Reviewerly/exHarmony huggingface dataset:
A Secured Image Communication With Dual Encryption and Reversible Watermarking

Secured communication is the optimal means of exchanging information without the risk of data leakage. Data encryption serves as a crucial method for safeguarding and fortifying sensitive information. This paper introduces a groundbreaking approach known as reversible information concealment, specifically designed for digital images. It employs an integer-to-integer wavelet transformation alongside a companding process to embed and retrieve confidential data, restoring the image to its pristine state. Furthermore, the paper explores the utilization of genetic operators in cryptography. Given the prevalence of general information tampering within networks, it becomes imperative to protect messages during transmission. To address this concern, the paper proposes a novel encryption technique leveraging genetic operators such as crossover and mutation. This method ensures message confidentiality during the transmission process, contributing to an enhanced and more secure environment. The overarching goal is to establish a robust security framework through the integration of encryption and digital image watermarking for discreet data concealment within images.
Using Augmented Reality Interfaces for Artificial Intelligence Systems

Augmented reality interfaces offer users an effective environment. In this study, a visualization approach with 3D augmented reality interfaces was introduced to enable users to understand and analyze complex deep learning models in a short time. It has been investigated whether the immersive experience that augmented reality creates on the user in other systems has the same effect when analyzing these models. Two-dimensional studies on deep learning models were examined and what could be done in three dimensions was emphasized. By adding another dimension with augmented reality interfaces, a threedimensional experience is offered to the user and the results are observed. A CNN model is visualized in the application. When test data was given to the model, the feature maps, filters and connections in the layers were displayed. The application was first run in 2 dimensions, then as a desktop application, and then in 3 dimensions, on Microsoft Hololens-2, a mixed reality headset. Tasks are given to users. Usability was measured with a test called the SUM model, which included completion or non-completion situations, errors, completion times and satisfaction. Here, satisfaction was measured using ASQ(After-Scenario Questionnaire), a user satisfaction measurement questionnaire. The usability of augmented reality 3D interfaces was found to be 80%. The conclusion reached with the answers; It has been stated that users are willing to use this system, their awareness in 3 dimensions is undeniable, and these systems can be used as a feature that increases human ability in artificial intelligence systems.
G-KMM: A Flexible Kernel Mean Matching Optimization Method for Density Ratio Estimation Involving Multiple Train &amp; Test Datasets

In the present paper we introduce new optimization algorithms for the task of density ratio estimation. More precisely, we consider extending the well-known KMM (kernel mean matching) method using the construction of a suitable loss function, in order to encompass more general situations involving the estimation of density ratio with respect to subsets of the training data and test data, respectively. The codes associated to our Python implementation can be found at https://github.com/CDAlecsa/ Generalized-KMM.
Development of a Laravel-based Publication, Research and Community Service Information System

The need for accurate data is really needed when departments or faculties are to be accredited, and to map lecturers who will have the opportunity in the following semester to carry out research or community service. Due to differences in data held by departments, faculties, or data at the LPPM (Institute for Research and Community Service), different data can occur due to lack of discipline in lecturers who have carried out research and service to collect reports to departments, faculties, or to LPPM. To facilitate reporting of research and community service activities by lecturers to the Department and to management, an information system is needed that can improve research and community service performance. The system development method used in this research is the SDLC (System Development Life Cycle) method. The SDLC method uses a system approach called the waterfall approach, which uses system development stages, namely requirements analysis, design, coding, system testing and support stages (maintenance), using the PHP programming language and MySQL database. The research results show that the information system developed makes it easier to manage and organize data from lecturers' research, service and publications and can help lecturers, departments and faculty leaders in obtaining the information they need online and in real time. It can be concluded that the Prisma Information System fully supports the submission of service research proposals, a faster and easier process, safer data and guaranteed credibility, dynamic configurability, more responsive design, and assistance documentation, as well as other new features.
Machine Learning Approaches for Investigating Breast Cancer

