Reviewerly/exHarmony huggingface dataset:
Research on the Construction Strategy of Practical Teaching System of Korean Education Major in Colleges and Universities

Korean education is becoming increasingly popular around the world, and the demand for graduates with knowledge and skills in this field is on the rise. As such, colleges and universities need to ensure that their Korean education majors are equipped with the necessary knowledge and skills to succeed in their future careers. One way to achieve this is through the construction of a practical teaching system that emphasizes hands-on learning and real-world experience. This paper explores the current state of practical teaching in Korean education majors in colleges and universities and proposes a construction strategy for a practical teaching system that can better prepare graduates for their careers. This includes the integration of technology, partnerships with industry stakeholders, and the incorporation of more practical and experiential learning methods.
iOS and Android Platform Development of Korean Professional Application-Oriented Talent Training Platform Based on Multi-Dimensional Information Transmission Algorithm

iOS and Android platform development of Korean professional application-oriented talent training platform based on multi-dimensional information transmission algorithm. Firstly, we consider the data security of the system. When the application executes the sensitive operation, the policy executor will request the security policy from the security decision center. If the security policy contains restriction rules for the sensitive operation, the security decision center instructs the policy executor to carry out corresponding permission control and security operation. It is necessary to first perform static analysis on the Android application specified by the user to obtain all sensitive API calls and dangerous permission requests involved in the application; then convert the obtained information into policy points, and decide whether to agree or not by operating the policy points. Dangerous requests from applications and more fine-grained API calls. With these technologies, the professional application-oriented talent training platform is implemented. Through the verification, the efficiency of the platform is tested.
Automatic Caption Generation for Aerial Images: A Survey

Aerial images have attracted attention from researcher community since long time. Generating a caption for an aerial image describing its content in comprehensive way is less studied but important task as it has applications in agriculture, defence, disaster management and many more areas. Though different approaches were followed for natural image caption generation, generating a caption for aerial image remains a challenging task due to its special nature. Use of emerging techniques from Artificial Intelligence (AI) and Natural Language Processing (NLP) domains have resulted in generation of accepted quality captions for aerial images. However lot needs to be done to fully utilize potential of aerial image caption generation task. This paper presents detail survey of the various approaches followed by researchers for aerial image caption generation task. The datasets available for experimentation, criteria used for performance evaluation and future directions are also discussed.
The Impact of Green Entrepreneurship Orientation on Sustainable Performance: The Mediating Effect of Knowledge Management

Green entrepreneurial orientation, knowledge management, and sustainable performance were explored in Jordanian five-star hotels in this study.The current study investigated the mediating influence of knowledge management on the link between Green entrepreneurial orientation and sustainable performance.A quantitative approach was adopted.Through a self-administered questionnaire, 108 questionnaires were collected from five-star employees in managerial positions.The gathered data was analyzed using structural equation modeling to assess the link between our research variables.According to the findings of structural equation modeling, Green entrepreneurial orientation has a beneficial impact on knowledge management and sustainable performance.Furthermore, the knowledge management has a favorable effect on the sustainable performance.The outcome also revealed that knowledge management partially mediates between Green entrepreneurial orientation and sustainable performance, suggesting that the favorable effects of Green entrepreneurial orientation on sustainable performance may be amplified by knowledge management's mediating action.The findings have implications on the Jordanian's tourism and hospitality industry.
5PSQ-109 Ransomware attack on a cytotoxic preparation unit (CPU): what now?

