Reviewerly/exHarmony huggingface dataset:
Recognition of Faces Wearing Masks Using Skip Connection Based Dense Units Augmented With Self Restrained Triplet Loss

Facial recognition-based systems are the most efficient and cost-effective of all the contactless biometric verification systems available. But, in the COVID-19 scenario, the performance of available facial recognition systems has been affected badly due to the presence of masks on people's faces. Various studies have reported the degradation of the performance of facial recognition systems due to masks. Therefore, there is a need for improvement in the performance of currently available facial recognition algorithms. In this research, we propose using Skip Connection based Dense Unit (SCDU) trained with Self Restrained Triplet Loss, to handle the embeddings produced by existing facial recognition algorithms for masked images. The SCDU is trained to make facial embeddings for unmasked and masked images of the same identity similar, as well as, embeddings for unmasked and masked images of different identities dissimilar. We have evaluated our results on the LFW dataset with synthetic masks as well as the real-world masked face recognition dataset, i.e., MFR2 and achieved improvement in verification performance in terms of Equal Error Rate, False Match Rate, False Non-Match Rate, and Fisher discriminant ratio.
SPoFC: A framework for stream data aggregation with local differential privacy

Abstract Collecting and analysing customers' data plays an essential role in the more intense market competition. It is critical to perform data analysis effectively while ensuring the user's privacy, especially after various privacy regulations are enacted. In this paper, we consider the problem of aggregating the stream data generated from wearable devices in a specific time period in a privacy‐preserving manner. Specifically, we adopt the local differential privacy mechanism to provide a strong privacy guarantee for users. One major challenge is that all values of the stream need to be perturbed. The additive noise makes it hard to release an accurate data stream. One way to reduce the noise scale is to select some data points to perturb instead of all. The intuition is that more privacy budgets are applied to a single data point, which ensures the statistical accuracy. The perturbed data points are used to predict the un‐selected data points without consuming an extra privacy budget. Based on this idea, we propose a novel stream data statistical framework, which includes four components, data fitting, skeleton point selection, noisy stream generation, and data aggregation. Extensive experiment results show that our proposed method achieves a much smaller mean square error given a fixed privacy budget compared with the state‐of‐the‐art.
Richards’s curve induced Banach space valued multivariate neural network approximation

Here, we present multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or RN,N∈N, by the multivariate normalized, quasi-interpolation, Kantorovich-type and quadrature-type neural network operators. We examine also the case of approximation by iterated operators of the last four types. These approximations are achieved by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its high-order Fréchet derivatives. Our multivariate operators are defined using a multidimensional density function induced by the Richards's curve, which is a generalized logistic function. The approximations are pointwise, uniform and Lp. The related feed-forward neural network is with one hidden layer.
Endoscopic Image Denoising Algorithm Based on Spatial Attention UNet

Abstract Endoscopic image has complex backgrounds and spatially different noise, bringing mainstream denoising methods to the problem of incomplete noise removal and the loss of image detail. Thus, an endoscopic image denoising algorithm based on spatial attention UNet network is proposed in this paper. UNet based on residual learning is utilized as the backbone network. Spatial attention modules based on noise intensity estimation and edge feature extraction modules are used to remove noise better while preserving the image details and improving generalization ability. We take endoscopic images of real scenes using gastroscopy and compare our method with mainstream methods. Experimental results show that our approach improves PSNR by 3.51 or 2.93 and SSIM by 0.03 or 0.015 compared with CBDNet or EDCNN, respectively. Our method can effectively improve the impact of noise on the image quality of endoscopic images, thus better assisting doctors in diagnosis and treatment.
Virtual reality and videogames: immersion, presence, and the performative spatiality of ‘being there’ in virtual worlds

Virtual worlds and their numerous digital geographies are ubiquitous in everyday life. This article addresses the intersection of two increasingly popular mediums: virtual reality and videogames. It argues that the immersion, presence, or sense of being in a virtual world is performative: that inhabitations of virtual space are constituted through social, cultural, and material relations. Through critical observations of virtual reality videogame play and in-depth interviews with players, this article shows how virtual worlds are socially and culturally shaped spaces of interaction. This article establishes the potential of ongoing geographic engagement with complex virtual worlds and how they exceed any one form or interface. It concludes by suggesting new directions for research in geographies of videogames, virtual worlds, virtual reality and their diverse spatialities.
Income inequality: a recipe for youth unemployment in Africa

