Reviewerly/exHarmony huggingface dataset:
An approach to enhance KNN based on data clustering using K-medoid

K-nearest-neighbor (KNN) is one of the state-of-the-art machine learning algorithms used for classification and regression tasks. In addition to being simple to understand, KNN is also versatile, spanning various applications. Despite its simplicity, it is considered a lazy classifier that does not generate a trained model but stores or memorizes training examples instead. Consequently, the prediction process using KNN becomes costly in resources and time, especially when the dataset becomes large. Also, there is no general way to choose the best distance metric during the prediction. This paper proposes a new algorithm called K-nearest Medoid KNN (KMKNN) which improves the performance of KNN in terms of prediction performance and time efficiency without a major effect on its result accuracy. The core idea of the proposed KMKNN is to cluster the dataset before the prediction to limit the distance measures to those data instances that belong to the nearest cluster of the new data. KMKNN when compared to the traditional KNN and other similar extended versions of KNN, achieves a noticeable improvement on 15 benchmark datasets. The importance of this work is primarily in large datasets or when the distance measure used is computationally expensive, which is common in the computer vision and pattern recognition domains.
Euclidean polynomials for certain arithmetic progressions and the multiplicative group of 𝔽p2

Let f(x) be a polynomial with integer coeﬃcients. We say that the prime p is a prime divisor of f(x) if p divides f(m) some integer m. For each positive integer n, we give an explicit construction of a polynomial all of whose prime divisors are ±1 modulo (8n + 4). Consequently, this specific polynomial serves as an “Euclidean” polynomial for the Euclidean proof of Dirichlet’s theorem on primes in the arithmetic progression ±1 (mod 8n + 4). Let be a finite field with p2 elements. We use that the multiplicative group of is cyclic in our proof.
Intelligent Visual Interface for Assisted Digital Teaching Using Computer Vision

The COVID-19 pandemic has impacted the world severely. With the onset of lockdowns and restrictions, daily activities have been hindered, forcing humanity to adapt to this new reality. With new arising necessities, several advancements have been made to help bridge the gap in our old way of life and our new, albeit forced reality. Education has been revolutionized out of necessity as well. Schools and universities have shifted to online mode in order to continue educating the future generations. But this has had its limitations. The goal of our research is to enable educators to seamlessly communicate and learn like they would on through regular blackboard. We have actuated with the use of computer vision and camera to capture their ideas onto the computer screen. Our approach involves a virtual keyboard that can be interfaced with hand gesture for providing input onto the system at a desired text area. Also, a virtual drawing system is developed that can capture finger movement and accurately draw, thereby envisioning free hands teaching gestures onto the screen.
Transition Probability Test for an RO-Based Generator and the Relevance between the Randomness and the Number of ROs

A ring oscillator is a well-known circuit used for generating random numbers, and interested readers can find many research results concerning the evaluation of the randomness with a packaged test suit. However, the authors think there is room for evaluating the unpredictability of a sequence from another viewpoint. In this paper, the authors focus on Wold’s RO-based generator and propose a statistical test to numerically evaluate the randomness of the RO-based generator. The test adopts the state transition probabilities in a Markov process and is designed to check the uniformity of the probabilities based on hypothesis testing. As a result, it is found that the RO-based generator yields a biased output from the viewpoint of the transition probability if the number of ROs is small. More precisely, the transitions 01→01 and 11→11 happen frequently when the number l of ROs is less than or equal to 10. In this sense, l&gt;10 is recommended for use in any application, though a packaged test suit is passed. Thus, the authors believe that the proposed test contributes to evaluating the unpredictability of a sequence when used together with available statistical test suits, such as NIST SP800-22.
Generated abstracts: evaluating automatic text summarization for blog posts in gray literature studies

Background: Researchers in software engineering have increasingly added gray literature (GL) to primary and, especially, secondary studies. Several reasons explain this decision, such as grasping practitioners’ view on the topic under study. However, the use of GL in research poses several challenges like the amount and unstructured nature of data. The lack of automated tools and approaches to aid this task creates a bottleneck in selecting documents for inclusion. Aims: We investigate how summaries generated by PositionRank, an unsupervised text summarization approach, could support the inclusion analysis of documents in a GL study. Method: We performed an evaluation of using PositionRank to summarize documents analyzed on an ongoing study on software engineering. We compared the rating among two raters in a cross-over setup using summaries and full-text documents. We calculated their agreement, the precision and miss-rate using summaries against the full-text. The raters also discussed the documents on which they had conflicted answers and reached categories of reasons to explain the disagreements. Results: The results indicate that some inclusion criteria, which might be positively determined by few sentences, is susceptible to be misclassified when using summaries. Conclusions: Our study presents an analysis of the use of automatic summarization to support the inclusion assessment in gray literature studies discussing when this solution is viable. Our results could guide further studies in this direction.
Digital Transformation on Education Learning Management

