Reviewerly/exHarmony huggingface dataset:
COMPUTER VISION SYNDROME IN MEDICAL STUDENTS IN THE ERA OF THE COVID-19 PANDEMIC

Introduction: In the era of the COVID-19 pandemic, there are pretty drastic lifestyle changes, one of which is in the field of education. A distance learning system (PJJ) increased the use of Visual Digital Terminal (VDT) such as computers, laptops, mobile phones, and tablets for lecture and non-lecture activities. This causes a group of problems in the eyes and sight called Computer Vision Syndrome. Some factors that cause Computer Vision Syndrome include long duration, the distance between the eyes and screen is not optimal, incorrect ergonomics, angle of vision above eye level, and using the air conditioner. Aim: To analyze the factors that affect the incidence of Computer Vision Syndrome in students in the era of the COVID-19 pandemic. Methods: This research uses a cross-sectional study with non-probability sampling techniques through consecutive sampling. The research period is online from July 31st to September 9th, 2021. Result: Respondents who experienced Computer Vision Syndrome were 92 people out of a total of 147 respondents. Multivariate analysis with multiple logistic regressions showed that total duration, distance, and room temperature significantly affected Computer Vision Syndrome (P&lt;0.05), with the most dominant factor being the use of air conditioning (OR 6.214). Conclusion: Factors that significantly affect the incidence of Computer Vision Syndrome in FKUKWMS students in the era of the COVID-19 pandemic with distance learning system are total duration, distance, and air conditioner use.
Correction: Privacy-preserving parallel kNN classification algorithm using index-based filtering in cloud computing

[This corrects the article DOI: 10.1371/journal.pone.0267908.].
Are eye movements and EEG on the same page?: A coregistration study on parafoveal preview and lexical frequency.

Readers extract visual and linguistic information not only from fixated words but also upcoming parafoveal words to introduce new input efficiently into the language processing pipeline. The lexical frequency of upcoming words and similarity with subsequent foveal information both influence the amount of time people spend once they fixate the word foveally. However, it is unclear from eye movements alone the extent to which parafoveal word processing, and the integration of that word with foveally obtained information, continues after saccade plans have been initiated. To investigate the underlying neural processes involved in word recognition after saccade planning, we coregistered electroencephalogram (EEG) and eye movements during a gaze-contingent display change paradigm. We orthogonally manipulated the frequency of the parafoveal and foveal words and measured fixation related potentials (FRPs) upon foveal fixation. Eye movements showed primarily an effect of preview frequency, suggesting that saccade planning is based on the familiarity of the parafoveal input. FRPs, on the other hand, demonstrated a disruption in downstream processing when parafoveal and foveal input differed, but only when the parafoveal word was high frequency. These findings demonstrate that lexical processing continues after the eyes have moved away from a word and that eye movements and FRPs provide distinct but complementary accounts about oculomotor behavior and neural processing that cannot be obtained from either method in isolation. Furthermore, these findings put constraints on models of reading by suggesting that lexical processes that occur before an eye movement program is initiated are qualitatively different from those that occur afterward. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Leveraging Labeling Representations in Uncertainty-Based Semi-supervised Segmentation

Semi-supervised segmentation tackles the scarcity of annotations by leveraging unlabeled data with a small amount of labeled data. A prominent way to utilize the unlabeled data is by consistency training which commonly uses a teacher-student network, where a teacher guides a student segmentation. The predictions of unlabeled data are not reliable, therefore, uncertainty-aware methods have been proposed to gradually learn from meaningful and reliable predictions. Uncertainty estimation, however, relies on multiple inferences from model predictions that need to be computed for each training step, which is computationally expensive. This work proposes a novel method to estimate the pixel-level uncertainty by leveraging the labeling representation of segmentation masks. On the one hand, a labeling representation is learnt to represent the available segmentation masks. The learnt labeling representation is used to map the prediction of the segmentation into a set of plausible masks. Such a reconstructed segmentation mask aids in estimating the pixel-level uncertainty guiding the segmentation network. The proposed method estimates the uncertainty with a single inference from the labeling representation, thereby reducing the total computation. We evaluate our method on the 3D segmentation of left atrium in MRI, and we show that our uncertainty estimates from our labeling representation improve the segmentation accuracy over state-of-the-art methods. Code is released at GitHub .
Development strategies as catalysts for provision of the RDM services in the South African higher education institutions

