Extractive summarization aims to produce a concise summary while retaining the key information through the way of selecting sentences from the original document. Under such background, learning inter-sentence relations has hitherto been the issue of most concern. In this study, we propose a Star architecture based model for extractive summarization (StarSum), that takes advantage of self-attention strategy based Transformer and star-shaped structure, models sentences within a document as satellite nodes and introduces a virtual star node, constructs a star model for each document to learn inter-sentence relations. Based on the constructed star-shaped model, we further develop two sentence representation learning algorithms, namely star guiding satellite (SGS) algorithm and star incorporating satellite (SIS) algorithm, in order to extract summary-worthy sentences. Experimental results on CNN/Daily Mail, New York Times (NYT) and XSum datasets prove that StarSum model achieves advanced performance for extractive summarization and has comparable performance to the state-of-the-art extractive summarization model. The results also demonstrate that the SIS algorithm is more effective than the SGS algorithm.

The automated steering controller is crucial for smooth and accurate path tracking of autonomous ground vehicles (AGVs). However, time-varying uncertainties and disturbances may deteriorate the path tracking performance. Moreover, it is difficult for the steering system to strictly follow the desired steering angle in practice. Therefore, this paper proposes an automated steering control algorithm consisting of two parts: 1) an output feedback model predictive controller (MPC) to solve the path tracking problem, which is formulated as an optimization problem in this paper, with strong robustness against time-varying uncertainty and disturbance; 2) a feedforward compensator for the steering angle calculated by MPC using deep deterministic policy gradient (DDPG) algorithm so that the steering system can execute the desired steering angle more quickly and more accurately. Simulation results demonstrate that the proposed control scheme can significantly improve response speed and accuracy for path tracking of AGVs with strong robustness.

Rapid growth of modern technologies is bringing dramatically increased e-commerce payments, as well as the explosion in transaction fraud. Many data mining methods have been proposed for fraud detection. Nevertheless, there is always a contradiction that most methods are irrelevant to transaction sequence, yet sequence-related methods usually cannot learn information at single-transaction level well. In this paper, a new “within→between→within” sandwich-structured sequence learning architecture has been proposed by stacking an ensemble model, a deep sequential learning model and another top-layer ensemble classifier in proper order. Moreover, attention mechanism has also been introduced in to further improve performance. Models in this structure have been manifested to be very efficient in scenarios like fraud detection, where the information sequence is made up of vectors with complex interconnected features.

In modern big data warehouse systems, we observe a common phenomenon that a column of data values can be derived from one or several other columns by transforming and concatenating these columns. We call this relationship between columns a Soft Concatenation Mapping (SCM). SCMs imply significant redundancy in the schema or data, and therefore can be exploited for data integration or data compression. In this paper, we formalize the problem of SCM detection and prove it is NP-hard. We then propose efficient approximate algorithms to detect all SCMs or an optimal set of SCMs in a table. Our experiments on both real-world and synthetic datasets show promising results.