Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Object Detection Models for the Detection of Multiple Face Masks

Deep learning has immense prospective in many real-life practices, one of them being object detection. Object detection based on deep learning has shown encouraging results. Since December 2019, deadly virus named CORONA or COVID-19 started to engulf the whole planet with its impact. One of the easiest and simplest ways to protect oneself from this virus is by wearing a mask. In order to detect whether a person is wearing mask or not, we propose a model to detect various face masks that include cloth masks, N-95 masks, medical masks, and no mask. The proposed model consists of two major components—annotating, labeling images and detection of face masks. A new dataset has been created by combining images from Medical Masks Dataset and Google Images, and then these images were annotated according to the mentioned categories. A comparative study has been presented among different object detection algorithms along with a proposed detection algorithm. Results show that YOLOv5 performs best in the detection of face masks when compared to other detection models. It achieved a mAP of 0.51 in just 0.24 h on our dataset. On comparing YOLOv5 to the proposed model, we found that our model achieved a precision of 0.9 as compared to 0.88 of YOLOv5. Among existing approaches YOLOv5 performed the best with precision of 0.88. The model proposed in the work results in precision of 0.90 outperforming all existing models.
Super-sensitive phase estimation using entanglement-assisted single-mode inputs

Interferometers are a versatile platform for achieving precise measurement. By using quantum resources such as entanglement and squeezing, quantum precision measurement exceeds its classical counterpart in phase sensitivity. In this paper, we propose an entanglement-assisted estimation scheme. One mode of the two-mode squeezed vacuum is injected to the interferometer, while the other mode is used as an assistant and does not participate in phase encoding. The advantage of entanglement is attainable via collective detection and the phase sensitivity can surpass the Heisenberg limit and reach the quantum Cramer–Rao bound. We analyze the phase sensitivity in the presence of transmission loss and imperfect detection efficiency. The results show that the phase sensitivity is fragile to transmission loss but extremely robust to imperfect detection efficiency.
Specific Wavelet Family Selection for Wavelet Domain-Based Super-Resolution Application

The prime need of image quality for accurate analysis with precise decisions is the key attraction for researchers in image processing field. The increasing need is due to degradation of image signals whilst capturing, transmission, compression, etc. The accuracy in super-resolution process for quality improvement of images or videos is achieved at the cost of time and complexity. This limitation is motivation for researcher to contribute themselves in the same field by developing conventional algorithms and methods specially categorised in spatial and frequency domain. In the recent decades, wavelet domain processing has remarkable results in super-resolution field. The results of wavelet domain processing are depending upon wavelet functions or families considered whilst analysis, as these families possess unique properties which gives different results according to application area. Unfortunately, there is no such theory or analysis available which shows specific wavelet family selection for particular super-resolution process. The author has tried to explore the concept behind selection of appropriate wavelet function with analysis on different video frames containing variety of scenes. The process is simple, i.e. decomposition and reconstruction of video frames using different wavelet families and comparative analysis of original and reconstructed image/frames with different quality measurement metrices. The exact reconstruction shows lossless wavelet domain process. The winner wavelet function is Haar which is simplest amongst all wavelets, despite the literature provided in wavelet processing preferred db2/db7/9 families. The assessment provided is beneficial for beginners to select appropriate wavelet function in super-resolution application.
The Changing Landscape of Identity and Access Management with Blockchain-Based Self-Sovereign Identity

Identity management solutions are widely used in real-world applications and are intended to simplify activities such as managing and authenticating digital identities. As public online presence grows and transactions take place around the world, it becomes important to identify and validate digital identities. In addition, larger organizations are collecting more and more personally identifiable information, leaving databases open to malicious intruders and untrusted individuals. The scenario of identity management is now changing with decentralized identity management solutions using blockchain technology which place the complete control of the user’s data with him/herself. Self-sovereign identity (SSI) management is a popular application for blockchain technology. During the six months that this investigation was conducted, several major attacks were carried out against companies that process large amounts of personal data. Equifax has lost over 140 million customer records, including blockchain identity management, digital identities, and self-governed identities. The SSI as a novelty in the identity management landscape is made possible with blockchain. It has been created for the ease of use, and anyone can understand and use the concept of self-sovereign identity. SSI can solve a variety of issues such as privacy, scalability, flexibility, proximity, and consent. This research paper attempts to answer the following research question. Is blockchain useful in IAM with SSI? The authors seek the answer in a survey of some key enabling technologies including IAM’s security, prevention, digital identity, decentralization, and blockchain technologies.
Image Generation Using Sketch and Attributes

