Deep learning models are recently applied to detect rumors on social media based on the information in the posts.However, at the early stage of rumor propagation, due to the lack of responses, the performance of these models often degrades.In this paper, we propose a method based on the conditional generative adversarial network, which can generate the responses like data and help the deep learning models in early detection.On two large-scale Sina Weibo datasets, the proposed method is applied on the existing convolution neural network model, the recurrent neural network model, and the recursive neural network model.The results show that the proposed method can significantly improve the performance of the models in the case of zero response, and has performance superiority in a certain early period.

The layout optimization problem is brought from the design of the recoverable satellite, where a set of objects (equipments or devices) are required to be installed on a circular load board.The aim of the problem is to find a layout of the objects with no interference, less unbalance, and less space occupied.Artificial bee colony (ABC) algorithms show good performance in many engineering problems.In this article, based on the analysis of the solution distribution, a problemspecific knowledge based ABC is proposed, which is configured with special initialization and parameter settings.On an open benchmark with ten instances, the proposed ABC is compared with two widely used algorithms.Its performance outperforms the genetic algorithm on all the instances, and outperforms the quasi-human algorithm on nine instances.

A hybrid satellite-aerial-terrestrial system has been recently studied as a promising candidate to meet the urgent communication needs of emergency relief, and a good resource allocation policy is important to solve the contradiction between the sudden growth of victims' demand and the shortage of wireless resource in emergency situation. This paper addresses the joint relay selection and power allocation for an OFDMA-based hybrid satellite-aerial-terrestrial cooperative network, aiming at maximizing the energy efficiency (EE) with power constraints, quality of service (QoS) requirements and backhaul capacity. The optimization problem not only is a mixed 0-1 nonlinear program, but also contains a fractional objective function and a non-convex constraint condition. To tackle this complicated optimization problem, we firstly relax the binary variables and then transform the fractional objective function into a subtractive one. In each iteration, the power allocation solution and relay selection policy are approached via dual decomposition method. Simulation results illustrate the impact of total transmit power and backhaul capacity on EE and system capacity. What is more, relay selections highly enhance the system performance.

In this paper we present a new algorithm for the Layout Optimization Problem: this concerns the placement of circular, weighted objects inside a circular container, the two objectives being to minimize imbalance of mass and to minimize the radius of the container. This problem carries real practical significance in industrial applications (such as the design of satellites), as well as being of significant theoretical interest. We present a genetic algorithm solution and compare it with two existing nature-inspired methods, one of which is the best published algorithm for this problem. Experimental results show that our approach out-performs these existing methods in terms of both solution quality and execution time.