Reviewerly/exHarmony huggingface dataset:
Design and Implementation a Web-Based Semester Learning Plan Management Information System

&lt;span lang="EN-US"&gt;This research designs and implements an information system to manage web-based Semester Learning Plan using cloud computing technology, software, computers, and repositories as well as a serverless architecture. So far, the preparation of Semester Learning Plan in Department of Electrical Engineering UIN Sunan Gunung Djati Bandung is still done manually using a template in Microsoft Excel format. This method is considered ineffective because filling in takes a long time and the output is sometimes not uniform, both between lecturers and between courses. Based on this background, the author proposed the idea of creating a web-based Semester Learning Plan management information system. The method used to develop this system is the waterfall method which consists of five stages, namely requirements analysis, system and software design, implementation, testing, operation and maintenance. This information system using cloud computing technology and serverless architecture. The result of this research is a web-based Semester Learning Plan management information system. Through this system, lecturers no longer type manually into doc or xls format, but lecturers can directly input into the system or the web. The output is that the Semester Learning Plan can be downloaded and is in accordance with the applicable format and is in pdf format. The existence of this system can increase the effectiveness of lecturers in managing Semester Learning Plan.&lt;/span&gt;
Partial Least Square Algorithm (PLS) with Technology Acceptance Model (TAM) in User Analysis of Public Health Center Management Information System (SIMPUS) Applications

Purpose: The most important information system application at the health center is the health center management information system or can be called SIMPUS. The SIMPUS is an application program specifically designed to help facilitate recording of patient data, processing and presenting data into information in a short time. With the SIMPUS application, it is necessary to examine whether the application is very helpful for users in completing work at the public health center or Puskesmas. Therefore, the purpose of this study is to analyze the SIMPUS application users by combining the Partial Least Square (PLS) algorithm with the Technology Acceptance Model (TAM) method.Methods: SIMPUS application user analysis is carried out using a combination of the Partial Least Square (PLS) algorithm with the Technology Acceptance Model (TAM) method. The variables used are Perceived Usefulness, Perceived Ease of Use, Attitude Towards Using, Behavioral Intention to Use, and Actual System Use. Data collection techniques by distributing closed questionnaires and taking samples with the solvency formula. Sampling was carried out using a multistage random sampling technique, the number of 12 Puskesmas in each Puskesmas from the calculation results determined 40 samples.Result: From the statistical test results, the effect of the perceived usefulness on the ease of use has the highest level of influence, which obtaining a test value of 3.6. Furthermore, the effect of the attitude towards using on the behavioral intention to use has the lowest level of influence, which obtaining a test value of 1.2.Value: Analysis and testing of variables that influence user acceptance of the SIMPUS application using the Partial Least Square (PLS) algorithm and the Technology Acceptance Model (TAM) approach, that acceptance of the SIMPUS application is influenced by the level of usability and ease of use of the application.
The Role of Social Network Sites in English Language Teaching_Harnessing the Potential of Facebook and YouTube as Learning Tools

This paper provides a comprehensive review of the role of Social Network Sites (SNSs) in the context of English as a Second Language (ESL) and English as a Foreign Language (EFL) teaching and learning. It examines the definitions and potential applications of SNSs, with a specific focus on Facebook and YouTube. The paper discusses the educational and instructional implementations of these SNSs, as well as the challenges and drawbacks faced by both teachers and students when utilizing them. Furthermore, it explores the pedagogical implications of incorporating the cutting-edge features offered by SNSs, aiming to bridge the gap between traditional and technologically driven learning environments
Study on Network Virtual Printing Sculpture Design using Artificial Intelligence

Sculptures are visionaries of a country’s culture from time immemorial. Chinese sculptures hold an aesthetic value in the global market, catalysed by opening the country's gates. On the other hand, this paved the way for many duplicates and replicates of the original sculptures, defaming the entire artwork. This work proposes a defrauding model that deploys a Siamese-based Convolutional Neural Network (S-CNN) that effectively detects the mimicked sculpture images. Nevertheless, adversarial attacks are gaining momentum, compromising the deep learning models to make predictions for faked or forged images. The work uses a Simplified Graph Convolutional Network (SGCN) to misclassify the adversarial images generated by the Fast Gradient Sign Method (FGSM) to combat this attack. The model's training is done with adversarial images of the Imagenet dataset. By transfer learning, the model is rested for its efficacy in identifying the adversarial examples of the Chinese God images dataset. The results showed that the proposed model could detect the generated adversarial examples with a reasonable misclassification rate.
Meteorological Drought Analysis Using The Theory Of Run Method In Lusi Watershed, Central Java

