As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

TOR（洋葱路由器）网络是一种广泛使用的开源匿名通信工具，滥用Tor使得很难监视在线犯罪的扩散，例如访问犯罪网站。大多数现有的TOR网络去匿名化的批准都在很大程度上依赖手动提取的功能，从而导致耗时和性能差。为了解决这些缺点，本文提出了一种神经表示方法，以根据分类算法识别网站指纹。我们构建了一个基于卷积神经网络（CNN）的新网站指纹攻击模型，并通过扩张和因果卷积，可以改善CNN的感知场并捕获输入数据的顺序特征。三个主流公共数据集的实验表明，与最先进的方法相比，提出的模型对网站指纹分类非常有效且有效，并将准确性提高了12.21％。

Practical applications employing deep learning must guarantee inference quality. However, we found that the inference quality of state-of-the-art and state-of-the-practice in practical applications has a long tail distribution. In the real world, many tasks have strict requirements for the quality of deep learning inference, such as safety-critical and mission-critical tasks. The fluctuation of inference quality seriously affects its practical applications, and the quality at the tail may lead to severe consequences. State-of-the-art and state-of-the-practice with outstanding inference quality designed and trained under loose constraints still have poor inference quality under constraints with practical application significance. On the one hand, the neural network models must be deployed on complex systems with limited resources. On the other hand, safety-critical and mission-critical tasks need to meet more metric constraints while ensuring high inference quality. We coin a new term, ``tail quality,'' to characterize this essential requirement and challenge. We also propose a new metric, ``X-Critical-Quality,'' to measure the inference quality under certain constraints. This article reveals factors contributing to the failure of using state-of-the-art and state-of-the-practice algorithms and systems in real scenarios. Therefore, we call for establishing innovative methodologies and tools to tackle this enormous challenge.

Anomaly detection is defined as discovering patterns that do not conform to the expected behavior. Previously, anomaly detection was mostly conducted using traditional shallow learning techniques, but with little improvement. As the emergence of graph neural networks (GNN), graph anomaly detection has been greatly developed. However, recent studies have shown that GNN-based methods encounter challenge, in that no graph anomaly detection algorithm can perform generalization on most datasets. To bridge the tap, we propose a multi-view fusion approach for graph anomaly detection (Mul-GAD). The view-level fusion captures the extent of significance between different views, while the feature-level fusion makes full use of complementary information. We theoretically and experimentally elaborate the effectiveness of the fusion strategies. For a more comprehensive conclusion, we further investigate the effect of the objective function and the number of fused views on detection performance. Exploiting these findings, our Mul-GAD is proposed equipped with fusion strategies and the well-performed objective function. Compared with other state-of-the-art detection methods, we achieve a better detection performance and generalization in most scenarios via a series of experiments conducted on Pubmed, Amazon Computer, Amazon Photo, Weibo and Books. Our code is available at https://github.com/liuyishoua/Mul-Graph-Fusion.

我们提出了一种简单而有效的自我训练方法，称为Stad，用于低资源关系提取。该方法首先根据教师模型所预测的概率将自动注释的实例分为两组：自信实例和不确定实例。与大多数以前的研究相反，主要的研究主要仅利用自信实例进行自我训练，我们利用了不确定的实例。为此，我们提出了一种从不确定实例中识别模棱两可但有用的实例的方法，然后将关系分为每个模棱两可的实例中的候选标签集和负标签集。接下来，我们建议对模棱两可的实例的负标签集和对自信实例的积极培训方法提出一种设定的培训方法。最后，提出了一种联合培训方法来在所有数据上构建最终关系提取系统。在两个广泛使用的数据集SEMEVAL2010任务8上进行的实验结果和低资源设置的重新攻击表明，这种新的自我训练方法确实在与几个竞争性自我训练系统相比时确实取得了显着和一致的改进。代码可在https://github.com/jjyunlp/stad上公开获取

