标准化的数据集和基准刺激了计算机视觉，自然语言处理，多模式和表格设置的创新。我们注意到，与其他经过良好研究的领域相比，欺诈检测有许多差异。差异包括高级失衡，多样化的特征类型，经常改变的欺诈模式以及问题的对抗性。由于这些差异，用于其他分类任务的建模方法可能对欺诈检测效果不佳。我们介绍了欺诈数据集基准（FDB），该基准是针对欺诈检测的公开可用数据集的汇编。 FDB包括各种与欺诈相关的任务，从识别欺诈性卡片 - 不出现交易，检测机器人攻击，对恶意URL进行分类，预测贷款的风险降至内容适度。来自FDB的基于Python的库为数据加载提供了一致的API，并具有标准化的训练和测试拆分。作为参考，我们还提供了FDB上不同建模方法的基线评估。考虑到各种研究和业务问题的自动化机器学习（AUTOML）的日益普及，我们使用了Automl框架进行基线评估。为了预防欺诈，拥有有限资源和缺乏ML专业知识的组织通常会聘请一个调查人员，使用区块列表和手动规则，所有这些规则效率低下且规模不佳。这些组织可以从易于在生产中部署并通过欺诈预防要求的汽车解决方案受益。我们希望FDB有助于开发适合不同欺诈模式操作数（MOS）的定制欺诈检测技术，以及改善汽车系统，这些系统可以很好地适用于基准中的所有数据集。

高质量的关节语音综合在语音科学和技术中具有许多潜在的应用。但是，将适当的映射从语言规范到关节手势是困难且耗时的。在本文中，我们构建了一个基于优化的框架，作为在不手动干预的情况下学习这些映射的第一步。我们证明了具有复杂的启用的音节的产生，并讨论了有关共插曲的关节手势的质量。

通常，基于学习的拓扑导航方法产生了本地政策，同时通过拓扑图保留了空间的一些松散连通性。然而，拓扑图中的伪造或缺失的边缘通常会导致导航故障。在这项工作中，我们提出了一种基于抽样的图形构建方法，与基线方法相比，导致较为稀疏的图形却具有更高的导航性能。我们还提出了图形维护策略，以消除伪边缘并根据需要扩展图形，从而改善终身导航性能。与从固定培训环境中学习的控制器不同，我们表明我们的模型只能使用来自部署代理的现实世界环境中的少量收集的轨迹图像进行微调。我们在现实世界环境进行了微调后证明了成功的导航，并且通过应用我们的终身图形维护策略，随着时间的推移，随着时间的推移表现出显着的导航改进。

Task-oriented dialog(TOD) aims to assist users in achieving specific goals through multi-turn conversation. Recently, good results have been obtained based on large pre-trained models. However, the labeled-data scarcity hinders the efficient development of TOD systems at scale. In this work, we constructed a weakly supervised dataset based on a teacher/student paradigm that leverages a large collection of unlabelled dialogues. Furthermore, we built a modular dialogue system and integrated coarse-to-fine grained classification for user intent detection. Experiments show that our method can reach the dialog goal with a higher success rate and generate more coherent responses.

In this work, we study the black-box targeted attack problem from the model discrepancy perspective. On the theoretical side, we present a generalization error bound for black-box targeted attacks, which gives a rigorous theoretical analysis for guaranteeing the success of the attack. We reveal that the attack error on a target model mainly depends on empirical attack error on the substitute model and the maximum model discrepancy among substitute models. On the algorithmic side, we derive a new algorithm for black-box targeted attacks based on our theoretical analysis, in which we additionally minimize the maximum model discrepancy(M3D) of the substitute models when training the generator to generate adversarial examples. In this way, our model is capable of crafting highly transferable adversarial examples that are robust to the model variation, thus improving the success rate for attacking the black-box model. We conduct extensive experiments on the ImageNet dataset with different classification models, and our proposed approach outperforms existing state-of-the-art methods by a significant margin. Our codes will be released.

Autonomous cars are indispensable when humans go further down the hands-free route. Although existing literature highlights that the acceptance of the autonomous car will increase if it drives in a human-like manner, sparse research offers the naturalistic experience from a passenger's seat perspective to examine the human likeness of current autonomous cars. The present study tested whether the AI driver could create a human-like ride experience for passengers based on 69 participants' feedback in a real-road scenario. We designed a ride experience-based version of the non-verbal Turing test for automated driving. Participants rode in autonomous cars (driven by either human or AI drivers) as a passenger and judged whether the driver was human or AI. The AI driver failed to pass our test because passengers detected the AI driver above chance. In contrast, when the human driver drove the car, the passengers' judgement was around chance. We further investigated how human passengers ascribe humanness in our test. Based on Lewin's field theory, we advanced a computational model combining signal detection theory with pre-trained language models to predict passengers' humanness rating behaviour. We employed affective transition between pre-study baseline emotions and corresponding post-stage emotions as the signal strength of our model. Results showed that the passengers' ascription of humanness would increase with the greater affective transition. Our study suggested an important role of affective transition in passengers' ascription of humanness, which might become a future direction for autonomous driving.

