Reviewerly/exHarmony huggingface dataset:
Interactive Augmented Reality Web for Introduction to Plants in the Sukolelo Prigen Herbal Village

The use of existing technology in Indonesia is still not widely applied. Even the information on the internet about the tourist attractions of the Kampung Herbal Sukolelo Prigen is still incomplete. Therefore, we need a solution to facilitate the introduction of existing herbal plants, so that tourists are more interested in coming to this tourist spot. Augmented Reality (AR) applications have the ability to display multimedia content from PCs, laptops, smartphones, and other devices. Augmented Reality (AR) is widely used in the fields of entertainment, games, advertising, education, health, commerce, as well as industry. This application will display 3D objects of herbal plants by scanning the camera from the user's device against a predetermined marker. Later the marker will be printed and placed in a special place. Then each marker will display a 3D object of herbal plants and the other supporting attributes. This application is expected to make it easier for tourists to recognize existing herbal plants. This application in the future is able to display 3D objects in the form of herbal plants and other attributes as supporting information.
Human-centred design, disability and bioethics

Human-centred design methodologies provide a means to align bioethical advocacy with the needs and desires of disabled people. As a method, human-centred design seeks to locate points of friction in an individual’s experience of everyday interactions, specifically in relation to technologies, but potentially in relation to processes and institutions. By focusing on disabled persons and their experiences of institutional organisation, human-centred design practices serve to create a foundation for a bioethical practice that addresses idiosyncratic needs and desires while providing support for disabled persons and their families. In considering how a design-focused bioethics might operate in this way, I focus on advanced sleep phase syndrome and delayed sleep phase syndrome as a way to show how the temporal ordering of institutions disable the participation of individuals with atypical sleep needs. I then turn to the education of deaf students through the exclusive use of sound, which puts them at a significant disadvantage relative to their hearing peers; this example shows how normative ableism obscures itself in attempts to aid disabled people, but an attention to the experience of deaf students show how exclusively auditory learning can be redesigned. Advocating for flexible institutional organisation and practices situates bioethical advocacy as a means to engage with social organisations in ways that create novel opportunities for able-bodied and disabled people alike.
Education by Means of Artistic Works of the Ability of Younger Schoolchildren to Follow the Rules of Etiquette

The article discusses the problem of familiarizing younger students with the rules of etiquette based on the analysis of works of art. The author proposes a scenario for a possible lesson in the second grade on the subject "World-Around-Us", dedicated to the study of the rules of behavior at the table, during which various creative exercises and didactic games are used on the content of the text of a fairy tale. This stimulates the interest of children in the topic being studied, allows them to successfully master the proposed material, as well as develop the abilities of younger schoolchildren in various types of creative activity and the ability to cooperate in the course of it.
To Monitor Yoga Posture Without Intervention of Human Expert Using 3D Kinematic Pose Estimation Model—A Bottom-Up Approach

Yoga is a technique which amplifies mental and physical powers of a human spec together. Yoga is a systematic method of doing specific physical activity, by controlling the breath along with concentration of mind. Systematic practicing of yoga daily results in enormous health benefits mentally and physically. Yoga should be practiced under the guidance of an expert if done without proper guidance could lead to adverse effects. So, it is always recommended to practice yoga under the guidance of an expert. In today’s world, the mankind is so busy in work-related matters, such that a significant amount of time is not given on activities which keep health in better condition. In such conditions, in many cases, yoga practice might not be done under the guidance of yoga experts due to lack of time. This paper proposes a system which monitors the posture of yoga aasanaas, without human expert guidance by using pose estimation algorithm for 3D video detection.
Analysis of Deep Learning Architecture-Based Classifier for the Cervical Cancer Classification

The incidence and mortality rates of cervical cancer in low-income states are more due to the constrained healthcare resources. For quick screening and early diagnosis, a very efficient and accurate intelligent system has to be developed which can diminish the incidence and mortal rate. Various image processing with machine learning algorithms and deep learning algorithms are applied for the diagnosis of cervical cancer to provide technical support for the healthcare pathologist department. Deep learning architecture achieves the classification task at higher accuracy. This paper brings the analysis of a fine-tuned deep learning architecture applied for the cervical cancer diagnosis. The proposed pre-trained model Alexnet and GoogleNet are fine-tuned with transfer learning for the classification task. The dataset considered is the HErlev Dataset (standard) and real-time clinical data comprising of 917 images and 10 images, respectively. The proposed model is trained, validated, and tested with 06 epochs and 30 epochs for the standard dataset. Accuracy, error rate, precision, and area under the curve are evaluated to analyze the proposed architectures. An accuracy achieved for the proposed architectures with 30 epochs is 93.99 and 89.07%. The proposed architectures trained with 30 epochs are validated on the clinical data. An accuracy of 0.9508 and 0.5 for the 30 epochs is presented by the proposed architecture with clinical dataset.
Gesture-Controlled Speech Assist Device for the Verbally Disabled

