Reviewerly/exHarmony huggingface dataset:
Supplemental Material: Sound velocity of eclogite at high pressures and implications for detecting eclogitization in subduction zones

&lt;p&gt;Supplemental Text S1–S2, Tables S1–S6, and Figures S1–S4.&lt;/p&gt;
Creating Engaging Powerpoint Supplementary Materials for Twelfth-Grade Students: Enhancing Procedure Text Learning

This research aimed to find the students’ needs and to design PowerPoint-based supplementary materials to teach writing procedure text for twelfth-grade students of a private school in Kalimantan Barat Province. In conducting this research and development (R&amp;D), the researcher applied the ADDIE procedure, which focused on ADD steps (analysis, design, and development). The participants of this research were an English teacher and 21 twelfth-grade social science students. The instruments used to collect the data were questionnaires and an unstructured interview sheet. The result of the needs analysis showed that the students needed various contents of writing procedure text in the form of PowerPoint. Moreover, the result of the internal evaluation showed that a design of supplementary materials of procedure text in PowerPoint scored high for all the criteria of material (89.8%) and media (83.3%). In conclusion, the design of supplementary materials for writing procedure text in PowerPoint was appropriate to implement.
Developing West Kalimantan Folklore Videos for Narrative Text Material

This research aims to enhance the teaching and learning of narrative texts for tenth-grade students by designing West Kalimantan Folklore Videos (WKFV) as supplementary learning materials. The study adopts a development research approach, utilizing the ADDIE (Analyze, Design, Develop, Implement, and Evaluate) procedure to design the materials. The research consists of three phases: analyzing, designing, and developing. In the analyzing phase, an English teacher from MAN 2 Pontianak was interviewed, and a questionnaire was distributed to X IPS 3 students. The WKFV was developed using various software tools such as Canva, Vimeo, WordPress, Google Drive, and Google Forms. An internal evaluation was conducted in the development phase to assess the usability of the WKFV. The results of the internal evaluation demonstrated that the WKFV met all the required criteria. In conclusion, the WKFV proves to be a practical and effective tool for supporting the teaching and learning of narrative texts.
Navigation of humanoids by a hybridized regression-adaptive particle swarm optimization approach

In the era of humanoid robotics, navigation and path planning of humanoids in complex environments have always remained as one of the most promising area of research.In this paper, a novel hybridized navigational controller is proposed using the logic of both classical technique and computational intelligence for path planning of humanoids.The proposed navigational controller is a hybridization of regression analysis with adaptive particle swarm optimization.The inputs given to the regression controller are in the forms of obstacle distances, and the output of the regression controller is interim turning angle.The output interim turning angle is again fed to the adaptive particle swarm optimization controller along with other inputs.The output of the adaptive particle swarm optimization controller termed as final turning angle acts as the directing factor for smooth navigation of humanoids in a complex environment.The proposed navigational controller is tested for single as well as multiple humanoids in both simulation and experimental environments.The results obtained from both the environments are compared against each other, and a good agreement between them is observed.Finally, the proposed hybridization technique is also tested against other existing navigational approaches for validation of better efficiency.
A Comprehensive Exploration of Product Description Ranking

&lt;p&gt;In this paper, we study the personalized ranking problem for item's recommendation text (product description). Item's recommendation text is the sentence that describe the item highlights for user decision (such as buy or click). The recommendation texts is shown under the item, and we also call them rec-texts. One item has multiple rec-texts, and different rec-texts have different affect for user decision. So the problem is to capture the user preference for each item's rec-texts and to personalized display. In this paper, we study multiple methods to train a model which learn the user preference scores for each item's rec-texts. The online experimental results demonstrate our methods work.&lt;/p&gt;
Penerapan Algoritma C4.5 Untuk Memprediksi Blog Yang Memiliki Peluang Juara

Blogs are currently not only used as online diaries or online journals. Blog content that supports text, images, videos, gifs, animations, pdfs and YouTube makes the use of blogs more than just online dairy. Currently blog is one of the media used by companies and organizations in promoting goods and services. The existence of bloggers is needed by companies and organizations to review their products and services with the aim of attracting online customers. One of the efforts to invite bloggers to be able to review products in an interesting way has led business actors to become interested in holding blog contests. In this research, a model was built to predict blogs that have a chance to win by applying the C4.5 decision tree algorithm. The prediction model was created using 100 blog contest participant data obtained from three blog competitions organized by ASUS which were obtained through the contest participant links found on the committee blog. From the processed dataset, supporting seven variables including Word Count, DA, PA, Image Template, Domain and Champion. The resulting prediction model is a decision tree with 7 attributes which produces 11 leaves and 18 trees with an accuracy of 74% with a precision of 0.735 and a recall of 0.740.
Rancang Bangun Sistem Informasi Geografis Pemetaan Wilayah Penderita Penyakit Stunting

