OCR for TIFF Compressed Document Images Directly in Compressed Domain Using Text segmentation and Hidden Markov Model

In today's technological era, document images play an important and integral part in our day to day life, and specifically with the surge of Covid-19, digitally scanned documents have become key source of communication, thus avoiding any sort of infection through physical contact. Storage and transmission of scanned document images is a very memory intensive task, hence compression techniques are being used to reduce the image size before archival and transmission. To extract information or to operate on the compressed images, we have two ways of doing it. The first way is to decompress the image and operate on it and subsequently compress it again for the efficiency of storage and transmission. The other way is to use the characteristics of the underlying compression algorithm to directly process the images in their compressed form without involving decompression and re-compression. In this paper, we propose a novel idea of developing an OCR for CCITT (The International Telegraph and Telephone Consultative Committee) compressed machine printed TIFF document images directly in the compressed domain. After segmenting text regions into lines and words, HMM is applied for recognition using three coding modes of CCITT-horizontal, vertical and the pass mode. Experimental results show that OCR on pass modes give a promising results. © 2022 IEEE.

Prevalence of pharmacological and non-pharmacological coping mechanisms for anxiety management during the COVID-19 pandemic: investigating the transition to online learning among medical students.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic had a devastating effect on college students worldwide. Here, the authors aimed to determine the prevalence of anxiety and its related coping strategies, provide a theoretical basis for understanding self-prescription, and identify the factors contributing to stress and anxiety in medical students during the pandemic. METHODS: The authors conducted a cross-sectional study among medical students in Saudi Arabia from September to November 2020. They assessed anxiety using the GAD-7 scale based on seven core symptoms. The authors also examined perceived psychological stress using a single-item measure of stress, the factors contributing to stress during the transition to online learning and examinations, and related coping strategies. The Statistical Package for Social Sciences (SPSS) version 26.0 was used to examine the data for both descriptive and inferential analyses. Chi-square test, one-way ANOVA, and univariate linear regression were used to test the research hypotheses. RESULTS: The authors collected and analyzed data from 7116 medical students distributed across 38 medical colleges. Among them, 40% reported moderate to severe anxiety symptoms. Pre-clinical and female students experienced more stress than clinical and male students. 12.19% (n = 868) of respondents reported using medication during their college years. Among those, 58.9% (n = 512) had moderate to severe anxiety, and the most commonly used drug was propranolol (45.4%, n = 394). Among the studied sample, 40.4% (n = 351) decreased their medication use after switching to online teaching. Most students used these medications during the final exam (35.8%, n = 311) and before the oral exam (35.5%, n = 308). In terms of coping strategies, males were much more likely to use substances than females, who mainly resorted to other strategies. CONCLUSIONS: This study provides a national overview of the impact of COVID-19 on the mental health of medical students. The results indicated that the pandemic is associated with highly significant levels of anxiety. These findings can provide theoretical evidence for the need for supportive psychological assistance from academic leaders in this regard.

Leveraging Free-Text Information to Detect Duplicates in COVID-19 Vaccine Adverse Event Reports: An International Journal of Medical Toxicology and Drug Experience

Introduction: Uppsala Monitoring Centre (UMC) manage VigiBase;the largest global database of reports of suspected adverse events (side effects) to medicines, on behalf of the World Health organisation (WHO). Following the emergency rollout of the vaccines against COVID-19, combined with a global focus on monitoring their safety, UMC saw a sharp increase in the volume of reports of suspected side effects of the vaccines. UMC sometimes receives multiple reports corresponding to the same suspected adverse event. This can have undesirable effects when it comes to both statistical signal detection and manual review of cases. Duplicate detection of vaccines has historically been especially challenging, due to homogeneity of patients. However, the extreme quantity of COVID-19 vaccine reports has highlighted the necessity for automated duplicate detection to be performant for them. Detecting duplicate reports is a non-trivial problem. Since reports do not always contain the same level of detail, and data errors can lead to different values in corresponding fields for duplicate reports, reports cannot simply be compared field by field. Several methods have been proposed for detecting duplicates based on information provided in structured form (sex, age, date of onset etc) (1,2). In our study we additionally incorporate free text information into a duplicate detection model. Objective: To leverage the free text information in suspected adverse event reports to identify duplicate reports which are referring to the same adverse event. Methods: Our method ensembles state-of-the-art machine learning methods.Narratives are placed in a spacewhere a smaller distance between two narratives conveys higher semantic similarity. This is done with vector embeddings using the SapBERT model, fine-tuned on a set of known duplicate reports (3). Two reports are then compared using the cosine similarity between the vector embeddings for the two narratives. This similarity is combined with representations of the structured information used in othermethods in a gradient boosted decision tree model, calibrated by a logistic regression model to fine tune the probability output (4). These methods are evaluated on a set of curated datasets of COVID- 19 vaccine reports comprising 1239 pairs of known duplicates. We use random pairs of COVID-19 vaccine reports as examples of nonduplicates. Results: Our model successfully identifies 78.9% of known duplicate pairs. It achieved a false positive rate (the number of non-duplicates erroneously marked as duplicates) of 0.001%. The full results can be seen in table 1. Conclusion: Not Applicable.

