Reviewerly/exHarmony huggingface dataset:
Temperature and Face Mask Detection with Contactless Sanitizer Dispenser

Abstract: Face mask identification is suggested since it has been evolving quickly since COVID-19 insisted on it last yearfor its many uses in the field of Law Enforcement Security purposes and other commercial uses. The provided system uses a convolution neural network to assist in face mask identification utilising the COVID-19 security measure in both photos and videos. The performance evaluation of the suggested strategies is presented in this study along with a large-scale experiment on the data. The project serves as a prototype for temperature detection and face mask identification for humans. The first techniqueuses a temperature sensor to determine the body's current temperature and then evenly distribute sanitizer. The goal of the next strategy is to provide people with a safety net so that COVID-19 cannot spread among them. The suggested method conducts a thorough experimentation on 50 different Image datasets to look at performance. For ten random trails, the author experimented with different training and testing percentages. The findings indicate that the symbolic method is superior than the traditional approach.
E-voting Maintained by Privacy and Transparency with Public Block Chain

Abstract: Electronic voting is frequently seen as a method for increasing the electoral system's monetary effectiveness and management trust. When implemented correctly, e-voting technologies can increase ballot integrity, accelerate the results mechanism, and simplify the voting process. However, the unit of methodological issues are well-organized. If not meticulously planned and managed, electronic voting has the tendency to endanger the reliability of the system electoral arena. Before selecting to employ new choosing innovations, it is essential to carefully evaluate essential concerns as they will have an impact on the performance of E-voting alternatives. Since the 1970s, electronic voting has been employed in a variety of ways, and it has a number of fundamental strengths beyond paper-based processes, including greater hyperbolic potency and fewer mistakes. Although the strength among these systems versus security vulnerabilities is growing with relevance, it is still complicated to understand their extensive applications. Block chain technology, which aims to increase the reliability of e-voting technologies, also might lead to riots. It demonstrates a potential way to make use of block chain's benefits, such as associated disclosure study, to comprehend a fiscally sustainable topic for electronic voting. The suggested motif satisfies the requirements for electronic voting systems and ensures end-to-end provability. The planned e-voting theme and how it will be implemented on the Multi chain platform are indeed thoroughly explained. The study offers in-depth analysis of a topic that consistently demonstrates its ability to understand a related, end-to-end, validated e-voting concern.
ANNEALING-BASED ALGORITHM FOR SOLVING CVP AND SVP

Inspired by quantum annealing, digital annealing computers specified for annealing computations have been realized on a large scale, such as the Digital Annealer (DA) developed by Fujitsu and the CMOS Annealing Machine developed by Hitachi. With the progress achieved using these computers, it has become necessary to estimate the computational hardness of cryptographic problems. This paper focuses on lattice problems, such as the closest vector problem (CVP) and shortest vector problem (SVP), which are a class of optimization problems. These problems form the basis of the security of lattice-based cryptography, which is a prime candidate for the NIST post-quantum cryptography standardization. For these lattice problems, we propose methods for generating an Ising model and solving the Ising model on annealing computers with a bit representation as the input, which represents encodings to map each integer variable in the SVP into binary variables. We propose two methods for SVPs, a basic method and a variant incorporating approximately the concept of the classical lattice enumeration. In our experimental results obtained using the second-generation DA, we succeeded in finding a shortest nonzero lattice vector in 40- and 45-dimensional lattices in the Darmstadt SVP Challenge. The basic method with a hybrid bit representation was the fastest among our methods with a bit representation, and the expected running time was estimated as 664 and 13,750 seconds for the 40- and 45-dimensional lattices, respectively. These results provide a benchmark for solving the SVP with annealing computers.
Monitoring the Modest Sensory Network Utility Device Watcher

Abstract: The proposed system introduces the android application using modest sensor device. The objective of the system is to develop a system to collect data from sensors, and to transmit this data through wireless network which passes the data to an application. Wireless transmission methods have been investigated, and it was decided that a HTTP GET method would be used. When the information is sent by different sensors it will displayed with the help of machine learning algorithms. Following an exploration of personal area networks (PAN) and mesh networking, a system was implemented to discover physical intrusion. To the end our network employed sensors are connected with motion sensors and accelerometers. Then it communicates with a generic infrastructure, adaptable to future wireless sensor projects, which stores the data in a database and through interfaces monitor the status of the entire system.
Real Time Object Detection Using Deep Learning

