Reviewerly/exHarmony huggingface dataset:
Enhanced Visual Clarity in Noise-Reduced Image Environment

This research introduces an advanced deep learning approach aimed at restoring highly degraded aging photographs. While conventional restoration tasks in supervised learning are effective, real-world image degradation is intricate, making it challenging for the network to generalize due to the disparity between artificial images and genuine aged photographs. The proposed research pioneers a distinctive triplet domain translation network, leveraging a comprehensive dataset containing both artificial image pairs and genuine snapshots to address these complexities. The approach involves training two variational autoencoders (VAEs) to encode clean images and aged photos into distinct latent spaces, facilitating translation between these latent representations. The existence of a discernible gap in the latent space enables the proposed translation strategy to effectively generalize to real photos, overcoming real-world degradation challenges. Moreover, this research incorporates a global branch integrated with a partial nonlocal block to tackle structural defects and multiple mixed deteriorations within a single aged image. Additionally, an extra branch is introduced to handle unstructured defects such as noise, blurriness, scratches, and dust spots. The fusion of these branches in the latent space translation significantly enhances the capability to address a wide array of defects commonly found in antique photos. Furthermore, a specialized face refinement network is employed to extract intricate facial details from the old photos, resulting in enhanced perceptual quality of the restored images. The effectiveness of the proposed method is demonstrated through extensive evaluations, showcasing its superior performance in terms of visual quality for restoring ancient images compared to both current state-of-the-art techniques and existing commercial technologies.
Implementation of Classification Algorithms for Data Traffic Analysis

This article poses the task of analyzing network traffic using machine learning methods. The division of instant message service traffic into classes of possible user activities is made, the following classes are selected for markup: text, video, idle. Methods of mathematical statistics, machine learning are used, k-NN and Random Forest algorithms are compared, training is implemented by the Kohonen network. In the course of research on the example of the one of the information exchange applications, a method based on statistical analysis of network traffic, which does not require the study of the contents of the packages, was applied. As a result, the classifier is implemented, which can help to analyze data traffic. The efficiency of the classifier is demonstrated and the efficiency tested by cross-validation. The difference between the developed algorithms and the known ones is in the ability to effectively classify and track traffic without studying and decrypting the contents of packages.
Comparative Evaluation of Fraud Detection in Online Payments Using CNN-BiGRU-A Approach

Online payment systems are now ubiquitous in people's daily lives. However, with this increased convenience comes a new security risk. Due to the nature of cybercrime involving an online payment service, which is often characterized by specialization, diversification, concealment, industrialization, scenario, and cross-regional elements, online payment security is notoriously difficult to implement and maintain. Effective and comprehensive online payment fraud detection is crucial. The first three stages of the proposed technique are preprocessing, feature selection, and model training. Remove Null Value, Data Transform, Resample, and Normalize Data are all preprocessing techniques used to improve the quality of raw data. Feature selection makes use of PCA and IG as a means of analysis. CNN-BiGRU-A is used to train the models after information gain is used to choose relevant features. When compared to the two most common alternatives, BiGRU and CNN, the proposed technique performs better.
Twitter Spam Detection Using Word Vector and Binary Classifier

In recent years, social media communication plays an important role in sharing messages, thoughts, comments and ideas around the world. Among them, Twitter is one of the most popular communication mediums. Twitter provides free services for sharing information's that are limited to 140 characters. Each month nearly 40 million new twitter accounts were created. Since usage of twitter increases rapidly, fake accounts were created for posting the spam messages and links for redirecting to phishing websites. Researchers proposed various machine learning methodologies for filtering the spam to handle and maintain the social network security problems. But still detecting spam tweets, malicious link in real world scenario is still a complex task. In the proposed work, a novel deep learning technique is introduced to address the above mention issues. Word Vector model is used for learning the syntax of each tweet. Later by using proceeding representation data set, binary classifier is developed. For experimental purpose 1-month twitter datasets are collected. Proposed work outperforms when compared with various existing methodologies and also to detect the non-text based as well. The ratio of spam is determined using the precision, recall, and F-measure metrics.
Design and Application of An Integrated Energy Management System for Park Enterprises

