Reviewerly/exHarmony huggingface dataset:
The Impact of E-Leadership Effectiveness on Turnover Intentions of Remote Employees

Purpose:The present article aims to analyze the relationship between e-leadership effectiveness and turnover intentions of employees working remotely, as well as to examine the mediating effect of relations with managers on that relationship.Design/methodology/approach: An empirical, quantitative study was conducted among a large sample of remote workers (N=3050) from five European countries (France, Spain, Poland, Netherlands, United Kingdom) and two North American (United States and Canada).To verify the hypotheses, stepwise multiple regression and mediation were conducted.Findings: Although all tested dimensions of e-leadership effectiveness (in terms of building involvement, consistency, adaptability, and mission, as well as in the areas of basic remote leader competencies: communicativeness, credibility, self-development and digital readiness) have a significant relationship with turnover intentions, the results of linear regression showed that only to a small degree the variance of turnover intentions is explained by the evaluation of remote competencies of leaders.However, the relationship with the supervisor significantly mediates the link between e-leadership effectiveness and turnover intentions. Practical implications:The obtained results can be applied in the context of the selection and development of remote team leaders, as they indicate which competencies and behaviors are related to a lower turnover intentions.It is particularly important to take care of developing communicativeness and building trust and credibility, as well as encouraging leaders to improve their technological skills.In addition to developing competencies, it is particularly important to care for building relationships with remote employees, as their positive evaluation significantly mediates the willingness to leave the job.Originality/value: This research significantly expands the literature on e-leadership, which is an emerging trend, as well as on turnover intentions of remote employees, which is of great importance in face of the "great resignation" economic trend of voluntarily quitting since the beginning of the pandemic.To the authors' knowledge, no studies have been published to date on the relationship between e-leadership effectiveness and turnover intentions of remote workers, nor has the mediating role of the relationship with superiors on that link been analyzed.
Gender Equality and Economic Growth in BSR and EAP Countries: A Quantitative Approach

Purpose:The main objective of the paper is to quantitatively analyze the relationship between gender equality and economic growth in selected countries worldwide.Design/Methodology/Approach: The study is based on the Solow Augmented model.Four hypotheses have been tested: Hypothesis 1 (H1): Higher educational levels of women lead to greater economic growth.Hypothesis 2 (H2): Greater participation of women in the labor market leads to greater economic growth.Hypothesis 3 (H3): Higher fertility and fecundity rates lead to lower economic growth.Hypothesis 4 (H4): Greater participation of women in the democratic system leads to greater economic growth.Findings: The research on links between gender, economic growth and development has proven that the power and mutual impact of these categories can differ significantly depending on the type of growth and key driving factors.The relation between GDP per capita and female wages was proved statistically significant.Practical Implications: Increased women's market activity means a more effective allocation of human resources, better use of people's talents (both women and men), the consequence of which is a positive impact on economic growth.Gender equality was thus described as "smart economics".We have proved that in the European countries under study plus Georgia and Armenia a 1 % increase of female wages will result in GDP per capita rising by 0.56%.
Women in Business-Prospects of SMEs in Gambia

Purpose: This article seeks to depict critical prospects that lieu most Gambia women especially those from the major business regions into SME's and opportunities they can explore.Women empowerment and involvement in economic and social activities are very crucial to the development of the Gambian economy.Design/Methodology/Approach: A qualitative research approach is used in the study to further gather responses from selected women participants using a semi-structured interview.This approach has enabled participants to share all those desiring factors behind their venture establishment, and the opportunities available.The primary research population, sampling, and interview.Regarding the topic, data from secondary source and literatures from other journal was also used for comparison and analysis.Findings: The study made by the authors shows that, women from major administrative towns are very keen on engaging into meaningful endeavors such as small and medium-term enterprises at a youthful age mainly for economic gains and to contribute to the societies and country's development.Originality/Value: There isn't much opportunity for Gambian women into micro and medium-term businesses, particularly from the side of government.And despite all constraints, the sector remains vibrant economic and social contributor in the advancement of this developing country.The issues that concentrate on women entrepreneurship in Africa in general is still a novelty since the originality and the value of the paper is considered to be high.
Can Cryptocurrencies Be Feasibly Adopted as a National Currency? The Perspective of the Younger Generation