For centuries, those who are verbally handicapped have struggled to interact with the rest of the world. For them, sign language is their only means of communication, which is not understood by the general public. The goal of this essay is to create a low-cost prototype that will help the speech challenged communicate more effectively. A wireless smart glove system that translates motions to text is included in this prototype. With the use of flex sensors, the glove tracks the finger alignment. To assist physically-disabled people, the flexes in various configurations make a gesture that can be translated into spoken signals or displayed as text. The Arduino Nano microcontroller then analyzes the data. The phrases associated with the flex sensor’s mapped value are sent to the liquid crystal display panel through a radio-frequency module. This approach is especially beneficial to caregivers and medical staff, since it allows them to respond quickly and effectively. The results of the assisted product showed that it can recognize hand movements during testing.
Analysis and Evaluation of Pre-processing Techniques for Fault Detection in Thermal Images of Solar Panels

Majority of solar panel thermal images require post-acquisition manipulations for optimization of contrast, brightness, and noise removal. Noise removal and contrast improvement are major part of pre-processing operations. Thermal imaging is one of the non-contact techniques used for fault detection in solar panels. Thermal images captured through thermal camera are often corrupted with noise due to various environmental conditions. Use of suitable denoising filter is an essential pre-processing step in case of thermal imaging. In this paper, various digital filters such as Gaussian, median, bilateral, mean, and Wiener filter are tested for removal of noise. The performance of these filters is evaluated using statistical measures such as mean square error (MSE), structural similarity index (SSIM), signal-to-noise ratio (SNR), and peak signal-to-noise ratio (PSNR). After filtering thermal images with suitable filter, contrast must be enhanced using good histogram equalization technique. To enhance the contrast of filtered images, various histogram equalization techniques are applied. This paper proposes use of brightness preserving dynamic fuzzy histogram equalization (BPDFHE) for solar panel thermal images by comparing the performance against histogram equalization (HE), mean preserving Bi-histogram equalization (BBHE), contrastive limited adaptive equalization (CLAHE), equal area dualistic sub-image histogram equalization (DSIHE) techniques. The qualitative attributes used for evaluation are peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and absolute mean brightness error (AMBE).
Gesture Detection Using Accelerometer and Gyroscope

Human hand gestures are a widely used real-time input modality for technologies that provide a human–machine interface. Computers mathematically analyze hand movements, and this technology can be used to exchange data or interact with the environment containing humans and computers or between them. Gesture-based interactions are one of the most comfortable and straightforward ways to communicate with the environment. The efficiency of vision-based approaches or the operation of such devices is highly dependent on lighting conditions and camera-facing angles. Sensor-based approaches thought are void of environmental conditions. However, they face the portability problem—this study attempts to address this limitation by proposing a sensor-based input device, Arduino Nano 33 BLE. On the other hand, hand gestures have limits in conveying the complexity and diversity of human intentions. Each application program demands various user intentions; we created a hybrid input device to solve these constraints. The computer already recognizes the presently running application. When the currently running program changes, the same gesture provides a new function required by the new app. As a result, the number and complexity of basic hand movements can be significantly reduced. As we want our physical model to be a simple stick fitted with sensors, we employ Arduino Nano 33 BLE containing IMU, which has one miniature three-axis accelerometer and one miniature three-axis gyroscope, the data of which is rasterized and converted to a rasterized image. For continuous gesture identification, we employ a CNN machine learning model. Five-unit hand gestures in our system successfully provide most functionality given by numerous input devices. Our approach’s characteristics are automatically modified by recognizing application programs or understanding user preferences. The technique exhibits good accuracy (90–96%) in various application programs, promising for building a summary. As users become increasingly familiar with the method, accuracy rises to 100%.
Conversion of Sign Language to Text Using Machine Learning

Hand motion detection is a critical step in the automatic translation of sign language, which allows deaf people to communicate. In the related discipline of Sign Language, a considerable study was done, and the same could be said for Indian Sign Language (ISL). In this paper, we proposed ISL detection and recognition using a machine learning framework. The convolutional neural network (CNN) has been used for detecting the activity using the ResNet-100 machine learning framework. The Indian Sign Language database contains 26 alphabets and 0–9 digits. The feature extraction and selection have been done in each convolutional layer, and optimization has been done in the pooling layer. Multiple activation functions have been utilized in dense layers for classification on real-time hand gesture as well as MNIST datasets. An extensive experimental analysis of proposed system and results have been evaluated with various machine learning algorithms that produce almost 5–7% higher accuracy with state-of-the-art methods. Sign language recognition is used to recognize alphabets, letters or words. Communication is a best way to exchange feelings. The deaf community have to be dependent on an interpreter to interact with normal people; due to this, they suffer extremely to interact with normal people. The aim of our system is to design a vision-based computer application that can convert the Indian Sign Language to text. We have discussed the dataset, system workflow, testing and training phases of convolutional neural network.
Hardware Implementation of Machine Vision System for Component Detection

