Reviewerly/exHarmony huggingface dataset:
RETRACTED: Blockchain technology in the future of healthcare

The overarching vision of Blockchain technology for the future would be to address many problems affecting the Healthcare industry today with the creation of a shared archive of health-related information for physicians and patients independent of their electronic diagnosis, improving safety and secrecy, investing fewer resources on the medical staff and more money on patient care. It is only the beginning of what is likely that a Blockchain movement has come out of cryptocurrency and has made its path to the medical industry. Within the sense of intelligent health, Blockchain could have distinctive benefits from a context-aware viewpoint, especially where people and society as a whole can profit from effective and customised solutions. In this paper, we discuss numerous use cases of Blockchain in the healthcare industry. The symbiotic relationship between Blockchain and intelligent health is discussed. Additionally, we address many obstacles for integrating Blockchain-based applications in the health sector and several future research prospects.
Sentiment Analysis in Tamil Texts using k-means and k-nearest neighbour

Sentiment analysis is an active research in the field of Natural Language Processing which aims to predict the sentiments expressed in the written text into positive or negative category. In this paper, we propose a method that uses k-means clustering and k-nearest neighbour classifier to predict the sentiments expressed in Tamil texts.In this proposed method, the data points are considered in two different ways for clustering the corpus: Clustering by considering class-wise information and clustering without considering class-wise information. These two clustering-based techniques are experimented using m-folds of training samples. These form four distinct approaches. These four experiments are conducted using BoW and fastText word embeddings. Altogether we have performed eight experiments. Each approach is tested using varying number of centroids (k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</inf> : 1..10), nearest neighbours (k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> : 1..k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</inf> ) and folds (m <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</inf> : 1..10) to study their influence in the accuracy. The accuracy is proportional to the values of k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</inf> . Accuracy is found to be more for fastText than for BoW. The method with fastText and class-wise clustering with m-folds of training set has given 89.87% accuracy for the corpus, UJ_MovieReviews that we created from various sources.
Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: From convolutional neural networks to visual transformers

Cervical cancer is a very common and fatal type of cancer in women. Cytopathology images are often used to screen for this cancer. Given that there is a possibility that many errors can occur during manual screening, a computer-aided diagnosis system based on deep learning has been developed. Deep learning methods require a fixed dimension of input images, but the dimensions of clinical medical images are inconsistent. The aspect ratios of the images suffer while resizing them directly. Clinically, the aspect ratios of cells inside cytopathological images provide important information for doctors to diagnose cancer. Therefore, it is difficult to resize directly. However, many existing studies have resized the images directly and have obtained highly robust classification results. To determine a reasonable interpretation, we have conducted a series of comparative experiments. First, the raw data of the SIPaKMeD dataset are pre-processed to obtain standard and scaled datasets. Then, the datasets are resized to 224 × 224 pixels. Finally, 22 deep learning models are used to classify the standard and scaled datasets. The results of the study indicate that deep learning models are robust to changes in the aspect ratio of cells in cervical cytopathological images. This conclusion is also validated via the Herlev dataset.
An improved arithmetic optimization algorithm with forced switching mechanism for global optimization problems

Arithmetic optimization algorithm (AOA) is a newly proposed meta-heuristic method which is inspired by the arithmetic operators in mathematics. However, the AOA has the weaknesses of insufficient exploration capability and is likely to fall into local optima. To improve the searching quality of original AOA, this paper presents an improved AOA (IAOA) integrated with proposed forced switching mechanism (FSM). The enhanced algorithm uses the random math optimizer probability (RMOP) to increase the population diversity for better global search. And then the forced switching mechanism is introduced into the AOA to help the search agents jump out of the local optima. When the search agents cannot find better positions within a certain number of iterations, the proposed FSM will make them conduct the exploratory behavior. Thus the cases of being trapped into local optima can be avoided effectively. The proposed IAOA is extensively tested by twenty-three classical benchmark functions and ten CEC2020 test functions and compared with the AOA and other well-known optimization algorithms. The experimental results show that the proposed algorithm is superior to other comparative algorithms on most of the test functions. Furthermore, the test results of two training problems of multi-layer perceptron (MLP) and three classical engineering design problems also indicate that the proposed IAOA is highly effective when dealing with real-world problems.
Blockchain technology as a Fog computing security and privacy solution: An overview

