Reviewerly/exHarmony huggingface dataset:
Exploring new strategies for comparing deep-learning models

With numerous technologies, seeking to utilize deep learning-based object detection algorithms, there is an increased need for an innovative approach to compare one model to another. Often, models are compared one of two over-arching ways: performance metrics or through statistical measures on the dataset. One common approach for training an object detector for a new problem is to transfer learn a model, often initially trained extensively on the ImageNet dataset; however, why one feature backbone was selected over another is overlooked at times. Additionally, while whether it was trained on ImageNet, COCO, or some other benchmark dataset is noted, it is not necessarily considered by many practitioners outside the deep learning research community seeking to implement a state-of-the-art detector for their specific problem. This article proposes new strategies for comparing deep learning models that are associated with the same task, e.g., object detection.
HDA: an iterative hyperplane-search method for discriminant analysis

In this paper, we introduce HDA, an iterative hyperplane-search method for Generalized Discriminant Analysis (GDA), designed to tackle challenges associated with high-dimensional data and limited sample sizes. Traditional LDA and GDA methods often struggle with poor covariance estimates, which lead to overfitting and reduced generalization capabilities. Furthermore, standard single-shot GDA incurs a high computational cost, particularly for high-dimensional data. Our proposed HDA method provides a tunable performance-complexity trade-off, enabling better model generalization by iteratively refining the discriminant analysis solution. We extensively evaluate HDA’s performance on various benchmark datasets and compare it to other GDA and LDA alternatives. The experimental results highlight the merits of HDA in terms of classification performance, balance between computational complexity, and enhanced model generalization. This study paves the way for improved discriminant analysis techniques, particularly in scenarios where data dimensionality and limited sample sizes pose significant challenges.
ILAW SA LILIM ANG PARA TEACHERS KONG NATAY: A PARENTS-INDUCED LEARNING TOWARDS ENHANCEMENT OF GRADE I PUPILS’ NUMERACY AND LITERACY SKILLS

This study aimed to determine the effectiveness of parent-induced learning, namely "Ilaw Sa Lilim Ang Para teachers Kong Natay": an intervention program to enhance Grade I Pupils’ Numeracy and Literacy Skills. Through descriptive research design, the study involved 11 parents of those pupils who struggle with language literacy and numeracy who had voluntarily undergone online parental training for two weeks. Also, the statistical tools used in this study were mean, standard deviation, and dependent t-test. Through the mean scores in pre-test and post-test, the results showed that pupils scored in language literacy in terms of phonological awareness, alphabet knowledge, fluency, and reading comprehension. And in numeracy skills in terms of counting, basic arithmetic, number symbols, and recognizing symbols, all significantly improved their rating after the intervention program. The results also revealed a significant difference between the pre-test and post-test mean scores of Grade 1 Pupils in Language Literacy and Numeracy Skills indicating an improvement in their statistical value. Thus, the researcher recommends strengthening the communication link among the parents by tapping them to be actively involved in school and academic-related activities. Also, integrate the intervention program into the curriculum to consistently check and upgrade its needs and applicability, especially in the challenge of the new normal, by considering including it in any future training among the parents. Effective, practical, and feasible pedagogical skills that would meet the different learning styles and interests of the children so as to maintain their span of attention.
Role of Digitalization in Election Voting Through Industry 4.0 Enabling Technologies

The election voting system is one of the essential pillars of democracy to elect the representative for ruling the country. In the election voting system, there are multiple areas such as detection of fake voters, illegal activities for fake voting, booth capturing, ballot monitoring, etc., in which Industry 4.0 can be adopted for the application of real-time monitoring, intelligent detection, enhancing security and transparency of voting and other data during the voting. According to previous research, there are no studies that have presented the significance of industry 4.0 technologies for improving the electronic voting system from a sustainability standpoint. To overcome the research gap, this study aims to present literature about Industry 4.0 technologies on the election voting system. We examined individual industry enabling technologies such as blockchain, artificial intelligence (AI), cloud computing, and the Internet of Things (IoT) that have the potential to strengthen the infrastructure of the election voting system. Based upon the analysis, the study has discussed and recommended suggestions for the future scope such as: IoT and cloud computing-based automatic systems for the detection of fake voters and updating voter attendance after the verification of the voter identity; AI-based illegal, and fake voting activities detection through vision node; blockchain-inspired system for the data integrity in between voter and election commission and robotic assistance system for guiding the voter and also for detecting disputes in the premises of election booth.
Hierarchical federated learning with global differential privacy