Stunting is a growth disorder in children and toddlers caused by lack of nutritional intake, so that children's height is stunted compared to children their age. Currently, stunting sufferers in West Sakra District can be said to be quite vulnerable. So it requires intensive treatment and attention quickly and precisely. In mapping the area of stunting sufferers in West Sakra District it is still manual so that in the process of searching for areas with stunting in West Sakra it takes a long time and tends to cause problems. Therefore, a study was conducted to create a geographic information system for mapping the areas of stunting sufferers in West Sakra District. The purpose of this system is to provide convenience and help the West Sakra District, especially the Rensing Health Center, which manages the data. The steps taken in making this system are system requirements analysis, system design, system creation, and system testing. And in making this system, using the PHP programming language and using MySQL as the database
Prediksi Tren Pergerakan Harga Saham PT Bank Central Asia Tbk, Dengan Menggunakan Algoritma Long Shot Term Memory (LSTM)

Shares are valuable documents that prove ownership of a company. Stock investment is one of the right choices to get more profit. There are various stocks in Indonesia, one of which is the shares of PT Bank Central Asia Tbk (BBCA). However, in making stock investments, it is necessary to analyze the data of a company that can determine the increase or decrease in a stock price. Very dynamic movements require data modeling to predict stock prices in order to get a high level of accuracy. In this study, modeling using the Long-Short Term Memory (LSTM) algorithm to predict BBCA stock prices. The data used is secondary daily data obtained from securities with a date range of January 3, 2011 to December 30, 2022. The main objective of this research is to analyze the accuracy of the LSTM algorithm in forecasting stock prices and to analyze the number of epochs in the formation of the optimal model. The optimal epoch variation is obtained with the number of epochs of 5 and batch size 1. The resulting values include Mean Absolute Error (MAE) of 96.92, Mean Squared Error (MSE) of 16185.22 and Root Mean Squared Error (RMSE) of 127.22. The results of this study provide further insight into the performance of the LSTM algorithm in stock price prediction and show that with the right parameter settings, it can be a useful tool for investors in making better investment decisions
Rancang Bangun Sistem Pakar Untuk Mendiagnosa Penyakit Dispepsia Dengan Metode Forward Chaining

Dyspepsia is a digestive disorder that causes discomfort or pain in the stomach. In expert systems, the forward chaining method is used to diagnose this disease. This method evaluates the facts or symptoms obtained from the patient, then determines the appropriate diagnosis. The application of the forward chaining method in an expert system allows for an efficient and systematic evaluation of patient symptoms, so that patients can make their own detections and can make further decisions so that their disease can be treated immediately. This technology-based approach enhances the diagnostic process and facilitates rapid intervention. The expert system developed to diagnose dyspepsia has been successfully implemented using knowledge that includes 21 symptoms and 3 types of related diseases. System validation was carried out by testing 30 data samples, and the results showed that the system has an accuracy rate of up to 90% in correctly identifying and diagnosing dyspepsia. This shows that the expert system has a high level of expertise in analyzing the symptoms presented and providing a reliable diagnosis
Privacy data protection scheme using memristive hyperchaos and multi-scale block compressive sensing

Abstract Recently, most meaningful image encryption schemes incorporated with various compressive sensing models have been developed to achieve dual protection of private image data and appearance. However, there exist performance constraints in these schemes in terms of anti-chosen-plaintext attack capability and key management. Aiming at the above issues, a new visually secure image encryption scheme is proposed using multi-scale block compressive sensing (MSB-CS) model and asymmetric integer wavelet transform (IWT) embedding. In this scheme, a memristor model with locally active tristable is first introduced into the oscillator to construct a new 5D memristive hyperchaotic system to generate cipher flows. Then, the non-linear MSB-CS model is designed to compress sparsely-represented plaintext coefficients. After a series of encryption operations, secret image without semantic features is asymmetrically embedded into the same-scale non-secret-involved carrier image. Additionally, both communicating parties, Alice and Bob , acquire shared secret key through the key sharing protocol based on matrix factorization problem. Finally, simulation experiments and comprehensive analysis indicate that the 5D memristive system has complicated hyperchaotic behaviours. In the meantime, the designed encryption scheme possesses better the anti-differential attack capability and reconstruction performance than other recently proposed schemes.
Multimodal Federated Learning: A Survey

