Reviewerly/exHarmony huggingface dataset:
Key methodological approaches to using free software in informatics course at the level of basic general education

The article discusses in detail the key methodological approaches developed by the authors to using free software at the level of basic general education. When developing these approaches, the authors proceeded from the advantages of using free software in the educational process of basic school, categorizing them as primary and secondary. In the study presented in the article analysis, synthesis, and a systemic approach were applied to identify the structure of benefits and methodological approaches to the use of free software. The new methodological approaches to using free software in teaching informatics considered in the article are structured as a system so a systematic approach can be applied. The system of proposed methodological approaches is of practical value to migrate the educational process in basic school to domestic free software under the import substitution program ongoing in the Russian educational system. The results of the study may be of practical interest to the managers of domestic educational organizations of basic general education who are going to migrate to domestic free software to ensure sustainable and safe school IT infrastructure operations under the increasing Western sanctions.
Import substitution of the software used in the educational process in Russian universities

The article aims to draw the attention of the faculty and academic as well as managerial staff of Russian higher education institutions to the necessity of switching the educational process to national software. The article presents the result of structural modeling of the problem under examination, namely import substitution of the software used in the educational process in Russian universities. The analysis of relevant scientific literature and the documentation available on the official websites of several Russian universities made it possible to describe the set of information and communication platforms used to run distance learning classes and secure other objectives of academic electronic and educational environment. Additionally, the examination of a wide array of educational and methodological literature provided by the leading national electronic library systems resulted in the description of the current state of the educational and methodological support for the use of national office software in the process of teaching informatics and the range of academic disciplines “Information Technologies” to higher education students studying humanities and social sciences. To conduct training sessions in these areas, the experience of using the Russian Р7-Office software package described in the article can be adopted in the educational process. To support the thesis that Russian office software and fonts can be used in educational and research work “here and now”, the author wrote this article in the PT Astra Serif font in the Р7-Office text editor. The work was saved without any loss of formatting as a Microsoft Word document with the Times New Roman font required by editors of scientific journals. Switching to the use of national software in the educational process poses a problem of varying degrees of seriousness to universities in other states, including strategic partners of the USA, the country under whose jurisdiction the copyright holders of most information and communication programs, office software reside.
Real‐time traffic cone detection for autonomous driving based on YOLOv4

A temporary road composed of traffic cones is an indispensable practical scene for the realization of automatic driving technology. However, the detection of traffic cones is a challenging issue because of their small volume and unfixed position. This work proposes a novel method that fuses colour and depth image information for traffic cone detection. Traffic cones are captured by a special machine vision system consisting of two monochrome cameras for cone distance perception and two colour cameras for cone colour acquisition. Via the YOLOv4 algorithm based on the Darknet platform and a detection result matching algorithm, the position of the traffic cone can be obtained and path planning can be performed. The results of experiments show that the proposed method can recognize red, blue, and yellow traffic cones in colour images with an average detection time of 35.46 ms and respective accuracies of 97.51%, 98.63%, and 97.29%. Compared with the previous traffic cone detection research, the proposed algorithm was found to exhibit advantages in small target sensitivity and overall detection accuracy in both static and dynamic experiments.
Nurilo is a Social Platform for the Sale and Purchase Of Framework Products Using Blockchain And Cloud Computing

A delivery chain is a gadget of companies, humans, activities, data, and assets concerned in shifting a service or product from provider to client. The management of the statistics system in most sectors drastically in healthcare, economics, meals, and education is centralized. There’s small information of the item beginnings, handling or shipping travel. The challenge gets to be not as quantitative but too subjective. The biggest challenge of the supply chain remains within the traceability and information administration framework. Blockchain technology is considered to be an important development after the invention of the Internet. Blockchain technology records transactions on databases of which are encrypted and distributed out of many computer networks as digital ledger out of lines of online transactions. This technology can be used in e-commerce industries to develop platforms to buy online and sell decentralized. E-commerce industry current faces many challenges face many challenges such as security measures of e-commerce system, transparency and trust, efficient, and conventional supply chain exercises include a few middle people, believe, and execution problems. The feasibility of block chain could be utilized to disturb delivery chain characteristics for way good execution, dispersed administration, and handle computerization. Blockchain innovation can make strides in the straight forwardness of money related exchanges, upgrade the adaptability of framework operation, and realize handling robotization.
Object identification-based stall detection and stall legitimacy analysis for traffic patterns

