Reviewerly/exHarmony huggingface dataset:
A low-light image enhancement method based on atmospheric scattering model

Since the traditional low-light image enhancement algorithm has poor generalization and is difficult to adapt to the complex and volatile real scenes, this paper proposes a low-light image enhancement method based on atmospheric scattering model. First, a low-light image is inverted for obtaining a foggy image. Subsequently, the inverted image is used as the input, which is passed through the atmospheric light estimation module and the transmission estimation module, respectively. Finally, the parameters obtained from this solution are incorporated into the atmospheric scattering model. Residual-in-residual dense blocks (RRDB) are introduced to extract high-level feature information from the inverted images. The dilated convolution is used in the atmospheric light estimation module with a larger receptive field and a minor number of parameters, thus the training speed is improved. A large number of experimental results demonstrate that our method achieves better quality compared with the state- of-the-art methods.
Research of Chinese Painting Inpainting Based on Improved Criminisi Algorithm

To address the problems of incorrect inpainting order and incorrect similar patch matching when restoring Chinese paintings using the Criminisi algorithm, an inpainting algorithm with improved priority calculation and similar patch search matching is proposed. Firstly, edge features are extracted from the image, and gradients are calculated on the edge feature map to replace the data items in the original algorithm. The distance and the difference in the colour mean of the patch are introduced into the similar patch searching matching formula to ensure the continuity of the inpainting results. Experiments show that the proposed improved algorithm has better inpainting results on Chinese painting inpainting and better processing for texture details.
A gesture recognition algorithm for smart gloves

This paper proposes a gesture recognition algorithm for smart gloves. The main innovations of the method are: (a) When the slope method is used to fit the curve, the two curve equations are assumed, and then the error size of the two curves is judged, and the curve where the smaller error is located is the fitted curve. (b) Jaccard coefficient is introduced into the gesture recognition algorithm, and by comparing the trend of two gestures in the same position segmentation interval, it judges whether they are the same gesture. Compared with the previous trend determination condition <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> the determination condition is simpler and the output result is more intuitive. The experiment was conducted by this algorithm, and better experimental results were achieved.
A Sudoku Image Recognition Method

The paper presents a recognition method for Sudoku Images. We use smart phone to capture Sudoku images on newspapers and books. Under different capturing conditions, Sudoku images happen several changes in gray levels and shooting angles and mix some noises. The proposed method is divided into several processing steps. We firstly use the contour characteristic of Sudoku images to extract Sudoku grid frames. Then, the images are transformed into the standard squares. Then, we partition the images into cells and extract HOG features of cells. Finally, an SVM classifier is employ to recognize digits. Because the directions of original images are variable, we add a correction procedure in the proposed method. The procedure is able to automatically rotate the images to the correct text directions. The experimental results indicate that our proposed method can quickly and recognize Sudoku images with different shooting angles and the recognition rate for digits inside cells is very high.
PENGARUH UNREAL ENGINE DALAM PERKEMBANGAN DUNIA GAME

The development of games industries today is speedy and has become more modern. The game company must develop their game to the next level, for example not long-ago Epic Games company release their new engine. The engine used for making the game can be used for 3D modeling as well. Unreal Engine 5 provided terrific graphics and more features than an older version of Unreal Engine. Epic Games company provided their engine free for all or open-source which can be used by anyone for free. Unreal Engine can be more useful for the game developer or 3D modeling. Without a doubt that Unreal Engine 5 by Epic Games can bring a new era to the game. A lot of developer wants to create a more powerful engine than Unreal Engine 5 but for now, there is no new engine that can beat Unreal Engine 5. For example, Frostbite engine by DICE adds more updates to catch up with Unreal Engine 5
Long-term preservation of legally significant electronic document

The article discusses the problem of ensuring the long-term preservation of legally significant electronic documents (LSED). An overview of the problem and existing options for solving it are provided in the article. The paper frames a formal problem of ensuring the long-term preservation of legally significant electronic documents as an optimal control problem under conditions of parametric disturbances. This study briefly outlines the problems that arise when organizing the long-term preservation of electronic documents. The author also proposes documents safety indicators through documents and mathematical models for assessmentof the overall document safety. The study has resulted in development of algorithms that allow, being implemented in the form of software and hardware, to control and organize security. Examples of the practical implementation of the proposed approaches within the framework of large information systems are provided alongside with their advantages. Implementation of suggested approaches can potentially make it possible to comprehensively solve the important scientific and technical problem of organizing the long-term preservation of legally significant electronic documents. At the end of the article, further prospects for research aimed at solving the problem of long-term preservation of big data and systems built using distributed ledger technologies are identified.
Blockchain technology and corporate law