Federated learning (FL) has become a burgeoning and attractive research area, which provides a collaborative training scheme for distributed data sources with privacy concerns. Most existing FL studies focus on taking unimodal data, such as images and text, as the model input and resolving the heterogeneity challenge, i.e., the non-identically distributed (non-IID) challenge caused by data distribution imbalance related to data labels and data amount. In real-world applications, data are usually described by multiple modalities. However, to the best of our knowledge, only a handful of studies have been proposed to improve the system performance by utilizing multimodal data. In this survey paper, we identify the significance of this emerging research topic – multimodal federated learning (MFL) and perform a literature review on the state-of-art MFL methods. Furthermore, we categorize multi-modal federated learning into congruent and incongruent multimodal federated learning based on whether all clients possess the same modal combinations. We investigate the feasible application tasks and related benchmarks for MFL. Lastly, we summarize the promising directions and fundamental challenges in this field for future research.
Steganography in Audio Files – COTS Software Analysis

The paper presents the analysis of the Commercial Off-The-Shelf (COTS) software regarding the ability to be used in audio steganography techniques.Such methods are a relatively new tool for hiding and transmitting crucial information, also being used by hackers.In the following work, the publicly available software dedicated to audio steganography is examined.The aim was to provide the general operating model of the information processing in the steganographic effort.The embedding method was analyzed for each application, providing interesting insights and allowing classifying the methods.The results prove that it is possible to detect the hidden message within the specific audio file and identify the technique that was used to create it.This may be exploited further during the hacking attack detection and prevention.
Physics-Informed Neural Networks for fault slip monitoring: simulation, frictional parameter estimation, and prediction on slow slip events in a spring-slider system

The episodic transient fault slips called slow slip events (SSEs) have been observed in many subduction zones. These slips often occur in regions adjacent to the seismogenic zone during the interseismic period, making monitoring SSEs significant for understanding large earthquakes. Various fault slip behaviors, including SSEs and earthquakes, can be explained by the spatial heterogeneity of frictional properties on the fault. Therefore, estimating frictional properties from geodetic observations and physics-based models is crucial for fault slip monitoring. In this study, we propose a Physics-Informed Neural Network (PINN)-based new approach to simulate fault slip evolutions, estimate frictional parameters from observation data, and predict subsequent fault slips. PINNs, which integrate physical laws and observation data, represent the solution of physics-based differential equations. As a first step, we validate the effectiveness of the PINN-based approach using a simple single-degree-of-freedom spring-slider system to model SSEs. As a forward problem, we successfully reproduced the temporal evolution of SSEs using PINNs and indicated how we should choose the appropriate collocation points depending on the residuals of physics-based differential equations. As an inverse problem, we estimated the frictional parameters from synthetic observation data and demonstrated the ability to obtain accurate values regardless of the choice of first-guess values. Furthermore, we discussed the potential of the predictability of the subsequent fault slips using limited observation data, taking into account uncertainties. Our results indicate the significant potential of PINNs for fault slip monitoring.
Parapan Sistem Informasi Penjualan dan Pemesanan Gerabah Berbasis Web Untuk memudahkan Masyarakat Melakukan Pembelian

Pottery is one of the works of applied art that are that are found in many regions in Indonesia, one of which is fottery in the hamlet of Manggis, Aikdewa village, Pringgasela district. In full also do not have a wide marketing area, because on average they stilluse a conventional system that only relies on the buyer coming home to make purchases and orders for the pottery. The results of pottery crafts are fruit piggy banks, animals, flowers pots, etc selling pottery, then a web-based pottery sales and ordering system was created so that it could be accessed by the wider community, the aim of which was to increase income from these crafts and introduce products online so that they could be accessed anywhere as long as they were connected. Crafts in the hamlet of Dasan Manggis can have an impact on increasing the economy for the community, it can also increase the interest and insight of the younger generation to better understand and understand the process of making the pottery that will be produced.
Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Untuk Klasifikasi Kelayakan Pemberian Pinjaman

Cooperatives are social organizations or economic bodies that have a very important role in the growth, development of economic potential and community success. One of the cooperative activities is the provision of credit or loans to community members. Cooperative credit is one of the most important banking activities and serves to provide credit to the community. In practice, errors often arise due to inaccurate credit analysis, or the behavior of the customers themselves. The purpose of this research is to compare the accuracy results between the Naive Bayes algorithm and Support Vector Machine (SVM), where the best accuracy results can later be used as a reference to determine the profitability of lending. The attributes used in this study consist of 11 attributes, namely: Gender, marital status, occupation, relatives, nominal income, income criteria, loan amount, loan term, interest rate, installments and class as income characteristics. The dataset used in this study includes 166 members of the Daru Nahdla Capita Shari'ah cooperative. The results of testing the naive bayes algorithm after dividing the data five times, dividing the data set 70% as test data and 30% as training data, obtained a precision value of 97.00%, recall 100.00%, F1 score 99.00%. and accuracy 98.00%. Thus, the Naive Bayesian algorithm is an algorithm that shows accurate classification and prediction
Komparasi Algoritma Data Mining Dalam Ketuntasan Belajar Daring Siswa Pada Masa Pandemi Covid 19

