The rumor detection task aims to identify unofficial and unconfirmed information that is spreading on social media. At any given moment, different users express their opinions, focusing on some propagation events, and the posts they make gradually form a social network that expands as it grows. Over time, nodes and edges form a dynamic graph that presents different states at different moments. However, most existing research focuses more on the text content, social context, propagation mode, etc., and they ignore the factors from many aspects and do not consider the dynamic relationships implied in the propagation development of social media. To analyze these dynamic properties, this article proposes a dynamic network-based multitask rumor detection method called dynamic GCN-based multitask model with temporal event sharing for rumor detection (DGCN-TES). This method can effectively capture the dynamic patterns of relationships in propagation events and change them over time to detect rumors. It is mainly divided into three modules: 1) dynamic-graph convolutional network (GCN) module, which uses dynamic graph neural network to construct the propagation graph of rumor events at different times, which can better capture the dynamic spatial features that change over time; 2) content-long short-term memory (LSTM), which uses the LSTM network as a benchmark model and has been improved to better capture time-series text features over time and for multitask shared interactions; and 3) temporal event sharing layer is the sharing layer, which uses time step as the basic unit of sharing, and realizes the sharing interaction between dynamic structural features and temporal text features between the first two modules. We tested the method on two real-world rumor detection datasets PHEME and WEIBO, and the final results show that the method improved F1-score by more than 2.63% and 3.91% compared to the other best baselines baseline.

Many real world optimization problems are dynamic optimization problems (DOPs) whose optima change over time. In this paper, we propose new variants of differential evolution (DE) to solve DOPs. A hybrid method that combines population core based multi-population strategy and prediction strategy and new local search scheme is introduced into DE to enhance its performance for solving DOPs. The population core based multi-population strategy is useful to maintain the diversity of population by using the multi-population and population core concept. The prediction strategy is useful to rapidly adapt to the dynamic environment by using the prediction area. The local search scheme is useful to improve the searching accuracy by suing the new chaotic local search method. Experimental results on the moving peaks benchmark show that the proposed schemes enhance the performance of DE in the dynamic environments.

An important problem in cloud computing faces the challenge of scheduling tasks to virtual machines to meet the cost and time demands, while maintaining the Quality of Service (QoS). Allocating tasks into cloud resources is a difficult problem due to the uncertainty of consumers’ future requirements and the diversity of providers’ resources. Previous studies, either on modeling or scheduling approaches, can no longer offer a satisfactory solution. In this paper, we establish a resource allocation framework and propose a novel task scheduling algorithm. An improved coral reef optimization (ICRO) is proposed to deal with this task scheduling problem. In ICRO, the better-offspring and multicrossover strategies increase the convergent speed and improve the quality of solutions. In addition, a novel load balance-aware mutation enhances the load balance among virtual machines and adjusts the number of resources provided to users. Experimental results show that compared with other algorithms, ICRO can significantly reduce the makespan and cost of the scheduling, while maintaining a better load balance in the system.