The advent of computerization has brought about several changes in the way we interact with the world, altering aspects such as political, economic, and social relationships. As an essential part for the functioning of society, education has also evolved greatly, especially with the use of active learning methodologies. After some years and even centuries of stagnant teaching, we have witnessed investments in ways of learning that have generated several positive impacts, not only for students, but also for teachers. It will explain the importance of active methodologies and how they work, also demonstrating their consequences that can help in the evolution of an educational institution. The academic management of any education institution includes processes that involve the entire community (coordinators, professors, technical-administrative employees, and students): planning, curricular and administrative activities, attention to students, organization of professors, institution evaluation, regulations, and standards, among others.
Investigations On Image Compression Methodologies Using Hybrid Algorithms

Compression of images with tolerable degradation is adopted for efficient data transmission. Because of the widespread use of computers and the resulting requirement for large-scale data storage and transfer, effective data storage solutions have become vital. With the advancement of technology and the onset of the Digital era, the globe has been awash in data.Dealing with such enormous information can often result in difficulties. Image compression minimizes the size in bytes of a graphic file without degrading image quality to an unacceptable level. You can store more photos in a given amount of disc or memory space by lowering the file size. It It decreases the time it takes to send or download images from Web pages over the Internet. The discrete cosine transform is a fast transform. This is a widely used and robust way to compress images. It has excellent compaction for high-correlation data. DCT has fixed basis images. DCT gives a good compromise between information packaging capability and computational complexity. JPEG The DWT image compression standard is used (DISCRETE WAVELET TRANSFORM). DWT can be used to shrink an image without losing most of the calculated resolutions, and values below a certain threshold are disregarded. This reduces the amount of memory required to display a given image. Huffman, CWT, JPEG, and DCT Compression algorithms along with a combined algorithm of Huffman and JPEG are performed.
Mammogram Image Retrieval System Using Texture and Semantic Features

Abstract Image retrieval systems might help radiologists in aiding their diagnostic decision-making by giving a way to discover and identify similar images from databases. Keyword-based search became the dominant paradigm for searching multimedia datasets in the early years of image retrieval. However, using keywords alone has numerous drawbacks: human annotation is time-consuming and intrinsically incomplete, and the relationship between words and concepts is sometimes complicated. These significant challenges have motivated research in the field of Content-Based Image Retrieval (CBIR). However, using visual content has its own set of restrictions, owing to the Semantic Gap, which describes the disparity between low-level information that can be extracted quickly from images and high-level descriptions that are meaningful to users. As a result, a system with integrated approaches is important. In this paper a mammogram image retrieval system based on low level visual features and high-level semantic features has been proposed. Based upon the proposed framework, a prototype of mammogram image retrieval system has been developed. The proposed framework for retrieval of mammograms consists of two phases. The system first exploits the textual features and then further refines the search using visual features. Further, if the user is unsatisfied with the search results, he or she can send Relevance Feedback (RF) to the retrieval system, which has methods to learn about the user’s information needs. A system like this might be utilised for Computer Aided Diagnosis, medical education, and research. The framework established is generalizable and adaptable to a variety of anatomic and diagnostic circumstances. The proposed system’s performance is demonstrated by the experimental findings.
Deep Learning based Traffic Analysis of Motor Cycles in Urban City

This exploration expects to tackle the issue of traffic stream gauges utilizing information from a video surveillance camera. The target challenge is to count and categorize automobiles based on their traveling directions. This field is still in its infancy, and the focus of this research is merely one of the Trichy, Tamilnadu, India. This research work has utilized the cutting-edge YOLO v3 two-stage finder related to the SORT tracker to settle the expressed test. Vehicle movement direction was classified using a basic regions-based heuristic method. A few changes to the Faster R-standard CNN's presentation were made: center misfortune, versatile component pooling, additional veil branch, and anchors improvement. The proposed model plan and assess the marker and accumulated 982 video stream including over 60,000 things under organized situations. The preliminary findings reveal that during peak traffic periods, the suggested structure can count vehicles and detect their driving route with a mean overall rate inaccuracy of less than 10%. The dataset offered here is used by several scientists as a rigorous test or for truly preparing material.
Satisfaction Survey of e-Learning Educational Programs Graduates as a Tool for Educational Process Quality Improvement and University Community Formation

