具有终身学习能力（LL）能力的质量检查模型对于实用的质量检查应用很重要，据报道，基于架构的LL方法是这些模型的有效实现。但是，将以前的方法扩展到质量检查任务是不平凡的，因为它们要么需要在测试阶段访问任务身份，要么不会从看不见的任务中明确对样本进行模拟。在本文中，我们提出了Diana：一种基于动态体系结构的终生质量检查模型，该模型试图通过迅速增强的语言模型来学习一系列QA任务。戴安娜（Diana）使用四种类型的分层组织提示来捕获来自不同粒度的质量检查知识。具体而言，我们专门介绍任务级别的提示来捕获特定任务的知识，以保留高LL性能并维护实例级别的提示，以学习跨不同输入样本共享的知识，以提高模型的概括性能。此外，我们专用于单独的提示来明确建模未看到的任务，并引入一组及时的密钥向量，以促进任务之间的知识共享。广泛的实验表明，戴安娜（Diana）的表现优于最先进的终身质量检查模型，尤其是在处理看不见的任务时。

作为一种主动网络安全保护方案，入侵检测系统（IDS）承担以恶意网络流量形式检测网络攻击的重要责任。入侵检测技术是ID的重要组成部分。目前，许多学者已经对入侵检测技术进行了广泛的研究。但是，为大规模网络流量数据开发有效的入侵检测方法仍然很困难。由于生成的对抗网络（GAN）具有强大的建模功能，可用于复杂的高维数据，因此它们为解决此问题提供了新的想法。在本文中，我们提出了一种基于Ebgan的入侵检测方法IDS-Ebgan，该方法将网络记录归类为正常流量或恶意流量。 IDS-Ebgan中的发电机负责将培训中的原始恶意网络流量转换为对抗性恶意示例。这是因为我们想使用对抗性学习来提高歧视者检测恶意流量的能力。同时，鉴别器采用自动编码器模型。在测试过程中，IDS-Ebgan使用歧视器的重建错误来对流量记录进行分类。

最近，基于GAN的神经声码器（如平行Wavegan，Melgan，Hifigan和Univnet）由于其轻巧和平行的结构而变得流行，从而导致具有高保真性的实时合成波形，即使在CPU上也是如此。 Hifigan和Univnet是两个Sota Vocoders。尽管它们质量很高，但仍有改进的余地。在本文中，由计算机视觉的视觉望远镜结构的激励，我们采用了一个类似的想法，并提出了一个有效且轻巧的神经声码器，称为Wolonet。在该网络中，我们开发了一个新颖的轻质块，该块使用位于曲线的动态凝胶核的位置变化，与通道无关和深度动态卷积内核。为了证明我们方法的有效性和概括性，我们进行了一项消融研究，以验证我们的新型设计，并与典型的基于GAN的歌手进行主观和客观的比较。结果表明，我们的Wolonet达到了最佳的一代质量，同时需要的参数少于两个神经SOTA声码器Hifigan和Univnet。

由于其便利性，使用第三方提供的预培训模型变得越来越普遍。然而，与此同时，这些模型可能容易受到中毒和逃避攻击的影响。我们引入了一个算法框架，当防御者无法获得清洁数据时，可以在预训练的模型中减轻潜在的安全漏洞。框架从给定的预训练模型进行了反向工程。然后，可以将所得的合成样品用作替代干净的数据以执行各种防御措施。我们考虑两种重要的攻击场景 - 后门攻击和逃避攻击 - 以展示合成样本的实用性。对于这两次攻击，我们表明，当提供我们的合成数据时，最新的防御能力的性能相当甚至比提供相同数量的清洁数据时的情况相当甚至更好。

Video action segmentation aims to slice the video into several action segments. Recently, timestamp supervision has received much attention due to lower annotation costs. We find the frames near the boundaries of action segments are in the transition region between two consecutive actions and have unclear semantics, which we call ambiguous intervals. Most existing methods iteratively generate pseudo-labels for all frames in each video to train the segmentation model. However, ambiguous intervals are more likely to be assigned with noisy and incorrect pseudo-labels, which leads to performance degradation. We propose a novel framework to train the model under timestamp supervision including the following two parts. First, pseudo-label ensembling generates pseudo-label sequences with ambiguous intervals, where the frames have no pseudo-labels. Second, iterative clustering iteratively propagates the pseudo-labels to the ambiguous intervals by clustering, and thus updates the pseudo-label sequences to train the model. We further introduce a clustering loss, which encourages the features of frames within the same action segment more compact. Extensive experiments show the effectiveness of our method.

