音频或视觉数据分析任务通常必须处理高维和非负信号。然而，当数据具有多维数减少预处理时，大多数数据分析方法遭受过度拟合和数值问题。此外，关于如何以及为什么滤波器为音频或可视应用的方式工作是所需的属性，特别是当涉及能量或频谱信号时。在这些情况下，由于这些信号的性质，滤波器重量的非承诺是所需的性质，以更好地理解其工作。由于这两个必需品，我们提出了不同的方法来减少数据的维度，而保证溶液的非承诺和可解释性。特别是，我们提出了一种广义方法，以在处理非负数据的应用程序中以监督方式设计过滤器银行，并且我们探讨了解决所提出的目标函数的不同方式，包括非负面的部分最小二乘法的非负图。我们分析了通过拟议的两种不同和广泛研究的应用方法获得的特征的辨别力：纹理和音乐类型分类。此外，我们比较我们的方法实现的滤波器银行，具体设计用于特征提取的其他最先进的方法。

多变量分析（MVA）包括用于特征提取的众所周知的方法，该方法提取，其利用表示数据的输入变量之间的相关性。大多数此类方法享有的一个重要属性是提取特征之间的不相关性。最近，MVA方法的正则化版本在文献中出现，主要是为了获得解决方案的解释性。在这些情况下，不再以封闭的方式获得解决方案，并且经常使用更复杂的优化方法，依赖于两个步骤的迭代。本文回到了替代方法来解决这个迭代问题。这种方法的主要新颖性在于保持原始方法的几个属性，最值得注意的是提取特征的不相关性。在此框架下，我们提出了一种新的方法，该方法利用L-21规范在特征提取过程中执行变量选择。不同问题的实验结果证实了与现有化配方的拟议配方的优点。

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.

User equipment is one of the main bottlenecks facing the gaming industry nowadays. The extremely realistic games which are currently available trigger high computational requirements of the user devices to run games. As a consequence, the game industry has proposed the concept of Cloud Gaming, a paradigm that improves gaming experience in reduced hardware devices. To this end, games are hosted on remote servers, relegating users' devices to play only the role of a peripheral for interacting with the game. However, this paradigm overloads the communication links connecting the users with the cloud. Therefore, service experience becomes highly dependent on network connectivity. To overcome this, Cloud Gaming will be boosted by the promised performance of 5G and future 6G networks, together with the flexibility provided by mobility in multi-RAT scenarios, such as WiFi. In this scope, the present work proposes a framework for measuring and estimating the main E2E metrics of the Cloud Gaming service, namely KQIs. In addition, different machine learning techniques are assessed for predicting KQIs related to Cloud Gaming user's experience. To this end, the main key quality indicators (KQIs) of the service such as input lag, freeze percent or perceived video frame rate are collected in a real environment. Based on these, results show that machine learning techniques provide a good estimation of these indicators solely from network-based metrics. This is considered a valuable asset to guide the delivery of Cloud Gaming services through cellular communications networks even without access to the user's device, as it is expected for telecom operators.

System identification, also known as learning forward models, transfer functions, system dynamics, etc., has a long tradition both in science and engineering in different fields. Particularly, it is a recurring theme in Reinforcement Learning research, where forward models approximate the state transition function of a Markov Decision Process by learning a mapping function from current state and action to the next state. This problem is commonly defined as a Supervised Learning problem in a direct way. This common approach faces several difficulties due to the inherent complexities of the dynamics to learn, for example, delayed effects, high non-linearity, non-stationarity, partial observability and, more important, error accumulation when using bootstrapped predictions (predictions based on past predictions), over large time horizons. Here we explore the use of Reinforcement Learning in this problem. We elaborate on why and how this problem fits naturally and sound as a Reinforcement Learning problem, and present some experimental results that demonstrate RL is a promising technique to solve these kind of problems.