An integrated energy management system (IEMS) based on energy internet architecture is proposed for the comprehensive energy management and energy-saving and emission reduction needs of park enterprises. The system has the characteristics of multi-energy data acquisition, diversity of communication channels, and excellent scalability. In order to solve the compatibility of communication between various acquisition terminals and the system, the interface converter communication conversion is added to upload to the concentrator via the RS485 bus to establish a connection with the master station. It can not only realize the acquisition of multi-energy information such as electricity, water, gas, heat/cold, etc. on the IEMS, but also introduces renewable energy and energy storage on the supply side, improves the energy supply reliability of park enterprises and reduces dependence on external energy networks. In order to meet the integrated energy business needs of enterprises in different parks, this paper studies and analyzes the applicable businesses of various algorithms in IEMS multi-energy collaborative optimal scheduling technology, designs the software architecture of the IEMS, and analyzes in detail the functions of each hierarchical unit module of IEMS and the deployment modes of the master stations in different parks. The system has been put into use and various applications have achieved corresponding results.
Comparative Analysis of Research Article Matching using SIF, RNN, Attention, and Hybrid Methods

Search engines make it easier to conduct literature reviews. However, for niche topics, search results are often poor. Snowballing can help, but it is limited by the initial articles, especially by the authors’ access when they were written. As an alternative, research paper databases have provided recommendation features; however, these are limited to their own articles. A tool to search for similar articles without relying on a specific database would be helpful, but before that, a proper method to match similar articles must be found. This research aims to match similar articles based on title, authors, and keywords using deep learning methods, which are SIF, RNN, Attention, and Hybrid methods, and evaluate them. This study also compares the combinations of features used in matching. The attention method using only the article title as a feature yielded the best result. The attention method was also faster than the hybrid method for training and use. Using only one feature should be even faster. In addition, the title field was found to be the best feature for predicting similarity matches. The author name feature was bad on its own but could improve the results when combined with the title. The keyword feature was found to be almost as good as the title, but combining them did not result in significant improvement.
Analysis of Effect of Image Augmentation with Image Enhancement on Fish Image Classification Using Convolutional Neural Network

Developing a fish species classification model using a Convolutional Neural Network (CNN) requires a diverse and abundant training dataset. In addition, the quality of the images in the dataset also affects the model’s performance. This research aims to investigate the effect of image enhancement through augmentation on fish image classification using CNN. The Fish-gres dataset is used in this study, and two image enhancement techniques, Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are applied to the training dataset. The experiment involved a non-pre-trained CNN and three pre-trained CNN models (ResNet50, Xception, and VGG16) trained using three different datasets, i.e., the original training data, training data augmented with HE, and training data augmented with CLAHE. We also experimented using different learning rates. The accuracies of each model are compared and evaluated. The experiment results showed that all models except Xception augmented with HE and CLAHE produced higher accuracy performance than those without augmentation. The best model for the Fish-gres dataset is CNN with ResNet50 pre-trained model, HE-augmented training data, and a learning rate of $10^{-3}$ with 100% accuracy.
Using a Digital Twin in Ultra-Large-Scale System

The article is devoted to the study of the possibility of using digital twin in Ultra-Large-Scale System (ULSS) on the example of the dynamic system “Intelligent electronic hitch”. ULSS it combines big data, complex algorithms for their processing and dynamic control processes of various levels in real-time. The sustainable development of ULSS depends on the effectiveness of the organization of interaction between its components, taking into account the properties of heterogeneity and self-organization. The capabilities of digital twin ensure the successful solution of modeling, analysis, management and forecasting tasks in ULSS. The application options of digital twin are considered on the example of the dynamic system “Intelligent electronic hitch”. The application of digital twin is considered in the following aspects: the realization of the possibility of forecasting and optimizing tasks for the implementation of the main activity of the control object; the realization of the possibility of issuing recommendations for control actions based on predictive analytics data obtained in real time; the realization of the possibility of making functional and structural changes to the life cycle of a physical object. The article proposes the implementation of types digital twin: component twin, asset twin, system twin and process twin at different levels of integration of smart-controller and “Intelligent electronic hitch” in ULSS.
Passive Sonar Multi-Target Frequency Feature-Aided Data Association Algorithm Based On DPGMM

In passive sonar system, it's always a challenge to accomplish multi-target data association. We propose a remedy for this problem by using a frequency feature-aided data association algorithm to track targets' frequency and bearing information simultaneously. Frequency and bearing information are extracted by Dirichlet Process Gaussian Mixture Model(DPGMM). Simulation results are presented to illustrate the tracking performance of the proposed algorithm in terms of incorrect association, association interrupt times and tracking accuracy aspect.
Explainable SCADA-Edge Network Intrusion Detection System: Tree-LIME Approach

