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.

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.

这项工作提出了两种统计方法，用于基于通用和用户依赖模型的击键生物识别数据的合成。两种方法在机器人检测任务上均经过验证，使用击键合成数据来更好地训练系统。我们的实验包括一个来自168,000名受试者的1.36亿击球事件的数据集。我们通过定性和定量实验分析了两种合成方法的性能。根据两个监督分类器（支持向量机和长期的短期内存网络）和一个包括人类和生成的样本在内的学习框架，考虑了不同的机器人探测器。我们的结果证明，所提出的统计方法能够生成现实的人类合成击键样品。此外，分类结果表明，在具有大型标记数据的情况下，可以高精度检测这些合成样品。但是，在几次学习方案中，它代表了一个重要的挑战。

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。

这项工作提出了基于眼闪烁频率的远程关注水平估计的可行性研究。我们首先提出了一种基于卷积神经网络（CNNS）的眼睛闪烁检测系统，对相关工程非常竞争。使用此探测器，我们通过在线会话期间通过实验评估眼睛眨眼率与学生的注意力水平之间的关系。实验框架是使用公共多模式数据库进行的用于眼睛眨眼检测和称为Mebal的注意力水平估计，包括来自38名学生的数据和倍数采集传感器，特别是i）提供时间信号的脑电图（EEG）频带从学生的认知信息和ii）RGB和NIR相机捕捉学生面部姿势。实现的结果表明眼睛闪烁频率与关注水平之间的反比相关性。在我们所提出的方法中使用该关系，称为ALEBK，用于估计注意力水平作为眼睛闪烁频率的倒数。我们的成果开设了新的研究线，以介绍这种技术的关注水平估计，以及这种行为生物识别基于面部分析的其他应用。

文献中的许多研究已经显示出出于身份验证目的的移动设备上生物识别技术的潜力。但是，已经表明，与生物识别系统相关的学习过程可能会暴露有关受试者的敏感个人信息。这项研究提出了Gaitprivacyon，这是一种新型的移动步态生物识别验证方法，可提供准确的身份验证结果，同时保留受试者的敏感信息。它包括两个模块：i）卷积自动编码器，该卷积自动编码器将生物识别原始数据的属性（例如性别或正在执行的活动）转换为新的隐私表示表示； ii）基于卷积神经网络（CNN）和复发性神经网络（RNN）与暹罗结构的相结合的移动步态验证系统。 Gaitprivacyon的主要优点是，第一个模块（卷积自动编码器）以无监督的方式进行了训练，而无需指定主题的敏感属性以保护。使用两个流行数据库（Motionsense和MobiACT）实现的实验结果表明，Gaitprivacyon有可能显着改善受试者的隐私，同时保持用户身份验证结果高于曲线下99％的面积（AUC）。据我们所知，这是第一种移动步态验证方法，它考虑了以无监督方式培训的隐私方法。

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.