Reviewerly/exHarmony huggingface dataset:
The experience of designing a single information and educational environment of the university "NMU Digital"

The publication considers theoretical and practical approaches to the design and creation of a single information and educational environment of the Bogomolets National Medical University. The main factors that were taken into account in the development of information and educational environment of the university are identified and substantiated. The basic educational and methodical resources that ensure the functioning of the information and educational environment of modern Medical (Pharmaceutical) Higher Educational Establishment (M(P)HEE) are given. The peculiarities of the educational process at the university are analyzed and the scheme of interaction of the educational-methodical department with the faculties of the university is given.A model of information and educational environment of the Bogomolets National Medical University "NMU Digital". The list of works related to the educational process performed by the structural units of the university in the automated control system is highlighted. The main advantages of use and functionality of the automated control system, electronic document management system and distance learning platform are revealed and analyzed. The results of an online survey of research and teaching staff and students of different faculties on determining the level of digital orientation are analyzed.
Generative Adversarial Networks: A Survey on Training, Variants, and Applications

In recent years, Generative Adversarial Network (GAN) and its variants have gained great popularity in both academia and industry. In this chapter, we survey different state-of-the-art GAN-based methods and their applications. These techniques vary in architecture and objective functions. The chapter firstly introduces generative models followed by the GAN's usual training problems, such as vanishing gradients, mode collapse, and convergence. Then, the proposed solutions and strategies for improving GAN's training and convergence are provided, including related tasks such as obtaining higher image quality when GANs are used in image processing applications. The chapter reviews state-of-the-art GANs and focuses on the main advancements that involve adjusting the loss function, modifying the training process, and adding auxiliary neural network(s). A summary of different applications of GANs is also provided.
Ageing Safely in the Digital Era: A New Unobtrusive Activity Monitoring Framework Leveraging on Daily Interactions with Hand-Operated Appliances

Supporting the elderly to maintain their independence, safety, and well-being through Active Assisted Living (AAL) technologies, is gaining increasing momentum. Recently, Non-intrusive Load Monitoring (NILM) approaches have become the focus of these technologies due to their non-intrusiveness and reduced price. Whilst some research has been carried out in this respect; it still is challenging to design systems considering the heterogeneity and complexity of daily routines. Furthermore, scholars gave little attention to evaluating recent deep NILM models in AAL applications. We suggest a new interactive framework for activity monitoring based on custom user-profiles and deep NILM models to address these gaps. During evaluation, we consider four different deep NILM models. The proposed contribution is further assessed on two households from the REFIT dataset for a period of one year, including the influence of NILM on activity monitoring. To the best of our knowledge, the current study is the first to quantify the error propagated by a NILM model on the performance of an AAL solution. The results achieved are promising, particularly when considering the UNET-NILM model, a multi-task convolutional neural network for load disaggregation, that revealed a deterioration of only 10% in the f1-measure of the framework's overall performance.
A Novel Speckle Suppression Method with Quantitative Combination of Total Variation and Anisotropic Diffusion PDE Model

Speckle noise seriously affects synthetic aperture radar (SAR) image application. Speckle suppression aims to smooth the homogenous region while preserving edge and texture in the image. A novel speckle suppression method based on the combination of total variation and partial differential equation denoising models is proposed in this paper. Taking full account of the local statistics in the image, a quantization technique—which is different from the normal edge detection method—is supported by the variation coefficient of the image. Accordingly, a quantizer is designed to respond to both noise level and edge strength. This quantizer automatically determines the threshold of diffusion coefficient and controls the weight between total variation filter and anisotropic diffusion partial differential equation filter. A series of experiments are conducted to test the performance of the quantizer and proposed filter. Extensive experimental results have demonstrated the superiority of the proposed method with both synthetic images and natural SAR images.
Corporate cultures and financial performance: The mediating role of firm innovation

The purpose of this study is to examine the association between cultural factors and the financial performance of the firm. The study includes power distance, clan culture, uncertainty avoidance, firm innovation, and market culture as independent variables and financial performance as a dependent variable. This study relies on a primary data collected from 216 respondents from various organizations in Nepal. Data were analyzed using structural equation modeling. The study results show that organizational culture has a significant impact on financial performance. Among the five cultural dimensions, firm innovation, and uncertainty avoidance have a strong direct impact on the financial performance of the firm whereas market culture has an indirect effect on the financial performance of the firm. This research encourages Nepalese firms to pay attention to the conducive working environment, especially the atmosphere of market culture, innovation, and uncertainty avoidance.
A systematic literature review of software vulnerability detection

