Edge Federated Learning (EFL) has demonstrated significant potential in the field of Artificial Intelligence of Things (AIoT) by protecting data privacy and reducing communication costs. However, in real-world scenarios, multiple independent edge networks seldom collaborate due to factors such as data heterogeneity and the absence of a central server. Mobile devices, acting as bridges across different environments, offer an opportunity to enable dynamic collaboration among multiple edge networks. Nevertheless, as mobile devices transition between edge networks, they may encounter new classes with only a few samples, leading to catastrophic forgetting of previous knowledge and overfitting in new environments. To address this challenge, we propose Cross-F$^{2}$SCIL, a Federated Few-Shot Class Incremental Learning method that enables on-demand dynamic collaboration in mobile edge network environments. Cross-F$^{2}$SCIL allows mobile devices to efficiently learn new class knowledge from few-shot samples upon entering new edge networks while consolidating prior knowledge to prevent forgetting. Specifically, to mitigate the forgetting caused by new class overwriting on devices and parameter dilution at the server, we design a two-phase training framework. In the first phase, we learn a local model using Prototype Augmentation to enhance the retention of prior knowledge. In the second phase, we obtain the global model via Hierarchical Personalized Parameter Aggregation to effectively integrate learned knowledge across devices. To effectively learn new class information while reducing overfitting, we incorporate Self-Supervised Knowledge Aggregation and Prototype Knowledge Fusion to enhance model generalization and seamlessly integrate new classes into the existing model. Compared to the best-performing baseline on each dataset, Cross-F$^{2}$SCIL achieves an average improvement of 5.52% in Average Accuracy across five datasets, with the maximum improvement reaching 7.97%.

The application of convolutional neural networks has been shown to significantly improve the accuracy of building extraction from very high-resolution (VHR) remote sensing images. However, there exist so-called semantic gaps among different kinds of buildings due to the large intraclass variance of buildings, and most of the present-day methods are ineffective in extracting various buildings in large areas that cover different scenes, for example, urban villages and high-rise buildings, because existing building extraction strategies are the same for various scenes. With the improvement of the resolution of remote sensing images, it is feasible to improve the image interpretation based on the scene prior. However, this idea has not been fully utilized in building extraction from VHR remote sensing imagery. This study proposes a scene-driven multitask parallel attention convolutional network (MTPA-Net) to resolve these limitations. The proposed approach classifies the input image into multilabel scenes and further separately maps the buildings in pixel level under different scenes. In addition, a simple postprocessing method is applied to integrate the building extraction results and scene prior. Our proposed method does not require multimodel training and the network can learn in an end-to-end manner. The performance of our proposed method is evaluated on a data set that includes various urban and rural scenes with diverse landscapes. The experimental results show that the proposed MTPA-Net outperforms state-of-the-art algorithms by reducing misclassification areas and maintaining improved robustness.

Presents corrections to the above named paper.

The Internet significantly reshapes traditional commerce mode, making online business ubiquitous and indispensable. Others' opinions are influential when we make a decision, which is complicated by tremendous and misleading information. According to opinion mining techniques, we propose a novel conceptual framework which is designed to exploit the potentials of opinion mining in e-commerce domain. Based on the various text corpora we crawled from targeted internet source, we utilize the sentiment analysis principle and algorithm to provide convenient and assistant services, such as opinion spam detection, opinion search and retrieval, opinion summary, opinion question answering and opinion recommendation, to e-commerce participants such as potential customers, manufacturers, third-party traders or retailers, and regulators. Besides, the system introduces a novel NLP toolkits excelling in handling the Chinese corpora than most of previous alike work. The system is especially helpful to regulators by providing useful applications which can be utilized to make administrating decisions.