Creating a photographic face picture from a sketch image or written description has always been a difficult problem in computer vision. Sketches normally just provide basic profile information and do not include facial detail. As a result, obtaining exact face characteristics is difficult. To address this issue, we present a photograph translation network that uses characteristics and the generative adversarial network (GAN) ( Image Quality Assessment: From Error Visibility to Structural Similarity Zhou Wang, Member, IEEE, Alan C. Bovik, Fellow, IEEE Hamid R. Sheikh, Student Member, IEEE, and Eero P. Simoncelli, Senior Member, IEEE, apr 2004.). By augmenting the sketch picture with the additional facial attribute feature, it will significantly contribute to the authenticity of the created face. The generator network is made up of two networks: a feature extraction network and a downsampling upsampling network, both of which employ skip connections to reduce the number of layers while maintaining network speed. The purpose of the human network is to check if the created faces have the necessary properties. A photographic face may be created with just a drawing and a simple language description. We have discovered via preliminary testing that sketch drawings include a lot of profile data, whereas attribute vectors provide high-level linguistics data like texture details and colors. When the two datasets are combined, the sketch gives approximately defined data while the attribute provides natural texture information, allowing you to create realistic photographic faces.
Axiomatically Designed Research Support Systems

Research support systems (RSS) aim at supporting researchers from various scientific disciplines in the production of new knowledge through effective interaction and collaboration. Despite numerous and isolated attempts to develop such systems, they remain specific to a particular discipline or institution, and thereby lack wide adoption, compared to their decision support systems counterparts. The proposed system herein applies axiomatic design theory and modern artificial intelligence (AI) tools for developing a generic and flexible software architecture of RSS across many scientific research disciplines. The proposed architecture can be implemented by the deployment of a set of well-specified software modules.
Automatic Documents Categorization Using NLP

Automated text classification has become a critical tool in the legal business in recent years. There is no doubt about the commercial value of being able to automatically categorize documents based on their content. Automatic document categorization divides and organizes text based on a set of specified categories, allowing for quick and easy retrieval of data during the search phase. Text classification, which aims to assign predetermined categories to a given text sequence, has long been a classic task and a popular research topic in the field of natural language processing (NLP). Through fine-tuning in downstream tasks, bidirectional encoder representations from transformer (BERT), one of the most popular transformer and general language models is more efficient, scalable, and objective, and it yields the best outcomes. As a result, BERT is used in this project, fine-tuned, and applied to documents for automatic document classification. Then, this BERT model is compared to another model called XLNet to see if BERT outperforms XLNet. The accuracy of the BERT and XLNet models are 96.8% and 97.30%, respectively, when tested with test data, indicating that the XLNet model slightly beats the BERT model.
Age Estimation from Face Images Using CNN Based on LBPH/LBP

Human face images have several biological characteristics. The face images are used in the field of research to estimate the age, race, gender, and facial expression of the person also for the facial authentication. Age estimation from face images has many applications including age restriction online services, human–computer interaction, and public/private ID renewal processes. The deep neural network proposed in this paper to estimate the age from face images. The proposed network has been validated with two datasets, FG-NET and AgeDB. The network is evaluated based on mean absolute error (MAE) and root mean square error (RMSE). The network outperformed using the FG-NET dataset with LBPH features, i.e., MAE is 6.3019 and RMSE is 8.9648. This model can be prefer for age estimation through face images.
Dehazing from a Single Remote Sensing Image

Various atmospheric conditions such as clouds and smog directly affect the quality of remote sensing images. Heavy presence of such atmospheric conditions is sometimes able to completely block most of the radiation from infrared to visible spectral region. However, a light presence of these conditions makes a hazy effect on such images. In the present study, we propose an effective method to remove hazy effect from a single satellite image. The proposed method is based on the robust estimate of air-light and assumption of modified dark channel (MDC) prior, which helps us to produce an efficient transmission map (TM), and hence ensures better enhancement; we show the efficiency of the proposed method by compare it with the existing state-of-art methods. The experimental results establish that the proposed method produces visually appealing resultant dehazed images and retains fine details of the given single-input hazy and low-contrast satellite image.
White Blood Cells Classification Using Deep Learning Technique

