Reviewerly/exHarmony huggingface dataset:
Psychometric Validation of the Information Area of Digital Competence in High School Students in Perú

Background/purpose -Few psychometric tests have been identified in the literature that analyze digital competencies in school-aged children.The Evaluation Test of Digital Competencies in Compulsory Education Students (ECODIES, Casillas-Martín et al., 2020) is considered one of the most robust for its measurement.The aim of the study was to assess the psychometric validity of the "ECODIES -Area 1: Information" test with high school students from two Peruvian cities in 2021.Materials/methods -The research was formed as a basic level, nonexperimental, and cross-sectional study.Content validity was analyzed through expert judges (n = 8), while construct validity, factorial invariance, and reliability were tested with a sample of high school students from Lima and Cusco (N = 201).Results -The content validity obtained satisfactory results (Aiken's V = 0.952, p < .01).Through construct validity, it was corroborated that the instrument measures the information competence (RMSEA = 0.052, CFI = 0.948, TLI = 0.928, PNFI = 0.67) and that this theoretical construct remains invariant between both of the groups studied.The instrument also demonstrates adequate internal consistency (α = 0.736, ω = 0.751, ordinal α = 0.869).Conclusion -"ECODIES -Area 1: Information" is a valid and reliable instrument for the assessment of the information digital competence, including cultural differences, in high school students.
Brunton, Finn. 2019. <i>Digital Cash: The Unknown History of the Anarchists, Utopians, and Technologists Who Created Cryptocurrency</i>. Princeton University Press

Abstract The book Digital Cash: The Unknown History of the Anarchists, Utopians, and Technologists Who Created Cryptocurrency is a very important piece of work in the field of digital currencies development. The author applies the media archaeology method of research and points out the most important ideas, projects, but as well barriers and beliefs that influenced the way of thinking about digital currencies and their development. This includes description of political agendas of different stakeholders, and furthermore processes of setting up a basis for robust and reliable currency, but independent of governmental forces (like Bitcoin). The book surprisingly offers several positions for the reader, in the introduction and conclusion of the book the form is rather popularly educational, sometimes reflective, the middle part is, on the contrary, a very dense academic body of work filled with theories and facts from actual research and the author's independent historical work.
Pemanfaatan Hand Tracking untuk Membuat Program Virtual Painter sebagai Alternatif Menggambar Digital

Technology will always develop every day. One of the widely used concepts is the Human Machine Interface. An example is the stylus which allows you to draw on a tablet, laptop, or other compatible device. The results of the images that are directly connected to the computer make many people like this use. However, not many people still use paper for drawing because the stylus is quite expensive and requires a device that is compatible with the stylus. The purpose of this journal is to create a program that allows users to draw digitally without the need for touch screen devices or other tools, just by moving their hands in front of the camera to draw and the results can be directly seen on the computer. The method used is the concept of hand tracking to track the movement of the fingers used for drawing. The results obtained are the program detects 21 keyponits hand landmark users using a 30 fps webcam. These keypoints are analyzed to further determine the current mode (select or draw). The resulting image can be directly seen in the input frame. Program accuracy can reach 100% with medium to high light intensity and with a distance of 50-100 cm.
Penerapan Algoritma Naive Bayes dan Multiple Linear Regression Untuk Prediksi Status dan Plafon Kredit (Studi Kasus: Bank ABC)

Along with changing technology, Human resources are still needed in many parts of decision making. The companies and organizations still use human to analysis data. Despite of that performance, human analysis took longer time and effort to complete. And sometimes there is always a negative factor of human resources such as unmanageable human. Therefore, it’s always important to provide an excellent training source so this human resource able to reach an outcome that needed. Machine learning is one of the most common knowledge that use in decision making. There are many forms of machine learning such as regression, classification, clustering, etc. two of which is used in this application, regression and classification. Naive Bayes regression is one of classification method which rooted on Bayes theorem. Naïve Bayes use historical data to predict future outcome based on the characteristic on that historical data. Multiple Linear Regression involves more than one independent variable or predictor. With machine learning and human resources, man can easily to analyse credit worthiness and determine the credit limit of one bank costumer without taking a long time and much effort.
ANALYSIS OF THE COMMUNICATION MODELS USAGE IN THE LIFE CYCLE OF INFORMATION SYSTEMS DEVELOPMENT

