在Robocup小型联盟（SSL）中，鼓励团队提出解决方案，以便仅使用嵌入式感应信息在SSL领域执行基本足球任务。因此，这项工作提出了一种嵌入式的单眼视觉方法，用于检测物体和估计足球场内的相对位置。通过假设对象放在地面上，并且板载摄像头的位置固定在机器人上，可以利用来自环境的先验知识。我们在NVIDIA Jetson Nano上实施了建议的方法，并使用SSD Mobilenet V2用于2D对象检测，并具有张力优化，检测球，机器人和目标，距离高达3.5米。球定位评估表明，所提出的解决方案克服了当前使用的SSL视觉系统，该系统的位置超过1米，距离板载摄像头14.37毫米。此外，所提出的方法以每秒30帧的平均处理速度实现实时性能。

在本文中，我们提出了一种新的贝叶斯在线预测算法，用于局部可观察性（ATPO）下的Ad Hoc团队的问题设置，这使得与未知的队友执行未知任务的运行协作，而无需预先协调协议。与以前的作品不同，假设环境的完全可观察状态，ATPO使用代理商的观察来确定队友正在执行哪项任务的部分可观察性。我们的方法既不假设队友的行为也不是可见的，也不是环境奖励信号。我们在三个域中评估ATPO - 追踪域的两个修改版本，具有部分可观察性和过核域。我们的研究结果表明，ATPO在识别可能的任务中的大型文库中，在近乎最佳的时间内求助，以及在适应越来越大的问题尺寸方面可以进行高效的速度，可以有效和强大。

Video segmentation consists of a frame-by-frame selection process of meaningful areas related to foreground moving objects. Some applications include traffic monitoring, human tracking, action recognition, efficient video surveillance, and anomaly detection. In these applications, it is not rare to face challenges such as abrupt changes in weather conditions, illumination issues, shadows, subtle dynamic background motions, and also camouflage effects. In this work, we address such shortcomings by proposing a novel deep learning video segmentation approach that incorporates residual information into the foreground detection learning process. The main goal is to provide a method capable of generating an accurate foreground detection given a grayscale video. Experiments conducted on the Change Detection 2014 and on the private dataset PetrobrasROUTES from Petrobras support the effectiveness of the proposed approach concerning some state-of-the-art video segmentation techniques, with overall F-measures of $\mathbf{0.9535}$ and $\mathbf{0.9636}$ in the Change Detection 2014 and PetrobrasROUTES datasets, respectively. Such a result places the proposed technique amongst the top 3 state-of-the-art video segmentation methods, besides comprising approximately seven times less parameters than its top one counterpart.

Scene change detection is an image processing problem related to partitioning pixels of a digital image into foreground and background regions. Mostly, visual knowledge-based computer intelligent systems, like traffic monitoring, video surveillance, and anomaly detection, need to use change detection techniques. Amongst the most prominent detection methods, there are the learning-based ones, which besides sharing similar training and testing protocols, differ from each other in terms of their architecture design strategies. Such architecture design directly impacts on the quality of the detection results, and also in the device resources capacity, like memory. In this work, we propose a novel Multiscale Cascade Residual Convolutional Neural Network that integrates multiscale processing strategy through a Residual Processing Module, with a Segmentation Convolutional Neural Network. Experiments conducted on two different datasets support the effectiveness of the proposed approach, achieving average overall $\boldsymbol{F\text{-}measure}$ results of $\boldsymbol{0.9622}$ and $\boldsymbol{0.9664}$ over Change Detection 2014 and PetrobrasROUTES datasets respectively, besides comprising approximately eight times fewer parameters. Such obtained results place the proposed technique amongst the top four state-of-the-art scene change detection methods.

