恶意软件检测在网络安全中起着至关重要的作用，随着恶意软件增长的增加和网络攻击的进步。以前看不见的恶意软件不是由安全供应商确定的，这些恶意软件通常在这些攻击中使用，并且不可避免地要找到可以从未标记的样本数据中自学习的解决方案。本文介绍了Sherlock，这是一种基于自学的深度学习模型，可根据视觉变压器（VIT）体系结构检测恶意软件。 Sherlock是一种新颖的恶意软件检测方法，它可以通过使用基于图像的二进制表示形式来学习独特的功能，以区分恶意软件和良性程序。在47种类型和696个家庭的层次结构中使用120万个Android应用的实验结果表明，自我监督的学习可以达到97％的恶意软件分类，而恶意软件的二进制分类比现有的最新技术更高。我们提出的模型还能够胜过针对多级恶意软件类型和家庭的最先进技术，分别为.497和.491。

非接触式和高效的系统迅速实施，以提倡对抗Covid-19大流行的预防方法。尽管此类系统的积极效益，但通过侵入用户隐私有潜力。在这项工作中，我们通过使用掩蔽面部图像预测隐私敏感的软生物测量来分析面部生物识别系统的隐私侵犯性。我们根据Reset-50架构培训并申请CNN，具有20,003个合成屏蔽图像并测量隐私侵犯性。尽管人们在人们中戴着面具的隐私益处存在受欢迎的信念，但我们表明，当面具磨损时，隐私侵犯性没有显着差异。在我们的实验中，我们能够准确地预测来自蒙面的面部图像的性别（94.7％），种族（83.1％）和年龄（MAE 6.21和RMSE 8.33）。我们所提出的方法可以作为基准实用程序来评估利用隐私敏感信息的人工智能系统的隐私侵犯性。我们开展研究界的重新提供和更广泛的使用贡献。

We present a graph convolutional network with 2D pose estimation for the first time on child action recognition task achieving on par results with an RGB modality based model on a novel benchmark dataset containing unconstrained environment based videos.

This paper presents an implementation on child activity recognition (CAR) with a graph convolution network (GCN) based deep learning model since prior implementations in this domain have been dominated by CNN, LSTM and other methods despite the superior performance of GCN. To the best of our knowledge, we are the first to use a GCN model in child activity recognition domain. In overcoming the challenges of having small size publicly available child action datasets, several learning methods such as feature extraction, fine-tuning and curriculum learning were implemented to improve the model performance. Inspired by the contradicting claims made on the use of transfer learning in CAR, we conducted a detailed implementation and analysis on transfer learning together with a study on negative transfer learning effect on CAR as it hasn't been addressed previously. As the principal contribution, we were able to develop a ST-GCN based CAR model which, despite the small size of the dataset, obtained around 50% accuracy on vanilla implementations. With feature extraction and fine-tuning methods, accuracy was improved by 20%-30% with the highest accuracy being 82.24%. Furthermore, the results provided on activity datasets empirically demonstrate that with careful selection of pre-train model datasets through methods such as curriculum learning could enhance the accuracy levels. Finally, we provide preliminary evidence on possible frame rate effect on the accuracy of CAR models, a direction future research can explore.