Adversarial attack on skeletal motion is a hot topic. However, existing
researches only consider part of dynamic features when measuring distance
between skeleton graph sequences, which results in poor imperceptibility. To
this end, we propose a novel adversarial attack method to attack action
recognizers for skeletal motions. Firstly, our method systematically proposes a
dynamic distance function to measure the difference between skeletal motions.
Meanwhile, we innovatively introduce emotional features for complementary
information. In addition, we use Alternating Direction Method of
Multipliers(ADMM) to solve the constrained optimization problem, which
generates adversarial samples with better imperceptibility to deceive the
classifiers. Experiments show that our method is effective on multiple action
classifiers and datasets. When the perturbation magnitude measured by l norms
is the same, the dynamic perturbations generated by our method are much lower
than that of other methods. What's more, we are the first to prove the
effectiveness of emotional features, and provide a new idea for measuring the
distance between skeletal motions.

我们提出了一种新的敌对攻击方法，通过引入情感特征和动态距离函数，使用交替方向乘法算法 (ADMM) 生成具有更好的隐蔽性的敌对样本，以欺骗动作识别器，实验证明我们的方法在多个动作分类器和数据集上是有效的。

情感丧失攻击：基于多维特征的骨架感知骨骼攻击

Emotion Loss Attacking: Adversarial Attack Perception for Skeleton based  on Multi-dimensional Features

This paper addresses the problem of risk prediction on social media data,
specifically focusing on the classification of Reddit users as having a
pathological gambling disorder. To tackle this problem, this paper focuses on
incorporating temporal and emotional features into the model. The preprocessing
phase involves dealing with the time irregularity of posts by padding
sequences. Two baseline architectures are used for preliminary evaluation: BERT
classifier on concatenated posts per user and GRU with LSTM on sequential data.
Experimental results demonstrate that the sequential models outperform the
concatenation-based model. The results of the experiments conclude that the
incorporation of a time decay layer (TD) and passing the emotion classification
layer (EmoBERTa) through LSTM improves the performance significantly.
Experiments concluded that the addition of a self-attention layer didn't
significantly improve the performance of the model, however provided easily
interpretable attention scores. The developed architecture with the inclusion
of EmoBERTa and TD layers achieved a high F1 score, beating existing benchmarks
on pathological gambling dataset. Future work may involve the early prediction
of risk factors associated with pathological gambling disorder and testing
models on other datasets. Overall, this research highlights the significance of
the sequential processing of posts including temporal and emotional features to
boost the predictive power, as well as adding an attention layer for
interpretability.

通过将时间特征和情感特征纳入模型，本文解决了在社交媒体数据上进行风险预测的问题，着重于对 Reddit 用户进行病态赌博障碍的分类。通过实验证明了序列模型在性能上优于基于连接的模型，提出的架构在包含 EmoBERTa 和 TD 层的情况下取得了高 F1 分数，在赌博障碍数据集上超过了现有基准。

社交媒体上的病理赌博风险预测

Risk prediction of pathological gambling on social media

This article presents our results for the sixth Affective Behavior Analysis
in-the-wild (ABAW) competition. To improve the trustworthiness of facial
analysis, we study the possibility of using pre-trained deep models that
extract reliable emotional features without the need to fine-tune the neural
networks for a downstream task. In particular, we introduce several lightweight
models based on MobileViT, MobileFaceNet, EfficientNet, and DDAMFN
architectures trained in multi-task scenarios to recognize facial expressions,
valence, and arousal on static photos. These neural networks extract
frame-level features fed into a simple classifier, e.g., linear feed-forward
neural network, to predict emotion intensity, compound expressions, action
units, facial expressions, and valence/arousal. Experimental results for five
tasks from the sixth ABAW challenge demonstrate that our approach lets us
significantly improve quality metrics on validation sets compared to existing
non-ensemble techniques.

本论文介绍了利用预训练的深度模型在静态照片上提取可靠情感特征的可能性，并通过多任务场景中的轻量级模型来识别面部表情、价值和唤起。实验结果表明，与现有的非集成技术相比，我们的方法显著提高了验证集上的质量指标。