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COVID-19 virus is raging across the planet. In countries where the epidemic is under control, the main mode of virus transmission is through the transport of imported refrigerated food from epidemic areas. Blockchain is a great way for the government to trace every piece of food. However, the high-performance requirements of the blockchain system for nodes limit its wide application. Several sharding-based blockchain systems have been proposed to solve this limitation. Which blocks should be saved by nodes in the sharding-based blockchain system is a new problem. To solve this problem, the optimized data storage method is proposed in this paper. Five features of block popularity are presented, including the objective feature of a block, the objective feature of the block associated with the node, the historical popularity, the hidden popularity and the storage requirements. Then the ELM classifier is used in the optimized model due to its high performance of training and classification. Finally, the experimental results on synthetic data demonstrate the accuracy and efficiency of the optimized data storage model.

Gene regulatory network can intuitively reflect the interaction between genes, and an in-depth study of these relationships plays a significant role in the treatment and prevention of clinical diseases. Therefore, correct reconstruction of gene regulatory network has become the first critical step in the study of disease treatment and prevention at the genetic level. Among the methods for gene regulatory network reconstruction, the Bayesian network model has been widely concerned because of its advantages of expressing both the regulatory relationship and the degree of strength between genes. Nevertheless, the complexity of the Bayesian network model in structure learning is extremely high, making the efficiency of the reconstruction network is low and the scale is limited. Therefore, this paper proposed a dynamic Bayesian network modeling based on structure prediction (DBN-SP). The method combines the correlation model with the dynamic Bayesian network model. On the premise of making full use of the search strategy of dynamic Bayesian network model structure learning, the candidate parent node set is selected based on the structure prediction firstly. Based on this, some redundant information can be removed and the search space can be reduced in the DBN structure learning to improves the efficiency of the network reconstruction. After the network is reconstructed, structure optimization by using the conditional mutual information method can further remove redundant edges and make the network more accurate. The experimental results show that DBN-SP greatly improves the efficiency and scale of the gene regulatory network reconstruction, and the accuracy and other indexes are also improved. DBN-SP is freely accessible at https://github.com/quluxuan/DBN-SP.git.

Extreme learning machine (ELM) is characterized by good generalization performance, fast training speed and less human intervention. With the explosion of large amount of data generated on the Internet, the learning algorithm in the single-machine environment cannot meet the huge memory consumption of matrix computing, so the implement of distributed ELM algorithm has gradually become one of the research focuses. In view of the research significance and implementation value of distributed ELM, this paper first introduced the research background of ELM and improved ELM. Secondly, this paper elaborated the implementation method of distributed ELM from the two directions: ensemble and matrix operation. Finally, we summarized the development status of distributed ELM and discussed the future research direction.

Extended Field of View Ultrasound Sonography (EFOV-US) uses the existing ultrasound images for image stitching, so as to display the shape and scope of organ occupation and the relationship with surrounding tissues comprehensively. However, there are still some problems in Extended Field of View Ultrasound Sonography, such as matching error and unstable quality of image stitching. In view of these problems, we propose Dual-enhanced EFOV-US method that overcomes the limitation and produces higher quality results. Firstly, the gray enhancement method is used to improve the image contrast and reduce the noise interference. Then the super-resolution method based on the generative adversarial network is used to improve the resolution of the ultrasonic image further and increase the number of feature point matching between stitching images. The high quality ultrasound wide-range image is gotten by stitching and fusing the double enhanced image. The experimental results show that the proposed method is effective and practical.

