Reviewerly/exHarmony huggingface dataset:
Direct generation of polarization-entangled photons from warm atomic ensemble

We experimentally realized a bright and robust polarization-entangled photon-pair source from Doppler-broadened atomic ensembles by exploiting the strong signal-idler polarization correlation. The photon pair is emitted from the cascade-type 5S1/2–5P3/2–5D5/2 transition of 87Rb. Without using an interferometric configuration, we robustly produce all four Bell states by applying the intrinsic polarization correlation achieved in a cascade-type atomic system. The coincidence count rate of the polarization-entangled photon pairs is measured as 10.0(1) kHz under a weak pump power of 20 μW. Our scheme enables the generation of a high rate of polarization-entangled photon pairs with a high fidelity of 0.933(2). We believe that our approach to realize bright and stable polarization-entangled photon-pair sources can contribute to the construction of practical scalable quantum networks based on atom–photon interactions.
Identifying the Effect of Data Breach Publicity on Information Security Awareness Using Hierarchical Regression

The technological evolution has formed new challenges for organizations to safeguard their information as digital assets.Information Security Awareness (ISA) is the cognitive state where individuals possess the comprehension of information security, threats, and the capability to develop preventive strategies.Prior studies discovered that human mistakes or misbehavior is the most vulnerable link in information security due to insufficient security awareness.There were massive data breaches reported throughout the years globally.Literature shows that individuals will develop their evaluations of risks and sense of security awareness when receiving security risk information such as data breach incidents.These indications motivated us to examine the effect of an unexplored factor, that is, data breach publicity (DBR) on ISA.The purpose of this research is to discover if DBR significantly improves a model's ability to predict ISA and its magnitude in influencing ISA.A 3-stage hierarchical linear regression approach was used to build up the model with prior known influential factors to predict ISA.To the extent of our knowledge, there is no study reported to date regarding the implication of DBR on ISA.Our main findings reveal that DBR significantly explains 6.7% of ISA and achieves the highest coefficient comparing with prior known factors.Our research contributes to a novel discovery of a new factor that significantly influences ISA and its magnitude in increasing ISA.This discovery implies the need to incorporate the knowledge of data breach incidents into ISA-related educative programs or strategies to increase ISA.
Improvement of English Teaching Model Based on Computer Network

The increase in the number of colleges and universities has brought great pressure to college English teaching. How to improve the teaching efficiency of English mode is an urgent problem to be solved. This article mainly studies the improvement of English teaching mode based on computer image assistance. In the preexperiment test, a unified English final test paper was selected, and the scores of 40 students were entered into the computer. The experimental group chose the English teaching mode of computer-assisted teaching. The control group still used traditional teaching methods to learn. At the end of the experiment, the students in the experimental group and the control group were given a posttest of writing in their respective classrooms, and the teachers gave their scores according to the scoring standard of the college entrance examination. SPSS 17.0 software is used to analyze the students’ scores, and the experimental results are obtained by comparing the changes of students’ scores before and after the test. In groups, tasks are done together, but it also helps students develop a correct sense of collective responsibility and fair play. Students can evaluate team members from the aspects of activity enthusiasm and learning participation. Students’ self-assessment can be evaluated objectively by observing their preclass learning level, chapter task completion, class attendance, group cooperation participation, and so on. In terms of learning motivation, the average value of students’ online learning initiative consciousness (3.04) and the average value of learning interest (3.05) are slightly higher than 3. The results show that computer image-assisted teaching can maximize the use of classrooms to improve students’ opportunities to use language and fully reflect students’ role as the main body of practice in English learning.
Enhanced sentiment extraction architecture for social media content analysis using capsule networks

Recent research has produced efficient algorithms based on deep learning for text-based analytics. Such architectures could be readily applied to text-based social media content analysis. The deep learning techniques, which require comparatively fewer resources for language modeling, can be effectively used to process social media content data that change regularly. Convolutional Neural networks and recurrent neural networks based approaches have reported prominent performance in this domain, yet their limitations make them sub-optimal. Capsule networks sufficiently warrant their applicability in language modelling tasks as a promising technique beyond their initial usage of image classification. This study proposes an approach based on capsule networks for social media content analysis, especially for Twitter. We empirically show that our approach is optimal even without the use of any linguistic resources. The proposed architectures produced an accuracy of 86.87% for the Twitter Sentiment Gold dataset and an accuracy of 82.04% for the CrowdFlower US Airline dataset, indicating state-of-the-art performance. Hence, the research findings indicate noteworthy accuracy enhancement for text processing within social media content analysis.
DO LEADERSHIP STYLES IMPACT ORGANIZATIONAL PERFORMANCE IN THE UAE CONTEXT? A STUDY