Research on remote sensing image classification significantly impacts essential human routine tasks such as urban planning and agriculture. Nowadays, the rapid advance in technology and the availability of many high-quality remote sensing images create a demand for reliable automation methods. The current paper proposes two novel deep learning-based architectures for image classification purposes, i.e., the Discriminant Deep Image Prior Network and the Discriminant Deep Image Prior Network+, which combine Deep Image Prior and Triplet Networks learning strategies. Experiments conducted over three well-known public remote sensing image datasets achieved state-of-the-art results, evidencing the effectiveness of using deep image priors for remote sensing image classification.

Machine Learning algorithms have been extensively researched throughout the last decade, leading to unprecedented advances in a broad range of applications, such as image classification and reconstruction, object recognition, and text categorization. Nonetheless, most Machine Learning algorithms are trained via derivative-based optimizers, such as the Stochastic Gradient Descent, leading to possible local optimum entrapments and inhibiting them from achieving proper performances. A bio-inspired alternative to traditional optimization techniques, denoted as meta-heuristic, has received significant attention due to its simplicity and ability to avoid local optimums imprisonment. In this work, we propose to use meta-heuristic techniques to fine-tune pre-trained weights, exploring additional regions of the search space, and improving their effectiveness. The experimental evaluation comprises two classification tasks (image and text) and is assessed under four literature datasets. Experimental results show nature-inspired algorithms' capacity in exploring the neighborhood of pre-trained weights, achieving superior results than their counterpart pre-trained architectures. Additionally, a thorough analysis of distinct architectures, such as Multi-Layer Perceptron and Recurrent Neural Networks, attempts to visualize and provide more precise insights into the most critical weights to be fine-tuned in the learning process.

模型透明度是许多领域的先决条件和机器学习研究中越来越受欢迎的地区。例如，在医学领域中，揭示疾病背后的机制通常具有比诊断本身更高的优先级，因为它可能决定或引导潜在的治疗和研究方向。解释模型全球预测的最受欢迎方法之一是允许置换数据的性能的置换重要性与基线为基准。然而，这种方法和其他相关方法将低估在协调因子存在中的重要性，因为这些涵盖其提供的信息的一部分。为了解决这个问题，我们提出了涵盖了信息解剖学（CID），一种考虑所有功能信息的方法重叠，以纠正释放重要性提供的值。我们进一步展示了如何在耦合Markov随机字段时有效地计算CID。在受控玩具数据集上首先展示其在调整权释放重要性中的效力，并讨论其对现实世界医疗数据的影响。

从开放式网络策略的大规模未过滤数据集培训的语言模型获取从其培训数据的系统偏差，偏见和有害视图。我们提出了一种从Web级数据集上以编程方式识别和删除有害文本的方法。预先训练的语言模型用于计算在特定文档上调节的研究员写入触发短语的日志可能性，该语言用于从数据集中识别和过滤文档。我们证明，在该过滤的数据集上培训的模型表现出较低的倾向，以产生有害文本，与未过滤的基线相比，标准语言建模基准的性能下降了下降。通过从标准语言建模基准测试的讨论语音和其他不良内容的介绍来提供对这种性能差异的部分解释。最后，我们讨论了这种方法的概括以及如何通过研究人员使用反映特定值的触发短语来构建与其值更紧密对齐的语言模型。

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.

Attention mechanisms form a core component of several successful deep learning architectures, and are based on one key idea: ''The output depends only on a small (but unknown) segment of the input.'' In several practical applications like image captioning and language translation, this is mostly true. In trained models with an attention mechanism, the outputs of an intermediate module that encodes the segment of input responsible for the output is often used as a way to peek into the `reasoning` of the network. We make such a notion more precise for a variant of the classification problem that we term selective dependence classification (SDC) when used with attention model architectures. Under such a setting, we demonstrate various error modes where an attention model can be accurate but fail to be interpretable, and show that such models do occur as a result of training. We illustrate various situations that can accentuate and mitigate this behaviour. Finally, we use our objective definition of interpretability for SDC tasks to evaluate a few attention model learning algorithms designed to encourage sparsity and demonstrate that these algorithms help improve interpretability.