Reviewerly/exHarmony huggingface dataset:
The Digital Pedagogy Competence Scale (DiPeCoS): development and validation

Digital pedagogy is the intentional integration of technology into teaching and learning to build rich learning experiences. Given this potential and the pace of the digitization of education, it is important to define, assess and develop teachers’ digital pedagogical competence. Although there are several self-report measures that assess digital pedagogy competence, these do not include scenario-based tools. Scenario-based assessments allow the evaluation of knowledge and skills in real-world applications. We present here the Digital Pedagogy Competence Scale (DiPeCoS), a short, scenario-based tool that assesses a teacher’s digital pedagogy competence through choices made in real-world teaching and learning scenarios. An initial pool of ten items was reduced to create an eight-item scale using item response analysis, which was subsequently validated on 1,315 teachers in India. The DiPeCoS demonstrates unidimensionality, and its constituent items show acceptable levels of discrimination, difficulty and guessing parameters and reliability. Our results indicate that such a tool is valuable in assessing teachers’ digital pedagogy competence, and we hope it finds value in the field of digital pedagogical training and evaluation.
PERTAHANAN PENCEGAHAN SERANGAN SOCIAL ENGINEERING MENGGUNAKAN TWO FACTOR AUTHENTICATION (2FA) BERBASIS SMS (SHORT MESSAGE SYSTEM)

&lt;em&gt;Advances in digital technology have made communication between humans faster and easier. On the other hand, a lot of personal information is available online through social media and services that do not have security measures in place to protect this information. Therefore, the communication system is very vulnerable and easily penetrated by intruder due to social engineering attacks. This attack aims to deceive individuals or companies by taking actions that benefit the attacker. The trick is to provide personal data such as PIN numbers, health records, and passwords. This attack phenomenon is one of the biggest challenges in maintaining the security of personal data because this attack model takes advantage of human nature which is easy to trust others. This paper provides an in-depth survey of high-success social engineering attacks, using a 2 Factor Authentication (2FA) model by examining user accounts, to detect and avoid attempted account fraud via SMS (Short Message System). Experimental results show that attack success can be reduced to 10% and aggressive intruders can be caught by 70% of users in forwarding user verification code.&lt;/em&gt;
Development of Google Sites-Assisted Learning Devices on Vibrations and Waves Material

Website-assisted learning in science learning can make it easier for students to increase their understanding of knowledge concepts and construct their knowledge based on problems encountered in everyday life to improve learning outcomes. One application that is quite well-known and can be used as a website-assisted learning site is the Google Sites site. Google Sites is a structured application that can easily create websites. Google Sites is an online application launched by Google for creating classes, schools, or other websites. This study aims to determine the quality (validity, practicality, and effectiveness) of the Google Sites Assisted Learning Device on Vibration, Waves, and Sound material. This type of development research refers to the 4-D Model development research consisting of four stages of development, namely Define, Design, Development, and Dissemination. After being validated by two validators, a limited trial was carried out by 58 students consisting of 29 class VIII-A students and 29 class VIII-B at SMP Negeri 1 Kwandang, then the most comprehensive test was carried out by 30 class VIII students at SMP Negeri 2 Kwandang. Data collection techniques in this study used expert validation sheets, student response questionnaires, and student learning outcomes tests. The results of this study indicate that the validation of Google Sites assisted learning devices is included in the “Valid” category. The practicality of Google Sites helped learning devices is seen from the results of the implementation of learning in class and the student's responses from both trials showing the “Good” category. The Google Sites assisted learning device developed meets the valid, practical, and effective criteria to be suitable for use in science learning in schools.
IMPELEMTASI TEKNOLOGI 3D AUGMENTED REALITY UNTUK PEMETAAN GEDUNG SMK YADIKA BANGIL

&lt;strong&gt;Abstract: &lt;/strong&gt;&lt;em&gt;At this time the Covid-19 pandemic was hit Indonesia. This new normal condition is very influential&lt;/em&gt;&lt;strong&gt; &lt;/strong&gt;&lt;em&gt;on activities in the school world. One of them was during the Introduction to School Environment Introduction as we call (MPLS) which should have been done face-to-face to get to know the school more deeply, replaced by online, so that new students could not get to know more about the school building. So it takes a picture of the building in the form of 3-dimensional visual multimedia (3D) using Augmented Reality (AR) technology. Based of these problems, an Android - based application was made using AR (Augmented Reality) technology. With this application, users, especially new students, can find out more details about the locations or rooms on the Yadika Vocational High school via their smartphones.&lt;/em&gt;
Role of Emotional Intelligence in the World of Artificial Intelligence

We all are living in a time of digital transformation, even if we don't know about it still, we can feel it.Cities are becoming digital and are getting ready and preparing their strategies and smart cities. Robots are taking some of the high preside jobs and they are doing the job better than humans.Emotional Intelligence is taking time to understand oneself and others' emotions which includes self-awareness, selfregulation, empathy, social skills, and Motivation.It is a relatively new field in research in Artificial Intelligence but it has great potential to do immense good for people.However, the technology can be misused.In this conceptual paper, we would like to focus on the same and make a point that it is up to people who would embrace the change because humans in technology will decide whether the technology will be used for good or bad.Everyone has the responsibility as human behavior to make a positive difference in the world of Artificial Intelligence.
Derek Lowe asks “What’s a Journal For?” – Knowledge graphs?

