Reviewerly/exHarmony huggingface dataset:
Cryptanalysis on Polynomial Congruence-Based Public Key with Chinese Remainder Theorem

Public key cryptography is an asymmetric cryptographic scheme that consists of two key pairs: a public key, which is publicly known, and a private key, which is kept secret. Public keys are typically constructed based on the properties of number theory, and one example is polynomial congruence. Karyadi’s public key algorithm is an example of a public key algorithm based on polynomial congruence. Cryptanalysis is the method of analyzing a cryptographic system by attempting to attack it, including public key cryptosystems. Cryptanalysis of public key systems can focus on obtaining the encrypted message or the secret private key through various attack methods. In this paper, cryptanalysis of Karyadi’s public key algorithm and Rabin Cryptosystem are conducted with the aim of obtaining the original message from the encrypted message without knowing the trapdoor information. The cryptanalysis results demonstrate that it is possible to easily recover the original plaintext of the encrypted message in Karyadi’s public key algorithm, Rabin Cryptosystem, and other polynomial congruence-like cryptosystems under specific conditions without requiring the trapdoor information. Furthermore, a general method is presented for cryptanalysis to obtain the plaintext without knowing the trapdoor information in public key systems based on polynomial congruence using the Chinese Remainder Theorem. Additionally, measures to avoid the impact of this cryptanalysis method on polynomial congruence-like cryptosystems are provided.
The Application of Computer Virtual Reality Technology in Improvisation Practice Assistance System Design

Computer virtual reality (VR), as an immersive type of virtual reality technology, is widely used in various industries and is gaining more and more attention because of its many advantages such as immersive experience, spanning time and space, and intelligence. Therefore, this paper designs and develops an improvisation practice assistance system based on computer VR technology, which tests the real-time changes in functional level by analyzing students' relevant information such as physiology, audio and video in detail, and then saving the practice in a database, so as to test the real-time changes in the functional level. The results show that this system, on the one hand, is able to provide all the parameters and visual information in an imaginative and intuitive way for teaching and training, facilitating the transformation of skill approach and theory to vivid image data information. On the other hand, it also contributes to the improvement of improvised music skill level and is worth promoting.
Tsunami Signal Classification Based on Sea Level Data using Extreme Gradient Boosting Method for Tsunami Early Warning System Modeling

Eruptive activity that started in June 2018 caused a landslide on Anak Krakatau volcano in December 2018. Volcanic material was released into the sea by the landslide and caused a tsunami that killed 437 people, making it one of the deadliest volcano-generated tsunamis. The tsunami early warning system deployed in Indonesia was ineffective since it could only detect tsunamis based on earthquake data. Therefore, a tsunami early warning system based on sea level data is needed. In this paper, we designed a machine-learning-based tsunami early warning system using the Extreme Gradient Boosting (XGBoost) method to classify tsunami and non-tsunami signals based on sea level data. As a study case, we use sea level data obtained from the Inexpensive Device for Sea Level Measurement (IDSL) in Marina Jambu that have been artificially added with tsunami signals from the 2018 tsunami caused by the landslide of Mount Anak Krakatau to increase the number of tsunami signals. After performing feature engineering and data imbalanced handling techniques such as adding lag and time features and applying class weight, we obtained good results from the XGBoost with a macro average F1-score of 0.76. The XGBoost outperformed other machine-learning methods, such as Support Vector Machine (SVM) and Random Forests.
An Automated Office and Vehicle Management System

Keeping track of the entry and exit time of vehicles and people in an office is an essential aspect of office management. This paper highlights the importance of a vehicle time logging system, which records the entry and exit time of vehicles and people, in improving the security, attendance management, billing and payroll, planning and scheduling, and compliance of an office. We propose a system that will generate a barcode for all the employee vehicles which will be on the vehicles. System records the vehicle entry and exit timings in the database by scanning the barcode on the vehicle by cameras on the gates. The system also helps to manage employee attendance, billing and payroll, and plan tasks more effectively. Additionally, in some industries, specific regulations around record-keeping and compliance must be followed, which can be facilitated by a vehicle time logging system. The paper concludes that an automated system, such as an access control system, can make the process of recording entry and exit times more efficient and accurate, leading to a more organized and secure office management system. By recording the time of arrival and departure, the system helps to maintain security by identifying any unauthorized or suspicious activity.
VRLVMix: Combating Noisy Labels with Sample Selection based on Loss Variation