<h3>Background and Importance</h3> The informatisation of processes has increased the susceptibility of the healthcare sector to computer attacks through ransomware. These can manifest by the impossibility of accessing the internet, and computer systems, with consequent interruption of electronic prescription, registration in patient diary, consultation of previous clinical data, request for means diagnostic tools, among others. Pharmaceutical Services are particularly vulnerable to these attacks. <h3>Aim and Objectives</h3> The aim of this work was to systematise the strategies adopted during the cyberattack, minimising the error and allowing the work to be carry out at the CPU, with the elaboration of a guideline to be adopted in a future cyberattack. <h3>Material and Methods</h3> Retrospective study between April 26 and May 10, 2022. <h3>Results</h3> CPU was restructured in order to guarantee its functionality. Through the information on paper from the production maps of April 2022, chemotherapy protocols, charts with reconstitution/dilution of used drugs, literature and coordination of information with the nursing and medical team, it was possible prevent the collapse of the unit. It was performed a daily survey of patients marked in agenda, and respective information about the cycle. In the first 15 days of attack, 28.8% (n=132) of scheduled patients (n=458) were unchecked due to lack of access to complementary means of diagnosis and history of chemotherapy protocols. The preparation of cytotoxics was possible through the elaboration of manual labels (n=615), using the validation of paper prescriptions. In the first week (April 26 to 29) 41.9% (n=67) of the patients (n=160) were unchecked and in the second week (May 2 to 6) 25% (n=54) of patients (n=216). In the initial days of cyberattack no new patients were scheduled. <h3>Conclusion and Relevance</h3> Faced with the reality of a computer attack, the CPU priority was to ensure a safety preparation of chemotherapy. On the other hand, this attack showed that it is crucial to have mechanisms of replacement of information such as the chemotherapy prescriptions file. Anticipating future cyberattacks, a guideline has been developed to ensure circuit safety in case of computer failure. <h3>References and/or Acknowledgements</h3> 1. Not applicable. <h3>Conflict of Interest</h3> No conflict of interest
The Impact of Guessing on the Accuracy of Estimating Simple Linear Regression Equation Parameters and the Ability to Predict

The current study aimed at identifying the impact of guessing on the accuracy of estimating simple linear regression equation parameters and the ability to predict.To achieve the objectives of the study, an achievement test was built in its final form of (40) multiple-choice items in a measurement and evaluation course.After verifying the psychometric properties of the test, it was applied to a sample of (134) male and female general diploma students registered at AL-Hussein Bin Talal University in the second semester of the year 2020/2021.The test was divided into two sections: the first section was tested based on the traditional correction method, while the second section was tested based on the correction formula for the impact of guessing.The results of the study concluded that there are statistically significant differences among the predicted values in the scores of the examinees when using the traditional method and the correction formula for the impact of guessing in favor of the traditional method, and that there are statistically significant differences among the means of residual squares for estimating the scores of the study sample attributed to the correction method and in favor of the traditional method.The results of the study also found that the value of the explained variance (R2) increases when using the correction equation for the guessing effect, and that the values of the parameters of the simple linear regression equation increase when using the correction equation for the impact of guessing, and that the values of the standard error in estimating the parameters of the simple linear regression equation decrease when using the correction equation for the impact of guessing.
Robust Subspace Clustering Via Multi-Affinity Matrices Fusion

Subspace clustering (SC) methods have attracted widespread attentions anda variety of SC methods have been proposed recently. In general, SCmethods obtain an affinity matrix, on which spectral clustering is used to cluster. Obviously, affinity matrices obtained by different algorithms are not the same, and their clustering effects are also different. Then, a question arises, i.e., whether the affinity matrices obtained by different algorithms can be merged to improve the clustering performance? The answer may be yes. In the paper, we design a new SC method, i.e., robust subspace clustering via multi-affinity matrices fusion (RSC/MAMF). In RSC/MAMF. Specifically, several classical SC algorithms, i.e., sparse subspace clustering (SSC), low-rank representation (LRR), least squares regression (LSR), are first chosen to derive their own affinity matrices. To make full use of the information of different affinity matrices and to mine the subspace structure thoroughly, we further fuse the derived affinity matrices, i.e., splice them into a 3-order tensor, and impose weighted tensor nuclear norm (WTNN) to it, which can not only mine and fuse the information of different affinity matrices, but remove the noise of different affinity matrices. Additionally, to further explore the consistency of different affinity matrices, spectral embedding is also unified into the final objective function. We propose an optimization algorithm to solve the optimization problem of RSC/MAMF, which utilizes the Augmented Lagrange Multiplier (ALM) method.Experiments show that the idea of multi-affinity matrix fusion is feasible, and RSC/MAMF outperforms the state-of-the-art SC methods.
Chaotic Stochastic Neural Network Quantization and its Robustness Against Adversarial Attacks

