Inter-subject or subject-independent emotion recognition has been a
challenging task in affective computing. This work is about an
easy-to-implement emotion recognition model that classifies emotions from EEG
signals subject independently. It is based on the famous EEGNet architecture,
which is used in EEG-related BCIs. We used the Dataset on Emotion using
Naturalistic Stimuli (DENS) dataset. The dataset contains the Emotional Events
-- the precise information of the emotion timings that participants felt. The
model is a combination of regular, depthwise and separable convolution layers
of CNN to classify the emotions. The model has the capacity to learn the
spatial features of the EEG channels and the temporal features of the EEG
signals variability with time. The model is evaluated for the valence space
ratings. The model achieved an accuracy of 73.04%.

通过使用 EEG 信号和 CNN 层，本文提出了一种易于实现的情感识别模型，使用 DENS 数据集进行评估，并在情感极性评分上达到 73.04％的准确性。

利用时空特征从 EEG 信号进行跨主体情感识别

Inter Subject Emotion Recognition Using Spatio-Temporal Features From  EEG Signal

The distributed nature and privacy-preserving characteristics of federated
learning make it prone to the threat of poisoning attacks, especially backdoor
attacks, where the adversary implants backdoors to misguide the model on
certain attacker-chosen sub-tasks. In this paper, we present a novel method
ARIBA to accurately and robustly identify backdoor attacks in federated
learning. By empirical study, we observe that backdoor attacks are discernible
by the filters of CNN layers. Based on this finding, we employ unsupervised
anomaly detection to evaluate the pre-processed filters and calculate an
anomaly score for each client. We then identify the most suspicious clients
according to their anomaly scores. Extensive experiments are conducted, which
show that our method ARIBA can effectively and robustly defend against multiple
state-of-the-art attacks without degrading model performance.

本文提出了一种名为 ARIBA 的新方法，它能够准确、有力地识别联邦学习中的后门攻击，并通过使用 CNN 层的过滤器和无监督异常检测来为每个客户端计算异常值以识别最可疑的客户端。经过大量实验证明，ARIBA 方法可以有效地防御多种最先进的攻击，而不会降低模型的性能。

ARIBA: 面向联邦学习中后门攻击的准确与鲁棒识别

ARIBA: Towards Accurate and Robust Identification of Backdoor Attacks in Federated Learning

The objective of this paper is the effective transfer of the Convolutional
Neural Network (CNN) feature in image search and classification.
Systematically, we study three facts in CNN transfer. 1) We demonstrate the
advantage of using images with a properly large size as input to CNN instead of
the conventionally resized one. 2) We benchmark the performance of different
CNN layers improved by average/max pooling on the feature maps. Our observation
suggests that the Conv5 feature yields very competitive accuracy under such
pooling step. 3) We find that the simple combination of pooled features
extracted across various CNN layers is effective in collecting evidences from
both low and high level descriptors. Following these good practices, we are
capable of improving the state of the art on a number of benchmarks to a large
margin.

本研究旨在有效转移卷积神经网络（CNN）在图像搜索和分类中的特征。通过系统研究 CNN 转移的三个要点，本研究得出了使用适当大小的图像作为 CNN 输入的优势，使用平均 / 最大池化改善特征映射的不同 CNN 层的性能的结果以及在不同 CNN 层中提取集合特征的简单组合有效地从低层次和高层次描述符中收集证据等结论。遵循这些良好实践，本研究在多个基准测试中显著提高了最先进技术水平。