Reviewerly/exHarmony huggingface dataset:
Interpretable Machine Learning for Inpatient COVID-19 Mortality Risk Assessments: Diabetes Mellitus Exclusive Interplay

People with diabetes mellitus (DM) are at elevated risk of in-hospital mortality from coronavirus disease-2019 (COVID-19). This vulnerability has spurred efforts to pinpoint distinctive characteristics of COVID-19 patients with DM. In this context, the present article develops ML models equipped with interpretation modules for inpatient mortality risk assessments of COVID-19 patients with DM. To this end, a cohort of 156 hospitalised COVID-19 patients with pre-existing DM is studied. For creating risk assessment platforms, this work explores a pool of historical, on-admission, and during-admission data that are DM-related or, according to preliminary investigations, are exclusively attributed to the COVID-19 susceptibility of DM patients. First, a set of careful pre-modelling steps are executed on the clinical data, including cleaning, pre-processing, subdivision, and feature elimination. Subsequently, standard machine learning (ML) modelling analysis is performed on the cured data. Initially, a classifier is tasked with forecasting COVID-19 fatality from selected features. The model undergoes thorough evaluation analysis. The results achieved substantiate the efficacy of the undertaken data curation and modelling steps. Afterwards, SHapley Additive exPlanations (SHAP) technique is assigned to interpret the generated mortality risk prediction model by rating the predictors' global and local influence on the model's outputs. These interpretations advance the comprehensibility of the analysis by explaining the formation of outcomes and, in this way, foster the adoption of the proposed methodologies. Next, a clustering algorithm demarcates patients into four separate groups based on their SHAP values, providing a practical risk stratification method. Finally, a re-evaluation analysis is performed to verify the robustness of the proposed framework.
Bio-Inspired Firefly Algorithm for Polygonal Approximation on Various Shapes

Polygonal approximation (PA) is a challenging problem in representation of images in computer vision, pattern recognition and image analysis. This paper proposes a stochastic technique-based firefly algorithm (FA) for PA. This technique customizes a kind of randomization by searching a set of solutions. In contrast, PA requires more combination of approximation to find an optimal solution. The algorithm involves several steps to produce better results. The attractiveness and brightness of the firefly have been used efficiently to solve the approximation problem. While compared to other similar algorithms, FA is independent of velocities which are considered as an advantage for this algorithm. Subsequently, the multi-swarm nature of FA allows finding multiple optimal solutions concurrently. This technique achieves the main goal of PA that is minimum error value with less number of dominant points. The experimental results show that proposed algorithm generates better solutions compared to other algorithms.
LO_v-Calculus: A Graphical Language for Linear Optical Quantum Circuits

We introduce the LOv-calculus, a graphical language for reasoning about linear optical quantum circuits with so-called vacuum state auxiliary inputs. We present the axiomatics of the language and prove its soundness and completeness: two LOv-circuits represent the same quantum process if and only if one can be transformed into the other with the rules of the LOv-calculus. We give a confluent and terminating rewrite system to rewrite any polarisation-preserving LOv-circuit into a unique triangular normal form, inspired by the universal decomposition of Reck et al. (1994) for linear optical quantum circuits.
PIS-YOLO: Real-Time Detection for Medical Mask Specification in an Edge Device

Wearing masks in a crowded environment can reduce the risk of infection; however, wearing nonstandard cloud does not have a good protective effect on the virus, which makes it necessary to monitor the wearing of masks in real time. You only look once (YOLO) series models are widely used in various edge devices. The existing YOLOv5s method meets the requirements of inference time, but it is slightly deficient in terms of accuracy due to its generality. Considering the characteristics of our driver medical mask dataset, a position insensitive loss which is cloud extract shared area feature in different categories and half deformable convolution net methods with cloud concern noteworthy features were introduced into YOLOv5s to improve accuracy, with an increase of 6.7% mean average in @.5 (mAP@.5) and 8.3% in mAP@.5:.95 for our dataset. To ensure that our method can be applied in a real scenario, TensorRT and CUDA were introduced to reduce the inference time in two edge devices (Jetson TX2 and Jetson Nano) and one desktop device, whose inference time was faster than that of previous methods.
Predicting the Potential Energy Yield of Bifacial Solar PV Systems in Low-Latitude Region