Although deep learning networks’ performance is satisfactory, research in these fields has continued to improve some other specifications, such as network’s robustness against adversarial attacks. Stochastic quantization is a promising technique in neural network compression, as it introduces a fast and straightforward solution for reaching a compact model. The randomness source in that stochastic quantization is the computer random number generator algorithms, which are repeatable and predictable.In this work, chaotic RNGs are used in stochastic quantization and are proven to have better results and robustness against adversarial attacks. Uniform, Gaussian, Lorenz and Henon RNGs are used in stochastic quantization and examined under Pixel attack, square attack, hopskipjump attack and Fast gradient sign method attack. Stochastic quantization for the activation functions is used once in the training only and another in the training and the inference resulting in two scenarios. A comparison between using a quantization network with variable temperature and a quantization network with stochastic quantization is made resulting in another two scenarios. The combinations of these four scenarios were studied comprehensively from the top-1 prediciton accuracy perspective and from the robustness against adversarial attack perspective. The study proved that using a quantization network with variable temperature and a quantization network with stochastic quantization are similar in top-1 prediction accuracy but stochastic quantization adds robustness against adversarial attacks to the model. The results also proved that using Lorenz and Henon (chaotic RNGs) is better than using uniform and Gaussian (normal RNGs) and increase the robustness against most adversarial attacks.
TailCmp - A Tail Latency Evaluation Solution of Public Cloud and Labeled von Neumann Architecture based Cloud Prototype

Tail latency is the key performance metric for cloud computing systems (CCSs). When latency-critical (LC) applications and best-effort batch (BE) jobs are co-located on CCSs, the unmanaged contention for computing resources usually leads to significant fluctuations in tail latency, which have a severe negative impact to the end user experience. Therefore, some promising computing systems and effective scheduling solutions are proposed to guarantee better quality of service (QoS), e.g., La-beled von Neumann Architecture (LvNA) based cloud prototype system. However, relatively few work focuses on the tail latency observation and evaluation of public CCSs and newest prototype systems. Therefore, we introduce an evaluation framework named TailCmp which includes nine representative workloads ranging over different tail latency requirements and application domains. With the TailCmp, we can collect and analyze four evaluation metrics including tail latency entropy. In this paper, we conduct a large number of experiments with TailCmp on three CCSs (two public CCSs and one LvNA-based prototype system). The results show that the co-location on existing public CCSs can seriously affect the QoS of LC workloads, while the labeling mechanism in Lv Na - based prototype can improve the performance of LC workloads in co-location.
Distribution Based Upper Lower Bound Estimation In Deep Neural Nets

A crucial part of forecasting is quantifying the uncertainties involved during prediction. Neural networks are great at making points estimates. However, the lack of uncertainty quantification through a predictive distribution has constrained their application in sensitive areas. In this paper we propose a simple distribution based upper lower bound estimation algorithm. We mathematically demonstrate a distribution based coverage probability and interval width assessment method. Our approach encompasses the quality metrics, prediction interval coverage probability (PICP) and mean prediction interval width (MPIW) in its bound estimation. Then, we developed a customized loss function with adaptive hyperparameter that balances the needed coverage probability in relation to the prediction interval. Finally, we evaluated the performance of our approach on a UCI regression data using the recent Quality Driven (QD) bound estimation method as a benchmark. The experiment has revealed the algorithm offers a simple tuning and a stable evaluation for a predictive distribution regardless of the network settings. In the simulation for the same 95% coverage probability, we observed the algorithm achieved the minimum prediction interval. Although, we assumed a logistically approximated Gaussian distribution for the algorithm derivation, its has a robust response to data with asymmetric distribution as well. Hence, the algorithm can be used as an alternative method to provide a predictive distribution for neural networks.
Research on Location Information and Privacy Protection Based on Big Data

In the context of big data era, in order to prevent malicious access and information leakage during data services, researchers put forward a location big data encryption method based on privacy protection in practical exploration. According to the problems arising from the development of information network in recent years, users often encounter the situation of randomly obtaining location information in the network environment, which not only threatens their privacy security, but also affects the effective transmission of information. Therefore, this study proposed the privacy protection as the core position of big data encryption method, must first clear position with large data representation and positioning information, distinguish between processing position information and the unknown information, the fuzzy encryption theory, dynamic location data regrouping, eventually build privacy protection as the core of the encryption algorithm. The empirical results show that this method can not only effectively block the intrusion of attack data, but also effectively control the error of position data encryption.
Keynote Speech

The emergence of cloud has significantly reduced the costs of hardware and software resources in computing infrastructure and the data is usually encrypted before outsourcing to the cloud. The explosion of big data mandates the use of cloud computing as the volume of the data becomes larger. This talk will focus on the effort to secure cloud storage, which is an important aspect of cloud computing adoption. We will discuss the theory and practice of secure cloud computing. More importantly, this talk will discuss new insights and research directions in cloud computing security. Particularly, we will focus on the research problems in this area and some possible solutions. We will close the talk with future directions in this area.
Patient reviews analysis using machine learning

