Reviewerly/exHarmony huggingface dataset:
DIGIDOC: A Handwritten Document Analysis Tool for Forensic Application

Analyzing questioned documents from the forensic viewpoint, measuring the genuineness of handwritten suspected contents and formulating a computer-based toolset for the assistance of forensic experts from forensic laboratories demands a lot of attention now-a-days. The reason being, the sudden rise of forgery and other crimes related to handwritten documents, notes, bank cheques, etc. In the real complex world, researchers from the document divisions of forensic labs are still conducting manual examinations for questioned handwritten documents, which is an extremely laborious and time-consuming task. In this work, a semi-automated tool for questioned handwritten document analysis has been described, composed of various image processing filters along with writer and signature verification for suspected handwriting notes. The tool is capable of performing pre-processing techniques over noisy handwritten contents, scanning documents attached to a network or standalone scanners integrated with the PCs, extracting ROI from suspected documents and matching writers and handwritten signature contents up to a certain label for text-dependent cases with other important features embedded into it. The system consists of a graphical user interface for handwritten writer and signature verification, which was developed using Python.
FLIME—Fast Low Light Image Enhancement for Real-Time and Low-Compute Environments Using a Data-Centric Approach

Low light image enhancement is a nontrivial task that finds application in autonomous driving, night vision devices for defence systems and other tasks that involve low light object detection. A majority of the existing solutions proposed, based on deep learning models, are resource intensive and take considerable time to process. Consequently, these are not suitable for processing a sequence of images for real time or even near-real-time video applications. This paper presents FLIME, a fast and efficient solution for the enhancement of low light images, which is amenable for use with a real-time video feed, and in low-compute environments because of its lightweight nature. FLIME is a pipeline of two steps: in the first step, a model is used to map input RGB values to output RGB values and, in the next step, contrast adjustment of the image is effected. The crux of the method comprises a linear transformation of the input RGB values. Since the processing uses a data-centric approach, a carefully curated dataset comprising images taken in low and bright light conditions is used in this study. Design considerations that underlie the preparation of the dataset are presented with the details of the proposed solution. Experimental results that include a qualitative comparison of the images enhanced by FLIME as well as a quantitative comparison of their PSNR, SSIM values and processing times against those of other enhancement methods on publicly available data for this purpose demonstrate the efficacy of the proposed solution.
Design of Equipment Data Analysis Algorithm Based on Convolution and BP Neural Network

In view of the increasingly rich types of equipment data, statistical algorithms have been unable to extract various types of feature information and evaluate equipment status. This paper aims to study the design of equipment data analysis algorithm based on convolution and BP neural network. Based on the analysis of the theoretical model of convolution neural network, the structure of BP neural network and the design of BP neural network topology, the equipment data collection and analysis. The system is designed, and the equipment data analysis algorithm is constructed using convolutional neural network and BP neural network. Finally, this paper has tested the system, and the test results show that the performance of the system can fully meet the needs of users.
Voice Recognition System for Desktop Assistant

Voice recognition is a biometric technique based on the unique qualities of a person’s voice that has become more convenient, safer, and secure than ever before thanks to recent breakthroughs in computing technology. Speech is the most basic method of human communication. A speech recognition system is a technology that converts a human voice into a signal that computers can understand. Users may communicate with technology just by speaking to it, which enables them to ask questions, create reminders, and carry out other basic tasks without the need for their hands. In the previous several years, voice recognition technology has advanced significantly. However, several significant research obstacles remain, such as linguistic diversity, ambient noise, vocabulary size, and a focus on easy tasks alone, to name a few. The goal of this work is to provide a comprehensive overview of speech recognition by outlining various processes and summarizing various programming methodologies. As a result, this article focuses on making advanced speech recognition simple to conduct basic and difficult tasks such as managing social media, searching on Google, sending WhatsApp messages, watching YouTube videos, obtaining an IP address, searching on Wikipedia, and more.
Homomorphic Encryption Technology Based on Computer Cloud Computing

