Tonight, with extensive fog the hills. Clearer skies are times. Eastern scotland and North Eastern england, touch of frost here. But temperatures mostly above freezing. Another fairly temperatures mostly above freezing. Anotherfairly dull temperatures mostly above freezing. Another fairly dull day. Extensive low cloud and craig first thing in the morning, patchy rain and drizzle. A Few Cloud Breaks East of wales, north east england, east of scotland, but for most, stick with the cloud. Wetter in Western Scotla nd the cloud. Wetter in Western Scotland later on. But once again, temperatures above where they should be for the time of year. As for the weekend, prepare forsomething be for the time of year. As for the weekend, prepare for something more winds to re. All in all, it turns colder. Not quite as cold and snowy as Eastern Europe but some of you may see some snow by thursday. See you in half an hour. Hello, this is bbc news with nicholas owen. The headlines at 8. 30pm. Labour has a
Recent studies have demonstrated that backdoor attacks can cause a significant security threat to federated learning. Existing defense methods mainly focus on detecting or eliminating the backdoor patterns after the model is backdoored. However, these methods either cause model performance degradation or heavily rely on impractical assumptions, such as labeled clean data, which exhibit limited effectiveness in federated learning. To this end, we propose FLPurifier, a novel backdoor defense method in federated learning that can effectively purify the possible backdoor attributes before federated aggregation. Specifically, FLPurifier splits a complete model into a feature extractor and classifier, in which the extractor is trained in a decoupled contrastive manner to break the strong correlation between trigger features and the target label. Compared with existing backdoor mitigation methods, FLPurifier doesn’t rely on impractical assumptions since it can effectively purify the backdoo
As a newly emerging task, audio-visual question answering (AVQA) has attracted research attention. Compared with traditional single-modality (e.g., audio or visual) QA tasks, it poses new challenges due to the higher complexity of feature extraction and fusion brought by the multimodal inputs. First, AVQA requires more comprehensive understanding of the scene which involves both audio and visual information; Second, in the presence of more information, feature extraction has to be better connected with a given question; Third, features from different modalities need to be sufficiently correlated and fused. To address this situation, this work proposes a novel framework for multimodal question answering task. It characterises an audiovisual scene at both global and local levels, and within each level, the features from different modalities are well fused. Furthermore, the given question is utilised to guide not only the feature extraction at the local level but also the final fusion of