加权模型集成（WMI）是一种流行的形式主义，旨在统一混合域概率推断的方法，涉及逻辑和代数约束。尽管最近的工作大量工作，但允许WMI算法随着混合问题的复杂性而扩展仍然是一个挑战。在本文中，我们重点介绍了现有最新解决方案的一些实质性局限性，并开发了一种结合基于SMT的枚举的算法，这是一种有效的正式验证技术，以及对问题结构的有效编码。这使我们的算法避免生成冗余模型，从而获得大量的计算节省。对合成数据集和现实世界数据集进行了广泛的实验评估，这证实了该解决方案比现有替代方案的优势。

在过去的十年中，强化学习成功地解决了复杂的控制任务和决策问题，例如Go棋盘游戏。然而，在将这些算法部署到现实世界情景方面的成功案例很少。原因之一是在处理和避免不安全状态时缺乏保证，这是关键控制工程系统的基本要求。在本文中，我们介绍了指导性的安全射击（GUS），这是一种基于模型的RL方法，可以学会以最小的侵犯安全限制来控制系统。该模型以迭代批次方式在系统操作过程中收集的数据中学习，然后用于计划在每个时间步骤执行的最佳动作。我们提出了三个不同的安全计划者，一个基于简单的随机拍摄策略，两个基于MAP-ELITE，一种更高级的发散搜索算法。实验表明，这些计划者可以帮助学习代理避免在最大程度地探索状态空间的同时避免不安全的情况，这是学习系统准确模型的必要方面。此外，与无模型方法相比，学习模型可以减少与现实系统的交互作用的数量，同时仍达到高奖励，这是处理工程系统时的基本要求。

在稀疏的奖励设置学习最优策略是困难的，因为学习代理人也鲜有其行动的质量没有反馈。在这些情况下，一个好的策略是专注于探索，希望能导致回报信号，以改善的发现。一个能够处理这种设置的学习算法必须能够（1）探讨可能的代理行为和（2）利用任何可能发现的奖励。高效勘探算法已经被提出，需要在被称为是一个值得探讨的空间中定义一个行为空间，即联营公司代理其产生的行为。需要定义这个空间是这些算法的限制。在这项工作中，我们介绍了STAX，旨在学习上的即时行为空间，并探索它的同时有效地优化发现任何报酬的算法。它通过分离的探索，并通过交替的两步过程中从奖励的剥削行为空间的学习这样做。在第一步骤中，建立STAX多样化策略的所有组成成分，同时学习策略评估过程中产生的高维观测值的低维表示。在开发步骤中，发射器用于优化发现有价值的解决方案的性能。在三个不同的稀疏奖励的环境进行的实验显示，STAX执行同等于现有基准，同时要求有关任务的要少得多的先验信息，因为它建立自主的行为空间。

基因 - 疾病的关联对于理解疾病机制和有效干预措施和治疗的发展至关重要。识别由于缺乏研究而尚未与疾病相关的基因是一项具有挑战性的任务，基于先验知识的优先次序可以有所帮助。对新候选疾病基因的计算搜索可以通过正标（PU）学习，机器学习（ML）设置来简化，其中只有一部分实例被标记为正，而其余数据集则未标记。在这项工作中，我们提出了一组有效的基于网络的特征，可用于新型的马尔可夫扩散基于推定疾病基因发现的多级标记策略。使用三种ML算法在五个不同的疾病数据集上测试了新标签算法的性能以及所提出特征的有效性。将这种特征与经典拓扑和功能/本体论特征进行了比较，表明它们在二进制分类和多级标签中的经典表现都优于经典。类似地，综合方法在搜索新疾病基因方面的预测能力与最新算法具有竞争力。

We are witnessing a widespread adoption of artificial intelligence in healthcare. However, most of the advancements in deep learning (DL) in this area consider only unimodal data, neglecting other modalities. Their multimodal interpretation necessary for supporting diagnosis, prognosis and treatment decisions. In this work we present a deep architecture, explainable by design, which jointly learns modality reconstructions and sample classifications using tabular and imaging data. The explanation of the decision taken is computed by applying a latent shift that, simulates a counterfactual prediction revealing the features of each modality that contribute the most to the decision and a quantitative score indicating the modality importance. We validate our approach in the context of COVID-19 pandemic using the AIforCOVID dataset, which contains multimodal data for the early identification of patients at risk of severe outcome. The results show that the proposed method provides meaningful explanations without degrading the classification performance.

Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps, and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A fully automatic and optimized segmentation process for different datasets is a prerequisite for classifying and diagnosing Indirect ImmunoFluorescence (IIF) raw data. This study describes a deterministic computational neuroscience approach for identifying cells and nuclei. It is far from the conventional neural network approach, but it is equivalent to their quantitative and qualitative performance, and it is also solid to adversative noise. The method is robust, based on formally correct functions, and does not suffer from tuning on specific data sets. Results: This work demonstrates the robustness of the method against the variability of parameters, such as image size, mode, and signal-to-noise ratio. We validated the method on two datasets (Neuroblastoma and NucleusSegData) using images annotated by independent medical doctors. Conclusions: The definition of deterministic and formally correct methods, from a functional to a structural point of view, guarantees the achievement of optimized and functionally correct results. The excellent performance of our deterministic method (NeuronalAlg) to segment cells and nuclei from fluorescence images was measured with quantitative indicators and compared with those achieved by three published ML approaches.

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.

Human Activity Recognition (HAR) is one of the core research areas in mobile and wearable computing. With the application of deep learning (DL) techniques such as CNN, recognizing periodic or static activities (e.g, walking, lying, cycling, etc.) has become a well studied problem. What remains a major challenge though is the sporadic activity recognition (SAR) problem, where activities of interest tend to be non periodic, and occur less frequently when compared with the often large amount of irrelevant background activities. Recent works suggested that sequential DL models (such as LSTMs) have great potential for modeling nonperiodic behaviours, and in this paper we studied some LSTM training strategies for SAR. Specifically, we proposed two simple yet effective LSTM variants, namely delay model and inverse model, for two SAR scenarios (with and without time critical requirement). For time critical SAR, the delay model can effectively exploit predefined delay intervals (within tolerance) in form of contextual information for improved performance. For regular SAR task, the second proposed, inverse model can learn patterns from the time series in an inverse manner, which can be complementary to the forward model (i.e.,LSTM), and combining both can boost the performance. These two LSTM variants are very practical, and they can be deemed as training strategies without alteration of the LSTM fundamentals. We also studied some additional LSTM training strategies, which can further improve the accuracy. We evaluated our models on two SAR and one non-SAR datasets, and the promising results demonstrated the effectiveness of our approaches in HAR applications.

Testing Deep Learning (DL) based systems inherently requires large and representative test sets to evaluate whether DL systems generalise beyond their training datasets. Diverse Test Input Generators (TIGs) have been proposed to produce artificial inputs that expose issues of the DL systems by triggering misbehaviours. Unfortunately, such generated inputs may be invalid, i.e., not recognisable as part of the input domain, thus providing an unreliable quality assessment. Automated validators can ease the burden of manually checking the validity of inputs for human testers, although input validity is a concept difficult to formalise and, thus, automate. In this paper, we investigate to what extent TIGs can generate valid inputs, according to both automated and human validators. We conduct a large empirical study, involving 2 different automated validators, 220 human assessors, 5 different TIGs and 3 classification tasks. Our results show that 84% artificially generated inputs are valid, according to automated validators, but their expected label is not always preserved. Automated validators reach a good consensus with humans (78% accuracy), but still have limitations when dealing with feature-rich datasets.

Digital media have enabled the access to unprecedented literary knowledge. Authors, readers, and scholars are now able to discover and share an increasing amount of information about books and their authors. Notwithstanding, digital archives are still unbalanced: writers from non-Western countries are less represented, and such a condition leads to the perpetration of old forms of discrimination. In this paper, we present the Under-Represented Writers Knowledge Graph (URW-KG), a resource designed to explore and possibly amend this lack of representation by gathering and mapping information about works and authors from Wikidata and three other sources: Open Library, Goodreads, and Google Books. The experiments based on KG embeddings showed that the integrated information encoded in the graph allows scholars and users to be more easily exposed to non-Western literary works and authors with respect to Wikidata alone. This opens to the development of fairer and effective tools for author discovery and exploration.