Deep neural networks (DNNs) are sensitive and susceptible to tiny perturbation by adversarial attacks which causes erroneous predictions. Various methods, including adversarial defense and uncertainty inference (UI), have been developed in recent years to overcome the adversarial attacks. In this paper, we propose a multi-head uncertainty inference (MH-UI) framework for detecting adversarial attack examples. We adopt a multi-head architecture with multiple prediction heads (i.e., classifiers) to obtain predictions from different depths in the DNNs and introduce shallow information for the UI. Using independent heads at different depths, the normalized predictions are assumed to follow the same Dirichlet distribution, and we estimate distribution parameter of it by moment matching. Cognitive uncertainty brought by the adversarial attacks will be reflected and amplified on the distribution. Experimental results show that the proposed MH-UI framework can outperform all the referred UI methods in the adversarial attack detection task with different settings.

We present Pre-trained Machine Reader (PMR), a novel method to retrofit Pre-trained Language Models (PLMs) into Machine Reading Comprehension (MRC) models without acquiring labeled data. PMR is capable of resolving the discrepancy between model pre-training and downstream fine-tuning of existing PLMs, and provides a unified solver for tackling various extraction tasks. To achieve this, we construct a large volume of general-purpose and high-quality MRC-style training data with the help of Wikipedia hyperlinks and design a Wiki Anchor Extraction task to guide the MRC-style pre-training process. Although conceptually simple, PMR is particularly effective in solving extraction tasks including Extractive Question Answering and Named Entity Recognition, where it shows tremendous improvements over previous approaches especially under low-resource settings. Moreover, viewing sequence classification task as a special case of extraction task in our MRC formulation, PMR is even capable to extract high-quality rationales to explain the classification process, providing more explainability of the predictions.

The opaqueness of the multi-hop fact verification model imposes imperative requirements for explainability. One feasible way is to extract rationales, a subset of inputs, where the performance of prediction drops dramatically when being removed. Though being explainable, most rationale extraction methods for multi-hop fact verification explore the semantic information within each piece of evidence individually, while ignoring the topological information interaction among different pieces of evidence. Intuitively, a faithful rationale bears complementary information being able to extract other rationales through the multi-hop reasoning process. To tackle such disadvantages, we cast explainable multi-hop fact verification as subgraph extraction, which can be solved based on graph convolutional network (GCN) with salience-aware graph learning. In specific, GCN is utilized to incorporate the topological interaction information among multiple pieces of evidence for learning evidence representation. Meanwhile, to alleviate the influence of noisy evidence, the salience-aware graph perturbation is induced into the message passing of GCN. Moreover, the multi-task model with three diagnostic properties of rationale is elaborately designed to improve the quality of an explanation without any explicit annotations. Experimental results on the FEVEROUS benchmark show significant gains over previous state-of-the-art methods for both rationale extraction and fact verification.

We present a simple yet effective end-to-end Video-language Pre-training (VidLP) framework, Masked Contrastive Video-language Pretraining (MAC), for video-text retrieval tasks. Our MAC aims to reduce video representation's spatial and temporal redundancy in the VidLP model by a mask sampling mechanism to improve pre-training efficiency. Comparing conventional temporal sparse sampling, we propose to randomly mask a high ratio of spatial regions and only feed visible regions into the encoder as sparse spatial sampling. Similarly, we adopt the mask sampling technique for text inputs for consistency. Instead of blindly applying the mask-then-prediction paradigm from MAE, we propose a masked-then-alignment paradigm for efficient video-text alignment. The motivation is that video-text retrieval tasks rely on high-level alignment rather than low-level reconstruction, and multimodal alignment with masked modeling encourages the model to learn a robust and general multimodal representation from incomplete and unstable inputs. Coupling these designs enables efficient end-to-end pre-training: reduce FLOPs (60% off), accelerate pre-training (by 3x), and improve performance. Our MAC achieves state-of-the-art results on various video-text retrieval datasets, including MSR-VTT, DiDeMo, and ActivityNet. Our approach is omnivorous to input modalities. With minimal modifications, we achieve competitive results on image-text retrieval tasks.

Recently, Vehicle-to-Everything(V2X) cooperative perception has attracted increasing attention. Infrastructure sensors play a critical role in this research field, however, how to find the optimal placement of infrastructure sensors is rarely studied. In this paper, we investigate the problem of infrastructure sensor placement and propose a pipeline that can efficiently and effectively find optimal installation positions for infrastructure sensors in a realistic simulated environment. To better simulate and evaluate LiDAR placement, we establish a Realistic LiDAR Simulation library that can simulate the unique characteristics of different popular LiDARs and produce high-fidelity LiDAR point clouds in the CARLA simulator. Through simulating point cloud data in different LiDAR placements, we can evaluate the perception accuracy of these placements using multiple detection models. Then, we analyze the correlation between the point cloud distribution and perception accuracy by calculating the density and uniformity of regions of interest. Experiments show that the placement of infrastructure LiDAR can heavily affect the accuracy of perception. We also analyze the correlation between perception performance in the region of interest and LiDAR point cloud distribution and validate that density and uniformity can be indicators of performance.