With more and more attention paid to the privacy protection of data, how to ensure the security of cloud platform when storing data is also a current research hotspot. At the same time, the rapid expansion of the amount of data has also caused people to study whether the ciphertext can be directly manipulated, and then put forward homomorphic encryption. The purpose of this paper is to study the application of homomorphic encryption technology based on computer cloud computing. A cloud data sharing scheme with access control is proposed, which is not limited to homomorphic encryption to encrypt data, but uses the homomorphism of homomorphic encryption technology to complete access control. Choose to compare the existing FHE scheme and RLWE scheme. From the results of the experiment, the related data expansion rate is 200 times. Now the efficiency of RLWE is close to the commercial standard. In some simple and small data it can be applied in the environment, and the use of RLWE to provide cloud computing security is no longer a matter of paper.
Aquila Algorithm: A Systematic Review of Variants and Applications

This paper presents a comprehensive survey of applications, variants, and improved versions of a novel swarm intelligence optimization technique known as Aquila optimization algorithm (AO). AO is inspired by hunting behavior of Aquila bird and is regarded as of the most successful metaheuristic optimization algorithm and is successfully used in a variety of issues across numerous domains. This paper has studied the variants, applications, hybridization, and modification of standard AO to improve convergence speed and enhance variety of solutions in population matrix using techniques such as opposition-based learning. This paper will be supportive for studies of academicians, scholars, and industry experts in the field optimization algorithms to understand the importance of AO and its variants in areas such as power system design, feature selection, unarmed vehicles, medical image processing, oil production forecasting, population forecasting, and engineering design problems.
Diversity of forms of socialization in science: Russian and world experience

The article reveals the main characteristics of the modern process of professional socialization of novice scientists, as it proceeds in Russia in comparison with foreign countries. It is shown that the widespread view of socialization as associated with the period of postgraduate studies and the preparation and defence of a dissertation for a degree does not correspond to modern realities in which socialization can be both early (postgraduate) and late (postdoctoral). At the same time, while in the countries of the European Union, obtaining a degree retains the role of a necessary condition for obtaining a research position in the academic environment, in Russia the share of researchers without a degree is more than 70%, and the number of decisions of the Ministry of Education and Science of Russia on issuing a diploma to those who defended their PhD theses steadily decreased in the period 2010–2018. During the designated period, the Russian training system went through a transformation associated with a change in the status of postgraduate studies, but instead of the desired approximation to the European system, this led to the emergence of a unique combination of features. It is shown that in European countries, the USA, China, Japan, etc., the role of the institute of socialization is gradually shifting from postgraduate studies to postdoctoral studies. In this configuration, postgraduate studies contribute to the development of professional independence, while postdoctoral studies provide a chance to realize this independence on a wider range of tasks than conducting and defending a dissertation research. At the same time, the state of the postdoctoral institute in a number of countries, for example, in Japan, demonstrates that under certain conditions it turns into a form of precarious employment, breaking the synonymy between socialization as obtaining recognition of the scientific community on the basis of professional achievements and obtaining a stable position in the social research hierarchy. The directions of minimizing the risk of failure of scientific socialization in Russian science are systematized, it is proved that they lead to an expansion of the diversity of socialization practices of a young scientist and thereby transform the transition situation itself. It acquires a prolonged and, due to this, less labor- and emotionally costly character.
Flight Path Planning of Aircraft Under Multiple Constraints Based on Genetic Algorithm

Flight path planning has become a significant subject of smart control. Because of system constraints, the location system of such airplane cannot precisely position. When the location error increase to a certain extent, the task could fail. Therefore, it is an key task in flight path to correct the location error. This paper processes the data given on attached annex 1 according to constraints, calculates the direct distance of any two samples and eliminates the path with a distance greater than 30,000 m between samples and a distance greater than 200,000 m from the target point. The 1000 feasible paths were generated by MATLAB as samples, and gradually iterated according to the genetic algorithm. Finally, the shortest route and the minimal correction points was obtained. The aircraft passed nine calibration points during this period, which is the optimal path. That is (A, 417, 210, 148, 155, 142, 254, 403, 594, 501, B), the track distance is 108.7758 km and the algorithm running time is 234.815 s.
An Application of Medical Care System During the COVID-19

The greatest approach to reach the most consumers is through a mobile application, which is also a brilliant idea and incredibly simple to handle. Numerous mobile applications that assisted the community in taking the necessary precautions during the Corona pandemic were developed as a result. As a result, we created a phone application that enables patients to schedule an appointment, select the best health and medical services, and submit a request for a PCR test to identify the coronavirus. This application's goal is to make it easier for people to access professionals in every region of Oman through online consultations or in-home visits and to make health services accessible reach of the hand and in the best quality and easiest way possible throughout the hours of the week (24/7). The survey distributed among 157 participants at AlBuraimi University College. The results show a significant acceptance to the application, and it's easy to understand through using two languages (Arabic and English).
The Application of Artificial Intelligence Technology in Computer-Aided Translation

