Reviewerly/exHarmony huggingface dataset:
impact of dynamic innovation capabilities on organizational agility and performance in saudi public hospitals

Despite the fact that the public healthcare sector has improved significantly, inefficiency persists, and little progress has been made in learning how to solve inefficiencies. Consequently, one of the most important concepts that affect the organizational agility and performance in healthcare are dynamic innovation capabilities. This study aims to investigate the impact of dynamic innovation capabilities on agility and performance in public hospitals in Saudi Arabia. The researcher utilized a dynamic capabilities theory to describe how four important contextual characteristics (sensing capabilities, combination capabilities, networking capabilities and learning capabilities) may affect the agility of healthcare organizations. The population sample for this research included 151 leaders and managers of public hospitals. After responding to the questionnaire, the Statistical Package for Social Science (SPSS) was used to analyse the data. The data collection process involved exploring the independent variables dynamic innovation capabilities that are expected to influence the dependent variable of organizational agility and performance in the public hospitals. The major results of the research showed that there is a positive relationship between the independent variables of the dynamic innovation capabilities and the dependent variables of organizational agility and organizational performance. &#x0D; Keywords: Healthcare Innovation (HCI), Dynamic Innovation Capabilities (DIC), Organizational Agility (OA).
Robot Coworkers: The Vision of Future Hoteliers

The purpose of this study is to understand how future employees in the hospitality and tourism industry envision the use of artificial intelligence in the organizations where they wish to work in the future. Through open-ended questions applied to undergraduate and master’s students in the area of tourism and hospitality, we capture their opinions when thinking about the partial or total use of robots in hospitality. Despite the increasing implementation of artificial intelligence in hospitality and tourism, existing research mainly focuses on current hoteliers and/or customers. However, anticipating how digital generations expect their future roles in a close engagement with robots allows researchers to predict and focus their attention on future problems. Their statements were subjected to a qualitative content analysis methodology, based on themes and sentiment. Participants expressed a negative view of the presence of robots in hospitality, mostly associated with a fear of job loss. Many also reported that interacting with robots is negative for both staff and customers due to robots’ lack of emotions. However, there is some division concerning the impact of robots on service quality: some believe that the service will be more efficient and with fewer failures; others believe that the limitations of robots will lead to worse service. The findings suggest that the acceptability and desirability of robotization may vary depending on the level of robotization in hotels, on the type of customer, and on the level of service provided.
Efficient deterministic preparation of quantum states using decision diagrams

Loading classical data into quantum registers is one of the most important primitives of quantum computing. While the complexity of preparing a generic quantum state is exponential in the number of qubits, in many practical tasks the state to prepare has a certain structure that allows for faster preparation. In this paper, we consider quantum states that can be efficiently represented by (reduced) decision diagrams, a versatile data structure for the representation and analysis of Boolean functions. We design an algorithm that utilises the structure of decision diagrams to prepare their associated quantum states. Our algorithm has a circuit complexity that is linear in the number of paths in the decision diagram. Numerical experiments show that our algorithm reduces the circuit complexity by up to 31.85% compared to the state-of-the-art algorithm, when preparing generic $n$-qubit states with different degrees of non-zero amplitudes. Additionally, for states with sparse decision diagrams, including the initial state of the quantum Byzantine agreement protocol, our algorithm reduces the number of CNOTs by 86.61% $\sim$ 99.9%.
Quantum Simulation of Z2 Lattice Gauge theory with minimal resources

The quantum simulation of fermionic gauge field theories is one of the anticipated uses of quantum computers in the NISQ era. Recently work has been done to simulate properties of the fermionic Z2 gauge field theory in (1+1) D and the pure gauge theory in (2+1) D. In this work, we investigate various options for simulating the fermionic Z2 gauge field theory in (2+1) D. To simulate the theory on a NISQ device it is vital to minimize both the number of qubits used and the circuit depth. In this work we propose ways to optimize both criteria for simulating time dynamics. In particular, we develop a new way to simulate this theory on a quantum computer, with minimal qubit requirements. We provide a quantum circuit, simulating a single first order Trotter step, that minimizes the number of 2-qubit gates needed and gives comparable results to methods requiring more qubits. Furthermore, variational approaches are investigated that allow to further decrease the circuit depth.
The Cultivation of Cross-Cultural Communicative Competence in English Teaching under the Background of Artificial Intelligence and Big Data