The article analyzes the existing communication models and their applicability to the tasksof organizing effective communication in the process of developing information systems. The useof communication models in combination with various, most commonly used models of the lifecycle of information systems – waterfall, spiral, SCRUM - is considered. The advantages and disadvantagesof such combinations and their causes are analyzed on the example of T. Newcomb'smodel. The main element of communication is to ensure understanding between the two participantsregarding the object that arises in the project at the next step of its implementation.The systematization of knowledge and information is presented, taking into account the subjectsand forms of its presentation. The analysis shows that the models, realizing the ideas embedded inthem, correspond to different life cycle models to varying degrees, but none of the communicationmodels fully solves communication problems in the development of information systems. The performedstudies show that none of the combinations of models allows to effectively organize communicationbetween participants in the development process. It is necessary to radically revisemodels and mechanisms in such a way that subjects can communicate at the stage of preparingactions to create software development artifacts, and not based on the results of their implementation.Approaches involving the development and use of communication tools – LOW/No code platforms,or other CASE-tools that allow the user and the developer to communicate while preservingthe context of the transmitted knowledge are focused on eliminating such problems. The study ofthe features and structure of such tools will be the subject of further research.
Analysis of Real-Time Applications of Blockchain

Data security and transparency the data are achieved in various application domains by incorporating blockchain technology. However, the blockchain shows under performance, scalability issues, and security threats at the time of operation in real life. This paper’s objective is to analyze the blockchain applications in the different technological domains and discuss the various issues that direct the research directions to provide suitable solutions to upgrade and enhance the blockchain application in the near future. In this paper, the analyzing of blockchain application in healthcare, smart contacts, digital certificate, crime-records, land records, Finance, agriculture, education, the internet of things (IoT), and the logistic system like the supply chain. It also analyzed the utilized area of blockchain in different domains and highlighted the issues still to be addressed. This paper aims to unearth the existing gaps and identify the scope of the future direction of works.
User Profile-Based Recommendation Engine Mitigating the Cold-Start Problem

Recommendation systems can be used in many situations in daily life. Recommending people on social media networks, products in various online shops, music, or movies are only a few use cases of these systems. The cold start problem, when no information about a new or infrequent user is available, is challenging for recommendation systems. We deal with creating restaurant and category recommendations for restaurant visitors. Recommendations are generated with different metrics and technologies based on user profiles to make recommendations as individual as possible. We use k-Means and Mean-Shift for clustering users to build a base for recommendations generated using user-based and content-based collaborative filtering methods. These suggestions consider the location of restaurants, the similarity between users and restaurants, and the ratings users give. We mitigate the cold-start problem by using matrix factorization and spatial information for users with few restaurant visits in the past. Recommendations are evaluated and adapted as a result of other user behavior to obtain better results. As a result, we can query recommendations via an Application Programming Interface (API), which consist of a mixture of location and user-based recommendation to please the users' needs by combining exploration and exploitation.
Pelatihan Public Speaking dengan Metode CCP bagi Siswa Siswi UPT SMAN 13 Maros

High school students are the young generation who have the potential and have the opportunity to play an active role in the development of the nation and the nation. To support their ability to compete in today's era, high school students are required to have self-confidence, be able to communicate politely, and be polite as part of public speaking. If you do not have the ability to communicate in public appropriately due to a lack of self-confidence, then the interaction cannot run smoothly. Therefore, to provide understanding, public speaking training is held using the CCP (Confident, Content and Performance) method. This dedication activity was a collaboration between the lecturer who held the tridharma activities of higher education and the UPTD SMAN 13 Maros which is located in Pucak Village. By using the practice method which begins with increasing self-confidence, followed by creating content and finally preparing for a live performance. the enthusiasm of the participants was very high, various questions were asked during the discussion so that this activity could provide training in the development of public speaking presentation skills
Curvature-Based Denoising of Vector-Valued Images

Salient visual information in images is often concentrated on contours or on regions where edges or curves change their direction abruptly. It is therefore of utmost importance in the processing of images to preserve this kind of information. Recently, a curvature-based denoising method has been proposed which first transforms an image into a level-line tree, then smoothes the level lines, and finally reassembles the image from those. Curvature information generated in this approach has also potential for further applications in image analysis. Focusing on denoising, we transfer curvature-based smoothing to vector-valued images. We replace level lines by pseudo-level lines (integral curves of the vector field of directions of least vectorial contrast) and design a robust algorithm for their extraction from a vector-valued image. In this context we also propose a modification of the level line extraction from grey-scale images for better rotational invariance. Since intensities along pseudo-level lines are not constant, our method stores this information along the pseudo-level lines, and performs an appropriate smoothing on intensities. Finally we adapt the reconstruction process. We present experiments on grey-scale and colour images to validate our proposed modification of the original grey-scale method as well as our new vector-valued curvature-based denoising method.
Augmented Reality in Holocaust Museums and Memorials

