深度学习（DL）是各种计算机视觉任务中使用的主要方法，因为它在许多任务上取得了相关结果。但是，在具有部分或没有标记数据的实际情况下，DL方法也容易出现众所周知的域移位问题。多源无监督的域适应性（MSDA）旨在通过从一袋源模型中分配弱知识来学习未标记域的预测指标。但是，大多数作品进行域适应性仅利用提取的特征并从损失函数设计的角度降低其域的转移。在本文中，我们认为仅基于域级特征处理域移动不足，但是在功能空间上对此类信息进行对齐也是必不可少的。与以前的工作不同，我们专注于网络设计，并建议将多源版本的域对齐层（MS-DIAL）嵌入预测变量的不同级别。这些层旨在匹配不同域之间的特征分布，并且可以轻松地应用于各种MSDA方法。为了显示我们方法的鲁棒性，我们考虑了两个具有挑战性的情况：数字识别和对象分类，进行了广泛的实验评估。实验结果表明，我们的方法可以改善最新的MSDA方法，从而在其分类精度上获得 +30.64％的相对增长。

The visual dimension of cities has been a fundamental subject in urban studies, since the pioneering work of scholars such as Sitte, Lynch, Arnheim, and Jacobs. Several decades later, big data and artificial intelligence (AI) are revolutionizing how people move, sense, and interact with cities. This paper reviews the literature on the appearance and function of cities to illustrate how visual information has been used to understand them. A conceptual framework, Urban Visual Intelligence, is introduced to systematically elaborate on how new image data sources and AI techniques are reshaping the way researchers perceive and measure cities, enabling the study of the physical environment and its interactions with socioeconomic environments at various scales. The paper argues that these new approaches enable researchers to revisit the classic urban theories and themes, and potentially help cities create environments that are more in line with human behaviors and aspirations in the digital age.

Automatic Text Summarization (ATS) is becoming relevant with the growth of textual data; however, with the popularization of public large-scale datasets, some recent machine learning approaches have focused on dense models and architectures that, despite producing notable results, usually turn out in models difficult to interpret. Given the challenge behind interpretable learning-based text summarization and the importance it may have for evolving the current state of the ATS field, this work studies the application of two modern Generalized Additive Models with interactions, namely Explainable Boosting Machine and GAMI-Net, to the extractive summarization problem based on linguistic features and binary classification.

Early recognition of clinical deterioration (CD) has vital importance in patients' survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network's performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models' performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data.

We can protect user data privacy via many approaches, such as statistical transformation or generative models. However, each of them has critical drawbacks. On the one hand, creating a transformed data set using conventional techniques is highly time-consuming. On the other hand, in addition to long training phases, recent deep learning-based solutions require significant computational resources. In this paper, we propose PrivateSMOTE, a technique designed for competitive effectiveness in protecting cases at maximum risk of re-identification while requiring much less time and computational resources. It works by synthetic data generation via interpolation to obfuscate high-risk cases while minimizing data utility loss of the original data. Compared to multiple conventional and state-of-the-art privacy-preservation methods on 20 data sets, PrivateSMOTE demonstrates competitive results in re-identification risk. Also, it presents similar or higher predictive performance than the baselines, including generative adversarial networks and variational autoencoders, reducing their energy consumption and time requirements by a minimum factor of 9 and 12, respectively.

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.

在本文中，我们研究了不确定性下的顺序决策任务中可读性的概念。以前的作品将易读性扩展到了机器人运动以外的方案，要么集中在确定性设置上，要么在计算上太昂贵。我们提出的称为POL-MDP的方法能够处理不确定性，同时保持计算障碍。在几种不同复杂性的模拟场景中，我们建立了反对最新方法的方法的优势。我们还展示了将我们的清晰政策用作反向加强学习代理的示范，并根据最佳政策建立了他们的优越性。最后，我们通过用户研究评估计算政策的可读性，在该研究中，要求人们通过观察其行动来推断移动机器人的目标。

在复杂，非结构化和动态环境中导航的董事会机器人基于在线事件的感知技术可能会遭受进入事件速率及其处理时间的不可预测的变化，这可能会导致计算溢出或响应能力损失。本文提出了尽快的：一种新型的事件处理框架，该框架将事件传输到处理算法，保持系统响应能力并防止溢出。尽快由两种自适应机制组成。第一个通过丢弃传入事件的自适应百分比来防止事件处理溢出。第二种机制动态调整事件软件包的大小，以减少事件生成和处理之间的延迟。ASAP保证了收敛性，并且对处理算法具有灵活性。它已在具有挑战性的条件下在船上进行了验证。

事件摄像机可以通过非常高的时间分辨率和动态范围来捕获像素级照明变化。由于对照明条件和运动模糊的稳健性，他们获得了越来越多的研究兴趣。文献中存在两种主要方法，用于喂养基于事件的处理算法：在事件软件包中包装触发的事件并将它们逐一发送作为单个事件。这些方法因处理溢出或缺乏响应性而受到限制。当算法无法实时处理所有事件时，处理溢出是由高事件产生速率引起的。相反，当事件包的频率太低时，事件包的生成率低时，缺乏响应率会发生。本文提出了尽快的自适应方案，该方案是通过可容纳事件软件包处理时间的可变大小软件包来管理事件流的。实验结果表明，ASAP能够以响应性和有效的方式喂食异步事件聚类算法，同时又可以防止溢出。

讽刺可以被定义为说或写讽刺与一个人真正想表达的相反，通常是为了侮辱，刺激或娱乐某人。由于文本数据中讽刺性的性质晦涩难懂，因此检测到情感分析研究社区的困难和非常感兴趣。尽管讽刺检测的研究跨越了十多年，但最近已经取得了一些重大进步，包括在多模式环境中采用了无监督的预训练的预训练的变压器，并整合了环境以识别讽刺。在这项研究中，我们旨在简要概述英语计算讽刺研究的最新进步和趋势。我们描述了与讽刺有关的相关数据集，方法，趋势，问题，挑战和任务，这些数据集，趋势，问题，挑战和任务是无法检测到的。我们的研究提供了讽刺数据集，讽刺特征及其提取方法以及各种方法的性能分析，这些表可以帮助相关领域的研究人员了解当前的讽刺检测中最新实践。