Illegal short-term vehicle stalls are frequent in developing countries, which disturb smooth traffic flow and results in congestion, road blockage, and even lead to accidents. Short-term stalls span a few seconds—e.g., a vehicle stopping to make way for people crossing the road. However, traditional stall detection techniques detect stalls that span 10 s or more. Moreover, existing techniques, such as background subtraction algorithms, are vulnerable to illumination changes and indicate too many false stalls with smaller stopping criteria. Furthermore, for violation indication, post-stall analysis is required to filter out the legitimate stalls where a vehicle stops for some valid reason. We present a two-fold methodology to detect illegally parked vehicles. An object identification-based technique is employed over stationary images to detect stall. Furthermore, the legitimacy of the stall is determined by performing pedestrian detection and its position analysis. We tested our proposed approach using publicly available advanced video and signal based surveillance 2007 PV dataset and ViSOR dataset. The technique successfully detects stalls with a recall of 0.96, after weaving the stopping criterion from the ground truth.
Neural-based automatic scoring model for Chinese-English interpretation with a multi-indicator assessment

Manual evaluation could be time-consuming, unreliable and unreproducible in Chinese-English interpretation. Therefore, it is necessary to develop an automatic scoring system. This paper proposes an accurate automatic scoring model for Chinese-English interpretation via a multi-indicator assessment. From the three dimensions (i.e. keywords, content, and grammar) of the scoring rubrics, three improved attention-based BiLSTM neural models are proposed to learn the text of the transcribed responses. In the feature vectorisation stage, the pre-training model Bert is utilised to vectorise the keywords and content, and a random initialisation is used for the grammar. In addition, the fluency is also taken into account based on the speech speed. The overall holistic score is obtained by fusing the four scores using the random forest regressor. The experimental results demonstrate that the proposed scoring method is effective and can perform as good as the manual scoring.
Design of adaptive hybrid classification model using genetic-based linear adaptive skipping training (GLAST) algorithm for health-care dataset

Machine-learning techniques are being used in the health-care industry to improve care delivery at a lower cost and in less time. Artificial Neural Network (ANN) is well-known machine-learning techniques for its diagnostic applications, but it is also increasingly being utilized to guide health-care management decisions. At the same time, in the healthcare industry, ANN has made significant progress in solving a variety of real-world classification problems that range from linear to non-linear and also from simple to complex. In this research work, an Adaptive Hybrid Classification Model named as Genetic-based Linear Adaptive Skipping Training (GLAST) Algorithm has been proposed for the health-care dataset. It has been designed as two-stage process. In first stage, Genetic Algorithm (GA) is adapted to optimize the Learning rate. After optimizing the Learning rate, the optimal Learning rate has been set to the ANN model is ŋ = 1e−4. In the second stage, The training process is carried out using the Linear Adaptive Skipping Training (LAST) algorithm, which reduces the total training time and thus increases the training speed. As a result, the highlighted characteristics of LAST have been integrated with GA to accomplish rapid classification and enhance computational efficiency. On 8 different health-care datasets extracted from the UCI Repository, the proposed GLAST algorithm outperforms both the BPN and LAST algorithms in terms of accuracy and training time, according to simulation results. The result analyses have proved that the efficiency of this proposed GLAST Algorithm outperforms over the existing techniques such as BPN and LAST in terms of accuracy and training time. On various datasets, experimental results show that GLAST improves accuracy from 4 to 17% over BPN training algorithm and reduces overall training time from 10 to 57% over BPN training algorithm.
Non‐convex nonlocal adaptive tight frame image deblurring

The challenge of the image restoration is to recover more detailed information from the degraded images. Based on the observations that wavelet frames have efficient representation ability to image details and the nonconvex regularization in the model may admit unbiased solutions, in this paper, in order to recover more details, a wavelet frames nonconvex ℓ p ( 0 < p < 1 $\ell _p(0<p<1$ ) regularization image restoration model is established that combing with a nonlocal adaptive mean doubly augmented Lagrangian (MDAL) method and context model. Specifically, a nonlocal adaptive MDAL method is proposed to solve the established nonconvex model. Furthermore, in order to mitigate the trade-off between the regularization error and the noise magnification error, spatially adaptive wavelet thresholding methods based on the context model and nonlocal mean filter are introduced in the algorithm. The convergence analysis of this nonlocal adaptive nonconvex algorithm is also obtained using the KL inequality. Numerical experiments demonstrate that the proposed method has a competent deblurring and denoising ability, also is efficient and is comparable to state-of-the-art methods.
Predicting lexical complexity in English texts: the Complex 2.0 dataset