Purpose:The aim of the paper is twofold.First, it is a presentation the cryptocurrencies as an interdisciplinary issue, however raising justified concerns.Second, it is the analysis of the opinion of the young generation on the possibility of converting their national currency into cryptocurrencies.Design/methodology/approach: The survey questionnaire was used and data collected from 778 university students were statistically analyzed.The survey was conducted in Poland, a country the cryptocurrencies are treated quite neutrally, they are not banned, and at the same time, they are not a full-fledged means of payment.The opinions of Poland-based students about the possibility of cryptocurrencies adaptation as a national currency considering their experience with cryptocurrencies were taken into consideration.Findings: The results showed that only 17% of students see the possibility of cryptocurrency adaptation as a national currency whereas most of them were opposed to that idea.Significant differences were found between students who had and not had experience with cryptocurrencies and their opinions on such variables as access to cryptocurrency wallets, using cryptocurrencies as a means of payment, and willingness for undertaking activities aimed at cryptocurrencies.However, there were no differences for such variables as cryptocurrency mining as well as the period students learnt about cryptocurrencies.Generally, students who have already had experience with cryptocurrencies rated higher the possibility of cryptocurrency adaptation as a national currency and saw more chances for a such adaptation.Practical implications: The majority of students found it difficult to assess cryptocurrencies and their further development.The presumed adaptation of the cryptocurrencies can encounter two main problems, i.e., (1) there is a large group of people that have no experience with cryptocurrencies and are not interested in them, (2) the opinion and evaluation of cryptocurrencies vary significantly considering people's experience even though only younger generation were analyzed, i.e. people for whom newest technologies and solution are an inseparable part of their lives and thus they are more eager to use them.It can be assumed that for older generations these problems can act as a major limitation and barrier and thus to hinder cryptocurrency adaptation.Originality/value: This study is the first attempt to empirically test the young generation's opinion on the potential adaptation of cryptocurrencies as national currencies.The study results enrich the literature by analyzing cryptocurrencies as a phenomenon rather than an economic variable through the lens of the young generation's opinion.
Skilled for the Future: Information Literacy for AI Use by University Students in Africa and the Role of Librarians

The role of libraries in preparing students to thrive during their studies and innovate after university is growing in importance. Information is more easily accessible through digital channels and is increasingly abundant. Generative Artificial intelligence (AI) adds to this reality and increases the need for digitally driven information literacy skills. This paper aims to guide librarians by discussing the digitalization of information creation, retrieval, and use. It recommends the training of both digital and information literacy for students. Librarians are called upon to provide clear guidelines to their universities to steer the use of generative AI. The implications of digital information sources and generative AI are discussed with the role of librarians in context.
Research on effective teaching in vocational education from the perspective of school-enterprise cooperation

This article explores the importance and development trends of vocational education, as well as vocational education models from the perspective of school-enterprise cooperation. Vocational education plays a crucial role in meeting market demands and enhancing the quality of the workforce, with future trends including technological integration and personalized learning. School-enterprise cooperation models encompass internships, dual-system education, and project-driven education, which help students gain practical work experience and problem-solving abilities. The article also discusses effective teaching methods applied in school-enterprise cooperation to improve the quality and practicality of vocational education. In summary, vocational education and school-enterprise cooperation provide essential avenues for nurturing professionals with practical skills and competencies, and they will continue to play a key role in the future.
A Signal-dependent Video Noise Estimator Via Inter-frame Signal Suppression

We propose a block-based signal-dependent noise estimation method on videos, that leverages inter-frame redundancy to separate noise from signal. Block matching is applied to find block pairs between two consecutive frames with similar signal. Then the Ponomarenko et al. method is extended to video by sorting pairs by their low-frequency energy and estimating noise in the high frequencies. Experiments on a real dataset of drone videos show its performance for different parameter settings and different noise levels. Two extensions of the proposed method using subpixel matching and for multiscale noise estimation are respectively analyzed.
Managing Cyber Threats in the Age of Technology

This chapter highlights the importance of risk detection and cybersecurity in modern computing systems. As technology continues to advance, these components become increasingly critical for ensuring the successful operation of these systems. The chapter suggests that failure to properly address these issues can result in serious consequences, including the compromise of sensitive information and the disruption of critical systems. Therefore, it is essential for organizations to prioritize risk detection and cybersecurity as a foundational element of their computing infrastructure.
Is $𝑴_𝑩$ really a constant?

is the Supernova absolute magnitude.Ref. [1] isolates the distance ladder () from the Baryon Acoustic Oscillations (BAO) and from the Type Ia supernova data to determine .The degeneracy between the absolute magnitude and the Hubble constant 0 for the supernova data is replaced by a degeneracy between and the sound horizon .Since the data is for certain redshifts, the Gaussian processes (GP) regression extrapolates the values for the whole redshift range.By canceling the distance ladder, the GP gives = -19.25 ± 0.39 as a mean for the full distribution when using the sound horizon from late time measurements.The estimations provide up to 1 possibility of a nuisance parameter presence () at higher redshifts.
Social Network Recommendations for Friends with Neo4j Graph Database

