Reviewerly/exHarmony huggingface dataset:
Dilation equations and the smoothness of compactly supported wavelets

In this chapter, the authors characterize all wavelets arising from finite-coefficient dilation equations which are smooth and compactly supported- such wavelets are necessarily well-localized in the time domain and have good decay in their Fourier transforms. Daubechies and Lagarias independently discovered the specific implementation, and gave sufficient conditions for the existence of smooth, compactly supported scaling functions and lower bounds for the corresonding Holder exponents of the last derivative in terms of a joint spectral radius. In general, the relationship between the smoothness of scaling functions and the coefficients of the dilation equation is much more complicated than is apparent from the discussion of four-coefficient dilation equations. Daubechies has shown that, for real-valued coefficients, symmetry precludes the possibility of an associated multiresolution analysis, with the single exception of the Haar system. Thus these scaling functions cannot be used to construct wavelet orthonormal bases.
Modern Educational Technologies

The article provides an overview of the technologies used in higher education institutions for the management of e-education, such as: educational technology, adaptive technology, personalization of learning using cloud technologies and big data technologies.
Design of Blockchain System for a Real Estate (a Revolution)

Blockchain the technology behind the highly successful Bitcoin has recently gained attention from researchers who want to use this new and disruptive technology in many fields, potentially redesigning businesses, politics, and society at large. Despite all the research in blockchain, no buyer-company-government buying and selling schema has been designed till date. This schema ensures no single point of failure, faster implementation of government schemes, e.g.- Pradhan Mantri Awas Yojna (PMAY), easy document recovery in case of disaster, and easier trade for everyone helps boost the economy etc. The prime objective of this paper is to fill in this gap and discuss the various key benefits of having a decentralized real estate ecosystem for buying and selling based on blockchain technology and the challenges are to replace the traditional real estate ecosystem and make this schema a standard for any buying or selling of property through the use of the platform of Real Chain providing the DLT (Distributed Ledger Technology) environment which will counter the corrupt middlemen and remove traditionally centralized institutions from the process of buying and selling.
Single Image Defogging Method Based on Image Patch Decomposition and Multi-Exposure Image Fusion

Bad weather conditions (such as fog, haze) seriously affect the visual quality of images. According to the scene depth information, physical model-based methods are used to improve image visibility for further image restoration. However, the unstable acquisition of the scene depth information seriously affects the defogging performance of physical model-based methods. Additionally, most of image enhancement-based methods focus on the global adjustment of image contrast and saturation, and lack the local details for image restoration. So, this paper proposes a single image defogging method based on image patch decomposition and multi-exposure fusion. First, a single foggy image is processed by gamma correction to obtain a set of underexposed images. Then the saturation of the obtained underexposed and original images is enhanced. Next, each image in the multi-exposure image set (including the set of underexposed images and the original image) is decomposed into the base and detail layers by a guided filter. The base layers are first decomposed into image patches, and then the fusion weight maps of the image patches are constructed. For detail layers, the exposure features are first extracted from the luminance components of images, and then the extracted exposure features are evaluated by constructing gaussian functions. Finally, both base and detail layers are combined to obtain the defogged image. The proposed method is compared with the state-of-the-art methods. The comparative experimental results confirm the effectiveness of the proposed method and its superiority over the state-of-the-art methods.
Remote Auditing : An Alternative Approach to Face the Internal Audit Challenges During The COVID-19 Pandemic

The research aims to define the roles that the internal audit activity can play in Egyptian business enterprises to confront the crisis of the spread of the Corona virus (Covid-19), and discover the challenges that the internal auditors may face while performing their work during the period of the epidemic. The research also aims to study the factors that should be taken into consideration when preparing and implementing the internal audit plan and reporting on the audit findings during the Corona virus (COVID-19) crisis, in addition to suggesting an alternative approach to facilitate the performance of internal audit tasks efficiently during this period, which is the remote auditing approach. To achieve these goals, the research relied on using the positive approach through the use of the deductive approach to analyze previous studies related to the research topic and derive the research hypotheses, in addition to using the inductive approach to test the validity of the research hypotheses in the real practice in the Egyptian business environment. This was done through a field study conducted on a sample of 95 individuals, which included three categories: the academics represented by the professors of accounting and auditing in the faculties of commerce in Egyptian universities, in addition to the members of the audit committees and the internal auditors of some joint stock companies listed on the Egyptian Stock Exchange. Statistical tests were used commensurate with the nature of the data collected. And based on the results of the statistical hypothesis test, the first hypothesis was accepted, which states that there is agreement between the study sample groups about the importance of the roles that the internal audit activity can play in business enterprises to confront the crisis of the Corona virus (Covid-19). Also, the second hypothesis was accepted , Which states that there is agreement between the study sample groups about the challenges that may face the internal auditors during the Corona virus (Covid-19) pandemic, as well as the third hypothesis which states that there is agreement between the study sample groups about the factors that should be considered when preparing and implementing the internal audit plan and reporting on the audit results during the Corona virus (Covid-19) crisis. Also, based on the results of the field study, it becomes clear the importance of developing the method of performing the internal audit activity during the Corona pandemic crisis, through applying modern audit methods, such as the remote audit approach, which relies on using information and communication technology to perform internal audit tasks efficiently , which enables the internal audit activity to provide the roles assigned to it and meet the needs of stakeholders , and overcome the challenges facing the internal auditors during the period of the epidemic on the other hand.
Online learning environments to stimulate in students the processes of mutual interaction between digital and analog artefacts to enhance student learning