What’s a Journal For? This debate has been raging ever since preprint servers were introduced as far back as 1991!
ZONING OF FLOOD PRONE AREAS IN SINGKAWANG CITY DUE TO RAINFALL

Singkawang City is a coastal and hilly city. Flooding incidents have caused considerable material damage, displacement of people, and loss of lives. Floods in Singkawang occur almost yearly. In general, the floods in Singkawang are caused by natural factors, exceptionally high rainfall. Considering the significant impact of floods on the development of Singkawang City, it is necessary to establish flood vulnerability zoning based on rainfall in the city.An analysis is conducted on parameters that influence flood incidents, including the slope of the land, elevation, soil type, land cover, and hydrological analysis of rainfall data to create a flood vulnerability zoning map in Singkawang City. Then, an overlay is performed on the four parameters that affect floods, resulting in a map showing the distribution of inundation due to the maximum planned flood discharge (Q) generated from the hydrological analysis based on rainfall.The flood vulnerability map analysis results based on 4 data parameters that affect flood events obtained that the flood-safe area in Singkawang City covers an area of 14863.97 ha (27.04%). In contrast, areas not prone to floods cover 24571.74 ha (44.70%). Areas vulnerable to floods cover 11476.22 ha (20.87%), and regions highly vulnerable to floods cover 3567.28 ha (6.49%). Based on the results of hydrological analysis based on rainfall, it is obtained that the area of Singkawang City mostly experiences inundation due to the maximum planned flood discharge occurring at a 2-year interval in the Small Semelagi River Zone (353.5 ha), at a 5-year gap in the Small Semelagi River Zone (327.4 hectares), at a 10-year gap in the Selakau River Zone (401.4 ha), at a 20-year interval in the Selakau River Zone (473.1 ha), at a 50-year interval in the Selakau River Zone (545.9 ha), and at a 100-year gap in the Selakau River Zone (617.7 ha).
Quantum chemistry interoperability (library): another step towards FAIR data.

To be FAIR, data has to be not only Findable and Accessible, but straightforwardly Interoperable. One of the best examples of interoperability in chemistry comes from the domain of quantum chemistry. This strives to describe a molecule by its electron density distribution, from which many interesting properties can then be computed. The process is split into two parts: Computation of the wavefunction.
RANCANG BANGUN APLIKASI PENGADAAN BARANG DISTRIBUSI PADA PT FAMOUS CHICKEN DENGAN MENGGUNAKAN METODE DISTRIBUTOR REQUIREMENT PLANNING (DRP)

&lt;span class="fontstyle0"&gt;The need for fast, precise and accurate information is absolute in this fast-paced era. The delay in presenting the required information will cause the information to become irrelevant for its users. Thus, a good system must be able to provide timely information, with accurate and precise data in the processing process.Goods Distribution Information System Through Transporter at PT Super Famous Chicken departs from the company's vision, mission and goals. Namely to be a Goods Distribution Information System through a Transporter at PT Super Famous Chicken that has quality in providing services to the community. Various ways to improve the quality of goods distribution services have been taken, among others by applying the distribution data archiving method which will conduct further studies more efficiently, simplify the transaction data processing, improve the overall quality of resources, and improve the company's accreditation status.This information system will further simplify the processes mentioned above by utilizing data communication network information technology. So that the processing of existing data will be faster and more accurate, and eliminate duplication of data that arises between existing sections.&lt;/span&gt; &lt;br /&gt;&lt;br /&gt;
Product Feasibility Decision Support System at Kadai Tangkelek with the Weighted Product (WP) Method

Speed and accuracy in work will be the key to the success of a company in providing satisfaction to its customers. The decision support system is also an interactive system that supports decisions in the decision-making process through alternatives obtained from the results of data processing, information and model design. Kadai Tangkelek Padang is a clothing business in West Sumatra that is well known to many tourists as souvenirs from West Sumatra, not only marketing clothes, they also sell other products such as bags and sandals, what happens is that the evaluation of the feasibility of a product is still done manually , so that feasibility calculations tend to be subjective and require a relatively long time. The purpose of this research is to assist companies in calculating product feasibility at Tangkelek Padang shops using the Weighted Product (WP) method. The Weighted Product (WP) method is a decision-making method by means of multiplication to link attribute ratings, where each attribute's rating must be raised to the first power of the attribute's weight. The result of this research is the creation of a system that can assist in the product feasibility decision-making process at the Tangkelek Padang Store, so that the final result of the calculation can determine whether the product is feasible or not suitable for distribution
A Study on Different Methods in Sentiment Analysis from Text

