Reviewerly/exHarmony huggingface dataset:
Factors associated with hospitalizations due to COVID-19 in a highly complex hospital service

Introduction: Understanding the profile of hospitalizations due to COVID-19 as well as the factors associated with the outcome is essential to establish better behaviors that guide quality care. Objective: To verify the existence of an association between sociodemographic and clinical variables with the outcome of hospitalization. Method: This is a retrospective cross-sectional cohort study, consisting of 170 medical records of patients hospitalized between September 2020 and January 2021 with a confirmed diagnosis of Covid-19. Data were collected using a structured instrument and analyzed according to descriptive statistics. Results: Most were male, non-health professionals, average of 61.53 years old, hospitalization of 10.77 days, 84.7% survived and 81.2% were discharged with symptoms such as dry cough, shortness of breath, fever, fatigue and tiredness, high blood pressure, obesity and type II diabetes. Death was associated with higher SBP and lower SPO2, chronic kidney disease on dialysis and malignant neoplasia. Conclusion: Survival rates were high with a low readmission rate and deaths were associated with higher SBP, having chronic kidney disease and malignant neoplasia.
A UAV Path Planning Method in Three-Dimensional Space Based on a Hybrid Gray Wolf Optimization Algorithm

Path planning, which is needed to obtain collision-free optimal paths in complex environments, is one key step within unmanned aerial vehicle (UAV) systems with various applications, such as agricultural production, target tracking, and environmental monitoring. A new hybrid gray wolf optimization algorithm—SSGWO—is proposed to plan paths for UAVs under three-dimensional agricultural environments in this paper. A nonlinear convergence factor based on trigonometric functions is used to balance local search and global search. A new relative-distance fitness adaptation strategy is created to increase the convergence speed of the SSGWO. Integrating the simulated annealing (SA) algorithm, an alternative position update strategy based on SA is proposed to improve the search process with diverse capabilities. Finally, a B-spline curve is introduced into a smooth path to ensure the path’s feasibility. The simulation results show that the SSGWO algorithm has better convergence accuracy and stability, and can obtain higher-quality paths in a three-dimensional environment, compared with GWO, MGWO, IGWO, and SOGWO.
ROLE OF COLLABORATIVE STRATEGIC READING IN POST-PANDEMIC EFL READING CLASS: A QUASI-EXPERIMENTAL STUDY

The transition of learning mode from online to offline in this post-pandemic era affects&#x0D; the EFL struggling students’ achievement in the reading context. This quasi-experimental&#x0D; study with a One-Group Pretest-Posttest design was intended to know the significant&#x0D; difference between the mean scores of the pretest and the posttest in reading obtained&#x0D; using Collaborative Strategic Reading (CSR) and reveal students’ reflections on using CSR&#x0D; in the reading classroom. Sixty-eight students in a senior high school in Indonesia were&#x0D; involved as participants. Data were collected through pretest-posttest and a&#x0D; questionnaire. The pretest and posttest results indicated that using CSR as a reading&#x0D; strategy significantly improved students’ reading skills in collaborative nuance. The&#x0D; students' perspectives on using CSR were positive since they felt highly motivated to&#x0D; practice reading collaboratively, where each reading phase has a relevant strategy,&#x0D; namely, Preview, Click and Clunk, Get the Gist, and Wrap up. Integrating CSR into reading&#x0D; may have positive pedagogical impacts for EFL teachers who want to try CSR in teaching&#x0D; reading in this post-pandemic nuance since it eliminates students' anxiety in the transition&#x0D; of learning mode after the COVID-19 outbreak.
CSST-Net: Channel Split Spatiotemporal Network for Human Action Recognition

Temporal reasoning is crucial for action recognition tasks. The previous works use 3D CNNs to jointly capture spatiotemporal information, but it causes a lot of computational costs as well. To improve the above problems, we propose a general channel split spatiotemporal network (CSST-Net) to achieve effective spatiotemporal feature representation learning. The CSST module consists of the grouped spatiotemporal modeling (GSTM) module and the parameter-free feature fusion (PFFF) module. The GSTM module decomposes features into spatial and temporal parts along the channel dimension in parallel, which focuses on spatial and temporal clues respectively. Meanwhile, we utilize the combination of group-wise convolution and point-wise convolution to reduce the number of parameters in the temporal branch, thus alleviating the overfitting of 3D CNNs. Furthermore, for the problem of spatiotemporal feature fusion, the PFFF module performs the recalibration and fusion of spatial and temporal features by a soft attention mechanism, without introducing extra parameters, thus ensuring the correct network information flow effectively. Finally, extensive experiments on three benchmark databases (Sth-Sth V1, Sth-Sth V2, and Jester) indicate that the proposed CSST-Net can achieve competitive performance compared to existing methods, and significantly reduces the number of parameters and FLOPs of 3D CNNs baseline.
Active methodologies in mathematics learning

