposes a split computing method to enhance the privacy of deep neural network models outsourced to trusted execution environments. The key metric that guides the approach is split computing performance that does not involve architectural modifications to deep neural network models. The model partitioning method enables stricter security guarantees while producing negligible levels of overhead. This chapter also discusses the challenges involved in developing a pragmatic solution against established Intel Software Guard Extensions attacks. The results demonstrate that the method introduces negligible performance overhead and reliably secures the outsourcing of deep neural network models.
Unmanned Aerial Vehicle-Based Automated Path Generation of Rollers for Smart Construction

The construction industry is continuously evolving, seeking innovative solutions to enhance efficiency, reduce costs, and improve safety. Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as a transformative technology in the construction sector, offering numerous advantages in data collection and site management. This paper presents a novel approach for utilizing UAVs to automate the path generation of rollers, a crucial element in the construction of roads and other large-scale infrastructure projects. A UAV was used to scan the target area to create a model; the next step was to generate the path for the rollers. Traditionally, the process of determining optimal roller paths is labor-intensive and reliant on manual surveys and engineering expertise. This study proposes a streamlined workflow that harnesses UAVs equipped with computer vision technology to capture high-resolution topographical data of construction sites. This data is then processed through an algorithm created by the authors that automatically generates optimized roller paths based on several factors.
Rental Price Prediction of Boarding Houses in Batam City Using Linear Regression and Random Forest Algorithms

Boarding houses, commonly known as "kost," are residential places typically rented by individuals, serving a function similar to hotels, but with more affordable pricing. With the proliferation of boarding house businesses, residents and newcomers in Batam city face challenges in selecting suitable accommodation based on both price and amenities. Leveraging machine learning, a branch of artificial intelligence (AI), and incorporating various algorithms, a system can be developed to predict the rental prices of boarding houses. This helps individuals make informed decisions regarding the suitability of a boarding house based on their preferences and budget. The algorithms utilized in this study are Linear Regression and Random Forest. The modeling process resulted in R2 Scores, with Linear Regression achieving a score of 64%, while Random Forest outperformed with an impressive 99% R2 Score. Due to the higher R2 Score of Random Forest, this model was selected for the development of a website using the Scrum framework. The outcome of this research is a predictive pricing website for boarding houses, offering a valuable tool for residents and visitors in Batam when seeking to rent or lease a boarding house.
Sentiment Analysis on Fuel Purchase Policy Through MyPertamina Application Using NB and SVM Methods Optimized by PSO as Weight Optimation

Sentiment analysis on the MyPertamina application can serve as a means to extract customer opinions about the application. This method involves collecting reviews from users who have utilized the MyPertamina application and classifying these reviews as positive or negative using sentiment analysis algorithms. After the reviews are classified, themes discussed in positive and negative reviews can be extracted, such as ease of use, payment speed, or technical issues. This provides a general overview of user expectations for the MyPertamina application and areas that may need improvement. Sentiment analysis of MyPertamina application comments using Naïve Bayes (NB) and Support Vector Machine (SVM) methods is a process to evaluate whether user comments on the MyPertamina application are positive or negative. NB and SVM are machine learning methods used to predict the category of an input based on given training data. In this study, user comments on the MyPertamina application are used as input and classified as positive, negative, or neutral based on previous training data. The goal of this sentiment analysis is to understand user perceptions of the MyPertamina application and enhance its quality. The research concludes that the implementation of data mining can assist in categorizing sentiments of MyPertamina reviews. The NB algorithm with the addition of Particle Swarm Optimization (PSO) proves to be the most effective method in this study compared to NB alone, SVM, and SVM + PSO. The NB algorithm with PSO optimization yields an accuracy of 79.49%, the highest precision of 79.57%, recall of 79.38%, and the highest AUC of 95.30%, falling into the category of excellent classification.
CLUSTERING K-MEANS UNTUK ANALISIS POLA PERSEBARAN BENCANA ALAM DI INDONESIA

Data clustering plays a crucial role in data analysis for identifying hidden patterns, trends, and structures within the data. The K-Means algorithm has gained popularity as a widely used method for data clustering due to its efficiency and ease of implementation. Clustering is a data analysis technique utilized to group similar objects together. The K-Means algorithm stands out as one of the most renowned and frequently employed clustering methods across various fields, including data science, pattern recognition, and artificial intelligence. In this research, we collected data on natural disasters from different regions in Indonesia and employed it as input for the K-Means clustering algorithm. K-Means was utilized to cluster the similarity patterns within the occurring natural disasters. The clustering results provide information about groups that may exhibit similar characteristics and disaster risks.
Does Cyber-Insurance Benefit the Insured or the Attacker? – A Game of Cyber-Insurance