Since deep neural networks can fully fit all data, including noisy labels, that is, mislabeled data, this will be detrimental to the robustness and generalization ability of the network. To address this problem, existing methods usually use small loss tricks to select clean samples for training. However, sample selection using only small loss techniques cannot distinguish between large loss “hard” samples and noise samples. In this work, we analyze the reasons why clean samples are misidentified as noise samples and propose a balanced selection mechanism based on loss variation. This method sorts the amount of loss change, uses variance to count the rankings of multiple epochs to describe the stability of loss variation, and selects samples with stable loss as clean samples to reduce the misclassification of “hard” samples. At the same time, semantic clustering is used to assist resampling and reweighting, thereby alleviating the negative impact <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> of class imbalance on sample selection. We conduct comparative experiments and ablation experiments on synthetic noise datasets and real-world datasets such as CIFAR-10/100 and Clothing1M. Our VRLVMix has been shown to outperform numerous state-of-the-art methods, as evidenced by the experimental results. Moreover, it demonstrates the ability to extract clean samples even from large loss sample.
Design of Fault Location System for Smart Distribution Network Based on Application of Clustering Algorithm

We focuses on the design of a fault location system for smart distribution networks using clustering algorithms. The paper discusses the challenges faced in fault detection and localization within smart distribution networks, highlighting the impact of distributed generation, the presence of intelligent devices, and the need for real-time fault identification. It also addresses the limitations of conventional fault location techniques in addressing these challenges. The objective of the paper is to design a fault localization technique that effectively utilizes the information obtained from the clustering algorithm to accurately locate faults within the smart distribution network. The analysis of the results demonstrates the system's accuracy in identifying fault types but also highlights certain limitations in precisely localizing the faults. The analysis of the results demonstrates the system's accuracy in identifying fault types but also highlights certain limitations in precisely localizing the faults.
Development Of Letter Learning Application For Early Childhood Using Mobile-Based Mda Framework

The learning methods and media used by teachers are less varied, there are still many teachers who are less creative in teaching tend to still do conventional methods so as to make children quickly feel bored. This study aims to create a letter recognition application for mobile-based early childhood using the waterfall software development method and the MDA Framework method in designing application games, which the results of this study contribute to the interactive learning process because the authors make applications to introduce alphabetic letters along with interesting pronunciation sounds so that early childhood is interested in learning to recognize letters. The design of the letter learning application was developed using Android Studio. This application is a solution that can fix problems for children who want to learn to recognize letters and can be used for ages 0 to 6 years.
Development of a Mathematical Model to Predict the Specific Growth Rate of Cancerous Tumour for Mammogram

Breast cancer is a disease with leading death rate among women as per the World Health Organization (WHO). Many researchers had worked to detect cancerous (malignant) and non-cancerous tumour (benign) in the breast. But the estimation of the tumour volume has not done till today. The identification of the malignant tumour is a big challenge as growth rate estimation because it is not sufficient to cure and avoid the cancer in advance. When tumour is in the higher stage there is a need of rigorous treatment for the curing of the disease. The research work is carried to estimate the tumour volume growth per day. Based on this we can not only classify as benign or malignant, the stage of the tumour is also identified by estimating the specific growth rate of tumour volume per day. The research work shows that the benign tumour will get doubled in 2100 days, malignant stage 1 tumour takes 376 days, stage 2 tumour doubles in 212 days and stage 3tumour doubles in 45 days. This work is carried out in three different phases, phase-1enhancement of tumour region in mammogram images. Further the correlation developed specific growth rate versus tumour volume doubling time for the fast estimation of the tumour progress.
Using User Behavior Models and Visual Immersion to Optimize Digital Space Content

This research paper delves into the relationship between user behavior and visual characteristics in digital immersive spaces. Based on this, we experimented with a method of content optimization. The goal of this research is to enhance user experience by analyzing user behavior data to optimize visual content to meet user experience needs. In creating digital content, we pay close attention to the diversity and complexity of the user's visual and behavioral elements to ensure our methods fully utilize this information for content optimization. By analyzing user behavior data and making real-time adjustments to visual content, we aim to enhance the user's immersive experience in digital spaces. Through empirical research methods such as observation, experimentation, and data analysis, we discovered that an effective combination of user behavior elements and visual components, along with our content optimization, has a significant effect on satisfying user needs and enhancing the user experience in digital immersive spaces. This research provides designers and developers with a new and effective method to better meet the needs of users, enhancing user satisfaction and engagement in digital immersive spaces.
Hindi News Article's Headline Generation based on Abstractive Text Summarization