Inspection of components using machine vision technologies provides solution for quality and process control. This technique is used in various applications such as automotive, pharmaceutical, food and beverage, electronics, packages, process control and special application. The basic idea is to use machine vision technology in the industries to improve the production quality, reduce the scrap product due to non-conformity by controlling the manufacturing process through machine vision and also to prevent the value addition for scrap product in the subsequent stage of manufacturing process. Thus, designing and developing a machine vision system for component identification used the image processing algorithm. Inside the component, small circles which are of 0.5 diameter are detected using Hough circle detection. Graphical user interface was developed for visual representation of the image processing. The training model of the system is developed.
Hardware Implementation of an Activation Function for Neural Network Processor

Neural network processors (NNPs) are designed to facilitate efficient implementation of modern deep learning neural networks. A typical neuron performs a weighted input aggregation followed by a nonlinear saturating operation such as a sigmoid, at the output. Therefore, hardware implementation of activation function is critical to performance of a NNP. Interestingly, output of activation function is sensitive to inputs in a limited range. Therefore, it is sufficient to implement a hardware module that implements this active region of the sigmoid efficiently. Number representation also plays a critical role in increasing the area utilization and speed performance of such module. In this paper, we present a novel method of implementing the sigmoid activation function. Performance parameters have been discussed and compared.
Recurrent Neural Networks

A recurrent neural network (RNN) is applied to inputs recurrently, with the network output from one time step sending an additional input to the next time step, augmenting the input for that time step. Inputs can be observations recorded at different time steps. Recurrent application of the network enables such networks to detect temporal relationships in input data that have a material impact in modeling output. The network’s output from one time step is passed as an input to the same network at the next time step, along with inputs for that time step. This enables RNNs to pass information learned from one time step to subsequent ones. This chapter illustrates the use of recurrent neural networks by focusing on gated recurrence unit (GRU), long-short-term memory (LSTM) cell, and customized recurrent neural network layers. RNNs are trained using backpropagation through time (BPTT), which involves unrolling the network through time and using backpropagation. Vanishing gradients pose a challenge to training RNNs, as the examples will demonstrate. A LSTM network was proposed by Hochreiter and Schmidhuber in 1997. In 2007, it was applied to speech recognition with outstanding results.
Overview

Deep neural networks have transformed virtually every scientific human endeavor – from image recognition, medical imaging, robotics, and self-driving cars to space exploration. The extent of transformation heralded by neural networks is unrivaled in contemporary human history, judging by the range of new products that leverage neural networks. Smartphones, smartwatches, and digital assistants – to name a few – demonstrate the promise of neural networks and signal their emergence as a mainstream technology. The rapid development of artificial intelligence and machine learning algorithms has coincided with increasing computational power, enabling them to run rapidly. Keeping pace with new developments in this field, various open source libraries implementing neural networks have blossomed. Python has emerged as the lingua franca of the artificial intelligence programming community. This book aims to equip Python-proficient programmers with a comprehensive knowledge on how to use the TensorFlow library for coding deep neural networks and reinforcement learning algorithms effectively. It achieves this by providing detailed mathematical proofs of key theorems, supplemented by implementation of those algorithms to solve real-life problems.
Secure quantum computing for healthcare sector: A short analysis

Abstract Quantum computing research might lead to “quantum leaps,” and it could have unanticipated repercussions in the medical field. This technique has the potential to be used in a broad range of contexts, some of which include the development of novel drugs, the individualization of medical treatments, and the speeding of deoxyribonucleic acid sequencing. This work has assembled a list of the numerous methodologies presently employed in quantum medicine and other disciplines pertaining to healthcare. This work has created a list of the most critical concerns that need to be addressed before the broad use of quantum computing can be realized. In addition, this work investigates in detail the ways in which potential future applications of quantum computing might compromise the safety of healthcare delivery systems from the perspective of the medical industry and the patient‐centric healthcare system. The primary objective of this investigation into quantum cryptography is to locate any potential flaws in the cryptographic protocols and strategies that have only very recently been the focus of scrutiny from academic research community members.
I Linked Open Data e gli archivi fotografici: il caso dell’Archivio Publifoto Intesa Sanpaolo

