A decade review and bibliometric analysis of the journal Compare

Compare is a leading journal in the comparative and international education research field. To assess this journal's productivity and influence, we conducted a bibliometric analysis of 428 papers published in Compare between 2010 and 2019. The findings show that in the past decade, Compare experienced significant growth in the number of publications and citations. This growth primarily stemmed from England, which yielded over half of the top 20 most productive authors and institutions. Among the numerous research topics discussed in Compare, the disciplinary development of comparative and international education, the internationalisation of education, gender studies in education, and citizenship education were the most frequently addressed. A detailed analysis of these four topics reveals that despite having published many papers falling within the scope of international education, Compare is encouraged to publish more papers about this subfield in the post-COVID-19 era.

The Phonetic Footprint of Covid-19?

Against the background of the ongoing pandemic, this year's Computational Paralinguistics Challenge featured a classification problem to detect Covid-19 from speech recordings. The presented approach is based on a phonetic analysis of speech samples, thus it enabled us not only to discriminate between Covid and non-Covid samples, but also to better understand how the condition influenced an individual's speech signal. Our deep acoustic model was trained with datasets collected exclusively from healthy speakers. It served as a tool for segmentation and feature extraction on the samples from the challenge dataset. Distinct patterns were found in the embeddings of phonetic classes that have their place of articulation deep inside the vocal tract. We observed profound differences in classification results for development and test splits, similar to the baseline method. We concluded that, based on our phonetic findings, it was safe to assume that our classifier was able to reliably detect a pathological condition located in the respiratory tract. However, we found no evidence to claim that the system was able to discriminate between Covid-19 and other respiratory diseases.

Self-interest Attribution Behavior in Annual Reports under the COVID-19 Pandemic-Taking restaurant companies for example

The outbreak of the COVID-19 pandemic at the end of 2019 has caused a profound impact on economic development. The catering, logistics and tourism industries have suffered a huge blow. This paper selects the catering industry as the research object, selects the 2019 and 2020 annual reports of five representative listed catering companies, classifies and summarizes the stated criteria for determination of the occurrence of self-interest attribution, calculates the degree of self-interest attribution, and compares and analyzes whether the self-interest attribution behavior of the five case companies before and after the COVID-19 pandemic stands out or amplifies the self-interest attribution behavior of the companies. The case studies showed that the degree of self-interest attribution was higher in the poor-performing companies, and that the impact of the COVID-19 pandemic on the self-interest behavior of restaurant companies was prominent, and that the poor external environment was more likely to lead to a higher degree of self-interest attribution behavior. © 2022 IEEE.

Wisdom of the Experts Versus Opinions of the Crowd in Hospital Quality Ratings: Analysis of Hospital Compare Star Ratings and Google Star Ratings.

BACKGROUND: Popular web-based portals provide free and convenient access to user-generated hospital quality reviews. The Centers for Medicare & Medicaid Services (CMS) also publishes Hospital Compare Star Ratings (HCSR), a comprehensive expert rating of US hospital quality that aggregates multiple measures of quality. CMS revised the HCSR methods in 2021. It is important to analyze the degree to which web-based ratings reflect expert measures of hospital quality because easily accessible, crowdsourced hospital ratings influence consumers' hospital choices. OBJECTIVE: This study aims to assess the association between web-based, Google hospital quality ratings that reflect the opinions of the crowd and HCSR representing the wisdom of the experts, as well as the changes in these associations following the 2021 revision of the CMS rating system. METHODS: We extracted Google star ratings using the Application Programming Interface in June 2020. The HCSR data of April 2020 (before the revision of HCSR methodology) and April 2021 (after the revision of HCSR methodology) were obtained from the CMS Hospital Compare website. We also extracted scores for the individual components of hospital quality for each of the hospitals in our sample using the code provided by Hospital Compare. Fractional response models were used to estimate the association between Google star ratings and HCSR as well as individual components of quality (n=2619). RESULTS: The Google star ratings are statistically associated with HCSR (P<.001) after controlling for hospital-level effects; however, they are not associated with clinical components of HCSR that require medical expertise for evaluation such as safety of care (P=.30) or readmission (P=.52). The revised CMS rating system ameliorates previous partial inconsistencies in the association between Google star ratings and quality component scores of HCSR. CONCLUSIONS: Crowdsourced Google star hospital ratings are informative regarding expert CMS overall hospital quality ratings and individual quality components that are easier for patients to evaluate. Improvements in hospital quality metrics that require expertise to assess, such as safety of care and readmission, may not lead to improved Google star ratings. Hospitals can benefit from using crowdsourced ratings as timely and easily available indicators of their quality performance while recognizing their limitations and biases.

DETERMINING THE BEST ACOUSTIC FEATURES FOR SMOKER IDENTIFICATION

Speech-based automatic smoker identification (also known as smoker/non-smoker classification) aims to identify speakers' smoking status from their speech. In the COVID-19 pandemic, speech-based automatic smoker identification approaches have received more attention in smoking cessation research due to low cost and contactless sample collection. This study focuses on determining the best acoustic features for smoker identification. In this paper, we investigate the performance of four acoustic feature sets/representations extracted using three feature extraction/learning approaches: (i) hand-crafted feature sets including the extended Geneva Minimalistic Acoustic Parameter Set and the Computational Paralinguistics Challenge Set, (ii) the Bag-of-Audio-Words representations, (iii) the neural representations extracted from raw waveform signals by SincNet. Experimental results show that: (i) SincNet feature representations are the most effective for smoker identification and outperform the MFCC baseline features by 16% in absolute accuracy;(ii) the performance of hand-crafted feature sets and the Bag-of-Audio-Words representations rely on the scale of the dimensions of feature vectors. © 2022 IEEE

