Semantic interpretation for convolutional neural networks: What makes a cat a cat?

The interpretability of deep neural networks has attracted increasing attention in recent years, and several methods have been created to interpret the "black box" model. Fundamental limitations remain, however, that impede the pace of understanding the networks, especially the extraction of understandable semantic space. In this work, we introduce the framework of semantic explainable AI (S-XAI), which utilizes row-centered principal component analysis to obtain the common traits from the best combination of superpixels discovered by a genetic algorithm, and extracts understandable semantic spaces on the basis of discovered semantically sensitive neurons and visualization techniques. Statistical interpretation of the semantic space is also provided, and the concept of semantic probability is proposed for the first time. Our experimental results demonstrate that S-XAI is effective in providing a semantic interpretation for the CNN, and offers broad usage, including trustworthiness assessment and semantic sample searching.Comment: 33 pages, 11 figure

FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality

Federated learning is a powerful technique that enables collaborative learning among different clients. Prototype-based federated learning is a specific approach that improves the performance of local models under non-IID (non-Independently and Identically Distributed) settings by integrating class prototypes. However, prototype-based federated learning faces several challenges, such as prototype redundancy and prototype failure, which limit its accuracy. It is also susceptible to poisoning attacks and server malfunctions, which can degrade the prototype quality. To address these issues, we propose FedRFQ, a prototype-based federated learning approach that aims to reduce redundancy, minimize failures, and improve \underline{q}uality. FedRFQ leverages a SoftPool mechanism, which effectively mitigates prototype redundancy and prototype failure on non-IID data. Furthermore, we introduce the BFT-detect, a BFT (Byzantine Fault Tolerance) detectable aggregation algorithm, to ensure the security of FedRFQ against poisoning attacks and server malfunctions. Finally, we conduct experiments on three different datasets, namely MNIST, FEMNIST, and CIFAR-10, and the results demonstrate that FedRFQ outperforms existing baselines in terms of accuracy when handling non-IID data

The Weighted Support Vector Machine Based on Hybrid Swarm Intelligence Optimization for Icing Prediction of Transmission Line

Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR). According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO), which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability

Nonlinearity of Subtidal Estuarine Circulation in the Pearl River Estuary, China

The Pearl River Estuary (PRE) is a bell-shaped estuary with a narrow deep channel and wide shoals. This unique topographic feature leads to different dynamics of the subtidal estuarine circulation (SEC) in the PRE compared with a narrow and straight estuary. In this study, the nonlinear dynamics of the SEC in the PRE under mean circumstance are analyzed by using a validated 3D numerical model. Model results show that the nonlinear advections reach leading order in the along-channel momentum balance. Modulated by tide, the nonlinear advections show significant temporal variations as they have much larger values during spring tide than that during neap tide. Unlike straight and narrow estuaries, both tidally and cross-sectionally averaged axial and lateral advections play important roles in driving the SEC in the PRE in which the axial advection dominates the nonlinear effect, but the two nonlinear terms balance each other largely resulting in a reduced nonlinear effect. Despite this, the total nonlinear advection is still comparable with other terms, and it acts as the baroclinic pressure to reinforce the SEC, especially during the ebb tide, suggesting a flood–ebb asymmetry of the nonlinear dynamics in the PRE. In addition, diagnostic analyses of the along-channel vorticity budget show that nonlinear advections also make significant contribution to drive the lateral circulation in the PRE as Coriolis and baroclinic pressure terms, indicating complex dynamics of the circulation in the PRE