Recently AI technologies, especially Deep Neural Network (DNN), have been widely used in the financial industry, such as stock price and movement prediction. In order to develop an AI-based solution for the global financial market, daily-based DNN model training for each stock is required to need collaboration among a large scale of entities. However, it is usually challenging due to data privacy, the cost of AI computing, and the lack of motivation to share information. This research proposes a novel Blockchain based platform, which utilizes the decentralized network, federated learning, and master-node to tackle these issues. The decentralized computing framework of federated learning, along with transfer learning, is applied to meet the data privacy requirements. Furthermore, the proposed federated learning platform with collaborative training is built on a decentralized AI computing cloud, which is highly affordable compared to centralized AI clouds. The master-node of Blockchain technology is further employed to enable the scalable global financial service, and effective rewards are applied to incentivize information sharing as well. We have applied the proposed Blockchain based platform to the stock prediction global service, which demonstrates the platform is practical and useful.

As Deep Web contains tremendous well-structured data sources, how to integrate data sources in Deep Web has become a hotspot in current research. Accurately discovering and identifying Deep Web data sources related to a specific domain become key issues. We propose a Domain-Oriented Deep Web data source Discovery method (DO-DWD) and a novel Domain Identification strategy of Deep Web data sources (DIDW). In the discovery stage, we use machine learning algorithms and some heuristic rules to find query interfaces of the data sources; In the identification stage, we identify Deep Web data sources associated with the domain by calculating the relevance between a query interface and the domain based on semantic similarity. Finally, we have extensive experiments on a real data set showing that DO-DWD and DIDW are of high correctness and accuracy.

Software coordination is central to the construction of large-scale high-performance distributed applications with software services scattered over the decentralized Internet. In this paper, a new mobile-agent-based architecture is proposed for the utilization and coordination of geographically distributed computing resources. Under this architecture, a user application is built with a set of software agents that can travel across the network autonomously. These agents utilize the distributed resources and coordinate with each other to complete their task. This approach’s advantages include the natural expression and flexible deployment of the coordination logic, the dynamic adaptation to the network environment and the potential of better application performance. This coordination architecture, together with an object-oriented hierarchical parallel application framework and a graphical application construction tool, is implemented in the HOOPE environment, which provides a systematic support for the development and execution of Internet-based distributed and parallel applications in the petroleum exploration industry.