An outbreak of a novel coronavirus (COVID‐19 or 2019‐CoV) infection has posed significant threats to international health and the economy. In the absence of treatment for this virus, there is an ...urgent need to find alternative methods to control the spread of disease. Here, we have conducted an online search for all treatment options related to coronavirus infections as well as some RNA‐virus infection and we have found that general treatments, coronavirus‐specific treatments, and antiviral treatments should be useful in fighting COVID‐19. We suggest that the nutritional status of each infected patient should be evaluated before the administration of general treatments and the current children's RNA‐virus vaccines including influenza vaccine should be immunized for uninfected people and health care workers. In addition, convalescent plasma should be given to COVID‐19 patients if it is available. In conclusion, we suggest that all the potential interventions be implemented to control the emerging COVID‐19 if the infection is uncontrollable.
Location privacy is one of the major challenges in vehicular ad hoc networks. Due to the open and broadcast nature of wireless communication, the safety messages of vehicles can be easily collected ...by malicious eavesdroppers to continuously track vehicles. Cryptographic mix-zone (CMIX) is a promising tool to enhance vehicle privacy, in which the safety messages of vehicles are encrypted using a group secret key. In that way, any outsider cannot monitor the safety messages broadcasted by the vehicles in the CMIX. Existing CMIX protocols need fully trusted dealers to distribute group secret keys and/or suffer from the problem of efficient key update. This paper proposes a novel method based on a new security tool referred to as one-time identity-based authenticated asymmetric group key agreement to create CMIXes which withstand malicious eavesdroppers. Different from the existing solutions, our proposal does not rely on the existence of fully trusted dealers and deals with efficient key update in CMIX for the first time. In our protocol, any vehicle in a CMIX could be a group secret key distributer. Furthermore, once the group secret key of the CMIX has to be updated, a vehicle in the CMIX just needs to broadcast a short ciphertext, then all the vehicles in the CMIX may refresh the group secret key to the new one efficiently.
This paper addresses an important issue known as sensor drift, which exhibits a nonlinear dynamic property in electronic nose (E-nose), from the viewpoint of machine learning. Traditional methods for ...drift compensation are laborious and costly owing to the frequent acquisition and labeling process for gas samples' recalibration. Extreme learning machines (ELMs) have been confirmed to be efficient and effective learning techniques for pattern recognition and regression. However, ELMs primarily focus on the supervised, semisupervised, and unsupervised learning problems in single domain (i.e., source domain). To our best knowledge, ELM with cross-domain learning capability has never been studied. This paper proposes a unified framework called domain adaptation extreme learning machine (DAELM), which learns a robust classifier by leveraging a limited number of labeled data from target domain for drift compensation as well as gas recognition in E-nose systems, without losing the computational efficiency and learning ability of traditional ELM. In the unified framework, two algorithms called source DAELM (DAELM-S) and target DAELM (DAELM-T) are proposed in this paper. In order to perceive the differences among ELM, DAELM-S, and DAELM-T, two remarks are provided. Experiments on the popular sensor drift data with multiple batches collected using E-nose system clearly demonstrate that the proposed DAELM significantly outperforms existing drift-compensation methods without cumbersome measures, and also bring new perspectives for ELM.
Preserving edge structures is a challenge to image interpolation algorithms that reconstruct a high-resolution image from a low-resolution counterpart. We propose a new edge-guided nonlinear ...interpolation technique through directional filtering and data fusion. For a pixel to be interpolated, two observation sets are defined in two orthogonal directions, and each set produces an estimate of the pixel value. These directional estimates, modeled as different noisy measurements of the missing pixel are fused by the linear minimum mean square-error estimation (LMMSE) technique into a more robust estimate, using the statistics of the two observation sets. We also present a simplified version of the LMMSE-based interpolation algorithm to reduce computational cost without sacrificing much the interpolation performance. Experiments show that the new interpolation techniques can preserve edge sharpness and reduce ringing artifacts
This study examines whether being located within a 100-year floodplain has an impact on the price of residential single-family house sales using house sales data in the Fargo-Moorhead Metropolitan ...Statistical Area between 2000 and 2013. A spatial quantile regression is applied to investigate the flood hazards impact on conditional higher- vs lower- priced homes, while accounting for spatial autocorrelation. The findings show that the location within a floodplain reduces property value. Furthermore, the negative impact of flood hazards on property values are stronger among lower-priced homes, and weaker among higher-priced homes. In addition, the study examines if a major flood in 2009 had an impact on the home buyers' perception about flood risk. The results show that about a year after the major flood, home buyers responded the most, however, the effect quickly diminished after 2010. Across quantiles, the 2009 flood had more effect on lower-priced than higher-priced homes.
•Spatial quantile regression is used to examine the flood hazards impact on house prices.•Location within a floodplain has a negative impact on house prices.•The negative impact is strongest among lower-priced houses.•A major flood in 2009 changes home buyers' perception about flood hazards.
Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising. However, these methods mostly learn a specific model for each noise level, ...and require multiple models for denoising images with different noise levels. They also lack flexibility to deal with spatially variant noise, limiting their applications in practical denoising. To address these issues, we present a fast and flexible denoising convolutional neural network, namely FFDNet, with a tunable noise level map as the input. The proposed FFDNet works on downsampled sub-images, achieving a good trade-off between inference speed and denoising performance. In contrast to the existing discriminative denoisers, FFDNet enjoys several desirable properties, including: 1) the ability to handle a wide range of noise levels (i.e., 0, 75) effectively with a single network; 2) the ability to remove spatially variant noise by specifying a non-uniform noise level map; and 3) faster speed than benchmark BM3D even on CPU without sacrificing denoising performance. Extensive experiments on synthetic and real noisy images are conducted to evaluate FFDNet in comparison with state-of-the-art denoisers. The results show that FFDNet is effective and efficient, making it highly attractive for practical denoising applications.
The body electromyography (EMG) signals contain a large amount of information related to the movement of the human body. Identifying the patients’ movement intention from the EMG signals is the key ...to controlling the exoskeleton to assist their movement. In order to accurately extract the information about the patients’ movement intention from the EMG signals, we preprocessed the EMG signals including signals amplification, denoising, biasing and normalization. Then we extracted the features of EMG signals from the time domain, frequency domain, and time-frequency domain respectively. Based on the features obtained, we used the Matlab neural network toolbox to train BP neural network and tested the established continuous movement control model. The results suggested that the angles estimated by the continuous movement control model had smaller errors. In addition, instead of the traditional working mode that used the PC to process the EMG signals, we used the STM32 microcontroller to perform real-time control of the upper limb exoskeleton, which greatly reduced the size of the control equipment and provided convenience for the patients’ rehabilitation training.
•A fully automated, real-time forecasting model for COVID-19 transmission to help frontline health workers and government policy makers.•Use of Artificial intelligence (AI) and Deep Learning to model ...Infectious diseases without loosing temporal components.•One of the early studies to use LSTM networks to predict the COVID-19 transmission.•We showed the trends of different countries and compared them with Canadian data to predict the future infections.
On March 11th 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the world within few weeks. Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Based on the results of our Long short-term memory (LSTM) network, we predicted the possible ending point of this outbreak will be around June 2020. In addition to that, we compared transmission rates of Canada with Italy and USA. Here we also presented the 2, 4, 6, 8, 10, 12 and 14th day predictions for 2 successive days. Our forecasts in this paper is based on the available data until March 31, 2020. To the best of our knowledge, this of the few studies to use LSTM networks to forecast the infectious diseases.
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