Reviewerly/exHarmony huggingface dataset:
Blink detection for off-angle iris images using deep learning

Iris recognition is one of the well-known areas of biometric research. However, in real-world scenarios, subjects may not always provide fully open eyes, which can negatively impact the performance of existing systems. Therefore, the detection of blinking eyes in iris images is crucial to ensure reliable biometric data. In this paper, we propose a deep learning-based method using a convolutional neural network to classify blinking eyes in off-angle iris images into four different categories: fully-blinked, half-blinked, half-opened, and fully-opened. The dataset used in our experiments includes 6500 images of 113 subjects and contains images of a mixture of both frontal and off-angle views of the eyes from -50o to 50o in gaze angle. We train and test our approach using both frontal and off-angle images and achieve high classification performance for both types of images. Compared to training the network with only frontal images, our approach shows significantly better performance when tested on off-angle images. These findings suggest that training the model with a more diverse set of off-angle images can improve its performance for off-angle blink detection, which is crucial for real-world applications where the iris images are often captured at different angles. Overall, the deep learning-based blink detection method can be used as a standalone algorithm or integrated into existing standoff biometrics frameworks to improve their accuracy and reliability, particularly in scenarios where subjects may blink.
Systemic and critical thinking as a competence of contemporary education

The article is devoted to students critical thinking skills’ development. Basing on the experiment, the author formulates a definition of the "critical thinking" concept and graphically presents its semantic composition. The article examines the invariant features of critical thinking and highlights the main strategies for its formation. They include: selecting and filtering information for the tasks set, evaluating information as "true" or "false", evaluating information as "implicit" or "explicit", identifying implicit manipulation strategies, systematizing the information selected according to established criteria, indicating correct relations between the facts highlighted, performing students’ own creative product of thought, reflection and the ability to argue their position. The author comes to the conclusion that these strategies should be taken into account when planning educational tasks with students of different ages.
Research on Blu-ray storage system technology

With the large-scale use of Blu-ray technology in the field of data storage, the management of Blu-ray storage and ensuring the safe storage of data have become urgent problems to be solved. Blu-ray storage system is mainly used to control the Blu-ray disc library, including the use of the changer to move the magazine, and put the magazine into the drive. The drive is responsible for loading and unloading the magazine, and recording and reading the data in the magazine. The tray in the Blu-ray disc library can be ejected. At the same time, the Blu-ray storage system can monitor the status of the entire library and the life of the drive and changer. When the library fails, the system can give an alarm in time. In order to improve the reading efficiency of disc, Blu-ray storage system can control multiple drives to read and write data at the same time. When multiple drives write data at the same time, EC (Erasure Coding) technology is adopted on multiple optical discs to realize data redundancy protection and improve the reliability and availability of Blu-ray disc. By using EC technology, even if one or even two Blu-ray discs are damaged, the data can still be read out completely.
Introduction to the application of augmented reality technology in the digital manufacturing process

Augmented Reality (AR) technology can skillfully blend virtual information with the real world, so it can greatly help the development of the manufacturing industry. The AR technology in this paper is mainly focused on 3D laser projection technology. This paper first describes the application background of AR technology. And then the basic principles of AR are analyzed and the key technologies such as virtual-real mapping and so on are systematically sorted out. On this basis, the basic composition of the AR system is described. And then, the application area is set out in the aviation manufacturing industry, aerospace manufacturing industry, shipbuilding, and so on. At last, we design an experiment to verify the laser spot error distribution of the calibration result point.
Using Conformal Navigation Transformations for Robot Navigation in Complex Polygonal Workspaces

This brief proposes a novel method for collision-free robot navigation in arbitrary polygonal workspaces by introducing a new transformation called the conformal navigation transformation. The properties of the proposed transformation in a polygonal workspace and its ability to provide solutions to path and motion planning subproblems are investigated. The original definition of the navigation function is extended to the workspace with corners. Using the extended navigation function and the proposed transformation, a provably correct feedback control law is developed to ensure convergence to the target and obstacle avoidance. Furthermore, a rapid method to compute conformal navigation transformations is presented, which is valid for arbitrary polygonal workspaces. Finally, numerical simulations demonstrate the efficacy and applicability of the proposed methodology.
Combinatorial Optimization Approach to Client Scheduling for Federated Learning