Collaboration with technology is the next revolution in health care, and they must adapt evolving healthcare technologies to be significant in the next years. Digitization in medicine and health care might help transform unsustainable healthcare systems into sustainable ones, enhance interactions between medical specialists and people, and provide inexpensive, speedier, and more effective illness remedies. The objective of this study is to devise a system that distinguishes the types of white blood cells using precise and programmed analysis, which has been considered an effective way of classifying the 4 different types of WBCs—eosinophil, lymphocyte, monocyte, neutrophil. Convolutional neural network (CNN) models had been used for detection and classification, providing an efficient solution with a good accuracy rate when compared to the existing system. The training was done using a publicly available dataset, and visualization methods were used to map the model accuracy and loss. It was discovered that neural networks can record the colors, and textures of lesions that are specific to their type, which resembles human decision-making. The final model is deployed using Django framework for classification, where the image is uploaded and the application displays the result.
Encrypto–Decrypto

In present scenario, cybercrimes are increasing in every area of technology. Especially, in text transfer, there is a high chance of a third party breach. Everyone needs to share information with each other through various platforms which is a daily part of life. These days it is a big concern that data might leak and can be misused by someone. To overcome these issues, a model has been created as an encryption and decryption platform to increase privacy in text transfer which leads to making the communication secure. A user-friendly, highly secure, and fast algorithm used in the platform.
Communicating qualitative uncertainty in data visualization

Abstract Qualitative uncertainty refers to the implicit and underlying issues that are imbued in data, such as the circumstances of its collection, its storage or even biases and assumptions made by its authors. Although such uncertainty can jeopardize the validity of the data analysis, it is often overlooked in visualizations, due to it being indirect and non-quantifiable. In this paper we present two case studies within the digital humanities in which we examined how to integrate uncertainty in our visualization designs. Using these cases as a starting point we propose four considerations for data visualization research in relation to indirect, qualitative uncertainty: (1) we suggest that uncertainty in visualization should be examined within its socio-technological context, (2) we propose the use of interaction design patterns to design for it, (3) we argue for more attention to be paid to the data generation process in the humanities, and (4) we call for the further development of participatory activities specifically catered for understanding qualitative uncertainties. While our findings are grounded in the humanities, we believe that these considerations can be beneficial for other settings where indirect uncertainty plays an equally prevalent role.
US Traffic Sign Recognition by Using Partial OCR and Inbuilt Dictionary

Advanced Driver Assistance Systems (ADAS) is playing a great role in the designing of driverless cars. However, in the designing of these cars, road sign detection and actions according to the respective instructions play a vital role. This paper discusses the detection of US-based signboard and recognition of the detected characters. The presented architecture is an entire pipeline comprising detection followed by segmentation and then recognition using an Optical Character Recognition (OCR) model using a CNN and an inbuilt dictionary. The SynthText ICDAR 2015 Incidental Text data set was used for detection and Chars74 data set for recognition. The paper also discusses the various quality assessments of images done for selecting the segments to be sent to the OCR model. The main evaluation parameter used is accuracy. The CNN model built shows an accuracy of 95%.
Binned Term Count: An Alternative to Term Frequency for Text Categorization

In text categorization, a well-known problem related to document length is that larger term counts in longer documents cause classification algorithms to become biased. The effect of document length can be eliminated by normalizing term counts, thus reducing the bias towards longer documents. This gives us term frequency (TF), which in conjunction with inverse document frequency (IDF) became the most commonly used term weighting scheme to capture the importance of a term in a document and corpus. However, normalization may cause term frequency of a term in a related document to become equal or smaller than its term frequency in an unrelated document, thus perturbing a term’s strength from its true worth. In this paper, we solve this problem by introducing a non-linear mapping of term frequency. This alternative to TF is called binned term count (BTC). The newly proposed term frequency factor trims large term counts before normalization, thus moderating the normalization effect on large documents. To investigate the effectiveness of BTC, we compare it against the original TF and its more recently proposed alternative named modified term frequency (MTF). In our experiments, each of these term frequency factors (BTC, TF, and MTF) is combined with four well-known collection frequency factors (IDF), RF, IGM, and MONO and the performance of each of the resulting term weighting schemes is evaluated on three standard datasets (Reuters (R8-21578), 20-Newsgroups, and WebKB) using support vector machines and K-nearest neighbor classifiers. To determine whether BTC is statistically better than TF and MTF, we have applied the paired two-sided t-test on the macro F1 results. Overall, BTC is found to be 52% statistically significant than TF and MTF. Furthermore, the highest macro F1 value on the three datasets was achieved by BTC-based term weighting schemes.
How is Perseveration in Dysgraphia Students? A Research at Yayasan Pantara Jakarta

