Reviewerly/exHarmony huggingface dataset:
Analysis and Implementation of 3-D Transpositional Cipher Algorithm Based on Rubik’s Cube Mechanism

The algorithms that are used for encrypting the text information for security purpose are referred as encryption algorithms. They are widely employed for the application on data transmission over networks. This is seen as a necessary part of data transmission since confidentiality and data privacy are important factors. Different encryption algorithms have different complexities, speeds of execution, and degrees of security. The Advanced Encryption Standard (AES) is one of the algorithms acknowledged by the U.S. government and other major organizations. The AES algorithm guarantees high security since there is no known public attack that can crack it. The Blowfish algorithm on the other hand is very fast but less secure. The encryption algorithms in the average zone of these two algorithms are highly useful for the transmission of general information among people. Such an algorithm also guarantees a good amount of speed as well as more security. Non-conventional algorithms such as transpositional cipher based on Rubik’s Cube mechanism can be seen as a viable option to use in common applications. These algorithms can be further deepened with the help of more complex key generation parts that can be done to enhance the security of the data. This paper focuses on analytically evaluating if this algorithm can be treated as a viable replacement in an application compared to other algorithms. Moreover, this research covers some pros and cons noted while evaluating.
Generative Adversarial Neural Approach Towards Construction of Warli-stick Figure Aboriginal Artwork

Image Synthesis or generation is evolving at a high rate helping from diagnosis of diseases to traffic control. Deep Learning has touched a new height in which domains like Computer Vision have successfully developed Deep learning models for image generation. Recent improvements in deep learning enabled Generative Adversarial Networks or GANs to achieve a remarkable level of success in image synthesis. GANs help in generating significant and relevant outcomes from convoluted patterns as input. This piece of research aims at making a call towards conservation of tribal art culture through deploying neural models and computer vision techniques, in line with Warli tribal human synthetic stick figures of Asian tribes. This paper presents several methods for synthesizing human stick figures in Asian Tribal Art using modified GAN architecture. The modified GANs were quite different in terms of layers as well as the range and distribution of latent vector space. To provide input data for the training process, a customized data set was created based on spherical polar coordinates and the laws of coordinate geometry. The process of training the GAN architecture involves insertion of random vector points, and addition of neural layers, to generate outputs of higher and better resolution. The paper includes training of Generator and Discriminator following the concepts of Transfer learning and providing relevant affirmation to draw conclusions that the generated samples exhibit similarities and resemble the original input sample. Further work extends towards using of different color space data set images for generating outputs with better accuracy through the modified GAN models. The final generated output images along with other visualizations are also attached with this work to provide evidence for the fact that the output samples are analogous to the actual data. This piece of research tries to open a gate way of using artificial intelligence in terms of cultivating and protecting the traditional art cultures of various tribes across the globe. The proposed methodology performed quite well in terms of producing warli figures from given latent vector space, which is vindicated by the comparison of images at pixel level and image histograms.
Hand-Drawn Electronic Component Recognition Using ORB

There is growth in interest to build a system that can recognize freely drawn symbols. In past two years, the situation in the world has become more difficult, almost all the work in their organizations has been digitalized. However, many challenges remain in terms of the recognition accuracy of different drawing styles. To address these challenges, a new approach is proposed to classify and predict the hand-drawn component. In this work Computer Vision approach using the ORB algorithm is used to recognize and predict the component. The drawing tools that are being used need to be upgraded in order to have a better learning experience in order to recognize the symbol that is drawn and directly convert it into digitized form. As it is very difficult to write it on an online platform every time this method would be very useful. Here 15 different electronic components are considered with three different orientations. In this approach, GUI is used to draw the symbol which is easy instead of picking and placing. This input-drawn image is given to the model developed using the CV algorithm, where the input image gets compared with the database image, according to ORB using the FAST method key points are generated for both images. Using a Brute force matcher both images get matched depending on the number of matched features, and the output image gets predicted. The results of the ORB and SIFT CV algorithms are analyzed and compared.
Quality Assessment Model for Handwritten Photo Document Images