Reduced water reserves can be caused by decreased rainfall. During the dry season along the Lusi River, Central Java, water quantity decreased, so that the area along the Lusi River passes in drought conditions. This study aims to determine the longest duration of drought and the largest cumulative number of droughts, in the Lusi watershed for a ten-year period. The level of drought in the Lusi watershed was analyzed using the Theory of Run method. The results obtained were the longest drought duration of 9 months and the largest cumulative drought amount of -486 mm and the Lusi watershed was classified as a meteorological drought.
Analysis of School Climate in the Unesa School Lab

This study aims to analyze the school climate in the Unesa Lab School environment. This study uses a descriptive quantitative approach. This research took place in nine schools, including two kindergartens, two elementary schools, three junior high schools, one high school, and one vocational school. The study results show that the overall school climate in the Unesa Lab environment is excellent, evidenced by the average gain of 4.19. However, suppose one looks at them one by one. In that case, two schools are in the excellent category: Unesa Lab School Kindergarten 1 and Unesa Lab School Elementary School 1. Of all the indicators that tend to get low scores compared to the others are (1) the happy atmosphere of the students is not maximized, and there are still doubts in doing the assignment; (2) there is secrecy in providing information, so mutual suspicion still appears; (3) the synergy between school principals and teachers, between teachers, and teachers and students in the teaching and learning process is not maximized; (4) the teaching and learning facilities provided are not by their purpose; and (5) the assistance given has not been maximized for any difficulties that arise as a result of assignments imposed on students.
Pharmacists' Perspectives on the Implementation of e-Prescribing in Hospitals and Community Pharmacies: A Literature Review

Electronic Prescribing is a system that utilizes information technology to replace or reduce the use of paper prescriptions in the process of prescribing medication to patients. The implementation of e-Prescribing poses significant challenges related to user readiness, facilities, and other factors. This study aims to determine the perspectives of hospital and community pharmacists on the implementation of e-Prescribing. The research design used in this paper is a literature review. The objects used in this study were taken from the results of research published in national and international journals. Articles were obtained from PubMed and Google Scholar using the keywords "Pharmacists" AND "Electronic prescribing" AND "Hospital". A total of 80 articles from PubMed and 280 articles from Google Scholar were obtained. The selection resulted in 6 articles that met the inclusion and exclusion criteria for use in this study. &#x0D; Based on the literature review of the 6 journals, information was obtained that the implementation of e-Prescribing from the perspectives of hospital and community pharmacists provides benefits in terms of ease of use, improving patient safety, speeding up service processes, and improving the quality of medication dispensing. Weaknesses identified in the implementation of e-Prescribing are associated with the availability of supporting facilities, skills and readiness of human resources, as well as the continued risk of medication errors.
URGENSI PENGGUNAAN MEDIA PEMBELAJARAN BERBASIS TEKNOLOGI

THE URGENCY OF USING TECHNOLOGY-BASED LEARNING MEDIA. The progress of the times has become a conversation every day, and has become a new habit in life. Technology plays a role in relation to the progress of the times, developments have spread to all sectors, including the education sector. In the learning process, the use of effective and efficient media to improve the quality and competence of students in order to be able to achieve learning objectives is very necessary. Various learning media provide flexibility for educators to design learning according to the characteristics of students. The presence of this technology-based learning media gives a new color and style to the world of education. With the technology that enters the world of education, it is expected to be able to change conventional habits towards modernization. In this study, several concrete facts are presented regarding the use of technology-based learning media so that they can be used in educational institutions in each educational unit, following the learning styles and characteristics of students. Where this learning has an important role in the order of human life, and as an alternative to an image and sound-based media to convey learning material.
Modeling The Results Of A Perceptron And Neuro-Fuzzy Neural Network Simulation (ANFIS)

The objective of this work is to model simulation data of a dust devils in Comsol using neuro-fuzzy methods (ANFIS: Adaptive Neuro Fuzzy Inference Systems) and perceptron neural networks. Since the number of simulations performed was insufficient, we used the Spline function to increase the amount of data. The results show that neuro-fuzzy is more effective than perceptron neural networks. The obtained models are excellent, with a Nash -Sutcliffe criterion value above 90%.
Design of CueAid: An Indian Sign Language Recognition Application for Differently Abled

