Reviewerly/exHarmony huggingface dataset:
A Systematic Review: Risk Management of Cloud Computing Projects in Healthcare

Cloud computing has become a major driver for innovation in this technological age. All sectors around the world have increasingly moved towards cloud adoption including the healthcare sector. However, cloud computing projects with other IT projects come with their own set of risks that could be costly for organizations if they materialized. This study aims to provide an understanding of the risks of cloud computing projects in healthcare by using a systematic literature review augmented by the constant comparison method.63 articles from five major databases written from the year 2010 to 2022 were reviewed as they are related to cloud computing projects. More specifically, this study shows 198 unique indicators that were categorized into risks, threats, vulnerabilities, probabilities, consequences, and control procedure categories which then were classified into 13 distinct risk classes that can be found in such projects.
IMPROVING COVERAGE AND NOVELTY OF ABSTRACTIVE TEXT SUMMARIZATION USING TRANSFER LEARNING AND DIVIDE AND CONQUER APPROACHES

Automatic Text Summarization (ATS) models yield outcomes with insufficient coverage of crucial details and poor degrees of novelty. The first issue resulted from the lengthy input, while the second problem resulted from the characteristics of the training dataset itself. This research employs the divide-and-conquer approach to address the first issue by breaking the lengthy input into smaller pieces to be summarized, followed by the conquest of the results in order to cover more significant details. For the second challenge, these chunks are summarized by models trained on datasets with higher novelty levels in order to produce more human-like and concise summaries with more novel words that do not appear in the input article. The results demonstrate an improvement in both coverage and novelty levels. Moreover, we defined a new metric to measure the novelty of the summary. Finally, we investigated the findings to discover whether the novelty is influenced more by the dataset itself, as in CNN/DM, or by the training model and its training objective, as in Pegasus.
Predicting breakthrough innovation engagement via hybrid intelligence: a moderated mediation model of self-extinction and social intelligence

Purpose This study aims at investigating how hybrid intelligence might enhance employee engagement in breakthrough innovation. Specifically, it empirically examines the mediating role of self-extinction and moderating role of social intelligence. Design/methodology/approach This study, using the lens of socio-technical system (STS) theory, collected data from 317 employees through cross-sectional survey. The hypotheses were tested using MPlus 8.3 by applying Structural Equation Modelling (SEM). Findings The results support the proposed model, suggesting that hybrid intelligence fosters employees' breakthrough innovation engagement and such a relationship is fully mediated by self-extinction. Besides, the findings provide support for the positive moderating impact of social intelligence on such indirect relationships in a way that high social intelligence will further strengthen the relationship. Originality/value As a pioneering contribution, the study uncovers the social mechanism that underlies hybrid intelligence–breakthrough innovation engagement relationship via self-extinction. The research suggests managers leveraging employees' social intelligence for playing a critical role in countering the negative impact of self-extinction by enhancing the employees' engagement in the breakthrough innovation process.
Approximation-Aware and Quantization-Aware Training for Graph Neural Networks

&lt;p&gt;Graph Neural Networks (GNNs) are one of the best-performing models for processing graph data. They are known to have considerable computational complexity, despite the smaller number of parameters compared to traditional Deep Neural Networks (DNNs). Operations-to-parameters ratio for GNNs can be tens and hundreds of times higher than for DNNs, depending on the input graph size. This complexity indicates the importance of arithmetic operation optimization within GNNs through model quantization and approximation. In this work, for the first time, we combine both approaches and implement quantization- and approximation-aware training for GNNs to sustain their accuracy under the errors induced by inexact multiplications. We employ matrix multiplication CUDA kernel to speed up the simulation of approximate multiplication within GNNs. Further, we demonstrate the execution speed, accuracy, and energy efficiency of GNNs with approximate multipliers in comparison with quantized low-bit GNNs. We evaluate the performance of state-of-the-art GNN architectures (i.e., GIN, SAGE, GCN, and GAT) on various datasets and tasks (i.e., Reddit-Binary, Collab for graph classification, Cora and PubMed for node classification) with a wide range of approximate multipliers. &lt;/p&gt;
Privacy-Preserving Distributed Learning via Newton Algorithm