Artificial intelligence refers to the intelligence expressed by machines made by humans and is a manifestation of human intelligence. On the whole, artificial intelligence includes two aspects: “artificial” and “intelligence.” Artificial is created by man, and information work covers a number of areas, for instance thinking and consciousness. The process from feeling memory to thinking is called “wisdom.” The result of wisdom is behavior and language. The expression process of behavior and language is called “ability,” and the two are collectively called “intelligence.” However, there is no unified explanation for intelligence at present, but there is no doubt about the widespread. Big data processing includes multiple data processing processes, but data quality is the most important link in the entire process, and each data processing link will have an impact on the quality of big data. The big data processing process mainly includes data collection, data preprocessing, data storage, data processing and analysis, data display/data visualization, data application, and other links. The aim of this paper is to explore the studies on the development of intercultural intercommunication skills in English language learning and teaching in the context of artificial intelligence and big data. It is hoped that this will be done in the light of AI and big data technologies. We can explore the current situation of cross-cultural communication, provide new learning directions for cross-cultural communication, and promote cultural exchange and dissemination. This paper investigates students’ attitudes towards cross-cultural communication by means of a questionnaire and then briefly tests students’ cross-cultural communication ability. The experimental results of this paper show that 4 students have mastered cross-cultural knowledge, accounting for 3%; 8 students have mastered cross-cultural knowledge very well, accounting for 4%, and 9 students have mastered cross-cultural knowledge, accounting for 7%. There are 12 people who are very knowledgeable about cross-cultural knowledge, accounting for 9%. These data show that the current students have very little knowledge of intercultural communication, and their intercultural communication skills need to be improved.
A Systematic Review on Blockchain in IoT

The IoT has greatly evolved and is drawing a lot of attention from academics and industries. Unfortunately, due to a lack of standard safety methods, IoT privacy concerns and security flaws have ascended. The current model of the IoT relies on a centralized cloud server, resulting in single-point failures. The blockchain method was introduced as a decentralized method to ensure security standards and linked to IoT owing to its decentralization and information disclosure. In the present study, initially, the basic structure of blockchain is explained following the integration of IoT with blockchain. The review of the wide-ranging hierarchy of blockchain-IoT applications across multiple segments defined as a systematic and operational content analysis of the scholarly papers is done. The foremost blockchain-based IoT applications are described in order to stimulate the functionalities and benefits of the blockchain approach on IoT systems.
BofAEG: Automated Stack Buffer Overflow Vulnerability Detection and Exploit Generation Based on Symbolic Execution and Dynamic Analysis

Stack buffer overflow vulnerability is a common software vulnerability that can overwrite function return addresses and hijack program control flow, causing serious system problems. Existing automated exploit generation (AEG) solutions cannot bypass position-independent executable (PIE) exploit mitigation and cannot cope with the situation where the standard output function is not introduced into the program. In this paper, we propose a solution to alleviate the above difficulties: BofAEG, which is based on symbolic execution and dynamic analysis to automatically detect stack buffer overflow vulnerability and generate exploit. We used to capture the flag (CTF) and common vulnerabilities and exposures (CVE) programs for experiments. Results show that BofAEG can not only detect and generate exploits effectively but also implement more exploit techniques and is faster than existing AEG solutions.
Progression Models for Imaging Data with Longitudinal Variational Auto Encoders

Disease progression models are crucial to understanding degenerative diseases. Mixed-effects models have been consistently used to model clinical assessments or biomarkers extracted from medical images, allowing missing data imputation and prediction at any timepoint. However, such progression models have seldom been used for entire medical images. In this work, a Variational Auto Encoder is coupled with a temporal linear mixed-effect model to learn a latent representation of the data such that individual trajectories follow straight lines over time and are characterised by a few interpretable parameters. A Monte Carlo estimator is devised to iteratively optimize the networks and the statistical model. We apply this method on a synthetic data set to illustrate the disentanglement between time dependant changes and inter-subjects variability, as well as the predictive capabilities of the method. We then apply it to 3D MRI and FDG-PET data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to recover well documented patterns of structural and metabolic alterations of the brain.
Application of Intelligent Inspection Robot in Coal Mine Industrial Heritage Landscape: Taking Wangshiwa Coal Mine as an Example