The photo document image's quality determines whether it has the potential to be used for information extraction. Document Image Quality Assessment (DIQA) is a difficult task since it is complicated to train a system to have a complete human-like vision and it might be tiresome and time-consuming to manually evaluate the quality of many document images. The educational system became online during the pandemic age, which led to the digital submission of exams and homework. Quality evaluation has gained more prominence recently as digitization has started to take precedence in some fields. In order to evaluate the document image quality of handwritten document images at the page level while taking into account the overall visual aesthetic, this research suggests a transfer-learning and classification-based model. ResNet50 exceeded all of the pre-trained models we tested and examined, with an accuracy of 91.00 % on validation sets and 83 % on test sets.
A short survey for Vietnamese micro-text augmentation techniques

The requisites of a powered-AI system is to have a big enough annotated data. Lack of the datasets is a big challenge to obtain the robustness of the predictive models so that it can broaden the AI ideas to various domains. The predictive models are less generalized and prone to overfit. Although the resources for Vietnamese have been investigated more and more, it has still been a low-resources language which is the biggest barrier in order to leverage the robustness of the AI applications. Building the datasets consumes so much time and money. This paper presents the text augmentation to generate the new annotated training data without user's intervention. This paper has summarized the potential methods, especially for the cross-languages methods to enhance the data, analyzed and evaluated the advantages and disadvantages of each method to apply to Vietnamese language processing. The synthetic presentation shows text augmentation has gained competitive performances and helped to save the time and money to build the data.
A Factor Based Multiple Imputation Approach to Handle Class Imbalance

Class imbalance and incompleteness are the two most serious problems faced in data science and machine learning when working on real-life datasets. Both of these cases have severe implications on the ability of classification algorithms to make accurate predictions. When a dataset used for training classifiers is both imbalanced as well as incomplete, the traditional approach is to address the missing data first and then handle class imbalance but it could lead to some issues such as overfitting as well as amplification of some errors due to random duplication. In this paper, an alternate factor-based multiple imputation oversampling method (FB-MIO) is proposed to handle class imbalance as well as missing values in the training dataset at the same time. First, a new factor is presented to evaluate the density of missing values belonging to the majority class with respect to the minority class in a particular region. With the help of this factor, an oscillator is developed to guide how imputation based oversampling should be carried out. Then the training set is divided into multiple smaller subsets and used the oscillator to determine whether missing values for the majority class belonging to that subsets should be imputed or not. This would help in preventing exaggerated duplication when not needed. Experiments were carried out on 27 imbalanced datasets after random addition of missing value and the F1 and AUROC scores of FB-MIO were compared to other dataset level resampling methods such as SMOTE, ADASYN, B-SMOTE etc. The effectiveness of the proposed method has been validated after experiments on benchmark datasets and the comparative results are presented in the form of average rank and number of wins.
Reading Pixels Parallel and Processing Pixels Parallel (RPP-PPP): An Approach to Detecting and Tracking Objects in Static and Dynamic camera

Object detection and Tracking are important tasks in both moving and static camera applications like unmanned vehicles, human-assisted vehicles, product parts inspection, video surveillance, and many more. These applications are required to detect moving objects and track static and dynamic cameras. It also needed to process these tasks with rate and lesser time. Many computer vision applications are always used to read the image/frame pixels one by one from row by column or column by row pattern. Reading pixels one by one from the image/frame took more time in real-time applications. In this proposed method we used to read the pixels from the entire image/frame in parallel using Parallel. For utilities and process these pixels in parallel. In the proposed work we used the background subtraction model for object detection and tracking and noted the difference in both sequential and parallel processes and compared the output with the existing background subtraction model. In this work analyzed the time taken to read the entire image using various tools and languages. The work was implemented using C#.NET parallel. For other thread-based functions to minimize the pixel reading time and processing time for object detection and tracking. Finally, the proposed work gave a considerable performance and compared with the existing system.
T5LSTM-RNN based Text Summarization Model for Behavioral Biology Literature

Behavioral biology is one of the crucial and trending topics these days which needs proper attention by scholars for rapid development in this field. Therefore, the purpose of this paper is to ease the process of collecting all kinds of relevant and vital data available on the internet regarding this topic in different forms of media, such as news articles, research papers, and YouTube lecture videos, all into one place into a single document in a proper summarized form. For proper training of the LSTM model, the lengthy video and journal datasets are pre-processed using the T5 transformer model to generate a uniform training dataset. So, in this work, a comprehensive approach is proposed based on an abstractive form of text summarization using the seq2seq encoder-decoder model combined with a stacked LSTM layer with an attention mechanism and a T5 transformer model pre-processor. Therefore, a proper hybrid model, T5LSTM-RNN, is implemented to generate the summarized data.
New GPU Sorting Algorithm Using Sorted Matrix

