In social network service platforms, crime suspects are likely to use
cybercrime coded words for communication by adding criminal meanings to
existing words or replacing them with similar words. For instance, the word
'ice' is often used to mean methamphetamine in drug crimes. To analyze the
nature of cybercrime and the behavior of criminals, quickly detecting such
words and further understanding their meaning are critical. In the automated
cybercrime coded word detection problem, it is difficult to collect a
sufficient amount of training data for supervised learning and to directly
apply language models that utilize context information to better understand
natural language. To overcome these limitations, we propose a new two-step
approach, in which a mean latent vector is constructed for each cybercrime
through one of five different AutoEncoder models in the first step, and
cybercrime coded words are detected based on multi-level latent representations
in the second step. Moreover, to deeply understand cybercrime coded words
detected through the two-step approach, we propose three novel methods: (1)
Detection of new words recently coined, (2) Detection of words frequently
appeared in both drug and sex crimes, and (3) Automatic generation of word
taxonomy. According to our experimental results, among various AutoEncoder
models, the stacked AutoEncoder model shows the best performance. Additionally,
the F1-score of the two-step approach is 0.991, which is higher than 0.987 and
0.903 of the existing dark-GloVe and dark-BERT models. By analyzing the
experimental results of the three proposed methods, we can gain a deeper
understanding of drug and sex crimes.

通过多级潜在表示及自动编码器模型，我们提出了一种新的两步骤方法来检测犯罪行为中的网络犯罪编码词，并通过分析实验结果深入了解毒品和性犯罪。

多级表示学习的两步自动网络犯罪密码词检测

Two-step Automated Cybercrime Coded Word Detection using Multi-level  Representation Learning

Detecting anomalies in electrocardiogram data is crucial to identifying
deviations from normal heartbeat patterns and providing timely intervention to
at-risk patients. Various AutoEncoder models (AE) have been proposed to tackle
the anomaly detection task with ML. However, these models do not consider the
specific patterns of ECG leads and are unexplainable black boxes. In contrast,
we replace the decoding part of the AE with a reconstruction head (namely,
FMM-Head) based on prior knowledge of the ECG shape. Our model consistently
achieves higher anomaly detection capabilities than state-of-the-art models, up
to 0.31 increase in area under the ROC curve (AUROC), with as little as half
the original model size and explainable extracted features. The processing time
of our model is four orders of magnitude lower than solving an optimization
problem to obtain the same parameters, thus making it suitable for real-time
ECG parameters extraction and anomaly detection.

通过使用基于心电图形状先验知识的重建头（即 FMM-Head）替代自编码器（AE）的解码部分来提高异常检测能力和解释性特征的心电图异常检测模型。

FMM-Head: 使用先验知识提升基于自编码器的心电异常检测

FMM-Head: Enhancing Autoencoder-based ECG anomaly detection with prior  knowledge

In medical applications, weakly supervised anomaly detection methods are of
great interest, as only image-level annotations are required for training.
Current anomaly detection methods mainly rely on generative adversarial
networks or autoencoder models. Those models are often complicated to train or
have difficulties to preserve fine details in the image. We present a novel
weakly supervised anomaly detection method based on denoising diffusion
implicit models. We combine the deterministic iterative noising and denoising
scheme with classifier guidance for image-to-image translation between diseased
and healthy subjects. Our method generates very detailed anomaly maps without
the need for a complex training procedure. We evaluate our method on the
BRATS2020 dataset for brain tumor detection and the CheXpert dataset for
detecting pleural effusions.

我们提出了一种基于去噪扩散内隐模型的弱监督异常检测方法，结合分类器指导进行病态和健康主体之间的图像翻译，生成非常详细的异常地图，无需复杂的训练过程。