Reviewerly/exHarmony huggingface dataset:
An investigation into emotional engagement of holography for museum display

This study delves into the emotional engagement elicited by displaying holograms in museum settings, offering a comprehensive analysis of visitors’ emotional responses and the technological features that influence them. Utilising a qualitative approach complemented by a TF-IDF analysis, the research gathered data from diverse participants who experienced holographic exhibits. The study revealed a range of emotional responses, with ‘Curiosity’ and ‘Amazement’ being predominant, signifying the ability of the display of holograms to enhance visitor engagement and learning experiences significantly. Key technological aspects of holography, such as display method, colour, and three-dimensionality, were crucial in eliciting strong emotional responses and deepening understanding of the exhibit content. The study also highlighted the long-term impact of these experiences, suggesting that the display of holograms can leave a lasting impression and foster sustained interest in museum visits. This study enriches the field of museology by shedding light on the emotional dimensions of visitor interactions with holographic technology. The findings reveal the significant potential of holography to deepen visitor engagement, suggesting that, when effectively implemented, the display of holograms can significantly elevate the museum experience. This research highlights the importance of understanding and catering to visitors’ emotional and experiential needs in the evolving landscape of museum exhibit design.
INTEGRATION OF BLOCKCHAIN TECHNOLOGY IN DIGITAL LIBRARIES: A SOFTWARE ENGINEERING DESIGN

This research aims to design software engineering that integrates blockchain technology in digital libraries to improve system security and reliability. This integration is expected to overcome challenges related to data security, service reliability, and efficiency in digital library management. The research methodology involves collecting data through literature, expert interviews, and observations, on the implementation of blockchain technology in digital libraries, then analyzing data to support data design such as etherum, smart contracts, address, node.js, solidity, metamask, and sublime text, then using the Agile Extreme Programming (XP) method for software development. The research results include the design of a decentralized blockchain architecture, the use of smart contracts, and the application of cryptographic techniques to enhance security. Immutability testing in the context of blockchain involves verifying data consistency, validating the process of adding data, testing the ability to delete data, testing against attacks, and activities on immutable data. These tests were conducted using the Truffle framework. The results show that the system is able to maintain data integrity well.
Analysis and Classification of Factors Contributing to Personalization in E-learning

The transformation brought about by digital technologies has significantly reshaped the field of education, giving rise to innovative approaches like E-learning that extend beyond the conventional confines of traditional education. One of E-learnings’ defining features is the concept of personalization, wherein learning approaches and content dynamically adapt to cater to the unique needs and preferences of individual learners. This research systematically classifies the myriad possibilities of personalization within the E-learning process. The developed classification encompasses factors of creating and delivering adaptive learning objects, employing learner-centered teaching methods, and personalization grounded in individual learner data. This classification structure enables the development of practical models for personalized learning by leveraging the features of contemporary Learning Management Systems (LMS), offering tangible benefits to both learners and tutors. This research enhances the academic discourse by providing a well-organized structure that elucidates the diverse factors of personalization within the context of e-learning. By classifying and discussing various factors, the research shows the perspective of integrating modern technologies in enhancing the personalized educational experience.
Police and robber game on infinite chessboard

It is considered two variants of the game "Policeman and a robber", on an infinite chessboard that is a graph giving a regular partition of the plane into squares. Heuristic and precise definitions of the concepts "the initial state is winning for the pursuer", and "the initial state is winning for the evader" are formulated. Then, criteria for determining if a given initial state is winning for the pursuer or for the evader is given.
Generation of Images from Text Using AI

Reading the words can be confusing, and it may be hard to picture what is happening. There are some circumstances where words can be misunderstood. It's much simpler to recognize text if it's displayed as an image. The use of visuals is proven to increase viewership and retention.Synthesizing realistic images automatically is a challenging undertaking, and even the most advanced artificial intelligence and machine learning algorithm has trouble meeting this standard. GANs (Generative Adversarial Networks) are just one example of a powerful neural network architecture that has shown promising results in recent years. Existing text-to-image methods can generate examples that generally reflect the meaning of the provided descriptions, but they often lack the necessary details and colorful object elements.The primary objective of our research was to explore diverse architectural methodologies with the intention of facilitating the generation of visual representations from textual descriptions. By delving into this investigation, we aimed to discover and examine various approaches that could effectively support the creation of visuals that accurately depict the content and context provided within written narratives. Our aim was to unlock new possibilities in the realm of visual storytelling by establishing a strong connection between language and imagery through innovative architectural techniques.
Locus of Control as Moderator between Work Motivation and Job Satisfaction among Bank Employees

