本文介绍了设计，开发，并通过IISC-TCS团队为穆罕默德·本·扎耶德国际机器人挑战赛2020年挑战1的目标的挑战1硬件 - 软件系统的测试是抓住从移动和机动悬挂球UAV和POP气球锚定到地面，使用合适的操纵器。解决这一挑战的重要任务包括具有高效抓取和突破机制的硬件系统的设计和开发，考虑到体积和有效载荷的限制，使用适用于室外环境的可视信息的准确目标拦截算法和开发动态多功能机空中系统的软件架构，执行复杂的动态任务。在本文中，设计了具有末端执行器的单个自由度机械手设计用于抓取和突发，并且开发了鲁棒算法以拦截在不确定的环境中的目标。基于追求参与和人工潜在功能的概念提出了基于视觉的指导和跟踪法。本工作中提供的软件架构提出了一种操作管理系统（OMS）架构，其在多个无人机之间协同分配静态和动态任务，以执行任何给定的任务。这项工作的一个重要方面是所有开发的系统都设计用于完全自主模式。在这项工作中还包括对凉亭环境和现场实验结果中完全挑战的模拟的详细描述。所提出的硬件软件系统对反UAV系统特别有用，也可以修改以满足其他几种应用。

Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.

Dataset scaling, also known as normalization, is an essential preprocessing step in a machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary within the same range. This transformation is known to improve the performance of classification models, but there are several scaling techniques to choose from, and this choice is not generally done carefully. In this paper, we execute a broad experiment comparing the impact of 5 scaling techniques on the performances of 20 classification algorithms among monolithic and ensemble models, applying them to 82 publicly available datasets with varying imbalance ratios. Results show that the choice of scaling technique matters for classification performance, and the performance difference between the best and the worst scaling technique is relevant and statistically significant in most cases. They also indicate that choosing an inadequate technique can be more detrimental to classification performance than not scaling the data at all. We also show how the performance variation of an ensemble model, considering different scaling techniques, tends to be dictated by that of its base model. Finally, we discuss the relationship between a model's sensitivity to the choice of scaling technique and its performance and provide insights into its applicability on different model deployment scenarios. Full results and source code for the experiments in this paper are available in a GitHub repository.\footnote{https://github.com/amorimlb/scaling\_matters}

Of late, insurance fraud detection has assumed immense significance owing to the huge financial & reputational losses fraud entails and the phenomenal success of the fraud detection techniques. Insurance is majorly divided into two categories: (i) Life and (ii) Non-life. Non-life insurance in turn includes health insurance and auto insurance among other things. In either of the categories, the fraud detection techniques should be designed in such a way that they capture as many fraudulent transactions as possible. Owing to the rarity of fraudulent transactions, in this paper, we propose a chaotic variational autoencoder (C-VAE to perform one-class classification (OCC) on genuine transactions. Here, we employed the logistic chaotic map to generate random noise in the latent space. The effectiveness of C-VAE is demonstrated on the health insurance fraud and auto insurance datasets. We considered vanilla Variational Auto Encoder (VAE) as the baseline. It is observed that C-VAE outperformed VAE in both datasets. C-VAE achieved a classification rate of 77.9% and 87.25% in health and automobile insurance datasets respectively. Further, the t-test conducted at 1% level of significance and 18 degrees of freedom infers that C-VAE is statistically significant than the VAE.

Air pollution is an emerging problem that needs to be solved especially in developed and developing countries. In Vietnam, air pollution is also a concerning issue in big cities such as Hanoi and Ho Chi Minh cities where air pollution comes mostly from vehicles such as cars and motorbikes. In order to tackle the problem, the paper focuses on developing a solution that can estimate the emitted PM2.5 pollutants by counting the number of vehicles in the traffic. We first investigated among the recent object detection models and developed our own traffic surveillance system. The observed traffic density showed a similar trend to the measured PM2.5 with a certain lagging in time, suggesting a relation between traffic density and PM2.5. We further express this relationship with a mathematical model which can estimate the PM2.5 value based on the observed traffic density. The estimated result showed a great correlation with the measured PM2.5 plots in the urban area context.

