Since the mid-10s, the era of Deep Learning (DL) has continued to this day, bringing forth new superlatives and innovations each year. Nevertheless, the speed with which these innovations translate into real applications lags behind this fast pace. Safety-critical applications, in particular, underlie strict regulatory and ethical requirements which need to be taken care of and are still active areas of debate. eXplainable AI (XAI) and privacy-preserving machine learning (PPML) are both crucial research fields, aiming at mitigating some of the drawbacks of prevailing data-hungry black-box models in DL. Despite brisk research activity in the respective fields, no attention has yet been paid to their interaction. This work is the first to investigate the impact of private learning techniques on generated explanations for DL-based models. In an extensive experimental analysis covering various image and time series datasets from multiple domains, as well as varying privacy techniques, XAI methods, and model architectures, the effects of private training on generated explanations are studied. The findings suggest non-negligible changes in explanations through the introduction of privacy. Apart from reporting individual effects of PPML on XAI, the paper gives clear recommendations for the choice of techniques in real applications. By unveiling the interdependencies of these pivotal technologies, this work is a first step towards overcoming the remaining hurdles for practically applicable AI in safety-critical domains.

电网已成为日常生活的重要组成部分，即使在日常生活中经常没有注意到它们。我们通常只会在不再可用的电网时特别了解这种依赖性。但是，重大变化，例如过渡到可再生能源（光伏，风力涡轮机等）以及具有复杂负载剖面（电动汽车，家用电池系统等）的越来越多的能源消费者，对电力构成了新的挑战网格。为了应对这些挑战，我们根据宽带电力线通信（PLC）基础架构中的测量结果提出了两个首先数据集。数据集FIN-1和FIN-2均在德国低压电网的一部分实际使用期间收集，该电网供应约440万人，并显示了超过5100个传感器收集的130亿个数据点。此外，我们在资产管理，网格状态可视化，预测，预测维护和新颖性检测中提出不同的用例，以突出这些类型的数据的好处。对于这些应用程序，我们特别强调了使用新颖的机器学习体系结构从现实世界数据中提取丰富信息，这些信息无法使用传统方法捕获。通过发布第一个大型现实世界数据集，我们旨在阐明PLC数据的先前很大程度上未识别的潜力，并通过呈现各种不同的用例来强调低压分布网络中基于机器的研究。

人们普遍认为，人类视觉系统偏向于识别形状而不是纹理。这一假设导致了越来越多的工作，旨在使深层模型的决策过程与人类视野的基本特性保持一致。人们对形状特征的依赖主要预计会改善协变量转移下这些模型的鲁棒性。在本文中，我们重新审视了形状偏置对皮肤病变图像分类的重要性。我们的分析表明，不同的皮肤病变数据集对单个图像特征表现出不同的偏见。有趣的是，尽管深层提取器倾向于学习对皮肤病变分类的纠缠特征，但仍然可以从该纠缠的表示形式中解码单个特征。这表明这些功能仍在模型的学习嵌入空间中表示，但不用于分类。此外，不同数据集的光谱分析表明，与常见的视觉识别相反，皮肤皮肤病变分类本质上依赖于超出形状偏置的复杂特征组合。自然的结果，在某些情况下，摆脱了形状偏见模型的普遍欲望甚至可以改善皮肤病变分类器。

在临床工作流程中成功部署AI的计算机辅助诊断（CAD）系统的一个主要障碍是它们缺乏透明决策。虽然常用可解释的AI方法提供了一些对不透明算法的洞察力，但除了高度训练的专家外，这种解释通常是复杂的，而不是易于理解的。关于皮肤病图像的皮肤病病变恶性的决定的解释需要特别清晰，因为潜在的医疗问题定义本身是模棱两可的。这项工作提出了exaid（可解释的ai用于皮肤科），是生物医学图像分析的新框架，提供了由易于理解的文本解释组成的多模态概念的解释，该概念由可视地图证明预测的视觉映射。 Exap依赖于概念激活向量，将人类概念映射到潜在空间中的任意深度学习模型学习的人，以及概念本地化地图，以突出输入空间中的概念。然后，这种相关概念的识别将用于构建由概念 - 明智地点信息补充的细粒度文本解释，以提供全面和相干的多模态解释。所有信息都在诊断界面中全面呈现，用于临床常规。教育模式为数据和模型探索提供数据集级别解释统计和工具，以帮助医学研究和教育。通过严谨的exaid定量和定性评估，即使在错误的预测情况下，我们展示了CAD辅助情景的多模态解释的效用。我们认为突然将为皮肤科医生提供一种有效的筛查工具，他们都理解和信任。此外，它将是其他生物医学成像领域的类似应用的基础。

