Reviewerly/exHarmony huggingface dataset:
The Impact of Organizational Culture on the Organizational Development: A Field Study at the Yemeni Banks

This study aimed to identify the impact of organizational culture on the organizational development in the Yemeni banks. To achieve the purpose of this study, a descriptive study was conducted in 13 Yemeni banks. The study population consisted of 1912 employees in the targeted banks and 314 employees were selected as a sample using a proportional stratified random sampling method. The data were gathered using a structured questionnaire. Descriptive data were processed and analyzed using SPSS. The results of the study revealed that there was a significant impact of organizational culture on the organizational development in the Yemeni Banks. The impact was found high in the responsiveness and technology variables and low in the control, human resources and relations variables. In light of the findings, the study recommended that the Yemeni banks should give further attention on promoting the organizational culture, especially culture of relations as this has a positive impact on achieving the organizational development.&#x0D; Keywords: organizational development, organizational culture, Yemeni banks.
View-consistent 4D Light Field Style Transfer using Neural Networks and Over-segmentation

Deep learning has shown promising results in several computer vision applications, such as style transfer applications. Style transfer aims at generating a new image by combining the content of one image with the style and color palette of another image. When applying style transfer to a 4D Light Field (LF) that represents the same scene from different angular perspectives, new challenges and requirements are involved. While the visually appealing quality of the stylized image is an important criterion in 2D images, cross-view consistency is essential in 4D LFs. Moreover, the need for large datasets to train new robust models arises as another challenge due to the limited LF datasets that are currently available. In this paper, a neural style transfer approach is used, along with a robust propagation based on over-segmentation, to stylize 4D LFs. Experimental results show that the proposed solution outperforms the state-of-the-art without any need for training or fine-tuning existing ones while maintaining consistency across LF views.
Optimization of Laminar Boundary Layers in Flow over a Flat Plate Using Recent Metaheuristic Algorithms

Heat transfer is one of the most fundamental engineering subjects and is found in every moment of life. Heat transfer problems, such as heating and cooling, where the transfer of heat between regions is calculated, are problems that can give exact solutions with parametric equations, many of which were obtained by solving differential equations in the past. Today, the fact that heat transfer problems have a more complex structure has led to the emergence of multivariate models, and problems that are very difficult to solve with differential equations have emerged. Optimization techniques, which are also the subject of computer science, are frequently used to solve complex problems. In this study, laminar thermal boundary layers in flow over a flat plate, a sub-problem of heat transfer, is solved with recent metaheuristic algorithms. Teaching learning-based optimization (TLBO), sine cosine optimization (SCO), gray wolf optimization (GWO), whale optimization (WO), salp swarm optimization (SSO), and Harris hawk optimization (HHO) algorithms are used in the study. In the optimization problem, the laminar boundary layer thickness, heat flow, and distance from the leading edge are determined. These three models’ minimum, maximum, and target values are found under the specified design variables and constraints. In the study, 540 optimization models are run, and it is seen that HHO is the most suitable optimization technique for heat transfer problems. Additionally, SSO and WO algorithms gave results close to HHO. Other algorithms also set model targets with an average of less than 0.07% and acceptable error rates. In addition, the average problem solution time of all optimization algorithms and all models was 0.9 s. To conclude, the recent metaheuristic algorithms are found to be powerful and fast in solving heat transfer problems.
Domain experts as owners of data: towards sustainable data science

Abstract Research on data science has largely viewed data as an abstract input in service of algorithms developed by data scientists. In this view, data science activities are made sustainable by the efficient flow of data to improve the algorithms. Recent studies in CSCW and HCI, however, point to how the effectiveness of algorithms critically depends on sustainably collecting reliable, complete data situated in domain experts’ practices and settings. Drawing on ethnographic fieldwork and a pilot machine learning project at a craft brewery, we describe three types of situations where brewers’ data practices led to unreliable, incomplete data, and how such data practices limited the effectiveness of data science activities. We analyze sources of misalignment between their data practices and data science activities, which we use to offer design implications for sustainability. Extending research on end-user software development that views sustainability as driven by domain experts as ‘owners of problems,’ our study proposes data science research driven by domain experts as ‘owners of data.’
Subjective evaluation of group user QoE in collaborative virtual environment (CVE)

