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.

事实证明，行为生物识别技术是有效的，可以防止身份盗用，并被视为用户友好的身份验证方法。文献中最受欢迎的特征之一是由于我们社会中计算机和移动设备的大量部署，击键动态。本文着重于改善自由文本方案的击键生物识别系统。由于不受控制的文本条件，用户的情绪和身体状态以及使用中的应用程序，这种情况的特征是非常具有挑战性的。为了克服这些缺点，在文献中提出了基于深度学习的方法，例如卷积神经网络（CNN）和经常性神经网络（RNN），表现优于传统的机器学习方法。但是，这些体系结构仍然需要进行审查和改进。据我们所知，这是第一个提出基于变压器的击键生物识别系统的研究。所提出的变压器体系结构在流行的AALTO移动击键数据库中仅使用5个注册会话实现了相等的错误率（EER）值，为3.84％，在文献中的大幅度优于其他最先进的方法。

增强隐私技术是实施基本数据保护原则的技术。关于生物识别识别，已经引入了不同类型的隐私增强技术来保护储存的生物特征识别数据，这些数据通常被归类为敏感。在这方面，已经提出了各种分类法和概念分类，并进行了标准化活动。但是，这些努力主要致力于某些隐私增强技术的子类别，因此缺乏概括。这项工作概述了统一框架中生物识别技术隐私技术的概念。在每个处理步骤中，详细介绍了现有概念之间的关键方面和差异。讨论了现有方法的基本属性和局限性，并与数据保护技术和原理有关。此外，提出了评估生物识别技术的隐私技术评估的场景和方法。本文是针对生物识别数据保护领域的进入点，并针对经验丰富的研究人员以及非专家。

本文对最近的ChildCI框架中提出的不同测试进行了全面分析，证明了其潜力可以更好地了解儿童的神经运动和随时间的认知发展，以及它们在其他研究领域的可能应用，例如电子学习。特别是，我们提出了一组与儿童与移动设备互动的运动和认知方面有关的100多个全球特征，其中一些是根据文献收集和改编的。此外，我们分析了拟议特征集的鲁棒性和判别能力，包括基于运动和认知行为的儿童年龄组检测任务的实验结果。在这项研究中考虑了两种不同的方案：i）单检验场景，ii）多测试场景。使用公开可用的childcidb_v1数据库（18个月至8岁的儿童超过400名儿童）实现了超过93％的精度，这证明了儿童年龄与与移动设备的互动方式之间的高度相关性。

可取消的生物识别性是指一组技术，其中生物识别输入在处理或存储前用键有意地转换。该转换是可重复的，可以实现后续生物特征比较。本文介绍了一种可消除生物识别性的新方案，旨在保护模板免受潜在攻击，适用于任何基于生物识别的识别系统。我们所提出的方案基于从变形随机生物识别信息获得的时变键。给出了面部生物识别技术的实验实施。结果证实，该方法能够在提高识别性能的同时抵抗泄漏攻击。

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.

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。