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Recent years have witnessed the rapid development of the China's stock market, but investment risks have also emerged. Stock price is always unstable and non-linear, affected not only by historical transaction data but also by national policies, news, and other data. Stock price and textual data are beginning to be employed in the prediction process. However, the challenge lies in effectively integrating feature information derived from stock price and textual information. To address the problem, in this paper, this paper proposes a Hybrid Data-driven Multi-task Learning(HDML) framework to predict stock price. HDML adopts hybrid data as model input, mining the transaction and capital flow data information in the stock market and considering the impact of investors' emotions on the stock market. In addition, we incorporate multi-task learning, which predicts the closing price range of stock based on structured data and then corrects the prediction results through investors' comment text data. HDML effectively captures the relationship between different modal data through multi-task learning and achieve improvements on both tasks. The experimental results show that compared with previous work, HDML reduces the RMSE of the evaluation set by 12.14% and improves the F1 score by an average of 13.64% at the same time. Moreover, value at risk (VaR), together with the HDML model, can help investors weigh the potential gains against the associated risks. [ABSTRACT FROM AUTHOR]

With the development of the big data era, the amount of data has entered an explosive growth phase. Limited by the constraints of cost, efficiency, and security of self-built storage systems, enterprises are forced to outsource files to cloud storage systems. However, the lack of file security and auditability in cloud storage systems continues to threaten the security of outsourced files. This paper designs and implements a large file security outsourcing and sharing method based on blockchain (BFSOut). It uses Hyperledger Fabric and Interplanetary File System (IPFS) as the underlying storage engine, which solves the problem of cloud storage security issues. In order to ensure the security of outsource files, the client-side offline encryption and decryption is used. Furthermore, a dynamic hybrid encryption scheme is adopted, making the overall encryption effect more efficient. Meanwhile, BFSOut effectively reduces the time consumption of large file encryption and decryption by 20%–30% through the parallel block encryption and decryption model. Experimental performance and security analysis show that the method has good performance and safety. [ABSTRACT FROM AUTHOR]

Automatic summarization is attracting increasing attention as one of the most promising research areas. This technology has been tried in various real-world applications in recent years and achieved a good response. However, the applicability of conventional evaluation metrics cannot keep up with rapidly evolving summarization task formats and ensuing indicator. After recent years of research, automatic summarization task requires not only readability and fluency, but also informativeness and consistency. Diversified application scenarios also bring new challenges both for generative language models and evaluation metrics. In this review, we analysis and specifically focus on the difference between the task format and the evaluation metrics. [ABSTRACT FROM AUTHOR]

With the continuous and rapid development of cloud-based data services, the total volume of Internet data is experiencing explosive growth. Nevertheless, contemporary centralized cloud storage-oriented data service providers encounter significant challenges in fully satisfying the requirements for user data security, fine-grained access control, and consistently high-performance standards. In this paper, we propose and implement an efficient peer-to-peer data storage and sharing system to address the challenges faced by service providers. Our solution utilizes the modified EOSIO blockchain and the InterPlanetary File System (IPFS) distributed storage as the underlying data storage framework. We employ hybrid encryption to ensure the secrecy of the shared data of the users and also to facilitate multiple uses and persistent storage of the shared data of the users. Moreover, with the expansion of the blockchain component, we provide a flexible transaction information audit solution that helps to trace the source of malicious behavior and reduces the cost of using blockchain information. In system analysis and experimental evaluation, compared to traditional blockchain storage, FIBPRO has theoretically achieved a 98.76% reduction in on-chain storage consumption. In practical concurrency testing, it achieved a comprehensive performance of approximately 1300 TPS (transactions per second), with an average upload efficiency of about 2.31MB/s and a download rate of about 5.29MB/s. These results demonstrate the system's availability and scalability. [ABSTRACT FROM AUTHOR]

Recovery of fragmented files is an important part of digital forensics. Video files are more likely to be fragmented since their sizes are relatively large that recovering video files without the file system information is meaningful. This paper presents a video recovery technique of a fragmented video file using the frame size information in every frame and the index. Many existing video recovery techniques attempt to recover videos using file system information or header/footer flag. These approaches may fail in the situations which the file system information is unknown or videos are fragmented. The proposed method addresses how to extract AVI file fragments from data images and map all the extracted fragments into original order. Experiments result show that mapping the AVI fragments according to the frame size information in every fragment and index is credible and the non-overwritten part of the fragmented video can be recovered using the method. [ABSTRACT FROM AUTHOR]