Wearable sensor-based human activity recognition (HAR) has emerged as a principal research area and is utilized in a variety of applications. Recently, deep learning-based methods have achieved significant improvement in the HAR field with the development of human-computer interaction applications. However, they are limited to operating in a local neighborhood in the process of a standard convolution neural network, and correlations between different sensors on body positions are ignored. In addition, they still face significant challenging problems with performance degradation due to large gaps in the distribution of training and test data, and behavioral differences between subjects. In this work, we propose a novel Transformer-based Adversarial learning framework for human activity recognition using wearable sensors via Self-KnowledgE Distillation (TASKED), that accounts for individual sensor orientations and spatial and temporal features. The proposed method is capable of learning cross-domain embedding feature representations from multiple subjects datasets using adversarial learning and the maximum mean discrepancy (MMD) regularization to align the data distribution over multiple domains. In the proposed method, we adopt the teacher-free self-knowledge distillation to improve the stability of the training procedure and the performance of human activity recognition. Experimental results show that TASKED not only outperforms state-of-the-art methods on the four real-world public HAR datasets (alone or combined) but also improves the subject generalization effectively.

In this work a novel recommender system (RS) for Tourism is presented. The RS is context aware as is now the rule in the state-of-the-art for recommender systems and works on top of a tourism ontology which is used to group the different items being offered. The presented RS mixes different types of recommenders creating an ensemble which changes on the basis of the RS's maturity. Starting from simple content-based recommendations and iteratively adding popularity, demographic and collaborative filtering methods as rating density and user cardinality increases. The result is a RS that mutates during its lifetime and uses a tourism ontology and natural language processing (NLP) to correctly bin the items to specific item categories and meta categories in the ontology. This item classification facilitates the association between user preferences and items, as well as allowing to better classify and group the items being offered, which in turn is particularly useful for context-aware filtering.

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.

Bi-encoders and cross-encoders are widely used in many state-of-the-art retrieval pipelines. In this work we study the generalization ability of these two types of architectures on a wide range of parameter count on both in-domain and out-of-domain scenarios. We find that the number of parameters and early query-document interactions of cross-encoders play a significant role in the generalization ability of retrieval models. Our experiments show that increasing model size results in marginal gains on in-domain test sets, but much larger gains in new domains never seen during fine-tuning. Furthermore, we show that cross-encoders largely outperform bi-encoders of similar size in several tasks. In the BEIR benchmark, our largest cross-encoder surpasses a state-of-the-art bi-encoder by more than 4 average points. Finally, we show that using bi-encoders as first-stage retrievers provides no gains in comparison to a simpler retriever such as BM25 on out-of-domain tasks. The code is available at https://github.com/guilhermemr04/scaling-zero-shot-retrieval.git

Graph learning problems are typically approached by focusing on learning the topology of a single graph when signals from all nodes are available. However, many contemporary setups involve multiple related networks and, moreover, it is often the case that only a subset of nodes is observed while the rest remain hidden. Motivated by this, we propose a joint graph learning method that takes into account the presence of hidden (latent) variables. Intuitively, the presence of the hidden nodes renders the inference task ill-posed and challenging to solve, so we overcome this detrimental influence by harnessing the similarity of the estimated graphs. To that end, we assume that the observed signals are drawn from a Gaussian Markov random field with latent variables and we carefully model the graph similarity among hidden (latent) nodes. Then, we exploit the structure resulting from the previous considerations to propose a convex optimization problem that solves the joint graph learning task by providing a regularized maximum likelihood estimator. Finally, we compare the proposed algorithm with different baselines and evaluate its performance over synthetic and real-world graphs.

Robust 2004是一种信息检索基准，其每个查询的大量判断使其成为可靠的评估数据集。在本文中，我们介绍了Mrobust04，这是一种多语言版本的robust04，使用Google Translate翻译为8种语言。我们还提供了该数据集上三个不同多语言检索器的结果。该数据集可在https://huggingface.co/datasets/unicamp-dl/mrobust上获得

序数模式的统计分析的最终目的是表征它们诱导的特征的分布。特别是，了解大类时间序列模型的对熵统计复杂性的联合分布将允许迄今无法获得的统计测试。在这个方向上工作，我们表征了Shannon经验的渐进分布，用于任何模型，在此模型中，真正的归一化熵既不为零也不为零。我们从中心极限定理（假设大时间序列），多元增量方法和其平均值的三阶校正获得了渐近分布。我们讨论了其他结果（精确，一阶和二阶校正）有关其准确性和数值稳定性的适用性。在建立有关香农熵的测试统计数据的一般框架内，我们提出了双边测试，该测试验证是否有足够的证据拒绝以下假设，即两个信号产生了具有相同Shannon熵的顺序模式。我们将此双边测试应用于来自三个城市（都柏林，爱丁堡和迈阿密）的每日最高温度时间序列，并获得了明智的结果。

在线作业问题在运营研究和计算机科学中起着重要作用，这就是为什么要引起了提高其解决方案质量的极大关注的原因。由于有关输入的不完整信息，在线算法很难产生最佳解决方案。使用竞争比率测量在线算法的解决方案的质量。没有在线确定性算法可以比（2N-1）更好地实现竞争比率。已经表明，在线计算中的建议改善了在线问题的竞争比率的下限。在线计算中的建议可以解释为在线算法的其他信息，以补偿缺乏有关整个输入序列的信息。在这项研究中，我们研究了引入机器学习建议如何改善此问题的竞争比率。通过模拟机器学习算法，我们为在线分配问题提供了在线算法，该算法预先预测了整个输入。我们利用一种最佳离线算法来提供预测输入的匹配解决方案。此外，我们研究了机器学习的预测错误如何影响在线算法的竞争比率。我们利用基准数据集来执行我们的经验分析。我们表明，随着机器学习预测误差的增加，解决方案质量会降低。此外，误差的大小与输入的大小成正比。该结果类似于在线分配问题最佳确定性算法的竞争比率，该算法也取决于参数n。

使用机器学习算法从未标记的文本中提取知识可能很复杂。文档分类和信息检索是两个应用程序，可以从无监督的学习（例如文本聚类和主题建模）中受益，包括探索性数据分析。但是，无监督的学习范式提出了可重复性问题。初始化可能会导致可变性，具体取决于机器学习算法。此外，关于群集几何形状，扭曲可能会产生误导。在原因中，异常值和异常的存在可能是决定因素。尽管初始化和异常问题与文本群集和主题建模相关，但作者并未找到对它们的深入分析。这项调查提供了这些亚地区的系统文献综述（2011-2022），并提出了共同的术语，因为类似的程序具有不同的术语。作者描述了研究机会，趋势和开放问题。附录总结了与审查的作品直接或间接相关的文本矢量化，分解和聚类算法的理论背景。

现代的3D计算机视觉利用学习来增强几何推理，将图像数据映射到经典结构，例如成本量或外观限制，以改善匹配。这些体系结构根据特定问题进行了专门化，因此需要进行大量任务的调整，通常会导致域的泛化性能差。最近，通才变压器架构通过编码几何学先验作为输入而不是执行约束，在诸如光流和深度估计等任务中取得了令人印象深刻的结果。在本文中，我们扩展了这一想法，并建议学习一个隐式，多视图一致的场景表示，并在增加视图多样性之前引入了一系列3D数据增强技术作为几何感应。我们还表明，引入视图合成作为辅助任务进一步改善了深度估计。我们的深度磁场网络（定义）实现了最新的目的，可以实现立体声和视频深度估计，而无需明确的几何约束，并通过广泛的边距改善了零局部域的概括。