Supervised and Self-Supervised Pretraining Based Covid-19 Detection Using Acoustic Breathing/Cough/Speech Signals

A rapid-accurate detection method for COVID-19 is rather important for avoiding its pandemic. In this work, we propose a bi-directional long short-term memory (BiLSTM) network based COVID-19 detection method using breath/speech/cough signals. Three kinds of acoustic signals are taken to train the network and individual models for three tasks are built, respectively, whose parameters are averaged to obtain an average model, which is then used as the initialization for the BiLSTM model training of each task. It is shown that such an initialization method can significantly improve the detection performance on three tasks. This is called supervised pre-training based detection. Besides, we utilize an existing pre-trained wav2vec2.0 model and pre-train it using the DiCOVA dataset, which is utilized to extract a high-level representation as the model input to replace conventional mel-frequency cepstral coefficients (MFCC) features. This is called self-supervised pre-training based detection. To reduce the information redundancy contained in the recorded sounds, silent segment removal, amplitude normalization and time-frequency masking are also considered. The proposed detection model is evaluated on the DiCOVA dataset and results show that our method achieves an area under curve (AUC) score of 88.44% on blind test in the fusion track. It is shown that using high-level features together with MFCC features is helpful for diagnosing accuracy.

The Relation of Career Adaptability to Values Realization Degree and Organizational Citizenship Behavior

The sudden outbreak of the pandemic COVID-19 inevitably has a great impact on economic and social development. Therefore, the innovation-driven value becomes more and more prominent. Through literature review, it is not difficult to find that values have gradually become an important reference standard for organizations to select talents for their teams, as well as an important reference factor for studying organizational citizenship behavior. In order to explore the relationship between values realization degree and organizational citizenship behavior, this investigation based on the social interaction theory was conducting using a sample of enterprise staff (N=358). In this paper, LISRELV9.2 and SPSS21.0 were used to analyze the sample data, including descriptive statistical analysis, common variance deviation test, reliability and validity test, one-way ANOVA, correlation analysis, regression analysis, and validation of mediating effects. The results showed that values realization degree positively predicted organizational citizenship behavior, and job satisfaction played an intermediary role in the relationship between values realization degree and organizational citizenship behavior. Besides, there were some differences between the relation that work values realization degree and organizational citizenship behavior acted on organizational citizenship behavior.