In view of the different spatial and temporal resolutions of observed multi-source heterogeneous carbon dioxide data and the uncertain quality of observations, a data fusion prediction model for observed multi-scale carbon dioxide concentration data is studied. First, a wireless carbon sensor network is created, the gross error data in the original dataset are eliminated, and remaining valid data are combined with kriging method to generate a series of continuous surfaces for expressing specific features and providing unified spatio-temporally normalized data for subsequent prediction models. Then, the long short-term memory network is used to process these continuous time- and space-normalized data to obtain the carbon dioxide concentration prediction model at any scales. Finally, the experimental results illustrate that the proposed method with spatio-temporal features is more accurate than the single sensor monitoring method without spatio-temporal features.

In order to solve the problems of copyrights infringement, high cost and complex process of rights protection in current media convergence center, a digital rights management system based on blockchain technology and IPFS (Inter Planetary File System) technology is proposed. Considering that large files such as video and audio cannot be stored on the blockchain directly, IPFS technology is adopted as the data expansion scheme for the data storage layer of the Ethereum platform, IPFS protocol is further used for distributed data storage and transmission of media content. In addition, smart contract is also used to uniquely identify digital rights through NFT (Non-fungible Tokens), which provides the characteristics of digital rights transferability and traceability, and realizes an open, transparent, tamper-proof and traceable digital rights management system for media convergence center. Several experimental results show that it has higher transaction success rate, lower storage consumption and transaction confirmation delay than existing scheme.

Existing weakly-supervised segmentation approaches based on image-level annotations may focus on the most activated region in the image and tend to identify only part of the target object. Intuitively, high-level semantics among objects of the same category in different images could help to recognize corresponding activated regions of the query. In this study, a scheme called Cycle-Consistency of Semantics Network (CyCSNet) is proposed, which can enhance the activation of the potential inactive regions of the target object by utilizing the cycle-consistent semantics from images of the same category in the training set. Moreover, a Dynamic Correlation Feature Selection (DCFS) algorithm is derived to reduce the noise from pixel-wise samples of low relevance for better training. Experiments on the PASCAL VOC 2012 dataset show that the proposed CyCSNet achieves competitive results compared with state-of-the-art weakly-supervised segmentation approaches.