The emergent of Fog computing as an extension of Cloud computing, from the center of the internet architecture to the IoT end user’s devices, aims to enhance the processing power of the resource-constraint IoT devices and deliver them other services since it locates close to these devices. This extension was also suggested to boost the standard of IoT system implementations thus decreasing energy consumption and latency for those applications that need fast responses. However, as stated in recent literature, Fog computing may have some important security and privacy challenges. On the other hand, Blockchain, which was generated and used in crypto-currencies, has been applied in a wider range of applications due to the security, privacy, distributed trust management, and reliability features provided. Among the applications, which have recently been attractive about blockchain is Fog computing. Blockchain in Fog computing may achieve a distributed and trusted, identity management, secure data, reputation, and payment systems. This survey discusses the state-of-the-art impact of the blockchain on the security and privacy of Fog computing. The findings elucidate the vision of blockchain in Fog computing-security and privacy-based enhancement and draw attention to open challenges and future research directions.
Prediction of hourly solar radiation in Tamil Nadu using ANN model with different learning algorithms

The solar radiation is a critical metric for design and optimal operation of the solar energy system. Most of the locations available solar radiation records are not available due to high cost for buying and maintaining the measuring instruments. Aim of this paper is building an efficient neural network model that can reliably estimate solar radiation. In this work, different ANN models with three popular algorithms admired from the literature are investigated. The models were trained using meteorological data collected over a year from six different places from the hot area locations of Tamil Nadu, India. Based on minimal Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), the optimum ANN algorithm and model are determined (R). Furthermore, the research illustrates that the ANN model’s prediction performance is dependent on the entire set of data utilised to train the network. Also, this article aims at finding out the accurate number of hidden layer neurons for the developed model. The proposed ANN model offers improved accuracy and applicability for estimating hourly average global radiation for the purpose of designing or evaluating photovoltaic (PV) installations in areas without meteorological data collection facilities.
Classroom management competency enhancement for student teachers using a fully immersive virtual classroom

The purpose of this study is to examine whether pre-service teachers studying classroom management (CM) in a virtual reality (VR)-supported setting enhance their CM competencies more than students do in a setting using conventional methods. With this aim in mind and to address a lack of available situations of practicing and reflecting CM competencies besides simply gaining theoretical knowledge about CM in education, we integrated a novel fully immersive VR application in selected CM courses. We evaluated the development of self-assessed and instructor-rated CM competencies and the learning quality in the different learning conditions. Additionally, we evaluated the presence, social presence, believability and utility of the VR application and the VR-assisted and video-assisted course. Participants were pre-service teachers (n = 55) of the University of Würzburg who participated in a quasi-experimental pre-test/post-test intervention. The students were randomly assigned to one of two intervention groups: the test group used the virtual classroom Breaking Bad Behaviors (BBB) during the term (n = 39), and the comparison group's learning was video-assisted (n = 16). The instructor rating shows significant differences between the VR group and the video group, the two points of measurement and for the interaction between the condition and time of measurement. It demonstrates a highly significant improvement in CM competencies in the VR setting between the pre-test and post-test (p < 0.001, Cohen's d = 1.06) as compared to the video-based setting. The participants of the VR setting themselves rated their CM competencies in the post-test significantly higher than in the pre-test (p = 0.02, Cohen's d = 0.39). Interestingly, the video group also rated themselves better in the post-test (p = 0.02, Cohen's d = 0.67), which reveals that self-assessment and external assessment show different results. In addition, we observed that even if both groups gained similar theoretical knowledge, the CM competencies developed to a greater degree in the VR-based settings. The participants rated the CM training system a useful tool to evaluate and reflect on individual teacher actions. Its immersion contributes to a high presence and the simulation of realistic scenarios in a CM course. These findings suggest that VR-based settings can lead to a higher benefit in the enhancement of pre-service teachers' CM competencies.
Quantum-dot single-photon sources for the quantum internet

High-performance quantum light sources based on semiconductor quantum dots coupled to microcavities are showing their promise in long-distance solid-state quantum networks.
Adaptive Harris hawks optimization with persistent trigonometric differences for photovoltaic model parameter extraction

In this paper, an adaptive Harris hawk optimization with persistent trigonometric (sine–cosine)-differences (ADHHO) is proposed for parameter identification of Photovoltaic (PV) systems. In the optimized version of HHO, we innovatively propose the persistent-trigonometric-differences mechanism for improving the global search capability of HHO; moreover, we improve the energy factor in the original algorithm so that ADHHO obtains a better balance between exploration and exploitation. Note that the proposed method can obtain lower CPU time in parameter extraction for the three-diode and PV module models with an enhanced parameter extraction performance. To validate the performance of ADHHO, we verified the parameter extraction capability of the single-diode model (SDM), double-diode model (DDM), triple-diode model (TDM), and PV module model (PVM), respectively. Further, we verified the parameter extraction effect of ADHHO in three commercial cells with different light intensity and temperature conditions. Experiments show that the method proposed in this paper can reasonably simulate the output performance of solar PV cells and can be used as a trustworthy method for the extraction of unknown parameters of solar PV systems.
Blockchain-Based Medical Certificate Generation and Verification for IoT-Based Healthcare Systems