The Indian Sign Language (ISL) is a widely used mode of communication among individuals with hearing and speech impairments in India. Unlike spoken language, sign language relies on visual cues such as hand gestures, arm posture, hand orientation, and facial expressions to convey a speaker's message. The majority of communication exchanges in today's society take place via video calls, which have been increasingly in demand. However, differently abled people find it challenging to communicate during video calls as it restricts their ability to express themselves. As a result, it is essential to convert sign language into a form that everyone can understand. The proposed application would help bridge the communication gap between the hearing and speech impaired community and the rest of society. In this study, we propose a system for recognizing action-based ISL. The designed application, CueAid, has two main functions: speech to gesture and gesture to text conversion. The system captures motion using OpenCV, identifies key points using MediaPipe, and employs a trained Long Short-Term Memory (LSTM) model for dynamic sign recognition. A custom dataset containing 15 everyday ISL words was created and used in the study, yielding an average accuracy of 93.334%. The created model has the potential to be integrated with the application for real-time recognition of ISL.
Hybrid Top-K Feature Selection to Improve High-Dimensional Data Classification Using Naïve Bayes Algorithm

Abstract. Purpose: The naive bayes algorithm is one of the most popular machine learning algorithms, because it is simple, has high computational efficiency and has good accuracy. The naive bayes method assumes each attribute contributes to determining the classification result that may exist between attributes, this can interfere with the classification performance of naive bayes. The naïve bayes algorithm is sensitive to many features so this can reduce the performance of naïve bayes. Efforts to improve the performance of the naïve bayes algorithm by using a hybrid top-k feature selection method that aims to handle high-dimensional data using the naïve bayes algorithm so as to produce better accuracy.Methods: This research proposes a hybrid top-k feature selection method with stages 1. Prepare the dataset, 2. Replace the missing value with the average value of each attribute, 3. Calculate the weight of the attribute value using the weight information gain method, 4. Select attributes using the top-k feature selection method, 5. Backward Elimination with the naïve bayes algorithm, 6. Datasets that have been selected new attributes, then validated using 10 fold-cross validation where the data is divided into training data and testing data, 7. Calculate the accuracy value based on the confusion matrix table.Result: Based on the experimental results of performance and performance comparison of several methods that have been presented (Naïve Bayes, deep feature weighting naïve bayes, top-k feature selection, and hybrid top-k feature selection). The experimental results in this study show that from 5 datasets from UCI Repository that have been tested, the accuracy value of the hybrid top-k feature selection method increases from the previous method. From the accuracy comparison results that the proposed hybrid top-k feature selection method is ranked the first best method.Novelty: Thus it can be concluded that the Hybrid top-k feature selection method can be used to handle dimensional data in the Naïve Bayes algorithm.
S-box Construction on AES Algorithm using Affine Matrix Modification to Improve Image Encryption Security

Abstract.Purpose: In this study, the AES algorithm was improved by constructing the S-box using a modified affine matrix and implementing it so that there was an increase in security in image encryption.Methods: The method used in this study starts from selecting the best irreducible polynomial based on previous studies. The irreducible polynomial chosen is . With this irreducible polynomial, an inverse multiplicative matrix is formed. The formed inverse mutiplicative matrix is implemented in the affine transformation process using the best 3 affine matrices based on previous research and 8-bit additional constants using AES S-box. This formulation produces 3 different S-boxes, i.e., S-box1, S-box2, and S-box3. Finally, the resulting S-boxes are implemented to carry out the image encryption process and are tested for their security level.Result: The test results show an increase in image encryption security compared to previous studies. The increase in security occurred at the entropy value of 7.9994 and the NPCR value of 99.6288%.Novelty: The novelty of this paper is the improvement of the S-box construction which is implemented in image encryption resulting in increased security in image encryption.
The Strategy of Constructing an Introduction for Scientific Writing at University

Academic writing was a mandatory thing needed to be written by academics, but at this time it was still being felt and experienced, especially among students, in terms of misunderstandings and a lack of understanding of knowledge about proper research and writing of scientific papers. Therefore, the purpose of this paper was to provide insight into strategies for making introductions, abstracts, and even how to make appropriate scientific paper titles because these 3 points were the initial basis for writing scientific papers. This study employed a literature review from several experts related to the method of making scientific papers. The data obtained from the results of scientific books, journals, and several research results in the last 5 years were compared. The results of this study found 3 findings, namely (1) Introduction strategy by 3 experts; (2) title strategy by 4 experts; and (3) abstract strategy by 3 experts. From these results, it can be concluded that the strategy in writing introductions, abstracts, and even research titles for scientific work quite needed to be considered by academics, especially students as novice researchers as a first step before going deeper. The strategy used must be followed and be consistent with one of the experts who was indeed an expert in writing scientific papers and had long been involved in the world of research and was still active in research and publications.
TECHNOLOGICAL, PEDAGOGICAL, CONTENT KNOWLEDGE (TPACK) OF SCIENCE TEACHERS: BASIS OF IN-SERVICE TRAINING DESIGN DEVELOPMENT