This study is developed to explore the role of intelligent inspection robot in the protection and utilization of coal mine industrial heritage. Based on the actual situation of the coal mine, the underground planning protection scope is analyzed. Aiming at the problems of imperfect fire early warning detection technology, management mechanism, high labor cost and low work efficiency in underground protection, the intelligent inspection robot technology is proposed to realize safety tour, underground intelligent management and early warning of underground security, fire protection facilities construction, and intelligent early warning system. This paper analyzes the key technology of intelligent inspection robot in coal mine industrial heritage protection, introduces the composition, structure and implementation method, and proposes its construction path and method. Besides, the path planning, motion obstacle avoidance and sensing detection of the robot are studied. The research shows that the intelligent inspection robot has comprehensive functions and stable performance, and can realize the scientific, intelligent and refined management of industrial heritage protection, which provides a guiding basis for the intelligent protection of coal mine industrial heritage.
A novel density deviation multi-peaks automatic clustering algorithm

Abstract The density peaks clustering (DPC) algorithm is a classical and widely used clustering method. However, the DPC algorithm requires manual selection of cluster centers, a single way of density calculation, and cannot effectively handle low-density points. To address the above issues, we propose a novel density deviation multi-peaks automatic clustering method (AmDPC) in this paper. Firstly, we propose a new local-density and use the deviation to measure the relationship between data points and the cut-off distance ( $$d_c$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>d</mml:mi> <mml:mi>c</mml:mi> </mml:msub> </mml:math> ). Secondly, we divide the density deviation into multiple density levels equally and extract the points with higher distances in each density level. Finally, for the multi-peak points with higher distances at low-density levels, we merge them according to the size difference of the density deviation. We finally achieve the overall automatic clustering by processing the low-density points. To verify the performance of the method, we test the synthetic dataset, the real-world dataset, and the Olivetti Face dataset, respectively. The simulation experimental results indicate that the AmDPC method can handle low-density points more effectively and has certain effectiveness and robustness.
Face Image Completion Based on GAN Prior

Face images are often used in social and entertainment activities to interact with information. However, during the transmission of digital images, there are factors that may destroy or obscure the key elements of the image, which may hinder the understanding of the image’s content. Therefore, the study of image completion of human faces has become an important research branch in the field of computer image processing. Compared with traditional image inpainting methods, deep-learning-based inpainting methods have significantly improved the results on face images, but in the case of complex semantic information and large missing areas, the completion results are still blurred, and the color of the boundary is inconsistent and does not match human visual perception. To solve this problem, this paper proposes a face completion method based on GAN priori to guide the network to complete face images by directly using the rich and diverse a priori information in the pre-trained GAN. The network model is a coarse-to-fine structure, where the damaged face images and the corresponding masks are first input to the coarse network to obtain the coarse results, and then the coarse results are input to the fine network with multi-resolution skip connections. The fine network uses the a priori information from the pre-trained GAN to guide the network to generate the face images, and finally uses the SN-PatchGAN discriminator to evaluate the completion results. The experiment is performed on the CelebA-HQ dataset. Compared with the latest three completion methods, the qualitative and quantitative experimental analysis shows that our method has obvious improvement in texture and fidelity.
The Effect of Storytelling and Peer Coaching Classes on Future Creativity Confluence and Creativity of University Students

The purpose of this study was to examine the effect of storytelling and peer coaching classes on the future creativity confluence competency (F3C) and Creativity of University students.To this purpose, an experiment was conducted on 96 university students who took storytelling class and the peer coaching class at S University located in Seoul in the second semester in 2020.The data of 89 students were used to evaluate the difference.This study used the future creativity confluence competency test and a simple integration creativity test.The result of this study was as follows.First, In the storytelling class, there was a statistically significant difference in creative ability among the sub-factors of the future creativity confluence competency, and there was no statistically significant difference in the remaining creative personality, integrated thinking ability, new knowledge and value creation ability.And there was a statistically significant difference in all sub-elements in creativity.Second, In the peer coaching class, there was a statistically significant difference in the future creativity confluence competency test and the creativity test.Third, the difference between the storytelling class and the peer coaching class in the future creativity confluence competency and the creativity was not statistically significant.Subjects utilizing storytelling and peer coaching can improve the future creativity confluence competency and creativity of university students, which can be beneficial to the development of various liberal arts courses in the university.
Towards Galaxy Foundation Models with Hybrid Contrastive Learning

