Anomaly detection on time series data is increasingly common across various industrial domains that monitor metrics in order to prevent potential accidents and economic losses. However, a scarcity of labeled data and ambiguous definitions of anomalies can complicate these efforts. Recent unsupervised machine learning methods have made remarkable progress in tackling this problem using either single-timestamp predictions or time series reconstructions. While traditionally considered separately, these methods are not mutually exclusive and can offer complementary perspectives on anomaly detection. This paper first highlights the successes and limitations of prediction-based and reconstruction-based methods with visualized time series signals and anomaly scores. We then propose AER (Auto-encoder with Regression), a joint model that combines a vanilla auto-encoder and an LSTM regressor to incorporate the successes and address the limitations of each method. Our model can produce bi-directional predictions while simultaneously reconstructing the original time series by optimizing a joint objective function. Furthermore, we propose several ways of combining the prediction and reconstruction errors through a series of ablation studies. Finally, we compare the performance of the AER architecture against two prediction-based methods and three reconstruction-based methods on 12 well-known univariate time series datasets from NASA, Yahoo, Numenta, and UCR. The results show that AER has the highest averaged F1 score across all datasets (a 23.5% improvement compared to ARIMA) while retaining a runtime similar to its vanilla auto-encoder and regressor components. Our model is available in Orion, an open-source benchmarking tool for time series anomaly detection.

对象异常的检测对于工业过程至关重要，但是由于难以获得大量有缺陷的样本以及现实生活中无法预测的异常类型，因此无监督的异常检测和定位尤为重要。在现有的无监督异常检测和定位方法中，基于NF的方案取得了更好的结果。但是，两个子网（复杂函数）$ s_ {i}（u_ {i}）$和$ t_ {i}（u_ {i}）在nf中通常是多层的perceptrons，需要从2D扁平至1D，破坏了特征图中的空间位置关系并丢失空间结构信息。为了保留并有效提取空间结构信息，我们在这项研究中设计了一个复杂的函数模型，该模型具有交替的CBAM嵌入在堆叠的$ 3 \ times3 $全卷积中，该卷积能够保留并有效地在标准化流程模型中提取空间结构信息。 MVTEC AD数据集的广泛实验结果表明，Cainnflow基于CNN和Transformer Backbone网络作为特征提取器达到高级准确性和推理效率，并且Cainnflow可在MVTEC广告中获得$ 98.64 \％的像素级AUC $ 98.64 \％\％。

本文提出了一种基于逆变器的Volt-VAR控制（IB-VVC）的一步两级深度强化学习（OSTC-DRL）方法。首先，考虑IB-VVC可以作为单周期优化问题进行配制，我们将IB-VVC作为单步马尔可夫决策过程而不是标准的Markov决策过程，从而简化了DRL学习任务。然后，我们设计了单步角色批判性DRL方案，该方案是最近DRL算法的简化版本，它可以成功地避免了Q值高估的问题。此外，考虑VVC的两个目标：最大程度地减少功率损耗并消除违反电压，我们利用两个批评家分别近似两个目标的回报。它简化了每个评论家的近似任务，并避免了评论家学习过程中两个目标之间的相互作用效果。 OSTC-DRL方法集成了单步角色批判性DRL方案和两批评技术。基于OSTC-DRL，我们设计了两种集中式DRL算法。此外，我们将OSTC-DRL扩展到分散的IB-VVC的多代理OSTC-DRL并设计两个多代理DRL算法。模拟表明，所提出的OSTC-DRL具有更快的收敛速度和更好的控制性能，并且多代理OSTC-DRL适用于分散的IB-VVC问题。