Active methodologies are necessary in the educational process because they comprise a fundamental tool for the student to achieve an adequate level of success in their educational process. It is important to mention that the student plays a very important role, where students build their knowledge based on scenarios and activities designed by the teacher. Analyzing the impact of the implementation of Problem-Based Learning, the research is relevant because active methodologies include interactive teaching-learning processes that are based on active communication and the interconnection between teachers, students, and teaching material. Its advantages lie in the application of methods where the role in the classroom passes from the teacher to the student, who must adopt a greater degree of involvement than in more traditional or classic classes. The objective of the research is to know the impact they produce between the use of active methodology in learning mathematics in students at the basic general education level, and upper basic sublevel of the Eloy Alfaro de Chone Educational Unit, during the 2023 school year, where the inductive method was used for the investigation Development. Obtaining as a result that the application of active methodologies in curricular programs and the classroom generates cognitive, social, and motivational advantages for students, providing improvements to the educational system.
MPCM: Multi-modal User Portrait Classification Model Based on Collaborative Learning

A social-media user portrait is an important means of improving the quality of an Internet information service. Current user profiling methods do not discriminate the emotional differences of users of different genders and ages on social media against a background of multi-modality and a lack of domain sentiment labels. This paper adopts the sentiment analysis of images and text to improve label classification, incorporating gender and age differences in the sentiment analysis of multi-modal social-media user profiles. In the absence of domain sentiment labels, instance transfer learning technology is used to express the learning method with the sentiment of text and images; the semantic association learning of multi-modal data of graphics and text is realized; and a multi-modal attention mechanism is introduced to establish the hidden image and text. Alignment relationships are used to address the semantic and modal gaps between modalities. A multi-modal user portrait label classification model (MPCM) is constructed. In an analysis of the sentiment data of User users on Facebook, Twitter, and News, the MPCM method is compared with the naive Bayes, Latent Dirichlet Allocation, Tweet-LDA and LUBD-CM(3) methods in terms of accuracy, precision, recall and the FL-score. At a 95% confidence, the performance is improved by 1% to 4% by using the MPCM method.
Linking Ethical Leadership and Organizational Social Capital Towards Enhancing Organizational Performance in the Somali Public Sector

Public administration scholars have continued to be interested in organizational performance and have attempted to understand its influencing factors. Public sector organizational performance is vital because it helps the sector accomplish better with limited resources. In the private sector, there is conclusive evidence of organizational performance and the factors that influence it. However, the issue is largely unknown and under-researched in the public sector, particularly in the Somali context. Thus, grounded in social capital and social learning theories, the study aims to unveil the impact of ethical leadership on public sector organizational performance in Somalia via the underlying mechanism of organizational social capital. Data were gathered from 343 public servants in two Somali regional administrations (the Banadir and Jubaland). SmartPLS 4 software was used to analyze the data. The results showed that the ethical leadership of public-sector organizations positively and significantly impacts both public-sector organizational performance and organizational social capital. Moreover, the findings showed that social capital partially mediates the association between ethical leadership and public-sector organizational performance. The results of this study have important implications for policymakers and public sector leaders in Somalia. The present study is the first to model the mediating effect of public-sector organizational social capital in the association between ethical leadership and public sector organizational performance in Somalia.
A Developed Framework for Studying Cyberethical Behaviour in North Central Nigeria

ICT advancements have enabled more online activities, resulting in several cyberethical behaviours. Literature documents prevalence of plagiarism, and online fraud, among other misbehaviours. While behaviour has been explained by several theories, as scholarship and research advances, frameworks are modified to include more constructs. The researchers modified Fishbein and Ajzen (2011)'s Reasoned Action Approach to study female postgraduate students' perspectives toward cyberethical behaviour in North Central Nigerian universities. The study focused on four variables, which were adequately captured by the model: perception, awareness, and attitude. An adapted questionnaire collected quantitative data. This study used multistage sampling. A sample size of 989 north central Nigerian female university postgraduate students were selected from a population of 9,000. The majority of respondents reported not using copyrighted materials without permission (X = 3.48, N = 834, SD = 0.68) and using student emails for academic purposes (X = 3.36, N = 834, SD = 0.673). Perception and attitude toward cyberethical behaviour were positively correlated (r =0.17; P &lt; 0.05). Perception, awareness, and attitude toward cyberethical behaviour correlated (R2 =0.22). Perception and awareness significantly affected cyberethical behaviour (R2 = 0.09). We recommend that university administrators implement intentional cyberethics policies and curricula to promote ethical internet use.
Automatic Graph Modeling of Power Transformer Management Data