This study was developed to identify the technological, pedagogical, and content knowledge of Science teachers in the context of the essential factors influencing the research gap in the quality of instruction delivered by Science teachers.Considering this study could provide baseline data, these findings might serve as the basis of any future training design plans.
Adversarial Likelihood Estimation With One-Way Flows

Generative Adversarial Networks (GANs) can produce high-quality samples, but do not provide an estimate of the probability density around the samples. However, it has been noted that maximizing the log-likelihood within an energy-based setting can lead to an adversarial framework where the discriminator provides unnormalized density (often called energy). We further develop this perspective, incorporate importance sampling, and show that 1) Wasserstein GAN performs a biased estimate of the partition function, and we propose instead to use an unbiased estimator; and 2) when optimizing for likelihood, one must maximize generator entropy. This is hypothesized to provide a better mode coverage. Different from previous works, we explicitly compute the density of the generated samples. This is the key enabler to designing an unbiased estimator of the partition function and computation of the generator entropy term. The generator density is obtained via a new type of flow network, called one-way flow network, that is less constrained in terms of architecture, as it does not require a tractable inverse function. Our experimental results show that our method converges faster, produces comparable sample quality to GANs with similar architecture, successfully avoids over-fitting to commonly used datasets and produces smooth low-dimensional latent representations of the training data.
PPN: Parallel Pointer-based Network for Key Information Extraction with Complex Layouts

Key Information Extraction (KIE) is a challenging multimodal task that aims to extract structured value semantic entities from visually rich documents. Although significant progress has been made, there are still two major challenges that need to be addressed. Firstly, the layout of existing datasets is relatively fixed and limited in the number of semantic entity categories, creating a significant gap between these datasets and the complex real-world scenarios. Secondly, existing methods follow a two-stage pipeline strategy, which may lead to the error propagation problem. Additionally, they are difficult to apply in situations where unseen semantic entity categories emerge. To address the first challenge, we propose a new large-scale human-annotated dataset named Complex Layout form for key information EXtraction (CLEX), which consists of 5,860 images with 1,162 semantic entity categories. To solve the second challenge, we introduce Parallel Pointer-based Network (PPN), an end-to-end model that can be applied in zero-shot and few-shot scenarios. PPN leverages the implicit clues between semantic entities to assist extracting, and its parallel extraction mechanism allows it to extract multiple results simultaneously and efficiently. Experiments on the CLEX dataset demonstrate that PPN outperforms existing state-of-the-art methods while also offering a much faster inference speed.
FACADE: A Framework for Adversarial Circuit Anomaly Detection and Evaluation

We present FACADE, a novel probabilistic and geometric framework designed for unsupervised mechanistic anomaly detection in deep neural networks. Its primary goal is advancing the understanding and mitigation of adversarial attacks. FACADE aims to generate probabilistic distributions over circuits, which provide critical insights to their contribution to changes in the manifold properties of pseudo-classes, or high-dimensional modes in activation space, yielding a powerful tool for uncovering and combating adversarial attacks. Our approach seeks to improve model robustness, enhance scalable model oversight, and demonstrates promising applications in real-world deployment settings.
Boundary State Generation for Testing and Improvement of Autonomous Driving Systems

Recent advances in Deep Neural Networks (DNNs) and sensor technologies are enabling autonomous driving systems (ADSs) with an ever-increasing level of autonomy. However, assessing their dependability remains a critical concern. State-of-the-art ADS testing approaches modify the controllable attributes of a simulated driving environment until the ADS misbehaves. Such approaches have two main drawbacks: (1) modifications to the simulated environment might not be easily transferable to the in-field test setting (e.g., changing the road shape); (2) environment instances in which the ADS is successful are discarded, despite the possibility that they could contain hidden driving conditions in which the ADS may misbehave. In this paper, we present GenBo (GENerator of BOundary state pairs), a novel test generator for ADS testing. GenBo mutates the driving conditions of the ego vehicle (position, velocity and orientation), collected in a failure-free environment instance, and efficiently generates challenging driving conditions at the behavior boundary (i.e., where the model starts to misbehave) in the same environment. We use such boundary conditions to augment the initial training dataset and retrain the DNN model under test. Our evaluation results show that the retrained model has up to 16 higher success rate on a separate set of evaluation tracks with respect to the original DNN model.