最近一年带来了电动汽车（EV）和相关基础设施/通信的大幅进步。入侵检测系统（ID）被广泛部署在此类关键基础架构中的异常检测。本文提出了一个可解释的异常检测系统（RX-ADS），用于在电动汽车中的CAN协议中进行入侵检测。贡献包括：1）基于窗口的特征提取方法； 2）基于深度自动编码器的异常检测方法； 3）基于对抗机器学习的解释生成方法。在两个基准CAN数据集上测试了提出的方法：OTID和汽车黑客。将RX-ADS的异常检测性能与这些数据集的最新方法进行了比较：HID和GID。 RX-ADS方法提出的性能与HIDS方法（OTIDS数据集）相当，并且具有超出HID和GID方法（CAR HACKING DATASET）的表现。此外，所提出的方法能够为因各种侵入而引起的异常行为产生解释。这些解释后来通过域专家使用的信息来检测异常来验证。 RX-ADS的其他优点包括：1）该方法可以在未标记的数据上进行培训； 2）解释有助于专家理解异常和根课程分析，并有助于AI模型调试和诊断，最终改善了对AI系统的用户信任。

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Plastic shopping bags that get carried away from the side of roads and tangled on cotton plants can end up at cotton gins if not removed before the harvest. Such bags may not only cause problem in the ginning process but might also get embodied in cotton fibers reducing its quality and marketable value. Therefore, it is required to detect, locate, and remove the bags before cotton is harvested. Manually detecting and locating these bags in cotton fields is labor intensive, time-consuming and a costly process. To solve these challenges, we present application of four variants of YOLOv5 (YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x) for detecting plastic shopping bags using Unmanned Aircraft Systems (UAS)-acquired RGB (Red, Green, and Blue) images. We also show fixed effect model tests of color of plastic bags as well as YOLOv5-variant on average precision (AP), mean average precision (mAP@50) and accuracy. In addition, we also demonstrate the effect of height of plastic bags on the detection accuracy. It was found that color of bags had significant effect (p < 0.001) on accuracy across all the four variants while it did not show any significant effect on the AP with YOLOv5m (p = 0.10) and YOLOv5x (p = 0.35) at 95% confidence level. Similarly, YOLOv5-variant did not show any significant effect on the AP (p = 0.11) and accuracy (p = 0.73) of white bags, but it had significant effects on the AP (p = 0.03) and accuracy (p = 0.02) of brown bags including on the mAP@50 (p = 0.01) and inference speed (p < 0.0001). Additionally, height of plastic bags had significant effect (p < 0.0001) on overall detection accuracy. The findings reported in this paper can be useful in speeding up removal of plastic bags from cotton fields before harvest and thereby reducing the amount of contaminants that end up at cotton gins.

Text-based personality computing (TPC) has gained many research interests in NLP. In this paper, we describe 15 challenges that we consider deserving the attention of the research community. These challenges are organized by the following topics: personality taxonomies, measurement quality, datasets, performance evaluation, modelling choices, as well as ethics and fairness. When addressing each challenge, not only do we combine perspectives from both NLP and social sciences, but also offer concrete suggestions towards more valid and reliable TPC research.

An article published on Medical News Today in June 2022 presented a fundamental question in its title: Can an earlobe crease predict heart attacks? The author explained that end arteries supply the heart and ears. In other words, if they lose blood supply, no other arteries can take over, resulting in tissue damage. Consequently, some earlobes have a diagonal crease, line, or deep fold that resembles a wrinkle. In this paper, we take a step toward detecting this specific marker, commonly known as DELC or Frank's Sign. For this reason, we have made the first DELC dataset available to the public. In addition, we have investigated the performance of numerous cutting-edge backbones on annotated photos. Experimentally, we demonstrate that it is possible to solve this challenge by combining pre-trained encoders with a customized classifier to achieve 97.7% accuracy. Moreover, we have analyzed the backbone trade-off between performance and size, estimating MobileNet as the most promising encoder.

人类机器人相互作用（HRI）对于在日常生活中广泛使用机器人至关重要。机器人最终将能够通过有效的社会互动来履行人类文明的各种职责。创建直接且易于理解的界面，以与机器人开始在个人工作区中扩散时与机器人互动至关重要。通常，与模拟机器人的交互显示在屏幕上。虚拟现实（VR）是一个更具吸引力的替代方法，它为视觉提示提供了更像现实世界中看到的线索。在这项研究中，我们介绍了Jubileo，这是一种机器人的动画面孔，并使用人类机器人社会互动领域的各种研究和应用开发工具。Jubileo Project不仅提供功能齐全的开源物理机器人。它还提供了一个全面的框架，可以通过VR接口进行操作，从而为HRI应用程序测试带来沉浸式环境，并明显更好地部署速度。