Abstract: Visually impaired people have difficulty moving safely and independently, which interferes with normal indoor and outdoor work and social activities. Similarly, they have a hard time identifying the basics of the environment. This paper presents a model for detecting the brightness and key colors of real-time images using the RGB method with an external camera and identifying basic objects and face recognition from human datasets.[2]. Object detection is a department of pc imaginative and prescient that appears for times of lexical entities in photographs and videos. The gadget makes use of the ESP-32 Cam's digital digicam to continuously seize severa frames, which can be sooner or later converted to audio segments. In this project, we use the You Only Look Once V3 (YOLO v3)algorithm, which runs thru a version of a really complex Convolutional Neural Network structure with OpenCV. Then with the aid of using the usage of Google Text to Speech, we convert the photo to textual content and afterwards textual content - to - speech for the visually impaired individual. Thus, the Visually Impaired individual receives the place of the gadgets withinside the digital digicam's view through audio. Distance calculation is aided with the aid of using an ultrasonic sensor. By the usage of The amassed consequences show that the proposed prototype is a hit in presenting visually impaired customers with the cappotential to realise surprising settings the usage of a user-pleasant machine that integrates this unique item detection Model
Augmentation of Blockchain and 5G in Green Computing

Abstract: Green Computing (GC) is a step toward environmental sustainability. More precisely, it is entirely about utilizing power on the way to carry out processes in the maximum green manner. With increasing energy costs &amp; rising ecological apprehensions, green computing is getting additional consideration. The Blockchain (BC) has emerged as a favourable implementation for decentralized, Peer-to-Peer (P2P) transparent ledger systems. 5G is the latest development in an exceedingly wireless communication network. The flexibility of 5G with BC allows many applications to become fast, clear, significant &amp; harmless for transmission of data in this smart era. Both technologies can give green solutions to the environment. In this very research paper, convergence of BC and 5G is and integrating it with Green Computing Approaches is shown via proposed model. With use of Network Slicing approach of 5G and Sharding approach of BC, their significance in decreasing computational power and energy usage is shown. Also, major focus is on the “Green Use” approach of green computing. A hypothetical model is proposed by converging Blockchain and Fifth Generation with Green Computing. Objective is on the physical hardware services layer of green computing which will directly impact physical environment in order to give ecofriendly solutions to sustainable projects.
Vision Based Banknote Recognition System

Abstract: People who are visually impaired face great difficulties in recognizing banknotes for day-to-day transactions as the texture and shape of many banknotes are very similar. Also, the Indian currency system does not have a separate banknote in circulation that might help people who are visually disabled. This paper presents a real-time Indian currency detection system for visually impaired persons. The proposed work makes use of the vision-based system to facilitate the visually impaired people to prosperously recognize banknotes. In this paper, a recognition system is implemented by making use of various machine learning and deep learning approaches which can produce an accuracy of up to 90%. In this system, totally two experiments are carried out on the banknote’s image. The first one is the analysis of the different regions of the banknote, the second experiment is the examination of the banknote image which was captured using a camera in a regulated environment. Our system is extended to support the visually impaired people by executing a “Voice-based assistance system” which would help us in giving the voice output. This system can identify all the seven banknotes used in India and exhibit the result with both textual and auditory output. People who are visually impaired will be able to easily use it in daily transactions.
Applications and Challenges of Augmente Reality in Education Sector: A Report

Abstract: Augmented Reality (AR) is one of the emerging advanced technologies which bridges the gap between the real and the virtual worlds, that gives students prodigious possibilities to learn the information in a completely unique way. With the rising use of personal smart devices in this modern era, the vast potentials of AR have begun to be explored as the smart devices are capable of producing AR environments. In this paper, we have the purpose of exploring applications and challenges of adopting AR in education. Since augmented reality in education can serve a number of purposes. It helps students easily acquire, process, and remember information. Additionally, AR makes learning itself more engaging and fun. Therefore, this paper is based on the research that has been conducted on AR. It is also not limited to single age group or level of education and can be used equally well in all levels of schooling: from preschool education up to college, or even at work. The potential of mixing smartphones and augmented reality for education is big, though it still has got to be fully discovered. AR, in various ways, could grant students extra digital information about any subject, and make complex information easier to know. Nowadays we may find some excellent samples of augmented reality in education worldwide. Ability to attach reality and digital content has been steadily improving, opening more options for adopting AR in education sector.
Blockchain-enabled construction contract administration: a practice-orientated review

Construction contract administration (CCA) is key to construction projects. Ineffective CCA is at the root cause of many of the industry’s disputes. Blockchain may offer necessary features for improving CCA. However, no review of blockchain’s potential against all functions of CCA exists. This study adopts a systematic review of relevant work, which is classified using a multifunctional approach to CCA. The findings reveal that existing research has focused on financial management whilst disregarding practice-based challenges. The study informs contracts policymakers and practitioners by showing how blockchain may revolutionise CCA, and establishes future research directions for researchers.
Conducting user studies in engineering education during the COVID-19 pandemic