In the recent years, numerous papers have discussed the use of concept maps in education. In this paper, we use the Dynamic Concept Maps (DCMs) in online learning environments as tools able to stimulate in students the processes of mutual interaction and hybridization between digital artefacts (DCMs) and analog artefacts (books) so as to encourage the development of significant learning. This method, called “DynaMap Remediation Approach” (DMRA), encourages and stimulates learners to study topics in greater detail, and supports the development of their own learning. The advantages of this method are listed below: DMRA is significantly effective in terms of reducing study time and improvement of learning outcomes. DMRA valorises the active role of the learners during their process of knowledge construction and may have significant implications for educators who would like to use innovative and engaging online learning environments to enhance student learning. DMRA is a simple and highly reproducible method.
A New Hybrid Stereo Disparity Estimation Algorithm with Guided Image Filtering-Based Cost Aggregation

Stereo matching algorithms are useful for estimating a dense depth characteristic of a scene by finding corresponding points from stereo images of the scene. Several factors such as occlusion, noise, and illumination inconsistencies in the scene affect the disparity estimates and make this process challenging. Algorithms developed to overcome these challenges can be broadly categorized as learning-based and non-learning based disparity estimation algorithms. The learning-based approaches are more accurate but computationally expensive. In contrary, non-learning based algorithms are widely used and are computationally efficient algorithms. In this paper, we propose a new stereo matching algorithm using guided image filtering (GIF)-based cost aggregation. The main contribution of our approach is a cost calculation framework which is a hybrid of cross-correlation between stereo-image pairs and scene segmentation (HCS). The performance of our HCS technique was compared with state-ofthe- art techniques using version 3 of the benchmark Middlebury dataset. Our results confirm the effective performance of the HCS technique.
Breast Cancer Detection in Mammograms Using Deep Learning

Pillai, Abhiram Nizam, Amaan Joshee, Minita Pinto, Anne Chavan, SatishkumarBreast cancer is the most lethal cancer among women. Early-stage diagnosis may reduce the mortality associated with breast cancer subjects. Diagnosis can be made with screening mammography. The main challenge of screening mammography is its high risk of false positives and false negatives. This paper presents the detection of breast cancer in mammograms using the VGG16 model of deep learning approaches. The VGG16 model is trained and tested on 322 images from the MIAS dataset. It performs better as compared to AlexNet, EfficientNet, and GoogleNet models. Classification of mammograms will improve mammograms’ efficient screening, which will be a support system to radiologists.
Formal Analysis of EDHOC Key Establishment for Constrained IoT Devices

The IETF is standardizing an authenticated key establishment (AKE) protocol named EDHOC for constrained IoT devices. In contrast to more powerful devices like web cameras and cars, which receive a lot of media attention, such devices operate under severe energy consumption and message size restrictions. EDHOC was first formally analyzed in 2018 by Bruni et al. Since then, the protocol has been significantly extended and now has three new key establishment methods. In this paper, we formally analyze all methods of EDHOC in a symbolic Dolev-Yao model, using the Tamarin verification tool. We show that the different methods provide sensible, but also rather heterogeneous security properties, and discuss various consequences of this. Our work has also led to improvements in the design and the specification of EDHOC.
Institutional Capacity of Informatization of Education

