Reviewerly/exHarmony huggingface dataset:
Post study of Blockchain in smart health environment

Blockchain technology is being popular day by day and also serving in many applications and areas such as Internet of Things, Cloud Computing, Big data, Healthcare and many more because of its security strengths and benefits. It has been proven that blockchain applications are being utilized in healthcare to deliver secure data and to manage the medical data safely. Moreover, blockchain is transforming the traditional medical practices in effective ways such as diagnosing the problems effectively and treating through secure information sharing. There is no doubt to say that in future, blockchain will be performing in personalized, valid and secure healthcare by combining the medical information of patient and providing in a secure and updated setup. In this research paper, securities of healthcare applications and traditional and recent security developments are discussed. Due to Covid-19, it has become challenging for healthcare officials and government to protect and record individuals sensitive data safely. Also, spread of misleading information has been also increased during the pandemic and inability of existing platform for information validation leads to public panic.Implementation of blockchain-based tracking systems is essential for accurate and valid information sharing among people and Government. This paper focus on information security issues raised by Covid-19 pandemic as well as implementation of blockchain-based platform in healthcare to record and protect covid-19 related information and contact tracing.
Implementation of Robot Operating System in Smart Garbage Bin Robot with Obstacle Avoidance System

Hygiene problem is a no solution problem in Malaysia due to the littering of the citizens. To solve the problem, the government has increased the garbage bin in the public space to encourage the citizens to throw rubbish in the garbage bin. However, the task of collecting the garbage will become difficult due to a large number of garbage bins. Thus, autonomous garbage bin that has the mobility to transport itself from one location to another should be the trend in future. In this paper, we proposed for developing an intelligent garbage bin robot utilizing robot operating system (ROS) for autonomous garbage retrieval. Using the robot operating system, the microcontroller was allowed to control the robot for running capacity detection, global positioning, garbage delivery and obstacle avoidance at the same time. The results shows that the robot was able to operate its bearing adjustment through motor control when confronting an obstacle.
A Novel Ensemble Model - The Random Granular Reflections

One of the most popular families of techniques to boost classification are Ensemble methods. Random Forests, Bagging and Boosting are the most popular and widely used ones. This article presents a novel Ensemble Model, named Random Granular Reflections. The algorithm used in this new approach creates an ensemble of homogeneous granular decision systems. The first step of the learning process is to take the training system and cover it with random homogeneous granules (groups of objects from the same decision class that are as little indiscernible from each other as possible). Next, granular reflection is created, which is finally used in the classification process. Results obtained by our initial experiments show that this approach is promising and comparable with other tested methods. The main advantage of our new method is that it is not necessary to search for optimal parameters while looking for granular reflections in the subsequent iterations of our ensemble model.
Comparison between Orthogonal and Bi-Orthogonal Wavelets

Theoretical aspects and analysis of wavelets have applications in mathematical modeling, artificial neural networks, digital signal processing, and image processing and numerical methods. The term orthogonal deals with the mathematical part which covers a wide area of digital signal processing and image processing. An orthogonal wavelet generates the wavelet whose nature is orthogonal. This means an inverse or transpose wavelet transform is nothing but the adjoint of a wavelet transform. If this condition fails by missing orthogonality it may result in biorthogonal wavelets. Single scaling functions and single wavelets are generated but orthogonal wavelet filter bank. A biorthogonal wavelet associated with the wavelet transformation is invertible. There is no need that if it is invertible so it should be orthogonal. The biorthogonal wavelet allows maximum freedom in the case of designing the orthogonal wavelet. It also supports the construction of symmetric wavelet functions. In biorthogonal wavelets, as the name indicates, two scaling factors or functions are responsible for the generation of the various multi-resolutions on the basis of different wavelets. For the image and signal reconstruction purpose we need that wavelet. We get a better result in the presence of biorthogonal wavelets. In the present work, we analyzed the performance of orthogonal and biorthogonal wavelet filters for image processing. We test the image and observed that the filter coefficient and image quality for the orthogonal and biorthogonal wavelet. On the basis of performance analysis it is concluded that biorthogonal wavelets are better than orthogonal wavelets.
More than a Fair Share: Network Data Remanence Attacks against Secret Sharing-based Schemes