Text summarization is a technique that converts large text into shorter ones by keeping its important information. Most of the available summarizers, summarize English language text. The availability of summarizers in other languages is rare. In the proposed work, abstractive text summarization (ATS) for the Hindi language has been used to generate headlines from news text. To build the model, a Hindi news dataset has been used that contains different news articles related to sports, politics, crime, etc. After pre-processing the text, Word2Vec method has been used for word embedding. ATS model has been built using the Seq2Seq model with attention. After that, we have calculated evaluation metrics like ROUGE and BLEU scores of the result summaries. We have also fine-tuned two pre-trained models, indicBART and multi-lingual variant of T5. After result comparison, it was found that the indicBART perform better than the other two models, considered for the experiment.
Implementation of Password Manager to Improve Data Security for Social Media Account

Social media has become one of the wider community's most widely used types of applications recently. Along with the increasing number of social media users, the level of data security of social media users should be addressed. One strategy to improve the security of social media user data is to use a password manager. This study seeks to determine and explain the efficacy of using a password manager to enhance the data security of social media account users. This study uses a quantitative method by distributing online questionnaires on January 2023 and obtaining data from as many as 136 respondents in the Jabodetabek area (cities in Indonesia). The data was collected using the purposive sampling method. Seven hypotheses show the correlation among eight variables: Personal Propensity to Trust (PPT), Initial Trust (IT), Structure Assurance (SA), Firm Reputation (FR), Attitude (AT) in a password manager, Perceived Usefulness (PU), Perceived Ease of Use (PEU), Intention to Use Password Manager (IU). The result found that all hypotheses are significant except the Perceived Ease of Use (PEU) variables, which do not significantly affect Attitude (AT) in a password manager.
Voice Enabled Deep Learning Based Image Captioning Solution for Guided Navigation

The use of technology to assist visually impaired individuals is crucial in addressing the global issue of vision impairment. Worldwide more than billion people suffer from a vision impairment that should have been avoided or is yet unaddressed. According to the statistics, there is a significant need for solutions that can help those who are visually impaired, mainly in the middle- and low-income countries where the vision impairment population is higher. It is anticipated that population expansion and ageing will increase the likelihood that more people may get vision impairment. The efficientnetB3 deep learning algorithm will be used in this project to caption images for blind people. so, they can learn about object identification, distance, and position. This has been accomplished by utilizing advanced picture captioning techniques, efficient net B3 algorithms, and tokenization approaches, where the computer learns the scenes with various captions. The computer recognizes and forecasts any image that is acquired using the camera. The significant objects are also anticipated, and the camera's distances are determined. Following the prediction, the user receives an audio output that can be used to determine the object's position and distance. Hence, with the aid of this research, we give the blind artificial eyesight that can give them confidence when they move on their own. The aim is to step forward in addressing the global issue of vision impairment. The use of technology to assist visually impaired individuals is crucial in providing them with the tools they need to navigate their environment and live their lives with greater ease. By utilizing advanced algorithms and image captioning techniques, the quality of life can be improved for people worldwide who are affected by vision impairment. The intension is to develop an artificial vision for vision impaired people by detecting real time objects, distance and the position of it from the person using Audio Output and to develop a model for image captioning to predict the captions.
A deep learning-based image stitching method for fiber-optic image bundles imaging

For the problem of small field of view of fiber-optic image bundles imaging, an unsupervised fiber-optic image bundles imaging images stitching algorithm based on deep learning is proposed. The algorithm consists of two stages: unsupervised coarse image alignment and image reconstruction. In the first stage, we design a new backbone network that utilizes the Focus structure instead of down-sampling operations. We incorporate the ELAN structure and add attention mechanisms, which effectively enhances the backbone ’s accuracy in predicting the homography matrix while significantly reducing the model size. In the second stage, we used a more efficient convolution module C3 instead of regular convolution, reducing the number of model parameters and improving computational efficiency. Experiments are conducted using a dataset comprising fiber-optic image bundles imaging dataset and two publicly available stitching datasets (Warped MS-COCO and UDIS-D). The experimental results demonstrate that our method achieves high evaluation metrics and performs better in stitching real scene images with parallax and poor-quality fiber-optic image bundles. The network model is improved in terms of accuracy, size, and speed, meeting the practical requirements for fiber-optic image bundles imaging images stitching.
User Profile Construction Based on Multi-Channel Fusion