Identifying words which may cause difficulty for a reader is an essential step in most lexical text simplification systems prior to lexical substitution and can also be used for assessing the readability of a text. This task is commonly referred to as Complex Word Identification (CWI) and is often modelled as a supervised classification problem. For training such systems, annotated datasets in which words and sometimes multi-word expressions are labelled regarding complexity are required. In this paper we analyze previous work carried out in this task and investigate the properties of CWI datasets for English. We develop a protocol for the annotation of lexical complexity and use this to annotate a new dataset, CompLex 2.0. We present experiments using both new and old datasets to investigate the nature of lexical complexity. We found that a Likert-scale annotation protocol provides an objective setting that is superior for identifying the complexity of words compared to a binary annotation protocol. We release a new dataset using our new protocol to promote the task of Lexical Complexity Prediction.
A Study on Lexicon Based Techniques of Twitter Sentiment Analysis

The world around us has changed rapidly in past few years and so has the medium of public voice. It has become common for every individual to voice his opinion using social media platforms like twitter. These platforms, as micro blogs, offer service not only to general public to went out their opinions, but also act as a huge resource of reflections of public emotions. Sentiments expressed over these platforms are mines of knowledge to researchers, marketers, product and services designers and all stakeholders. Over the years many techniques have evolved to mine these repositories. These techniques are primarily based on supervised machine learning approaches, ensemble approaches and lexicon-based approaches. In this paper authors present a study on various techniques based on lexicon-based approaches developed and followed by various researchers for sentiment analysis using twitter data. Authors have also prepared a comparative analysis of various techniques based on certain performance parameters to evaluate most accurate polarity of sentiments reflected in tweets.
Web Application Penetration Testing &amp; Patch Development Using Kali Linux

Nowadays, safety is a first-rate subject for all applications. There has been an exponential growth year by year in the number of businesses going digital since the few decades following the birth of the Internet. In these technologically advanced times, cyber security is a must mainly for internet applications, so we have the notion of diving deeper into the Cyber security domain and are determined to make a complete project. We aim to develop a website portal for ease of communication between us and the end user. Utilizing the power of python scripting and flask server to make independent automated tools for detection of SQLI, XSS &#x0026; a Spider(Content Discovery Tool). We have also integrated skipfish as a website vulnerability scanner to our project using python and Kali Linux. Since conducting a penetration test on another website without permission is not legal, we thought of building a dummy website prone to OS Command Injection in addition to the above-mentioned attacks. A well-documented report will be generated after the penetration test/ vulnerability scan. In case the website is vulnerable, patching of the website will be done with the user&#x0027;s consent.
Text Detection and Recognition Using Fusion Neural Network Architecture

Text recognition possesses various application which attracted wide number of research interests in past decade. Recent method includes the deep learning-based model and it follows the attention-based mechanism. However, this deep learning mechanism suffers problem with recurrence alignment and also the feature extraction and labelling sequence becomes hard due to involvement of image as well as text data. Hence, this research designs and develops a deep learning-based Fusion Neural Network for detection and recognition of text. FNN aims to improve the accuracy of identification. Fusion Neural Network comprises different layer of different Neural Network; moreover, convolutional layer is used to obtain the feature sequence and optimal training model is designed using recurrent layer to enhance the accuracy. FNN is evaluated considering Devanagari MLT-19 dataset, evaluation is carried out considering different parameter, at first script cropping and recognising is carried out, also comparison with existing methodology is carried out to prove proposed model efficiency. Furthermore, FNN model observes 98.67&#x0025; of script word identification accuracy.
Detection of Face Mask Using Convolutional Neural Network