With progress toward a practical quantum computer has come an increasingly rapid search for quantum-safe, secure communication schemes that do not rely on discrete logarithm or factorization problems.One such encryption scheme, Multi-path Switching with Secret Sharing (MSSS), combines secret sharing with multi-path switching to achieve security as long as the adversary does not have global observability of all paths and thus cannot capture enough shares to reconstruct messages.MSSS assumes that sending a share on a path is an atomic operation and all paths have the same delay.We identify a side-channel vulnerability for MSSS, created by the fact that in real networks, sending a share is not an atomic operation as paths have multiple hops and different delays.This channel, referred to as Network Data Remanence (NDR), is present in all schemes like MSSS whose security relies on transfer atomicity and all paths having the same delay.We demonstrate the presence of NDR in a physical testbed.We then identify two new attacks that aim to exploit the side-channel, referred to as NDR Blind and NDR Planned, propose an analytical model to analyze the attacks, and demonstrate them using an implementation of MSSS based on the ONOS SDN controller.Finally, we present a countermeasure for the attacks and show its effectiveness in simulations and Mininet experiments.
Blockchain-Based Initiatives in Social Security Sector

Blockchain technology has emerged as a major technological platform that can record the various transaction and records in a secure and immutable format.It is being used in all sorts of transaction and record management in multiple fields.Social security is a domain that requires a system to correctly validate and store records in a secure and tamper-proof manner to avoid any case of fraud and to reach it to genuinely intended applicants.Blockchain technology can provide such a platform.In this paper, various existing projects in the field of social security have been reviewed and analysed that presents a futuristic approach for blockchain applications in the social security sector.
Red-blue team exercises for cybersecurity training during a pandemic

The COVID-19 pandemic has given rise to a remote workforce and education system which is vulnerable to a number of unique cybersecurity risks. There is a need for training cybersecurity professionals to deal with cyber threats during the pandemic and its aftermath. We present a series of red-blue team exercises related to pandemic threats, developed for undergraduate cybersecurity students. Implementation and experimental verification of macro virus attacks leveraging social engineering, building command-and-control services and conscripting devices into a botnet, and mitigating man-in-the middle attacks using a Raspberry Pi for website filtering will be discussed.
Teacher performance evaluation model in Covid-19 times

This study purpose was to validate a model for evaluating teacher performance at university level, in the modality of non-attendance education, based on the management of teaching and learning processes with the LMS - Moodle and its integration with the MS Teams, according to the adaptation provided by the National Superintendence of Higher University Education (SUNEDU) in Peru, due to the social distancing declared by the government in Covid-19 times. The evaluation model involves first validating the 15-item questionnaire, with 5 factors each one, with 3 items using the 5-value Likert scale, applying the Item Response Theory (IRT) model for graduated response polytomics items. The latent structure of the items was analyzed, applying firstly the exploratory factorial analysis using SPSS, secondly, for the confirmatory analysis the PLS-SEM methodology was used, which is a second generation multivariate method called Structural Equation Modeling with Partial Least Squares. The model was validated with a sample of 358 undergraduate students in 5 subjects of the Professional School of Psychology. It is concluded that the statistical tests for exploratory and confirmatory analyzes have revealed acceptable results in their evaluation, therefore, it is affirmed that the instrument is reliable and consistent, so that its relationship with the variable index of student satisfaction through the Net Promoter Score® survey. The results obtained confirm the validity of the model in its entirety.
CVRRICULUM Program: Benefits and Challenges of Embedding Virtual Reality as an Educational Medium in Undergraduate Curricula

Since the release of more affordable, portable, and easy-to-use virtual reality (VR) systems in 2014, there has been renewed interest in using this technology in education, as an alternative to traditional learning, because it creates more opportunities for experiential education. Despite the many benefits and affordances of VR, widespread adoption in post-secondary education has been limited, and gaps remain in the provisioning of detailed guidelines for implementing this technology in curricula. Our team developed the CVRRICULUM (CVR) initiative: a pilot program that recruited instructors to adapt a traditional written assignment into a VR format. A mixed-methods approach was used to collect data from five instructor and 18 student participants. In this manuscript we describe the implementation process, report the identified challenges, and provide suggestions that should improve subsequent offerings. Our team addressed raised challenges by creating a set of resources available on the CVR website.
Institutional Environment and Financial Inclusion in Sub-Saharan Africa

The purpose of this study is to analyze the effect of institutional factors on financial inclusion through the lens of the integration of mobile money.To achieve this objective, a multiple regression model is chosen, justified by the acceptance of the null hypothesis that the specified endogenous regressor, in our case GDP per capita, can indeed be treated as exogenous.The estimation of this model by Ordinary Least Squares (OLS) using data from the World Development Indicators (WDI) and the World Governance Indicators (WGI) databases reveals that the quality of regulation is, a priori, the only institutional factor affecting financial inclusion since it is a driver of financial inclusion in SSA.This result, which corroborates the theory of financial liberalization, makes it possible to identify economic policy implications supporting the implementation of incentive-based regulation in the banking system and the regulation of the mobile money sector.
The concentration and health risk assessment of heavy metals and microorganisms in the groundwater of Lagos, Southwest Nigeria