Teaching dance in digital environments: copying through video

The central question in this paper is: What kind of knowledge is acquirable in the process of copying dance via a video? This question is approached through choreographic practice, teaching and learning experiences, as well as theoretical research. Subjects that are raised include the transmission of embodied knowledge in digital environments, the experience of failure or success, and the relationship between dance teachers and students. The possibilities offered to dance educators and students are explored, through an analysis of two dance experiments set into practice within distance education, due to COVID-19, shedding light into new perspectives in asynchronous teaching methods.

Copy and paste in the electronic medical record: A scoping review

Copy and paste (CPF) can be defined as the act of duplicating medical documentation from one section of the electronic medical record (EMR) and placing it verbatim in another section. The objective of this scoping review is to 1) describe the prevalence of copy and paste usage in EMR documentation, 2) detail the known measurable safety hazards associated with its use, and 3) identify potential solutions and/or strategies that can be used to mitigate the negative consequences of the CPF while preserving its essential role in documentation efficiency. The Joanna Briggs Institute guidelines were used to identify, screen, and assess the text of articles for final inclusion in CPF article review. The primary search strategy for copy-paste articles was developed in PubMed® and then translated to CINAHL®, ScienceDirect®, and IEEExplore® to extract additional articles. Identified copy-paste articles were imported into Covidence®. Two reviewers determined the final articles that were included in the review. The search retrieved 63 publications of which 17 were identified for final inclusion. The scoping review revealed CPF of medical text is a common occurrence that cuts across all clinician types (e.g., physicians and nurses). The scoping review revealed that automated methods for finding duplication in electronic documentation had emerged. A limited number of studies with quantifiable harms associated with CPF were found. Clinicians stated that CPF 1) had a negative impact on critical thinking, 2) led to medical complications being more likely to be overlooked, and 3) led to safety issues being missed with copy-paste content. A few different approaches were tested by researchers as alternatives to CPF. They included dictation systems, practice guidelines, note templates, highlighting of copied information, note splitting, and text insertion. CPF is long overdue for innovative approaches to minimizing patient risk and maximizing provider efficiency. https//doi.org/10.34105/j.kmel.2021.13.028

A Multi-Objective Optimization-based Clustering Approach for CORD-19 Scholarly Articles

The pandemic disease COVID-19, originated from the SARS-CoV-2 virus has spread globally. Researchers are working tirelessly on areas including studying the transmission of COVID-19, promoting its identification, designing new vaccines and therapies, and recognizing its socio-economic consequences. This extensive research leads to the exploration of thousands of scientific papers related to biology, chemistry, genetics, health, and economy. Therefore, it is essential to develop an intelligent text mining technique for segregating this rich source of data to perform easy access, information retrieval, and interpretation within minimum time and resources. We propose a multi-objective optimization-based document clustering approach for the CORD-19 (COVID-19 Open Research Dataset) dataset in this paper. Here, a new technique utilizing BioBERT has been proposed, which benefits from the and the document text, rather than only the brief , to perceive a concise understanding of the text to generate clusters with better definitions. The main contributions of the proposed work are two-fold: in the first step, we have used BioBERT to generate the sentence embedding which is further used for the document representation. In the next step, we have developed a multi-objective optimization (MOO) based clustering algorithm for grouping the generated document vector representations. In this MOO-based clustering, we have used Non-dominated Sorting Genetic Algorithm-II and Fuzzy c-means algorithm as the underlying MOO and clustering technique, respectively. This model is evaluated using the Silhouette Score (Silhouette score) and Calinski-Harabasz index (CH index), and the clustering solutions are visualized using word clouds. The clustering results exhibit significant improvements over various other existing clustering models. © 2021 IEEE.

Understanding the Emotion Coping Strategies During Public Emergencies - From the Perspective of Psychological Distance.

Research has investigated behavioral coping strategies for the negative emotions that public emergencies elicit. Accordingly, our current research explored how people coped with negative emotions in response to the coronavirus disease (COVID-19) outbreak, from a cognitive perspective. Building on the theory of psychological distance and self-construal, we proposed that people who experienced fear, sadness and anxiety responded with independent-self construal, focusing on information that related to themselves and the novel virus (independent information). On the other hand, people who experienced fear, sadness and anger responded with interdependent-self construal, focusing on information that pertained to "us", the virus and nature (interdependent information). We collected data from 1,142 participants at both the initial peak of the outbreak and when its spread had subsided. Based on this longitudinal data, we examined the effectiveness of these strategies, and our findings suggested that independent information was effective in decreasing fear and anxiety, while interdependent information effectively mitigated sadness. The findings could help researchers, practitioners, governments, and organizations to implement appropriate information strategies to regulate individuals' negative emotions during and after the COVID-19 pandemic.