食源性疾病是一个严重但可以预防的公共卫生问题 - 延迟发现相关的暴发导致生产力损失，昂贵的召回，公共安全危害甚至生命丧失。尽管社交媒体是识别未报告的食源性疾病的有前途的来源，但缺乏标记的数据集来开发有效的爆发检测模型。为了加快基于机器学习的疫苗爆发检测模型的开发，我们提出了推文-FID（Tweet-Foodborne疾病检测），这是第一个用于多种食源性疾病事件检测任务的公开注释的数据集。从Twitter收集的Tweet-FID带有三个方面：Tweet类，实体类型和老虎机类型，并带有专家以及众包工人生产的标签。我们介绍了利用这三个方面的几个域任务：文本相关性分类（TRC），实体提及检测（EMD）和插槽填充（SF）。我们描述了用于支持这些任务模型开发的数据集设计，创建和标签的端到端方法。提供了这些任务的全面结果，以利用Tweet-FID数据集上的最新单项和多任务深度学习方法。该数据集为未来的Foodborne爆发检测提供了机会。

文件级关系提取旨在识别整个文件中实体之间的关系。捕获远程依赖性的努力大量依赖于通过（图）神经网络学习的隐式强大的表示，这使得模型不太透明。为了解决这一挑战，在本文中，我们通过学习逻辑规则提出了一种新的文档级关系提取的概率模型。 Logire将逻辑规则视为潜在变量，包括两个模块：规则生成器和关系提取器。规则生成器是生成可能导致最终预测的逻辑规则，并且关系提取器基于所生成的逻辑规则输出最终预测。可以通过期望最大化（EM）算法有效地优化这两个模块。通过将逻辑规则引入神经网络，Logire可以明确地捕获远程依赖项，并享受更好的解释。经验结果表明，Logire在关系性能（1.8 F1得分）和逻辑一致性（超过3.3逻辑得分）方面显着优于几种强大的基线。我们的代码可以在https://github.com/rudongyu/logire提供。

旨在识别来自新型类别的新型类别，几个参考样本，几次拍摄学习（FSL）是一个具有挑战性的问题。我们发现现有的作品通常通过混合所有本地级别来基于图像级功能来构建其几拍模型，这导致本地细节中的辨别位置偏差和信息丢失。为了解决问题，本文将返回本地级别功能的视角，并提出了一系列本地级策略。具体而言，我们展示（a）局域不可知的训练策略，以避免基本和新型类别之间的辨别位置偏差，（b）一种新的本地级相似度量，以捕获本地级别特征之间的准确比较（c ）可以根据不同的位置特征综合来自基本类别的不同知识传输的本地级知识转移。广泛的实验证明，我们拟议的本地级别战略可以显着提高性能，并在不同的基准数据集中实现基线的2.8％-7.2％，这也实现了最先进的准确性。

Deep learning has shown impressive performance on hard perceptual problems. However, researchers found deep learning systems to be vulnerable to small, specially crafted perturbations that are imperceptible to humans. Such perturbations cause deep learning systems to mis-classify adversarial examples, with potentially disastrous consequences where safety or security is crucial. Prior defenses against adversarial examples either targeted specific attacks or were shown to be ineffective. We propose MagNet, a framework for defending neural network classifiers against adversarial examples. MagNet neither modifies the protected classifier nor requires knowledge of the process for generating adversarial examples. MagNet includes one or more separate detector networks and a reformer network. The detector networks learn to differentiate between normal and adversarial examples by approximating the manifold of normal examples. Since they assume no specific process for generating adversarial examples, they generalize well. The reformer network moves adversarial examples towards the manifold of normal examples, which is effective for correctly classifying adversarial examples with small perturbation. We discuss the intrinsic difficulties in defending against whitebox attack and propose a mechanism to defend against graybox attack. Inspired by the use of randomness in cryptography, we use diversity to strengthen MagNet. We show empirically that Mag-Net is effective against the most advanced state-of-the-art attacks in blackbox and graybox scenarios without sacrificing false positive rate on normal examples. CCS CONCEPTS• Security and privacy → Domain-specific security and privacy architectures; • Computing methodologies → Neural networks;

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.