The validation of the potential energy yield of bifacial PV systems of various configurations at low latitudes under West African climatic conditions is critical for evaluating performance and for promoting market expansion of the technology since validation has mostly occurred in high-latitude regions. In this paper, the potential energy yield from an inclined south-facing bifacial PV module and a vertically mounted east–west bifacial PV module are compared to an inclined south-facing monofacial PV module using an analytical model, field-measured data, and simulations. For measured/modelled and PVsyst/modelled monofacial systems, the model predicts RMSE values of 1.49 and 9.02, respectively. An inclined bifacial PV system has RMSEs of 1.88 and 7.97 for measured/modelled and PVsyst/modelled, respectively, and a vertically installed system has RMSEs of 10.03 for measured/modelled and 3.76 for PVsyst/modelled. Monthly energy yield is predicted by the model, with deviations from measured data ranging from 0.08% to 1.41% for monofacial systems, from 0.05% to 4.06% for inclined bifacial systems, and from 4.63% to 9.61% for vertical bifacial systems. The average bifacial gains from the modelled, measured, and simulated data of an inclined south-facing stand-alone bifacial PV system over an inclined south-facing stand-alone monofacial system are 9.05%, 10.15%, and 5.65%. Finally, at 0.25 albedo, the inclined monofacial PV system outperforms and yields more energy than the vertically installed bifacial PV system.
Relationship between Human Resource Management Practices and Employee Engagement

Purpose: Employee Engagement is mainstream attention for organizations in the current business scenario. The purpose of the current research focused on analysing the relationship between HRM Practices and Employee Engagement considering employees working in various IT industries, especially considering employees working in HR Department. Design/Methodology/Approach: The research was cross-sectional in nature and survey method was employed to collect the data. Exploratory Factor Analysis, Confirmatory Factor Analysis, Structural Equation Modelling were used to analyse the hypothesis. Data was collected among 345 employees employed in the HR department of various private sector organizations. Results: The model fit indices were adequate and acceptable. The model fit indices obtained were: CMIN/DF = 2.909; Goodness of Fit index (GFI) = 0.945; adjusted goodness of fit index (AGFI) = 0.909; incremental fit index (IFI) = 0.976; Tucker-Lewis index (TLI) = 0.981; comparative fit index (CFI) = 0.966; root mean square error of approximation (RMSEA) = 0.06. Findings: The results of the study disclose that HRM practices and Employee Engagement were related to each other. HRM practices in organizations empowered employees and made them more engaged to perform better in their job roles. The study results imply that HRM practices need to be considered as priority in business organizations for the long term success. Research Limitations: Non probabilistic sample method was adopted and it was possible to gather data only from 345 employees working in the HR Department of various private sector organizations. Practical Implications: Business organizations are constantly working towards adopting new strategies and programs in the workplace to empower employees and keep them engaged in their work roles and responsibilities. Therefore, organizations to focus on human resource management practices and implement them effectively to make their employees committed and engaged towards their job. In this context, the present research attempts to analyse the association between human resource practices and employee engagement. The results provide insights for business managers on the importance of having effective Human Resource practices to enhance employees level of engagement towards their work roles. Originality/ Value: Current research contributes to the existing literature on Human Resource Practices and Employee Engagement. While considering the current business scenario, it becomes very important to understand the role of human resource management practices on the engagement level of employees.
Distillation of Indistinguishable Photons

A reliable source of identical (indistinguishable) photons is a prerequisite for exploiting interference effects, which is a necessary component for linear optical based quantum computing, and applications thereof such as Boson sampling. Generally speaking, the degree of distinguishability will determine the efficacy of the particular approach, for example by limiting the fidelity of constructed resource states, or reducing the complexity of an optical circuits output distribution. It is therefore of great practical relevance to engineer heralded sources of highly pure and indistinguishable photons. Inspired by magic state distillation, we present a protocol using standard linear optics which can be used to increase the indistinguishability of a photon source, to arbitrary accuracy. In particular, in the asymptotic limit of small error $\epsilon$, to reduce the error to $\epsilon' < \epsilon$ requires $O((\epsilon/\epsilon')^2)$ photons. We demonstrate the scheme is robust to detection and control errors in the optical components, and discuss the effect of other error sources.
Deep Learning-Based Image Regression for Short-Term Solar Irradiance Forecasting on the Edge

Photovoltaic (PV) power production is characterized by high variability due to short-term meteorological effects such as cloud movements. These effects have a significant impact on the incident solar irradiance in PV parks. In order to control PV park performance, researchers have focused on Computer Vision and Deep Learning approaches to perform short-term irradiance forecasting using sky images. Motivated by the task of improving PV park control, the current work introduces the Image Regression Module, which produces irradiance values from sky images using image processing methods and Convolutional Neural Networks (CNNs). With the objective of enhancing the performance of CNN models on the task of irradiance estimation and forecasting, we propose an image processing method based on sun localization. Our findings show that the proposed method can consistently improve the accuracy of irradiance values produced by all the CNN models of our study, reducing the Root Mean Square Error by up to 10.44 W/m2 for the MobileNetV2 model. These findings indicate that future applications which utilize CNNs for irradiance forecasting should identify the position of the sun in the image in order to produce more accurate irradiance values. Moreover, the integration of the proposed models on an edge-oriented Field-Programmable Gate Array (FPGA) towards a smart PV park for the real-time control of PV production emphasizes their advantages.
An Evolutionary Dynamic Control Cuckoo Search Algorithm for Solving the Constrained Engineering Design Problems

