Very few eXplainable AI (XAI) studies consider how users understanding of explanations might change depending on whether they know more or less about the to be explained domain (i.e., whether they differ in their expertise). Yet, expertise is a critical facet of most high stakes, human decision making (e.g., understanding how a trainee doctor differs from an experienced consultant). Accordingly, this paper reports a novel, user study (N=96) on how peoples expertise in a domain affects their understanding of post-hoc explanations by example for a deep-learning, black box classifier. The results show that peoples understanding of explanations for correct and incorrect classifications changes dramatically, on several dimensions (e.g., response times, perceptions of correctness and helpfulness), when the image-based domain considered is familiar (i.e., MNIST) as opposed to unfamiliar (i.e., Kannada MNIST). The wider implications of these new findings for XAI strategies are discussed.

Counterfactual explanations have emerged as a popular solution for the eXplainable AI (XAI) problem of elucidating the predictions of black-box deep-learning systems due to their psychological validity, flexibility across problem domains and proposed legal compliance. While over 100 counterfactual methods exist, claiming to generate plausible explanations akin to those preferred by people, few have actually been tested on users ($\sim7\%$). So, the psychological validity of these counterfactual algorithms for effective XAI for image data is not established. This issue is addressed here using a novel methodology that (i) gathers ground truth human-generated counterfactual explanations for misclassified images, in two user studies and, then, (ii) compares these human-generated ground-truth explanations to computationally-generated explanations for the same misclassifications. Results indicate that humans do not "minimally edit" images when generating counterfactual explanations. Instead, they make larger, "meaningful" edits that better approximate prototypes in the counterfactual class.

从课堂上学习不平衡数据集对许多机器学习算法带来了挑战。许多现实世界域通过定义，通过拥有多数阶级的多数阶级，自然具有比其少数级别更多的阶级（例如，真正的银行交易比欺诈性更频繁）。已经提出了许多方法来解决类别不平衡问题，其中最受欢迎的过采样技术（例如Smote）。这些方法在少数群体类中生成合成实例，以平衡数据集，执行提高预测机器学习（ML）模型的性能的数据增强。在本文中，我们推进了一种新的数据增强方法（改编自解释的AI），它在少数类中生成合成，反事实情况。与其他过采样技术不同，该方法使用实际特征值，而不是实例之间的内插值，自适应地将存在于数据集的实例。报告了使用四种不同分类器和25个数据集的几个实验，这表明该反事实增强方法（CFA）在少数类中生成有用的合成数据点。实验还表明，CFA与许多其他过采样方法具有竞争力，其中许多过采样方法是Smote的变种。讨论了CFAS性能的基础，以及在未来测试中可能更好或更糟的情况下的条件。

Despite the huge advancement in knowledge discovery and data mining techniques, the X-ray diffraction (XRD) analysis process has mostly remained untouched and still involves manual investigation, comparison, and verification. Due to the large volume of XRD samples from high-throughput XRD experiments, it has become impossible for domain scientists to process them manually. Recently, they have started leveraging standard clustering techniques, to reduce the XRD pattern representations requiring manual efforts for labeling and verification. Nevertheless, these standard clustering techniques do not handle problem-specific aspects such as peak shifting, adjacent peaks, background noise, and mixed phases; hence, resulting in incorrect composition-phase diagrams that complicate further steps. Here, we leverage data mining techniques along with domain expertise to handle these issues. In this paper, we introduce an incremental phase mapping approach based on binary peak representations using a new threshold based fuzzy dissimilarity measure. The proposed approach first applies an incremental phase computation algorithm on discrete binary peak representation of XRD samples, followed by hierarchical clustering or manual merging of similar pure phases to obtain the final composition-phase diagram. We evaluate our method on the composition space of two ternary alloy systems- Co-Ni-Ta and Co-Ti-Ta. Our results are verified by domain scientists and closely resembles the manually computed ground-truth composition-phase diagrams. The proposed approach takes us closer towards achieving the goal of complete end-to-end automated XRD analysis.

This report summarizes the 3rd International Verification of Neural Networks Competition (VNN-COMP 2022), held as a part of the 5th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), which was collocated with the 34th International Conference on Computer-Aided Verification (CAV). VNN-COMP is held annually to facilitate the fair and objective comparison of state-of-the-art neural network verification tools, encourage the standardization of tool interfaces, and bring together the neural network verification community. To this end, standardized formats for networks (ONNX) and specification (VNN-LIB) were defined, tools were evaluated on equal-cost hardware (using an automatic evaluation pipeline based on AWS instances), and tool parameters were chosen by the participants before the final test sets were made public. In the 2022 iteration, 11 teams participated on a diverse set of 12 scored benchmarks. This report summarizes the rules, benchmarks, participating tools, results, and lessons learned from this iteration of this competition.