Aiming at the shortcomings of existing user profile construction algorithms that do not fully utilize contextual structural information and multidimensional feature information., a user profile construction model based on the pre-training model NEZHA, Deep Pyramidal Convolutional Neural Networks (DPCNN), Gated Recurrent Unit (GRU), and attention mechanism is proposed, NCDGA. The method uses multiple channels to process the text, capturing different dimensions of information and constructing features to reduce the prediction bias that occurs when a single model captures a single feature. The experimental results show that the NCDGA model has better accuracy and effectiveness compared with other modeling methods.
Expert Validation of Online Social Networks Forensic Investigation Metamodel (OSNFIM)

This paper validates the Online Social Networks Forensic Investigation Metamodel (OSNFIM) by employing an expert validation approach. The OSNFIM was created to address the issue of non-uniform, ad hoc forensic methods, and processes in the domain of Online Social Networks (OSNs) Forensic Investigation (OSNFI). This not only diminishes the effectiveness of the procedure but also has an impact on the dependability and integrity of proof in criminal cases. To construct OSNFIM, several models were identified, collected, and reviewed. The developed OSNFIM, on the other hand, lacked the expert validation-based methodology that would have ensured OSNFIM’s applicability in the OSNFI domain. Experts were required to validate the developed OSNFIM’s completeness, usefulness, and logic. Therefore, this study aims at validating the created OSNFIM using an expert review validation approach. An expert review is an informed opinion provided by someone with a track record in the subject who is considered a competent expert by others and who can provide data, facts, opinions, and assessments. It is a well-informed view expressed by someone with experience in the field who is regarded as a competent expert by others and who can provide data, facts, opinions, and assessments. For this, three (3) experts were chosen to validate the created OSNFIM. The developed OSNFIM is comprehensive, logical, and suitable for the OSNFI domain after expert assessment.
Dynamical Decoupling (DD) to Improve Fidelity in Quantum Computing

Dynamical decoupling (DD) and Decoherence free subspace (DFS) are powerful techniques in quantum computing aimed at enhancing fidelity by mitigating the detrimental effects of noise and environmental interactions. In quantum systems, noise and decoherence can degrade the coherence of quantum states, leading to errors and reduced computational accuracy. DD employs carefully designed sequences of control pulses to periodically intervene and counteract these undesired interactions, while DFS utilizes subspace encoding techniques to protect quantum information from decoherence. To evaluate and compare the fidelity achieved by DD and DFS, this paper presents a series of experiments considering the effect of time and the number of qubits. The experiments provide valuable insights into the performance of both methods under different conditions. By systematically examining the fidelity results, the impact of time and the scaling behavior with increasing qubits can be assessed. The experimental findings showcase the effectiveness of DD in reducing the impact of noise and extending the coherence time of quantum states. By strategically manipulating the quantum system, DD minimizes error accumulation and preserves the integrity of fragile quantum information. It is observed that the decrease in fidelity with increasing t is relatively small and gradual. Despite being outperformed by DD, the DFS method exhibits robustness and maintains a high degree of fidelity even in the presence of extended time durations.
Enhancement Method of Reservoir Numerical Simulation Based on Generative Adversarial Networks

In practical simulation research of fluid flow in reservoir porous media, the demand for computational resources in numerical simulation greatly limits its accuracy and practical usage. While the numerical solution of Darcy flow’s PDE is based on the idea of discretizing the continuous geology models into an upscaled grid to proximate the analytical solution. The grid size which can significantly affect the computational effort becomes an essential factor. In our research, we borrow the idea from super-resolution technique to map the relationship between the simulation results by coarse and fine grids and propose a simulation enhancement method based on GAN structure. The designed network called petrophysical constrained simulation generative adversarial network (PCSGAN) employs RRDB and attention mechanism to achieve the above target. Different from traditional super-resolution methods, PCSGAN innovatively uses the petrophysical grid data to constrain and guide the enhancement procedure to improve its accuracy. To demonstrate the PCSGAN’s ability, we design some simulation experiments by finite volume of the 2D anisotropy reservoir models. Each model has two scale sizes, the fine one is 16 times larger than the coarse one. The target of PCSGAN is to enhance the simulation result from coarse gird to match the one from fine grid. From these experiments, the effect of PCSGAN is fully evaluated, the analysis and discussion of these results are also provided.