The complex relationship between the archives and the web must also take into account the extension of the latter, the Semantic Web, which requires cultural institutes to question the opportunity to use Linked Data for the representation of their datasets and the provision of their information assets online. Among the different entities and different examples of use available at national level, here we will include an in-depth study on the use of Linked Open Data for the description of the photographic archives, which can certainly benefit greatly from joining the Semantic Web. In particular, the text will focus on the case of Intesa Sanpaolo Publifoto Archive, describing the methods of LOD publication of the catalogue and the choices made.
Explore the Method for College English Teaching and Improvement—Basing on the Use of Intelligent Classroom

Recently, with more and more modern applications being put into use, the college English teaching innovation is facing a huge change. How to integrate those new apps with the real classroom teaching has become a big challenge. This paper will discuss about the process and frequency of using the app (represented by Rainclassroom) , based on the students’ performance, from several aspects, and further providing some benefits deriving from the use of the app .
Target Detection in Video Images Using HOG-Based Cascade Classifier

Detecting small objects using computer vision is a challenging task due to their small size in the image and therefore the lack of features when describing them. In this paper, a computer was trained to detect three small balls using 20 levels of the AdaBoost cascade classifier. The features of the balls in each level are described using the HOG feature descriptor. Three balls were recorded in practice at various distances (d = 2, 3, 4, ..., 10 m) from the camera and the targets (balls). The frames are then taken from the videos and resized using five magnification factors (RS = 1, 3, 5, 7, and 9) to make the balls seem as they should. According to the results, the detection rate of balls at all distances was 80% when using the magnification factor RS = 1, 90% when using the magnification factor RS = 3, 5, and 7, and 100% when using the magnification factor RS = 9. The suggested approach was also used in calculating the height and width of the detected balls. The overall results indicated that the height and width of the balls dwindle as the distance between the camera and the targets increases.
On the Kana notations of Group Shan(山) in Mako-Inkyo: Focusing on grade 1・2 on Group Shan(山)

In this paper, we studied the principle of the Left and Right phonetic notations of Mako-Inkyo and compared the notations with Go’on・Kan’on focousing on grade 1・2 on Group Shan(山). The results are summarized as follows.BR 1) The first rhymes Han(寒)・Huan(桓) appear as -(w)an/-(w)at type in Go’on・Kan’on in Mako-Inkyo. However, the Final -t of all rhymes is described only as Go’on [チ] and as Kan’on [ツ] in Mako-Inkyo.BR 2) The second opened rhymes Shan(刪)・Shan(山) appear as -en/-et type and some -an/-at types are mixed in Go’on, but are described mainly as -en/-et type in Mako-Inkyo. Further, [セチ察] should be changed to [サツ察] in Go’on in Mako-Inkyo.BR 3) The second rounded rhymes Shan(刪)・Shan(山) appear as -wen/-wet, -(w)an/-(w)at types in Go’on, but are described mainly as -(w)an/-(w)at type and -en/-et types are mixed in Mako-Inkyo. Further, [クワン關] should be changed to [クヱン關] in Go’on in Mako-Inkyo.BR 4) The Final -t of Go’on is described only as [チ] and of Kan’on only as [ツ] in Mako-Inkyo. It can also be seen in rhyme tables(韻圖) that appeared after Mako-Inkyo and modern Sino-Japanese dictionaries.
Het nominale genus in het Nederlands: minder arbitrair dan wij denken

Amsterdam University Press is a leading publisher of academic books, journals and textbooks in the Humanities and Social Sciences. Our aim is to make current research available to scholars, students, innovators, and the general public. AUP stands for scholarly excellence, global presence, and engagement with the international academic community.
ViMRC VLSP 2021: XLM-R versus PhoBERT on Vietnamese Machine Reading Comprehension

The development of industry 4.0 in the world is creating challenges in Artificial Intelligence (AI) in general and Natural Language Processing (NLP) in particular. Machine Reading Comprehension (MRC) is an NLP task with real-world applications that require machines to determine the correct answers to questions based on a given document. MRC systems must not only answer questions when possible but also determine when no answer is supported by the document and abstain from answering. In this paper, we present the description of our system to solve this task at the VLSP shared task 2021: Vietnamese Machine Reading Comprehension with UIT-ViQuAD 2.0. We propose a model to solve that task, called MRC4MRC. The model is a combination of two MRC components. Our MRC4MRC based on the XLM-RoBERTa pre-trained language model is 79.13% of F1-score (F1) and 69.72% of EM (Exact Match) on the public-test set. Our experiments also show that the XLM-R language model is better than the powerful PhoBERT language model on UIT-ViQuAD 2.0.