In recent years, along with the development of the internet, the number of social network users is increasing day by day. Through social networks, users can share, exchange information or make friends with each other. However, with new relationships, users often have a need to assess the credibility of a new friend before making friends on social networks. This paper proposes a friend recommendation system on social networks based on how to calculate the reliability between users. The recommendation system is implemented using the Neo4j graph database. The results of the truth algorithm proposed in this paper are higher than other similar algorithms.
The Effect of Logistics Service Quality on Customer Satisfaction and Customer Loyalty in Chinese Retail

Purpose: This study verifies the relationship between customer satisfaction and customer loyalty with logistics service quality selected as a leading factor. The mediating effect of customer satisfaction is also verified in the relationship between logistics service quality and customer loyalty.&#x0D; Research design, data, and methodology: The design and hypothesis setting of the research model are based on previous studies, and SPSS 19.0 is used for basic data analysis, reliability, validity, correlation analysis, multiple regression analysis, and hierarchical analysis of a survey.&#x0D; Results: Economic feasibility, which is the quality of logistics services, did not significantly affect customer satisfaction. Speed, accuracy, and convenience had a significant effect on customer satisfaction, while customer satisfaction had a significant effect on customer loyalty. Customer satisfaction had a mediating effect on the relationship between logistics service quality and customer loyalty.&#x0D; Conclusions: The logistics industry is in a period of rapid development, and the introduction of logistics talent should be increased to improve the core competitiveness of the retail industry, increase work efficiency, reduce logistics costs, and promote the improvement and development of corporate management levels.
An Improvemant of the IGEPVO-k Reversible Data Hiding Method

PVO-K method is an expansion of PVO method in which all ???? largest (???? smallest pixels) are used as a single unitto embed a bit. In PVOK-method, each pixel of an original image is modified by one at most. GePVO-K method is again a development of the PVOK-method by embedding k bits in k largest pixels and ???? bits in ???? smallest pixels. However, this method has two drawbacks. First, each pixel of the original image is changed by two at most, so marked image quality is not good. Second, the location map consisting of two bits for each pixel is so large that in some cases compression code length of the map is greater method’s embedding capacity, and then data embedding capacity is zero. Both drawbacks are overcome in the iGePVO-K method. Specifically, in this method, each pixel only is changed by one at most and the two-bit location map as in iGePVO-K method is replaced bya one-bit map and a sequence of binary flags. This paper is a development of the iGePVO-K method by discarding the sequence of binary flags. That means it is not necessary to embed flags with the data in the original image. Therefore, the proposed method not only has a larger data embedding capacity but also has a better image quality than the methods mentioned above.
A Two-Stream Hybrid Convolution-Transformer Network Architecture for Clothing-Change Person Re-Identification

Long-term (also called Clothing-Change) person re-identification (CC-reID) aims at confirming the identity of pedestrians captured at diverse locations and/or times. Current CC-reID methods heavily rely on ID features learned by the CNN architecture. However, with limited receptive fields, CNN is hard to effectively explore some unique but discriminative ID features (e.g., hair style, tattoo and accessories) from small body regions. Compared with CNN, Transformer has certain merits in exploring more diverse ID-unique features <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> and retaining more details by the multi-head self-attention design and the removal of down-sampling operation. In this paper, a two-stream hybrid Convolution-Transformer Network (CT-Net) is proposed for CC-reID by combining both CNN and Transformer parallelly in an end-to-end learning scheme. Specifically, CT-Net contains a CNN-based stream (C-Stream) and a Transformer-based stream (T-Stream). Compared with using C-Stream only, T-Stream is used to encourage the C-Stream to explore more detailed ID-unique features when the clothing information is no reliable in CC-reID. Specifically, a Feature Supplement Module (FSM) is proposed to transfer features learned by T-Stream to C-Stream from low-level to high-level for mining more ID-unique feature. In order to further enhance the discriminability <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and complementary of ID features learned by our CT-Net, we also introduce a hierarchical supervision with bilinear pooling (HSBP). Experimental results demonstrate that CT-Net performs favorably against the state-of-the-art methods over three CC-reID benchmarks. Meanwhile, CT-Net also demonstrates good generalization ability by achieving comparable performance on traditional person re-ID datasets such as Market-1501 and DukeMTMC-reID.
DSAM-GN: Graph Network Based on Dynamic Similarity Adjacency Matrices for Vehicle Re-identification