Data for electrical components is maintained by different information systems in China. Currently, the relational database is commonly used to obtain and manage data for equipment maintenance purposes. However, as the modern power system develops, there is a growing need for massive data processing from different sources, which poses challenges to relational databases of its limitations in model flexibility, query efficiency, and scalability. Compared to traditional relational databases, graph databases handle fast-changing, inter-connected data well, offer flexibility, and have higher query efficiency. This paper introduces a graph modeling technique for transformer equipment to quickly acquire and maintain data for transformer equipment operation and management purposes. An initial data screening is performed to select data with different sources, structures, and characteristics. Word2vec and K-means are adapted to define and cluster model candidate sets. TextRank is applied to perform disambiguation for those transformer model sets. The transformer management graph model is then optimized based on its business scenario. The automatic resulting model provides comprehensive transformer data management, which opens transformer equipment operation and maintenance scenarios. This work significantly improves transformer automatic modeling and data management efficiency and compatibility. The results demonstrate the importance and potential of applying graph database in electrical component data management.
Three Perspectives on the Value of Bridgers Within IT: A Longitudinal Study of Eight Years of Job Placement of IT Graduates

Job placement data is important for understanding where graduates get jobs. We present an empirical study of eight years of undergraduate and graduate IT placement data to explore IT jobs obtained within IT functions. Niederman, Ferratt, and Trauth (2016) propose clustering IT jobs into four macro-level categories based on the knowledge, skills, and abilities (KSAs) required for these jobs: IT bridgers, technical specialists, application domain specialists, and IT managers. They argue an increased need for and value placed on “bridgers” who can fill bridging jobs within IT. Bridgers within IT possess both technical and “soft skills” like communications/managerial and change/project management and work within the IT function but can liaise between IT and business units. Using a longitudinal sample of 1,980 IT graduates from one Information School, the data supports that IT bridgers are hired within IT, are hired earlier, and are largely hired in consulting, technology, and finance industry sectors. Contributions include exploring the utility of the macro-level categorization of NFT IT job categories to analyze job placement, the importance of tracking IT job placement in employer sectors, and the overall value of IT placement data for IT educators and administrators.
FedTA: Federated Worthy Task Assignment for Crowd Workers

Crowdsourcing is a promising computing paradigm for processing computer-hard tasks by harnessing human intelligence. How to protect online workers' privacy is a hindrance for deploying crowdsourcing in the real world. Attempts have been made to address this issue by injecting noise or encrypting sensitive data, which cause quality loss and/or heavy computation and communication load. In this paper, we propose an approach, called FedTA (Federated Worthy Task Assignment for Crowd Workers), to protect a crowd worker's private data while ensuring quality. FedTA trains a client model based on the private data and annotations owned by a worker and uploads client models to aggregate the server model, without leaking the privacy of task data. To account for the varying task distributions (i.e., non-i.i.d.) and error-prone annotations of tasks, it leverages the feature similarity and semantic similarity separately derived from client and server models on local tasks, to quantify the quality of annotations and clients. Based on those, it further introduces a task assignment strategy to notify the clients which tasks are worthy and suitable for annotations. This strategy can incrementally improve the performance of client and server models. At the same time, it disregards the unworthy tasks to save the budget and to avoid their negative impact. Experimental results show that FedTA can complete secure crowdsourcing projects with high quality and low budget.
Cinematica Sentiment Analysis

Sentiment analysis, a subfield of natural language processing, holds significant importance in understanding the emotions and opinions expressed in textual data. This project focuses on applying sentiment analysis techniques to movie reviews, aiming to develop an efficient model for automatically classifying sentiments as positive, negative, or neutral. The primary goal of this project is to create a robust sentiment analysis of accurately categorizing the sentiment conveyed in movie reviews. By leveraging machine learning algorithms and natural language processing techniques, the model aims to provide insights into audience reactions and contribute to the broader field of sentiment analysis.
The Role of Forensic Accounting and Non-Financial Measurement for the Financial Audit

Due to a lack of competency, including knowledge and experience, non-financial measurement skills are now required by auditors in order to prove fraud. As a result, this research aims to identify the gap between the practice requirements and the output of the audit training centre and the role of the forensic accounting curriculum in filling that gap. This research examines the best strategies for establishing competent and reliable auditing practice results. The non-participatory observation data mining method is used in this study. The study discovered that the auditing education and training centre had not taught and did not have a fraud prevention and detection curriculum based on non-financial measurement. As a result, this study recommends that Training Centre develop the audit curriculum in accordance with The International Education Standard. Furthermore, this study suggests that auditing education and training centres involve forensic accounting practitioners in team-teaching, particularly investigative audit topics.
ANDRAGOGIC CONCEPT OF ACCOMPANYING TEACHERS IN THE CONTEXT OF DIGITAL TRANSFORMATION OF EDUCATION: LEADING IDEAS, PRINCIPLES AND PATTERNS

