The emerging decentralized storage systems (DSSs), such as InterPlanetary File System (IPFS), Storj, and Sia, provide people with a new storage model. Instead of being centrally managed, the data are sliced up and distributed across the nodes of the network. Furthermore, each data object is uniquely identified by a cryptographic hash (ObjectId) and can only be retrieved by ObjectId. Compared with the search functions provided by the existing centralized storage systems, the application scenarios of the DSSs are subject to certain restrictions. In this paper, we first apply decentralized B+Tree and HashMap to the DSSs to provide keyword search. Both indexes are kept in blocks. Since these blocks may be scattered on multiple nodes, we ensure that all operations involve as few blocks as possible to reduce network cost and response time. In addition, the version control and version merging algorithms are designed to effectively organize the indexes and facilitate data integration. The experimental results prove that our indexes have excellent availability and scalability.

A rapidly spreading epidemic, COVID-19 had a serious effect on millions and took many lives. Therefore, for individuals with COVID-19, early discovery is essential for halting the infection’s progress. To quickly and accurately diagnose COVID-19, imaging modalities, including computed tomography (CT) scans and chest X-ray radiographs, are frequently employed. The potential of artificial intelligence (AI) approaches further explored the creation of automated and precise COVID-19 detection systems. Scientists widely use deep learning techniques to identify coronavirus infection in lung imaging. In our paper, we developed a novel light CNN model architecture with watershed-based region-growing segmentation on Chest X-rays. Both CT scans and X-ray radiographs were employed along with 5-fold cross-validation. Compared to earlier state-of-the-art models, our model is lighter and outperformed the previous methods by achieving a mean accuracy of 98.8% on X-ray images and 98.6% on CT scans, predicting the rate of 0.99% and 0.97% for PPV (Positive predicted Value) and NPV (Negative predicted Value) rate of 0.98% and 0.99%, respectively.

As the microblogging systems such as Twitter and Sina Weibo become more and more popular in recent years, the requirement for real-time and personalized search over microblogging systems also becomes more important. In general, a user may expect a quick response that also satisfies her personalized requirements. Unfortunately, since there exist a huge number of users and massive updating microblogs in a microblogging system, personalized search on the system becomes a challenging task. In this paper, we design a new search engine containing four modules to infer the topics of microblogs and update the interests of users, build indexes efficiently, return microblogs for a keyword search, and personalize the order of microblogs, respectively. We also conduct a series of experiments on a real dataset to illustrate the effectiveness and efficiency of the proposed methods.