Reviewerly/exHarmony huggingface dataset:
Enhancing Generative Adversarial Networks with Attention Mechanisms for Image Synthesis

Abstract Generative Adversarial Networks (GANs) have significantly advanced image syn- thesis, but challenges persist in generating realistic and coherent images. This research paper introduces a novel Attention-Enhanced Generative Adversarial Network (A-GAN) that integrates attention mechanisms into the GAN frame- work to address these limitations. The attention module guides the generator’s focus on relevant image regions, leading to improved image quality, fine-detail capture, and global coherence preservation. Extensive experiments on diverse datasets demonstrate the superiority of A-GAN over traditional GANs, show- casing enhanced image realism, diversity, and spatial consistency. The proposed A-GAN model is applicable across various image synthesis tasks, such as image- to-image translation, style transfer, and image inpainting. The incorporation of attention mechanisms offers promising avenues for advancing computer vision and artificial intelligence applications.
Unsupervised probabilistic machine learning applied to seismicity declustering: a new approach to represent earthquake catalogues with fewer assumptions

Many applications in seismology require to isolate earthquake clusters from a background activity. Relative declustering methods essentially find a 2D representation of an earthquake catalogue that distinguishes between two classes of events: crisis and non-crisis events. However, the number of statistical and/or physical parameters to be used is often limited due to the difficulty of concatenating the information onto a physically meaningful 2D grid. In this study, we propose to alleviate the declustering task by using the ability of unsupervised artificial intelligence to model complex spatio-temporal relationships directly from data. Through a data-driven approach, we define an easily transferable declustering model that provides declustering results with fewer assumptions and no prior selection of thresholds. We first obtain this model by training a self-organising neural network (SOM) that learns to cluster data points according to their feature similarity on a 2D map. We then assign each SOM cluster a label (crisis or non-crisis class) using an agglomerative clustering procedure. We quantify the classification uncertainty by developing a probabilistic function based on the projection learned by SOM. Our method is applied to a synthetic dataset and to real catalogues from the Gulf of Corinth, Central Italy and Taiwan. We discuss the validity of the method by estimating its classification accuracy. For real data, we qualitatively compare our results to previous declustering attempts. We show that our approach is easy to handle, provides a fairly new representation of earthquake catalogues and has the potential to reduce classification ambiguities between nearby events.
THE EFFECT OF TEAMS GAMES TOURNAMENT (TGT)STRATEGY WITH THE HELP OF WORD WALL APPLICATION ON THE STUDENTS’MASTERY OF SIMPLE PRESENT TENSE AT X GRADE OF SMA N 7 TANJUNGBALAI

&lt;p&gt;The goal of this research is to determine if the effect of Teams Games Tournament (TGT) Strategy With The Help of Word Wall Application on The Students’ Mastery of Simple Present Tense at X Grade of SMA N 7 Tanjungbalai. This research employed quantitative research with a two-class design, exprimental class and control class. This research's population consisted of all class X students from SMA Negeri 7 Tanjungbalai. Simple random sampling was used to choose two classes for the research sample: 32 students from the experimental class (X IPS 1) and 32 students from the control class (X IPS 2). A multiple choice test was used to collect data. The t-test formula was used to analyze the data. From this analysis, by comparing pre-test and post-test scores, the exprimental class had a lower pre-test mean score (53,4&amp;gt;56,6) and a higher post-test mean score (76,5&amp;gt;72,3) than the mean score in control class. This research found that using Teams Games Tournament (TGT) Strategy With The Help of Word Wall Application has significant effect in simple present tense. It can be proved by the result that t&lt;sub&gt;score &lt;/sub&gt;(t&lt;sub&gt;o&lt;/sub&gt;) = 11,25 was higher than the t&lt;sub&gt;table &lt;/sub&gt;(t&lt;sub&gt;t&lt;/sub&gt;) = 1,999 with the significant 0,05 or it can be understood as T&lt;sub&gt;o &lt;/sub&gt;&amp;gt; T&lt;sub&gt;table&lt;/sub&gt; (5% ) = 11,25&amp;gt;1,999. So, Ha is accepted. In another word, using Teams Games Tournament (TGT) Strategy With The Help of Word Wall Application more effective and more significant than using conventional way.&lt;/p&gt;
Detection of Book and Borrower Communities Based on Book Borrowing Records in the Library Using Complex Network Analysis