Augmented reality (AR) is a new medium with the potential to revolutionize education in both schools and museums by offering methods of immersion and engagement that would not be attainable without technology. Utilizing augmented reality, museums have the capability to combine the atmosphere of their buildings and exhibits with interactive applications to create an immersive environment and change the way that audiences experience them and therefore providing the ability to perform additional historical perspective taking. Holocaust museums and memorials are candidates for augmented reality exhibits; however, using this technology for them is not without concerns due to the sensitive nature of the subject. Ethically, should audiences be immersed in a setting like the Holocaust? How is augmented reality currently being used within Holocaust museums and memorials? What measures should be taken to ensure that augmented reality experiences are purely educational and neither disrespectful to the victims nor cause secondary trauma? These are the questions that this chapter will seek to answer in order to further develop the field of augmented reality for Holocaust education. To achieve this, previous AR apps in Holocaust museums and memorials have been reviewed, and a series of studies on the usage of AR for Holocaust education have been examined to identify the ethical considerations that must be made and the ramifications of utilizing AR technology to recreate tragic periods of history.
Semantic-based vulnerability detection by functional connectivity of gated graph sequence neural networks

In computer security, semantic learning is helpful in understanding vulnerability requirements, realizing source code semantics, and constructing vulnerability knowledge. Nevertheless, learning how to extract and select the most valuable features for software vulnerability detection remains difficult. In this paper, we first derive a subset of vulnerability knowledge representations from the Functional Connectivity (FC) of Graph Gated Sequence Neural Networks (GGNNs). The Gated Graph Sequence Neural Networks can be utilized to capture the long-term dependency to understand a high-level representation of potential vulnerabilities in order to detect vulnerabilities on a target project. Studying functional connectivity-based Graph Neural Networks ensures our deep understanding of the operation of sequence graph networks as highly complex interconnected systems. This ensures that the model focuses on vulnerability-related code, which makes it more appropriate for vulnerability mining tasks. Which constructs a composite semantic code property graph for code representation based on the causes of vulnerabilities. The experimental findings indicate that the suggested Model can select relevant discriminative features and achieve superior performance than benchmark methods.
Efficient Crawler Robot Modelling in Gazebo: A Tutorial

Simulation and virtual experiments are important stages of robotics research.Robotics simulators allow testing new hypothesis and algorithms without taking risks to destroy valuable hardware.Therefore, most of popular robots are sup-plied with simulation models from their makers or associated research groups.This tutorial paper describes a process of creating of a robot model for the Gazebo simulator starting from the model construction and physics setup, and ending up with sensors and Robot Operating System based control integration.The process is illustrated with an example of a step-by-step modelling of a crawler-type robot Servosila Engineer supplied with an open-source code via a public Gitlab repository.
CNN-based Single Image Dehazing via Attention Module

Methods based on deep learning have achieved remarkable performance in image deblur processing. Most of the deep learning methods used for image restoration and enhancement have no end-to-end modeling, and too many network layers lead to slow calculation and can not effectively integrate the overall features. Therefore, we propose a neural network with convolution module attention mechanism to jointly estimate the projected picture and atmospheric light, and embed the altered atmospheric scattering model. The CD-Net with Convolutional Block Attention Module proposed by us integrates global features and avoids CNN losing the low-level semantics learned in the early layers. Meanwhile, the reconstructed the fog degradation model model can greatly reduce the amount of computation, making our model simple to construct and efficient to compute. We test our network on NYU2 dataset and SOTS dataset and verify that our method is superior to other advanced image deblurring techniques by quantitative and qualitative methods, and the images after processing are easier for computer recognition or better suited to human eyes' visual qualities. Automatic driving, medical imaging, remote sensing imaging, character photography, and other industries can all benefit from image enhancing technology.
Comments on: “Machine learning-based re-classification of the geochemical stratigraphy of the Rajahmundry Traps, India” by Hoyer, et al.: J. Volc.Geother.Res. v. 428, no: 107594

We seek the views of Hoyer et al. 2022 on alternative models / conflicting observations of the Rajahmundry lavas in the perspective of their ML analytics.
An Intrusion Signal Recognition Method for Substation Perimeter Security