Background: The global banking sector faces numerous challenges that necessitate a reevaluation of employee attitudes, behaviors, and the working environment. The critical roles of job satisfaction and work motivation in enhancing organizational efficiency and productivity are well-documented, with research consistently showing a positive correlation between these factors and organizational outcomes. The concept of locus of control, distinguishing between internal and external beliefs about control over life events, is a significant personality trait influencing job satisfaction and work motivation. Objective: This study aims to explore the moderating role of locus of control on the relationship between work motivation and job satisfaction among bank employees. It hypothesizes that an internal locus of control will enhance the positive correlation between work motivation and job satisfaction. Methods: A quantitative research methodology was employed, involving the survey of 212 bank employees. Data were collected on work motivation, locus of control (internal and external), and job satisfaction. Correlation analysis was used to examine the relationships between these variables. Further, multiple hierarchical regression analysis was conducted to assess the moderating effect of locus of control on the relationship between work motivation and job satisfaction. Cronbach’s alpha was used to evaluate the reliability of the constructs. Results: The study found a strong positive correlation between work motivation and job satisfaction (r = .53, p &lt; .01), a positive correlation between work motivation and internal locus of control (r = .44, p &lt; .01), and a negative correlation between work motivation and external locus of control (r = -.26, p &lt; .01). Job satisfaction also correlated positively with internal locus of control (r = .46, p &lt; .01) and negatively with external locus of control (r = -.34, p &lt; .01). The regression analysis revealed that internal locus of control significantly predicted job satisfaction and moderated the relationship between work motivation and job satisfaction, with a total variance explained by the model of 68% (R² = .68, F = 86.83, p &lt; .01). Conclusion: The findings confirm the hypothesis that locus of control moderates the relationship between work motivation and job satisfaction, with an internal locus of control enhancing this positive correlation. These insights highlight the importance of considering personality traits, such as locus of control, in developing strategies to enhance employee motivation and satisfaction, thereby contributing to organizational success and productivity.
Multi-scale and multi-patch feature fusion network for person re-identification

Person re-identification relies on discriminative features. However, most researches focus on extracting features from the high-layer of network while ignoring the middle-layer features, some important details are overlooked frequently. To address this issue, we propose a Multi-Scale and Multi-Patch Feature Fusion Network(MSPF). We employ modified OSFA to extract, align, and fuse the feature maps in the middle-layer of network, which can compensate for the lack of detailed information in the high-level network features. To obtain richer detailed global features of pedestrian, we construct a multi-patch feature fusion module(MPF). We concatenate the global features extracted from modified OSFA and MPF to obtain global features with richer detailed representations. Cross-entropy loss, triplet loss and center loss are combined to constrain our model. We evaluate the performance of our model on Market-1501, CUHK03_labeled and DukeMTMC. The results prove that our method is superior to the state-of-the-art approaches.
Test of Good Governance in Nigeria using World Bank indicators

The concern for good governance and its role in development process has increased since 1980s. Nigeria has experienced uninterrupted democracy since 1999. This study carryout a test of good governance in Nigeria using the six indicators of good governance used by World Bank. Specifically, the study determine the effect of voice and accountability, political stability and absence of violence and terrorism, government effectiveness, regulatory quality, rule of law and control of corruption on economic development in Nigeria using per capita GDP as a proxy for economic development. Inflation rate and oil price were included in the model in order to produce a robust model. Annual data covering 1999 to 2022 were collected from World Bank website. Descriptive statistics were used to summarize data while Autoregressive Distributed Lag Model and Bound tests were used to estimate relationship. Findings revealed that the performance of governance in Nigeria in all the six indicators during the period of study was weak. This consequently affect per capita income negatively both in the short-run and long-run. It was concluded that governance has negative effect on economic development in Nigeria. It was recommended that the government should use worldwide indicators to conduct self-evaluation and carry out reforms that will help to improve governance in Nigeria for the purpose of achieving development or improving the wellbeing of Nigerians.
Efficient quantum digital signatures over long distances with likely bit strings

Quantum digital signatures (QDSs) can provide information-theoretic security of messages against forgery and repudiation. Compared with previous QDS protocols that focus on signing one-bit messages, hash function--based QDS protocols can save quantum resources and are able to sign messages of arbitrary length. Using the idea of likely bit strings, we propose an efficient QDS protocol with hash functions over long distances. Our method of likely bit strings can be applied to any quantum key distribution--based QDS protocol to significantly improve the signature rate and dramatically increase the secure signature distance of QDS protocols. In order to save computing resources, we propose an improved method where Alice participates in the verification process of Bob and Charlie. This eliminates the computational complexity relating to the huge number of all likely strings. We demonstrate the advantages of our method and our improved method with the example of sending-or-not-sending QDS. Under typical parameters, both our method and our improved method can improve the signature rate by more than $100$ times and increase the signature distance by about $150\phantom{\rule{0.2em}{0ex}}\mathrm{km}$ compared with hash function--based QDS protocols without likely bit strings.
At the frontier of the metaverse: NFTs, artistic expression, and digital immersions