To improve user services, libraries need to have deep insight into users and the books they manage. This study aims toidentify the community of users and borrowed books based on book borrowing records using complex network analysis. It starts with collecting book-borrowing data, then converting it into a bipartite book-borrowing network using python programming and visualizing it with Gephi. Network analysis is performed to investigate network properties, and to use the BIMLPA method to find communities. The results of the investigation show that the structure of the book borrowing network is divided into two separate components. One main component of the network represents the optimal process of borrowing books, and the rest consists of many small components representing suboptimal borrowing. Community detection on the main component found 217 borrowing communities and 141 book communities. Most of the communities are small, where the book community is 2-4 members, while the borrowing community is in the form of individual borrowers. This research also produces the top 5 book communities, and borrower communities, the most popular books, and the most active borrowers. The characteristics of the users and the books found can be used as a library reference for a more effective and efficient book development policy strategy, as well as book recommendations for more targeted users.
Classification of Basurek Batik Using Pre-Trained VGG-16 and Support Vector Machine

By introducing Indonesian batik motifs, we know that the island of Sumatra, especially Bengkulu and Jambi provinces, has a distinctive batik called Basurek batik. This research aims to classify the two batik motifs using the Support Vector Machine (SVM) algorithm. First, we extract the image of the batik motif with a pre trained VGG-16 model and then use them as a dataset for the SVM classification process. The classification process itself uses linear, polynomial, and sigmoid kernels. We divided the data 90:10 and used 10-fold cross-validation to analyze each training and testing data classification result. The results of this study are the highest values of accuracy, precision, and recall of 76.4%, 76.5%, and 76.4% produced by the linear kernel for the training data classification. For the testing data classification, both the linear and polynomial kernels generate the best accuracy, precision, and recall values of 87.5%, 90%, and 85.5%. On average, incorporating the training and testing classification results, we found that the linear kernel is the best function for classifying the Basurek batik motif using the collected images from the internet.
Projected Augmented Reality for Industrial Design: Challenges and Opportunities

Spatial-Augmented Reality (SAR) technology has the potential to impact decision-making in various design fields, such as architecture, engineering, and product design. However, despite its benefits, SAR technology has not yet been fully embraced by professionals. This study aims to identify the essential features of a SAR platform that would meet the demands and expectations of different design activities, including product appearance, interface, and ergonomics. The research involved experts who used a SAR platform in real-life product development, and data was collected through semi-structured questionnaires and interviews. The analysis revealed a significant correlation between the features of the technology, considering both potential and already available ones, and the needs of specific design sectors during product development processes. The study’s outcomes provide valuable insights for advancing SAR technology in the design field and promoting its adoption by professionals.
Vehicle Monitoring with Web-Based Applications Utilizing High-Precision Positioning

Motorized vehicles have facilitated various human activities in everyday life. As technology develops, various features in driving have been added to the vehicle itself. Starting with the aim of adding comfort, even to increase the safety in the use of vehicles. In the current digital era, the vehicle monitoring process has been widely applied, especially in a number of smart vehicles that are integrated with a monitoring system from various vendors. A web-based application or commonly known as webapp, designed by PT. Telkom Indonesia, Tbk. named IndiCar, is an example of a monitoring system, which can be applied to vehicles that are commonly used daily. This is in line with the main goal of IndiCar, which is the digitalization of public vehicles, into smart vehicles. The implementation of monitoring using the IndiCar webapp can make it easier for vehicle users, both in personal and institutional use, to track and diagnose a vehicle, regarding a number of factors such as precise location, track traveled, to the condition of various vehicle components, utilizing a number of sensors applied to the IndiCar system.
A Combination Of Support Vector Machine And Inception-V3 In Face-Based Gender Classification

Differences in human facial structures, especially those recorded in a digital image, can be used as an automatic gender comparison tool. This research utilizes machine learning using the support vector machine (SVM) algorithm to perform gender identification based on human facial images. The transfer learning technique using the Inception-v3 model is combined with the SVM algorithm to produce six models that implement polynomial, radial basis function (RBF), and sigmoid kernel functions. The results obtained are models with excellent performance, as seen from the lowest values of accuracy = 0.852, precision = 0.856, recall = 0.852, and the highest values of 0.957, 0.957, and 0.957. This combination also produces a model with excellent reliability, where the probability of overfitting or underfitting obtained is below 1%.
Smart Security in Smart City Using Naïve Bayes and RSA

As all major cities adopt the concept of smart cities, concerns arise among the public regarding data security and privacy. The constant threat of attacks on confidentiality, integrity and accessibility of data makes it vulnerable to cyber attacks. The increasing use of IoT devices also increases the potential for cyber attacks that can harm all IoT users. Therefore, it is crucial for city governments to be aware of data security issues related to smart spaces, services, and citizen security, and to provide solutions to existing problems by making maximum policies related to the implementation of smart city concepts. From the above explanation, the author is taking the analysis step with the title "Analysis of Naive Bayes Classifier and Rsa (Rivest Shamir Adleman) Combination in Smart Security in the Implementation of Smart City in Pemko Medan" where the benefits that can be obtained are to gain deeper understanding of Smart Security level, obtain information about the Smart Securty level, and classify the stage of Smart Securty using the combination of Naive Bayes Classifier and Rsa (Rivest Shamir Adleman) in the implementation of Smart City in Pemko Medan.
Hyperteroid Disease Analysis With Backpropagation Artificial Neural Network