Nowadays, medical certificates are very important for many users as they want to avail health benefits like tax purposes, insurance claims, legal procedures, and many more. Generating, issuing, and maintaining medical certificates remain a significant problem; before the invention of the computer, they were available as hard copies. The digitization of medical certificates and documents leads to potential security issues, such as forging of certificates risks the privacy of healthcare documents. Moreover, individuals still need to be physically present and wait at the issuing healthcare centers to get the certificates. Currently, the infrastructure of any healthcare industry connects the Internet of Things (IoT) devices and application software that communicates with the information technology systems. Blockchain technology with IoT can significantly affect the healthcare industry by improving efficiency, security, transparency, and can provide more business opportunities. Therefore, a privacy-preserving technique has been proposed in this article for IoT-based healthcare systems using blockchain technology. The proposed architecture provides an interface between the users and healthcare centers to generate and maintain healthcare documents. Furthermore, the proposed scheme ensures security by specifying rules with a smart contract. Results and discussion show that the proposed scheme is more efficient than the existing schemes.
Assessing citizens' behavior towards blockchain cryptocurrency adoption in the Mano River Union States: Mediation, moderation role of trust and ethical issues

Digital transformation and technologies have drastically channeled innovative global market trends from conventional commerce to digital currency. Therefore, this study examined the influence of trust on citizens' behavior (CB) in decision-making towards blockchain cryptocurrency. The study employed a quantitative method to collect data from Africans in the diaspora of the Mano River Union sub-region. We combined SPSS and Smart PLS for data analysis. The proportion of males in the population was 52%, females were 48%. The analysis outcome found that citizen's behavior R2 = 43% and trust in cryptocurrency R2 = 45% variance were explained by the study model. Results also show a positive relationship between technology attachment and citizen's behavior (r2 = 25%), blockchain transparency (BT) on trust crypto (r2 = 68%), BT on CB (r2 = 38%) as well as trust in Crypto on CB (r2 = 25%). Meanwhile, the moderation effects of ethical issues negate the relationship between trust and consumer's behaviors, while the mediation of trust supports the association between cryptocurrency and citizen's behavior (68%). The development of BT should entail an inclusive approach; as such, the Mano River Union must not be left behind. “The internet is central to data transfer, but the blockchain is central to value transfer,” hence the ethical issues and trust in crypto-enabler will ensure easy adaptability across the globe and Africa in particular.
Applicability of the multispectral remote sensing on determining the natural rock complexes distribution and their evaluability on the radiation protection applications

The mafic and ultramafic rocks of Mettupalayam belong to Southern Granulite Terrain (SGT), and it is prominent with economically valuable resources. The radiation shielding properties of the rock types, along with the remote sensing aspects, are considered in the study. The image processing techniques like Principal Component Analysis, Band ratio, Minimum Noise Fraction are performed using Landsat data to discriminate the litho units. The spectral characters derived from Landsat data identified with the significant absorptions of Fe2+, Fe3+and OPx. The Monte Carlo simulation was applied to evaluate the radiation protection ability of the studied rock samples. The examination showed a high shielding capacity for Gabbro rocks compared to the other studied rocks. The linear attenuation coefficient of the mentioned Gabbro rocks varied among 47.838 and 0.075 cm−1 when the gamma-ray energy increases in the energy interval 0.015 and 15 MeV. Moreover, the Radiation defensive capacity of the Gabbro samples decreased from 100 to 20.095% while the halve value layer increases from 0.014 to 9.269 cm with raising the gamma photon energy among 0.015 and 15 MeV, correspondingly.
Interactive video retrieval evaluation at a distance: comparing sixteen interactive video search systems in a remote setting at the 10th Video Browser Showdown

The Video Browser Showdown addresses difficult video search challenges through an annual interactive evaluation campaign attracting research teams focusing on interactive video retrieval. The campaign aims to provide insights into the performance of participating interactive video retrieval systems, tested by selected search tasks on large video collections. For the first time in its ten year history, the Video Browser Showdown 2021 was organized in a fully remote setting and hosted a record number of sixteen scoring systems. In this paper, we describe the competition setting, tasks and results and give an overview of state-of-the-art methods used by the competing systems. By looking at query result logs provided by ten systems, we analyze differences in retrieval model performances and browsing times before a correct submission. Through advances in data gathering methodology and tools, we provide a comprehensive analysis of ad-hoc video search tasks, discuss results, task design and methodological challenges. We highlight that almost all top performing systems utilize some sort of joint embedding for text-image retrieval and enable specification of temporal context in queries for known-item search. Whereas a combination of these techniques drive the currently top performing systems, we identify several future challenges for interactive video search engines and the Video Browser Showdown competition itself.
DenseRes-Unet: Segmentation of overlapped/clustered nuclei from multi organ histopathology images