The article is devoted to the conduct and analysis of the results of the satisfaction survey of graduates of online educational programs. Monitoring the satisfaction of consumers of educational services makes it possible to improve the educational process and ensure effective interaction of all parties interested in higher and additional professional education. Based on the hypothesis of creating a club of alumni, a questionnaire was drawn up for a survey of former students of the institute who graduated from programs of higher and additional education using online technologies. An analysis of the respondents' answers was carried out and recommendations were made for organizing long-term work with graduates. The results of the survey demonstrate the positive attitude of graduates to the university community creation and the identification of the need for interaction, even taking into account the electronic form of education.
AspectNet: Aspect-Aware Anchor-Free Detector for Autonomous Driving

The anchor-free-based object detection method is a crucial part in an autonomous driving system because of its low computing cost. However, the under-fitting of positive samples and over-fitting of negative samples affect the detection performance. An aspect-aware anchor-free detector is proposed in this paper to address this problem. Specifically, it adds an aspect prediction head at the end of the detector, which can learn different distributions of aspect ratios between other objects. The sample definition method is improved to alleviate the problem of positive and negative sample imbalance. A loss function is designed to strengthen the learning weight of the center point of the network. The validation results show that the AP50 and AP75 of the proposed method are 97.3% and 93.4% on BCTSDB, and the average accuracies of the car, pedestrian, and cyclist are 92.7%, 77.4%, and 78.2% on KITTI, respectively. The comparison results demonstrate that the proposed algorithm is better than existing anchor-free methods.
Analysis of Indian and American poetry using topic modeling and Deep learning

Text classification is a supervised machine learning technique that assigns a set of predefined categories or classes to the given text corpora based on the content of the processed text using Natural language processing techniques. Text classification is widely used in numerus applications such as categorizing the sentiment of the tweets and reviews, classification of news and web pages into multiple categories and automatic classification of emails in to spam or not spam. Under the text categories poetry is a literary text and it is special when compared with the regular prose text. A very less focus is given to the task of classification of poetry by the research community. In this context, this work aimed to classify poetry using machine learning and deep learning models and to analyze the performance of the algorithms. To perform this task, poetry corpus is categorized into multiple classes using Latent Dirichlet Allocation a topic modeling technique. The classification task is carried using Multinomial Bayesian, SGD models under machine learning methods and LSTM, Bi-LSTM and CNN models under the deep learning methods. The results are evaluated with parameter accuracy. As a result of this experiment the best classification accuracy is achieved using CNN model with 87% by outperforming other models. This shows that for literary text classification CNN can be considered as a best classifier in comparative with the LSTM and Bi-LSTM models.
Idiotic agents within home

Intelligent Personal Assistants (IPAs) are voice-enabled virtual agents providing automated functionality through very specific interactions structured around embedded and cloud-based intelligence. However, concerns related to privacy and data extraction, along with users’ frustration over limited interactions, have rendered them controversial. Their ‘intelligence’ is the main control point through which tech companies turn human life into quantifiable data sold to advertisers. Hence, users are instrumentalized and IPAs become passive tools promoting efficiency or enemies trespassing on users’ privacy.
Uncertainty estimation of the bounding box centroid for autonomous driving

Object detection provides information needed for target tracking and plays a core role in autonomous driving. In this work, we study the uncertainty in the estimation of the centroid (position) of a bounding box of the measurements from an object detected by the sensor of an autonomous vehicle (AV). The estimated centroid uncertainty will be used in object tracking as measurement noise variance, which is not available from the sensor manufacturer, for measurement association and target state estimation. When the (position) uncertainty that captures the noise inherent in the sensor observations is available for each detected point (this can be done using Bayesian deep learning), the bounding box centroid uncertainty is obtained using a Least-Squares estimator (LS). When the uncertainty for each detected point is not available, one can assume a uniform distribution of the clustered points in a single rectangular bounding box. A Maximum Likelihood estimator is used for the bounding box centroid estimation. Experiments using real data are carried out to show the performance of proposed methods for autonomous driving applications. A comparison with the sample mean approach showed the superiority of new algorithm.
Information System for Automatic News Text Classification