Federated learning (FL) is a prominent distributed learning framework. The main barriers of FL include communication cost and privacy breaches. In this work, we propose a novel privacy-preserving second-order-based FL method, called GDP-LocalNewton. To improve the communication efficiency, we use Newton’s method to iterate and allow local computations before aggregation. To ensure strong privacy guarantee, we make use of the notion of differential privacy (DP) to add Gaussian noise in each iteration. Using advanced tools of Gaussian differential privacy (GDP), we prove that the proposed algorithm satisfies the strong notion of GDP. We also establish the convergence of our algorithm. It turns out that the convergence error comes from the local computation and Gaussian noise for DP. We conduct experiments to show the merits of the proposed algorithm.
Perceptions of Undergraduate ESL Learners towards Syntax Contents &amp; Teaching Methodology

Syntax is one of the important branches of theoretical linguistics that deals with the study of structural constructions of various languages. However, learners often find studying and internalizing the contents of syntax a challenging and daunting task. To address this issue, mixed-method research was conducted to evaluate the perceptions of fifty non-randomly selected sixth-semester undergraduate ESL students with reference to the course contents and teaching methodology. The survey research design was employed, and the data were collected through a comprehensive questionnaire. Findings reveal that most learners studied grammar at schools and colleges through the deducted approach. This grammar background has enabled them to grasp course contents of syntax rather effortlessly. Moreover, the learning process was facilitated by comparing English syntactic constructions to those of the Urdu language in addition to the structures of other languages. However, there are some topics that learners find somewhat laborious and challenging to internalize. These contents include tree diagrams, structural ambiguity, forms and functions, phrase structure rules, and X-bar rules. Personalized instruction and extensive practice can make these course contents less challenging. The learners generally have felt their overall satisfaction with the teacher's interactive and learner-centered teaching methodology.
Formation of a responsible attitude of the children of primary school age to their health

Today, children's health problems need new approaches. In this regard, the educational function of teachers and psychologists is important in order to form a child's idea of a healthy lifestyle, to form right attitudes to their's own health. This is of particular importance for young people, children, since they are the future generation that will continue our history, which has determined the relevance of this work related to the formation of a responsible attitude to a healthy lifestyle of children, which is the subject of this work. A psychological and pedagogical program for 4th grade students has been developed to form a responsible attitude to their health and healthy lifestyle. The implementation of the program includes three main levels: 1) Emotional involvement (emotional-value component of a responsible attitude); 2) semantic (cognitive component); activity (activity component). As a result of the implementation of the psychological and pedagogical program, the level of responsible attitude of children to their health has significantly increased. Namely, the number of children with a conscious attitude to their health has increased (cognitive component), the level of mastery of cultural norms has increased (emotional and value component), the number of schoolchildren participating in health-preserving and promoting a healthy lifestyle has increased
Outlier Detection Model of Electric Power Big Data Based on Fuzzy K-means Clustering Algorithm

Abnormal operation of big data in electric power will affect users' normal electricity consumption, increase of line loss and loss of electricity, or even cause fire, affect the stability and security of power grid, and bring serious economic loss and reputation loss to power grid enterprises. In this paper, based on fuzzy K-means clustering algorithm, the outlier detection of power big data is studied, and the outlier detection model of power big data is established. Finally, through the simulation experiment, it can be found that the number of detection results based on fuzzy K-means clustering algorithm is reduced compared with the actual results, and it also shows good performance in the face of data from multiple transformers with different magnitude of data, which shows that the algorithm can be applied to larger data sets. Compared with the density-based detection algorithm, the algorithm in this paper is more feasible, and it can better realize power big data fusion and anomaly detection, so it is worth popularizing and applying. The basic idea of the power big data outlier detection model based on fuzzy K-means clustering algorithm is to optimize the initial clustering center, improve the complexity of power big data outlier detection, and improve the detection accuracy and efficiency.
When to Use Large Language Model: Upper Bound Analysis of BM25 Algorithms in Reading Comprehension Task

Large language model (LLM) is a representation of a major advancement in AI, and has been used in multiple natural language processing tasks. Nevertheless, in different business scenarios, LLM requires fine-tuning by engineers to achieve satisfactory performance, and the cost of achieving target performance and fine-turning may not match. Based on the Baidu STI dataset, we study the upper bound of the performance that classical information retrieval methods can achieve under a specific business, and compare it with the cost and performance of the participating team based on LLM. This paper gives an insight into the potential of classical computational linguistics algorithms, and which can help decision-makers make reasonable choices for LLM and low-cost methods in business R& D.
Nexus between technological innovation, trade, education and institutional quality: Evidence from dynamic SUR estimation