Cancer is the second deadliest disease globally that can affect any human body organ. Early detection of cancer can increase the chances of survival in humans. Morphometric appearances of histopathology images make it difficult to segment nuclei effectively. We proposed a model to segment overlapped nuclei from H&E stained images. U-Net model achieved state-of-the-art performance in many medical image segmentation tasks; however, we modified the U-Net to learn a distinct set of consistent features. In this paper, we proposed the DenseRes-Unet model by integrating dense blocks in the last layers of the encoder block of U-Net, focused on relevant features from previous layers of the model. Moreover, we take advantage of residual connections with Atrous blocks instead of conventional skip connections, which helps to reduce the semantic gap between encoder and decoder paths. The distance map and binary threshold techniques intensify the nuclei interior and contour information in the images, respectively. The distance map is used to detect the center point of nuclei; moreover, it differentiates among nuclei interior boundary and core area. The distance map lacks a contour problem, which is resolved by using a binary threshold. Binary threshold helps to enhance the pixels around nuclei. Afterward, we fed images into the proposed DenseRes-Unet model, a deep, fully convolutional network to segment nuclei in the images. We have evaluated our model on four publicly available datasets for Nuclei segmentation to validate the model's performance. Our proposed model achieves 89.77% accuracy 90.36% F1-score, and 78.61% Aggregated Jaccard Index (AJI) on Multi organ Nucleus Segmentation (MoNuSeg).
An analysis of the blockchain and COVID-19 research landscape using a bibliometric study

The scientific community has become concerned about the impact of blockchain technology as a motivational tool in the COVID-19 era. Since the beginning of the pandemic, the number of scientific articles published in this field has been increasing, making it highly desirable to carry out a bibliometric study to identify research efforts. Therefore, the aim of this work was to conduct a literature review of blockchain and COVID-19 technology in the field of Business Management to identify recent lines of research. To do so, we used a text mining technique on a corpus composed of 37 articles in the Web of Science database. The results obtained clearly show 3 distinct clusters. The first represents the blockchain technology framework in organizations and stakeholders in which the research methodology in artificial intelligence is very important. The second shows the need to take into account business sustainability caused by COVID-19. The third indicates the impact of the pandemic on the supply chain industry.
Gaussian Differential Privacy

Abstract In the past decade, differential privacy has seen remarkable success as a rigorous and practical formalization of data privacy. This privacy definition and its divergence based relaxations, however, have several acknowledged weaknesses, either in handling composition of private algorithms or in analysing important primitives like privacy amplification by subsampling. Inspired by the hypothesis testing formulation of privacy, this paper proposes a new relaxation of differential privacy, which we term ‘f-differential privacy’ (f-DP). This notion of privacy has a number of appealing properties and, in particular, avoids difficulties associated with divergence based relaxations. First, f-DP faithfully preserves the hypothesis testing interpretation of differential privacy, thereby making the privacy guarantees easily interpretable. In addition, f-DP allows for lossless reasoning about composition in an algebraic fashion. Moreover, we provide a powerful technique to import existing results proven for the original differential privacy definition to f-DP and, as an application of this technique, obtain a simple and easy-to-interpret theorem of privacy amplification by subsampling for f-DP. In addition to the above findings, we introduce a canonical single-parameter family of privacy notions within the f-DP class that is referred to as ‘Gaussian differential privacy’ (GDP), defined based on hypothesis testing of two shifted Gaussian distributions. GDP is the focal privacy definition among the family of f-DP guarantees due to a central limit theorem for differential privacy that we prove. More precisely, the privacy guarantees of any hypothesis testing based definition of privacy (including the original differential privacy definition) converges to GDP in the limit under composition. We also prove a Berry–Esseen style version of the central limit theorem, which gives a computationally inexpensive tool for tractably analysing the exact composition of private algorithms. Taken together, this collection of attractive properties render f-DP a mathematically coherent, analytically tractable and versatile framework for private data analysis. Finally, we demonstrate the use of the tools we develop by giving an improved analysis of the privacy guarantees of noisy stochastic gradient descent.