In the era of technology 4.0, a system is needed to support the development of a company, both in industry, education, and others to help solve problems. In this study, the authors used the Backpropagation Neural Network Algorithm in recognizing hyperthyroid disease patterns. In this study, in the recognition of hyperthyroid disease patterns. The author uses 11 data variables that will be trained using the back-propagation algorithm where the weighting is done randomly and the second data is trained using the backpropagation algorithm. In this study using Matlab application for processing. From the results of testing data derived from kaggle, namely hyperthyroid disease data above, we can see in the 2-2-1 architecture which shows that the target is reduced by the jst output that the SSE is 0.06571 which indicates that there is an increase in hyperthyroid disease activity in humans. From the data obtained, that the performance of artificial neural network calculations with the Backpropagation Algorithm is 86%. Can be seen by comparing the desired target with the prediction target.
Sentiment Analysis of IDAHOBIT Celebrations using Naïve Bayes and Decision Tree Algorithms

The development of LGBTIQ in Indonesia reflects the shift in culture and the emergence of this phenomenon has attracted the attention of the Indonesian people. The use of NLP, ML, and statistics technology in tweet analysis can be used to identify sentiments contained in tweets. This study compares Naïve Bayes algorithm and Decision Tree in sentiment analysis classification, in which the multilingual sentiment analysis method is used in the labeling process of training data. Naïve Bayes results give the best classification with 100% accuracy, precision, and recall, and the number of positive sentiments is 385, negative sentiments are 3117, and neutral sentiments are 899. It looks that the negative class is the most superior compared to other classes. This proves that the Indonesian people have an unfavorable response to the IDAHOBIT celebration.
Comparison Of SGD, Rmsprop, and Adam Optimation In Animal Classification Using CNNs

Many measures have been taken to protect endangered species by using "camera trap" technology which is widespread in the field of technology-based nature protection field research. In this study, a machine learning-based approach is presented to identify endangered wildlife images with a data set containing 5000 images taken from Kaggle and some other sources. The Gradient Descent optimization method is often used for Artificial Neural Network (ANN) training. This method plays a role in finding the weight values that give the best output value. Three optimization methods have been implemented, namely Stochastic Gradient Descent (SGD), ADADELTA, and Adam on the Artificial Neural Network system for animal data classification. In some of the studies reviewed there are differences in the results of SGD and ADAM, which on the one hand SGD is superior, and on the one hand ADAM is superior with the appropriate learning rate. The results of this study show that the CNN method with the Adam optimization function produces the highest accuracy compared to the SGD and RMSprop optimization methods. The model trained using Adam's optimization function achieved an accuracy of 89.81% on the test, showing the feasibility of the approach.
Sentiment Classification on Twitter Social Media Using K-Means Clustering, C4.5 and Naive Bayes (Case Study: Blocking Paypal by Kominfo)

Kominfo (Ministry of Communication and Information) requires all PSEs (Electronic System Providers) to register themselves so that their access is not blocked, as shown in the case of Paypal and several other PSEs. The blocking case reaps mixed opinions from netizens, especially Twitter social media users. We use the sentiment values obtained from the content of tweets collected through the crawling process and employ the K-Means Clustering to group them into clusters. Finally, we use these clusters as the target in a dataset and classify them using the C4.5 and Naive Bayes algorithms. Of the 1000 netizen tweets studied, we found that 6.5% of netizens supported the blocking action, 75.4% did not care or felt that the blocking action had no effect on them, and 15.4% did not support the blocking by Kominfo. The classification results in this study resulted in a 98.2% accuracy value, a 95% precision value, and a 95.5% recall value.
Comparison Of K-nearest Neighbor (KNN) And Linear Discriminant Analysis (LDA) Algorithms For Mature Ajwa Date Fruit Classification

Currently, many applications of artificial intelligence in various fields of life, especially in image data, require digital image processing. One example of the use of digital images often encountered is image processing of fruit ripeness. Dates are a fruit in great demand by the people of Indonesia, and one of the most popular dates is the Ajwa date. The author is interested in developing previous research regarding identifying the ripeness of Ajwa Dates, where previous research used RGB color image processing with the HIS method. Therefore, the authors want to apply a different method, namely the K-Nearest Neighbor (K-NN) method and Linear Discriminant Analysis (LDA), in classifying the ripeness of the Ajwa Dates by applying a statistical feature algorithm. This research aims to develop a classification model for the maturity level of Ajwa Dates. Furthermore, it is expected to provide better classification results than the previous algorithm. The test results using the KNN method can produce higher accuracy than the LDA, where the KNN method is obtained from the calculation of the Euclidean distance k = 1 100% and Manhattan with a value of k = 1 and k = 2 worth 100%, but the minimum accuracy value is 53.33 % is found at k = 9 in the Euclidean distance calculation, while the LDA accuracy value can reach 93.33%.
Reduksi Kejenuhan Belajar Sejarah Kebudayaan Islam; Analisis Variasi Metode dan Media Pembelajaran Berbasis Digital