Cyber-insurance is an insurance policy that protects the insured from a variety of cybersecurity incidents such as cyber-attacks, ransomware, and data breaches. The rapid expansion of cyber-insurance in recent years hints the strong demand for cyber-insurance and its benefits. However, the impacts of cyber-insurance practice on cybersecurity enhancement and cyber-attackers are largely unknown. In this paper we study the optimal cybersecurity investment and cyber-insurance decision-making systematically with special attention paid to the effects of the attacker’s strategies. The economic modeling analysis and simulation study suggest that although cyber-insurance may be beneficial for the insured from a financial perspective, cyber-insurance practice may not be optimal from the societal cybersecurity perspective. Purchasing cyber-insurance decreases organizations’ optimal cybersecurity investment and increases the attacker’s expected payoffs. Therefore, the attacker has a motive to manipulate cyber-insurance by selective cyber-attacks on organizations up to a critical point, beyond which we discovered that imposing further threat will force organizations to invest more in cybersecurity. The attacker is capable of “playing god” by controlling the probabilities of initiating cyber-attacks and acts strategically to influence organizations’ incentives to whether to purchase cyber-insurance to harvest benefits. This study of cyber-insurance’ effects on attackers and their strategic manipulation of cyber-insurance provides insights for the future of the cyber-insurance market.
QChi: A Faster Classification Algorithm Based on Wang-Mendel Algorithm

To address learning problems in which many examples and variables are involved, the use of algorithms that are capable of processing the data using simple and efficient techniques is required. The Wang and Mendel algorithm applied to classification (also known as the Chi algorithm) satisfies these properties in many cases. However, in particularly complex problems, such as those provided by Big Data, it has become necessary to use architectures for parallel processing, such as MapReduce, whose base algorithm is just this algorithm. The use of these architectures is necessary because the bottleneck of the algorithm is the calculation of the weight of the rules. This calculation requires, for each selected rule, to consider the adaptation of all the examples with this rule. Although this calculation is polynomial-order, in problems with many examples and where many rules are generated, it is very time consuming. In this paper, we propose QChi, a new algorithm based on the Wang-Mendel algorithm that uses, among other changes to make it efficient, a new way of calculating the weights of the rules that is much less computationally expensive. The reduction in learning time is so significant that it allows the algorithm to tackle Big Data problems without parallel processing structures. In addition to the description of the algorithm, this paper checks experimentally on a wide variety of problems, including those that other authors have used as Big Data problems, that this algorithm drastically reduces the learning time while maintaining the classification performance.
SISTEM APLIKASI ADMINISTRASI PENDATAAN PENDUDUK RT.003 RW.001 CILIWUNG KEMIRIMUKA KOTA DEPOK

Neighborhood association is an organizing structure in which the Indonesian government recognizes and fosters to assist administrative tasks in building communities at the village and sub-district levels. But on RT. 003 RW. 001 Ciliwung Kemirimuka Depok City there are various problems in the management of population data and its services which consist of traditional filings into a general ledger used as physical archives so that they are vulnerable to damage and often loss of population data and cause inaccuracies in the number of population data who move, experiencing death, and residents who came. The purpose of this study is to build a population data administration system into an integrated application whose data can be stored into a database to help the service process that can be managed easily. The method used by the Research and Development (R&amp;D) method to create products is in the form of an administrative application for RT.003 RW.001 Ciliwung Kemirimuka Depok City population data collection. The results of this study can be used for population data collection applications by RT.003 RW.001 officers as a user so it does not require more time and data search can be done easily.
The development of website games as learning media for 4th-grade elementary students

Abstrak: Penelitian ini bertujuan untuk mengembangkan game website sebagai media pembelajaran untuk siswa kelas 4 sekolah dasar pada semester dua. Metode penelitian yang digunakan dalam penelitian ini adalah desain dan pengembangan (D&amp;D) oleh Richey &amp; Klein (2007) dengan model ADDIE (analisis, desain, pengembangan, implementasi, dan evaluasi). Terdapat beberapa instrumen penelitian yang digunakan dalam proses pengambilan data, yang bernama panduan wawancara kepada guru bahasa Inggris, lembar observasi untuk mengobservasi proses pembelajaran di kelas, analisis dokumen dalam bentuk modul pembelajaran dan alur tujuan pembelajaran, lembar penilaian ahli untuk ahli pendidikan, dan lembar kuesioner pengguna untuk guru dan lima siswa sekolah dasar kelas 4. Hasil penelitian ini adalah prototype game website. Hasil dari analisis data menunjukkan bahwa prototype game website dikualifikasi sebagai media yang sangat valid. Oleh karena itu, game website ini layak untuk digunakan sebagai media pembelajaran untuk siswa kelas 4 sekolah dasar.Abstract: This study aims to develop a website game as learning media for 4th-grade elementary students in the second semester. The subjects of this study were 4th-grade elementary students and an English teacher. The research method used is design and development (D&amp;D) by Richey &amp; Klein (2007) using ADDIE (analysis, design, development, implementation, and evaluation) mode. Several research instruments were used in the data collection process, namely interview guides for English teachers, observation sheets to observe the learning process in the classroom, documents analysis in the form of teaching modules and objective learning flow, expert judgment sheet for the educational experts, user questionnaire sheet for the teacher and five 4th-grade elementary students. The outcome of this research is a website game prototype. The results of the data analysis show that the website game prototype qualified as a very valid product. Therefore, this website game is feasible to use as a learning media for 4th-grade elementary students.
Implementation of Port Knocking with Telegram Notifications to Protect Against Scanner Vulnerabilities