This study presents an Explainable AI (XAI) conceptual framework for intrusion detection in the SCADAedge network based on an optimizable tree algorithm for attack classification and a Decision Tree-based local interpretable model agnostic explanations (LIME) approach. This approach creates plausible explanations that capture nonlinear interactions between data features, resulting in more reliable interpretability, fidelity, and efficiency, as demonstrated in experimentation with the Edge dataset. Experimentation shows the Tree-LIME XAI approach giving faithful and local explanations of the 12 relevant features of the recently publicly available Edge-IIoTset.
Prediction Technology of Photovoltaic Power in Sandstorm Weather Based on Upstream and Downstream Coordination

Based on the influence of sand and dust storms on upstream PV stations, a sand and dust storm photovoltaic output impact model is constructed. Considering the dynamic characteristics of sandstorms, a geographically located model for the evolution and attenuation of sandstorms in upstream and downstream photovoltaic power plants is constructed. Based on the evolution model of upstream and downstream sandstorms and the impact model of sandstorms on photovoltaic output, a photovoltaic output prediction model for downstream photovoltaic power stations during sandstorms is constructed. Based on the similarity characteristics of output between distributed PV power stations and centralized PV power stations, a distributed photovoltaic sandstorm output prediction model is established to acquire the output prediction of photovoltaic sandstorms in a certain area.
Usability Testing of User Experience and User Interface Design on Mobile Map Applications: A Comparative Study of User Perception and Interaction

Map applications play a crucial role in everyday life, assisting users in finding locations and optimizing travel routes. This study aims to evaluate the usability of map applications, focusing on UX testing using GOMS and SUS techniques and UI testing using the A/B Testing method. The results show the effectiveness of the three applications (GMaps, Petal, and Waze) in completing the given tasks. GMaps demonstrated the fastest task completion time and the highest SUS score, followed by Petal and Waze. GMaps’ advantage may be due to most respondents being accustomed to using Android and the native applications of this operating system. Furthermore, A/B Testing of UI elements revealed a nearly balanced preference between GMaps and Petal, indicating Petal’s potential despite not being an Android-native application. Based on these findings, the development of map applications with good usability can be achieved by combining the strengths of each tested application.
Evaluating Cryptoanalysis on Low-Exponent Implementation of Rivest-Shamir-Adleman

In information security, cryptography plays an important role in disguising information so that it can only be accessed by authorised parties. One of the cryptographic algorithms that is still widely used today is RSA (Rivest-Shamir-Adleman). RSA is an asymmetric cryptographic algorithm that uses a key pair, public and private keys. The main concept that makes RSA secure is the infeasibility to factorise large numbers. Despite being mathematically secure, implementations of cryptographic algorithms may still create a security hole. This security hole can be caused by several factors, for example, the use of weak keys or weak padding schemes. To find out the strength of an algorithm used in cryptography, of course it is necessary to do a test. This test is done by cryptanalysis. Cryptanalysis is an action to decrypt a message that has been encrypted without permission or without a key. In this research, cryptanalysis is carried out to determine the strength of the RSA algorithm of an existing cryptosystem using the Coppersmith method. The Coppersmith method is a cryptanalysis method using the lattice basis reduction algorithm, namely LLL. Based on the results of the cryptanalysis carried out, the test for the value of e = 3 has a success rate of 25 out of 49 total cases or 51.02%. For the value of e = 5, it has a success of 8 out of 49 or 16.33% of the total cases. The value of e = 7 only has success in 8 of 65 cases or around 12.31% of the total cases.
Close-Range Indoor Proximity Detection for COVID-19 Exposure Notifications Using Smartphone Magnetometer Traces

Smartphone apps for exposure notification and contact tracing have been shown to be effective in controlling the COVID-19 pandemic. However, there is evidence that the Bluetooth Low Energy approach used for proximity detection by existing apps can be error-prone in areas with high numbers of metallic objects. In this paper, we present a new method for detecting whether or not two smartphones are 2 or fewer meters apart, intended to augment BLE-based proximity detection methods. We design a set of binary machine learning classifiers that take as input pairs of 10-second-long segments of magnetometer traces. These classifiers identify pairs of trace segments for which the two recording devices were 2 or fewer meters apart for at least 75% of the segment duration. We introduce a simple method of compensating for different magnetometer biases in heterogeneous devices. We confirm that our classifiers can generalize well to both new buildings and new devices whose traces are not present in their training data, and characterize their overall accuracy for heterogeneous-device proximity detection to be between 93.1% and 96.2%.
Efficient Automation of Web Application Development and Deployment Using Jenkins: A Comprehensive CI/CD Pipeline for Enhanced Productivity and Quality

