在本文中，我们根据两个模型提出了一个端到端情感感知的对话代理：答复情绪预测模型，该模型利用对话的上下文来预测适当的情感，以便代理人在其答复中表达表达；以及一个基于预测的情感和对话的上下文的条件的文本生成模型，以产生既适合上下文又适合情感的答复。此外，我们建议使用情感分类模型来评估代理商在模型开发过程中表达的情感。这使我们能够自动评估代理。自动和人类评估结果都表明，用预定义的句子集明确指导文本生成模型导致了明确的改进，包括表达的情感和生成文本的质量。

网络安全已经关注了很长一段时间。在最近几年，网络理念的规模和复杂程度越来越大，通过技术的重大进步推动。如今，保护系统和数据对于业务连续性至关重要的不可避免的必要性。因此，已经创建了许多入侵检测系统，以试图减轻这些威胁并有助于及时检测。这项工作提出了一种可解释和解释的混合性入侵检测系统，它利用人工智能方法来实现更好，更持久的安全性。该系统将专家的书面规则和动态知识与决策树算法连续生成，因为从网络活动中出现了新的证据。

By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project's website and videos can be found at robotics-transformer.github.io

Text classification is a natural language processing (NLP) task relevant to many commercial applications, like e-commerce and customer service. Naturally, classifying such excerpts accurately often represents a challenge, due to intrinsic language aspects, like irony and nuance. To accomplish this task, one must provide a robust numerical representation for documents, a process known as embedding. Embedding represents a key NLP field nowadays, having faced a significant advance in the last decade, especially after the introduction of the word-to-vector concept and the popularization of Deep Learning models for solving NLP tasks, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based Language Models (TLMs). Despite the impressive achievements in this field, the literature coverage regarding generating embeddings for Brazilian Portuguese texts is scarce, especially when considering commercial user reviews. Therefore, this work aims to provide a comprehensive experimental study of embedding approaches targeting a binary sentiment classification of user reviews in Brazilian Portuguese. This study includes from classical (Bag-of-Words) to state-of-the-art (Transformer-based) NLP models. The methods are evaluated with five open-source databases with pre-defined data partitions made available in an open digital repository to encourage reproducibility. The Fine-tuned TLMs achieved the best results for all cases, being followed by the Feature-based TLM, LSTM, and CNN, with alternate ranks, depending on the database under analysis.

A major challenge in machine learning is resilience to out-of-distribution data, that is data that exists outside of the distribution of a model's training data. Training is often performed using limited, carefully curated datasets and so when a model is deployed there is often a significant distribution shift as edge cases and anomalies not included in the training data are encountered. To address this, we propose the Input Optimisation Network, an image preprocessing model that learns to optimise input data for a specific target vision model. In this work we investigate several out-of-distribution scenarios in the context of semantic segmentation for autonomous vehicles, comparing an Input Optimisation based solution to existing approaches of finetuning the target model with augmented training data and an adversarially trained preprocessing model. We demonstrate that our approach can enable performance on such data comparable to that of a finetuned model, and subsequently that a combined approach, whereby an input optimization network is optimised to target a finetuned model, delivers superior performance to either method in isolation. Finally, we propose a joint optimisation approach, in which input optimization network and target model are trained simultaneously, which we demonstrate achieves significant further performance gains, particularly in challenging edge-case scenarios. We also demonstrate that our architecture can be reduced to a relatively compact size without a significant performance impact, potentially facilitating real time embedded applications.

The preservation, monitoring, and control of water resources has been a major challenge in recent decades. Water resources must be constantly monitored to know the contamination levels of water. To meet this objective, this paper proposes a water monitoring system using autonomous surface vehicles, equipped with water quality sensors, based on a multimodal particle swarm optimization, and the federated learning technique, with Gaussian process as a surrogate model, the AquaFeL-PSO algorithm. The proposed monitoring system has two phases, the exploration phase and the exploitation phase. In the exploration phase, the vehicles examine the surface of the water resource, and with the data acquired by the water quality sensors, a first water quality model is estimated in the central server. In the exploitation phase, the area is divided into action zones using the model estimated in the exploration phase for a better exploitation of the contamination zones. To obtain the final water quality model of the water resource, the models obtained in both phases are combined. The results demonstrate the efficiency of the proposed path planner in obtaining water quality models of the pollution zones, with a 14$\%$ improvement over the other path planners compared, and the entire water resource, obtaining a 400$\%$ better model, as well as in detecting pollution peaks, the improvement in this case study is 4,000$\%$. It was also proven that the results obtained by applying the federated learning technique are very similar to the results of a centralized system.