A strategy in this paper was viewed as a plan of action for achieving the mission and vision of an organisation. This paper presents preliminary findings of the larger study which aimed to determine the strategies for research data management (RDM) at selected universities in KwaZulu-Natal. The current study used the community capability maturity model framework (CCMF) and the digital curation centre (DCC) lifecycle model as theoretical support to determine the strategies for RDM service provision with specific reference to the University of Zululand. The interpretive paradigm, following the qualitative research approach through a single case study, was used. Primary data was gathered through online interviews using Zoom and Teams with Librarians, Technicians, HODs, and DVC Research due to the Covid-19 pandemic and availability of technologies. The findings of the study revealed the University of Zululand does not have an RDM policy; however, research activities are practiced. The University lacks the infrastructure and investment to support RDM services and activities. The study is significant for providing the background for developing RDM in the public university through RDM strategy and policy. The findings also sought to inform the university’s RDM agenda.
Mask Scoring R-CNN

Letting a deep network be aware of the quality of its own predictions is an interesting yet important problem. In the task of instance segmentation, the confidence of instance classification is used as mask quality score in most instance segmentation frameworks. However, the mask quality, quantified as the IoU between the instance mask and its ground truth, is usually not well correlated with classification score. In this paper, we study this problem and propose Mask Scoring R-CNN which contains a network block to learn the quality of the predicted instance masks. The proposed network block takes the instance feature and the corresponding predicted mask together to regress the mask IoU. The mask scoring strategy calibrates the misalignment between mask quality and mask score, and improves instance segmentation performance by prioritizing more accurate mask predictions during COCO AP evaluation. By extensive evaluations on the COCO dataset, Mask Scoring R-CNN brings consistent and noticeable gain with different models, and outperforms the state-of-the-art Mask R-CNN. We hope our simple and effective approach will provide a new direction for improving instance segmentation. The source code of our method is available at \url{https://github.com/zjhuang22/maskscoring_rcnn}.
Ledger to ledger: off- and on-chain auditing of stablecoin

To assess the financial statements of companies that invest in stablecoin, a digital representation of a fiat currency managed and backed by a blockchain, the auditor must collect evidence of transactions from the blockchain (on-chain) or from an off-chain ledger managed by an intermediary. This study aims to expand the understanding of such transactions, outline possible configurations for the recognition of stablecoin balances and transactions in financial statements, and audit procedures for collecting evidence of these transactions. Based on actual transactions of stablecoin registered on the Ethereum blockchain, we present a hypothetical case of the accounting history of an audited company to demonstrate the challenges in establishing accounting and audit procedures for these novel transactions. We observe an abundance, diversity, and unprecedentedness in the stablecoin transactions studied. We further identify the need to adapt current audit procedures and create new ones, and rethink the very process of doing so. The findings could help auditors obtain more significant knowledge of the information required to assess a company’s financial statements when such statements include stablecoin transactions. In addition, the study addresses the evolving relationship between auditing, accounting, and information technology, and the problems in integrating accounting and information technology.
TempAMLSI: Temporal Action Model Learning Based on STRIPS Translation

Hand-encoding PDDL domains is generally considered difficult, tedious and error-prone. The difficulty is even greater when temporal domains have to be encoded. Indeed, actions have a duration and their effects are not instantaneous. In this paper, we present TempAMLSI, an algorithm based on the AMLSI approach to learn temporal domains. TempAMLSI is the first approach able to learn temporal domains with single hard envelopes, and TempAMLSI is the first approach able to deal with both partial and noisy observations. We show experimentally that TempAMLSI learns accurate temporal domains, i.e., temporal domains that can be used without human proofreading to solve new planning problems with different forms of action concurrency.
Research on the Optimization of College Teachers’ Flow Management Strategy under the Support of Artificial Intelligence Technology

In recent years, with the establishment and improvement of the academic labor market system, the flow of college teachers has become more frequent. On the one hand, the rational and orderly flow of teachers, as a social construction, is of great significance for deconstructing the solidification of academic structure and promoting academic development and progress. But on the other hand, we should also see that the disorderly flow of teachers also has a strong destructive effect on destroying the inherent academic structure, leading to vicious competition in the academic labor market, and weakening the willpower of teachers to devote themselves to learning. Therefore, it is of great significance to analyze the dynamic factors of college teachers’ mobility, scientifically explain the internal mechanism of college teachers’ mobility, promote the improvement of college teachers’ mobility policies, and defend against disorderly mobility such as “poaching wars.” Based on the mobility dynamic model in colleges and universities, this paper puts forward some thoughts on promoting the mobility of college teachers. Colleges can improve the promotion and development channels of teachers and optimize their working conditions. The study also emphasizes on developing basic salary standard of teachers thereby establish a remuneration system for distribution on the basis of their performance. For good measure, they can also stabilize the current teachers while attracting more outstanding talents by exploiting the advantages of location, making more efforts to build high-level disciplines, and offering more favorable conditions for talent introduction.
A Meta-Analysis on Mobile-Assisted Language Learning Applications: Benefits and Risks