The article analyses legislative initiatives aimed at regulating the use of blockchain in corporate governance. The tokenisation equity opens up new opportunities for companies to attract investment. As a result, many traditional companies are interested in converting traditional securities into security tokens. Countries aspiring to lead the blockchain industry are seeking to establish a legal framework for security tokens and a blockchain-based registration system for them. The use of blockchain brings with it not only the digital transformation of companies, but also the emergence of a new type of organization - decentralized autonomous organization (DAO). Existing legal forms are not appropriate for the DAO, which requires the creation of a new type of legal entity. Changes to corporate law that address these trends will eliminate legal risk and drive digital transformation of companies
RANCANG BANGUN JARINGAN SITE TO SITE VPN (VIRTUAL PRIVATE NETWORK) DENGAN PROTOCOL OPENVPN

The use of wireless media such as the internet in building computer networks is so widely used. However, performance and security must also be considered. VPN (Virtual Private Network) is one of the methods in network security that works by creating a tunnel so that a trusted network can connect to networks outside via the internet. PT. Laris Manis Utama is a private company engaged in the field of fresh fruits export-import and distributor. And already has several branch offices located outside the city. Designing a VPN (Virtual Private Network) with a Site to site (OpenVPN) system at PT. Laris Manis Utama functions as a communication liaison for media and data transfer between the head office server and branch servers so that they are connected in real-time and can develop application systems and updated databases
Data Masking for Chinese Electronic Medical Records with Named Entity Recognition

With the rapid development of information technology, the electronification of medical records has gradually become a trend. In China, the population base is huge and the supporting medical institutions are numerous, so this reality drives the conversion of paper medical records to electronic medical records. Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence, and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field. However, electronic medical records contain a large amount of private patient information, which must be desensitized before they are used as open resources. Therefore, to solve the above problems, data masking for Chinese electronic medical records with named entity recognition is proposed in this paper. Firstly, the text is vectorized to satisfy the required format of the model input. Secondly, since the input sentences may have a long or short length and the relationship between sentences in context is not negligible. To this end, a neural network model for named entity recognition based on bidirectional long short-term memory (BiLSTM) with conditional random fields (CRF) is constructed. Finally, the data masking operation is performed based on the named entity recognition results, mainly using regular expression filtering encryption and principal component analysis (PCA) word vector compression and replacement. In addition, comparison experiments with the hidden markov model (HMM) model, LSTM-CRF model, and BiLSTM model are conducted in this paper. The experimental results show that the method used in this paper achieves 92.72% Accuracy, 92.30% Recall, and 92.51% F1_score, which has higher accuracy compared with other models.
Gender Identification Using Marginalised Stacked Denoising Autoencoders on Twitter Data

Gender analysis of Twitter could reveal significant socio-cultural differences between female and male users. Efforts had been made to analyze and automatically infer gender formerly for more commonly spoken languages’ content, but, as we now know that limited work is being undertaken for Arabic. Most of the research works are done mainly for English and least amount of effort for non-English language. The study for Arabic demographic inference like gender is relatively uncommon for social networking users, especially for Twitter. Therefore, this study aims to design an optimal marginalized stacked denoising autoencoder for gender identification on Arabic Twitter (OMSDAE-GIAT) model. The presented OMSDAE-GIAR technique mainly concentrates on the identification and classification of gender exist in the Twitter data. To attain this, the OMSDAE-GIAT model derives initial stages of data pre-processing and word embedding. Next, the MSDAE model is exploited for the identification of gender into two classes namely male and female. In the final stage, the OMSDAE-GIAT technique uses enhanced bat optimization algorithm (EBOA) for parameter tuning process, showing the novelty of our work. The performance validation of the OMSDAE-GIAT model is inspected against an Arabic corpus dataset and the results are measured under distinct metrics. The comparison study reported the enhanced performance of the OMSDAE-GIAT model over other recent approaches.
Bug Prioritization Using Average One Dependence Estimator

Automation software need to be continuously updated by addressing software bugs contained in their repositories. However, bugs have different levels of importance; hence, it is essential to prioritize bug reports based on their severity and importance. Manually managing the deluge of incoming bug reports faces time and resource constraints from the development team and delays the resolution of critical bugs. Therefore, bug report prioritization is vital. This study proposes a new model for bug prioritization based on average one dependence estimator; it prioritizes bug reports based on severity, which is determined by the number of attributes. The more the number of attributes, the more the severity. The proposed model is evaluated using precision, recall, F1-Score, accuracy, G-Measure, and Matthew’s correlation coefficient. Results of the proposed model are compared with those of the support vector machine (SVM) and Naive Bayes (NB) models. Eclipse and Mozilla datasetswere used as the sources of bug reports. The proposed model improved the bug repository management and outperformed the SVM and NB models. Additionally, the proposed model used a weaker attribute independence supposition than the former models, thereby improving prediction accuracy with minimal computational cost.
A Proposed Architecture for Local-Host and AWS with Multiagent System