&lt;strong&gt;&lt;strong&gt;&lt;/strong&gt;&lt;/strong&gt;&lt;p&gt;This paper explores the multifaceted landscape of Non-Fungible Tokens beyond their role in digitizing artworks. It delves into the intersection of art, utility, and cultural preservation within the NFT realm. The discussion revolves around the duality of “art for art’s sake” and functional utility, sparking ongoing debates about the essence of art in both the analog and digital realms. The convergence of contemporary aesthetics with practical token utility challenges conventional definitions, offering an alternative to narrative retrieval and contributing to cultural preservation in the virtual sphere. As one enters the NFT ecosystem, a utopian quest for autonomy in healthy production becomes evident, albeit not without formidable obstacles. The paper examines the promise of NFTs to liberate creators from intermediary control and the entwined complexities accompanying their evolution. It sheds light on the cautious and ethical approach required for the mass adoption of these technologies, contrasting this with the current global context characterized by rampant consumerism, individualism, and the relentless pursuit of easy financial gains.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;
Dementia Research on Facebook and Twitter: Current Practice and Challenges

Background: Social media is a powerful tool for engaging diverse audiences in dementia research. However, there is little data summarizing current content exchange in this context. Objective: To inform ethical dementia research engagement on social media, we characterized current practices by analyzing public social media posts. Methods: We retrieved Facebook (2-year period, N = 7,896) and Twitter (1-year period, N = 9,323) posts containing dementia research-related keywords using manual and machine learning-based search strategies. We performed qualitative and quantitative content and sentiment analyses on random samples (10%) of the posts. Results: Top Facebook users were advocacy (45%) and health organizations (25%). On Twitter, academics/researchers were the largest user group. Prevention was the most frequently coded theme (Facebook 30%; Twitter 26%), followed by treatment (Facebook 15%; Twitter 18%). Diagnostics had the highest Facebook engagement. Sharing knowledge was the primary form of content exchange (Facebook 63%; Twitter 80%). Most shared journal articles were peer-reviewed and open access. Emotional tone was overall more positive on Facebook. Justice was a prominent ethics topic regarding inequalities related to identity and intersecting modes of marginalization in dementia research. Conclusion: The findings indicate the importance of social media as an engagement tool of current topics in health research and reveal areas of potential for increased engagement. These data can inform consensus-based best practices for ethical social media application in dementia research.
Habits, Motivation and Performance: An Integrative Model for Organisational Improvement Grounded in Brain Science

Purpose: Organizations aim to cultivate motivated and productive workforces, yet often struggle to understand the complex neurological factors shaping employee habits, engagement, and performance. This paper presents an integrative model linking psychological needs, motivation systems, and contextual supports to optimize collective habits and outcomes.&#x0D; Methodology: A mixed methods study of 400 employees in Zambia’s public health sector combined surveys, interviews, statistical analysis, and prior organizational research.&#x0D; Findings: Results indicated the limitations of existing habit frameworks in capturing neurological dynamics. Just 31% felt current models reliably predict long-term impacts on performance. Key findings revealed that the top motivators were financial rewards, career growth, and training. On-the-job skill-building is also rated highly. Clear expectations, collaboration, and resources further enabled productivity.&#x0D; Unique Contribution to Theory, Policy and Practice: Based on the data, a Motivation-Performance Model is proposed, encompassing elements like empowerment, open communication, and flexibility to fulfil psychological needs. Individual, team, and organizational factors intersect to form collective habits and effectiveness. Implementing diagnostic and enhancement strategies based on the model offers an actionable approach to leveraging habits for competitive advantage.
The digital economy brings new opportunities for arts and culture

We are currently in the digital era of the 21st century, and the rapid advancement of artificial intelligence has brought new energy to the evolution of museums. Museums must inevitably advance towards digitisation. Therefore, the variety of applications for artificial intelligence and other related technologies should be broadened. At the same time, numerous arising issues need to be addressed. The focus of this paper is the objective of increasing young visitors’ satisfaction. Herein, we leverage an assortment of digital technology tools and continuously evaluate their commercial feasibility. It is hoped that a rise in satisfaction can be accomplished through the exemplar business plan provided.
Workshop Media Pembelajaran Berbasis Artificial Intelegence