This study was conducted to investigate the relationship between leadership styles and organizational performance.It also examined the impact of different leadership styles on the performance of an organization.This study focused on understanding the effect of Autocratic, Democratic, Laissez Faire, Transactional and Transformational leadership styles on organizational performance in UAE based organization.The relevant data was collected with the aid of a structured questionnaire which was distributed to 150 respondents.Convenient sampling was used to select the respondents.Pearson correlation was used to examine the relationship between leadership styles and organizational performance.Regression analysis was performed to study the significance of the impact of the different leadership styles on organizational performance.The study showed that leadership styles have both positive and negative influence on the performance of an organization and that leadership styles alone were responsible for 25% of organizational performance.The study concluded that Democratic leadership and Transformational leadership had a positive and significant influence on organizational performance in UAE based Organization.
Toward Fairness-Aware Time-Sensitive Asynchronous Federated Learning for Critical Energy Infrastructure

Critical energy infrastructure (CEI) systems are vital to underpin the national economy and social development, but vulnerable to cyber attack and data privacy leakage when distributed machine learning technologies are deployed on them.Although federated learning (FL) has promoted distributed collaborative learning while keeping natural compliance with the privacy protection, it is tremendously difficult to schedule edge nodes of CEI collaboratively when asynchronous FL tasks are applied in CEI system, since the CEI system must make an irrevocable immediate decision on whether to hire a participant who arrives and departs dynamically without knowing future information.We tackle this issue by designing fairness-aware and time-sensitive task allocation mechanisms in asynchronous FL for CEI.First, we design an optimal multi-dimensional contract to guarantee the reliability, honesty and fairness, and maximize the learning accuracy for the fixed deadline scenario.Second, we design a multi-metric participant recruitment mechanism to control time consumption for the limited budget scenario, prove that the problem of optimizing this mechanism is NP-Hard, and propose an e-approximation algorithm accordingly.Finally, extensive experiments using both real-world data and simulated data further demonstrate the effectiveness and efficiency of our proposed mechanisms compared to the state-ofthe-art approaches.
Two-Stage Self-supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images

The thick-slice magnetic resonance (MR) images are often structurally blurred in coronal and sagittal views, which causes harm to diagnosis and image post-processing. Deep learning (DL) has shown great potential to reconstruct the high-resolution (HR) thin-slice MR images from those low-resolution (LR) cases, which we refer to as the slice interpolation task in this work. However, since it is generally difficult to sample abundant paired LR-HR MR images, the classical fully supervised DL-based models cannot be effectively trained to get robust performance. To this end, we propose a novel Two-stage Self-supervised Cycle-consistency Network (TSCNet) for MR slice interpolation, in which a two-stage self-supervised learning (SSL) strategy is developed for unsupervised DL network training. The paired LR-HR images are synthesized along the sagittal and coronal directions of input LR images for network pretraining in the first-stage SSL, and then a cyclic interpolation procedure based on triplet axial slices is designed in the second-stage SSL for further refinement. More training samples with rich contexts along all directions are exploited as guidance to guarantee the improved interpolation performance. Moreover, a new cycle-consistency constraint is proposed to supervise this cyclic procedure, which encourages the network to reconstruct more realistic HR images. The experimental results on a real MRI dataset indicate that TSCNet achieves superior performance over the conventional and other SSL-based algorithms, and obtains competitive qualitative and quantitative results compared with the fully supervised algorithm.
Network approaches for formalizing conceptual models in ecosystem-based management