Youth unemployment is a problem in Africa such that young people face almost double the unemployment rate as adults. With the booming population on the rise, youth unemployment can turn into a major catastrophe in the continent if not addressed. This study presents empirical evidence on how income inequality accelerates the problem. The study uses panel data from 42 African countries spanning 29 years from 1991 to 2020. The dependent variable is youth unemployment, and the independent variable is income inequality. The control variables are gross domestic product (GDP) per capita, population growth, political stability, foreign direct investment, gross capital formation, and political stability. The study employs the Generalized Method of Moment (GMM) model for estimations. The results imply that income inequality positively impacts African youth unemployment, which varies across different income levels. Therefore, measures must be formulated to combat income inequality, such as increasing productivity among small-scale farmers, robust social protection programs, minimum wages, and better access to financial services for young people on the continent.
Cloud Gamification: Bibliometric Analysis and Research Advances

Research on gamification in the cloud has been increasing in recent years. The main objective of this work was to analyse the advances and progress reported in the scientific literature published internationally in cloud gamification from a bibliometric perspective. The scientific production in this field was identified using the Web of Science (WoS) database. The analysis was carried out with the support of the VOSviewer software, version 1.6.18, developed by van Eck and Waltman, for the graphical visualisation of bibliometric networks. The study period covered the time from the first publication on the subject in 2012 to 31 July 2022, with 108 documents detected. The most prolific author was Jacub Swacha from the University of Szczecin, Poland. Forty-seven countries published on Cloud Gamification, with Spain and Italy being the countries with the highest scientific production. The most productive organisations were Bucharest University of Economic Studies, Complutense University of Madrid, Liverpool John Moores University and the University of Szczecin. The journal with the highest output was Information. The groups in the producing countries, the authors, the organisations to which they belonged and the thematic areas of the studies were identified, as well as their evolution over time.
Stochastic cluster embedding

Abstract Neighbor embedding (NE) aims to preserve pairwise similarities between data items and has been shown to yield an effective principle for data visualization. However, even the best existing NE methods such as stochastic neighbor embedding (SNE) may leave large-scale patterns hidden, for example clusters, despite strong signals being present in the data. To address this, we propose a new cluster visualization method based on the Neighbor Embedding principle. We first present a family of Neighbor Embedding methods that generalizes SNE by using non-normalized Kullback–Leibler divergence with a scale parameter. In this family, much better cluster visualizations often appear with a parameter value different from the one corresponding to SNE. We also develop an efficient software that employs asynchronous stochastic block coordinate descent to optimize the new family of objective functions. Our experimental results demonstrate that the method consistently and substantially improves the visualization of data clusters compared with the state-of-the-art NE approaches. The code of our method is publicly available at https://github.com/rozyangno/sce .
Comprehension of polarity of articles by citation sentiment analysis using TF-IDF and ML classifiers

Sentiment analysis has been researched extensively during the last few years, however, the sentiment analysis of citations in a research article is an unexplored research area. Sentiment analysis of citations can provide new applications in bibliometrics and provide insights for a better understanding of scientific knowledge. Citation count, as it is used today to measure the quality of a paper, does not portray the quality of a scientific article, as the article may be cited to indicate its weakness. So determining the polarity of a citation is an important task to quantify the quality of the cited article and ascertain its impact and ranking. This article presents an approach to determine the polarity of the cited article using term frequency-inverse document frequency and machine learning classifiers. To analyze the influence of an imbalanced dataset, several experiments are performed with and without the synthetic minority oversampling technique (SMOTE) and uni-gram and bi-gram term frequency-inverse document frequency (TF-IDF). Results indicate that the proposed methodology achieves high accuracy of 99.0% with the extra tree classifier when trained on SMOTE oversampled dataset and bi-gram features.
An Improved Whale Optimization Algorithm for Web Service Composition