The aim of this community service is to enhance teachers' creativity in utilizing Artificial Intelligence (AI) technology in teaching activities. The service method was conducted in-person, utilizing LCD Projectors and sound systems to support workshop activities. The results of the implementation show that teachers have gained an understanding of creating instructional media using Canva and CapCup. This is evident as teachers can operate both AI applications and produce instructional media that are sufficiently good and diverse. In conclusion, the workshop proceeded smoothly as scheduled, providing a positive impact on teachers.
Benchmarking hybrid digitized-counterdiabatic quantum optimization

Hybrid digitized-counterdiabatic quantum computing (DCQC) is a promising approach for leveraging the capabilities of nearterm quantum computers, utilizing parameterized quantum circuits designed with counterdiabatic protocols. However, the classical aspect of this approach has received limited attention. In this study, we systematically analyze the convergence behavior and solution quality of various classical optimizers when used in conjunction with the digitized-counterdiabatic approach. We demonstrate the effectiveness of this hybrid algorithm by comparing its performance to the traditional QAOA on systems containing up to 28 qubits. Furthermore, we employ principal component analysis to investigate the cost landscape and explore the crucial influence of parametrization on the performance of the counterdiabatic ansatz. Our findings indicate that fewer iterations are required when local cost landscape minima are present, and the SPSA-based BFGS optimizer emerges as a standout choice for the hybrid DCQC paradigm.
Separability criteria based on the correlation tensor moments for arbitrary dimensional states

As one of the most profound features of quantum mechanics, entanglement is a vital resource for quantum information processing. Inspired by the recent work on PT-moments and separability [Phys. Rev. Lett. 127, 060504 (2021)], we propose two sets of separability criteria using moments of the correlation tensor for bipartite and multipartite quantum states, which are shown to be stronger in some aspects in detecting entanglement.
AI-assisted classification of AVM endothelial cell phenotypes based on dynamic features

Biological tissues frequently display cellular heterogeneity, characterized by the coexistence of diverse cellular phenotypes. These phenotypes encompass a range of physical traits, including shape, motion, and gene expression patterns that indicate distinct functions and behaviors. Heterogeneous cellular phenotypes are particularly prevalent in many diseases, including arteriovenous malformations (AVMs), a type of vascular anomaly. Recent research has emphasized the significance of angiogenic potential in the development and treatment of AVMs, as AVM endothelial cells exhibit considerable genetic mutations in angiogenesis signaling pathways. Since angiogenesis is closely associated with dynamic behaviors such as migration and proliferation, a quantitative approach is necessary for classifying heterogeneous cell phenotypes based on their dynamic features. In this study, we propose a dynamic feature-based approach to classify heterogeneous cell phenotypes, which accounts for time variability and the number of division cycles. We apply this approach to classify AVM endothelial cell phenotypes and group them into subcategories based on their angiogenic potential. In order to facilitate dealing with a vast volume of images, we employ cellpose, a deep convolutional neural network designed for cellular segmentation, to generate cell masks from phase contrast time-lapse images of AVM endothelial cells. We extract dynamic features that not only capture the periodic and progressive changes in morphology but also incorporate motility features. These features are then subjected to principal component analysis, followed by K-means clustering and hierarchical clustering. We have identified three distinct subgroups based on their angiogenic potential, and notably, these subgroups exhibit distinct temporal variations in the solidity and aspect ratio of the cell body.
Analyzing octocoral morphology using network analysis

The effects of warming oceans due to climate change have had an immediate and devastating effect on coral ecosystems. Amid this crisis, the need to accurately identify and understand these organisms is more crucial than ever. In this work, we use methods from network science to analyze the transport distribution networks of octocorals, a group of more climate change-resistant corals. Octocorals are colonies of Cnidarian polyps connected by a network made of biological tissue that contains a through-gut that allows for nutrient sharing between individuals. We first develop a diverse database of over 350+ images of quasi-two-dimensional soft corals. We then quantify topological and geometric graph theory metrics that consider the spatial embedding and identify relevant functions of the transport network within the corals. These metrics include the number of nodes and edges, robustness, total length, and centrality of the nodes and edges. From this, we identify two main classes of coral morphologies: tree-like corals, whose branches do not reconnect, and mesh-like corals, whose branches reestablish connections throughout their network. We compare the metrics from these two types of coral to minimal and maximal models to discover that tree-like corals are expensive but in turn robust and transport efficient networks whereas mesh-like corals strike an ideal balance between the marginal cost of their network versus the returns in efficiency.