Sentiment analysis (SA) research has predominantly focused on monolingual SA over the past four years. However, there is a growing need to explore SA in languages with mixed code and expand analysis to underrepresented languages. This study aims to address this gap by investigating the creation of a gold standard dataset for sentiment analysis in mixed code languages and exploring techniques for underrepresented languages. The objective is to understand the construction of high-quality, standardized datasets encompassing mixed code languages, serving as benchmarks for evaluating SA models in languages with diverse code-mixing patterns. Currently, resources for SA in mixed code languages are limited, and comprehensive datasets will facilitate advancements in SA research. The study explores sentiment analysis in languages that have received limited attention, especially those involving code-mixing scenarios. Analyzing sentiment in these languages enhances the applicability of SA in diverse linguistic contexts and provides insights into sentiment expression and perception across cultures and language combinations. Through an extensive literature review, the study highlights the need for expanded SA research beyond monolingual analysis. By addressing the formation of gold standard datasets for mixed code languages and exploring SA techniques in underrepresented languages, this research pushes the boundaries of SA and extends its applicability to complex linguistic landscapes. The findings will contribute to advancing SA in diverse language contexts and provide a foundation for future research in sentiment analysis across multiple languages.
Sentiment Analysis of Hybrid Network Model

The existing text sentiment analysis models based on deep learning and neural network usually have problems such as incomplete text feature extraction and failure to consider the impact of key information on text sentiment tendency. Based on the parallel hybrid network and the two-way attention mechanism, an improved text sentiment analysis model is proposed. The model first takes the word vector trained by the BERT language model as the input, and then extracts the global and local features of the context simultaneously through the parallel hybrid neural network constructed by the Convolution Neural Network (CNN) and The Bidirectional Gated Recurrent Unit (BiGRU), so as to improve the feature extraction ability of the model. It also integrates the dual-way attention mechanism to strengthen the key information in the global feature and local feature, and the feature vectors obtained by feature fusion are used for sentiment analysis. The experimental results on the ChnSentiCorp hotel review data set and the NLPcc2013-NLPcc2014 microblog review data set show that the model has significantly improved the classification accuracy compared with the benchmark model.
TranKrypto: A Blockchain-Based Platform for Secure and Transparent Transactions

The concept of blockchain has recently gained a lot of popularity because it can be applied to practically every industry and commercial transaction while maintaining sufficient security measures. A comprehensive solution for safe and transparent transactions is offered by the blockchain-based platform TranKrypto. The platform uses Ethereum as its blockchain network and leverages several technologies, such as React, Solidity, Vite, Tailwind, Node.js, and MongoDB, to create a decentralized network of nodes that work together to maintain the ledger. User identification, smart contracts, an admin panel, and a decentralized ledger are just a few of the platform's features that offer a safe and open method to do business transaction. The objective of this research activity is to provide a detailed overview of TranKrypto, which makes use of the technologies node, react, solidity, vite, tailwind, and MetaMask. Our proposed approach includes an admin panel built with React, MUI, and Redux, a server built with Node.js, and user data stored on MongoDB. Additionally, this endeavour provides users with a service that enables them to test Ethereum transactions using the Goerli Ethereum test net on their computer. Key Words: Blockchain, TranKrypto, MongoDB, Node.js, React, Ethereum, MetaMask
AI Path Prediction &amp; Planning for Autonomous Vehicles

Autonomous navigation of vehicles at various places can tremendously improve daily traversal, especially while driving in unfamiliar territory. Path-planning systems have been continuously improved to cope with increasingly difficult issues and advances in computing technology. One example is the rise of sampling-based planners, which made difficult planning issues easier to solve. As a result, problems that were previously thought difficult can now be addressed, and evendynamical constraints can be overcome. The ongoing modifications and study in path planning are aimed at achieving robotic systems' fullautonomy. By relying solely on path planning approaches, this goal has not been fully realized. The majority of path-planning algorithms work well in simulations and laboratory robots, but real- world robots operate in unknown surroundings and interact with other agents whose motion is often unpredictable and whose conduct can be uncooperative. Real robots have inherent kinematical and dynamical (kinodynamical) constraints that make autonomous collision-free motion impossible in particular circumstances. This research demonstrates the implementation, testing, and comparison of two path-planning algorithms for autonomous navigation of a vehicle/rover, namely rapidly-exploring random trees (RRT) and second probabilistic roadmap (PRM). Using random samples from the search space, an RRT generates a tree rooted at the beginning configuration. As each sample is drawn, a link is made between it and the state closest to it in the tree. This results in the adding of the new state to the tree if the link is viable (goes wholly acrossfree space and obeys all constraints). The main idea underlying PRM, on the other hand, is to pick random samples from the robot's configuration space, test them to see whether they are in the free space, and then use a local planner to connect these configurations to other adjacent configurations.The beginning and goal configurations are brought in, and the resulting graph is subjected to a graph search algorithm in order to find a path between them. To assess the performance of the recommended approaches, simulations and experiments are carried out.
A STUDY ON THE DIFFERENT TECHNIQUES FOR FACIAL EMOTION IDENTIFICATION AND CLASSIFICATION