In the current circumstance, the Web Service Composition (WSC) was introduced to address complex user needs concerning the Quality of Services (QoS). In the WSC problem, the user needs are divided into a set of tasks. The corresponding web services are retrieved from the web services discovery according to the functionality of each task, and have different non-functional constraints, such as QoS. The WSC problem is a multi-objective optimization problem and is classified as an NP-hard problem. The whale optimization algorithm (WOA) is proven to solve complex multi-objective optimization problems, and it has the advantage of easy implementation with few control parameters. In this work, we contribute to improving the WOA algorithm, where different strategies are introduced to enhance its performance and address its shortcomings, namely its slow convergence speed, which produces low solution accuracy for the WSC problem. The proposed algorithm is named Improved Whale Optimization Algorithm (IWOA) and has three different strategies to enhance the performance of the WOA. Firstly, the Sine chaos theory is proposed to initiate the WOA’s population and enhance the initialization diversity. Secondly, a Lévy flight mechanism is proposed to enhance the exploitation and exploration of WOA by maintaining the whales’ diversity. Further, a neighborhood search mechanism is introduced to address the trade-off between exploration and exploitation searching mechanisms. Different experiments are conducted with datasets on 12 different scales (small, medium, and large), and the proposed algorithm is compared with standard WOA and five state-of-the-art swarm-based algorithms on 30 different independent runs. Furthermore, four evaluation criteria are used to validate the comparison: the average fitness value, best fitness values, standard deviation, and average execution time. The results show that the IWOA enhanced the WOA algorithm’s performance, where it got the better average and best fitness values with a low variation on all datasets. However, it ranked second regarding average execution time after the WOA, and sometimes third after the WOA and OABC, which is reasonable because of the proposed strategies.
ConDPC: Data Connectivity-Based Density Peak Clustering

As a relatively novel density-based clustering algorithm, Density peak clustering (DPC) has been widely studied in recent years. DPC sorts all points in descending order of local density and finds neighbors for each point in turn to assign all points to the appropriate clusters. The algorithm is simple and effective but has some limitations in applicable scenarios. If the density difference between clusters is large or the data distribution is in a nested structure, the clustering effect of this algorithm is poor. This study incorporates the idea of connectivity into the original algorithm and proposes an improved density peak clustering algorithm ConDPC. ConDPC modifies the strategy of obtaining clustering center points and assigning neighbors and improves the clustering accuracy of the original density peak clustering algorithm. In this study, clustering comparison experiments were conducted on synthetic data sets and real-world data sets. The compared algorithms include original DPC, DBSCAN, K-means and two improved algorithms over DPC. The comparison results prove the effectiveness of ConDPC.
Very Short-Term Forecast: Different Classification Methods of the Whole Sky Camera Images for Sudden PV Power Variations Detection

Solar radiation is by nature intermittent and influenced by many factors such as latitude, season and atmospheric conditions. As a consequence, the growing penetration of Photovoltaic (PV) systems into the electricity network implies significant problems of stability, reliability and scheduling of power grid operation. Concerning the very short-term PV power production, the power fluctuations are primarily related to the interaction between solar irradiance and cloud cover. In small-scale systems such as microgrids, the adoption of a forecasting tool is a brilliant solution to minimize PV power curtailment and limit the installed energy storage capacity. In the present work, two different nowcasting methods are applied to classify the solar attenuation due to clouds presence on five different forecast horizons, from 1 to 5 min: a Pattern Recognition Neural Network and a Random Forest model. The proposed methods are tested and compared on a real case study: available data consists of historical irradiance measurements and infrared sky images collected in a real PV facility, the SolarTechLAB in Politecnico di Milano. The classification output is a range of values corresponding to the future value assumed by the Clear Sky Index (CSI), an indicator allowing to account for irradiance variations only related to clouds passage, neglecting diurnal and seasonal influences. The developed models present similar performance in all the considered time horizons, reliably detecting the CSI drops caused by incoming overcast and partially cloudy sky conditions.
Improved Bald Eagle Search Optimization with Synergic Deep Learning-Based Classification on Breast Cancer Imaging

Medical imaging has attracted growing interest in the field of healthcare regarding breast cancer (BC). Globally, BC is a major cause of mortality amongst women. Now, the examination of histopathology images is the medical gold standard for cancer diagnoses. However, the manual process of microscopic inspections is a laborious task, and the results might be misleading as a result of human error occurring. Thus, the computer-aided diagnoses (CAD) system can be utilized for accurately detecting cancer within essential time constraints, as earlier diagnosis is the key to curing cancer. The classification and diagnosis of BC utilizing the deep learning algorithm has gained considerable attention. This article presents a model of an improved bald eagle search optimization with a synergic deep learning mechanism for breast cancer diagnoses using histopathological images (IBESSDL-BCHI). The proposed IBESSDL-BCHI model concentrates on the identification and classification of BC using HIs. To do so, the presented IBESSDL-BCHI model follows an image preprocessing method using a median filtering (MF) technique as a preprocessing step. In addition, feature extraction using a synergic deep learning (SDL) model is carried out, and the hyperparameters related to the SDL mechanism are tuned by the use of the IBES model. Lastly, long short-term memory (LSTM) was utilized to precisely categorize the HIs into two major classes, such as benign and malignant. The performance validation of the IBESSDL-BCHI system was tested utilizing the benchmark dataset, and the results demonstrate that the IBESSDL-BCHI model has shown better general efficiency for BC classification.
Blockchain Design and Implementation Techniques, Considerations and Challenges in the Banking Sector: A Systematic Literature Review