Interest in the applications of Extended Reality is growing across many different domains. Collaborative or shared experiences are seen as a primary use case. However, there is a surprisingly little research efforts on collaborative design tasks not considering social experiences using Virtual Reality (VR). In addition, there are very few research studies that have focused on the Quality of Experience (QoE) of small user groups working together on collaborative tasks. In this paper, the authors present the results of an experimental study conducted to understand user experience of collaborative tasks using Virtual Reality. The paper presents some initial analysis from self-reported questionnaire data. Two users allocated different roles (Describer and Finder) join remotely to perform the design task collaboratively in immersive VR. The results presented compare user QoE between the two groups (Describer Group vs Finder Group) and considers how different roles and position produces different levels of immersion, interaction, collaboration, post-usage acceptability and system-related consequences. Self-reported measures via post-test questionnaire (15-questions) shows statistically-significant differences in terms of the perceived QoE aspects between the two groups.
Generative Adversarial Networks and Other Generative Models

Generative networks are fundamentally different in their aim and methods compared to CNNs for classification, segmentation, or object detection. They have initially not been meant to be an image analysis tool, but to produce naturally looking images. The adversarial training paradigm has been proposed to stabilize generative methods, and has proven to be highly successful -- though by no means from the first attempt. This chapter gives a basic introduction into the motivation for Generative Adversarial Networks (GANs) and traces the path of their success by abstracting the basic task and working mechanism, and deriving the difficulty of early practical approaches. Methods for a more stable training will be shown, and also typical signs for poor convergence and their reasons. Though this chapter focuses on GANs that are meant for image generation and image analysis, the adversarial training paradigm itself is not specific to images, and also generalizes to tasks in image analysis. Examples of architectures for image semantic segmentation and abnormality detection will be acclaimed, before contrasting GANs with further generative modeling approaches lately entering the scene. This will allow a contextualized view on the limits but also benefits of GANs.
Data-Driven Digital Twins in Surgery utilizing Augmented Reality and Machine Learning

On the one hand, laparoscopic surgery as medical state-of-the-art method is minimal invasive, and thus less stressful for patients. On the other hand, laparoscopy implies higher demands on physicians, such as mental load or preparation time, hence appropriate technical support is essential for quality and suc-cess. Medical Digital Twins provide an integrated and virtual representation of patients' and organs' data, and thus a generic concept to make complex information accessible by surgeons. In this way, minimal invasive surgery could be improved significantly, but requires also a much more complex software system to achieve the various resulting requirements. The biggest challenges for these systems are the safe and precise mapping of the digital twin to reality, i.e. dealing with deformations, movement and distortions, as well as balance out the competing requirement for intuitive and immersive user access and security. The case study ARAILIS is presented as a proof in concept for such a system and provides a starting point for further research. Based on the insights delivered by this prototype, a vision for future Medical Digital Twins in surgery is derived and discussed.
A methodology for training homomorphicencryption friendly neural networks

Privacy-preserving deep neural network (DNN) inference is a necessity in different regulated industries such as healthcare, finance and retail. Recently, homomorphic encryption (HE) has been used as a method to enable analytics while addressing privacy concerns. HE enables secure predictions over encrypted data. However, there are several challenges related to the use of HE, including DNN size limitations and the lack of support for some operation types. Most notably, the commonly used ReLU activation is not supported under some HE schemes. We propose a structured methodology to replace ReLU with a quadratic polynomial activation. To address the accuracy degradation issue, we use a pre-trained model that trains another HE-friendly model, using techniques such as trainable activation functions and knowledge distillation. We demonstrate our methodology on the AlexNet architecture, using the chest X-Ray and CT datasets for COVID-19 detection. Experiments using our approach reduced the gap between the F1 score and accuracy of the models trained with ReLU and the HE-friendly model to within a mere 0.32-5.3 percent degradation. We also demonstrate our methodology using the SqueezeNet architecture, for which we observed 7 percent accuracy and F1 improvements over training similar networks with other HE-friendly training methods.
The Relationship between Achievement Goal Orientation and Learning Immersion of College Students Majoring in Physical Education: Focusing on the mediating effects of Positive Psychological Capital and Self-Determining Motivation

Purpose: The purpose of this study is to study the relationship between achievement goal propensity, positive psychological capital, self-determination motivation, and learning commitment among college students majoring in physical education.BR Methods: 878 college students majoring in physical education at some universities were selected in the northeastern part of China
Sentiment Analysis based on Feature Selection Classification model