This study analyzes the various factors that promote technological advancement in lower-income nations. Additionally, it aims to provide policy recommendations that can effectively stimulate and nurture such advancements. The study employs a robust methodology, which includes the utilization of a comprehensive dataset and the application of rigorous statistical analysis techniques. A thorough examination of the current body of literature supports the foundation of this work. The study uncovers a noteworthy positive correlation among institutional quality, trade openness, and education about technological innovation. The statement implies that countries with stronger institutions create a favorable environment for the growth and progress of innovative practices. The study further emphasizes the positive influence of both domestic and international trade on the innovation process. Moreover, it emphasizes the positive influence of education on technological advancement, underscoring the importance of accessible and high-quality education in fostering innovation. Based on the empirical evidence presented in this study, it is recommended that policymakers give significant priority to the implementation of institutional reforms, facilitation of trade integration, and allocation of resources towards education. These measures are crucial for stimulating and nurturing technological advancements within lower-income nations. The study provides valuable insights that can benefit policymakers and practitioners promoting innovation-driven development in lower-income countries. Nevertheless, it is crucial to consider alternative explanations and contextual factors that may impact the interaction between these variables.
Ensuring privacy and confidentiality of cloud data: A comparative analysis of diverse cryptographic solutions based on run time trend

The cloud is becoming a hub for sensitive data as technology develops, making it increasingly vulnerable, especially as more people get access. Data should be protected and secured since a larger number of individuals utilize the cloud for a variety of purposes. Confidentiality and privacy of data is attained through the use of cryptographic techniques. While each cryptographic method completes the same objective, they all employ different amounts of CPU, memory, throughput, encryption, and decryption times. It is necessary to contrast the various possibilities in order to choose the optimal cryptographic algorithm. An integrated data size of 5 n *10 2 ( KB (∈ 1,2,4,10,20,40) is evaluated in this article. Performance metrics including run time, memory use, and throughput time were used in the comparison. To determine the effectiveness of each cryptographic technique, the data sizes were run fifteen (15) times, and the mean simulation results were then reported. In terms of run time trend, NCS is superior to the other algorithms according to Friedman’s test and Bonferroni’s Post Hoc test.
Speeding lectures to make time for retrieval practice: Can we improve the efficiency of interpolated testing?

Testing is increasingly recognized as an important tool in learning. One form of testing often used in lectures, particularly recorded lectures, is interpolated testing wherein tests are interspersed throughout the lecture. Like testing in general, interpolated testing appears to benefit performance on content tests among other outcome variables (e.g., mind wandering). While beneficial, adding testing also increases instructional time. In the present investigation, we examine one strategy to mitigate the costs of this increase in instructional time in the context of recorded lectures. Specifically, we examine the interaction between increasing the playback speed of a recorded lecture and adding interpolated tests. Results demonstrate that the conjoint effects of these two interventions are largely additive. That is, the benefit of testing was as robust in a normal speed lecture and a lecture that was sped up 1.5×. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Research on the Impact of AI on University Student Learning based on Principal Component Analysis and Factor Analysis Methods

Artificial intelligence (AI) was introduced in 1956 by scientists from McCarthy and Minsky held at Dartmouth College in the United States. Its rapid development has a different depth of impact on all social strata, but also has a great impact on the education industry, especially for the future college students of the motherland. This paper uses the formal statistical data of the questionnaire to analyze and establish the mathematical model to understand the influence of artificial intelligence on college students in different aspects. First, the raw data is visualized and numericized. Secondly, the analysis results of the data are taken as the evaluation index and comprehensively considered from many aspects such as priority, scientificity and operability. We used principal component analysis and factor analysis to measure each index reasonably. Finally, a comprehensive evaluation method is used to evaluate the influence of AI on college students' learning.
Discussion on the Influence of Accounting mputerization on Audit

The implementation of accounting computerization has greatly improved the speed and accuracy of accounting information processing, which is an unprecedented leap in the history of accounting development. However, the popularization and application of accounting computerization are bound to affect audit work; the traditional audit has been unable to adapt to the new situation and new requirements of computerization. Accounting computerization promotes the change of audit theory, technology, and method, but at the same time, it also causes new situations such as computer fraud, brings new audit risks, and puts forward higher requirements for auditors. This paper discusses the influence of accounting computerization on audit work and puts forward some suggestions and countermeasures for the existing problems of audit work under the condition of computerization.
Confidence-based Utterance Selection for a Recognizer-free Spoken Dialogue System