This research was conducted at SMA Negri 3 Selong and became the focus of students in class XI IPA and Social Studies. The sampling technique used purposive sampling method. This study aims to describe the extent to which the level of completeness of students during post-covid-19 pandemic learning with online media. This study uses a classification algorithm that functions to find a model that distinguishes data classes or data concepts, with the specific objective of determining the class of unknown object labels. The method used is the PSO-based Naïve Bayes and Naïve Bayes Comparison Algorithms. The results of this study indicate that the use of online media during online learning using the naïve Bayes algorithm is 83.91%, and the PSO-based naïve Bayes algorithm is 91.98%, from the experimental results and testing of the two algorithms, the results of the confusion matrix and AUC testing can be obtained which can be determined the best accuracy value is the PSO-based Naïve Bayes algorithm. As for the comparison of the results in the form of an accuracy value obtained by the Naïve Bayes Algorithm of 83.91% and the PSO-Based Naïve Bayes Algorithm of 91.98% and the difference in the level of accuracy of 8.07%, so it can be concluded that the algorithm that is suitable for classifying student learning completeness during the covid 19 pandemic is Naive Bayes based on particle swarm optimization.
Penerapan Python Dalam Data Mining Untuk Prediksi Kangker Paru

Lung cancer is one of the groups of cancer that causes the most deaths, including in Indonesia. Many people with lung cancer do not realize that they are infected with lung cancer, which causes delays in treating this disease. For this reason, it is necessary to have a method that has a good level of accuracy in making a prediction so that later with a good level of accuracy it can be a reference for the development of an Artificial Intelligence (AI) in the world of health to detect lung cancer early. The proposed study uses the c4.5 algorithm to predict the likelihood of patients with lung cancer by providing the final result in the form of the prediction accuracy of the proposed algorithm. To carry out data mining implementation using the Python programming language by utilizing the library that has been provided to make it easier to implement machine learning. In this study the use of c4.5 was able to predict with an accuracy rate of 86%. This level of accuracy can be said to be worthy of being used as a reference to be able to predict lung cancer patients based on the symptoms that appear in the patient
Penerapan Metode Waterfall dalam perancangan aplikasi sistem pembelian alat kesehatan berbasis Dekstop

The purchasing process at PT Primajaya Putra Mandiri has not been carried out effectively and efficiently. This becomes an obstacle in making purchase reports every month. For this reason, a purchasing information system design is required. Purchasing information systems can streamline the purchase transaction process and can produce more accurate and timely purchase reports and are able to record accounting transactions in a short time. Timely and accurate purchase reports are necessary for the decision-making process for business entity owners. The objective of this research is to develop software using the waterfall development method, so that it can produce software that is capable of improving the purchasing system. The development of information systems in the manufacture of software uses the Waterfall model and in the manufacture of this software uses JavaScript as the script language used to create desktop pages and MySQL as the database for data storage. The results of the design of a purchasing information system using the waterfall method can reduce errors caused by human factors (human error), avoid duplicate data entry and presentation of sales reports presented in a fast and precise time so as to help the owner to carry out the decisionmaking process
Boosting Question Answering Systems with Multi-Label Classification Techniques

Abstract DBpedia is one of the most resourceful link databases today, and to access information in DBpedia databases, we need to use query syntax (e.g., SPARQL). However, not all users know SPARQL, so we must use a natural language query system to translate the user's query into the corresponding query syntax. It is costly and time-consuming for the query system to generate query syntax. Therefore, this paper proposes generating query syntax from natural language. Two multi-label learning methods are used for question transformation: Binary Relevance (BR) and Classifier Chains (CC). To predict all the labels that match the query intentions, we use Recurrent Neural Networks (RNNs) to build a multi-label classifier for generating RDF triples. To better consider the relationship between RDF triples, the Binary Relevance is integrated into an ensemble learning approach to propose an Ensemble BR. The experiments perform better than the other research to improve the query accuracy. (This article is submitted to IEEE Access with #Access-2023-19693 entitled Enhancing SPARQL Query Performance with Recurrent Neural Networks）
Contrasting the Usage of Punctuations in Chinese and Indonesian Language

Knowing the punctuation marks in Chinese and Indonesian is very important. Therefore, in order to avoid mistakes or inaccuracies in the use of punctuation marks, this research was conducted to clearly describe the similarities and differences in the use of punctuation marks in Mandarin and Indonesian.This study is a descriptive qualitative research using the literature method and the contrastive analysis method. The results show that of the 12 punctuation marks in this study, there are 7 punctuation marks that have the same form and use, 3 punctuation marks that have different forms with the same use, and there are 2 punctuation marks in Mandarin that are not found in Indonesian.