In this paper, we report about our experiences on adapting ongoing laboratory research of a supervised Virtual Reality (VR) study to a remotely conducted unsupervised desktop-based application.Challenges in the context of participant recruitment, pre-/post-test, and our application are illustrated in conjunction with the corresponding transformations.In addition, based on a qualitative problem analysis by an interdisciplinary team of experts, recommendations for future studies and transformations of VR lectures are derived from the findings and experiences of this project.
Precise and dense AI-based mobile 3D reconstruction of indoor scenes by camera-lidar fusion and odometry

We propose a mobile 3D reconstruction method for improving the precision and density of point clouds.It is suitable for hand-held scanners comprised of a colour camera and a lidar.We fuse time-synchronized and spatially registered images and lidar sweeps using deep learning techniques into dense scans, which are then used for progressive reconstruction in an odometry-like manner.We build a prototypic scanner and test our method in an indoor case-study.The results show that our pipeline outperforms reconstructions by other devices and methods, yielding relatively denser and detail-preserving point clouds with a 67% reduction in noise of reconstructed planar surfaces.
An ontological approach for a recommendation system of a requirement tool: the case of a national standard framework for hospital design

There is a need to facilitate cross-project learning in hospital projects.To do this, the Swedish national healthcare project frame and database, PTS (Program for Technical Standard), has been created to provide a framework for Swedish regions when conducting hospital projects.However, the fragmented information currently available and overall structure makes it difficult to embed knowledge and requirements for cross-project learning.In this paper we use an ontological framework to review the current structure of PTS and also propose a conceptual information-structure for machine-readable functional and spatial requirements that can be utilized in a recommendation system for hospital room layout.
The influence of Japanese popular culture on learning Japanese

This article presents the case that because students like Japanese popular culture (JPC) it can motivate them to take up Japanese language study, and not only that, but can influence them to continue their study. This stance is supported with findings from a research project undertaken in Sydney Australia. Learner motivational trajectories reveal that a strong interest in JPC is intertwined with the motivation to learn Japanese sometimes to the point of becoming a passion. Consequently, harmonious passion (HP) is examined in regards to Motivation and a striking similarity is found. Learning Japanese can take place effortlessly through engagement in JPC and is described as ‘Effortless effort’ (e-e). It is linked to directed motivational current (DMC), an attractor state within complex dynamic systems theory (CDST). Questionnaire outcomes and excerpts from interviews with university learners portray the influence of JPC on learning Japanese and the role it plays in developing language identities.
Adaptive Dynamic Surface Control for a Class of Time Delay Nonlinear Systems with Extreme Learning Machine

Abstract This paper is aimed at investigating the problem of adaptive track- ing control for a class of strict-feedback nonlinear systems with unknown ex- ternal disturbance and time delay. First of all, the original system state equa- tion is transformed into a new form of state equation by coordinate trans- formation. In the next part, extreme learning machine is applied to approx- imate the unknown functions which exist in the whole system states. Based on this, adaptive dynamic surface controller is invented to cope with the dif- ferential explosion problem. In addition, the combination of the control law and the compensation signal of the filter improves the accuracy performance. Lyapunov-Krasovskii functional is introduced to handle the influence of time delay successfully. Subsequently, all signals of the whole closed-loop system can be ultimately uniformly bounded through a series of proofs. In the end, simulation examples are presented to verify the feasibility and effectiveness of the proposed algorithm.
A Conjoint Study and Segmentation on the Preferred Online Learning Attributes of Senior High School Learners amidst COVID-19 Pandemic

Abstract The educational systems were disrupted due to the COVID-19 pandemic that has resulted in the sudden shift in the instructional implementation from face-to-face to fully online distance learning. This study examined the socio-demographic characteristics and access to technological resources, and explored senior high school learners' preferred online learning attributes and their segmentation. The study employed a descriptive and exploratory research design. Using Cochran's formula, the study determined 300 respondents who were identified through a random sampling technique. The statistical treatments utilized were frequency count, percentage, conjoint, and cluster analysis. The data were gathered through an online-administered survey instrument. Results revealed that multiple-choice and multiple-type exams, synchronous delivery type with slides, individual tasks, and learning materials with visual aids were the most valued online learning attributes by the learners. Further analysis revealed that learners could be classified into two groups based on their preferences. This study provides insights for academic institutions in redesigning their online learning systems.
Towards Adversariality in Depth Estimation Based on Local Equilibrium Disparity Consensus and Ego-motion