&lt;abstract&gt;&lt;p&gt;Federated learning (FL) is a framework which is used in distributed machine learning to obtain an optimal model from clients' local updates. As an efficient design in model convergence and data communication, cloud-edge-client hierarchical federated learning (HFL) attracts more attention than the typical cloud-client architecture. However, the HFL still poses threats to clients' sensitive data by analyzing the upload and download parameters. In this paper, to address information leakage effectively, we propose a novel privacy-preserving scheme based on the concept of differential privacy (DP), adding Gaussian noises to the shared parameters when uploading them to edge and cloud servers and broadcasting them to clients. Our algorithm can obtain global differential privacy with adjustable noises in the architecture. We evaluate the performance on image classification tasks. In our experiment on the Modified National Institute of Standards and Technology (MNIST) dataset, we get 91% model accuracy-layer HFL-DP, our design is more secure while as being accurate.&lt;/p&gt;&lt;/abstract&gt;
Medical Image Segmentation Using Deep Learning: A Survey

During the last few years, medical image segmentation using deep learning has become the most active research area in computer vision. Effectively, researchers become more and more interested in this accurate technique that has a direct impact on the decisions made in different medical fields. The deep learning image segmentation success in different areas, including the medical area, enable us to have the best results. The aim of this paper is two folds, firstly, it presents a study about the most important deep learning architectures used in the medical image segmentation such as the Fully Convolutional Network (FCN), the DeepLab Family and the Convolutional networks for biomedical image segmentation (U-Net) and Generative Adversarial Networks (GANs). Secondly, it provides an analysis for each implemented model in these architectures, which allows highlighting the various common challenges between those models and their adopted approaches.
Methods of Separable RGB-D 3D Human Pose Estimation for Different Scenes

Human pose estimation refers to a technique for recovering the key points of the human body in a given input. Owing to the wide use of RGB-D cameras with rapidly improving performance, RGB-D scenes will be widely used in the near future along with the rapid development of deep learning. In this paper, two new methods for the scenes of precision improvement and fast calculation are proposed by exploring the task of RGB-D 3D human pose estimation. The test results on the MKT data set shows that the methods proposed in this paper to improve the precision perform better than the best methods currently used in terms of the precision-related index, and the methods proposed in this paper for the fast calculation can run the overall process at a speed of nearly 30 frames with a quite small precision error. The good performance of the methods proposed in this paper makes it possible to apply the methods proposed in this paper to scenes with low computational power terminals requiring real-time running. It is believed that the methods proposed in this paper are promising methods of RGB-D 3D human pose estimation.
IMPLEMENTATION OF MACHINE LEARNING TECHNIQUES AND CREATION OF AN ARTIFICIAL NEURAL NETWORK FOR THE PREDICTION OF THE ACADEMIC PERFORMANCE OF STUDENTS IN UNIVERSITY ENVIRONMENTS THAT USE E-LEARNING AND STREAMING

This work describes the implementation of machine learning (ML) techniques: Random Forest, Xtreme Boosting Gradient, Support Vector Machine, K-Nearest-Neighbor and Logistic Regression as well as the creation of an artificial neural network (ANN), which were compared to determine the technique that can learn to predict with greater accuracy, the low academic performance of university students, to improve the mechanisms of e-learning and streaming that help them raise academic performance. The e-learning methodology was established for the first time in the late 1990s, however, since the Covid-19 pandemic, it has established itself as the best alternative to traditional education, placing it as a benchmark worldwide. One of the concerns in the university environment where this study was carried out is to be able to determine the impact that virtual teaching has had compared to face-to-face teaching, since there are factors (gender, number of children, sex, age, type of study) that could influence the academic performance of students. Using the classification metrics within the comparative process, it was determined that among the implemented ML techniques, the XGBoost reached 78.4% accuracy, but was surpassed by the artificial neural network (ANN) that learned to predict with 82.4%. of accuracy. Due to the above, the use of the artificial neural network is recommended for the prediction of the academic performance of university students since, in addition, with its massive predictions can be made due to its high processing capacity. Key Words: e-learning, covid19, streaming, academic performance, machine learning, artificial neural networks,
Dóris Chatbot: Personal assistant to help indicate a medical specialty