Angluin的L*算法使用会员资格和等价查询了解了常规语言的最低（完整）确定性有限自动机（DFA）。它的概率近似正确（PAC）版本用足够大的随机会员查询替换等效查询，以使答案获得高级信心。因此，它可以应用于任何类型的（也是非规范）设备，可以将其视为合成自动机的算法，该算法根据观测值抽象该设备的行为。在这里，我们对Angluin的PAC学习算法对通过引入一些噪音从DFA获得的设备感兴趣。更确切地说，我们研究盎格鲁因算法是否会降低噪声并产生与原始设备更接近原始设备的DFA。我们提出了几种介绍噪声的方法：（1）嘈杂的设备将单词的分类W.R.T.倒置。具有很小概率的DFA，（2）嘈杂的设备在询问其分类W.R.T.之前用小概率修改了单词的字母。 DFA和（3）嘈杂的设备结合了W.R.T.单词的分类。 DFA及其分类W.R.T.柜台自动机。我们的实验是在数百个DFA上进行的。直言不讳地表明，我们的主要贡献表明：（1）每当随机过程产生嘈杂的设备时，盎格鲁因算法的行为都很好，（2）但使用结构化的噪声却很差，并且（3）几乎肯定是随机性的产量具有非竞争性语言的系统。

我们将图形神经网络训练来自小工具N体模拟的光晕目录的神经网络，以执行宇宙学参数的无现场级别可能的推断。目录包含$ \ Lessim $ 5,000 HAROS带质量$ \ gtrsim 10^{10} 〜h^{ - 1} m_ \ odot $，定期卷为$（25〜H^{ - 1} {\ rm mpc}）{\ rm mpc}） ^3 $;目录中的每个光环都具有多种特性，例如位置，质量，速度，浓度和最大圆速度。我们的模型构建为置换，翻译和旋转的不变性，不施加最低限度的规模来提取信息，并能够以平均值来推断$ \ omega _ {\ rm m} $和$ \ sigma_8 $的值$ \ sim6 \％$的相对误差分别使用位置加上速度和位置加上质量。更重要的是，我们发现我们的模型非常强大：他们可以推断出使用数千个N-n-Body模拟的Halo目录进行测试时，使用五个不同的N-进行测试时，在使用Halo目录进行测试时，$ \ omega _ {\ rm m} $和$ \ sigma_8 $身体代码：算盘，Cubep $^3 $ M，Enzo，PKDGrav3和Ramses。令人惊讶的是，经过培训的模型推断$ \ omega _ {\ rm m} $在对数千个最先进的骆驼水力动力模拟进行测试时也可以使用，该模拟使用四个不同的代码和子网格物理实现。使用诸如浓度和最大循环速度之类的光环特性允许我们的模型提取更多信息，而牺牲了模型的鲁棒性。这可能会发生，因为不同的N体代码不会在与这些参数相对应的相关尺度上收敛。

在本文中，我们将预处理技术应用于具有不同长度的多通道时间序列数据，我们称之为对齐问题，用于下游机器学习。多种原因可能发生多种渠道时间序列数据的未对准，原因有多种原因，例如丢失的数据，变化的采样率或不一致的收集时间。我们考虑从MIT SuperCloud高性能计算（HPC）中心收集的多渠道时间序列数据，其中不同的工作开始时间和HPC作业的运行时间不同，导致数据不对准。这种未对准使得为计算工作负载分类等任务构建AI/ML方法具有挑战性。在先前使用MIT SuperCloud数据集的监督分类工作的基础上，我们通过三种宽阔的低间接空间方法解决了对齐问题：从全职系列中抽样固定子集，在全职系列上执行摘要统计信息，并对系数进行取样。从映射到频域的时间序列。我们最佳性能模型的分类精度大于95％，以先前的方法对MIT SuperCloud数据集的多通道时间序列分类的表现优于5％。这些结果表明，我们的低间接费用方法与标准机器学习技术结合使用，能够达到高水平的分类准确性，并作为解决对齐问题（例如内核方法）的未来方法的基准。

我们将定量探测作为模型 - 非局部框架，用于在存在定量域知识的情况下验证因果模型。该方法被构造为基于相关的机器学习中火车/测试拆分的类似物，并增强了与科学发现逻辑一致的当前因果验证策略。在进行彻底基于模拟的研究之前，使用Pearl的洒水示例说明了该方法的有效性。通过研究示例性失败方案来识别该技术的限制，这些方案还用于提出一系列主题，以供未来的研究和改进定量探测的版本。在两个单独的开源python软件包中提供了将定量探测的代码以及基于模拟的定量探测有效性的基于仿真的研究的代码。