由于对社会，经济学和环境的巨大影响，智能电网（SG）的研究和发展引起了学术界，行业和政府的重视。确保SG是一个很大的重大挑战，因为增加了通信网络以协助物理过程控制，将它们暴露于各种网络威胁。除了使用假数据喷射（FDI）技术改变测量值的攻击之外，通信网络上的攻击可能通过拦截消息来破坏电力系统的实时操作，或者通过泛洪与不必要的数据泛换通信信道。解决这些攻击需要跨层方法。在本文中，呈现了一种交叉层策略，称为具有自适应统计（CECD-AS）的交叉层集合RORDET，其集成了故障的SG测量数据的检测以及不一致的网络到达时间和传输延迟，以便更可靠地进行传输延迟准确的异常检测和攻击解释。数值结果表明，与当前方法相比，CECD-AS可以检测多个错误数据喷射，拒绝服务（MITM）攻击中的拒绝服务（MITM）攻击的攻击（MITM）攻击。基于传统的基于物理的状态估计，具有自适应统计策略和基于机器学习分类的检测方案的集合RORDET。

The broad usage of mobile devices nowadays, the sensitiveness of the information contained in them, and the shortcomings of current mobile user authentication methods are calling for novel, secure, and unobtrusive solutions to verify the users' identity. In this article, we propose TypeFormer, a novel Transformer architecture to model free-text keystroke dynamics performed on mobile devices for the purpose of user authentication. The proposed model consists in Temporal and Channel Modules enclosing two Long Short-Term Memory (LSTM) recurrent layers, Gaussian Range Encoding (GRE), a multi-head Self-Attention mechanism, and a Block-Recurrent structure. Experimenting on one of the largest public databases to date, the Aalto mobile keystroke database, TypeFormer outperforms current state-of-the-art systems achieving Equal Error Rate (EER) values of 3.25% using only 5 enrolment sessions of 50 keystrokes each. In such way, we contribute to reducing the traditional performance gap of the challenging mobile free-text scenario with respect to its desktop and fixed-text counterparts. Additionally, we analyse the behaviour of the model with different experimental configurations such as the length of the keystroke sequences and the amount of enrolment sessions, showing margin for improvement with more enrolment data. Finally, a cross-database evaluation is carried out, demonstrating the robustness of the features extracted by TypeFormer in comparison with existing approaches.

The detection and prevention of illegal fishing is critical to maintaining a healthy and functional ecosystem. Recent research on ship detection in satellite imagery has focused exclusively on performance improvements, disregarding detection efficiency. However, the speed and compute cost of vessel detection are essential for a timely intervention to prevent illegal fishing. Therefore, we investigated optimization methods that lower detection time and cost with minimal performance loss. We trained an object detection model based on a convolutional neural network (CNN) using a dataset of satellite images. Then, we designed two efficiency optimizations that can be applied to the base CNN or any other base model. The optimizations consist of a fast, cheap classification model and a statistical algorithm. The integration of the optimizations with the object detection model leads to a trade-off between speed and performance. We studied the trade-off using metrics that give different weight to execution time and performance. We show that by using a classification model the average precision of the detection model can be approximated to 99.5% in 44% of the time or to 92.7% in 25% of the time.

A tractogram is a virtual representation of the brain white matter. It is composed of millions of virtual fibers, encoded as 3D polylines, which approximate the white matter axonal pathways. To date, tractograms are the most accurate white matter representation and thus are used for tasks like presurgical planning and investigations of neuroplasticity, brain disorders, or brain networks. However, it is a well-known issue that a large portion of tractogram fibers is not anatomically plausible and can be considered artifacts of the tracking procedure. With Verifyber, we tackle the problem of filtering out such non-plausible fibers using a novel fully-supervised learning approach. Differently from other approaches based on signal reconstruction and/or brain topology regularization, we guide our method with the existing anatomical knowledge of the white matter. Using tractograms annotated according to anatomical principles, we train our model, Verifyber, to classify fibers as either anatomically plausible or non-plausible. The proposed Verifyber model is an original Geometric Deep Learning method that can deal with variable size fibers, while being invariant to fiber orientation. Our model considers each fiber as a graph of points, and by learning features of the edges between consecutive points via the proposed sequence Edge Convolution, it can capture the underlying anatomical properties. The output filtering results highly accurate and robust across an extensive set of experiments, and fast; with a 12GB GPU, filtering a tractogram of 1M fibers requires less than a minute. Verifyber implementation and trained models are available at https://github.com/FBK-NILab/verifyber.

