Label noise is a significant obstacle in deep learning model training. It can have a considerable impact on the performance of image classification models, particularly deep neural networks, which are especially susceptible because they have a strong propensity to memorise noisy labels. In this paper, we have examined the fundamental concept underlying related label noise approaches. A transition matrix estimator has been created, and its effectiveness against the actual transition matrix has been demonstrated. In addition, we examined the label noise robustness of two convolutional neural network classifiers with LeNet and AlexNet designs. The two FashionMINIST datasets have revealed the robustness of both models. We are not efficiently able to demonstrate the influence of the transition matrix noise correction on robustness enhancements due to our inability to correctly tune the complex convolutional neural network model due to time and computing resource constraints. There is a need for additional effort to fine-tune the neural network model and explore the precision of the estimated transition model in future research.

Non-negative matrix factorisation (NMF) has been widely used to address the problem of corrupted data in images. The standard NMF algorithm minimises the Euclidean distance between the data matrix and the factorised approximation. Although this method has demonstrated good results, because it employs the squared error of each data point, the standard NMF algorithm is sensitive to outliers. In this paper, we theoretically analyse the robustness of the standard NMF, HCNMF and L2,1-NMF algorithms, and implement sets of experiments to show the robustness on real datasets, namely ORL and Extended YaleB. Our work demonstrates that different amounts of iterations are required for each algorithm to converge. Given the high computational complexity of these algorithms, our final models such as HCNMF and L2,1-NMF model do not successfully converge within the iteration parameters of this paper. Nevertheless, the experimental results still demonstrate the robustness of the aforementioned algorithms to some extent.

Gaze estimation is the fundamental basis for many visual tasks. Yet, the high cost of acquiring gaze datasets with 3D annotations hinders the optimization and application of gaze estimation models. In this work, we propose a novel Head-Eye redirection parametric model based on Neural Radiance Field, which allows dense gaze data generation with view consistency and accurate gaze direction. Moreover, our head-eye redirection parametric model can decouple the face and eyes for separate neural rendering, so it can achieve the purpose of separately controlling the attributes of the face, identity, illumination, and eye gaze direction. Thus diverse 3D-aware gaze datasets could be obtained by manipulating the latent code belonging to different face attributions in an unsupervised manner. Extensive experiments on several benchmarks demonstrate the effectiveness of our method in domain generalization and domain adaptation for gaze estimation tasks.

有了来自多个输入模式的信息，基于传感器融合的算法通常在机器人技术中表现出其单模式的表现。带有互补语义和深度信息的相机和激光镜头是复杂驾驶环境中检测任务的典型选择。但是，对于大多数摄像头融合算法，传感器套件的校准将极大地影响性能。更具体地说，检测算法通常需要多个传感器之间的准确几何关系作为输入，并且通常假定这些传感器的内容是同时捕获的。准备此类传感器套件涉及精心设计的校准钻机和准确的同步机制，并且制备过程通常是离线进行的。在这项工作中，提出了一个基于分割的框架，以共同估计摄像机套件校准中的几何和时间参数。首先将语义分割掩码应用于传感器模式，并通过像素双向损失优化校准参数。我们专门合并了来自光流的速度信息，以进行时间参数。由于仅在分割级别进行监督，因此在框架内不需要校准标签。提出的算法在KITTI数据集上进行了测试，结果显示了几何和时间参数的准确实时校准。

With its capability to deal with graph data, which is widely found in practical applications, graph neural networks (GNNs) have attracted significant research attention in recent years. As societies become increasingly concerned with the need for data privacy protection, GNNs face the need to adapt to this new normal. Besides, as clients in Federated Learning (FL) may have relationships, more powerful tools are required to utilize such implicit information to boost performance. This has led to the rapid development of the emerging research field of federated graph neural networks (FedGNNs). This promising interdisciplinary field is highly challenging for interested researchers to grasp. The lack of an insightful survey on this topic further exacerbates the entry difficulty. In this paper, we bridge this gap by offering a comprehensive survey of this emerging field. We propose a 2-dimensional taxonomy of the FedGNNs literature: 1) the main taxonomy provides a clear perspective on the integration of GNNs and FL by analyzing how GNNs enhance FL training as well as how FL assists GNNs training, and 2) the auxiliary taxonomy provides a view on how FedGNNs deal with heterogeneity across FL clients. Through discussions of key ideas, challenges, and limitations of existing works, we envision future research directions that can help build more robust, explainable, efficient, fair, inductive, and comprehensive FedGNNs.

Aiot技术的最新进展导致利用机器学习算法来检测网络物理系统（CPS）的操作失败的越来越受欢迎。在其基本形式中，异常检测模块从物理工厂监控传感器测量和致动器状态，并检测这些测量中的异常以识别异常操作状态。然而，由于该模型必须在存在高度复杂的系统动态和未知量的传感器噪声的情况下准确地检测异常，构建有效的异常检测模型是挑战性的。在这项工作中，我们提出了一种新的时序序列异常检测方法，称为神经系统识别和贝叶斯滤波（NSIBF），其中特制的神经网络架构被构成系统识别，即捕获动态状态空间中CP的动态模型;然后，通过跟踪系统的隐藏状态的不确定性随着时间的推移，自然地施加贝叶斯滤波算法的顶部。我们提供定性的和定量实验，并在合成和三个现实世界CPS数据集上具有所提出的方法，表明NSIBF对最先进的方法比较了对CPS中异常检测的最新方法。

加密协议已被广泛用于保护用户的隐私，并避免暴露私人信息。 Quic（快速UDP Internet连接），包括由Google（GQUIC）最初设计的版本和IETF（IQUIC）标准化的版本，作为传统HTTP的替代品，演示了它们独特的传输特性：基于UDP进行加密资源传输，加速网页呈现。然而，基于TCP的现有加密传输方案容易受到网站指纹（WFP）攻击，允许通过窃听传输信道通过窃听用户访问的网站。无论是GQUIC和IQUIC是否可以有效抵制此类攻击值得调查。在本文中，我们从交通分析的角度研究了GQQUIC，IQUIC和HTTPS对WFP攻击的脆弱性。广泛的实验表明，在早期的交通方案中，GQQUIC是最容易受到原始攻击的攻击，而lequic比https更容易受到影响，但三个协议的脆弱性在正常的完全交通方案中类似。特征传输分析表明，在正常的完整流量方案时，大多数功能在协议之间可在协议之间传输。然而，与潜在特征表示的定性分析相结合，我们发现在早期流量时，转移效率低下，作为GQQ，IQUIC和HTTPS显示出在早期交通上的交通分布的显着不同的变化。通过将一次性WFP攻击升级到多个WFP顶级攻击，我们发现攻击准确性分别达到95.4％和95.5％，只有40个数据包，只需使用简单的功能，而且只达到60.7％什么时候在https。我们还证明IQUIC的漏洞仅略微依赖于网络环境。

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.

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.