近年来，自我监督学习（SSL）已广泛探索。特别是，生成的SSL在自然语言处理和其他AI领域（例如BERT和GPT的广泛采用）中获得了新的成功。尽管如此，对比度学习 - 严重依赖结构数据的增强和复杂的培训策略，这是图SSL的主要方法，而迄今为止，生成SSL在图形上的进度（尤其是GAES）尚未达到潜在的潜力。正如其他领域所承诺的。在本文中，我们确定并检查对GAE的发展产生负面影响的问题，包括其重建目标，训练鲁棒性和错误指标。我们提出了一个蒙版的图形自动编码器Graphmae，该图可以减轻这些问题，以预处理生成性自我监督图。我们建议没有重建图形结构，而是提议通过掩盖策略和缩放余弦误差将重点放在特征重建上，从而使GraphMae的强大训练受益。我们在21个公共数据集上进行了大量实验，以实现三个不同的图形学习任务。结果表明，Graphmae-A简单的图形自动编码器具有仔细的设计-CAN始终在对比度和生成性最新基准相比，始终产生优于性的表现。这项研究提供了对图自动编码器的理解，并证明了在图上的生成自我监督预训练的潜力。

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

Text clustering and topic extraction are two important tasks in text mining. Usually, these two tasks are performed separately. For topic extraction to facilitate clustering, we can first project texts into a topic space and then perform a clustering algorithm to obtain clusters. To promote topic extraction by clustering, we can first obtain clusters with a clustering algorithm and then extract cluster-specific topics. However, this naive strategy ignores the fact that text clustering and topic extraction are strongly correlated and follow a chicken-and-egg relationship. Performing them separately fails to make them mutually benefit each other to achieve the best overall performance. In this paper, we propose an unsupervised text clustering and topic extraction framework (ClusTop) which integrates text clustering and topic extraction into a unified framework and can achieve high-quality clustering result and extract topics from each cluster simultaneously. Our framework includes four components: enhanced language model training, dimensionality reduction, clustering and topic extraction, where the enhanced language model can be viewed as a bridge between clustering and topic extraction. On one hand, it provides text embeddings with a strong cluster structure which facilitates effective text clustering; on the other hand, it pays high attention on the topic related words for topic extraction because of its self-attention architecture. Moreover, the training of enhanced language model is unsupervised. Experiments on two datasets demonstrate the effectiveness of our framework and provide benchmarks for different model combinations in this framework.

An increasing number of public datasets have shown a marked clinical impact on assessing anatomical structures. However, each of the datasets is small, partially labeled, and rarely investigates severe tumor subjects. Moreover, current models are limited to segmenting specific organs/tumors, which can not be extended to novel domains and classes. To tackle these limitations, we introduce embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models, dubbed the CLIP-Driven Universal Model. The Universal Model can better segment 25 organs and 6 types of tumors by exploiting the semantic relationship between abdominal structures. The model is developed from an assembly of 14 datasets with 3,410 CT scans and evaluated on 6,162 external CT scans from 3 datasets. We rank first on the public leaderboard of the Medical Segmentation Decathlon (MSD) and achieve the state-of-the-art results on Beyond The Cranial Vault (BTCV). Compared with dataset-specific models, the Universal Model is computationally more efficient (6x faster), generalizes better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks. The design of CLIP embedding enables the Universal Model to be easily extended to new classes without catastrophically forgetting the previously learned classes.

Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views. However, the preserved high-level semantics do not contain enough local information, which is vital in medical image analysis (e.g., image-based diagnosis and tumor segmentation). To mitigate the locality problem of comparative SSL, we propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics. We also address the preservation of scale information, a powerful tool in aiding image understanding but has not drawn much attention in SSL. The resulting framework can be formulated as a multi-task optimization problem on the feature pyramid. Specifically, we conduct multi-scale pixel restoration and siamese feature comparison in the pyramid. In addition, we propose non-skip U-Net to build the feature pyramid and develop sub-crop to replace multi-crop in 3D medical imaging. The proposed unified SSL framework (PCRLv2) surpasses its self-supervised counterparts on various tasks, including brain tumor segmentation (BraTS 2018), chest pathology identification (ChestX-ray, CheXpert), pulmonary nodule detection (LUNA), and abdominal organ segmentation (LiTS), sometimes outperforming them by large margins with limited annotations.