The sorting of elements is vital in various real-life applications such as CPU scheduling in operating systems, recommendation systems based on search time, Television channels sorted based on view time, etc. But it becomes complex and time-consuming if the number of elements becomes larger. For this problem, multiple solutions have been proposed in the past; among them, one efficient solution uses the concept of finding the kth element in a column and row-wise sorted matrix. If a sorted matrix is used to represent a list's items, it can be used to determine any element's position in the sorted list. In this paper, a Graphics Processing Unit (GPU) based parallel method has been proposed to sort a list using the concept of the sorted matrix. A mathematical proof of the proposed method and its implementation for the GPU-based machine is also presented. Experimental results of the proposed algorithm are compared with the CUDA Thrust Library Radix sorting implementation. The proposed algorithm has achieved a five-fold speed increase compared to the CUDA Thrust library sorting performance.
Personalized Multi-document Text Summarization using Deep Learning Techniques

The summary of a large piece of text or a document is a concise description of the same thing. It must retain most of the important points from the document and remove any kind of verbosity. The text summarization task is an information processing scheme that given a document or a set of documents, mines the most essential content from the source by considering the user or task at hand and presents the summary in a well-formed and brief text. Summarization can be done not just on one document, it can be done on a set of documents as well, which is called multi-document summarization. The proposed approach considers and in-corporates user preferences for the summarization task. Depending on the current user or current task, the summary could differ essentially. For an instance, a summary on 'photosynthesis', would be very different for the kid in 4th Grade vs. a student doing a bachelor's in Science. Therefore, while writing a summary, the reading history of the user is employed as a source of personalizing text summarization tasks. The proposed method is experimentally evaluated in the domain of news articles and obtained better summaries capable of extracting important concepts based on user preferences explained in the document when considering the relevant domain terms in the process of multi-document text summarization.
Automatic Detection and Categorization of Road Traffic Signs using a Knowledge-Assisted Method

Deep Learning is proving to be a boon for the latest smart city applications. For efficient mobility-based applications, Artificial Intelligence (AI)-based Intelligent Transportation Systems (ITS) are indispensable. In an autonomous driving scenario, a Traffic Sign Detection and Recognition system (TSDR) helps the drivers. Eventually, with its proper use, the number of accidents on the road can also be reduced. TSDR is an important technique in ITS but it can be hard to implement because of the mess, different levels of light, different sign sizes, and changing weather. In the past, different object detection models such as Fast regions with convolutional neural networks (RCNN) and Faster R-CNN were utilised for this purpose but due to the sluggish pace of detection, these models are inefficient. In this article, a state-of-the-art approach using ”You Only Look Once” version 4 (YOLOv4) is being used for the correct detection and recognition of traffic signs. YOLOv4 utilizes convolutional neural networks (CNN) for the instant detection of objects. YOLO framework processes input image in a single instance and it does not require a complex detection pipeline, making it extremely fast. Therefore, the proposed approach is feasible for real-time traffic sign detection and recognition. The detection accuracy of the proposed YOLOv4 model is improved by adjusting the relevant parameters and retraining the model on manually annotated data set. It has been demonstrated in results that the performance of proposed model is adequate.
LAD in finance: accounting analytics and fraud detection

The paper explores advancements in accounting analytics using the logical analysis of data (LAD) approach by identifying fraudulent firms and transactions. The straightforward approach to fraud detection with an analytic model is to identify possible predictors of fraud associated with known fraudsters and their historical actions. LAD is a machine learning methodology that combines Boolean functions, optimization, and logic ideas in alignment with the traditional approach. The key characteristic of the LAD is discovering minimal sets of features necessary for explaining all observations and detecting hidden patterns in the data capable of distinguishing observations describing “positive” outcome events from “negative” outcome events. The combinatorial optimization model described in the paper represents a variation on the general theme of set covering and concludes with an outline of LAD applications to detect fraudulent firms and financial frauds. The dataset consists of Annual data of 777 firms from 14 different sectors. The results demonstrate 97.4% accuracy with an F1 score of 0.97. Another dataset on credit card transactions and finance is also used to test the effectiveness of LAD in finance. With the appearance of the immense growth of financial fraud cases, these promising results lead to future advancements in analytical audit fieldwork.
Performance Evaluation of Time-based Recommendation System in Collaborative Filtering Technique