The opening of the service port on the Mikrotik router provides an opening for hackers to enter the Mikrotik service to access the router illegally. This research aimed to close certain ports that are gaps for hackers and uses port knocking and telegram bots. The Telegram bot was used as a message notification to managers in real-time to provide information that occurs when the vulnerability scanning process is carried out to find and map weaknesses in the network system. Searching for weaknesses also includes looking for open router service ports such as ports 22, 23, 80, and 8291. This research used the Network Development Life Cycle method, which started from analysis design and prototype simulation to implementation. The research results after testing were able to secure local network service ports against vulnerability scanners on routers using the port knocking method, and testing attack schemes carried out from each scheme could run well on the router’s local network and obtain notifications via telegram bots in real time to administrators. This research contributes to administrators’ ability to secure networks so irresponsible people do not easily infiltrate them.
A Novel Dynamic Movement Primitives for Learning from Demonstrations

Dynamic Movement Primitives (DMPs) represent one of the most commonly employed frameworks for Learning from Demonstration (LfD). Nonetheless, a noteworthy drawback of DMPs pertains to their limited ability to learn from a sole demonstration, thus possibly constraining their practical utility in actual robotic scenarios. In this research, a modified DMP approach is proposed whereby the spring and damping term in DMPs are replaced by a pre-set system possessing intrinsic char-acteristics of converging towards the target, while the force term in DMPs is modeled through employment of the kernel trick. Subsequently, the modified DMPs are experimentally validated via simulation experiments and a writing task executed with the Franka Emika robot. The experimental findings affirm that the proposed algorithm not only facilitates learning from multiple demonstrations but also allows for adaptability in traversing via-ooints while preserving the primary motion features.
Estimation of Underwater Monocular Depth Using Laplacian Pyramid-Based Depth Residuals

With the success of deep learning, an increasing number of architectures and models have been proposed. Among these, the encoder-decoder architecture is one of the most renowned, and monocular depth estimation has also received enthusiastic research. In the current landscape of deep neural network models, two mainstream computational approaches prevail: convolution and transformer. However, the latter's substantial computational and parameter demands make it challenging to run on end devices. As a result, convolution-based research continues to evolve. To enhance the utilization of finely encoded lower-level features, we have implemented a straightforward yet highly effective approach by incorporating the Laplacian pyramid into the structure of the decoder. This entails feeding encoded features into different depth levels for deep residual decoding and progressively merging their respective outputs to reconstruct the final depth map from coarse to fine. This methodology greatly enhances accurate depth boundary estimation and global context comprehension. Furthermore, we introduce an improved activation function that enhances gradient flow, leading to faster convergence.Experimental results on benchmark datasets from various underwater environments demonstrate the effectiveness of this approach in monocular underwater depth estimation, especially when compared to existing models.
Comparison of Distance Measurements Based on k-Numbers and Its Influence to Clustering

Heuristic data requires appropriate clustering methods to avoid casting doubt on the information generated by the grouping process. Determining an optimal cluster choice from the results of grouping is still challenging. This study aimed to analyze the four numerical measurement formulas in light of the data patterns from categorical that are now accessible to give users of heuristic data recommendations for how to derive knowledge or information from the best clusters. The method used was clustering with four measurements: Euclidean, Canberra, Manhattan, and Dynamic Time Warping and Elbow approach for optimizing. The Elbow with Sum Square Error (SSE) is employed to calculate the optimal cluster. The number of test clusters ranges from k = 2 to k = 10. Student data from social media was used in testing to help students achieve higher GPAs. 300 completed questionnaires that were circulated and used to collect the data. The result of this study showed that the Manhattan Distance is the best numerical measurement with the largest SSE of 45.359 and optimal clustering at k = 5. The optimal cluster Manhattan generated was made up of students with GPAs above 3.00 and websites/ vlogs used as learning tools by the mathematics and computer department. Each cluster’s ability to create information can be impacted by the proximity of qualities caused by variations in the number of clusters.
A diffusion-based phase shifting network for fringe analysis

This paper proposes a frige phase-shifting technique based on the diffusion model. Phase-shifting interferometry is a commonly used method in white-light interferometric measurements, where phase shift values used as known quantities for analyzing the fringes. However, when the measurement environment introduces significant disturbances, the quality of recorded fringe patterns at different time intervals for phase shifting decreases, making it difficult to perform real-time measurements and in-line inspections in highly interfered processing environments. Additionally, the phase shift error introduced by the lead zirconate titanate (Pb[Zr<sub>x</sub> Ti<sub>1−x</sub>]O<sub>3</sub> or PZT) phase shifter easily deviates the phase shift values from their ideal values. The implemented virtual phase shifter in this study utilizes a diffusion model, which allows for the input of a raw, noise-contaminated fringe pattern and produces a noise-free fringe pattern after phase shifting. Experimental results demonstrate that our method can generate clear fringe patterns and accurately restore phase information even when the signal-to-noise ratio is 0.5 dB. The residual Peak-to-Valley (PV) and Root Mean Square Error (RMSE) values compared to the true phase are 0.8867 rad and 0.0779 rad, respectively.