Nowadays software developers are building web applications, to the main requirement is that it work well, be free from issues and reach users quickly. But doing this can be challenging. That is where Continuous Integration (CI) and Continuous Deployment (CD) come with a concept called Jenkins. It has become a vital tool for software developers globally because of automation. This research study explores the impact of Jenkins automation on improving web application development by automating essential tasks like testing and deployment. It also examines notable benefits such as early issue detection, increased developer focus, time savings, and enhanced reliability. Through a practical example using the Angular framework for web applications, this study underscores the considerable benefits of integrating Jenkins automation. Overall, this research highlights the importance of Jenkins in enhancing software development processes, making them more efficient and effective.
Silicon Technologies for Quantum Computing

LSI technology has continuously supported the progress of computer performance. Today, transistor miniaturization almost reaches its limitation; however, efforts of semiconductor society have realized a continuous increment of the number of transistors per unit area, thanks to the development of new technologies. Also, new device- and circuit-level technologies have come to continue the trend of low power consumption. There is no doubt that the transistor and its integrated circuit technologies continue to be the driving force for the progress of computer performance. However, the speed of the progress has slowed down. There has been a demand to seek a new driving force in recent years. Then, quantum computing technology has been quite attractive and promising for the future. The most promising applications of quantum computers are combinatorial optimization problems with social significance in many cases and quantum chemical calculation, which accounts for much of modern supercomputer usage. Today, quantum computers have been realized. We can use quantum computers; however, certainly, their performance is not satisfactory. This is because the number of qubits we can use is still insufficient. Toward realizing their full potential, we must succeed in the integration of million-scale qubits. Therefore, the integration technology for quantum computers is now on focus. Silicon quantum computers are attractive from this point of view. Needless to say, silicon technology can achieve large-scale integration of small devices. Recently, companies that have supported the semiconductor industry, such as Intel and Global Foundries, have participated in quantum technology R&D. For example, Intel has demonstrated the operation of their silicon qubits fabricated in their mass-production-ready fabrication line using 300mm-scale wafers. This presentation summarizes the issues toward large-scale integration of silicon qubits and introduces our recent significant research results.
Early Cervical Cancer Diagnostics Detection Based on RMSE and Extreme Learning Machine

Among Indian women, cervical cancer continues to be the most prevalent form of the disease. The death and incidence rates from cervical cancer are dramatically higher in India than in any other country. Cervical cancer is unique among malignancies in that it can be prevented through screening by detecting and treating precancerous lesions at any point in the disease's lengthy natural history. Women can be screened for cervical precancers and malignancies using a number of different procedures, both older and more cutting-edge technologies. Given the limitations of any given screening tool, selecting the appropriate tool for a given situation is essential. Cervical precancers can be treated in a number of ways; the one chosen will rely on a number of factors, including the patient's preferences, the available budget, the quality of the biopsy specimens, etc. Cervical cancer screening guidelines for Indian women are examined. The proposed method consists of three main stages: preprocessing, feature extraction, and model training. In the preprocessing phase, image processing techniques like HE and sharpening and smoothing are applied. The Fourier Transform is used for feature extraction in GLCM. The RMSE-ELM technique is utilized for model evaluation. Better results are achieved with the proposed technique than with CNN or ELM.
Enhancing Profanity Detection in Textual Data Using Bidirectional Long Short-Term Memory Networks

The proliferation of offensive language and content, often referred to as 'profanity,’ poses significant challenges across various digital platforms. Profanity detection in textual data plays a crucial rolein various domains such as social media monitoring, online content moderation, and cyberbullying prevention. This approach aims to enhance profanity detection in textual data using Bidirectional Long Short-Term Memory (BiLSTM) networks to prevent offensive content. The performance of profanity detection is improved in this work by utilizing a novel approach that makes use of cutting-edge deep learning techniques. To be more precise, This approach uses a Bidirectional Long Short-Term Memory network, a powerful deep learning architecture for sequence classification, to model the intricate relationships between words in textual data. Furthermore, this incorporates fine-tuned pre-trained word embeddings to capture semantic information and contextual cues, thereby augmenting the performance of the model. Through extensive experimentation and evaluation of exemplary datasets, the approach achieves a remarkable accuracy rate of 88.3%. Modern methodologies are outperformed by the suggested approach in terms of memory, accuracy, recall, and precision. The efficiency of the method opens up avenues for its application in real-world scenarios, facilitating more effective social media monitoring, online content moderation, and proactive cyberbullying prevention. The findings contribute to advancing the field of profanity detection and hold promise for future research in this domain.