For machine learning in situations where data is scattered and cannot be aggregated, federated learning, in which aggregators and agents send and receive model parameters, is one of the most promising methods. The scheduling problem of deciding which agents to communicate with has been studied in various ways, but it is not easy to solve due to its combinatorial optimization nature. In this letter, we attempt to solve this scheduling problem using combinatorial optimization theory. Specifically, we propose an efficient exact solution method based on dynamic programming and a greedy method whose superiority is confirmed by numerical examples. We also discuss the applicability of the proposed methods to a more dynamic and uncertain environment.
Federated Multi-Task Learning for Complaint Identification Using Graph Attention Network

Prior study on automatically identifying complaints on social media has relied on extensive feature engineering in centralized settings, with no consideration for the decentralized, non-identically independently distributed (Non-IID), and privacy-conscious aspect of complaints, which can hinder data collection, distribution, and learning. In this work, we propose a Graph Attention Network (GAT) based multi-task framework that intends to learn two closely related tasks, complaint detection (primary task) and sentiment classification (auxiliary task), simultaneously in federated-learning scenarios. We propose the Federated Combination (FedComb) algorithm, a two-sided adaptive optimization technique that simultaneously optimizes global and local models. The proposed methodology outperforms several baselines for the intended task of recognizing complaints in decentralized settings, according to quantitative and qualitative studies on two benchmark datasets. The resources are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/appy1608/IEEE-TAI_FedCI_GAT</uri> .
Outsourced Privately Verifiable Proofs of Retrievability Via Blockchain

Outsourced Proofs of Retrievability (OPoR) with private verification enables a third party verifier to periodically check cloud data on behalf of users. However, such a scheme requires the verifier to keep a copy of the user's data and generate tags for the data like the data owner. In other words, in addition to storing the data and tags from the user, the cloud server also needs to store tags uploaded by the verifier. To overcome this limitation, we propose a concrete construction of outsourced privately verifiable PoR (OPVPoR) without requiring the additional tag storage from the verifier. Furthermore, we extend the OPVPoR scheme to the multi-user setting and build a MOPVPoR scheme, where users storing the same data to the cloud server also share the tag information to further reduce the storage cost. Finally, we implement both schemes to evaluate their performance in practice.
A Study on Cross Site Scripting Attacks

Web applications face many protection issues. Cross-site scripting (XSS) is one of the most serious security threats that web users have to deal with. The risk of XSS attacks is growing as more related devices engage various Web Applications for various tasks. Hackers can acquire victims’ gathering data or additional delicate information by misusing XSS exposures in Web applications. Most critical systems today depend on these apps, including those in health management, investment, and even emergency response. They must, accordingly, incorporate, apart from the bulged value they supply to their consumers, trustworthy security procedures. In this work, we study various cross-site scripting threats against web applications.
Impact of the Role of Artificial Intelligence on Mental Health

Artificial Intelligence (AI) consists of a machine technology which is associated with using a computer system with a form of minimized human intervention in order to conduct a behavior that is more intelligent in nature. Artificial intelligence may also be considered as technique by which the intelligence of a human being forms a coordination with machines especially in the field of computer science. There are various applications of Artificial Intelligence like professional systems, natural language processing, recognition of speech, and machine learning. The technology related to Artificial intelligence (AI) and its strategies could prove to be very much useful in almost all domains which are related to human behavior and intelligence in the field of decision-making, healthcare applications, treatment of medically ill patients, business management in the form of leadership, emotional intelligence, group activity, management research, and many more (Kritikos, Artificial intelligence ante portas: legal and ethical reflections. EPRS, European Parliament, 2019 [1]). Healthcare application is one of the major prominent areas which has gained significance in the present day out of which mental health is considered to be one of the thrust areas. Mental health is one of the most important and vital troubles that desires to be emphasized via all of the healthcare employees (Madiega, EU guidelines on ethics in artificial intelligence: context and implementation. EPRS, European Parliament, 2019 [2]). The present-day technological revolution is more emphasized in order to understand the capabilities and advancement of development in this field of artificial intelligence. Therefore, the basic objective of this paper is to analyze the position of artificial intelligence generation and its applicability inside the intellectual health of girls in the use of a sensible model [3].
Development and Validation of Unsupervised Machine Learning Clustering Techniques

Nowadays a greater number of users participates is used to create new issues and discussion on social media that form into different kinds of groups such as Positive and negative comments. This paper is focused on a group of user’s discussions and specifies from which category they belong to. The user messages are parsed in the social media data then they identified network relationships and applied the data mining techniques to a group of different types of communities. The collection of objects is considered as similar or non-similar Clustering structures in unsupervised approaches. The aim of this paper is to develop clusters depending on features and their characteristics that are included in the proposed model. This work helps the system to categorize people into groups which also helps to identify people groups that are participated in discussions. This paper shows the clustering algorithms like K-Means, DBSCAN, and Agglomerative to cluster data and to find large streams of clustering community messages in social media data. This paper throws light on a novel use-case of communities and a proposed algorithm that shows the best clustering results. This application tells us which group of people saw the post and who gave their opinions on the post; by this, we can categorize the users.
Face Recognition at Various Angles