在接下来的十年中，社交机器人将在许多公共场所中实施，以向人类提供服务。我们质疑这些社交机器人的特性，以提供接受和自发的情感互动。更具体地说，在本研究中，我们报告了机器人在与人类参与者面对面互动任务中情绪传染中空闲运动频率的影响。机器人系统的伙伴被编程为采用悲伤的姿势和面部表情，同时讲述了三个悲伤的故事，并以低，中和高频向上/向下移动头部。每个参与者（n = 15）被邀请坐在好友面前，听故事。使用3D运动捕获系统（质量）记录了人类参与者姿势的无意识变化。结果表明，在高频试验中，肩膀/躯干在高频试验中的倾斜度更大。当Buddy以缓慢的频率移动时，自发运动的数量也更大。当两个人从事社交互动时，这些发现与实验心理学报道的结果相呼应。在Godspeed问卷中获得的分数进一步表明，当Buddy移动缓慢时，可能会发生情绪传染，因为机器人系统被认为是更自然和知识渊博的，例如，以速度与表达的情感相干。我们的工作探讨了机器人系统概念中身体姿势和空闲运动频率的重要性。这样的补充可以提供社交机器人，这些机器人在轻松的机器人人类协作任务中提供情感传染。

主动感知和凹觉视觉是人类视觉系统的基础。虽然动脉凹视觉减少了在注视期间要处理的信息的量，但主动感知会将凝视方向转变为视野中最有前途的部分。我们提出了一种方法，以模仿人类和机器人使用中央摄像机探索场景，并以最少的凝视转移来识别周围环境中存在的物体。我们的方法基于三种关键方法。首先，我们采用现成的深度对象检测器，并在大量的常规图像数据集上进行了预训练，并将分类输出校准为foveateat图像的情况。其次，考虑了几种数据融合技术，对对象分类和相应的不确定性编码对象分类和相应的不确定性进行了依次更新。第三，下一个最好的目光固定点是基于信息理论指标确定的，旨在最大程度地减少语义图的总预期不确定性。与随机选择的下一个凝视转移相比，提出的方法可以使检测的F1分数增加2-3个百分点，以相同数量的凝视偏移，并减少三分之一，而三分之一则是所需的凝视转移数量以达到相似的性能。

在胸部计算机断层扫描（CT）扫描中，自动分割地面玻璃的不透明和固结可以在高资源利用时期减轻放射科医生的负担。但是，由于分布（OOD）数据默默失败，深度学习模型在临床常规中不受信任。我们提出了一种轻巧的OOD检测方法，该方法利用特征空间中的Mahalanobis距离，并无缝集成到最新的分割管道中。简单的方法甚至可以增加具有临床相关的不确定性定量的预训练模型。我们在四个胸部CT分布偏移和两个磁共振成像应用中验证我们的方法，即海马和前列腺的分割。我们的结果表明，所提出的方法在所有探索场景中有效地检测到遥远和近型样品。

本文提出了一种深度学习方法，用于在历史文档的数字收集中进行图像检索和图案斑点。首先，区域建议算法检测文档页面图像中的对象候选。接下来，考虑了两个不同的变体，这些模型用于特征提取，这些变体提供了实用值或二进制代码表示。最后，通过计算给定输入查询的特征相似性来对候选图像进行排名。一项强大的实验协议评估了DOCEXPLORE图像数据库上的每个表示方案（实用值和二进制代码）的建议方法。实验结果表明，所提出的深层模型与历史文档图像的最新图像检索方法相比，使用相同的技术用于模式斑点，优于2.56个百分点。此外，与基于实价表示的相关作品相比，提议的方法还将搜索时间缩短了200倍，并且存储的成本高达6,000倍。