The state-of-the-art dimensionality reduction approaches largely rely on complicated optimization procedures. On the other hand, closed-form approaches requiring merely eigen-decomposition do not have enough sophistication and nonlinearity. In this paper, we propose a novel nonlinear dimensionality reduction method -- Inverse Kernel Decomposition (IKD) -- based on an eigen-decomposition of the sample covariance matrix of data. The method is inspired by Gaussian process latent variable models (GPLVMs) and has comparable performance with GPLVMs. To deal with very noisy data with weak correlations, we propose two solutions -- blockwise and geodesic -- to make use of locally correlated data points and provide better and numerically more stable latent estimations. We use synthetic datasets and four real-world datasets to show that IKD is a better dimensionality reduction method than other eigen-decomposition-based methods, and achieves comparable performance against optimization-based methods with faster running speeds. Open-source IKD implementation in Python can be accessed at this \url{https://github.com/JerrySoybean/ikd}.

使用深度学习来产生类似人类的文本的自回归语言模型已变得越来越普遍。这样的模型为智能健康，金融和自动驾驶等领域的流行虚拟助手提供动力。尽管这些大语言模型的参数正在改善，但担心这些模型可能对社会中的所有亚组都没有平等。尽管对跨学科的AI公平性进行了越来越多的讨论，但缺乏系统的指标来评估公平在对话系统中的意义以及如何使不同人群参与评估循环。本文基于审议民主和科学技术研究的理论，提出了一个分析框架，以解开人类对话中的公平意义。使用此框架，我们进行了一项审计研究，以研究GPT-3如何应对有关关键科学和社会主题的不同亚人群的反应：气候变化和黑人生活问题（BLM）运动。我们的语料库包括在性别，种族和种族，教育水平，英语作为第一语言的GPT-3和3290个人之间的超过20,000轮对话，以及对问题的看法。我们发现，在观点和教育少数群体中，对GPT-3的用户经验实质上较差；但是，这两个小组获得了最大的知识增长，改变了聊天后对BLM和气候变化工作的态度改变。我们将这些用户的经验划分为对话差异，发现GPT-3在对教育和舆论少数群体群体做出反应时，与对多数群体的反应相比，它使用了更多的负面表达。我们讨论了我们的发现对集中多样性，公平和包容性的审议对话AI系统的含义。

基于骨架的动作识别会受到越来越多的关注，因为骨架表示通过消除与动作无关的视觉信息来减少训练数据的量。为了进一步提高样本效率，为基于骨架的动作识别而开发了基于元学习的一局学习解决方案。这些方法根据实例级全局平均嵌入之间的相似性找到最近的邻居。但是，由于对局部不变和嘈杂特征的广义学习不足，这种测量具有不稳定的代表性，而直觉上，更细粒度的识别通常依赖于确定关键的局部身体运动。为了解决这一限制，我们介绍了自适应的局部成分感知图卷积网络，该网络将比较指标替换为相似性测量的集中之和，以对对齐的局部局部嵌入行动至关重要的空间/时间段。 NTU-RGB+D 120公共基准的全面单发实验表明，我们的方法比全球嵌入提供了更强的表示，并有助于我们的模型达到最新的最新能力。

当环境标签未知时，我们研究不变学习的问题。当贝叶斯最佳条件标签分布在不同环境中相同时，我们将重点放在不变的表示概念上。先前的工作通过最大化不变风险最小化（IRM）框架的罚款来进行环境推理（EI）。 EI步骤使用的参考模型侧重于虚假相关性，以有效地达到良好的环境分区。但是，尚不清楚如何找到这样的参考模型。在这项工作中，我们建议重复EI过程，并在先前的EI步骤推断出的\ textit {多数}环境上重复ERM模型。在温和的假设下，我们发现这种迭代过程有助于学习比单一步骤更好地捕获虚假相关性的表示。这会导致更好的环境推理和更好的不变学习。我们表明，该方法在合成数据集和现实世界数据集上的表现优于基准。