In recent years, vehicle re-identification (Re-ID) has gained increasing importance in various applications such as assisted driving systems, traffic flow management, and vehicle tracking, due to the growth of intelligent transportation systems. However, the presence of extraneous background information and occlusions can interfere with the learning of discriminative features, leading to significant variations in the same vehicle image across different scenarios. This paper proposes a method, named graph network based on dynamic similarity adjacency matrices (DSAM-GN), which incorporates a novel approach for constructing adjacency matrices to capture spatial relationships of local features and reduce background noise. Specifically, the proposed method divides the extracted vehicle features into different patches as nodes within the graph network. A spatial attention-based similarity adjacency matrix generation (SASAMG) module is employed to compute similarity matrices of nodes, and a dynamic erasure operation is applied to disconnect nodes with low similarity, resulting in similarity adjacency matrices. Finally, the nodes and similarity adjacency matrices are fed into graph networks to extract more discriminative features for vehicle Re-ID. Experimental results on public datasets VeRi-776 and VehicleID demonstrate the effectiveness of the proposed method compared with recent works.
OpenRAND: A Performance Portable, Reproducible Random Number Generation Library for Parallel Computations

We introduce OpenRAND, a C++17 library aimed at facilitating reproducible scientific research through the generation of statistically robust and yet replicable random numbers. OpenRAND accommodates single and multi-threaded applications on CPUs and GPUs and offers a simplified, user-friendly API that complies with the C++ standard's random number engine interface. It is portable: it functions seamlessly as a lightweight, header-only library, making it adaptable to a wide spectrum of software and hardware platforms. It is statistically robust: a suite of built-in tests ensures no pattern exists within single or multiple streams. Despite the simplicity and portability, it is remarkably performant\textemdash matching and sometimes even outperforming native libraries by a significant margin. Our tests, including a Brownian walk simulation, affirm its reproducibility and highlight its computational efficiency, outperforming CUDA's cuRAND by up to 1.8 times.
Deer Hunting Optimization with Deep Learning Enabled Emotion Classification on English Twitter Data

Currently, individuals use online social media, namely Facebook or Twitter, for sharing their thoughts and emotions. Detection of emotions on social networking sites’ finds useful in several applications in social welfare, commerce, public health, and so on. Emotion is expressed in several means, like facial and speech expressions, gestures, and written text. Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning (DL) and natural language processing (NLP) domains. This article proposes a Deer Hunting Optimization with Deep Belief Network Enabled Emotion Classification (DHODBN-EC) on English Twitter Data in this study. The presented DHODBN-EC model aims to examine the existence of distinct emotion classes in tweets. At the introductory level, the DHODBN-EC technique pre-processes the tweets at different levels. Besides, the word2vec feature extraction process is applied to generate the word embedding process. For emotion classification, the DHODBN-EC model utilizes the DBN model, which helps to determine distinct emotion class labels. Lastly, the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN technique. An extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach. A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%.
Generating Moire-Like Videos from RGB-D Videos Using Bilateral Filter

A non-photorealistic rendering method (NPR) has been proposed for automatically generating moire-like images from photographic images using a bilateral filter and unsharp mask. Extensions to the conventional method have also been proposed for generating moire-like videos from videos or for generating moire-like images from RGB-D images. In this paper, we propose an NPR method for generating moire-like videos from RGB-D videos. Flickering is a common problem in NPR videos, but our method can suppress it. We evaluate the flicker of moire-like videos generated by our method visually and quantitatively through experiments using an RGB-D video captured by us.
COOL: A Constraint Object-Oriented Logic Programming Language and its Neural-Symbolic Compilation System

This paper explores the integration of neural networks with logic programming, addressing the longstanding challenges of combining the generalization and learning capabilities of neural networks with the precision of symbolic logic. Traditional attempts at this integration have been hampered by difficulties in initial data acquisition, the reliability of undertrained networks, and the complexity of reusing and augmenting trained models. To overcome these issues, we introduce the COOL (Constraint Object-Oriented Logic) programming language, an innovative approach that seamlessly combines logical reasoning with neural network technologies. COOL is engineered to autonomously handle data collection, mitigating the need for user-supplied initial data. It incorporates user prompts into the coding process to reduce the risks of undertraining and enhances the interaction among models throughout their lifecycle to promote the reuse and augmentation of networks. Furthermore, the foundational principles and algorithms in COOL's design and its compilation system could provide valuable insights for future developments in programming languages and neural network architectures.