The application of artificial intelligence technology (AIT) in computer-aided translation (CAT) can effectively improve work efficiency, promote the accuracy of information data, and improve the whole social and economic benefits. Firstly, this paper briefly introduces and analyzes AI language. Secondly, it expounds its concept, characteristics, history of development and current situation. Then, combined with specific examples, this paper discusses in detail the existing problems and relevant improvement methods in the process of CAT. Finally, based on AIT, the effect of applying AIT to CAT is tested. The final test results show that CAT takes different time for different semantic materials in translation time, but it can be concluded that the more corpus, the longer time it takes. But the overall time consumption is within 10 s. At the same time, the accuracy and integrity rates were maintained above 80%.
Penta Cancer Detection Using Deep Learning Technique

Cancer is the result of unregulated cell division caused by genetic flaws. Initial diagnosis and primary causes of death are difficult due to its complexity. Cancers such as skin cancer, lung cancer, breast cancer, prostate cancer, and brain cancer kill people all over the world. Traditionally, a biopsy is used to diagnose cancer. This method of manually interpreting medical photographs takes a long time and is prone to errors. Deep learning algorithms are employed in this study to classify several types of cancer, such as lung cancer, brain tumours, cervical cancer, thyroid cancer, and skin cancer and detect their presence without the need for multiple medical consultations. Each step is carried out using different modules such as data set collection, data set augmentation, data set pre-processing, training with architecture, web application development, and cancer prediction. There are numerous techniques that are available to predict the particular cancer type. The proposed work leads to earlier prediction of the presence of five types of diseases and allows us to take prior actions immediately to avoid further consequences effectively and cheaply avoiding human error rate.
Broad Learning Inference Based on Fully Homomorphic Encryption

Distributed big data computing environments such as machine learning are widely deployed and applied on the cloud. However, since cloud servers can easily access user data, it leads to serious data leakage problems. As a potential technology, Fully Homomorphic Encryption (FHE) is often used in the field of privacy-preserving machine learning. However, in order to reduce the multiplicative depth of FHE, the neural network prunes the number of network layers, resulting in low inference accuracy. A learning model named Broad Learning System (BLS) has the characteristics of shallow model depth and low complexity. Based on this mode, we propose a privacy-preserving inference algorithm with low multiplicative depth, namely broad learning inference based on fully homomorphic encryption. We also extend the algorithm to the BLS model with incremental learning. We implement the privacy-preserving BLS for the first time using TENSEAL’s CKKS scheme, and also verify the effectiveness of BLS inference with incremental learning. Experimental evaluations demonstrate the inference accuracy of 0.928 and 0.672 for datasets of MNIST and NORB, respectively.
Application of Probabilistic Common Set on an Open World Set for Vertical Federated Learning

Vertical federated learning (VFL) is a distributed machine learning technology that is suitable for model building in organizations across different industries. It enables the identification of a common set of data that co-occur across organizations. However, VFL uses private set intersection (PSI) protocols, which requires making all data shareable, and satisfying the data minimization principle in the General Data Protection Regulation is difficult. To mitigate noncompliance in privacy regulations, we propose a new VFL method that uses horizontal federated learning to identify the common set instead of PSI. The method consists of two concepts: The first is to use a common data structure between organizations to avoid using PSI. The second is to identify the common set from machine learning classifiers of unseen data of a certain class. Our proposed method considers that the data labeled as the desired class is unseen data and it is not in the common set. Experimental results show that the F-measure is 0.8 or higher in 40% of the common set ratios.
Improved Breast Cancer Detection Using Ensembled Machine Learning Models