Face Recognition (FR) and surveillance video analytics are well-defined and solved problems in the applications of Computer Vision. FR aims to identify an already known person in a given image. Surveillance video analytics seeks to identify the occurrence of abnormal events or things in public places. But, recognizing the movements of most wanted criminals or suspects in public areas using FR systems with unclear surveillance video inputs is a very challenging problem. This work analyses the performance of three existing popular machine learning-based FR systems. They are (i) Viola–Jones detector, (ii) HOG-based FR, and (iii) PCA-based FR. This work analyzes the performance of these FR models on two different datasets. One is a benchmark dataset that has only the frontal view of the faces of various subjects. Another dataset we created with 10000 images. These images are collected from 50 subjects. From each subject, 200 images are taken from various angles. This work observes that the above models will improve their performance from 7 to 10% in terms of accuracy by training them on the proposed dataset.
USE-Based Deep Learning Approach to Detect Spammers on Twitter

Spammers disseminating obscene content on Twitter have been studied and detected using various hybrid features and machine learning approaches in past. To have greater insight into data prevailing in form of text on platforms like Twitter, their correct vector representation is paramount. Our goal is to understand what encoding techniques are more suitable for representing long text documents. We proposed a novel deep learning model consisting of a Universal Sentence Encoder (USE) as a feature extractor and an artificial neural network (ANN) as a classifier. We transform all the sentence vectors representing the tweets of a user into a document vector. These vectors are used as high-quality features to be processed by the artificial neural network for classification. To check the effectiveness of our proposed model, different sentence embedding techniques such as Doc2Vec, Infersent, and Sentence-Bert have been used and compared with the proposed model. Experimental results show that the proposed model outperforms all of them in terms of recall, precision, f1-score, and AUROC. Our results show that a simple ANN combined with USE-based deep learning approach can be a robust solution for the detection of spammers on Twitter.
Online Handwriting Recognition in Multiple Languages

In this study, handwritten Hindi characters are recognized using a convolutional neural network that is CNN-based technique. After being recognized, the characters can be used in a variety of ways and stored digitally on your computer. The characters in these pictures are all written in the Devanagari script. Every one of the 46-character classes has 2000 patterns. The training set makes up 85% of the data set, whereas the test set makes up 15%. OCR systems’ classification algorithms can be tested using image data sets. The highest Curacy score in the test set was 98.47. For the development and testing of handwritten text recognition systems, it offers a sizable collection of Devanagari handwriting styles created by numerous authors. Three completely connected detection layers are added after his four CNN layers. The input takes the form of a handwritten image in grayscale. Utilize filters to extract unique information from each layer in your photos. Convolution is used to achieve this. The processes of bunching and flattening are also crucial. A fully linked layer receives the output of the CNN layer and processes it. The character with the highest score is shown as the outcome after computing the chance or probability value for each character. There are 98.94 curates for acknowledgment. For this goal, there are already models that are similar, but the new model was more effective and precise than some of the earlier versions.
Innovative Task Scheduling and Allocation Algorithm for E-Governance with Multi-cloud Environment

Nowadays, cloud computing is one of the prominent tools for offering on-demand services via the Internet. It’s possible that cloud datacentres won’t always be able to provide the services that are needed during peak use times for diverse applications in e-governance. So, planning tasks through various clouds in a decentralized way for e-governance has drawn a lot of interest in recent years. This study proposes ZDSCN work scheduling methods to enhance performance. By using conventional normalizing methods, including the decimal scaling and z-score employed in data mining. The best VMs are then allocated based on similarity while taking cost into account.
A Comparative Study of Haze Removal for Dehazing Remote Sensing Images

The satellite images usually suffered from the problem of dehazing of images due to hazy weather conditions. Haze and fog cause poor visibility of remote sensing images and it is difficult to interpret those images. Most computer vision systems use remote sensing images for interpretation. The hazy remotely sensed images cause serious errors during the interpretability of the images. There are various ways to solve this issue in order to improve the image details. This study explores some of the haze removal methods and estimates the performance of those methods using quantifiable measures. The experimental results are evaluated in terms of visual analysis and quantifiable measures.
Finding Data: Teaching Science and Technology Data Information Literacy Skills to Researchers