In the context of the digital transformation of education, it is of interest to study the structural elements of the andragogical concept of accompanying teachers. We have systematized ideas and views on the organization of procedural and effective characteristics of the author’s concept as a new form of scientific psychological and pedagogical knowledge. The semantic core of the pedagogical goal-setting of the author’s concept and the system of fundamental leading ideas of the concept: 1) timely identification of problem areas of digital transformation of education and barriers that complicate the formation of digital literacy of teachers for optimal minimization of the identified risky areas of professional and pedagogical activity based on the implementation of various types and strategies of support; 2) the need to take into account andragogical invariants when implementing support for teachers in the context of the digital transformation of education in a complementary combination of the desire of teaching staff for full selfrealization in the profession and increasing the level of their own digital literacy; 3) implementation of organizational facilitating aspects of support for the optimal transition of teachers from the traditional teaching model to an integrated one using the multifunctional capabilities of the electronic information and educational environment, digital technologies and resources. The patterns of the author’s concept set the logic for the functioning of the phenomenon under study: a) the productivity of the formation of digital literacy of teachers (as a result of the concept) depends on the complementary use of strategies to support teaching staff in the electronic information and educational environment of the organization; b) the active development by teachers of new social-role functionality and methods of professional activity in the field of digitalization of education depends on the timely implementation of the andragogical position of the teacher-mentor; c) the constructiveness of the development of andragogical invariants of teachers in the context of the digital transformation of education depends on the directions of support for updating the mechanisms of social partnership of educational subjects and the development of professional collaborations. Based on the identified patterns, principles have been identified that are designed to fulfill the regulatory function of pedagogical activity: openness of education to external demands of the digital world; subjective focus on continuity of education; collaboration and digital dialogue; facilitation; social and professional activity; personal self-realization of teachers as digital professionals.
Digital Certificate Security: A Blockchain-based Approach for Fraud Prevention and Verification

With the rise of digitalization and increased internet usage, digital certificates' security issues have gained significant importance. The utilization of counterfeit certificates can lead to deceptive identity and capability verifications, allowing untrustworthy individuals to assume misleading positions. Simultaneously, factors such as data leaks and technical glitches can jeopardize the security of certificates. These challenges can complicate talent verification processes and result in unsuitable individuals being placed in incorrect roles. The proposed work aims to thwart forged certificates and ensure their verifiability within this context. The methodology employed in the study strives to mitigate potential data loss or accessibility problems by adopting a decentralized storage structure. The primary objective is to establish certificates that are reliable, traceable, and capable of being verified. A rapid and efficient user interface was developed using React.js to achieve this aim. Smart contracts were scripted on the Ethereum blockchain using Solidity, and data, including user information and certificate details, were stored within components like MongoDB. In the phase of practical implementation, diverse scenarios have been designed to facilitate the generation, verification, and monitoring of certificates. The proposed approach is geared towards deterring forgery and enhancing the credibility of certificates. Through this system, users and organizations can authenticate certificates and prevent the proliferation of counterfeit ones. Furthermore, the enhanced security and ease of sharing certificates on digital platforms offer substantial advantages to certificate holders. In summary, this study strives to enhance the safeguarding and dependability of certificates by addressing the security concerns posed by the era of digitalization.
Inter-class Sparsity Based Non-negative Transition Sub-space Learning

Least squares regression has shown promising performance in the supervised classification. However, conventional least squares regression commonly faces two limitations that severely restrict their effectiveness. Firstly, the strict zero-one label matrix utilized in least squares regression provides limited freedom for classification. Secondly, the modeling process does not fully consider the correlations among samples from the same class. To address the above issues, this paper proposes the inter-class sparsity-based non-negative transition sub-space learning (ICSN-TSL) method. Our approach exploits a transition sub-space to bridge the raw image space and the label space. By learning two distinct transformation matrices, we obtain a low-dimensional representation of the data while ensuring model flexibility. Additionally, an inter-class sparsity term is introduced to learn a more discriminative projection matrix. Experimental results on image databases demonstrate the superiority of ICSN-TSL over existing methods in terms of recognition rate. The proposed ICSN-TSL achieves a recognition rate of up to 98% in normal cases. Notably, it also achieves a classification accuracy of over 87% even on artificially corrupted images.