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The purpose of our study is to figure out the transitions of the cryptocurrency market due to the outbreak of COVID-19 through network analysis, and we studied the complexity of the market from different perspectives. To construct a cryptocurrency network, we first apply a mutual information method to the daily log return values of 102 digital currencies from January 1, 2019, to December 31, 2020, and also apply a correlation coefficient method for comparison. Based on these two methods, we construct networks by applying the minimum spanning tree and the planar maximally filtered graph. Furthermore, we study the statistical and topological properties of these networks. Numerical results demonstrate that the degree distribution follows the power-law and the graphs after the COVID-19 outbreak have noticeable differences in network measurements compared to before. Moreover, the results of graphs constructed by each method are different in topological and statistical properties and the network’s behavior. In particular, during the post-COVID-19 period, it can be seen that Ethereum and Qtum are the most influential cryptocurrencies in both methods. Our results provide insight and expectations for investors in terms of sharing information about cryptocurrencies amid the uncertainty posed by the COVID-19 pandemic.

Secure computation, a methodology of computing on encrypted data, has become a key factor in machine learning. Homomorphic encryption (HE) enables computation on encrypted data without leaking any information to untrusted servers. In machine learning, the model selection method is a crucial algorithm that determines the performance and reduces the fitting problem. Despite the importance of finding the optimal model, none of the previous studies have considered model selection when performing data analysis through the HE scheme. The HE-based model selection we proposed finds the optimal complexity that best describes given data that is encrypted and whose distribution is unknown. Since this process requires a matrix calculation, we constructed the matrix multiplication and inverse of the matrix based on the bitwise operation. Based on these, we designed the model selection of the HE cross-validation approach and the HE Bayesian approach for homomorphic machine learning. Our focus was on evidence approximation for linear models to find goodness-of-fit that maximizes the evidence. We conducted an experiment on a dataset of age and Body Mass Index (BMI) from Kaggle to compare the capabilities and our model showed that encrypted data can regress homomorphically without decrypting it.

In the big data era, data scientists explore machine learning methods for observed data to predict or classify. For machine learining to be effective, it requires access to raw data which is often privacy sensitive. In addition, whatever data and fitting procedures are employed, a crucial step is to select the most appropriate model from the given dataset. Model selection is a key ingredient in data analysis for reliable and reproducible statistical inference or prediction. To address this issue, we develop new techniques to provide solutions for running model selection over encrypted data. Our approach provides the best approximation of the relationship between the dependent and independent variable through cross validation. After performing 4-fold cross validation, 4 different estimates of our model’s errors are calculated. And then we use bias and variance extracted from these errors to find the best model. We perform an experiment on a dataset extracted from Kaggle and show that our approach can homomorphically regress a given encrypted data without decrypting it.

Background: Auditory hallucinations often influence schizophrenia patients in many aspects. In order to develop effective behavioral interventions for overcoming enduring auditory hallucinations, it is necessary to understand how the annoying symptom affects the daily lives of the patients. This study evaluated the effect of hearing unusual voices on performing the activities of daily life in schizophrenia patients. Methods: Eighteen hallucinating patients, 18 nonhallucinating patients and 20 normal controls performed the virtual daily-life task of packing 8 items for travel under 3 conditions: (1) without unusual voices and without avatars, (2) with unusual voices and without avatars and (3) with unusual voices and with avatars. Task completion time and the number of times the packing list was checked were recorded as a measure of the task performance. Results: When exposed to unusual voices without avatars, hallucinating patients checked the packing list fewer times than nonhallucinating patients, and they required longer to complete the task, as positive and negative symptoms were worse. Subjective responses to unusual voices were stronger in hallucinating patients than in nonhallucinating patients. Conclusions: Daily-life activities of hallucinating patients may be less easily influenced by odd auditory stimuli in a nonsocial situation than those of nonhallucinating patients; however, hallucinating patients may feel more strongly affected by unusual voices. To better evaluate and thereby understand the difficulties faced by hallucinating patients in their daily life, the discrepancies between objective and subjective measures as well as social situations should be taken into consideration.