Cloud computing has become one of the leading technologies in the world today. The benefits of cloud computing affect end users directly. There are several cloud computing frameworks, and each has ways of monitoring and providing resources. Cloud computing eliminates customer requirements such as expensive system configuration and massive infrastructure while improving dependability and scalability. From the user’s perspective, cloud computing makes it easy to upload multiagents and operate on different web services. In this paper, the authors used a restful web service and an agent system to discuss, deployments, and analysis of load performance parameters like memory use, central processing unit (CPU) utilization, network latency, etc., both on localhost and an Amazon Web Service Elastic Cloud Computing (AWS-EC2) server. The Java Agent Development Environment (JADE) tool has been used to propose an architecture and conduct a comparative study on both local and remote servers. JADE is an open-source tool for maintaining applications on AWS infrastructure. The focus of the study should be to reduce the complexity and time of load performance parameters by using an agent system on a cloud server instead of establishing a massive infrastructure on a local system, even for a small application.
Multiple Extreme Learning Machines Based Arrival Time Prediction for Public Bus Transport

Due to fast-growing urbanization, the traffic management system becomes a crucial problem owing to the rapid growth in the number of vehicles The research proposes an Intelligent public transportation system where information regarding all the buses connecting in a city will be gathered, processed and accurate bus arrival time prediction will be presented to the user. Various linear and time-varying parameters such as distance, waiting time at stops, red signal duration at a traffic signal, traffic density, turning density, rush hours, weather conditions, number of passengers on the bus, type of day, road type, average vehicle speed limit, current vehicle speed affecting traffic are used for the analysis. The proposed model exploits the feasibility and applicability of ELM in the travel time forecasting area. Multiple ELMs (MELM) for explicitly training dynamic, road and trajectory information are used in the proposed approach. A large-scale dataset (historical data) obtained from Kerala State Road Transport Corporation is used for training. Simulations are carried out by using MATLAB R2021a. The experiments revealed that the efficiency of MELM is independent of the time of day and day of the week. It can manage huge volumes of data with less human intervention at greater learning speeds. It is found MELM yields prediction with accuracy in the range of 96.7% to 99.08%. The MAE value is between 0.28 to 1.74 minutes with the proposed approach. The study revealed that there could be regularity in bus usage and daily bus rides are predictable with a better degree of accuracy. The research has proved that MELM is superior for arrival time predictions in terms of accuracy and error, compared with other approaches.
Hyperparameter Tuned Deep Hybrid Denoising Autoencoder Breast Cancer Classification on Digital Mammograms

Breast Cancer (BC) is considered the most commonly scrutinized cancer in women worldwide, affecting one in eight women in a lifetime. Mammography screening becomes one such standard method that is helpful in identifying suspicious masses’ malignancy of BC at an initial level. However, the prior identification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in diagnosis. Deep learning (DL) techniques were broadly utilized for medical imaging applications, particularly breast mass classification. The advancements in the DL field paved the way for highly intellectual and self-reliant computer-aided diagnosis (CAD) systems since the learning capability of Machine Learning (ML) techniques was constantly improving. This paper presents a new Hyperparameter Tuned Deep Hybrid Denoising Autoencoder Breast Cancer Classification (HTDHDAE-BCC) on Digital Mammograms. The presented HTDHDAE-BCC model examines the mammogram images for the identification of BC. In the HTDHDAE-BCC model, the initial stage of image preprocessing is carried out using an average median filter. In addition, the deep convolutional neural network-based Inception v4 model is employed to generate feature vectors. The parameter tuning process uses the binary spider monkey optimization (BSMO) algorithm. The HTDHDAE-BCC model exploits chameleon swarm optimization (CSO) with the DHDAE model for BC classification. The experimental analysis of the HTDHDAE-BCC model is performed using the MIAS database. The experimental outcomes demonstrate the betterments of the HTDHDAE-BCC model over other recent approaches.
Detection of Phishing in Internet-of-Things Using Hybrid Deep Belief Network

