任何机器学习（ML）算法的性能受到其超参数的选择影响。由于培训和评估ML算法通常很昂贵，因此需要在实践中有效地计算高参数优化（HPO）方法。多数目标HPO的大多数现有方法都使用进化策略和基于元模型的优化。但是，很少有方法可以解释性能测量中的不确定性。本文提出了多目标超参数优化的结果，并在评估ML算法的情况下进行了不确定性。我们将树结构化parzen估计量（TPE）的采样策略与训练高斯过程回归（GPR）在异质噪声后获得的元模型相结合。关于三个分析测试功能和三个ML问题的实验结果表明，相对于超量指标，多目标TPE和GPR的改善。

由于其良好的特性，诸如高强度重量比，设计灵活性，限量的应力浓度，平面力传递，良好损害耐受性和疲劳性，因此越来越多地应用于各种应用的各种应用。寻找粘合剂粘合过程的最佳过程参数是具有挑战性的：优化是固有的多目标（旨在最大限度地提高断裂强度，同时最小化成本）和受约束（该过程不应导致材料的任何视觉损坏，应应对压力测试不会导致粘附相关的故障。实验室中的现实生活实验需要昂贵;由于评估所需的禁止的实验，传统的进化方法（如遗传算法）被否则适合解决问题。在本研究中，我们成功地应用了特定的机器学习技术（高斯过程回归和逻辑回归），以基于有限量的实验数据来模拟目标和约束函数。该技术嵌入贝叶斯优化算法中，该算法成功地以高效的方式检测静态过程设置（即，需要有限数量的额外实验）。

时间序列加工是风力涡轮机健康监测的重要方面。尽管在这一领域进展，但新方法仍有空间来提高建模质量。在本文中，我们提出了两种新方法来分析风力涡轮机健康。这两种方法都基于抽象概念，使用模糊集实现，概述并汇总了底层的原始数据。通过观察概念的变化，我们推断涡轮机健康的变化。分析分别进行不同的外部条件（风速和温度）。我们提取代表相对低，中等和高功率生产的概念。第一种方法旨在评估相对较高和低功率生产的降低或增加。使用回归式模型执行此任务。第二种方法评估提取的概念的整体漂移。大漂移表明电力生产过程及时经历波动。使用语言标签标记概念，从而用改善的解释性功能配备了我们的模型。我们应用了提出的方法来处理描述四种风力涡轮机的公开数据。仿真结果表明，所有风力涡轮机的老化过程在所有风力涡轮机中都不均匀。

汽车公司越来越多地寻找制作产品更轻，使用小说材料和新型粘合工艺将这些材料一起加入产品。寻找这种粘合剂粘合过程的最佳过程参数是具有挑战性的。在这项研究中，我们通过高斯过程回归和逻辑回归成功地应用了贝叶斯优化，以有效地（即，需要少数实验）指导对Pareto-Optimal Process参数设置的实验设计。

HyperParameter Optimization（HPO）是一种确保机器学习（ML）算法最佳性能的必要步骤。已经开发了几种方法来执行HPO;其中大部分都集中在优化一个性能措施（通常是基于错误的措施），并且在这种单一目标HPO问题上的文献是巨大的。然而，最近似乎似乎侧重于同时优化多个冲突目标的算法。本文提出了对2014年至2020年的文献的系统调查，在多目标HPO算法上发布，区分了基于成逐的算法，Metamodel的算法以及使用两者混合的方法。我们还讨论了用于比较多目标HPO程序和今后的研究方向的质量指标。

Modelling and forecasting real-life human behaviour using online social media is an active endeavour of interest in politics, government, academia, and industry. Since its creation in 2006, Twitter has been proposed as a potential laboratory that could be used to gauge and predict social behaviour. During the last decade, the user base of Twitter has been growing and becoming more representative of the general population. Here we analyse this user base in the context of the 2021 Mexican Legislative Election. To do so, we use a dataset of 15 million election-related tweets in the six months preceding election day. We explore different election models that assign political preference to either the ruling parties or the opposition. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods. These results demonstrate that analysis of public online data can outperform conventional polling methods, and that political analysis and general forecasting would likely benefit from incorporating such data in the immediate future. Moreover, the same Twitter dataset with geographical attributes is positively correlated with results from official census data on population and internet usage in Mexico. These findings suggest that we have reached a period in time when online activity, appropriately curated, can provide an accurate representation of offline behaviour.

In this work, we address the problem of unsupervised moving object segmentation (MOS) in 4D LiDAR data recorded from a stationary sensor, where no ground truth annotations are involved. Deep learning-based state-of-the-art methods for LiDAR MOS strongly depend on annotated ground truth data, which is expensive to obtain and scarce in existence. To close this gap in the stationary setting, we propose a novel 4D LiDAR representation based on multivariate time series that relaxes the problem of unsupervised MOS to a time series clustering problem. More specifically, we propose modeling the change in occupancy of a voxel by a multivariate occupancy time series (MOTS), which captures spatio-temporal occupancy changes on the voxel level and its surrounding neighborhood. To perform unsupervised MOS, we train a neural network in a self-supervised manner to encode MOTS into voxel-level feature representations, which can be partitioned by a clustering algorithm into moving or stationary. Experiments on stationary scenes from the Raw KITTI dataset show that our fully unsupervised approach achieves performance that is comparable to that of supervised state-of-the-art approaches.

Content moderation is the process of screening and monitoring user-generated content online. It plays a crucial role in stopping content resulting from unacceptable behaviors such as hate speech, harassment, violence against specific groups, terrorism, racism, xenophobia, homophobia, or misogyny, to mention some few, in Online Social Platforms. These platforms make use of a plethora of tools to detect and manage malicious information; however, malicious actors also improve their skills, developing strategies to surpass these barriers and continuing to spread misleading information. Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems. In response to this recent ongoing issue, this paper presents an innovative approach to address this linguistic trend in social networks through the simulation of different content evasion techniques and a multilingual Transformer model for content evasion detection. In this way, we share with the rest of the scientific community a multilingual public tool, named "pyleetspeak" to generate/simulate in a customizable way the phenomenon of content evasion through automatic word camouflage and a multilingual Named-Entity Recognition (NER) Transformer-based model tuned for its recognition and detection. The multilingual NER model is evaluated in different textual scenarios, detecting different types and mixtures of camouflage techniques, achieving an overall weighted F1 score of 0.8795. This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content on social networks, making the fight against information disorders more effective.

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the ``IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method.

The proliferation of automatic faithfulness metrics for summarization has produced a need for benchmarks to evaluate them. While existing benchmarks measure the correlation with human judgements of faithfulness on model-generated summaries, they are insufficient for diagnosing whether metrics are: 1) consistent, i.e., decrease as errors are introduced into a summary, 2) effective on human-written texts, and 3) sensitive to different error types (as summaries can contain multiple errors). To address these needs, we present a benchmark of unfaithful minimal pairs (BUMP), a dataset of 889 human-written, minimally different summary pairs, where a single error (from an ontology of 7 types) is introduced to a summary from the CNN/DailyMail dataset to produce an unfaithful summary. We find BUMP complements existing benchmarks in a number of ways: 1) the summaries in BUMP are harder to discriminate and less probable under SOTA summarization models, 2) BUMP enables measuring the consistency of metrics, and reveals that the most discriminative metrics tend not to be the most consistent, 3) BUMP enables the measurement of metrics' performance on individual error types and highlights areas of weakness for future work.