Conversational interfaces have gained visibility due to recent technological advances in the use of Artificial Intelligence. In the health area, this type of interface can be very useful for applications that directly interact with the patient, therefore, simulating contact with a health agent. In addition, many patients, when having certain symptoms, usually make a partial self-diagnosis, without a clinical basis, and make an appointment with a specialist doctor, without first asking the opinion of a general practitioner. In this context, the objective of the research was to develop a chatbot platform in the health area, using the agent Dóris, with the functionality of pre-medical consultation to indicate the most appropriate medical specialty for patients with cough. The platform was developed using IBM Watson for natural language processing together with an inference engine for decision making.
GEOTAGGING WITH VIRTUAL TOUR ON BHARAT MAP

Associating geotags with multimedia resources allows online multimedia repositories to be browsed and searched using geographic criteria, but millions of already online but untagged videos and images remain invisible to the eyes of this type of system.This situation calls for the development of automatic geotagging techniques capable of estimating the location where the video or image was taken.This project presents a geotagging system for 360° videos based on the extraction and augmentation of geographic information contained in textual metadata and visual similarity criteria.The system is specifically designed to geotag and display 360° videos for a virtual tour of colleges.Thanks to the rapidly growing modern technology, we can sit at home and take a virtual tour of some of the world's famous places.Solving this problem will allow parents and students to get a complete virtual tour of colleges and instantly get information about courses and facilities.This will also save travel time and expenses and enable parents/guardians to make better decisions about schools, courses and hostel facilities.Geotagging of all educational institutions integrated with virtual tour on Bharat map so that all students/parents can visit any part of the country on the map and take virtual tour along with facilities/courses available across the country is a much-needed solution to help students.and their parents/guardians make decisions at home.This will allow parents and students to get a complete virtual tour of the colleges and instantly get information about courses and facilities.This will save time and in the current COVID crisis this solution will benefit many students as the risk of disability while traveling will be completely reduced.
Instrumental Variable-Driven Domain Generalization with Unobserved Confounders

Domain generalization (DG) aims to learn from multiple source domains a model that can generalize well on unseen target domains. Existing DG methods mainly learn the representations with invariant marginal distribution of the input features, however, the invariance of the conditional distribution of the labels given the input features is more essential for unknown domain prediction. Meanwhile, the existing of unobserved confounders which affect the input features and labels simultaneously cause spurious correlation and hinder the learning of the invariant relationship contained in the conditional distribution. Interestingly, with a causal view on the data generating process, we find that the input features of one domain are valid instrumental variables for other domains. Inspired by this finding, we propose an instrumental variable-driven DG method (IV-DG) by removing the bias of the unobserved confounders with two-stage learning. In the first stage, it learns the conditional distribution of the input features of one domain given input features of another domain. In the second stage, it estimates the relationship by predicting labels with the learned conditional distribution. Theoretical analyses and simulation experiments show that it accurately captures the invariant relationship. Extensive experiments on real-world datasets demonstrate that IV-DG method yields state-of-the-art results.
SWAYAM SARATHI (AI BASED AUTONOMOUS CAR)

The project aims to represent a prototype of self-driving car using IOT with raspberry pi and Arduino UNO working as a main processor chip, pi camera will provide the necessary information and the raspberry pi will analyze the data(samples) and it will get trained in pi with image processing and machine learning algorithm which would result in detecting road lanes, traffic signals and the car will take turns accordingly.In addition to these features the car will overtake with proper LED indications if it comes across an obstacles.The system automatically detects the Traffic signs, controls the vehicle and command certain actions.The system consists of Raspberry Pi processor and web camera which automatically captures the video data and converts them into number of frames which are processed by the proposed algorithm in OpenCV to detect the traffic signs and control the vehicle.Based on the detected signs, the vehicle is controlled by two DC motors interfaced with Raspberry Pi.
ONLINE PARKING SLOT BOOKING WEBSITE

The world is facing new challenges of parking vehicles.Today, as the number of vehicles in the world continues to grow, more parking spaces are required, making efficient parking management a key issue.This paper proposes a parking space reservation website to solve the problem of wasting time looking for parking spaces in commercial parking lots.An online parking reservation site is a web-based technology that manages vehicle entry and exit records for parking lots.It is easy for managers to retrieve data when vehicles are visited by number.he can get this data.Users reserve parking slots in advance.All the information you need is available on the server.Each user has a unique username and password.In case of misuse, the responsible user will receive relevant information.
Image Retrieval Algorithm of Intangible Cultural Heritage Based on Artificial Intelligence Technology