Automated Covid-19 Respiratory Symptoms Analysis from Speech and Cough

An effective contact tracking strategy helps to maintain control over the Covid-19 pandemic. People without visible symptoms make it a complex problem because there has to be an unobtrusive way to discover that they are virus carriers and have to be isolated. Automated Covid-19 respiratory symptoms analysis helps to focus on people with respiratory symptoms. In our approach, a telephone call system leads a dialog and discovers Covid-19 disease by analyzing a person's speech and cough. After a positive match, it invites the person to PCR testing to confirm or reject the diagnosis. We compare our speech and cough detection system with Interspeech Computational Paralinguistics Challenge (ComParE) 2021. The results of Covid-19 Speech Sub-Challenge sub-challenge (CSS) show that we outperform the baseline results by 4.3% of the Unweighted Average Recall value. © 2022 IEEE.

FUSION OF MODULATION SPECTRAL AND SPECTRAL FEATURES WITH SYMPTOM METADATA FOR IMPROVED SPEECH-BASED COVID-19 DETECTION

Existing speech-based coronavirus disease 2019 (COVID-19) detection systems provide poor interpretability and limited robustness to unseen data conditions. In this paper, we propose a system to overcome these limitations. In particular, we propose to fuse two different feature modalities with patient metadata in order to capture different properties of the disease. The first feature set is based on modulation spectral properties of speech. The second comprises spectral shape/descriptor features recently used for COVID-19 detection. Lastly, we fuse patient metadata in order to improve robustness and interpretability. Experiments are performed on the 2021 INTERSPEECH COVID Speech Sub-Challenge dataset with several different data partitioning paradigms. Results show the importance of the modulation spectral features. Metadata, in turn, did not perform very well when used alone but provided invaluable insights when fused with the other features. Overall, a system relying on the fusion of all three modalities showed to be robust to unseen conditions and to rely on interpretable features. The simplicity of the model suggests that it can be deployed in portable devices, hence providing accessible COVID-19 diagnostics worldwide. © 2022 IEEE

MODELING OF PRE-TRAINED NEURAL NETWORK EMBEDDINGS LEARNED FROM RAW WAVEFORM FOR COVID-19 INFECTION DETECTION

COVID-19 is a respiratory system disorder that can disrupt the function of lungs. Effects of dysfunctional respiratory mechanism can reflect upon other modalities which function in close coupling. Audio signals result from modulation of respiration through speech production system, and hence acoustic information can be modeled for detection of COVID-19. In that direction, this paper is addressing the second DiCOVA challenge that deals with COVID-19 detection based on speech, cough and breathing. We investigate modeling of (a) ComParE LLD representations derived at frame- and turn-level resolutions and (b) neural representations obtained from pre-trained neural networks trained to recognize phones and estimate breathing patterns. On Track 1, the ComParE LLD representations yield a best performance of 78.05% area under the curve (AUC). Experimental studies on Track 2 and Track 3 demonstrate that neural representations tend to yield better detection than ComParE LLD representations. Late fusion of different utterance level representations of neural embeddings yielded a best performance of 80.64% AUC. © 2022 IEEE

Machine Learning Applications in Forecasting of COVID-19 Based on Patients' Individual Symptoms

Predicting the COVID-19 outbreak has been studied by many researchers in recent years. Many machine learning models have been used for the prediction of the transmission in a country or region, but few studies aim to predict whether an individual has been infected by COVID-19. However, due to the gravity of this global pandemic, prediction at an individual level is critical. The objective of this paper is to predict if an individual has COVID-19 based on the symptoms and features. The prediction results can help the government better allocate the medical resources during this pandemic. Data of this study was taken on June 18th from the Israeli Ministry of Health on COVID-19. The purpose of this study is to compare and analyze different models, which are Support Vector Machine (SVM), Logistic Regression (LR), Naive Bayesian (NB), Decision Tree (DT), Random Forest (RF) and Neural Network (NN). © 2021 ACM.

COMPARE Analysis, a Bioinformatic Approach to Accelerate Drug Repurposing against Covid-19 and Other Emerging Epidemics.

A novel bioinformatic approach for drug repurposing against emerging viral epidemics like Covid-19 is described. It exploits the COMPARE algorithm, a public program from the National Cancer Institute (NCI) to sort drugs according to their patterns of growth inhibitory profiles from a diverse panel of human cancer cell lines. The data repository of the NCI includes the growth inhibitory patterns of more than 55,000 molecules. When candidate drug molecules with ostensible anti-SARS-CoV-2 activities were used as seeds (e.g., hydroxychloroquine, ritonavir, and dexamethasone) in COMPARE, the analysis uncovered several molecules with fingerprints similar to the seeded drugs. Interestingly, despite the fact that the uncovered drugs were from various pharmacological classes (antiarrhythmic, nucleosides, antipsychotic, alkaloids, antibiotics, and vitamins), they were all reportedly known from published literature to exert antiviral activities via different modes, confirming that COMPARE analysis is efficient for predicting antiviral activities of drugs from various pharmacological classes. Noticeably, several of the uncovered drugs can be readily tested, like didanosine, methotrexate, vitamin A, nicotinamide, valproic acid, uridine, and flucloxacillin. Unlike pure in silico methods, this approach is biologically more relevant and able to pharmacologically correlate compounds regardless of their chemical structures. This is an untapped resource, reliable and readily exploitable for drug repurposing against current and future viral outbreaks.