Blockchain is transforming the banking sector and offering opportunities for significant cost reduction and efficient banking services.However, implementing blockchain is a challenge due to lack of adequate knowledge and skills on how to implement the technology.As a result, there are very few market-ready blockchain banking products and organisations are unable to realise the promised value.This paper presents an overview of the banking sector's blockchain use cases, design and implementation considerations and techniques.The aim is to offer an evidence-based primer to guide researchers and practitioners.The study relies on the systematic literature review method and reviews a total of 45 papers comprising 26 peer-reviewed scholarly articles and 19 technical reports from the banking industry.Leximancer software is used to support the thematic data analysis.The results show for the banking sector an increase in experimentation efforts geared towards the development of payment systems.The results also indicate key considerations from a technological, organisational and environmental perspective.The study highlights that platform selection, scalability and resilience are some of the critical technical considerations for implementing blockchain banking systems.Organisational considerations include collaboration and governance-related challenges.From an environmental perspective, the study notes several legal and regulatory considerations.This study contributes to the existing literature on blockchain adoption in banking, which is still in the nascent stage.The study also offers a research agenda for further understanding of blockchain implementation in the banking sector.Opportunities for further research are noted in the areas of interoperability, governance, security and privacy.
An Uninterrupted Processing Technique-Based High-Throughput and Energy-Efficient Hardware Accelerator for Convolutional Neural Networks

This article proposes an uninterrupted processing technique for the convolutional neural network (CNN) accelerator. It primarily allows the CNN accelerator to simultaneously perform both processing element (PE) operation and data fetching that reduces its latency and enhances the achievable throughput. Corresponding to the suggested technique, this work also presents a low latency VLSI-architecture of the CNN accelerator using the new random access line-buffer (RALB)-based design of PE array. Subsequently, the proposed CNN-accelerator architecture has been further optimized by reusing the local data in PE array, incurring better energy conservation. Our CNN accelerator has been hardware implemented on Zynq-UltraScale + MPSoC-ZCU102 FPGA board, and it operates at a maximum clock frequency of 340 MHz, consuming 4.11 W of total power. In addition, the suggested CNN accelerator with 864 PEs delivers a peak throughput of 587.52 GOPs and an adequate energy efficiency of 142.95 GOPs/W. Comparison of aforementioned implementation results with the literature has shown that our CNN accelerator delivers 33.42% higher throughput and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.24\times $ </tex-math></inline-formula> better energy efficiency than the state-of-the-art work. Eventually, the field-programmable gate array (FPGA) prototype of the proposed CNN accelerator has been functionally validated using the real-world test setup for the detection of object from input image, using the GoogLeNet neural network.
Radiooculogram (ROG) for eye movement sensing with eyes closed

This paper presents radiooculogram (ROG), a novel sensor for non-invasive eye movement (EM) monitoring with eyes closed. We have experimentally demonstrated accurate measurements of EM frequency and directions for 5 participants and benchmarked ROG with electrooculogram (EOG). Compared with biopotential-based sensors, ROG has higher user comfort due to touchless operation and can capture direct muscle activity even in deep tissues. This work on voluntary EM sensing can serve as the baseline implementation for eventual sleep rapid EM monitoring.
Amharic-Kistanigna Bi-directional Machine Translation using Deep Learning

Machine translation is an application of NLP, which can be used to translate text from one natural language to another natural language. This research attempted to design Amharic-Kistanigna bidirectional machine translation. Previously, there is no research conducted on machine translation between Kistanigna and Amharic. Due to an increase in the number of language users, to address the issues of the endangered of the Kistanigna language and to increases the content of the language in web, it is essential to develop machine translation between Kistanigna and Amharic language. Neural Machine Translation (NMT) is a recently proposed approach to machine translation that has achieved the state-of-the-art translation quality. So, we conduct the experiments using five deep learning encoder-decoder model. Such as, LSTM, Bi-LSTM, LSTM + attention, CNN + attention, and Transformer. We have used 9,225 parallel sentences for each model. Finally, we proposed Transformers model with a BLEU score of 7.73 and 4.43 from Amharic to Kistanigna and Kistanigna-Amharic translation respectively. The lack of sufficient data to conduct an extensive experiment is the study’s major weakness. As a result, additional parallel corpora for conducting similar research are required.