Nowadays, the wide application of digital image has involved national defence industry, military, manufacturing, medical treatment, news media, mass entertainment and family daily life. In this wide use, a large number of different image databases have been formed. At the same time, the protection of intangible cultural heritage has entered the digital era. Many regions in China have done a lot of research work and in-depth excavation in the digital preservation of intangible historical and cultural heritage. In order to better preserve and disseminate intangible historical and cultural heritage, the retrieval and management of image database system has become an urgent topic.
Design a Computer-Aided Diagnosis System to Find Out Tumor Portion in Mammogram Image with Classification Technique

For the original, mammogram is a one type of X-ray image which is used to create detailed image of breast. Nowadays, medical images are playing important role in diagnosis and plays important role in prognosis. Images captured by machines may not be directly diagnosed the problem or find out tumor, so it is very important to process image and analyze on the basis of its features. Manual reading of an image may cause misdiagnosis. Computer vision supports scholars to analyze images to get required information, understand information or descriptions, and various pattern. In this paper, we are designing a CAD system which finds region of interest, calculates GLCM features, and compares classification technique to decide the one which gives highest accuracy with this algorithm. For the experimental work, images are taken from mini-MIAS database, and for experimental work, MATLAB has been used.
A Study on the Operation of a Balancing Group Consisting of PVs and On-site Storage Batteries

The renewable energy resources, such as photovoltaic generation and wind turbine, are rapidly being integrated. In order to compensate for the variable output and imbalance between actual and scheduled outputs, introduction of a battery storage would be one of the promising solutions. In this paper, we focus on the scheduled operation of a balancing group (BG) consisting of the widely distributed photovoltaic generation with storage battery. BG forming would bring reduction of total imbalance due to cancellation of forecast errors among PV stations, and could enable cooperative usage of battery storages for imbalance compensation. This paper proposes a new battery storages operation scheduling for the BG considering the imbalance risk, which are assumed based on the confidence interval forecast for PV output. Effectiveness of the proposed scheduling method is ascertained through year-basis computational simulation of the BG. Advantages of BG forming is also discussed.
Protocol Built by Chaotic Map and Elliptic Curve Cryptography for Key Exchange

The elliptical curve system has received great interest in the science of security systems, and this has a great number of advantages. When linked with chaotic systems, it gave a broad comprehensiveness in the science of finding common keys between the two parties or in a system as a server that distributes the common keys among the participants in the distress. The proposed algorithm reduces processor load, reduces power consumption, increases processing speed, enhances storage efficiency , requires smaller certificates, and is good at saving bandwidth. Where ECC gives high-level arithmetic operations ECC is an algebraic structure in wide areas for a large number of points and any point in the Cartesian coordinates, as well as for a number of prime numbers generated from the chaotic three-dimensional system using the multiplication and addition algorithm of the elliptical curved system. This paper suggested a new event in the implementation method: the inputs to the elements of the controlling parameters and the initial conditions of the symmetric chaotic functions produce an ECC by a secret shared key between two parties or a number of parties participating in the group, and all these points of the share keys lie on the points of the curve. and this key technology is available for authentication, confidentiality, and non-repudiation.
The Degree of Effectiveness of Distance Learning at Yarmouk University during COVID-19 Crisis from Students' Point of View

The study aimed to find out the degree of effectiveness of distance learning at Yarmouk University during Corona Virus crisis from students' point of view. The study sample consisted of (500) university students. To achieve the study objectives, a descriptive survey methodology was used to measure the degree of effectiveness of using distance learning. The results showed that the effectiveness degree of distance learning during COVID-19 crisis at Yarmouk University from students' point of view was moderate. The study found significant differences in students’ perceptions about the effectiveness degree of distance learning at Yarmouk University due to faculty type, in favor of humanities colleges, while there were no significant differences in students’ perceptions about the degree of effectiveness of distance learning in Jordanian universities due to gender and academic level variables.
Aerial Images Enhancement Using Perceptual Dark Channel Prior

Improvements in aerial images have an important role, given the increase in dust storms due to global warming and climatic changes.Apart from improving aerial images, image enhancement is important in many applications, such as tracking and monitoring studies during wars and for the environment.However, aerial images often suffer from a lack of contrast due to dust and pollutants.In this study, we propose a perceptual dark channel prior (PDCP).In this method, the light value (Y) is improved by using stretch and perceptual color space, and then the chromatic compounds (CbCr) are improved by dark channel prior (DCP).The quality measures lightness order error (LOE), entropy, and color-fullness metrics (CM) and then compares these with the improved methods (DCPMV, YCBCR, MLA, SEA, DCP, and IEIF) to determine the method efficiency.Results show that the proposed PDCP effectively enhances the aerial images better than other methods, because owning it the average are LOE (194.096),EN (7.361), and CM (39.403).