In order to improve the accuracy of intelligent substation perimeter alarm and reduce the false alarm rate, this paper proposes CEEMDAN combined with wavelet denoising method to preprocess the intrusion signal, using the support vector machine (SVM) as the core algorithm of the classifier, and optimizing the support vector machine (SVM) by the particle swarm (PSO) algorithm, because the particle swarm (PSO) algorithm is easy to achieve the disadvantage of local optimization. We used the gray wolf (GWO) algorithm and particle swarm algorithm (PSO) to optimize the support vector machine (SVM), experimental results show that the method has achieved a certain effect, in the perimeter intrusion signal to provide a scheme.
Coordination of Multi-Robot Systems

The multi-robot system is composed of a collection of two or more autonomous robots. The multi-robot system aims to create robust, flexible, and scalable approaches to collaborate with all robots. In multi-robot systems, robots work together to achieve several specific tasks. Although multi-robot systems provide wide applications such as path planning, cooperative localization, and dynamic task allocation, the research in the multi-robot system field is still developing. This paper analyzes previous research on multi-robot systems and divides multi-robot systems into two taxonomies. In the first taxonomy, we review the centralized multi-robot system. In the second taxonomy, we review the distributed multi-robot system. Then we analyze two typical algorithms (ant colony algorithm and particle swarm algorithm) applied to distributed multi-robot systems and compare the centralized systems with distributed systems in terms of characteristics and applications. We conclude by discussing the current limits and future research directions in the multi-robot system.
Visualization of Aerological Diagram and Analysis of Atmospheric Sounding Information Using Raob with Model Data During Low Visibility Conditions at Cengkareng Meteorological Station

The limitations of in-situ observations of the upper air are one of the obstacles in analyzing the weather. The use of data models can be one solution. The purpose of this study was to determine the accuracy of the data model in providing upper air information using RAOB as a visualization tool for aerological diagrams and sounding information analyzers. The data used are radiosonde observation data from the Cengkareng meteorological station and 1000 – 100 mb ECMWF pressure level models at the same location as the in-situ observations. The time chosen is when the haze and mist occur at the observation time 00 UTC for 5 events each. The method used is pearson correlation and simple visual verification. The results obtained that the correlation of the significant point plot data diagram when the mist occurs is 0.76 and when the haze occurs is 0.67 and visually as a whole show that the model data is quite close to the observation data. Correlation of 59 sounding information as a whole produces a value of 0.85 – 0.99 when the Mist occurs and a value of 0.89 – 0.99 when the Haze occurs. It is hoped that these results can be used as a consideration for the use of data models in filling in the gaps in radiosonde observation data. Keywords: Sounding Information, RAOB, Radiosonde, ECMWF Models
Effective SNOMED-CT Concept Classification from Natural Language using Knowledge Distillation

Recently, as natural language processing (NLP) methods have been developed a lot, research on predicting rule languages such as medical terminology systems such as Systemized Nomenclature of Medicine Clinical Term (SNOMED-CT) in natural language is becoming active. In this paper, we propose a prediction model with the SNOMED-CT code using medical natural language. Thus, natural language is encoded with the existing pre-trained model and used as a teacher model to learn a lightweight student model using knowledge distillation techniques. To improve the performance of the model, augmented data are used for learning with the augmentation technique, and performance improvement is attempted using the Teacher model in the same domain with BioBert.When the Teacher model used BioBert and the Student model used simple LSTM, the distillation results obtained an accuracy of 0.86. Only the Teacher model fine-tuned with the pre-trained model obtained a result of 0.88, but the result of the only student model, which is a simple LSTM, was 0.8695. Although we did not obtain a student model with better performance than the Teacher; however, it is useful to interpret the language of SNOMDE-CT by applying knowledge distillation (KD) to the NLP.
A Method Combined with User’s Scores for Optimizing

In personalized recommendation services, association rules are often used to provide users with appropriate recommendations.However, it is often difficult to choose the results of the optimal rule.Association rule recommendation methods, usually only need to make use of the transaction data set, and do not use many more accessible domain knowledge, the recommendation results are not satisfactory.This paper combines the collaborative filtering thinking and uses the user score information to propose a new rule result selection method, which combines the rules with consistent recommendation results, and then determines the similarity between the user and the combined rules by using user's Scores, and then obtains the optimal recommendation result.Experimental results show that the proposed method has better user satisfaction compared to traditional methods.