Increase in the use of internet of things owned devices is one of the reasons for increased network traffic. While connecting the smart devices with publicly available network many kinds of phishing attacks are able to enter into the mobile devices and corrupt the existing system. The Phishing is the slow and resilient attack stacking techniques probe the users. The proposed model is focused on detecting phishing attacks in internet of things enabled devices through a robust algorithm called Novel Watch and Trap Algorithm (NWAT). Though Predictive mapping, Predictive Validation and Predictive analysis mechanism is developed. For the test purpose Canadian Institute of cyber security (CIC) dataset is used for creating a robust prediction model. This attack generates a resilience corruption works that slowly gathers the credential information from the mobiles. The proposed Predictive analysis model (PAM) enabled NWAT algorithm is used to predict the phishing probes in the form of suspicious process happening in the IoT networks. The prediction system considers the peer-to-peer communication window open for the established communication, the suspicious process and its pattern is identified by the new approach. The proposed model is validated by finding the prediction accuracy, Precision, recalls F1score, error rate, Mathew’s Correlation Coefficient (MCC) and Balanced Detection Rate (BDR). The presented approach is comparatively analyzed with the state-of-the-art approach of existing system related to various types of Phishing probes.
Enhanced Deep Learning for Detecting Suspicious Fall Event in Video Data

Suspicious fall events are particularly significant hazards for the safety of patients and elders. Recently, suspicious fall event detection has become a robust research case in real-time monitoring. This paper aims to detect suspicious fall events during video monitoring of multiple people in different moving backgrounds in an indoor environment; it is further proposed to use a deep learning method known as Long Short Term Memory (LSTM) by introducing visual attention-guided mechanism along with a bi-directional LSTM model. This method contributes essential information on the temporal and spatial locations of &#x2018;suspicious fall&#x2019; events in learning the video frame in both forward and backward directions. The effective &#x201C;You only look once V4&#x201D; (YOLO V4)&#x2013;a real-time people detection system illustrates the detection of people in videos, followed by a tracking module to get their trajectories. Convolutional Neural Network (CNN) features are extracted for each person tracked through bounding boxes. Subsequently, a visual attention-guided Bi-directional LSTM model is proposed for the final suspicious fall event detection. The proposed method is demonstrated using two different datasets to illustrate the efficiency. The proposed method is evaluated by comparing it with other state-of-the-art methods, showing that it achieves 96.9% accuracy, good performance, and robustness. Hence, it is acceptable to monitor and detect suspicious fall events.
Political Optimizer with Probabilistic Neural Network-Based Arabic Comparative Opinion Mining

Opinion Mining (OM) studies in Arabic are limited though it is one of the most extensively-spoken languages worldwide. Though the interest in OM studies in the Arabic language is growing among researchers, it needs a vast number of investigations due to the unique morphological principles of the language. Arabic OM studies experience multiple challenges owing to the poor existence of language sources and Arabic-specific linguistic features. The comparative OM studies in the English language are wide and novel. But, comparative OM studies in the Arabic language are yet to be established and are still in a nascent stage. The unique features of the Arabic language make it essential to expand the studies regarding the Arabic text. It contains unique features such as diacritics, elongation, inflection and word length. The current study proposes a Political Optimizer with Probabilistic Neural Network-based Comparative Opinion Mining (POPNN-COM) model for the Arabic text. The proposed POPNN-COM model aims to recognize comparative and non-comparative texts in Arabic in the context of social media. Initially, the POPNN-COM model involves different levels of data pre-processing to transform the input data into a useful format. Then, the pre-processed data is fed into the PNN model for classification and recognition of the data under different class labels. At last, the PO algorithm is employed for fine-tuning the parameters involved in this model to achieve enhanced results. The proposed POPNN-COM model was experimentally validated using two standard datasets, and the outcomes established the promising performance of the proposed POPNN-COM method over other recent approaches.
Automated Disabled People Fall Detection Using Cuckoo Search with Mobile Networks

Falls are the most common concern among older adults or disabled people who use scooters and wheelchairs. The early detection of disabled persons’ falls is required to increase the living rate of an individual or provide support to them whenever required. In recent times, the arrival of the Internet of Things (IoT), smartphones, Artificial Intelligence (AI), wearables and so on make it easy to design fall detection mechanisms for smart homecare. The current study develops an Automated Disabled People Fall Detection using Cuckoo Search Optimization with Mobile Networks (ADPFD-CSOMN) model. The proposed model’s major aim is to detect and distinguish fall events from non-fall events automatically. To attain this, the presented ADPFD-CSOMN technique incorporates the design of the MobileNet model for the feature extraction process. Next, the CSO-based hyperparameter tuning process is executed for the MobileNet model, which shows the paper’s novelty. Finally, the Radial Basis Function (RBF) classification model recognises and classifies the instances as either fall or non-fall. In order to validate the betterment of the proposed ADPFD-CSOMN model, a comprehensive experimental analysis was conducted. The results confirmed the enhanced fall classification outcomes of the ADPFD-CSOMN model over other approaches with an accuracy of 99.17%.