A collaborative cloud-based control and defense framework is presented to address the challenges of low-carbon economy and compound attacks such as false data injection (FDI) and denial-of-service (DoS) in distribution networks. The framework consists of two main layers: the cloud-based multi-objective optimization layer and the control & defense layer. The upper cloud computing layer focuses on multi-objective optimization, aiming to minimize generation cost, line losses, and node voltage deviations in low-carbon conditions. Meanwhile, the lower layers combine control with attack defense strategies. State-feedback control is utilized to regulate the dynamics of the distributed generation, and defense strategies are employed to protect against potential compound attacks (FDI and DoS attacks). The defense strategy employs a dual control law sliding mode observer for attack reconfiguration, complemented by periodic event triggering. Also, the input-state stability of the control strategy is demonstrated. To validate the effectiveness of the proposed control strategy, simulations are conducted both on a computer and on the StarSim hardware-in-the-loop experimental platform.

Deception detection has garnered increasing attention in recent years due to the significant growth of digital media and heightened ethical and security concerns. It has been extensively studied using multimodal methods, including video, audio, and text. In addition, individual differences in deception production and detection are believed to play a crucial role. Although some studies have utilized individual information such as personality traits to enhance the performance of deception detection, current systems remain limited, partly due to a lack of sufficient datasets for evaluating performance. To address this issue, we introduce a multimodal deception dataset MDPE. Besides deception features, this dataset also includes individual differences information in personality and emotional expression characteristics. It can explore the impact of individual differences on deception behavior. It comprises over 104 hours of deception and emotional videos from 193 subjects. Furthermore, we conducted numerous experiments to provide valuable insights for future deception detection research. MDPE not only supports deception detection, but also provides conditions for tasks such as personality recognition and emotion recognition, and can even study the relationships between them. We believe that MDPE will become a valuable resource for promoting research in the field of affective computing.

A cloud-based collaborative framework is proposed for the low-carbon economic dispatch problem in distribution networks (DNs). The framework integrates multi-objective optimization and control layers in a hierarchical control approach by coordinating active and reactive power from distributed generation (DG). The upper cloud computing layer comes to multi-objective optimization to minimize the generation cost, line loss and bus voltage deviation under low carbon conditions. The lower control layer regulates the dynamics of the DG using state feedback control combined with a game theory-basedH∞filter. Lastly, the proposed framework effectiveness is verified by StarSim hardware-in-the-loop simulation.

This article presents a collaborative cloud-based control and defense framework designed to address scheduling challenges and interlayer false data injection (FDI) attacks in a low carbon economy. The proposed framework integrates the principles of low carbon economy strategy and new energy (wind turbine, photovoltaic) modeling to coordinate active and reactive power of distributed generation (DG) using a layered control approach. The framework consists of two main layers: a lower layer and an upper layer. The lower layer combines control and attack defense strategies. State feedback control is utilized to regulate the dynamics of the DG and defense strategies are employed to defend against potential controller FDI attacks. The upper layer, on the other hand, consists of interlayer defense strategies and cloud computing. The FDI defense from the lower control layer to the upper cloud computing layer obtains the actual operating state of the DG. And these data are used for cloud computing to get the next reference power. Cloud computing focuses on multiobjective optimization with the aim of minimizing generation cost, line loss, and bus voltage deviation under low carbon conditions. In order to verify the effectiveness of the proposed control strategy, simulations are conducted on a computer and StarSim hardware-in-the-loop experimental platform. The results show that the framework can effectively manage energy consumption in a low-carbon economy.

The novel coronavirus disease (COVID-19) was declared a pandemic by the World Health Organization. The cumulative number of deaths is more than 4.8 million. Epidemiology experts concur that mass testing is essential for isolating infected individuals, contact tracing, and slowing the progression of the virus. In recent months, some machine learning methods have been proposed utilizing audio cues for COVID-19 detection. However, many works are based on hand-crafted features and deep features to detect COVID-19. There is no evidence that these features are optimal for COVID-19 detection. Therefore, we proposed an end-to-end network based on transformer for automatic detection of COVID-19. It directly learns features from the raw waveform for end-to-end learning, rather than extracting features in advance. We propose a feature extraction module to automatically extract features. And we use the transformer architectures to model the dependencies between the extracted features. It is the first end-to-end learning based on raw waveform for COVID-19 detection. Experiments on COUGHVID dataset show that our method has achieved competitive results.