Mobile language learning applications are a pervasive facet of modern life, however evidence on their effectiveness on L2 learning outcomes is lacking. In the current work, we sought to determine the effect of mobile language learning applications on L2 proficiency between groups who used mobile language learning applications and control groups who learned with traditional methods on L2 achievement. We systematically searched journal articles and grey literature between 2007-2019 and performed a quantitative meta-analysis based on 23 synthesized effect sizes. We also performed risk of bias and quality of evidence assessments on our included papers. We found a moderate-to-strong overall effect (g = 0.88) of learning achievement using mobile language applications compared to control groups who learned with traditional approaches. At the same time, we found high risk of bias and low quality of evidence across all included studies. Our results provide evidence for mobile applications as a beneficial tool for second language learning. However, findings should be treated with caution due to risks of high bias and low quality of evidence. Improvements for future studies are discussed.
I2MB: Intelligent Immersive Multimedia Broadcast in Next-Generation Cellular Networks

The popularity of immersive multimedia content is prevalent and the consumption of 360° videos is increasing rapidly in varied domains. The broadcast of such content in cellular networks will be challenging in terms of dynamic content adaptation and efficient resource allocation to serve heterogeneous consumers. In this work, we propose an intelligent immersive new radio multimedia broadcast multicast system (NR-MBMS), I2MB, for next-generation cellular networks. I2MB intelligently forecasts the users’ viewing angle and the 360° video tiles to be broadcast beforehand using long short-term memory network. We define broadcast areas by using modified K-means clustering. The complex multivariable optimization problem that integrates efficient adaptive 360-degree video encoding and tiled broadcast using optimized transmission parameters is defined as as a Markov Decision Process (MDP) . In a dense urban scenario with a large MBSFN synchronization area, the state and action space dimensionality is very high, in which the solution is obtained by using Deep Deterministic Policy Gradient (DDPG) algorithm. I2MB incorporates Deep Reinforcement Learning based radio resource allocation (modulation-coding scheme and frequency-time resource blocks) and tiled video encoding to maximize the viewport video quality experienced by the broadcast mobile users. I2MB provides improved immersive video broadcast streaming quality while serving a higher number of mobile users. Adaptive encoding of 360° video tiles and radio resource allocation are performed based on users’ forecasted viewing angle, spatial distribution, channel conditions, and service request. The performance evaluation of our proposed scheme, I2MB, shows considerable gains in viewport quality ( 46.83%) and number of users served ( 30.52%), over a recent state-of-the-art method VRCAST.
Cognitive Artificial Intelligence Using Bayesian Computing Based on Hybrid Monte Carlo Algorithm

Cognitive artificial intelligence (CAI) is an intelligent machine that thinks and behaves similar to humans. CAI also has an ability to mimic human emotions. With the development of AI in various fields, the interest and demand for CAI are continuously increasing. Most of the current AI research focuses on the realization of intelligence that can make optimal decisions. Existing AI studies have not conducted in-depth research on human emotions and cognitive perspectives. However, in the future, the demand for the use of AI that can imitate human emotions in various fields, such as healthcare and education, will continue. Therefore, we propose a method to build CAI in this paper. We also use Bayesian inference and computing based on the hybrid Monte Carlo algorithm for CAI development. To show how the proposed method for CAI can be applied to practical problems, we create an experiment using simulation data.
Perbandingan Kualitas Air Sumur Galian dan Bor Berdasarkan Parameter Kimia dan Parameter Fisika

This study aims to analyze and compare the quality of dug well water and bore well water around the UNIB Kandang Limun campus. The descriptive method was used to analyze 60 samples, consisting of 30 water samples for each dug well and drilled well. The parameters measured were the degree of acidity (pH), total dissolved particles (TDS), salt content, and electrical conductivity (DHL). This study found that only 35% of the samples met the class I water quality standards. The samples consisted of 7 dug well samples and 14 drilled well samples. Most of the samples (53%) had an acidic pH below 6.5, while the other 12% did not meet the quality standards from TDS, DHL, or both. The pH value of dug well water was significantly lower (more acidic) than drilled well water (p &lt; 0.0001, F1.58=27.28), but the TDS and DHL values ​​in drilled well water were significantly higher (p &lt; 0.01, F1.58=9.09 for TDS; p &lt; 0.001, F1.58=15.89 for DHL). In conclusion, the water quality of drilled wells in the Kandang Limun Campus area is better than that of dug wells.&#x0D; Keywords: Lemonade Cage, Water Quality, Drilling Well, Excavation Well
Perceptions of Environmental Protection of University Students: A Look through Digital Competences in Mexico