ABSTRACT: This study aims to predict whether the case is malignant or benign and concentrate on the anticipated diagnosis; if the case is malignant, it is advised to admit the patient to the hospital for treatment. The primary goal of this work is to put together models in two distinct datasets to predict breast cancer more accurately, faster, and with fewer errors than before. Then contrast the techniques that produced datasets with the highest accuracy. In this study, the datasets were processed using Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbours, Artificial Neural Network, Nave Bayes, Stochastic Gradient Descent (SGD),Gradient boosting classifiers(GBC), Stochastic Gradient Boosting (SGB), Extreme Gradient Boosting (XGBoost),and Random Forest. Two datasets—the Wisconsin Diagnostic Breast Cancer dataset and the Breast Cancer dataset—are used to test these methods. to evaluate the findings and choose the algorithm that is more adept in predicting breast cancer. Seven algorithms that operate on both datasets in the AI platform were used to build the article. Breast cancer prediction has gotten much harder because so many people die from the disease in its early stages. Consequently, using two real-time datasets, one for Wisconsin diagnosis and the other for research on breast cancer. The same methods are applied to both datasets, and it is found that SVM provides the best accuracy in the shortest time and with the lowest error rate.
An Efficient Image Encryption Method Based on Enhanced Josephus Problem and a Non-Invertible Economic Map

As an increasing number of digital images are created and transmitted over the internet, there is growing concern over their unauthorized use, which has a big impact on both security and privacy concerns. In this research, we provide a fast and secure image encryption scheme by using an enhanced Josephus problem and two-dimensional non-invertible economic chaotic map (2D-ECM) to safely and covertly protect digital image information throughout public channel transmission. First, the initial values of the 2D-ECM map are generated based on Secure Hash Algorithm (SHA-256) and the input secret key. Then, the Josephus problem is enhanced by substituting the extract operation with location exchange and dynamic start location and step size is employed to scramble the image pixels. In order to integrate the confusion process and diffusion process, the enhanced Josephus problem is utilized indirectly to choose two random columns from the scrambled image and random image to XOR them with the current column. The evaluation results prove that the proposed image cryptosystem is more efficient compared to existing cryptosystems.
Teacher And Student Perception Of Learning English Via Online During The Covid-19 Outbreak At Smpn 1 Gondang

To prevent the life-threatening transmission of COVID-19, the Indonesian government requires educational institutions across the country to switch to online learning. These contactless meetings ensure that teaching and learning activities continue as they should be. The current research sought to find out what challenges teachers and students faced while online learning was applied and how they perceived the method. A total of three students and three teachers from SMPN 1 Gondang, Mojokerto, were interviewed to share their views on online learning. The researcher used guided questions to explore the participants’ responses. From the data collected, technological barriers and demotivation are common challenges faced by teachers and students of SMPN 1 Gondang, Mojokerto, in organizing and participating in online classes. Although they favored the flexibility, they cultivated rather negative views of it. Several recommendations have been made to address these issues, ranging from improving the school’s digital infrastructure, organizing training programs to fill literacy gaps, and encouraging parents to establish an effective learning environment for students, ensuring they can keep up with the classes.
Model Regresi Nonparametrik Spline Truncated Pada Indeks Pembangunan Manusia di Indonesia

Truncated spline nonparametric regression is a nonparametric regression analysis using a segmented polynomial model. This segmented nature provides flexibility so that the regression model can adapt more effectively to the local characteristics of the data. The purpose of this study was to obtain a regression model and determine the factors that influence the Human Development Index (HDI) in all provinces in Indonesia using multivariable truncated spline nonparametric regression. The Human Development Index is an important indicator in measuring success in efforts to build the quality of human life. The Human Development Index can determine the rank or level of development of a region and a country. In development, a high Human Development Index is something that is expected to be achieved, especially for developing countries. The Human Development Index data used in this study is based on BPS data published in 2020 from all provinces in Indonesia. In this study, based on the results of the analysis, the best nonparametric truncated spline regression was obtained using 1 knot point, 2 knot point and 3 knot point. Based on the minimum Generalized Cross Validation (GCV) value, the best truncated spline regression model is 3 knots with an R2 value of 83.70%. The factors that influence the Human Development Index are the variables expected length of schooling, life expectancy at birth, and population
Analysis of Solar Radiation Estimation under Shaded Sky Conditions using Experimental Measurements

Solar energy is a renewable energy source used in different applications that takes advantage of global and direct irradiance. In this context, the estimation of irradiance and its direct and diffuse components is essential for better energy use. In the present work, the Perrin Brichambaut approach was used to estimate the global irradiance; then, based on specific factors, the diffuse and direct irradiance were estimated. The factor for the diffuse component should be in the range of 1 to 1.5, while the factor for the direct component ought to be in the range of 0 to 1, considering that, with this approach, the normalized mean square error can be reduced up to 40% in comparison to other methods. The statistical evaluation for the proposed approach and the comparison with others has been carried out with more than 70 days of measurement in the 1-minute range through the pyranometer CMPIO Class A Kipp & Zonen installed in the city of Huancayo, Peru. The use of the proposed method allowed researchers to obtain an error rate of up to 2%. The factors for both irradiance components do not perform better if they present high variability during very cloudy sky conditions or intermittent sunny and cloudy conditions during the day. Rather, the factors should capture the irradiance variability as established in the present research.
Enhancing Real-World Image Deblurring: Algorithm Selection and Performance Evaluation for CCTV