Abstract We ask whether Adversariality in left-right stereo images can learn to estimate an optimal depth map through a consensus-based loss function and ego-motion. We describe the workflow as merging supervised learning (AS) and unsupervised learning (U) models. The supervised learning model optimizes a depth estimation network with prior knowledge of ground-truth depth maps. Inspired by the recent works on adversarial neural networks, we formulate the supervised model as an adversarial learning task. Thus we generate two depth maps from left-right stereo images, respectively. Based on their learning behavior–that is, the loss function, we take the then optimal depth map. On the contrary, the unsupervised learning module has no knowledge of the ground-truth depth yet optimizes the depth estimation network using 3D geometry. Our framework is trained and benchmarked on the KITTI driving dataset.
Impacts of Remote Learning Measures on Educational Access and Quality in Ecuador

This analysis explores the ways in which the digital measures taken by Ecuador’s MINEDUC affected the access and quality of education in Ecuador, as well as impacts of these changes on existing socioeconomic gaps in the country. The exploratory study was based on two semi-structured interviews, one with an educational practitioner and one with a policymaker. The first interviewee was the director and sole multi-grade teacher of a public IBE elementary school in a rural zone of Ecuador. This interview took place in December, 2020. The second interviewee had served in a high rank position in the MINEDUC during the pandemic, and this interview was carried out in August, 2021. The analysis of the data indicated that participants conceptualized academic quality as care for the holistic wellbeing of students as well as taking opportunities provided by remote learning for innovation in education. Educational policy, curriculum, and practitioners emphasized these forms of quality education during the pandemic. Another finding was the roles of technology and teachers as educational gatekeepers. Educational access was mediated by access to technology, and/or teacher responsibility to deliver other forms of remote learning. Teacher responsibility was especially high in rural areas where access to technology is scarcer. Finally, digital divide is a term used in recent decades to describe the gap between those who are able to access and effectively use new technologies, and those who cannot (Van Dijk, 2012). Social distancing highlighted the digital divide in Ecuador, but the diversity of measures offered helped to mitigate adverse effects of the digital divide. Even where technological access was limited, the wealth of digital resources developed in response to the pandemic provided professional support to teachers.
The Provision of Learning in Mexico During the COVID-19 Pandemic

To understand how Aprende en Casa contributed to the spread of digital learning and the implementation of the Educational Digital Agenda in Mexico, this report presents the analysis of an exploratory study that consisted of three semi-structured interviews. The interviewees for this inquiry were a leader of the Aprende en Casa strategy at SEP, the director of a basic education public school in Tijuana, northern Mexico, and the director of a private pre-school establishment in Mexico City. The interviews were conducted from February to August 2021 and centered on each interviewee's perspectives according to their professional standpoint. This report presents the results according to three topics that complemented each other among the interviews: the duty of the State as the main provide of education, the progress and setbacks in the use of technologies for educational purposes, and the factors that define the digital divide in Mexico. Findings suggest that the digital divide in Mexico cannot be merely understood as the access to devices and the Internet that the US National Telecommunications and Information Administration (NTIA) defined in 1999, but as a holistic set of factors that intervene in the use of technologies: Internet and mobile, social class, economic capital, emotions and perceptions, and public policy, results which are very much in line with Robinson et al. (2015). These factors interact within an already unequal society whose educational backbone is its Secretary of Education, but whose implementation burden ultimately lies on the teaching staff.
Gesture Recognition Using Deep Learning Techniques

Abstract Sign language is a method of communication that uses hand gestures and movements rather than words. It is mostly used by Deaf or hearing-impaired individuals. The paper discusses the process of using the hand gestures for Gesture Recognition Based on deep convolutional neural networks (CNN). In this a custom CNN model of five layers is built from the scratch and trained over 2400 images of hand gestures to identify 26 alphabets and 10 numerical from 0 to 9. The diversity and complexity of gestures, on the other hand, have a significant influence on gesture recognition owing to the surroundings, light source, cover, and other considerations. To improve the characteristics of gesture identification, the hand skin color is filtered through the YCbCr color space to segregate the gesture region to be identified, and the noise of the gesture edge is processed using the Gaussian filter. Another task of Speech recognition, often known as speech-to-text, refers to a machine’s or program’s capacity to recognise and transform spoken words into legible text. The vocabulary of rudimentary voice recognition software is restricted, and it can only recognise words and sentences uttered clearly. The paper also discusses the neural networks’ pattern matching ability on audio signals for the speech recognition process. In this an audio is generated and recognised using speech recognition google API which results in text processing using NLP. As a result a GIF or hand gestures are output on the screen corresponding to the audio.