The key contribution of this work is the dynamic control cuckoo search (DCCS) method. Nonetheless, the adaptive cuckoo search (ACS) appears to be effective in utilizing the exploitation and exploration by using the best solution followed by an adaptive step size to determine the next-generation solutions. However, its convergence rate is limited. To solve this problem, the authors use dynamic control, adaptive step size, and randomization in the cuckoo search path for the following generations. A better tradeoff between exploitation and exploration is achieved, allowing for a faster convergence rate. The 23 traditional and 10 CEC2019 benchmark functions are used for validations. When the DCCS results are compared to the well-known methods using scalability and statistical tests like Wilcoxon's rank-sum test, it shows a significant improvement. Friedman's mean rank test is also ranked the strategic DCCS top. Furthermore, constrained engineering design problems 1) welded beam design and 2) pressure vessel design are solved. The DCCS would be useful for optimization.
A Study of Neural Network Framework for Power Generation Prediction of a Solar Power Plant

In the process of creating a prediction model using artificial intelligence by utilizing a deep neural network, it is of utmost significance to know the amount of insolation that has an absolute effect on the quantity of power generation of a solar cell. To predict the power generation quantity of a solar power plant, a deep neural network requires previously accumulated power generation data of a power plant. However, if there is no equipment to measure solar radiation in the internal facilities of the power plant and if there is no record of the existence of solar radiation in the past data, it is inevitable to obtain the solar radiation information of the nearest point in an effort to accurately predict the quantity of power generation. The site conditions of the power plant are affected by the geographical topography which acts as a stumbling block while anticipating favorable weather conditions. In this paper, we introduce a method to solve these problems and predict the quantity of power generation by modeling the power generation characteristics of a power plant using a neural network. he average of the error between the actual quantity and the predicted quantity for the same period was 1.99, that represents the predictive model is efficient to be used in real-time.
Rain Removal of Single Image Based on Directional Gradient Priors

Images taken on rainy days often lose a significant amount of detailed information owing to the coverage of rain streaks, which interfere with the recognition and detection of the intelligent vision systems. It is, therefore, extremely important to recover clean rain-free images from the rain images. In this paper, we propose a rain removal method based on directional gradient priors, which aims to retain the structural information of the original rain image to the greatest extent possible while removing the rain streaks. First, to solve the problem of residual rain streaks, on the basis of the sparse convolutional coding model, two directional gradient regularization terms are proposed to constrain the direction information of the rain stripe. Then, for the rain layer coding in the directional gradient prior terms, a multi-scale dictionary is designed for convolutional sparse coding to detect rain stripes of different widths. Finally, to obtain a more accurate solution, the alternating direction method of multipliers (ADMM) is used to update the multi-scale dictionary and coding coefficients alternately to obtain a rainless image with rich details. Finally, experiments verify that the proposed algorithm achieves good results both subjectively and objectively.
Image Forgery Detection using Deep Learning Model

The act of tampering with or altering an image is known as image forgery. This image manipulation may appear to be harmless. When an image is used in legal proceedings or forensic investigations, however, it must be unaltered to maintain the integrity of the evidence. Manipulation of an image can be done in a variety of ways. There are numerous ways to predict image forgery, just as there are numerous ways to predict forgery. However, the problem with those methods is that they are so advanced that they are inaccessible to the general public. To resolve this issue, this study comes up with the idea of the developing of a Deep Learning (DL) model. For this purpose, a dataset consisting of both original and forged images is collected from a database named CASIA. This dataset is then preprocessed. The preprocessing techniques include image resizing and denoising. The processed images are then used to train the DL model. The working of the model is then tested using the accuracy and loss as the main parameters. In the end, it is found that the model is capable of producing an accuracy greater than 95% which is greater than many other DL models. Just like the accuracy, the loss value is also found to be very low that it can be neglected. This makes the model more efficient. In the future the model can be deployed into a website, making it useful for the common people.
Digitalization of society: implications and perspectives in the context of the psycho-dimensionality of social reality / psychosynertics