According to data acquired by the World Health Organization, the worldwide universal of COVID-19 bears harshly hit the realm and bears immediately contaminate eight heaps of human beings in general. Wearing face masks and following cautious public leave behind are two of the embellished protection from harm rules of conduct that need to take the place of honestly held places in consideration of keeping from happening or continuing the spread of the virus. To develop in mind or physically conservative surroundings that contribute to public protection from harm, we suggest an adept data processing machine located in close contact with the genuine in existence-period made or done by a human being to discover two reliable public dissociate themselves and face masks honestly placed by the model ahead of the start of the model to monitor special interests or pursuits and discover rape through photographic equipment. In addition to presenting an alarm to the public, in this proposed structure, we have designed mask detection along which indicates people to wear their mask properly before permitting in to the area which they prefer. We have used machine learning with supports the accuracy for the prediction.
Deep learning-based Video Captioning Technique using Transformer

Automatic Video captioning is the process by which a meaningful natural language sentence description is generated for a given video. Video understanding has got several applications in the field of automatic object identification, real-time calamity detection, navigation for visually impaired people, autonomous driving, human robot interaction and many more. The process of video captioning is a tedious task for machines, and it needs a collaborated effort of CV-Computer Vision techniques and NLP-Natural Language Processing techniques. In the last decade, the technologies that were used to perform this task includes CNN, RNN, LSTM, GRU and its variants. Recently the focus has been shifted to Transformer networks which sounds more promising than any of these. This work proposes a transformer-based Video captioning architecture, and the evaluation has been made over standard dataset with metrics and is found to perform superior to existing methods.
Multikernel Support Vector Machine with GLCM For Truthful Brain Signal Classification of Magnetic Resonance Imaging

This paper works on the novel hierarchical transformation technique (HTT)on the root of gray level co-occurrence matrix (GLCM) texture features which has to be extracted and sequentially processed with multi-kernel support vector machine (SVM) classifier to categorize MRI brain image clinical states. This processing technique possess 3 different phases. First, pre-processing steps are applied to enhance the image quality by applying hierarchical transformation technique. Then, texture features are extracted with the help of GLCM. Finally, multi-kernel support vector machine algorithm enables the classification. The novel proposed HTT methodology makes the incorporation of optimal selection of mask with disk shaped bottom and top hat morphological processing and few mathematical processes for the cumulative enhancement and the pre-processing of the image. The computation of GLCM is being made for extracting the probabilistic texture features. These features are measurement of contrast, energy, correlation, entropy and homogeneity. The extracted GLCM texture features are based on co-occurrence and then applied with SVM classification which can categorize the acquired MRI brain image into two clinical states as abnormal and normal. Additionally, the enumeration of comparison is also made with traditional feature extraction technique using the GLCM textures.
An Optimized Neural Network for Content Based Image Retrieval in Medical Applications

The Content-Based Image Retrieval (CBIR) is a widely used approach for finding and retrieving related pictures. It is useful in medical applications for detecting disorders like cancer in the individuals. A lot of previous strategies for increasing the performance of the CBIR were described. CBIR&#x0027;s retrieval time and accuracy, however, can yet be improved. In CBIR, an optimum classifier will be employed to address this problem. In this work, the PSO-ANN is a classifier to detect the events. In addition, visual properties are extracted and these retrieved features will be grouped using the k-means clustering algorithm to enhance the classification process. The suggested classifier is used to recover the similar images of the query image are from clustered features. The simulation results shows that this framework have high accuracy and minimum time with previous approaches.
Review of Movie Recommendation System

A movie recommendation plays a crucial role in our social life. A recommendation system provides a set of movies to the users based on the movie&#x0027;s popularity or depending on the users&#x0027; interests. A recommendation system is also used to propose goods to buy or view. They employ an extensive collection of information to steer consumers to the things that will best match their needs. This paper discusses various movie recommendation algorithms used in recent research. We have summarized the movie recommendation algorithm&#x0027;s methodology, merits, and demerits. Moreover, we have discussed the future challenges in the movie recommendations system.
Robotics and Artificial Intelligence in the Hotel Industry: A Systematic Literature Review

The paper provides an overview of robotics, and artificial intelligence (AI) research in the realm of hotel industry. In recent years hotel industry has incorporated many technological tools, such as smart sensors, chat bots, service robots, AI embedded equipments in their day to day operations. A comprehensive literature review from 1984 to 2021 evaluated 551 papers. The information extracted is analyzed to understand the work done in this field in terms of publications, authorship, subject area, source, keywords and citations. Findings suggest that the research work in AI and robotics has increased significantly in the hotel business.