The Collaborative Filtering (CF) technique is the most common neighbourhood-based recommendation strategy, that provides personalized recommendation to a user for the items using a similarity measure. Hence, the selection of the appropriate similarity measure becomes crucial in the CF based recommendation system. The traditional similarity measures merely focus only on the historical ratings provided by the users to compute the similarity, completely ignoring the fact that preferences change over a period of time. Considering this, the paper aims to develop an effective Recommendation System that uses temporal information to capture the changes in the preferences over a period of time. For this, the existing exponential and power time decay functions are integrated with Cosine, Pearson Correlation, and Gower's similarity measures to compute similarity. The similarity is computed at the similarity computation and prediction levels of recommendation processes. Experimental findings in terms of MAE and RMSE on the MovieLens-100k demonstrate that performance of Gower's coefficient is better when applied with the exponential function at the similarity computation level of the recommendation process.
SCS-Net: An efficient and practical approach towards Face Mask Detection

Much work has been done in the computer vision domain for the problem of facial mask detection to curb the spread of the Coronavirus disease (COVID-19). Preventive measures developed using deep learning-based models have got enormous attention. With the state-of-the-art results touching perfect accuracies on various models and datasets, two very practical problems are still not addressed - the deployability of the model in the real world and the crucial cases of incorrectly worn masks. To this end, our method proposes a lightweight deep learning model with just 0.12M parameters having up to 496 times reduction as compared to some of the existing models. Our novel architecture of the deep learning model is designed for practical implications in the real world. We also augment an existing dataset with a large set of incorrectly masked face images leading to a more balanced three-class classification problem. A large collection of 25296 synthetically designed incorrect face mask images are provided. This is the first of its kind of data to be proposed with equal diversity and quantity. The proposed model achieves a competitive accuracy of 95.41% on two class classification and 95.54% on the extended three class classification with minimum number of parameters in comparison. The performance of the proposed system is assessed with various state-of-the-art literature and experimental results indicate that our solution is more realistic and rational than many existing works which use overly massive models unsuitable for practical deployability.
Application of Learning Technology in Domino Card Games on a large scale and individually to the responses of senior high school students

Learning by applying game technology in learning media will attract students' attention. This study uses a domino physics game with quantities and units as materials. Each card will be printed with a barcode containing information regarding the principal amount, unit, symbol, and dimensions. The process of making physics domino card media uses the ADDIE development model. The trial was conducted on 133 class X students at one of the SHS in the Pamekasan and Surabaya Regencies. The results of the validation of the physics domino cards in the media aspect obtained a very valid average category value, and the material aspects got a very valid average category value. The results of the average percentage of responses of Al Amien Pamekasan senior high school students after using the domino physics media with indicators of ease of obtaining, interest, and usefulness obtained scores in the very good category. The results of the average percentage of responses from Pamekasan 4 senior high school students after using the domino physics media with indicators of ease of obtaining, interest, and usefulness obtained scores in the very good category. The average percentage of senior high school 9 Surabaya responses after using the domino physics media with indicators of ease of obtaining, interest, and usefulness received scores in the very good category. Domino physics learning media can make it easier for students to understand the material in quantities and units and instill a strong will to learn physics.
Big Safety and the Future of Energy: The State-of-the-World

Paper investigates interconnection of concepts Big Safety (BS), Global Trusted Dependability (GTD), and others considering challenges and solutions in area of energy and thermonuclear fusions. GTD is based on the ReSeT model developed as an extension of the PMC methods for computer system reliability. GTD is added by BS, AI based Wellness Engineering, and fractal description of knowledge and observation. The theme of BS and GTD is extended to the pursuit of Controlled Thermonuclear Fusion as the foundation of a Safe and Secure World.
Relevant Objectives of Developing SQL Adaptive Learning Technology

An analysis of the existing adaptive computer means for teaching professional skills in various subject areas is given in terms of the functions and pedagogical principles embodied in them. Modern developments are described as those that teach the ability to form queries in the Structured Query Language (SQL), which is one of the most common and important requirements for information technology specialists today. The goal of the research is to provide comprehensive analysis of the existing adaptive SQL E-Learning systems and formulate actual objectives that could be used in further researches. Actual tasks of developing an adaptive SQL intelligent tutoring system have been presented. Were proposed 5 tasks for further researches.