The article reflects the experience and gives fragments of the state of affairs for today and discusses the trends of future informatization, computerization and digitalization of education. The article analyses the activities of specialized organizations in the field of informatization of education on the example of the Academy of Informatization of Education; Criteria for the performance of these organizations have been identified; the algorithm of productive functioning of specialized organizations in the field of informatization of education has been determined.
No-reference Stereoscopic Image Quality Predictor using Deep Features from Cyclopean Image

With the expanding use of stereoscopic imaging for 3D applications, no-reference perceptual quality evaluation has become important to provide good viewing experience. The effect of the quality distortion is related to the scene’s spatial details. Taking this into account, this paper introduces a blind stereoscopic image quality measurement using synthesized cyclopean image and deep feature extraction. The proposed method is based on Human Visual System (HVS) modeling and quality-aware indicators. First, the cyclopean image is formed, taking on the existence of binocular rivalry / suppression that includes the asymmetric distortion case. Second, the cyclopean image is decomposed into four equivalent parts. Then, four Convolutional Neural Network (CNN) models are deployed to automatically extract quality feature sets. Finally, a feature bank is then created from the four patches and mapped to quality score using a Support Vector Regression (SVR) model. The best known 3D LIVE phase I and phase II databases were used to evaluate the efficiency of our technique. Compared with the state-of-the-art stereoscopic image quality measurement metrics, the proposed method has shown competitive outcomes and achieved good performance.
Backpropagation Neural Network for Processing of Missing Data in Breast Cancer Detection

A complete dataset is essential for biomedical implementation. Due to the limitation of objective or subjective factors, missing data often occurs, which exerts uncertainty in the subsequent data processing. Commonly used methods of interpolation are interpolating substitute values that keep minimum error. Some applications of statistics are usually used for handling this problem. We are trying to find a higher performance interpolation method compared with the usual statistic methods, by using artificial intelligence which is in full swing today. The prediction and classification of backpropagation neural network are used in this paper, describes a missing data interpolation method to propose the interpolation model that mines association rules in the data. In the experiment, depending on a multi-layer network structure, the model is trained and tested by sample data, constantly revises network weights and thresholds. The error function decreases along the negative gradient direction and approaches the expected real output. The model is validated on the breast cancer dataset, and we select real samples from the data set for validation, moreover, add four traditional methods as a control group. The proposed method has great performance improvement in the interpolation of missing data. Experimental results show that the interpolation accuracy of our proposed method (84%) is higher than four traditional methods (1.33%, 74.67%, 73.33%, 77.33%) as mentioned in this paper, BPNN stays low in MSE evaluation. Finally, we analyze the performance of various methods in processing missing data. The study in this paper has estimated missing data with high accuracy as much as possible to reduce the negative impact in the diagnosis of real life. At the same time, it can also assist in missing data processing in the biomedical field.
The Fourth Industrial Revolution

In this reflective chapter, we undertake a mapping exercise from the anticipated demands arising from the Fourth Industrial Revolution to the foreseeable mathematical readiness and disposition incumbent on our secondary school students. Industry 4.0 is categorized by the increasing automaticity and interoperability of production systems which are becoming extensively data-driven. As technologies are developed within Industry 4.0, they also become available as by-products for the field of education, changing the rules and norms of learning. An immediate concern is an extent to which school mathematics is preparing our youngsters to develop a mindset that can embrace these technologies and be ready for new jobs that we may not have considered. We attempt to provide anticipative answers to the following two questions: (i) What are the enablers that can nurture the appropriation of the affordances of Industry 4.0 in the teaching and learning of school mathematics? (ii) How should the school mathematics curriculum be revamped to align it to the demands of Industry 4.0 and develop the incumbent dispositions in preparation for future vocational and functional obligations? To answer the two questions, firstly, we review steps that have already been initiated to accommodate the tools of Industry 4.0 in the domain of mathematics education. Secondly, as a didactic exercise, we map out the mathematical skills that new technological frontiers may require in light of projected developments. We also preempt some of the challenges that this new revolution may engender such as resistance to changes from influential stakeholders and changes in teachers’ mindset and preparation. We use the knowledge gathered in mathematics education as a backdrop to chart a trajectory that can potentially embody the new technologies from Industry 4.0 into our discipline.
Collaboration in the IETF

RFC 9000, published in May 2021, marks an important milestone for the Internet's standardization body, the Internet Engineering Task Force (IETF): finally, the specification of the QUIC protocol is available. QUIC is the result of a five-year effort - and it is also the second of two major protocols (the first being SPDY, which later became HTTP/2) that Google LLC first deployed, and then brought to the IETF for standardization. This begs the question: when big players follow such a "shoot first, discuss later" approach, is IETF collaboration still "real", or is the IETF now being (mis-)used to approve protocols for standardization when they are already practically established, without really actively involving anyone but the main proponents?
[Migration, Transformation Characteristics, and Ecological Risk Evaluation of Heavy Metal Fractions in Cultivated Soil Profiles in a Typical Carbonate-Covered Area].