Contaminated water is increasingly linked to diseases worldwide, necessitating the safety evaluation of the sources of domestic and drinking water in every locality. The present study aimed to assess water safety in terms of lead (Pb), nickel (Ni), cadmium (Cd), copper (Cu), chromium (Cr), zinc (Zn), and microorganisms in the borehole and well water in Iwaya, Makoko, and Ilaje in Lagos, Nigeria. Water samples were subjected to atomic absorption spectroscopy (AAS) and microbiological examinations using standard protocols and compared with the World Health Organization (WHO) permissible limits. The average daily ingestion (ADI) and hazard quotient (HQ) of the selected heavy metals were also calculated using standard formulas. The AAS indicated that the borehole and well water of the three locations contained non-permissible levels of Pb, Ni, and Cd (only the well water in Ilaje), while Zn, Cu, and Cr were normal. In addition, the microbiological examinations showed that the borehole and well water of the three locations contained abnormal bacteria and coliform counts (well water only). The ADI and HQ of the selected heavy metals were less than one, which is the threshold at which a substance is considered safe for consumption. Water could pose some health risks, and the consumers in high-risk areas should consider water treatment before consumption.
Group ring based public key cryptosystems

In this paper, we propose two cryptosystems based on group rings and existing cryptosystem. First one is Elliptic ElGamal type group ring public key cryptosystem whose security is greater than security of cryptosystems based on elliptic curves discrete logarithmic problem (ECDLP). Second is ElGamal type group ring public key cryptosystem, which is analogous to ElGamal public key cryptosystem but has comparatively greater security. Examples are also given for both the proposed cryptosystems.
SWVBiL-CRF: Selectable Word Vectors-based BiLSTM-CRF Power Defect Text Named Entity Recognition

The construction of intelligent informatization of the power grid has prompted to accumulate a large amount of text data, and deeply mining the valuable information is significant for the development of the industry. A large number of basic information and information of production process are recorded in the defect text of power equipment. However, repeated expression, unclear logical expression and colloquialism will appear in the process of recording, which makes the operation and maintenance personnel unable to accurately and efficiently manage the logical relationship between the contents of the text. In this paper, we propose a named entity recognition model of BiLSTM-CRF power defect text based on selectable word vector. The model can recognize the category information of professional named entities in the power field from a large number of power defect texts, thereby structuring the massive power defect text data and facilitating the management of text data. In order to further improve the recognition accuracy of the model, we propose an effective optimization scheme. Simulation experiment results show that the proposed model can accurately recognize the information of named entity of defective text in power field. This research can be seen as the foundation for the future defect analysis of power equipment, auxiliary decision support and so on.
Efficient Technique for Facial Image Recognition With Support Vector Machines in 2D Images With Cross-validation in Matlab

This article presented in the context of 2D global facial recognition, using Gabor Wavelet's feature extraction algorithms, and facial recognition Support Vector Machines (SVM), the latter incorporating the kernel functions: linear, cubic and Gaussian. The models generated by these kernels were validated by the cross validation technique through the Matlab application. The objective is to observe the results of facial recognition in each case. An efficient technique is proposed that includes the mentioned algorithms for a database of 2D images. The technique has been processed in its training and testing phases, for the facial image databases FERET [1] and MUCT [2], and the models generated by the technique allowed to perform the tests, whose results achieved a facial recognition of individuals over 96%.
A Review of Genetic Programming Popular Techniques, Fundamental Aspects, Software Tools and Applications

Genetic Programming (GP) is one of the evolutionary computation (EC) methods followed with great interest by many researchers. When GP first appeared, it has become a popular computational intelligence method because of its successful applications and its potentials to find effective solutions for difficult practical problems of many different disciplines. With the use of GP in a wide variety of areas, numerous variants of GP methods have emerged to provide more effective solutions for computation problems of diverse application fields. Therefore, GP has a very rich literature that is progressively growing. Many GP software tools developed along with process of GP algorithms. There is a need for an inclusive survey of GP literature from the beginning to today of GP in order to reveal the role of GP in the computational intelligence field. This survey study aims to provide an overview of the growing GP literature in a systematic way. The researchers, who need to implement GP methods, can gain insight of potentials in GP methods, their essential drawbacks and prevalent superiorities. Accordingly, taxonomy of GP methods is given by a systematic review of popular GP methods. In this manner, GP methods are analyzed according to two main categories, which consider the discrepancies in their program (chromosome) representation styles and their methodologies. Besides, GP applications in diverse problems are summarized. This literature survey is especially useful for new researchers to gain the required broad perspective before implementing a GP method in their problems.
Eff-YNet: A Dual Task Network for DeepFake Detection and Segmentation