Abstract As the use of social media and other online forums for exchanging ideas and exchanging opinions has grown, so has the field of sentiment analysis. Many people use social media like Twitter and Facebook to share their thoughts and ideas with the rest of the world. Opinion mining or sentiment analysis focuses on classifying and predicting a target's opinion. Text documents or sentences can be categorized based on whether they represent a favorable or negative view on a certain subject. Text categorization may seem like an easy process compared to sentiment analysis, but numerous difficulties have prompted many studies into this area. The use of Firefly with Levy and Multilayer Perceptron approaches combined with a feature selection-based classification model to automate sentiment analysis has been proposed. Online product reviews can be improved by incorporating feature selection and classification into the SA model presented in this research. Feature extraction from online product evaluations was accomplished using the firefly (FF) algorithm, while sentiment classification was accomplished using a multi-layer perceptron (MLP). Four datasets are used in the experiment, and the outcomes are evaluated according to various criteria. As a result of these tests, it can be concluded that the FF-MLP model provided here has the best classification performance over the whole dataset tested.
First-Order Modal Semantics and Existence Predicate

In the article we study the existence predicate \(\varepsilon\) in the context of semantics for first-order modal logic. For a formula \(\varphi\) we define \(\varphi^{\varepsilon}\) - the so called existence relativization. We point to a gap in the work of Fitting and Mendelsohn concerning the relationship between the truth of \(\varphi\) and \(\varphi^{\varepsilon}\) in classes of varying- and constant-domain models. We introduce operations on models which allow us to fill the gap and provide a more general perspective on the issue. As a result we obtain a series of theorems describing the logical connection between the notion of truth of a formula with the existence predicate in constant-domain models and the notion of truth of a formula without the existence predicate in varying-domain models.
Comparison of Decision Tree and Linear Regression Algorithms in the Case of Spread Prediction of COVID-19 in Indonesia

COVID-19 is a disease that was first discovered in Wuhan, China and caused the 2019-2020 coronavirus pandemic. This virus can cause respiratory tract infections such as flu when infecting humans. According to Ministry of Health of the Republic of Indonesia, the number of confirmed cases of COVID-19 in Indonesia at March 2021 is 1,511,712 with 40,858 deaths and 1,348,330 recovered. For that, Indonesia is declared to have the highest confirmed cases in ASEAN. Several studies have been carried out to handle some cases by using the data mining techniques such as Decision Tree or Linear Regression algorithm, as example to classify the respiratory diseases and predict pregnancy hypertension. In this study, we tried to analyze COVID-19 cases in Indonesia and conducted an experiment of predicting COVID-19 new cases with the Decision Tree (CART) and Linear Regression algorithms. Then we will compare the values of these two algorithms by using R2 Score to evaluate the prediction performance. The results of this analysis state that DKI Jakarta province has the highest number of positive cases, cures and deaths in Indonesia. The value of the comparison results from the R2 Score obtained in the Decision Tree algorithm reached 95.69% (training) and 92.15% (testing) while the Linear Regression algorithm reached 79.93% (training) and 77.25% (testing).
Feasibility of intraprocedural augmented reality visualisation of 3D rotational angiography in congenital cardiac catheterisation

Three-dimensional rotational angiography has become a mainstay of congenital cardiac catheterisation. Augmented reality is an exciting and emerging technology that allows for interactive visualisation of 3D holographic images in the user's environment. This case series reports on the feasibility of intraprocedural augmented reality visualisation of 3D rotational angiography in five patients with CHD.
Language Translator Application

Abstract: A language translator is a mobile application that can be utilized for translating from one language to another. The problemof language difference has hindered effective information communication over the years. This traditional approach used for solving the problem of language differences has not been productive and favorable. The study develops an android language converter app in order to make learning and language translation easy and facilitates stress-free communication. The system will also be able to evaluate language translation to determine their suitability for everyday conversation.
Tighter monogamy relations of entanglement measures based on fidelity

Abstract We study the Bures measure of entanglement and the geometric measure of entanglement as special cases of entanglement measures based on fidelity, and find their tighter monogamy inequalities over tri-qubit systems as well as multi-qubit systems. Furthermore, we derive the monogamy inequality of concurrence for qudit quantum systems by projecting higher-dimensional states to qubit substates.
Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review