This study provided a systematic literature review of software vulnerability detection (SVD) by searching ACM and IEEE databases for related literatures. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart, a total of 55 studies published in the selected journals and conference proceeding of IEEE and ACM from 2015 to 2021 were reviewed. The objective is to identify, select and critically evaluate research works carried out on software vulnerability detection. The selected articles were grouped into 7 categories across various vulnerability detection evaluation criteria such as neural network – 5 papers, machine learning – 11 papers, static and dynamic analysis – 8 papers, code clone – 3 papers, classification – 4 papers, models – 3 papers, and frameworks – 6 papers. There are 15 articles that could not fall into any of these 7 categories, thus, they were place in others row that used different criteria to implement vulnerability detection. The result showed that many researchers used machine learning strategy to detect vulnerability in software since large volume of data can be reviewed easily with machine learning. Although many systems have been developed for detecting software vulnerability, none is able to show the type of vulnerability detected.
Augmented Reality and the Flipped Classroom—A Comparative Analysis of University Student Motivation in Semi-Presence-Based Education Due to COVID-19: A Pilot Study

Background: This study aims to analyze university student motivation during the period of semi-presence-based education due to the COVID-19 pandemic, based on the type of educational material used: Augmented Reality (AR) videos and traditional videos associated with the Flipped Classroom (FC) methodology. Methods: A sample of 129 university students were asked to take the Instructional Materials Motivation Survey (IMMS). Results indicate that participants in the study were generally more motivated by AR educational material than by FC material; although mean motivation levels with both instruments are extremely high, with scores of 4.57 and 5.64 for FC and AR, respectively. Conclusions: Therefore, a conclusion was met which determines that these results are a starting point to continue the research of the motivational impact of using different types of videos in active methodologies.
Unsupervised single image dehazing with generative adversarial network

Abstract Most recent learning algorithms for single image dehazing are designed to train with paired hazy and corresponding ground truth images, typically synthesized images. Real paired datasets can help to improve performance, but are tough to acquire. This paper proposes an unsupervised dehazing algorithm based on GAN to alleviate this issue. An end-to-end network based on GAN architecture is established and fed with unpaired clean and hazy images, signifying that the estimation of atmospheric light and transmission is not required. The proposed network consists of three parts: a generator, a global test discriminator, and a local context discriminator. Moreover, a dark channel prior based attention mechanism is applied to handle inconsistency haze. We conduct experiments on RESIDE datasets. Extensive experiments demonstrated the effectiveness of the proposed approach which outperformed previous state-of-the-art unsupervised methods by a large margin.
RNS-Based FPGA Accelerators for High-Quality 3D Medical Image Wavelet Processing Using Scaled Filter Coefficients

Medical imaging using different modalities has many problems. The main ones are low informativeness, various distortion noises, and a large amount of information. Fusion, denoising, and visual data compression are used to solve them in practice. Discrete wavelet transform is one way to implement various fusion, denoising, and compression methods for 2D and 3D medical image processing. Medical imaging systems produce increasingly accurate images with scanning technology and digital devices development. These images have improved quality using both higher spatial resolutions and color bit-depth. Processing a large volume of medical imaging data requires considerable resources and processing time. Modern wavelet-based devices for medical image processing do not meet the current performance demand. Hardware accelerators are being designed to solve this problem. This paper proposes new (field-programmable gate array) FPGA accelerators using wavelet processing (WP) with scaled filter coefficients (SFC) and parallel computing in residue number system (RNS) to improve the performance of high-quality 3D medical image WP systems. The computational complexity is reduced using the developed WP method with SFC and the proposed wavelet filter coefficients scaling algorithm. Parallel computing is organized in RNS using moduli sets of a particular type. Hardware implementation of 3D medical image WP using the proposed FPGA accelerators increases device performance by 2.89-3.59 times, increasing the hardware resources by 1.18-3.29 times compared to state-of-the-art solutions. The device performance improvement is achieved while maintaining high-quality 3D medical image processing in peak signal-to-noise ratio terms.
Reconstructing the Spatial Parameters of a Laser Beam Using the Transport-of-Intensity Equation