This study aims to determine the emergence of learning obstacles experienced by students at MAS PAB 4 Klumpang. This research uses qualitative methods with the type of field research and descriptive methods. Data collection by observation, interviews, and documentation. This study produced several findings, namely: (1) Factors causing saturation of learning SKI (History of Islamic Culture) experienced by grade XI students include: experiencing fatigue in the body due to carrying out routine activities, fatigue felt by limbs such as fingers and toes due to a lot of writing, looking weak and there are students who put their heads on the table when the teacher is explaining due to the teacher's lack of attention to Students, experiencing lazy learning due to food intake that is not good for the body and the absence of effective methods carried out by teachers. (2) Variations of digital-based learning methods and media in reducing SKI learning saturation by Giving Assignments to Students, Increasing Teacher Attention to Students, Learning Outside the Classroom, Adding Reading Resources or SKI Learning References (3) Learning achievement with a variety of digital-based learning methods and media in MAS PAB 4 Klumpang schools. Personalized Learning, Breadth of Information, More Interesting Lessons, Develop Accountability, Learning Flexibility, Recorded Content, 24-Hour Resources, Connected Learning, Easily Track Student Progress, and Improve Written Language Skills.
Program Synthesis with Best-First Bottom-Up Search

Cost-guided bottom-up search (BUS) algorithms use a cost function to guide the search to solve program synthesis tasks. In this paper, we show that current state-of-the-art cost-guided BUS algorithms suffer from a common problem: they can lose useful information given by the model and fail to perform the search in a best-first order according to a cost function. We introduce a novel best-first bottom-up search algorithm, which we call Bee Search, that does not suffer information loss and is able to perform cost-guided bottom-up synthesis in a best-first manner. Importantly, Bee Search performs best-first search with respect to the generation of programs, i.e., it does not even create in memory programs that are more expensive than the solution program. It attains best-first ordering with respect to generation by performing a search in an abstract space of program costs. We also introduce a new cost function that better uses the information provided by an existing cost model. Empirical results on string manipulation and bit-vector tasks show that Bee Search can outperform existing cost-guided BUS approaches when employing more complex domain-specific languages (DSLs); Bee Search and previous approaches perform equally well with simpler DSLs. Furthermore, our new cost function with Bee Search outperforms previous cost functions on string manipulation tasks.
Enlazados con la primera infancia: sistematización de una experiencia de Responsabilidad Social Universitaria en contexto de pandemia

The article systematizes a University Social Responsibility project carried out by students of Early Childhood Education and Audiovisual Communication programs of a private university in Lima. The initiative "Linked with early childhood: playing and learning at home with the family" won the USR Student Initiatives Fund in response to COVID-19 in 2020 and aimed to promote the development of pedagogical strategies and digital resources for an adequate interaction in learning activities between teachers and pre-school children, in the context of remote education. The survey was used to identify teachers' learning and resources' implementation. This experience strengthened the students their leadership skills, committed participation, connection with the environment, and decision-making in the face of a crisis situation for the benefit of early childhood. In addition, the improvement of the teachers' digital skills for the development of interactive classes was observed. Keys words: Early childhood, University Social Responsibility, Interactions, Distance education.
Developing Youtube Content Using Podcast to Improve Students Speaking Skill

This research was designed to develop learning media products to improve students' abilities at Madrasah Aliyah Mathlaul Anwar Pusat Menes, Pandeglang, Banten. This was done based on research questions 1) how to design a product using a podcast through the ADDIE Model stage (analysis, design, development, implementation, evaluation) on improving the speaking skills of class XI IPA 2 students of Madrasah Aliyah Mathlaul Anwar Pusat Menes through the use of podcasts? 2) how is the effectiveness of the product on improving the speaking ability of students of class XI IPA 2 Madrasah Aliyah Math'laul Anwar Pusat Menes through the use of podcasts and the research. the research objectives are 1) to design teaching media products through the ADDIE Model stage (analysis, design, development, implementation, evaluation ) using podcasts 2) to find out the results of product effectiveness on students' speaking abilities using podcasts. This study uses a development research design and data collected from 22 students in one class (control class). The results of this study are shown from the pre-test and post-test value data, the results of the data obtained from the use of the T-Paired Table on the aspect of assessing speaking ability. Then for the result of the research found that have significance for students.