This research reflects on the environmental problems that we are currently experiencing as a society and discusses education as an opportunity to solve them. Several frameworks of university digital competencies are identified and environmental care is highlighted as an important part of them, so the objective is to know the perceptions of university students in Mexico on digital competency and environmental protection and verify the following hypothesis: “currently there a is little development of environmental awareness and the relationship between the use of Information Technology (IT) as well as its environmental impact among university students”. For this purpose, a quantitative, non-experimental approach was used, specifically, a descriptive cross-sectional design using a Likert-type questionnaire was applied on 22 and 23 November 2021 to 135 students from two public universities belonging to four undergraduate programs in Mexico. The results indicate that most students are aware that the use of electronic devices for educational purposes has an impact on the environment and that, therefore, it is necessary to adopt effective measures and habits for an adequate and optimal use of digital environments.
Determination of Flood Susceptibility Index Using Overlay-Scoring Data Method based on Geographic Information System (GIS) in Semarang City, Central Java, Indonesia

Semarang City is the capital of the Central Java Province, located in the lowlands and directly adjacent to the north side of the Java Sea. This geographical condition makes this city very vulnerable to being affected by floods. This study aims to determine the distribution of flood-prone locations and classify the level of flood susceptibility that occurs in the Semarang City. Quantitative descriptive is the type of research with an overlay method based on scoring parameters related to environmental conditions. These parameters include slope, soil type, rainfall, altitude, land use and river flow buffers. The scored parameters are then entered into the overlay stage with other parameters in the geographic information system (GIS) application to form a map of the flood susceptibility level. The data analysis technique used the descriptive method. The level of flood susceptibility map resulting from this study is in one area of the Pemali-Juana Sub-watershed. The results also show that the scattered highly vulnerable areas in the northern part of Semarang City are areas with low elevations and flat slopes, then dominated by settlement land use that lacks infiltration areas, small river buffer sizes. These are what make the area classified as an area that is very prone to flooding.
Penentuan Siswa Berprestasi Dengan Metode Simple Additive Weighting Berbasis Web

Decision support system (SPK) is one part of the information system that uses problem solving in decision making. The selection of high achieving students in Yapim High School and Vocational School Taruna Marelan discusses based on the highest grades, the activeness and concern of students towards the teacher and also based on the rankings of the guardians of each class that are counted manually with the Microsoft excel application, therefore the application required problem solving application Web-based outstanding students. Data obtained through teachers and homeroom teachers who teach at Yapim Taruna Marelan High School and Vocational School, the method used in this application is weighting simple additives (SAW) in the assessment of student achievement based on data from student and high school students 'and Yapim Taruna's students' judgment Marelan This application is used to facilitate teachers and homeroom teachers in favor of outstanding students and also transparent by all students. This system is applied to support decisions made by humans from manual to digital and can be shown in full which becomes an assessment made by the user and proven by the system.
Analisis Sentimen Ulasan Aplikasi Mola Pada Google Play Store Menggunakan Algoritma Support Vector Machine

MOLA is one of the video streaming platform applications on the google play store. The mola application has been downloaded 5 million times but only has a 3.2 rating. On the Google Play Store app rating is followed by user reviews of the app. There are quite a lot of reviews that are unstructured and contain opinions from users about their satisfaction with the application so that it is often taken into consideration by potential users to choose the application used. Based on this, sentiment analysis was carried out using the Support Vector Machine algorithm to find out how the sentiments of users towards the MOLA application on the google play store were carried out. This study uses the Knowledge Discovery in Database (KDD) method. The data used is a review of the MOLA application with as many 520 data consisting of 312 positive reviews and 208 negative reviews. The best results are obtained in scenario 1 (90:10) using the RBF (Radial Basis Function) kernel which produces 92.31% accuracy, 96.3% precision, 89.66% recall, and 92.86% f1-score.&#x0D; &#x0D; Keywords: Sentiment Analysis, Support Vector Machine, MOLA
Wild Animal Classifier Using CNN

Classification and identification of wild animals for tracking and protection purposes has become increasingly important with the deterioration of the environment, and technology is the agent of change which augments this process with novel solutions. Computer vision is one such technology which uses the abilities of artificial intelligence and machine learning models on visual inputs. Convolution neural networks (CNNs) have multiple layers which have different weights for the purpose of prediction of a particular input. The precedent for classification, however, is set by the image processing techniques which provide nearly ideal input images that produce optimal results. Image segmentation is one such widely used image processing method which provides a clear demarcation of the areas of interest in the image, be it regions or objects. The Efficiency of CNN can be related to the preprocessing done before training. Further, it is a well-established fact that heterogeneity in image sources is detrimental to the performance of CNNs. Thus, the added functionality of heterogeneity elimination is performed by the image processing techniques, introducing a level of consistency that sets the tone for the excellent feature extraction and eventually in classification.
Digital Pathology: New Initiative in Pathology

Digital pathology (DP) is an emerging field of pathology that manages information generated from digitized specimen slides [...]