This presentation explores and provides instructional tips on how to find data to support researchers, students, and scholars in science and technology disciplines. Finding and sharing data requires understanding the research and data information needs of our researchers and an awareness of existing and evolving new data repositories. Keeping current about the existence, scope, features, and availability of these repositories is critical in today's research environment. This session will help you develop a methodology for teaching students and researchers how to find and evaluate appropriate data sets. Expected interactions and exchange of ideas during this talk can further stimulate curiosity of our community members and inspire them to develop and implement innovative teaching methods in imparting information skills.
A Study on the Design of Elementary School Convergence Teaching Using Metaverse Based on the Rationality of Digital Citizens

작금의 민주주의 교육은 학생들에게 시민적 삶의 태도를 부여하고자 하기에 새로운 시민 정체성으로 등장한 디지털 시민으로서의 방향성이 적절히 제시되어야 한다. 이 연구의 목적은 디지털 시민교육의 구성요소로서 합리성의 근거들을 확립하고, 에듀테크로서의 메타버스 활용성을 탐색하여 관련된 초등 융합수업 설계 방안을 마련하는 것이다. 먼저 디지털 시민교육에서 합리성의 중요도를 분석한 결과 첫째, 디지털 사회가 심화됨에 따라 합리적 사고의 기본이 되는 리터러시 역량이 디지털 기반 용어와 합성되어 급격히 강조되고 있었다. 둘째, 무분별한 정보의 무비판적 수용 사례가 끼친 악영향으로 인해 비판적 사고력에 대한 필요성이 증대되고 있었다. 또한 디지털 기반의 교육 혁명으로 인해 에듀테크 사용 사례가 급증하였는데, 탐색 결과 메타버스 기술은 에듀테크의 전반적인 효과성과 독자적인 이점을 고루 갖추어 디지털 시민교육의 방법론으로 유의하였다. 이러한 연구 결과를 종합하여 최종적으로 디지털 시민교육의 합리성에 근거하여 메타버스를 활용한 초등 융합수업 설계 방안을 제시하였다.The current democratic education aims to provide students with the behavior of civic life, so the direction as a digital citizen that has emerged as a new civic identity should be appropriately presented. The purpose of this study is to establish grounds for rationality as a component of digital citizenship education, explore the metaverse utility as edutech, and prepare a related elementary school convergence teaching design plan. There are two insights of analyzing the importance for the rationality of digital citizenship education. First, as the digital society deepens, the literacy competency, which is the basis of rational thinking, has been rapidly emphasized by combining it with digital -based terms. Second, the need for critical thinking ability has increased due to the adverse effects of the uncritical acceptance of reckless information. In addition, the use of edutech has increased rapidly due to the digital -based educational revolution. As a result of the search, metaverse technology is considered as a methodology for digital citizenship education, with the overall effectiveness of edutech and unique advantages. In conclusion, a plan for elementary school convergence teaching design using metaverse is proposed based on the rationality of digital citizenship education.
Scenario analysis using machine learning to inform sensor design

Target detection is a key component of any constructive simulation. Detecting the target is the first step in the beginning of the constructive simulation. Detection predictions can have significant effect on the results in any constructive simulation. This paper explores the utility of advances in machine learning to inform sensor design by analyzing the outcomes of the time and detection models in constructive simulations. A simple scenario will be designed, and execution of the scenario will be performed to create datasets for analysis. Traditional metrics such as probability of detection and time to detect will be evaluated by the algorithms to determine the optimal sensor(s) designs to achieve the best possible performance across the scenario. A summary of the results and recommendations for machine learning algorithm design for this type of data analysis will then be presented.
For Maker’s Sake: A Somatic Exploration of Making

This half-day workshop brings together practitioners, researchers, educators, and students that design, research, and engage in making activities to have fun and create artifacts using a laser cutter. Participants will engage in an interactive discussion around their own making practices and experiences. Together we aim to identify existing considerations and practices that remain to be articulated around feelings and making. That is, what does it feel or look like when making serves as a form of personal expression, or as an extension of being? How might making create and support somatic openings that frame making less as an activity toward production, but rather, an end in itself–for fun?
When Children Chat with Machine Translated Text: Problems, Possibilities, Potential

Two cross-lingual (Nepalese and English) letter exchanges took place between school children from Nepal and England, using Digipal; an Android chatting application. Digipal uses Google Translate to enable children to read and reply in their native language. In two studies we analysed the errors made and the effect of errors on children’s understanding and on the flow of conversation. We found that errors of input negatively affected translation, although this can be reduced through initial grammar cleaning. We highlight features of children’s text that cause errors in translation whilst showing how children worked with and around these errors. Errors sometimes added humour and contributed to continuing the conversations.