A simple method for reconstructing the spatial parameters of a laser beam, based on the transport-of-intensity equation, is presented. Registration of cross-section intensity distributions in several planes was carried out using a single CMOS camera. The processing of the experimental measurements with the help of specialized software helped to reconstruct all of the spatial parameters, namely, the radius and position of the waist, Rayleigh length, angular divergence, quality parameter M2 The method was compared with measurements made according to the international standard ISO 11146 and showed that the difference in the spatial parameters is 10% or less, which shows good agreement.
Finite Cross-Section Method for Mode Shape Recognition of Highly Coupled Beam-Type Structures

Abstract The mode shapes of beam-type structures, such as aircraft wings and wind turbine blades, involve a high degree of coupling between bending and torsional deformations. In the case of wind turbine blades, different types of deformation are typically easily recognized by visual observation. However, this visual approach is sometimes challenging for high-order mode shapes, which involve complex coupling of both bending and torsional deformations. This work proposes a novel mode shape recognition algorithm, called the finite cross-section method (FCSM), for application to highly coupled beam-type structures not only to identify the deformation components of complex beam mode shapes but also more importantly to quantify their respective relative contribution. In the application case study for the FCSM, the entire structure is discretized into multiple cross sections. The flap-wise, edge-wise, and torsional deformation components of the entire structure are determined at the cross-section level. The deformation components of the entire structure and their respective contribution are obtained from assembling all cross sections. To validate the mode shape recognition performance, FCSM is applied to and demonstrated on four test cases: (1) numerical mode shapes of a simple cantilever beam, (2) numerical mode shapes from a straight wind turbine blade, (3) numerical mode shapes of a swept wind turbine blade, and (4) experimental mode shapes from a high spatial resolution 3D scanning laser Doppler vibrometer (SLDV) modal test. Both numerical and experimental studies demonstrate that FCSM can successfully recognize the quantitative contribution of flap-wise, edge-wise, and torsional deformation.
A Novel Approach for ABO Blood Group Prediction using Fingerprint through Optimized Convolutional Neural Network

The fingerprints of humans hold many potentials and having plenty of unique characteristics.It is one of the primary diagnostic tools used from accent's era because of their distinctive identity.It opens a lot of possibilities for human science research, by analyzing fingerprints, some researchers have tried to predict an individual's gender or age.In the 20th century, fingerprint identification and analysis have become commonplace also it has evolved into a crucial component of forensic at crime scenes.Similarly, like a fingerprint, the blood group is also unique for each individual.This study focuses on ABO and Rh systems, which are among the most prominent blood grouping methods.This paper proposed an optimized Convolutional Neural Network (CNN) which is designed as an extension of an AlexNet, that correlates the fingerprint patterns or different features of the fingerprint with the blood group of an individual.Researchers have only attempted to connect fingerprint patterns with blood types prior to this proposed method.The result and performance of proposed CNN framework is compared with three different CNN variations like LeNet-5, ZFNet, and AlexNet.The design of proposed CNN used for the prediction of the blood group having noticeable performance with 95.27 % accuracy rate.
Cybersecurity Awareness Based on Software and E-mail Security with Statistical Analysis

The aim of this study is to discover the impact of software security and e-mail security on overall cybersecurity among the students of Imam Abdulrahman Bin Faisal University in Dammam. Another main purpose to conduct this study is to know the level of knowledge students have in the developing countries about the cybersecurity and how much are they mindful of cyber-attacks and the level of awareness among the university students. Two important hypotheses were studied to discover their importance in awareness of cybersecurity. One is software security, and the other is e-mail security. A total of 11 relevant questions were drafted, and then these questions were distributed among the university students, and around 390 responded to the questionnaires. Statistical analysis was performed on the responses using tools. Initial tests such as validity and reliability test, feasibility test of a variable, correlation test, multicollinearity test, multiple regression, and Heteroskedasticity test were conducted using SPSS. And furthermore, multiple linear regression model and coefficient of determination, hypothesis test, ANOVA test, and partial test were conducted using ANOVA. The outcome of the analysis is software security variable (X1) that has a significant and positive effect on cybersecurity awareness ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>p</mi> </math> value ≤0.001, β = 0.192). This shows that having a thorough understanding of software security can raise cybersecurity awareness up to 19.2%. E-mail security variable (X2) has a significant and positive effect on cybersecurityawareness ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>p</mi> </math> -value ≤0.000). This shows that having a thorough understanding of email security can raise cybersecurity awareness up to 31.3%. Software security (X1) and e-mail security (X2) variables simultaneously have a significant effect on cybersecurity awareness ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>p</mi> </math> -value ≤0.000) with a correlation coefficient of 12.1% (R2 = 0.121). This shows that the independent variable used can explain the level of cybersecurity awareness up to 12.1%. Research results show that students are aware of software or application updates. Furthermore, students’ awareness of email security is also good.
Geochemical Quantitative Assessment of Mineral Resource Potential in the Da Hinggan Mountains in Inner Mongolia, China