To restore clear photos from blurred photographs due to camera movement, several image deblurring techniques have been developed. Deblurring is th process of taking off blurs and bringing back the sharp image. Defocus, motion, Gaussian, average, and other types of blur are only a few examples. We want to identify the bes algorithms for real-world problems (such as CCTV and traffic camera images) in this project. We look at the algorithms used to solve a certain issue. The PSNR and SSIN values will be used as benchmark scores as these chosen methods are tested on actual blurred photographs. Existing datasets can be used to evaluate the performance of algorithms; however, actual data may differ slightly from these. Three major categories can be used to categorize deblurring techniques: blind and non-blind, regularization, deep learning, and hybrid methods. We are focusing on applying the best algorithm for deblurring photos in this to produce incredibly effective results.
Revolutionizing Gas Management: An Integrated Approach To Leakage Detection, Monitoring, And Automated Booking System

This solution integrates revolutionary technologies in order to provide customers with unmatched control and security in gas usage. It presents an innovative gas safety and management system. The system offers real-time insights into gas concentration and residual contents by integrating an MQ-6 gas sensor for leakage detection with a precision weight sensor for gas level monitoring. Visualizations of augmented reality (AR) overlay gas levels on the user’s surroundings to increase engagement. Intuitive interactions are made possible by voice recognition and user customization. Particularly noteworthy is a feature that automatically schedules refills. By combining automated convenience, dynamic visualization, and proactive safety precautions into a single, user-centric platform, the technology redefines gas monitoring.
Artificial Intelligence Assisted Service Marketing Using Deep Assisted Neural Network

Improving marketing abilities, the quality of one product, and one connections with one target audience are at the service marketing approach. The marketing of goods and services can both be considered to come within the scope of this category. This data is stored in a centralized location in these databases for ease of access and analysis. Based on the findings of this study, AI has played a crucial role in big data analytics, making it possible to foresee and provide guided experiences that are individualized based on the needs of customers. In this paper, we develop a AI assisted customer support system to deliver experiences that result in advocates and consumers that remain loyal over time, the usage of AI and predictive analytics is crucial.
American Sign Language Recognition System for Numerical and Alphabets

Sign language is a helpful visual technique that can be utilized by individuals who have difficulty communicating verbally. This language is used frequently by people as well. However, the vast majority of individuals don't understand sign language very well, and as a result, they frequently struggle when attempting to communicate with someone who is fluent in it. The community of people who have trouble speaking or hearing will benefit from an initiative called the Sign Language Recognition System. It is extremely challenging for deaf mutes to communicate, which is why we require a linguistic framework to understand what it is that they are attempting to express. In light of this, we present in this research an American Sign Language (ASL) handwriting translator that is built on convolutional neural networks. Instructing computers to recognize specific characters is one way to accomplish this goal. The Support Vector Machine (SVM) and the Convolutional Neural Network are two types of artificial neural networks that are being utilized in our project we have suggest the identification of American Sign Language. In addition, the Decision Tree and Voting Classifier are applied to the dataset in order to do an analysis. We have demonstrated the design and execution of an American Sign Language (ASL) translator that also incorporates fingerspelling as a direct consequence of this.
A Generative Adversarial Networks-Based Integer Overflow Detection Model for Smart Contracts

Due to the rapid development of blockchain technology in recent years, smart contracts have been widely applied in critical fields such as finance, insurance, healthcare, and the Internet of Things. However, smart contracts face increasingly serious security issues due to their unique operating environment and programming characteristics. We focus on Ethereum-based smart contracts and propose a high-precision and versatile detection method to address the integer overflow vulnerability, which significantly affects smart contract development and execution. Our method can also solve the problem of possible data shortage. Specifically, we utilize code embedding algorithms to convert Solidity-compiled smart contracts into spatial vectors, thereby retaining as much syntax and semantic information as possible. Based on this, we use Generative Adversarial Network (GAN) to train a small sample vector dataset to generate a substantial number of synthetic datasets. Our proposed model combines GAN discriminator feedback and vector similarity analysis to identify smart contracts that contain integer overflow vulnerabilities.
Video Anomaly Detection Using Self-Attention-Enabled Convolutional Spatiotemporal Autoencoder