The purpose of this article is to analyze the process of «digitalization» of modern society from the point of view of psychosynergistic approach, psycho-dimensionality of social reality as subjective association of people. The concept of «digitalization» is used mainly in scientific and philosophical publications where it is presented as a modern «world trend». The concept of «digitization» is associated with profound changes in the social life of a modern person who in the near future will be in a fully digital world where every aspect of his life will be deeply influenced by digital data: how people will communicate and relate to each other; how they will work, learn, stay healthy and participate in politics and the economy. The introduction of digital technologies promises, on the one hand, enormous benefits for better health, more efficient social mobility, more efficient use of energy, more business and more prosperous companies, etc. But it is also obvious that such a fundamental social transformation will have certain complexities of an individual, intra-psychic nature, i.e. that unexpected challenges and problems that did not exist before have already arisen and may arise further. The main theoretical base of the research are scientific and philosophical publications devoted to the problem of «digitization» and its influence on social life in English and Ukrainian languages. In the works of Ukrainian researchers, attention is mainly paid to economic and technological aspects of «digitalization», but we propose to solve this problem with transdisciplinary approach in which the efforts of scientists from different fields of knowledge (humanities, doctors, psychologists, representatives of computer sciences, etc.) will find general recommendations and regulations, which will help to adapt the person in new social conditions «digital society». One such transdisciplinary area could be psychosynergetics, as a post-non-classical area of knowledge that implements synergistic and other ideas.
Anomaly detection by using a combination of generative adversarial networks and convolutional autoencoders

Abstract With the development of full digitalization, the amount of time series data generated by sensors is ever-increasing; thus, time series outlier detection has become crucial. Moreover, in practice, discovering and flagging anomalies is very time-consuming and expensive. To solve this problem, unsupervised anomaly detection methods have often been used in the past, in which the model is trained with normal data to learn its behavioral patterns. Generative adversarial networks (GANs) can simulate complex and high-dimensional distributions of data and can be used to learn the behavioral patterns of normal data for unsupervised anomaly detection. However, because of the problem of convergence, GANs are difficult to train. Thus, USADs (an unsupervised anomaly detection model) utilize an autoencoder (AE) to undertake the task of the generator and discriminator and enhance the stability during adversarial training by using the AE to alleviate the problem of non-convergence encountered in GANs. Therefore, in this study, we used the USAD’s generative adversarial training architecture combined with convolutional AEs to improve the model’s feature extraction capabilities. In addition, to reduce false-positive outcomes caused by the prominent sharp points in the reconstructed data, we used the exponential weighted moving average method to smooth the reconstruction error, thereby improving the anomaly detection accuracy of the model. Finally, we experimented with real-world time-series data (ECG and 2D gesture) and verified that our approach could improve accuracy. Compared to the best in the comparison method, our model improved by 0.028% in AUROC, 0.233% in AUPRC, and 0.187% in F1 on average.
Graph-Transporter: A Graph-based Learning Method for Goal-Conditioned Deformable Object Rearranging Task

Rearranging deformable objects is a long-standing challenge in robotic manipulation for the high dimensionality of configuration space and the complex dynamics of deformable objects. We present a novel framework, Graph-Transporter, for goal-conditioned deformable object rearranging tasks. To tackle the challenge of complex configuration space and dynamics, we represent the configuration space of a deformable object with a graph structure and the graph features are encoded by a graph convolution network. Our framework adopts an architecture based on Fully Convolutional Network (FCN) to output pixel-wise pick-and-place actions from only visual input. Extensive experiments have been conducted to validate the effectiveness of the graph representation of deformable object configuration. The experimental results also demonstrate that our framework is effective and general in handling goal-conditioned deformable object rearranging tasks.
GPGCN: A General-Purpose Graph Convolution Neural Network Accelerator Based on RISC-V ISA Extension

In the past two years, various graph convolution neural networks (GCNs) accelerators have emerged, each with their own characteristics, but their common disadvantage is that the hardware architecture is not programmable and it is optimized for a specific network and dataset. They may not support acceleration for different GCNs and may not achieve optimal hardware resource utilization for datasets of different sizes. Therefore, given the above shortcomings, and according to the development trend of traditional neural network accelerators, this paper proposes and implements GPGCN: a general-purpose GCNs accelerator architecture based on RISC-V instruction set extension, providing the software programming freedom to support acceleration for various GCNs, and achieving the best acceleration efficiency for different GCNs with different datasets. Compared with traditional CPU, and traditional CPU with vector expansion, GPGCN achieves above 1001×, 267× speedup for GCN with the Cora dataset. Compared with dedicated accelerators, GPGCN has software programmability and supports the acceleration of more GCNs.