In China, high heavy metal concentrations in cultivated soil are mainly distributed in carbonate-covered areas. The migration and transformation of heavy metals in such soils are influenced by interactions between natural processes and human activities. This study examined the profiles of nine paddy soils, derived from carbonate rocks in Guangxi. The Cd, As, Zn, Cr, Cu, Hg, Ni, and Pb contents we determined, and soil properties such as pH, Corg content, and fractions of Cd, As, Zn, and Cr were tested. Based on the above data, we assessed the vertical distribution of heavy metal fractions, as well as the ecological risks and factors affecting the migration ability of heavy metals, under the influence of human activities and natural soil formation. The results show that compared with the carbonate rocks in Guangxi, the soil profile of the study area is significantly enriched with all eight heavy metals. Among them, Cd, As, Zn, and Cr exceeded China's agricultural land (paddy field) pollution risk screening values, and Cd and As partially exceed the risk intervention values. All fractions of Cd in the soil profiles are widely distributed. The proportion of water-soluble and ion-exchange Cd fractions with high ecological risk decreased significantly from the surface to greater depths in the soil profile. As, Zn, and Cr in the soil profile were mainly in residual states, and the proportion of water-soluble and ion-exchange fractions did not change considerably with increasing depth. The evaluation results of RAC and RSP show that Cd pollution risk in the study area is relatively high, whereas As, Zn, and Cr are generally pollution-free or risk-free. In naturally developed soil in the study area, the clay mineral content and degree of soil development have significant effects on Cd migration, whereas in the cultivated layer affected by human activities, the soil pH and organic matter content are the main controlling factors. The migration ability of soil As is mainly related to soil organic matter, Fe2O3 content, and soil development degree, but organic matter has an obviously enhanced effect in the tillage layer. The main controlling factor of Zn and Cr migration in soil is pH, and the effects are more intense under the disturbance of human activities.
Answer-Set Programs for Reasoning About Counterfactual Interventions and Responsibility Scores for Classification

We describe how answer-set programs can be used to declaratively specify counterfactual interventions on entities under classification, and reason about them. In particular, they can be used to define and compute responsibility scores as attribution-based explanations for outcomes from classification models. The approach allows for the inclusion of domain knowledge and supports query answering. A detailed example with a naive-Bayes classifier is presented.
Eye-Smoker: A Machine Vision-Based Nose Inference System of Cigarette Smoking Detection using Convolutional Neural Network

In the Philippines, at least 16 million Filipinos reported smoking cigarettes amid the campaign against tobacco products due to various concerns about their adverse health effects. Due to health, environmental, and safety concerns, the President of the Philippines issued Executive Order 26 s. 2017, imposing a nationwide ban on smoking (use of tobacco including e-cigarettes) in all public places in the Philippines. Despite the implementation of this order, many are still seen smoking in prohibited smoking areas. A smoke detector can be helpful in this situation. This study proposed a smoker detection system that uses a deep learning algorithm that can detect people that are smoking cigarettes. The study used the Pascal VOC format and LabelImg tool for annotating the datasets. Training, validation, and evaluation of the system is done by presenting images, videos, and live detection using the webcam of a camera. Overall, the system produced 90% testing accuracy.
Towards Remote Inspections of FPSO's Using Drones Instrumented with Computer Vision and Hyperspectral Imaging

Abstract The Class Society DNV has performed production surveys in enclosed spaces using drones since 2016, demonstrating cost savings and increased personnel safety. The goal is to develop autonomous inspection drones to reduce the need to enter tanks and enable remote inspection. The vision is a drone that can fly by itself, track where it is, and spot rust and cracks, and measure steel thickness. We expect that drone-assisted remote inspection will reduce survey costs for the clients and be a major safety improvement for surveyors. Several drone capabilities are required to enable visual close-up inspection and non-destructive testing in enclosed, GPS-denied, and poorly lit environments. In this study, we report the most recent status from an ongoing research project, including several industry partners. We highlight technical challenges and preliminary results on drone navigation functionalities, computer vision for detection of cracks, and the use of hyperspectral imaging to detect and classify the chemical composition of coatings, rust, and other use cases.