Organizations responsible for seismic and volcanic monitoring worldwide mainly gather information from instrumental networks composed of specialized sensors, data-loggers, and transmission equipment. This information must be available in seismological data centers to improve early warning diffusion. Furthermore, this information is necessary for research purposes to improve the understanding of the phenomena. However, the acquisition data systems could have some information gaps due to unstable connections with instrumental networks and repeater nodes or exceeded waiting times in data acquisition processes. In this work, we performed a systematic review around information availability issues and solutions in data acquisition systems, instrumental networks, and their interplay with transmission media for seismic and volcanic monitoring. Based on the SLR methodology proposed by Kitchenham, B., a search string strategy was considered where 1938 articles were found until December 2021. Subsequently, through selection processes, 282 articles were obtained and 51 relevant articles were extracted using filters based on the content of articles mainly referring to seismic–volcanic data acquisition, data formats, monitoring networks, and early warnings. As a result, we identified two independent partial solutions that could complement each other. One focused on extracting information in the acquisition systems corresponding to continuous data generated by the monitoring points through the development of mechanisms for identifying sequential files. The other solution focused on the detection and assessment of the alternative transmission media capabilities available in the seismic–volcanic monitoring network. Moreover, we point out the advantage of a unified solution by identifying data files/plots corresponding to information gaps. These could be recovered through alternate/backup transmission channels to the monitoring points to improve the availability of the information that contributes to real-time access to information from seismic–volcanic monitoring networks, which speeds up data recovery processes.
Underwater Image Enhancement in Epicontinental Sea Based on the Improved Algorithm of Dark Channel

Enhancing underwater images in epicontinental sea is a challenging problem owing to the influence of ocean currents, the refraction, absorption and scattering of light by suspended particles, and the weak illumination intensity. Recently, different methods have relied on the underwater image formation model and deep learning techniques to restore the underwater image, but they tend to degrade underwater image, interference of background clutter and miss boundary details of blue regions. Improved image fusion and enhancement algorithm based on a priori dark channel is proposed in this paper. Image edge features sharpening and dark detail enhancement by homomorphism filtering in CIELab color space is realized. In RGB color space, the multi-scale retinal with color restoration (MSRCR) algorithm is used to improve color deviation and enhance color saturation, and the contrast-limited adaptive histogram equalization (CLAHE) algorithm is used to de fog and enhance image contrast. Finally, according to the dark channel images of the three processing results, the final enhanced image is obtained by linear fusion of multiple images and multiple channels. Experimental results demonstrate the effectiveness and practicality of the proposed method on various datasets.
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity

Transformers have quickly shined in the computer vision world since the emergence of Vision Transformers (ViTs). The dominant role of convolutional neural networks (CNNs) seems to be challenged by increasingly effective transformer-based models. Very recently, a couple of advanced convolutional models strike back with large kernels motivated by the local-window attention mechanism, showing appealing performance and efficiency. While one of them, i.e. RepLKNet, impressively manages to scale the kernel size to 31x31 with improved performance, the performance starts to saturate as the kernel size continues growing, compared to the scaling trend of advanced ViTs such as Swin Transformer. In this paper, we explore the possibility of training extreme convolutions larger than 31x31 and test whether the performance gap can be eliminated by strategically enlarging convolutions. This study ends up with a recipe for applying extremely large kernels from the perspective of sparsity, which can smoothly scale up kernels to 61x61 with better performance. Built on this recipe, we propose Sparse Large Kernel Network (SLaK), a pure CNN architecture equipped with sparse factorized 51x51 kernels that can perform on par with or better than state-of-the-art hierarchical Transformers and modern ConvNet architectures like ConvNeXt and RepLKNet, on ImageNet classification as well as a wide range of downstream tasks including semantic segmentation on ADE20K, object detection on PASCAL VOC 2007, and object detection/segmentation on MS COCO.
Research of content parameters of the professional self-realization of future fire safety specialists

The research purpose is a theoretical analysis and empirical study of content parameters of professional self-realization of future fire safety specialists. The author implemented a retrospective analysis and identified key parameters of the professional self-realization of a future rescuer. A summative assessment with the construction of a correlation matrix of significant relationships between key parameters was applied, and graphical visualization of the obtained data was presented. The psychophysiological state was analyzed at the training facility “Psychological training ground”. Significant relationships were established within the parameters: “Propensity for risk” and “Need for recognition” (rs = .291; p ≤ .01) and “Need to set complex goals and achieve them” (rs = .208; p ≤. 01); “Will, Openness and Democracy” with “Need for recognition” (rs = .219; p ≤ .01). It was found that the vital goal-value of “Will, Openness and Democracy” has a high operational capacity and is a key value orientation in respondents’ professional self-realization. It is generalized that obtained scientific facts of the empirical research should be operationalized in academic and professional training of fire safety specialists.