Studying surface geochemical anomalies is important for detecting the presence of mineral deposits. However, distinguishing inconspicuous geochemical anomalies is a challenge for geochemists. This paper studies geochemical quantitative prediction for Pb–Zn metallic mineral deposits by identifying inconspicuous surface geochemical anomalies mainly associated with the Permian and Jurassic strata in the middle-southern Da Hinggan Mountains metallogenic belt. Some new methods are employed to highlight weak surface geochemical anomalies. The weak surface geochemical anomalies of Pb and Zn are effectively highlighted by the average contrast values of Pb–Zn–Ag–Cd. The similarity coefficient with the large typical discovered deposits is used to identify new Pb–Zn mineralized anomalies and delineate new prospecting target areas. The denudation degree of mineral deposit is determined by the ratio of (W × Sn)/(As × Sb). The analogy method and areal productivity method are employed for resource prediction. Thirty-six prediction areas with Pb–Zn resources of 307.73 million tons are delineated. Five prediction areas are verified, and some new mineral deposits are proven by drilling. The verification results show that the predicted resources are very reasonable and credible. This paper is a successful case of quantitative prediction assessment of mineral resource potential, which can be used as a reference for future prospecting activities.
Double Image Encryption Scheme Based on Compressive Sensing and Double Random Phase Encoding

In order to overcome the shortcomings of the standard compressive sensing (CS) encryption framework, a novel fusion application scheme of CS and optical transformation technology is proposed. The proposed scheme, making full use of the feature of CS to achieve compression and encryption simultaneously, compresses and encrypts two images into one image, which not only reduces storage space and transmission bandwidth, but also improves the security performance of encryption. In the proposed scheme, the two original images are first sampled with CS, and then double random phase coding is performed to obtain two small-sized images. Meanwhile, the two original images are directly encrypted with double random phase coding to obtain the authentication information. Next, we combine two small-sized images and authentication information into one image, and finally perform double random phase coding again to obtain the final encrypted image. It should be emphasized that the proposed scheme has the function of image authentication. Experiment results validate the effectiveness and advancement of the proposed fusion application scheme.
HGVul: A Code Vulnerability Detection Method Based on Heterogeneous Source-Level Intermediate Representation

Vulnerability detection on source code can prevent the risk of cyber-attacks as early as possible. However, lacking fine-grained analysis of the code has rendered the existing solutions still suffering from low performance; besides, the explosive growth of open-source projects has dramatically increased the complexity and diversity of the source code. This paper presents HGVul, a code vulnerability detection method based on heterogeneous intermediate representation of source code. The key of the proposed method is the fine-grained handling on heterogeneous source-level intermediate representation (SIR) without expert knowledge. It first extracts graph SIR of code with multiple syntactic-semantic information. Then, HGVul splits the SIR into different subgraphs according to various semantic relations, which are used to obtain semantic information conveyed by different types of edges. Next, a graph neural network with attention operations is deployed on each subgraph to learn representation, which captures the subtle effects from node neighbors on their representation. Finally, the learned code feature representations are utilized to perform vulnerability detection. Experiments are conducted on multiple datasets. The F1 of HGVul reaches 96.1% on the sample-balanced Big-Vul-VP dataset and 88.3% on the unbalanced Big-Vul dataset. Further experiments on actual open-source project datasets prove the better performance of HGVul.
Teaching Research of Material Mechanics Aimed at Stimulating Vocational College Students’ Interest Based on Simulation Technology

Material Mechanics is an important course for science and engineering students in higher vocational colleges. However, its unfavorable teaching quality has been a problem for a long time. One of the key factors affecting the teaching effect is the students’ lack of interest in learning. In order to solve this problem, this paper analyzes the common problems in the teaching of Material Mechanics and applies simulation technology to classroom teaching. The results revealed that with the application of simulation technology, abstract concepts can be displayed visually and vividly, thus making it easier for students to understand. In addition, Mises stress nephograms and animations significantly improve students’ learning interest. The teaching method expounded in this paper should be applied to more courses in vocational education.