In the present scenario, health-related tweets are available in several online forums for communication. Doctors as well as patients share their views in different discussion forums that help people seek similar information. In this work, an investigation was done by the authors on unstructured patient reviews collected from different forums regarding different diseases. The dataset was collected from Figshare which helps in identifying several features from the text provided by patients into numerical forms. Sentiment analysis (SA) was applied on the dataset to determine positive and negative tweets. Then, the bag-of-words model was used for disease detection. From the considered dataset, specific features were selected and machine learning classification algorithms such as support vector machine (SVM), Gaussian naive Bayes (NB), and random forest (RF) were applied to classify the features. Finally, the performance of classifiers was measured in terms of the parameters such as precision, recall, F1-score, support and accuracy. From the experimental results, it was found that RF results in higher accuracy of 98% as compared to SVM and Gaussian NB.
An Adaptability of Online Engineering Education Amidst the COVID19 Pandemic

The COVID19 pandemic has disturbed the daily lives of people, including those in the education sector. It has tested the readiness of Higher Education Institutes (HEIs) and put in question the adaptability of students and faculty in using online teaching-learning modalities. This paper describes the development of an online engineering education in the department of Electronics and Telecommunication Engineering of Rajarambapu Institute of Technology, Islampur. The authors sampled 211 engineering students and 16 faculty members from the department of Electronics and Telecommunication Engineering using online survey forms. The author highlighted the requirements of online teaching-learning, the implementation methodology, online platforms, sample ICT tools and active learning tools used for the delivery of contents and conducting examinations. From the feedback of students and faculties members, it was found out that availability of resources, institute support, skill development and engagement have significant contribution to the adaptability of faculties and students to online teaching-learning. The pandemic situation has enhanced the adaptability of online teaching-learning during the COVID19 pandemic and also students and faculties are ready if any other similar man-made or natural disruptions occur. Keywords— Online teaching-learning, ICT Tools, online platforms, adaptability, pandemic.
Can we visualize equity and sustainable development without Inclusivity?

The study followed a descriptive research design to assess the employee readiness for inclusive workplaces that permit transgender. The sampling method used is cluster sampling. 185 enterprises were contacted for collecting data through the structured questionnaire, out of which 152 respondents from 79 companies have participated in the research study. To test the hypothesis, Partial least square based approach was adopted. WarpPLS version 8.0 was used for hypothesis testing. The research attempted to address the stigma in the minds of the employees, to accommodate transgenders at workplace. Results demonstrated that the co-worker and senior management support was influential in reducing the stigma of peers and helped in accommodating transgenders in the workplace. Using sensitization programs and creation of structural measures, the team succeeded in placing nine transgender employees in the textile unit. Keywords: Transgender, Partial Least square, WrapPLS, Inclusivity
Development of Survey Instrument to Evaluate Freshmen Protégé Understandingon Mentoring in Engineering Education

Mentoring has a good impact and influence in undergraduate STEM students' perseverance. It is crucial in any profession and engineering is no exception. By supporting them in directing their efforts and by helping them create objectives and receive feedback, mentors may encourage and facilitate both professional and personal growth. In order for a mentee to maximize their potential, develop their skills, and transform into the person they want to be, mentoring involves guidance and support in the way they manage their learning. With this as the driving force, an effort is made in this study to develop a survey instrument to capture the understanding of the first-year engineering students in relation to the roles and responsibilities of mentors in engineering course, mentors' attention to the physical and mental health of students, goal-setting, mentees' skill development and maintaining trust and confidentiality. In fall 2022, a survey instrument was developed for quantitative analysis and administered to first-year engineering students at an autonomous college HITAM in South of India, 263 participants responded. The survey instrument's factor structure was determined using exploratory factor analysis (EFA), which yielded four factors. The Cronbach's α ranged from 0.67 and 0.92 and for factors' minimum and highest loading were 0.43 and 0.92. This survey instrument could be utilized by a varied institution in order to evaluate students' present levels of understanding for accountabilities and duties of mentors, to assist and encourage mentees learning so that they can reach their full potential and enhance their abilities.