The process of automatically detecting abnormal video patterns in the intelligent surveillance framework is known as video anomaly detection. However, video anomaly detection is challenging due to inherent research challenges such as equivocal nature, data imbalances, data scarcity, the complex nature of the entities involved in the anomaly, etc. Hence, a self-attention-enabled convolutional spatiotemporal autoencoder is proposed to detect video anomalies efficiently. The proposed Self-Attention-enabled Convolutional Long-Short-Term-Memory Auto-Encoder (SA-ConvLSTM2D- AE)-based video anomaly detector is comprised of three sequential stages: spatial encoder to learn spatial (appearance) features of individual frames, temporal encode-decoder to learn temporal (motion) features of encoded spatial features, and spatial decoder to decode the encoded spatial features for reconstructing the individual frames. Here, the self-attention mechanism is embedded into the convolutional Long Short Term Memory block present in the temporal encoder-decoder section to generate the Spatial-Attention-enabled ConvLSTM block for learning better spatiotemporal features. An efficient threshold selection criteria based on the finding of the optimized Geometric mean value of the sensitivity and specificity from the Receiver Operating Characteristics curve is implemented. The model is trained on only the video frame sequences corresponding to the normal incidents. However, the model poorly reconstructed test frame sequences with video anomalies, as anomalous samples are never exposed during training. Hence, when the anomaly score of individual frames exceeds the selected optimum threshold level, then an anomaly is said to be detected.
Automatic Bengali Image Captioning using EfficientNet-Transformer Network

The task of image captioning is a complex process that involves generating textual descriptions for images. Much of the research done in this particular domain, especially using transformer models, has been focused on English language. However, there has been relatively little research dedicated to the context of the Bengali language. This study addresses the lack of research in the context of Bengali language and proposes a novel approach to automatic image captioning that involves a multi-modal, transformer-based, end-to-end model with an encoder-decoder architecture. Our approach utilizes a pre-trained EfficientNet Transformer Network. To evaluate the effectiveness of our approach, we compare our model with a Vision Transformer that utilizes a non-convolutional encoder pre-trained on ImageNet. The two models were tested on the BanglaLekhaImageCaptions dataset and evaluated using BLEU metrics.
XGBoost and Random Forest Algorithms: An in Depth Analysis

Machine learning is playing an increasingly important role in many facets of our lives as technology develops, including forecasting weather, figuring out social media trends, and predicting prices on the world market. This significance invoked the demand of some efficient predicting models that can easily handle complex data and provide maximum accurate results. XGBoost and Random Forest are upgradable ensemble techniques used to solve regression and classification problems that have evolved and proved to be dependable and reliable machine learning challenge solvers. In this research paper, we undertake a comprehensive analysis and comparison of these two prominent machine learning algorithms. The first half of the research includes relevant overview on both of the techniques, significance and evolution of both algorithms. The latter part of this study involves a meticulous comparative analysis between Random Forest and XGBoost, scrutinizing facets such as time complexity, precision, and reliability. We examine their distinctive approaches to handling regression and classification problems while closely examining their subtle handling of training and testing datasets. A thorough quantitative evaluation using a variety of performance metrics, such as the F1-score, Recall, Precision, Mean Squared Erro
Analisis Peranan Sistem Informasi Akuntansi Pembelian Tunai Dan Penjualan Tunai Dalam Pengendalian Persediaan Barang Dagang Pada Toko Salah Parkir Metro

This type of research is field research with a qualitative descriptive approach. The data collection tools in this study are observation, interviews and documentation. The data analysis technique is through the process of data collection, data reduction, data presentation, and drawing conclusions. Based on the results of the study, it can be concluded that the implementation of an accounting information system for cash purchases and cash sales at the Metro Salah Parkir Stores is already computerized. This can be seen from the elements of cash purchases at Metro Salah Parkir Stores, namely: Related functions are the Purchasing Function and Receiving Function, Warehouse Function and Accounting Function. The role of the accounting information system for cash purchases and cash sales in controlling merchandise inventory at the Metro Salah Parkir Stores is in accordance with the internal control components. But there are still a number of problems including excess or shortage between the goods ordered and the goods that have been sent to the Metro Salah Parkir Stores.