In this work, a re-design of the Moodledata module functionalities is presented to share learning objects between e-learning content platforms, e.g., Moodle and G-Lorep, in a linkable object format. The e-learning courses content of the Drupal-based Content Management System G-Lorep for academic learning is exchanged designing an object incorporating metadata to support the reuse and the classification in its context. In such an Artificial Intelligence environment, the exchange of Linkable Learning Objects can be used for dialogue between Learning Systems to obtain information, especially with the use of semantic or structural similarity measures to enhance the existent Taxonomy Assistant for advanced automated classification.

Emerging applications such as Deep Learning are often data-driven, thus traditional approaches based on auto-tuners are not performance effective across the wide range of inputs used in practice. In the present paper, we start an investigation of predictive models based on machine learning techniques in order to optimize Convolution Neural Networks (CNNs). As a use-case, we focus on the ARM Compute Library which provides three different implementations of the convolution operator at different numeric precision. Starting from a collation of benchmarks, we build and validate models learned by Decision Tree and naive Bayesian classifier. Preliminary experiments on Midgard-based ARM Mali GPU show that our predictive model outperforms all the convolution operators manually selected by the library.

Despite the impact of psychiatric disorders on clinical health, early-stage diagnosis remains a challenge. Machine learning studies have shown that classifiers tend to be overly narrow in the diagnosis prediction task. The overlap between conditions leads to high heterogeneity among participants that is not adequately captured by classification models. To address this issue, normative approaches have surged as an alternative method. By using a generative model to learn the distribution of healthy brain data patterns, we can identify the presence of pathologies as deviations or outliers from the distribution learned by the model. In particular, deep generative models showed great results as normative models to identify neurological lesions in the brain. However, unlike most neurological lesions, psychiatric disorders present subtle changes widespread in several brain regions, making these alterations challenging to identify. In this work, we evaluate the performance of transformer-based normative models to detect subtle brain changes expressed in adolescents and young adults. We trained our model on 3D MRI scans of neurotypical individuals (N=1,765). Then, we obtained the likelihood of neurotypical controls and psychiatric patients with early-stage schizophrenia from an independent dataset (N=93) from the Human Connectome Project. Using the predicted likelihood of the scans as a proxy for a normative score, we obtained an AUROC of 0.82 when assessing the difference between controls and individuals with early-stage schizophrenia. Our approach surpassed recent normative methods based on brain age and Gaussian Process, showing the promising use of deep generative models to help in individualised analyses.

In deep learning, transfer learning (TL) has become the de facto approach when dealing with image related tasks. Visual features learnt for one task have been shown to be reusable for other tasks, improving performance significantly. By reusing deep representations, TL enables the use of deep models in domains with limited data availability, limited computational resources and/or limited access to human experts. Domains which include the vast majority of real-life applications. This paper conducts an experimental evaluation of TL, exploring its trade-offs with respect to performance, environmental footprint, human hours and computational requirements. Results highlight the cases were a cheap feature extraction approach is preferable, and the situations where an expensive fine-tuning effort may be worth the added cost. Finally, a set of guidelines on the use of TL are proposed.

The task of motion forecasting is critical for self-driving vehicles (SDVs) to be able to plan a safe maneuver. Towards this goal, modern approaches reason about the map, the agents' past trajectories and their interactions in order to produce accurate forecasts. The predominant approach has been to encode the map and other agents in the reference frame of each target agent. However, this approach is computationally expensive for multi-agent prediction as inference needs to be run for each agent. To tackle the scaling challenge, the solution thus far has been to encode all agents and the map in a shared coordinate frame (e.g., the SDV frame). However, this is sample inefficient and vulnerable to domain shift (e.g., when the SDV visits uncommon states). In contrast, in this paper, we propose an efficient shared encoding for all agents and the map without sacrificing accuracy or generalization. Towards this goal, we leverage pair-wise relative positional encodings to represent geometric relationships between the agents and the map elements in a heterogeneous spatial graph. This parameterization allows us to be invariant to scene viewpoint, and save online computation by re-using map embeddings computed offline. Our decoder is also viewpoint agnostic, predicting agent goals on the lane graph to enable diverse and context-aware multimodal prediction. We demonstrate the effectiveness of our approach on the urban Argoverse 2 benchmark as well as a novel highway dataset.