Advances in generative models and manipulation techniques have given rise to digitally altered videos known as deepfakes. These videos are difficult to identify for both humans and machines. Modern detection methods exploit various weaknesses in deepfake videos, such as visual artifacts and inconsistent posing. In this paper, we describe a novel architecture called Eff-YNet designed to detect visual differences between altered and unaltered areas. The architecture combines an EfficientNet encoder and a U-Net with a classification branch into a model capable of both classifying and segmenting deepfake videos. The task of segmentation helps train the classifier and also produces useful segmentation masks. We also implement ResNet 3D to detect spatiotemporal inconsistencies. To test these models, we run experiments against the Deepfake Detection Challenge dataset and show improvements over baseline classification models. Furthermore, we find that an ensemble of these two approaches improves performance over a single approach alone.
Compression of MRI brain images based on automatic extraction of tumor region

In the compression of medical images, region of interest (ROI) based techniques seem to be promising, as they can result in high compression ratios while maintaining the quality of region of diagnostic importance, the ROI, when image is reconstructed. In this article, we propose a set-up for compression of brain magnetic resonance imaging (MRI) images based on automatic extraction of tumor. Our approach is to first separate the tumor, the ROI in our case, from brain image, using support vector machine (SVM) classification and region extraction step. Then, tumor region (ROI) is compressed using Arithmetic coding, a lossless compression technique. The non-tumorous region, non-region of interest (NROI), is compressed using a lossy compression technique formed by a combination of discrete wavelet transform (DWT), set partitioning in hierarchical trees (SPIHT) and arithmetic coding (AC). The classification performance parameters, like, dice coefficient, sensitivity, positive predictive value and accuracy are tabulated. In the case of compression, we report, performance parameters like mean square error and peak signal to noise ratio for a given set of bits per pixel (bpp) values. We found that the compression scheme considered in our setup gives promising results as compared to other schemes.
Quantum illumination with a parametrically amplified idler

Quantum illumination uses a quantum state of the electromagnetic field to detect the presence of a target against a bright background more sensitively than any classical state. Most often, the quantum state is a two-mode squeezed vacuum consisting of signal and idler modes with a non-zero phase-sensitive cross correlation, which serves as the signature for target detection, and a zero phase-insensitive cross correlation, which means the modes produce no fringes in second order interference. Here it is shown that applying phase-sensitive amplification to the idler modes of a two-mode squeezed vacuum results in a non-zero phase-insensitive cross correlation enabling reception by a simple beam splitter and photodetectors. It is shown that quantum illumination with a parametrically amplified idler has a lower probability of error than an asymptotically optimal classical-state scheme in discriminating between a present target and an absent target with equal prior probabilities.
Design of temperature and humidity control system on oyster mushroom plant house based on Internet of Things (IoT)

Abstract Oyster mushrooms are generally cultivated in kumbung covered by black colored plastic. This condition results in high temperatures and low humidity, so mushroom cultivation’s failure rate is very high. This research aimed to design and test temperature and humidity control systems’ performance based on the Internet of Things (IoT) on Oyster mushrooms kumbung. This study used an experimental method by controlling the microclimate inside the kumbung in Tanjung Karang Permai Sub-District, Sekarbela District, Mataram City. Research data were analyzed using the Microsoft Excel application. DHT 11 temperature sensor calibration on hygrometer with warm water showed a linear equation of y = 1.0402 x – 0.1423 and R 2 = 0.9905, whereas using cold water was y = 1.081x – 1.7457 and R 2 = 0.973. For the humidity calibration linear equation of y = 1.1203x – 8.3253 and R 2 = 0.8606 was observed. Mean Absolute Percentage Error (MAPE) for warm water and cold water was 3,48% and 3.58%, respectively. In comparison, MAPE for RH was 0.082%. The temperature and humidity control system had been tested at a set point temperature of 28°C and humidity of 85%. The test results showed that mushrooms without a control system were yellowish, dry, and hard texture. Whereas with treatment, the fungus appeared white, wet, and soft texture. The number of mushrooms with treatments was higher and grew more evenly than without treatment. Based on these, it can be concluded that the control system could regulate microclimate inside mushroom kumbung, also successfully monitor it through mobile phones using the Blynk application.