In this paper, the design and development of an information system for the classification of Russian-language news texts using machine learning algorithms are considered. The information system under consideration consists of the automatic classification system and the website. The text data has been preprocessed. A number of experiments were conducted to train classifiers using grid search algorithm. Four classification methods have been tried: naive Bayesian classifier, logistic regression, random forest classifier and artificial neural network. The classification quality of the trained classifiers has been evaluated using a number of metrics: precision, recall and F-score. The website was also designed in order to provide a convenient information system usage.
Advanced Visual Interfaces for Augmented Video

The growing use of online videos across a wide range of applications, including education and training, demands new approaches to enhance their utility and the user experience, and to minimise or overcome their limitations. In addition, these new approaches must consider the needs of users with different requirements, abilities, and usage contexts. Advances in human-computer interaction, immersive video, artificial intelligence and adaptive systems can be effectively exploited to this aim, opening up exciting opportunities for enhancing the video medium. The purpose of this workshop is to bring together experts in the fields above and from popular application domains in order to provide a forum for discussing the current state-of-the-art and requirements for specific application domains, in addition to proposing experimental and theoretical approaches.
IMPLEMENTASI PEMBUATAN LAPORAN KEUANGAN PADA ADI JAYA GROUP BERBASIS WEB

The problem that occurs in Adi jaya Grosir is that the administrative process and the making of the financial statements of profit or loss obtained by the administration officer must go through a manual and not integrated process. Difficulties occur in calculating the amount of income earned by each sales. And it is difficult to know the number of stores that actively order products from this because there is no accounting information system, so the process is still manual. In this study the software used to build the system is Balsamiq Mockups for designing systems, PHP programming scripts and using MySQL as the database. The results obtained are financial data recapitulation. The system provides a report menu that is integrated between sales transaction data and financial statement data, so that it can obtain reports based on date. This system also generates income reports for each salesperson. With this system, the operation manager and administration staff can see the number of stores / customers who are actively ordering products from Adi jaya Grosir. This system produces accurate financial reports because the process is integrated. Reports generated by accounting information systems for making web-based financial statements can be printed and exported in the form of a .pdf file.
An Enhanced Multifactor Multiobjective Approach for Software Modularization

Complex software systems, meant to facilitate organizations, undergo frequent upgrades that can erode the system architectures. Such erosion makes understandability and maintenance a challenging task. To this end, software modularization provides an architectural-level view that helps to understand system architecture from its source code. For modularization, nondeterministic search-based optimization uses single-factor single-objective, multifactor single-objective, and single-factor multiobjective, which have been shown to outperform deterministic approaches. The proposed MFMO approach, which uses both a heuristic (Hill Climbing and Genetic) and a meta-heuristic (nondominated sorting genetic algorithms NSGA-II and III), was evaluated using five data sets of different sizes and complexity. In comparison to leading software modularization techniques, the results show an improvement of 4.13% in Move and Join operations (MoJo, MoJoFM, and NED).
Generative Deep Belief Model for Improved Medical Image Segmentation

Medical image assessment is based on segmentation at its fundamental stage.Deep neural networks have been more popular for segmentation work in recent years.However, the quality of labels has an impact on the training performance of these algorithms, particularly in the medical image domain, where both the interpretation cost and inter-observer variation are considerable.For this reason, a novel optimized deep learning approach is proposed for medical image segmentation.Optimization plays an important role in terms of resources used, accuracy, and the time taken.The noise in the raw medical image are processed using Quasi-Continuous Wavelet Transform (QCWT).Then, feature extraction and selection are done after the pre-processing of the image.The features are optimally selected by the Golden Eagle Optimization (GEO) method.Specifically, the processed image is segmented accurately using the proposed Generative Heap Belief Network (GHBN) technique.The execution of this research is done on MATLAB software.According to the results of the experiments, the proposed framework is superior to current techniques in terms of segmentation performance with a valid accuracy of 99%, which is comparable to the other methods.
Enhancing the Performance of Hash Function Using Autonomous Initial Value Proposed Secure Hash Algorithm 256

To verify the integrity and confidentiality of data communicated through the web is a very big issue worldwide because every person wants very fast computing and secure electronic data communication via the web. The authentication of electronic data is done by hashing algorithms. Presently researchers are using one-time padding to convert variable-length input messages into a block of fixed length and also using constant initial values that are constant for any input message. So this reason we are proposing the autonomous initial value proposed secure hash algorithm-256 (AIVPSHA256) and we are enhancing the performance of the hash function by designing and compuiting its experimental results in python 3.9.5 programming language.