This work aims to develop a spoken dialogue system without a speech recognizer, which is used for a CALL system of endangered languages for language revitalization. A problem in realizing such a system is the accuracy of choosing a most-matched example utterance because of the difference in voice characteristics. Thus, this paper proposes a method to choose the selection candidate among multiple candidates selected by different speech features. The selection is based on the confidence measure calculated from the distance values between the input and the database utterances. We conducted an experiment to choose a candidate among those by MFCC and PPG and obtained a 2.6-point accuracy improvement.
Blind video watermarking scheme for medical video authentication

Medical video watermarking is one of the beneficial and efficient tools to prohibit important patients' data from illicit enrollment and redistribution. In this paper, a new blind watermarking scheme has been proposed to improve the confidentiality, integrity, authenticity, and perceptual quality of a medical video with minimum distortion. The proposed scheme is based on 2D-DWT and dual Hessenberg-QR decomposition, where the input medical video is initially processed into frames. Then, the processed frames are transformed into sub-bands using 2D-DWT, followed by applying Hessenberg-QR decomposition on the selected wavelet HL2 sub-band. The watermark is scrambled via Arnold cat map to raise confidentiality and then concealed in the modified selected features. The watermark is extracted in a fully blind mode without referencing the original video, which reduces the extraction time. The proposed scheme maintained a fundamental tradeoff between robustness and visual imperceptibility compared to existing methods against many commonly encountered attacks. The visual imperceptibility has been evaluated using well-known metrics PSNR, SSIM, Q-index, and histogram analysis. The proposed scheme achieves a high PSNR value of (70.6899 dB) with minimal distortion and a high robustness level with an average NC value of (0.9998) and BER value of (0.0023) while conserving a large payload capacity. The obtained results show superior performance over similar video watermarking methods. The limitation of this scheme is the elapsed time during the embedding process since we utilized dual Hessenberg-QR decomposition. One possible solution to reduce time consumption is simple decompositions like bound-constrained SVM or similar decompositions.
The Effectiveness of Situational Language Teaching (SLT) Approach in Teaching Speaking for Islamic Junior High School

SLT approach is an approach that involves the systematic principles of selection (the procedure by which lexical and grammatical content were chosen), graduation (principles by which organization and sequencing of contents were determined), and presentation (technique used for presentation and practice of items in a course). The purposes of this research are to know whether situational language teaching approach effective in teaching speaking for the eighth graders of Islamic junior high school, and then the extent of the effectiveness of situational language teaching. The design is pre-experimental research. The sample is randomly selected as the experimental class of the eighth graders. The data are collected by administering pre-test and post-test to the students. t-test is used to analyze whether the alternative hypothesis is accepted or not. The statistical analysis shows mean of the class on pre-test is 16.43 and post-test is 20.47 by using t-test at the level of significance (a) 0.05, gotten r-test = 0.561 and t-test is bigger than t-table that is t-test = 5.691 &gt; t-table = 2.045, meaning that there is a significant difference between the students’ achievement in speaking before and after SLT approach implemented. It then portrays SLT approach significantly effective in teaching speaking and SLT approach has positive correlation with students’ speaking skill at the level of believe 95%. It is suggested that SLT approach in teaching speaking is recommended for the English teachers, but SLT approach is better implemented in small class because teachers are easier in managing the class.
Research on the Application of Question Chains Based on Deep Learning in Senior English Reading

Thinking quality, as one of the important goals of reading teaching, is an indispensable way to implement the cultivation of core competencies. However, in the current reading teaching, there are problems such as shallow thinking and separation of learning and thinking, which hinder the development of students’ higher-order thinking. Question chains, as an effective teaching method, can promote students’ output of language knowledge and enhance their thinking ability to achieve deep learning. Therefore, this paper aims to explore how to apply question chains based on deep learning of senior English reading teaching, so as to activate students’ higher-order thinking ability, optimize high school teachers’ question design ability, and improve the effectiveness of senior English reading teaching.
Prediction and Analysis of House Prices in Boston Based on Regression Model

In artificial intelligence learning, Boston housing price forecast analysis problem is a classic regression problem. Based on the housing price information collected by the U.S. Census Bureau in Boston, Massachusetts. this paper divides the housing price data set of Boston and builds the regression model linear regression, decision tree regression and support vector machine regression SVR and trains the data set, so as to obtain the relationship between different data related to Boston house price, and use this relationship to connect all data, it can finally predict the future house price trend in Boston and display it through visual operation. Through three regression model prediction value, respectively compared with the actual value, the trend of overall and actual and estimated values of the same, but there is a certain error, especially when spot prices higher or lower, often cannot get accurate forecast, so the data for the selection of the characteristic value still exists space for improvement, future study needs to get more data and the characteristics of abundant data.