随着机器学习的出现，在医疗保健和能源等关键基础设施的应用中，隐私是利益相关者的思想中越来越令人担忧。它是衡量的，确保模型和数据都不能用于提取攻击者对个人使用的敏感信息或通过利用关键基础设施来伤害整个社会。由于缺乏关于透明度和隐私约束的信任，机器学习在这些域中的适用性主要是有限的。各种安全关键用例（主要依赖于时间序列数据）目前在隐私相关的考虑因素方面受到了代表性。通过评估有关其在时间序列数据的适用性的若干隐私保留方法，我们验证了加密对深度学习的影响，差异隐私的强大数据集依赖性以及联合方法的广泛适用性。

通过Navier-Stokes方程的数值解决方案的计算流体动力学（CFD）仿真是从工程设计到气候建模的广泛应用中的重要工具。然而，CFD代码所需的计算成本和内存需求对于实际兴趣的流动可能变得非常高，例如在空气动力学形状优化中。该费用与流体流动控制方程的复杂性有关，其包括具有困难的解决方案的非线性部分衍生术语，导致长的计算时间和限制在迭代设计过程中可以测试的假设的数量。因此，我们提出了DeepCFD：基于卷积神经网络（CNN）的模型，其有效地近似于均匀稳态流动问题的解决方案。所提出的模型能够直接从使用最先进的CFD代码生成的地面真实数据的速度和压力场的完整解决方案的完整解决方案。使用DeepCFD，与标准CFD方法以低误差率的成本相比，我们发现高达3个数量级的加速。

The performance of the Deep Learning (DL) models depends on the quality of labels. In some areas, the involvement of human annotators may lead to noise in the data. When these corrupted labels are blindly regarded as the ground truth (GT), DL models suffer from performance deficiency. This paper presents a method that aims to learn a confident model in the presence of noisy labels. This is done in conjunction with estimating the uncertainty of multiple annotators. We robustly estimate the predictions given only the noisy labels by adding entropy or information-based regularizer to the classifier network. We conduct our experiments on a noisy version of MNIST, CIFAR-10, and FMNIST datasets. Our empirical results demonstrate the robustness of our method as it outperforms or performs comparably to other state-of-the-art (SOTA) methods. In addition, we evaluated the proposed method on the curated dataset, where the noise type and level of various annotators depend on the input image style. We show that our approach performs well and is adept at learning annotators' confusion. Moreover, we demonstrate how our model is more confident in predicting GT than other baselines. Finally, we assess our approach for segmentation problem and showcase its effectiveness with experiments.

Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple degrees of freedom via user-generated signals remains challenging. To address this issue, various machine learning controllers have been developed to better predict movement intent. As these controllers become more intelligent and take on more autonomy in the system, the traditional approach of representing the human-machine interface as a human controlling a tool becomes limiting. One possible approach to improve the understanding of these interfaces is to model them as collaborative, multi-agent systems through the lens of joint action. The field of joint action has been commonly applied to two human partners who are trying to work jointly together to achieve a task, such as singing or moving a table together, by effecting coordinated change in their shared environment. In this work, we compare different prosthesis controllers (proportional electromyography with sequential switching, pattern recognition, and adaptive switching) in terms of how they present the hallmarks of joint action. The results of the comparison lead to a new perspective for understanding how existing myoelectric systems relate to each other, along with recommendations for how to improve these systems by increasing the collaborative communication between each partner.

Nowadays, the current neural network models of dialogue generation(chatbots) show great promise for generating answers for chatty agents. But they are short-sighted in that they predict utterances one at a time while disregarding their impact on future outcomes. Modelling a dialogue's future direction is critical for generating coherent, interesting dialogues, a need that has led traditional NLP dialogue models that rely on reinforcement learning. In this article, we explain how to combine these objectives by using deep reinforcement learning to predict future rewards in chatbot dialogue. The model simulates conversations between two virtual agents, with policy gradient methods used to reward sequences that exhibit three useful conversational characteristics: the flow of informality, coherence, and simplicity of response (related to forward-looking function). We assess our model based on its diversity, length, and complexity with regard to humans. In dialogue simulation, evaluations demonstrated that the proposed model generates more interactive responses and encourages a more sustained successful conversation. This work commemorates a preliminary step toward developing a neural conversational model based on the long-term success of dialogues.

In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph. HNN derives multiple embeddings per node in the hypergraph where each embedding for a node is dependent on a specific hyperedge of that node. Notably, HNN is accurate, data-efficient, flexible with many interchangeable components, and useful for a wide range of hypergraph learning tasks. We evaluate the effectiveness of the HNN framework for hyperedge prediction and hypergraph node classification. We find that HNN achieves an overall mean gain of 7.72% and 11.37% across all baseline models and graphs for hyperedge prediction and hypergraph node classification, respectively.