在最大的状态熵探索框架中，代理商与无奖励环境进行交互，以学习最大程度地提高其正在引起的预期国有访问的熵的政策。 Hazan等。 （2019年）指出，马尔可夫随机策略类别足以满足最大状态熵目标，而在这种情况下，利用非马克维亚性通常被认为是毫无意义的。在本文中，我们认为非马克维亚性是有限样本制度中最大状态熵探索至关重要的。尤其是，我们重新阐明了目标在一次试验中针对诱发的国有访问的预期熵的目标。然后，我们表明，非马克维亚确定性政策的类别足以满足引入的目标，而马尔可夫政策总体上遭受了非零的遗憾。但是，我们证明找到最佳的非马克维亚政策的问题是NP-HARD。尽管结果有负面的结果，但我们讨论了以一种可行的方式解决该问题的途径，以及非马克维亚探索如何使未来工作中在线增强学习的样本效率受益。

In this new computing paradigm, named quantum computing, researchers from all over the world are taking their first steps in designing quantum circuits for image processing, through a difficult process of knowledge transfer. This effort is named Quantum Image Processing, an emerging research field pushed by powerful parallel computing capabilities of quantum computers. This work goes in this direction and proposes the challenging development of a powerful method of image denoising, such as the Total Variation (TV) model, in a quantum environment. The proposed Quantum TV is described and its sub-components are analysed. Despite the natural limitations of the current capabilities of quantum devices, the experimental results show a competitive denoising performance compared to the classical variational TV counterpart.

预测过程分析已成为组织的基本援助，从而为其流程提供在线运营支持。但是，需要向流程利益相关者提供解释为什么预测给定流程执行以某种方式行事的原因。否则，他们将不太可能相信预测性监测技术，从而采用它。本文提出了一个预测分析框架，该框架还具有基于Shapley值的游戏理论的解释功能。该框架已在IBM Process采矿套件中实施，并为业务用户商业化。该框架已在现实生活事件数据上进行了测试，以评估预测的质量和相应的评估。特别是，已经执行了用户评估，以了解系统提供的解释是否可以使流程利益相关者可理解。

最近，Wong等人。表明，使用单步FGSM的对抗训练导致一种名为灾难性过度拟合（CO）的特征故障模式，其中模型突然变得容易受到多步攻击的影响。他们表明，在FGSM（RS-FGSM）之前添加随机扰动似乎足以防止CO。但是，Andriushchenko和Flammarion观察到RS-FGSM仍会导致更大的扰动，并提出了一个昂贵的常规化器（Gradalign），DEMATER（GARGALIGN）DES昂贵（Gradalign）Dust Forrasiniger（Gradalign）Dust co避免在这项工作中，我们有条不紊地重新审视了噪声和剪辑在单步对抗训练中的作用。与以前的直觉相反，我们发现在干净的样品周围使用更强烈的噪声与不剪接相结合在避免使用大扰动半径的CO方面非常有效。基于这些观察结果，我们提出了噪声-FGSM（N-FGSM），尽管提供了单步对抗训练的好处，但在大型实验套件上没有经验分析，这表明N-FGSM能够匹配或超越以前的单步方法的性能，同时达到3 $ \ times $加速。代码可以在https://github.com/pdejorge/n-fgsm中找到

通过有效的监控和调整电池操作条件，促进了锂离子电池的寿命和安全性。因此，为电池管理系统上的健康状况（SOH）监测提供快速准确的算法至关重要。由于对电池劣化的复杂性和多种因素的复杂性和多种因素的复杂性，特别是因为不同的劣化过程发生在各种时间尺度，并且它们的相互作用发挥着重要作用。数据驱动方法通过用统计或机器学习模型近似复杂进程来绕过这个问题。本文提出了一种数据驱动方法，在电池劣化的背景下，尽管其简单性和易于计算：多变量分数多项式（MFP）回归。模型从一个耗尽的细胞的历史数据训练，并用于预测其他细胞的SOH。数据的特征在于模拟动态操作条件的载荷变化。考虑了两个假设情景：假设最近的容量测量是已知的，则另一个仅基于标称容量。结果表明，在考虑到电池寿命的电池结束时，通过其历史数据的历史数据受到它们的历史数据的影响，电池的降解行为受到其历史数据的影响。此外，我们提供了一种多因素视角，分析了每个不同因素的影响程度。最后，我们与长期内记忆神经网络和其他来自相同数据集的文献的其他作品进行比较。我们得出结论，MFP回归与当代作品有效和竞争，提供了几种额外的优点。在可解释性，恒定性和可实现性方面。