Computer-Aided Diagnosis (CAD) can improve the accuracy of diagnosis effectively, reduce the rate of misdiagnosis, and provide the support for the valid decision. In clinical applications, high requirements are often imposed on the execution speed and accuracy of CAD systems. The classifier is regarded as the core of the CAD system, that is, the performance of the classifier will have a decisive influence on the operating affection of the CAD system. Extreme Learning Machine (ELM) is a fast learning algorithm using Single Hidden Layer Feedforward Neural Network (SLFN) structure. With its advantages in training speed, generalization performance and accuracy, ELM has draw attention in many research fields, including the development of CAD system. The applications of ELM in CAD are reviewed in this research. First, the mathematical model of ELM and framework of CAD system are briefly introduced. Then, the application of ELM in CAD is reviewed in detail, including the feature modeling method combined with ELM in CAD and the specific application of ELM. Finally, we summarized the current research status of CAD systems based on ELM, and the future work is prospected.

A computer-aided diagnosis (CAD) system based on mammograms enables early breast cancer detection, diagnosis, and treatment. However, the accuracy of the existing CAD systems remains unsatisfactory. This paper explores a breast CAD method based on feature fusion with convolutional neural network (CNN) deep features. First, we propose a mass detection method based on CNN deep features and unsupervised extreme learning machine (ELM) clustering. Second, we build a feature set fusing deep features, morphological features, texture features, and density features. Third, an ELM classifier is developed using the fused feature set to classify benign and malignant breast masses. Extensive experiments demonstrate the accuracy and efficiency of our proposed mass detection and breast cancer classification method.

Outsourced data, as significant service offered by the cloud service provider (CSP), can effectively facilitate the data owner (DO) overcoming the storage limitations on massive data. To ensure the availability of data, DO usually outsources the data replications to multiple CSPs (multi-CSPs) and utilizes a third party metadata management (TPMM) to dominate the metadata of the corresponding replications. However, during the outsourced procedures, DO can hardly confirm the confidence of the TPMM who may take some malicious behaviors to affect the reliability of data. Thus, DO inevitably faces data security issues caused by the over-reliance on the semi-trusted TPMM to manage the metadata of replications. In this paper, we focus on the problem of reliable outsourced data service among multi-CSPs in untrusted environment, that is, how to reliably store and verify the metadata of the data replications in untrusted multi-CSPs environment. To address the problem, we use the novel blockchain technology as a medium to build a trusted outsourced service platform. Moreover, we fully consider the innovative characteristics of blockchain including decentralized architecture, redundancy storage, collective maintenance, and tamper resistant to ensure the data cannot be changed maliciously. We first design a blockchain-based outsourced service framework for storing data replications in untrusted environment, which contains three key layers, that is, storage layer, verification layer, and blockchain layer. Then, we devise a novel concept of verification peer (VP) for maintaining metadata stored by a form of blockchain, and each of which holds the entire blockchain locally to prevent metadata from being maliciously tampered with. Finally, based on the proposed model, we introduce a collaborative algorithm invoked by VPs to store and verify the metadata of replications. We present a completed analysis and conduct extensive experiments on multi-CSPs scenario. The evaluation results demonstrate that our proposed approach achieves superior performance.

Skyline query is widely used in multi-standard decision making process. As the extension to skyline query, p-dominant skyline query controls the base of the result set by adjusting data proportion, which is suitable for the application on fast deciding data stream. However, skyline query on data stream cannot satisfy the need for skyline query of corresponding value ratio, and the traditional p-dominant skyline query cannot provide skyline query with frequent data update. As a result, a data stream-based algorithm that can achieve the goal of p-dominant skyline query is proposed. First, the definitions of full-dominant relationship and p-dominance are given, after which the definitions of p-dominant skyline and p-dominant skyline based on data stream are proposed. Second, by analyzing the characters of data stream-based p-dominant skyline, a new data filtering method is established. Based on the method, p-Dominant Skyline Query on data stream over Sliding Window (DSSW) is proposed in order to improve the efficiency of p-dominant skyline query. After that, we expend the p-dominant skyline query of data stream into n-of-N p-dominant skyline query and the (n1, n2)-of-Np-dominant skyline query in data stream. Finally, we prove that DSSW has obvious advantages in responding time and storage skyline space by comparing with the traditional p-dominant skyline query algorithms through abundant experiments.