This research focused on handwriting disorders for dysgraphia students. The objective of this research are to analyze and to describe the types of perseveration and types of error in perseveration of 10 dysgraphia elementary school students. This research uses descriptive-qualitative method The data collection technique is from the worksheet that provided to students fill out. Research result discovered there were some perseveration types in dysgraphia found in elementary school students of Yayasan Pantara Jakarta. In this study, only one perseveration type is founded and the type was “Substitution”. The discussion of perseverations part involves ten words. Some students use misspelled words when they confuse one or more characters with different characters. This study also found that it is important to note that most of students have no problem in writing the first letter of a word, the error usually occurs on the next consecutive letter. it can be concluded that there were dysgraphia occure in elementary school students.
Noised Phase Unwrapping Based on the Adaptive Window of Wigner Distribution

A noised phase-unwrapping method is presented by using the Wigner distribution function to filter the phase noise and restore the gradient of the phase map. By using Poisson’s equation, the unwrapped phase map was obtained. Compared with the existing methods, the proposed method is theoretically simple, provides a more accurate representation, and can be implemented in light-field hardware devices, such as Shack-Hartmann sensors.
IoT-Enabled Sustainable Solution for Smart Shopping Experience

The Internet of things (IoT) is transforming people’s lives by connecting common objects. Retail marketing sectors are greatly benefited by IoT-enabled technologies in modernizing the billing and cart-filling activities. Customers experience many delays by waiting in the long queues within the marts. This problem is addressed by designing an affordable radio frequency identification (RFID)-based smart shopping cart that can be used in all retail marketing scenarios. When an item is placed in a smart shopping cart, it can be instantly scanned by a cart equipped with an RFID reader. As a result, billing can be done directly from the shopping cart, saving customers from having to wait in a long line at the checkout. Further, the authors enhance the smart shopping cart which includes smart shelving that is equipped with a video sensor that reads the QR code on the item and may monitor stock, possibly also updating a central server. The major advantage of this type of system is that inventory management becomes much easier because all items can be automatically added after scanning the QR code of the item rather than being manually scanned by a laborer.
A novel <scp>YOLOv4</scp>‐modified approach for efficient object detection in satellite imagery

Abstract Interpreting high‐resolution satellite imagery could be an expensive and time‐consuming task for human eyes. Computer Vision and Deep Learning techniques can help to solve this major problem by applying detection algorithms, which can ease the task of analysing such images for the benefit of humans. It can help in changing the way we comprehend and anticipate the economic activity around the world. Such techniques help us to observe the urban development in high security areas such as national and international borders. Constant progressions in improving and making satellites deployment, a cost‐effective process to strengthen the networks of satellite orbiting the earth is one of the reasons such tasks can be easily solved with the help of high‐resolution images. Current computer vision research works have achieved significant milestones in accuracy and speed but, there are still room for improvements. In this paper, we addressed some of these methods to bring them to a combined pipeline and proposed a set of improvements to further improve the speed and the accuracy of the detections. We proposed a unified framework, which combines several object detection algorithms and the state‐of‐art architecture of YoloV4 along with the TensorFlow object detection API. This framework can detect small and well as large objects with improved speed and accuracy by using two detectors for different scales. Evaluation ran on these high‐resolution images yield mAP of 85.6% F1‐score of 0.84.
Designing a Model of Cryptosecurity of Information in the System of Countering the Spread of COVID-19

Information security of information systems is vital for the protection of information resources. If coronavirus 229E, NL63, OC43, HKU1 enters the human body, one of the therapeutic treatment tactics is used to restore the immune system. By analogy with this process, an equivalent of the cryptographic information protection model is proposed in this paper. The study of the attack of the COVID-19 virus and the therapeutic treatment of the human body made it possible to propose a cryptoprotection model based on the algorithm for combating the virus in question. The modern studies carried out by scientists from various countries, but most of all by Russian specialists, devoted to the adaptive immune response, were of decisive importance for the development of vaccines and preparations of monoclonal antibodies; on this basis, an algorithm for designing cryptographic protection of information that circulates in the system of transmission and reception through channels of various physical nature. Taking into account the equivalent of the therapeutic treatment of COVID-19, it is proposed to filter the user by biometric, characteristics or human parameters (BCP), (HBP) for the purpose of high-quality and prompt identification. For the scientific approval of the presented proposals, a mathematical confirmation based on the interpolation of the Lagrange polynomial is given.