In recent years, unmanned aerial vehicle (UAV) related technology has expanded knowledge in the area, bringing to light new problems and challenges that require solutions. Furthermore, because the technology allows processes usually carried out by people to be automated, it is in great demand in industrial sectors. The automation of these vehicles has been addressed in the literature, applying different machine learning strategies. Reinforcement learning (RL) is an automation framework that is frequently used to train autonomous agents. RL is a machine learning paradigm wherein an agent interacts with an environment to solve a given task. However, learning autonomously can be time consuming, computationally expensive, and may not be practical in highly-complex scenarios. Interactive reinforcement learning allows an external trainer to provide advice to an agent while it is learning a task. In this study, we set out to teach an RL agent to control a drone using reward-shaping and policy-shaping techniques simultaneously. Two simulated scenarios were proposed for the training; one without obstacles and one with obstacles. We also studied the influence of each technique. The results show that an agent trained simultaneously with both techniques obtains a lower reward than an agent trained using only a policy-based approach. Nevertheless, the agent achieves lower execution times and less dispersion during training.

We present edBB-Demo, a demonstrator of an AI-powered research platform for student monitoring in remote education. The edBB platform aims to study the challenges associated to user recognition and behavior understanding in digital platforms. This platform has been developed for data collection, acquiring signals from a variety of sensors including keyboard, mouse, webcam, microphone, smartwatch, and an Electroencephalography band. The information captured from the sensors during the student sessions is modelled in a multimodal learning framework. The demonstrator includes: i) Biometric user authentication in an unsupervised environment; ii) Human action recognition based on remote video analysis; iii) Heart rate estimation from webcam video; and iv) Attention level estimation from facial expression analysis.

Reinforcement learning (RL) operating on attack graphs leveraging cyber terrain principles are used to develop reward and state associated with determination of surveillance detection routes (SDR). This work extends previous efforts on developing RL methods for path analysis within enterprise networks. This work focuses on building SDR where the routes focus on exploring the network services while trying to evade risk. RL is utilized to support the development of these routes by building a reward mechanism that would help in realization of these paths. The RL algorithm is modified to have a novel warm-up phase which decides in the initial exploration which areas of the network are safe to explore based on the rewards and penalty scale factor.

在许多现实世界和高影响力决策设置中，从分类过程中说明预测性不确定性的神经网络的概率预测至关重要。但是，实际上，大多数数据集经过非稳定神经网络的培训，默认情况下，这些神经网络不会捕获这种固有的不确定性。这个众所周知的问题导致了事后校准程序的开发，例如PLATT缩放（Logistic），等渗和β校准，这将得分转化为校准良好的经验概率。校准方法的合理替代方法是使用贝叶斯神经网络，该网络直接建模预测分布。尽管它们已应用于图像和文本数据集，但在表格和小型数据制度中的采用有限。在本文中，我们证明了与校准神经网络相比，贝叶斯神经网络在各种数据集中进行实验，从而产生竞争性能。

实验数据的获取成本很高，这使得很难校准复杂模型。对于许多型号而言，鉴于有限的实验预算，可以产生最佳校准的实验设计并不明显。本文介绍了用于设计实验的深钢筋学习（RL）算法，该算法通过Kalman Filter（KF）获得的Kullback-Leibler（KL）差异测量的信息增益最大化。这种组合实现了传统方法太昂贵的快速在线实验的实验设计。我们将实验的可能配置作为决策树和马尔可夫决策过程（MDP），其中每个增量步骤都有有限的操作选择。一旦采取了动作，就会使用各种测量来更新实验状态。该新数据导致KF对参数进行贝叶斯更新，该参数用于增强状态表示。与NASH-SUTCLIFFE效率（NSE）指数相反，该指数需要额外的抽样来检验前进预测的假设，KF可以通过直接估计通过其他操作获得的新数据值来降低实验的成本。在这项工作中，我们的应用集中在材料的机械测试上。使用复杂的历史依赖模型的数值实验用于验证RL设计实验的性能并基准测试实现。

我们提出了一种在多孔培养基中使用物理知识的神经网络（PINNS）中多相热力学（THM）过程中的参数鉴定的解决方案策略。我们采用无量纲的理事方程式，特别适合逆问题，我们利用了我们先前工作中开发的顺序多物理Pinn求解器。我们在多个基准问题上验证了所提出的反模型方法，包括Terzaghi的等温固结问题，Barry-Mercer的等温注射产生问题以及非饱和土壤层的非等热整合。我们报告了提出的顺序PINN-THM逆求器的出色性能，从而为将PINNS应用于复杂非线性多物理问题的逆建模铺平了道路。