Breast cancer (BC) is one of the most deadly cancers for the women across the world. Early detection of breast cancer is very important for life saving. Though it is one of the most critical diseases, yet permanent cure has not been developed till now. Artificial intelligence (AI) and machine learning (ML) have been playing a vital role for effective and quick detection of this disease and increasing the rate of survivals. Deep learning (DL) technologies are helping for the analysis of very important features affecting prediction and detection of serious breast cancer diseases. This research paper focuses on solving the problem of BC detection using Wisconsin Diagnosis Breast Cancer (WDBC) data set by applying different ML models after training and validation. Additionally, various types of performance metrics have been calculated and studied. Various ensemble models have also been developed for improved detection of BC. Recurrent neural network (RNN) and gated recurrent unit (GRU)-based DL models are used for feature extraction and training. Different classification models used in this literature are random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR). After classification, the majority voting and stacking ensemble models have been applied for better performances. After exhaustive simulation and analysis, the performance measures in terms of accuracy, precision, recall, F1-score and area under the curve (AUC) values of the ensembled model are 97.3%, 0.97, 0.971, 0.97 and 0.974, respectively. The proposed ensembled model gives higher performances compared to the individual ML models.
A Novel Method of Low-light Image Enhancement Based on Gaussian Filtering and Gamma Correction

Images taken in low-light situations frequently have poor vision. Aside from less illumination, a few kinds of deterioration, which include noise and color distortion induced by poor camera quality, lurk in the shadows may be present in images. To put it another way, simply improving the brightness of dark areas will increase the noise in images. Image enhancement in low-light should thus not only brighten the dark areas but also eliminate hidden artifacts. Researchers have proposed many methods for enhancing images taken in low light. However, most of them have issues such as poor performance for images which are dark and noise amplification. To address these problems, we present a strategy that divides the issues of pre-enhancing and post-refinement in low-light image enhancement into two categories. To improve the quality of a low-light image, we first pre-process it. Then, in the following stage, we use a refinement network. Our method performs better than traditional methods, according to the experimental findings. We also show that feature point matching, simultaneous localization and mapping in dimly lit environments can all be significantly enhanced by our method.
Virtual Game Play and Pose Detection Based on Computer Vision

Human pose detection is an active research topic in the field of computer vision. Numerous methods and theories have been proposed for pose detection, however the research studies reveal that they fail to recognize due to interference within the segments, similar movements, and other external factors. In this paper, virtual gameplay is built based on the machine learning algorithm. A neural network model called DBLSTM is developed by utilizing the deep learning techniques for training and testing the proposed model. Here, the laptop camera is used as the motion capture device to track the movements of the human body. Data extracted from the image/videos by using OpenPose connects the obtained data with the virtual environment. The result analysis shows that the proposed SVM algorithm outperforms all other algorithms in terms of recognition rate.
Application of Computer Electronic Information Technology in Engineering Project Management

In recent years, the integration of information technology and civil engineering project management has never stopped. It can be said that information technology has penetrated into all stages and links of civil engineering project management, and can be seen in all aspects of civil engineering project management. This paper discusses the application of computer electronic information technology in engineering project management. In this paper, systems engineering theory, process management, information process, Based on technical knowledge such as historical science and integration process, combined with the uniqueness of technical production management, a set of overall research framework and conceptual model of engineering construction project integration and management has been established. The attrition rate of project management has been reduced by 0.7%, and the attrition rate of mechanical costs has been reduced by 1.5%. By informatizing project management, it can also reduce the difficulty of outsiders participating in project management, break industry boundaries, eliminate technical barriers, and allow management to return to its roots.
A Systematic Literature Review on Cybersecurity Issues in Healthcare

Good health is a crown on the well person’s head that only the ill person can see, famous wisdom that everyone knows so, most surely there is nothing most important than health, with the increasing use of technology to market our life easier, technology becomes playing an essential role in healthcare from storing patients’ information to employing the Internet of Things to using the network and everything in between; for this reason, healthcare sector has been increasingly targeted by cyberattacks. This paper will discuss the most critical cybersecurity issues in healthcare and address security and privacy challenges. Finally, we will show significant cybersecurity threats during the COVID-19 pandemic and some suggested solutions to reduce the damages.
Deep Web Search Log Mining Using Spark for Efficient and Scalable Web Information Retrieval

Search engines are widely used for information retrieval on the web and therefore generate the huge volume of web usage data. The increase in the magnitude and complexity of the web data makes pattern recognition a challenge. Deep learning model has been used widely for pattern recognition but requires multiple days for big data processing on a single machine. To improve the training time of deep learning model on big web usage data, a framework is proposed for the execution of convolution neural network (CNN) on web usage data on spark and generates the web document semantic vector for web page ranking. Experiment results show the use of proposed framework reduces the training time of deep learning model CNN and model prediction accuracy is increased. The average precision of the ranked search results based on document semantic vector is improved efficiently using CNN with spark in comparison with CNN without spark.