开发有效的自动分类器将真实来源与工件分开，对于宽场光学调查的瞬时随访至关重要。在图像差异过程之后，从减法伪像的瞬态检测鉴定是此类分类器的关键步骤，称为真实 - 博格斯分类问题。我们将自我监督的机器学习模型，深入的自组织地图（DESOM）应用于这个“真实的模拟”分类问题。 DESOM结合了自动编码器和一个自组织图以执行聚类，以根据其维度降低的表示形式来区分真实和虚假的检测。我们使用32x32归一化检测缩略图作为底部的输入。我们展示了不同的模型训练方法，并发现我们的最佳DESOM分类器显示出6.6％的检测率，假阳性率为1.5％。 Desom提供了一种更细微的方法来微调决策边界，以确定与其他类型的分类器（例如在神经网络或决策树上构建的）结合使用时可能进行的实际检测。我们还讨论了DESOM及其局限性的其他潜在用法。

科学机器学习（SCIML）是对几个不同应用领域的兴趣越来越多的领域。在优化上下文中，基于SCIML的工具使得能够开发更有效的优化方法。但是，必须谨慎评估和执行实施优化的SCIML工具。这项工作提出了稳健性测试的推论，该测试通过表明其结果尊重通用近似值定理，从而确保了基于多物理的基于SCIML的优化的鲁棒性。该测试应用于一种新方法的框架，该方法在一系列基准测试中进行了评估，以说明其一致性。此外，将提出的方法论结果与可行优化的可行区域进行了比较，这需要更高的计算工作。因此，这项工作为保证在多目标优化中应用SCIML工具的稳健性测试提供了比存在的替代方案要低的计算努力。

味道是遵循社会趋势和行为的风味行业的焦点。新调味剂和分子的研究和开发在该领域至关重要。另一方面，自然风味的发展在现代社会中起着至关重要的作用。鉴于此，目前的工作提出了一个基于科学机器学习的新颖框架，以在风味工程和行业中解决新的问题。因此，这项工作带来了一种创新的方法来设计新的自然风味分子。评估了有关合成可及性，原子数以及与天然或伪天然产物的相似性的分子。

我们提出了一个数据收集和注释管道，该数据从越南放射学报告中提取信息，以提供胸部X射线（CXR）图像的准确标签。这可以通过注释与其特有诊断类别的数据相匹配，这些数据可能因国家而异。为了评估所提出的标签技术的功效，我们构建了一个包含9,752项研究的CXR数据集，并使用该数据集的子集评估了我们的管道。以F1得分为至少0.9923，评估表明，我们的标签工具在所有类别中都精确而始终如一。构建数据集后，我们训练深度学习模型，以利用从大型公共CXR数据集传输的知识。我们采用各种损失功能来克服不平衡的多标签数据集的诅咒，并使用各种模型体系结构进行实验，以选择提供最佳性能的诅咒。我们的最佳模型（CHEXPERT-FRECTER EDIDENENET-B2）的F1得分为0.6989（95％CI 0.6740，0.7240），AUC为0.7912，敏感性为0.7064，特异性为0.8760，普遍诊断为0.8760。最后，我们证明了我们的粗分类（基于五个特定的异常位置）在基准CHEXPERT数据集上获得了可比的结果（十二个病理），以进行一般异常检测，同时在所有类别的平均表现方面提供更好的性能。

ICECUBE是一种用于检测1 GEV和1 PEV之间大气和天体中微子的光学传感器的立方公斤阵列，该阵列已部署1.45 km至2.45 km的南极的冰盖表面以下1.45 km至2.45 km。来自ICE探测器的事件的分类和重建在ICeCube数据分析中起着核心作用。重建和分类事件是一个挑战，这是由于探测器的几何形状，不均匀的散射和冰中光的吸收，并且低于100 GEV的光，每个事件产生的信号光子数量相对较少。为了应对这一挑战，可以将ICECUBE事件表示为点云图形，并将图形神经网络（GNN）作为分类和重建方法。 GNN能够将中微子事件与宇宙射线背景区分开，对不同的中微子事件类型进行分类，并重建沉积的能量，方向和相互作用顶点。基于仿真，我们提供了1-100 GEV能量范围的比较与当前ICECUBE分析中使用的当前最新最大似然技术，包括已知系统不确定性的影响。对于中微子事件分类，与当前的IceCube方法相比，GNN以固定的假阳性速率（FPR）提高了信号效率的18％。另外，GNN在固定信号效率下将FPR的降低超过8（低于半百分比）。对于能源，方向和相互作用顶点的重建，与当前最大似然技术相比，分辨率平均提高了13％-20％。当在GPU上运行时，GNN能够以几乎是2.7 kHz的中位数ICECUBE触发速率的速率处理ICECUBE事件，这打开了在在线搜索瞬态事件中使用低能量中微子的可能性。