Advances in computer vision and machine learning techniques have led to significant development in 2D and 3D human pose estimation from RGB cameras, LiDAR, and radars. However, human pose estimation from images is adversely affected by occlusion and lighting, which are common in many scenarios of interest. Radar and LiDAR technologies, on the other hand, need specialized hardware that is expensive and power-intensive. Furthermore, placing these sensors in non-public areas raises significant privacy concerns. To address these limitations, recent research has explored the use of WiFi antennas (1D sensors) for body segmentation and key-point body detection. This paper further expands on the use of the WiFi signal in combination with deep learning architectures, commonly used in computer vision, to estimate dense human pose correspondence. We developed a deep neural network that maps the phase and amplitude of WiFi signals to UV coordinates within 24 human regions. The results of the study reveal that our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches, by utilizing WiFi signals as the only input. This paves the way for low-cost, broadly accessible, and privacy-preserving algorithms for human sensing.

Due to the environmental impacts caused by the construction industry, repurposing existing buildings and making them more energy-efficient has become a high-priority issue. However, a legitimate concern of land developers is associated with the buildings' state of conservation. For that reason, infrared thermography has been used as a powerful tool to characterize these buildings' state of conservation by detecting pathologies, such as cracks and humidity. Thermal cameras detect the radiation emitted by any material and translate it into temperature-color-coded images. Abnormal temperature changes may indicate the presence of pathologies, however, reading thermal images might not be quite simple. This research project aims to combine infrared thermography and machine learning (ML) to help stakeholders determine the viability of reusing existing buildings by identifying their pathologies and defects more efficiently and accurately. In this particular phase of this research project, we've used an image classification machine learning model of Convolutional Neural Networks (DCNN) to differentiate three levels of cracks in one particular building. The model's accuracy was compared between the MSX and thermal images acquired from two distinct thermal cameras and fused images (formed through multisource information) to test the influence of the input data and network on the detection results.

The advances in Artificial Intelligence are creating new opportunities to improve lives of people around the world, from business to healthcare, from lifestyle to education. For example, some systems profile the users using their demographic and behavioral characteristics to make certain domain-specific predictions. Often, such predictions impact the life of the user directly or indirectly (e.g., loan disbursement, determining insurance coverage, shortlisting applications, etc.). As a result, the concerns over such AI-enabled systems are also increasing. To address these concerns, such systems are mandated to be responsible i.e., transparent, fair, and explainable to developers and end-users. In this paper, we present ComplAI, a unique framework to enable, observe, analyze and quantify explainability, robustness, performance, fairness, and model behavior in drift scenarios, and to provide a single Trust Factor that evaluates different supervised Machine Learning models not just from their ability to make correct predictions but from overall responsibility perspective. The framework helps users to (a) connect their models and enable explanations, (b) assess and visualize different aspects of the model, such as robustness, drift susceptibility, and fairness, and (c) compare different models (from different model families or obtained through different hyperparameter settings) from an overall perspective thereby facilitating actionable recourse for improvement of the models. It is model agnostic and works with different supervised machine learning scenarios (i.e., Binary Classification, Multi-class Classification, and Regression) and frameworks. It can be seamlessly integrated with any ML life-cycle framework. Thus, this already deployed framework aims to unify critical aspects of Responsible AI systems for regulating the development process of such real systems.

Model calibration, which is concerned with how frequently the model predicts correctly, not only plays a vital part in statistical model design, but also has substantial practical applications, such as optimal decision-making in the real world. However, it has been discovered that modern deep neural networks are generally poorly calibrated due to the overestimation (or underestimation) of predictive confidence, which is closely related to overfitting. In this paper, we propose Annealing Double-Head, a simple-to-implement but highly effective architecture for calibrating the DNN during training. To be precise, we construct an additional calibration head-a shallow neural network that typically has one latent layer-on top of the last latent layer in the normal model to map the logits to the aligned confidence. Furthermore, a simple Annealing technique that dynamically scales the logits by calibration head in training procedure is developed to improve its performance. Under both the in-distribution and distributional shift circumstances, we exhaustively evaluate our Annealing Double-Head architecture on multiple pairs of contemporary DNN architectures and vision and speech datasets. We demonstrate that our method achieves state-of-the-art model calibration performance without post-processing while simultaneously providing comparable predictive accuracy in comparison to other recently proposed calibration methods on a range of learning tasks.

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}