Facial expression plays a significant role in our everyday lives. These expressions help in identifying the frame of mind of people. Henceforth, it is used in various fields like medical psychology, criminal investigations, job recruitment, etc., to study human behaviour and analyze people. In the emerging field of artificial intelligence, various categories and analyses are developed to identify and classify facial emotions. Various methods have been implemented, from image analysis to live facial emotion recognition. In our proposed work, we have implemented micro-expression based real-time facial emotion recognition (FER) using a convolution neural network in conjunction with the artificial bee colony algorithm (ABC). Datasets like FER2013, Google Data Set, CK+ and in the wild dataset were used for the training process. We achieved a 95% to 99% accuracy in real- time video-based facial recognition. Key Words: Facial Emotion Recognition (FER), Artificial Bee Colony (ABC), Multi-channel Convolution Neural Network (MCNN), Optical Flow Analysis, Empirical Wavelet Transform (EWT).
Multilevel Thresholding of Color Image Segmentation Using Memory-based Grey Wolf Optimizer With Otsu Method, Kapur, and M.Masi Entropy

Determining the optimal threshold value for image segmentation has become more attention in recent years because of its varied uses. Otsu-based thresholding methods, minimum cross entropy, and Kapur entropy are efficient for solving bi-level thresholding image segmentation problems (BL-ISP), but not with multi-level thresholding image segmentation problems (ML-ISP). The main problem is exponentially increasing computational complexity. This study uses the memory-based Gray Wolf Optimizer (mGWO) to determine the optimal threshold value for solving ML-ISP on RGB images. The mGWO method is a variant of the standard grey wolf optimizer (GWO) that utilizes the best track record of each individual grey wolf for the global exploration and local exploitation phases of the problem solution space. The solution candidates are represented by each grey wolf using the image intensity values and optimized according to mGWO characteristics. Three objective functions, namely the Otsu method, Kapur Entropy, and M.Masi Entropy are used to evaluate the solutions generated in the optimization process. The GridSearch method is used to determine the optimal parameter combination of each method based on 10 training images. Evaluation of the performance of the mGWO method was measured using several benchmark images and compared with five standard swarm intelligence (SI) methods as benchmarks. Analysis of the results was carried out qualitatively and quantitatively based on the average PSNR, RMSE, SSIM, UQI, fitness value, and CPU processing time from 30 tests. The results were analyzed further with the Wilcoxon signed-rank test. The experimental results show that the performance of the mGWO method outperforms the benchmark method in most experiments and metrics. The mGWO variant also proved to be superior to the standard GWO in resolving multi-level color image segmentation problems. The mGWO performance results are also compared with other state-of-the-art SI methods in solving ML-ISP on grayscale images and was able to outperform those methods in most experiments.
Modification of Non-local Mean Algorithm Using Parallel Calculation for Image Noise Reduction

Noise in digital image processing is a noise that occurs at pixel values due to random colour intensity. Several types of noise models include Gaussian noise, speckle noise, impulse noise, and Poisson noise. Before processing image data, a noise reduction process is required. One of the noise reduction algorithms used for gaussian noise models is Non-local Mean. This algorithm performs calculations sequentially on each pixel in the search block. Due to a large number of pixels and search block area in the image, the noise reduction process using the Non-local Mean algorithm is very slow. This study proposes the concept of parallel calculations for the Non-local Mean algorithm. This concept divides the search block into three parts and performs calculations on each part simultaneously. The experimental results show that the Non-local Mean algorithm with parallel calculations can reduce noise up to 30% faster if the noise standard deviation is above 30.
Feedback Attention Neural Network

The hidden semantics are a crucial analysis element in the NLP field for performing tasks by analyzing the semantic context of text. Although self-attention mechanisms have achieved outstanding results in numerous tasks in recent years, their calculation solely based on input data results in excessive focus on the attention relationship between input data, reducing their ability to model contextual dependencies or develop global semantics and overall understanding. We have designed a novel feedback attention neural network architecture with a structure of hierarchical weight sharing, which enables distant interaction between the model's input and output layers and guides the calculation of input data attention based on the acquired global information. Through careful evaluation, we demonstrate the effectiveness and robustness of our proposed model.