Trading volume movement prediction is the key in a variety of financial applications. Despite its importance, there is few research on this topic because of its requirement for comprehensive understanding of information from different sources. For instance, the relation between multiple stocks, recent transaction data and suddenly released events are all essential for understanding trading market. However, most of the previous methods only take the fluctuation information of the past few weeks into consideration, thus yielding poor performance. To handle this issue, we propose a graph-based approach that can incorporate multi-view information, i.e., long-term stock trend, short-term fluctuation and sudden events information jointly into a temporal heterogeneous graph. Besides, our method is equipped with deep canonical analysis to highlight the correlations between different perspectives of fluctuation for better prediction. Experiment results show that our method outperforms strong baselines by a large margin.

Stock movement prediction is a hot topic in the Fintech area. Previous works usually predict the price movement in a daily basis, although the market impact of news can be absorbed much shorter, and the exact time is hard to estimate. In this work, we propose a more practical objective to predict the overnight stock movement between the previous close price and the open price. As no trading operation occurs after market close, the market impact of overnight news will be reflected by the overnight movement. One big obstacle for such task is the lacking of data, in this work we collect and publish the overnight stock price movement dataset of Reuters Financial News. Another challenge is that the stocks in the market are not independent, which is omitted by previous works. To make use of the connection among stocks, we propose a LSTM Relational Graph Convolutional Network (LSTM-RGCN) model, which models the connection among stocks with their correlation matrix. Extensive experiment results show that our model outperforms the baseline models. Further analysis shows that the introduction of the graph enables our model to predict the movement of stocks that are not directly associated with news as well as the whole market, which is not available in most previous methods.

In this paper, we propose a fast non-iterative camera pose voting method for 3D object identification. The proposed method improves the accuracy and speed upon the conventional local feature based 2D-to-3D matching between a 2D image and a 3D model reconstructed by the structure-from-motion (SfM) pipeline. Instead of performing iterative RANSAC based method for geometric verification, the proposed method computes a hypothesis of camera pose from each feature correspondence between the query image and the 3D model. The camera pose is computed using scale, orientation and coordinate of the local features, calibrated by the camera matrix of the database image used to construct the 3D model. Then the most likely hypothesis is found by carrying out a two-stage clustering on the estimated camera poses in the parameter space. Experiment results on the 3D machine datasets show that our method improves the identification accuracy from 82.8% to 84.9% when FPR is 1%, compared with conventional RANSAC based method. In addition, the processing speed for the geometric verification is improved up to 25 times compared to the conventional method.

Management of ship designing is difficult because of the complexity in modeling of the process and large amounts of data throughout the process. Though existing technologies of BPMN can solve the first problem, BPMN does not provide sufficient supports on dealing with complex dependencies in the process, e.g., storage and search problems of correlated data with “many to many” relations. In this paper, we introduce a series of annotations of data objects to solve this problem. First, we extend the annotations of data objects based on BPMN and utilize foreign key in relational database to manage the relations between data objects. Second, we use SQL queries to execute the common operations to data objects in the process. Our approach is the extension of mature BPMN and database technologies, so it is standard and reusable. We implemented our approach on the Produce Management System to verify the availability and efficiency.