SKA脉冲星搜索管道将用于实时检测脉冲星。SKA等现代射电望远镜将在其全面运行中生成数据。因此，基于经验和数据驱动的算法对于诸如候选检测等应用是必不可少的。在这里，我们描述了我们的发现，从测试一种称为Mask R-CNN的最先进的对象检测算法来检测SKA PULSAR搜索管道中的候选标志。我们已经训练了蒙版R-CNN模型来检测候选图像。开发了一种自定义注释工具，以有效地标记大型数据集中感兴趣的区域。我们通过检测模拟数据集中的候选签名成功证明了该算法。本文介绍了这项工作的详细信息，并重点介绍了未来的前景。

我们研究图形神经网络（GNN）的节点分类任务，并在群体公平性（通过统计平等和均等机会衡量）之间建立联系，以及局部分类性，即连接节点的趋势具有相似的属性。这种分类性通常是由同质性诱导的，即相似特性的节点连接的趋势。同质性在社交网络中可能很常见，在社交网络中，系统性因素迫使个人进入具有敏感属性的社区。通过合成图，我们研究了本地发生的同质和公平预测之间的相互作用，发现并非所有节点邻居在这方面都相等 - 社区以敏感属性的一类类别为主导，通常会努力获得公平的治疗，尤其是在分化本地类别和敏感属性同质。在确定存在局部同质和公平之间的关系之后，我们研究了不公平的问题是否与应用的GNN模型的设计相关联。我们表明，通过采用能够处理拆卸组标签的异性GNN设计，与真实和合成数据集中的同质设计相比，可以将本地异性邻居中的群体公平提高25％。

我们介绍了DeepNash，这是一种能够学习从头开始播放不完美的信息游戏策略的自主代理，直到人类的专家级别。 Stratego是人工智能（AI）尚未掌握的少数标志性棋盘游戏之一。这个受欢迎的游戏具有$ 10^{535} $节点的巨大游戏树，即，$ 10^{175} $倍的$倍于GO。它具有在不完美的信息下需要决策的其他复杂性，类似于德克萨斯州Hold'em扑克，该扑克的游戏树较小（以$ 10^{164} $节点为单位）。 Stratego中的决策是在许多离散的动作上做出的，而动作与结果之间没有明显的联系。情节很长，在球员获胜之前经常有数百次动作，而Stratego中的情况则不能像扑克中那样轻松地分解成管理大小的子问题。由于这些原因，Stratego几十年来一直是AI领域的巨大挑战，现有的AI方法几乎没有达到业余比赛水平。 Deepnash使用游戏理论，无模型的深钢筋学习方法，而无需搜索，该方法学会通过自我播放来掌握Stratego。 DeepNash的关键组成部分的正则化NASH Dynamics（R-NAD）算法通过直接修改基础多项式学习动力学来收敛到近似NASH平衡，而不是围绕它“循环”。 Deepnash在Stratego中击败了现有的最先进的AI方法，并在Gravon Games平台上获得了年度（2022年）和历史前3名，并与人类专家竞争。

描述了一种用于分析摄像机陷阱延时记录的新的开源图像处理管道。该管道包括机器学习模型，以帮助人类的视频细分和动物重新识别。我们在为期一年的项目中使用了该管道的实用性的一些绩效结果和观察结果，研究了Gopher Tortoise的空间生态和社会行为。

深层生成模型已成为检测数据中任意异常的有前途的工具，并分配了手动标记的必要性。最近，自回旋变压器在医学成像中取得了最先进的性能。但是，这些模型仍然具有一些内在的弱点，例如需要将图像建模为1D序列，在采样过程中误差的积累以及与变压器相关的显着推理时间。去核扩散概率模型是一类非自动回旋生成模型，最近显示出可以在计算机视觉中产生出色的样品（超过生成的对抗网络），并实现与变压器具有竞争力同时具有快速推理时间的对数可能性。扩散模型可以应用于自动编码器学到的潜在表示，使其易于扩展，并适用于高维数据（例如医学图像）的出色候选者。在这里，我们提出了一种基于扩散模型的方法，以检测和分段脑成像中的异常。通过在健康数据上训练模型，然后探索其在马尔可夫链上的扩散和反向步骤，我们可以识别潜在空间中的异常区域，因此可以确定像素空间中的异常情况。我们的扩散模型与一系列具有2D CT和MRI数据的实验相比，具有竞争性能，涉及合成和实际病理病变，推理时间大大减少，从而使它们的用法在临床上可行。