Abstract Qualitative Network Models (QNMs), Fuzzy Cognitive Maps (FCMs), and Bayesian Belief Networks (BBNs) have been proposed as methods to formalize conceptual models of social–ecological systems and project system responses to management interventions or environmental change. To explore how these different methods might influence conclusions about system dynamics, we assembled conceptual models representing three different coastal systems, adapted them to the network approaches, and evaluated outcomes under scenarios representing increased fishing effort and environmental warming. The sign of projected change was the same across the three network models for 31–60% of system variables on average. Pairwise agreement between network models was higher, ranging from 33 to 92%; average levels of similarity were comparable between network pairs. Agreement measures based on both the sign and strength of change were substantially worse for all model comparisons. These general patterns were similar across systems and scenarios. Different outcomes between models led to different inferences regarding trade-offs under the scenarios. We recommend deployment of all three methods, when feasible, to better characterize structural uncertainty and leverage insights gained under one framework to inform the others. Improvements in precision will require model refinement through data integration and model validation.
Electronic Health Records System Using Blockchain Technology

Blockchain technology is one of the most important and disruptive technologies in the world. Nowadays the healthcare center needs to share patient databases over all departments of the healthcare centers. Although, electronic healthcare records overcome several problems compared with manual records, but still suffer from many issues such as security, the privacy of patient data overall as we should transfer over a database from a central database to a decentralized database. In this paper, we proposed a good security system to manage the data of patients based on blockchain technology and a decentralized database. Depending on decentralized database and blockchain. Our proposed system provides the secure exchange of patient data, reliability, and high efficiency in sharing data during transaction data network equivalence checking to perform this validation of patient information in the blockchain and healthcare centers.
Emotional expressions as appeals to recipients.

Emotion expressions convey information. One important kind of information, from a communicative perspective, is information about what is demanded of the recipients of the expression. Compared to the vast body of research that focuses on whether, and to what degree, emotion expressions convey information about inner states, there is surprisingly little systematic research on the information emotion expressions convey about what the expresser wants the recipient to do. The present research documents for the first time the set of appeals associated with the expressions of anger, fear, happiness, sadness, and disgust. In two studies, we found that (a) generally, for each emotional expression, a core appeal is perceived as being made on recipients, but also a range of complementary demands, and that (b) context matters, as is the case for social perception of emotions in general. In this case, who caused the emotion expression affects the mapping between emotional expressions and appeals. Finally, (c) recipients report intentions to comply with these appeals, as can be expected from a functional point of view. Overall, these findings put the focus squarely on the imperative dimension of emotional expressions and lend support to the view that emotional communication entails an attempt to influence recipients rather than simply to inform them about how expressers feel or what they may do next. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Trust, transaction transparency, volatility, facilitating condition performance expectancy towards cryptocurrency adoption through intention to use

The cryptocurrency enhances today's digital currency ecosystem. The use of cryptocurrencies leads to better understanding, higher intention, trust, and acceptance. However, the lack of cryptocurrency acceptance is a severe issue in the digital market. Cryptocurrency adoption is a requirement for implementing technology functionality supported by intentioned patterns. The main goal of this study is to examine the factors that influence cryptocurrency adoption in the digital market in Malaysia. The research also confidently concludes that cryptocurrencies would continue to increase in prevalence. This empirical study examined the role of trust (TR), transaction transparency (TT), volatility (VO), facilitating conditions (FC), performance expectancy (PE), and intention to use (ITU) in cryptocurrency adoption (AD). Therefore, the study identifies intention to use as a mediator of cryptocurrency adoption. Therefore it examined the significance of intention in the context of cryptocurrency adoption. Systematic random sampling was utilized in this study to ensure the most rigorous analysis of research objectives. Besides that, the survey questions were asked at the cryptocurrency consumers' location in Malaysia. Also, a total of 263 valid responses were taken for the final assessment. This study considered Partial Least Squares Structural Equation Modeling (PLS-SEM) for data analysis. Finally, the findings showed that TR, TT, VO, and FC are all found to help in the AD (dependent variable) through the mediation of intention to use (ITU) in Malaysia's digital market. On the other hand, performance expectancy negatively impacts the digital market of Malaysia. The findings in this study may be replicated by future researchers in different locations, in various industries, and then use equivalent constructions to expand our current body of knowledge. This paper extends the limited literature on the cryptocurrency and digital currency perspectives. Besides that, it helps to understand mediating impact on cryptocurrency adoption and intention to use. Moreover, identify the significant factors that affect the cryptocurrency in Malaysia's digital market.
Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge

In this paper, we investigate the Aspect Category Sentiment Analysis (ACSA) task from a novel perspective by exploring a Beta Distribution guided aspect-aware graph construction based on external knowledge.That is, we are no longer entangled about how to laboriously search the sentiment clues for coarsegrained aspects from the context, but how to preferably find the words highly sentimentrelated to the aspects in the context and determine their importance based on the public knowledge base, so as to naturally learn the aspect-related contextual sentiment dependencies with these words in ACSA.To be specific, we first regard each aspect as a pivot to derive aspect-aware words that are highly related to the aspect from external affective commonsense knowledge.Then, we employ Beta Distribution to educe the aspect-aware weight, which reflects the importance to the aspect, for each aspect-aware word.Afterward, the aspect-aware words are served as the substitutes of the coarse-grained aspect to construct graphs for leveraging the aspectrelated contextual sentiment dependencies in ACSA.Experiments on 6 benchmark datasets show that our approach significantly outperforms the state-of-the-art baseline methods.
A Decentralized Mechanism Based on Differential Privacy for Privacy-Preserving Computation in Smart Grid

As one of the most successful industrial realizations of Internet of Things, a smart grid is a smart IoT system that deploys widespread smart meters to capture fine-grained data on residential power usage. Unfortunately, it always suffers diverse privacy attacks, which seriously increases the risk of violating the privacy of customers. Although some solutions have been proposed to address this privacy issue, most of them mainly rely on a trusted party and focus on the sanitization of metering masurements. Moreover, these solutions are vulnerable to advanced attacks. In this paper, we propose a decentralized mechanism for privacy-preserving computation in smart grid called DDP, which leaverages the differential privacy and extends the data sanitization from the value domain to the time domain. Specifically, we inject Laplace noise to the measurements at the end of each customer in a distributed manner, and then use a random permutation algorithm to shuffle the power measurement sequence, thereby enforcing differential privacy after aggregation and preventing the sensitive power usage mode informaton of the customers from being inferred by other parties. Extensive experiments demonstrate that DDP shows an outstanding performance in terms of privacy from the non-intrusive load monitoring (NILM) attacks and utility by using two different error analysis.
Comparing Three Countries’ Higher Education Students’ Cyber Related Perceptions and Behaviours during COVID-19

In 2020, a global pandemic led to lockdowns, and subsequent social and business restrictions. These required overnight implementation of emergency measures to permit continued functioning of vital industries. Digital technologies and platforms made this switch feasible, but it also introduced several cyber related vulnerabilities, which students might not have known how to mitigate. For this study, the Global Cyber Security Index and the Cyber Risk literacy and education index were used to provide a cyber security context for each country. This research project—an international, cross-university, comparative, quantitative project—aimed to explore the risk attitudes and concerns, as well as protective behaviours adopted by, students at a South African, a Welsh and a Hungarian University, during the pandemic. This study’s findings align with the relative rankings of the Oliver Wyman Risk Literacy and Education Index for the countries in which the universities reside. This study revealed significant differences between the student behaviours of students within these universities. The most important differences were identified between students’ risk attitudes and concerns. It was also discovered that South African students reported having changed their protective online behaviours to the greatest extent, since the pandemic commenced. Recommendations are made suggesting that cyber security training and education, as well as improving the digital trust and confidence in digital platforms, are critical.
Design, Implementation, and Evaluation of an Immersive Virtual Reality-Based Educational Game for Learning Topology Relations at Schools: A Case Study

Education has always been modified by employing different technologies to enhance the knowledge acquisition and performance of students. Virtual Reality (VR) along with the Game Industry is among those evolving technologies for educational applications. This study aimed to design, implement, and evaluate an immersive VR-based Educational Game (IVREG) for learning topology relations. Topology relations are one of the fundamental topics which exist in Geospatial Information Science (GIS); due to the great capabilities offered by GIS, learning these basic topis is of great importance. A total of thirty-seven male middle-school students participated in this study. A total of four questionnaires were designed to evaluate the suitability of the proposed learning environment and its components at schools, particularly for learning geospatial topics. In conclusion, students found the IVREG useful and effective in classrooms. Additionally, the results showed that the components, namely the integrated pedagogical approach, gamification, and VR technology, were all suitable for being used at schools. On a final note, however, the study indicated that the immersion aspect of such learning environments should be enhanced in future studies.