New astronomical tasks are often related to earlier tasks for which labels have already been collected. We adapt the contrastive framework BYOL to leverage those labels as a pretraining task while also enforcing augmentation invariance. For large-scale pretraining, we introduce GZ-Evo v0.1, a set of 96.5M volunteer responses for 552k galaxy images plus a further 1.34M comparable unlabelled galaxies. Most of the 206 GZ-Evo answers are unknown for any given galaxy, and so our pretraining task uses a Dirichlet loss that naturally handles unknown answers. GZ-Evo pretraining, with or without hybrid learning, improves on direct training even with plentiful downstream labels (+4% accuracy with 44k labels). Our hybrid pretraining/contrastive method further improves downstream accuracy vs. pretraining or contrastive learning, especially in the low-label transfer regime (+6% accuracy with 750 labels).
Federated Learning and Internet of Medical Things – Opportunities and Challenges

The recent advancements in artificial intelligence (AI) and the Internet of Medical Things (IoMT) have opened new horizons for healthcare technology. AI models, however, rely on large data that must be shared with the centralized entity developing the model. Data sharing leads to privacy preservation and legal issues. Federated Learning (FL) enables the training of AI models on distributed data. Hence, a large amount of IoMT data can be put into use without the need for sharing the data. This paper presents the opportunities offered by FL for privacy preservation in IoMT data. With FL, the complicated dynamics and agreements for data-sharing can be avoided. Furthermore, it describes the use cases of FL in facilitating collaborative efforts to develop AI for COVID-19 diagnosis. Since handling data from multiple sites poses its challenges, the paper also highlights the critical challenges associated with FL developments for IoMT data. Addressing these challenges will lead to gaining maximum benefit from data-driven AI technologies in IoMT.
Self-Supervision and Self-Distillation with Multilayer Feature Contrast for Supervision Collapse in Few-Shot Remote Sensing Scene Classification

Although the means of catching remote sensing images are becoming more effective and more abundant, the samples that can be collected in some specific environments can be quite scarce. When there are limited labeled samples, the methods for analyzing remote sensing images for scene classification perform drastically worse. Methods that classify few-shot remote sensing image scenes are often based on meta-learning algorithms for the handling of sparse data. However, this research shows they will be affected by supervision collapse where features in remote sensing images that help with out-of-distribution classes are discarded, which is harmful for the generation of unseen classes and new tasks. In this work, we wish to remind readers of the existence of supervision collapse in scene classification of few-shot remote sensing images and propose a method named SSMR based on multi-layer feature contrast to overcome supervision collapse. First of all, the method makes use of the label information contained in a finite number of samples for supervision and guides self-supervised learning to train the embedding network with supervision generated by multilayer feature contrast. This can prevent features from losing intra-class variation. Intra-class variation is always useful in classifying unseen data. What is more, the multi-layer feature contrast is merged with self-distillation, and the modified self-distillation is used to encourage the embedding network to extract sufficiently general features that transfer better to unseen classes and new domains. We demonstrate that most of the existing few-shot scene classification methods suffer from supervision collapse and that SSMR overcomes supervision collapse well in the experiments on the new dataset we specially designed for examining the problem, with a 2.4–17.2% increase compared to the available methods. Furthermore, we performed a series of ablation experiments to demonstrate how effective and necessary each structure of the proposed method is and to show how different choices in training impact final performance.
Education in Universities in the Context of Distance Education

The aim of this work is to analyze the framework and approaches to the organization of distance learning in schools and universities. To achieve this goal, the article used both theoretical and empirical methods of scientific research. Thus, the work was based on functional and systematic methodological approaches, as well as the method of analysis and synthesis, comparison, deduction, lesson observation, generalization of independent characteristics. As a result of the study both theoretical and practical aspects of the study were obtained. Accordingly, in the theoretical part the essence of the concept of distance education, its characteristics and features of the organization was disclosed. On the basis of the information received, a practical part was established in which ways and mechanisms for conducting distance learning classes in both secondary and higher education institutions in Ukraine were established. In future scientific papers related to distance learning, it is necessary to consider ways of using artificial intelligence to organize this type of learning process.