增加分类变量的基数可能会降低ML算法的整体性能。本文介绍了一种新颖的计算预处理方法，用于转换为机器学习（ML）算法的数值变量的分类。在此方法中，我们选择并将三个分类特征转换为数值特征。首先，我们根据变量中类别的分布选择阈值参数。然后，我们使用条件概率将每个分类变量转换为两个新的数字变量，总共产生六个新的数变量。之后，我们将这六个数值送入到主成分分析（PCA）算法。接下来，我们选择主组件（PCS）的整个或部分数量。最后，通过使用十种不同的分类器应用二进制分类，我们测量了新编码器的性能，并将其与其他17个众所周知的类别编码器进行比较。所提出的技术实现了使用众所周知的网络安全NSLKDD DataSet对高基数分类变量下的曲线（AUC）下的最高性能。此外，我们定义了谐波平均指标，在火车和测试性能之间找到最佳权衡，并防止磨损和过度装备。最终，新创建的数字变量的数量很少。因此，该数据减少改善了计算处理时间，这可能减少5G未来电信网络中的处理数据。

Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles by improving coordination and removing the barrier of non-line-of-sight sensing. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system, which can suffer from loss of information due to the inherent issues of their different components, such as sensors failures or the poor performance of V2X technologies under dense communication channel load. Particularly, information loss affects the target classification module and, subsequently, the safety application performance. To enable reliable and robust CVS systems that mitigate the effect of information loss, we proposed a Context-Aware Target Classification (CA-TC) module coupled with a hybrid learning-based predictive modeling technique for CVS systems. The CA-TC consists of two modules: A Context-Aware Map (CAM), and a Hybrid Gaussian Process (HGP) prediction system. Consequently, the vehicle safety applications use the information from the CA-TC, making them more robust and reliable. The CAM leverages vehicles path history, road geometry, tracking, and prediction; and the HGP is utilized to provide accurate vehicles' trajectory predictions to compensate for data loss (due to communication congestion) or sensor measurements' inaccuracies. Based on offline real-world data, we learn a finite bank of driver models that represent the joint dynamics of the vehicle and the drivers' behavior. We combine offline training and online model updates with on-the-fly forecasting to account for new possible driver behaviors. Finally, our framework is validated using simulation and realistic driving scenarios to confirm its potential in enhancing the robustness and reliability of CVS systems.

Are extralinguistic signals such as image pixels crucial for inducing constituency grammars? While past work has shown substantial gains from multimodal cues, we investigate whether such gains persist in the presence of rich information from large language models (LLMs). We find that our approach, LLM-based C-PCFG (LC-PCFG), outperforms previous multi-modal methods on the task of unsupervised constituency parsing, achieving state-of-the-art performance on a variety of datasets. Moreover, LC-PCFG results in an over 50% reduction in parameter count, and speedups in training time of 1.7x for image-aided models and more than 5x for video-aided models, respectively. These results challenge the notion that extralinguistic signals such as image pixels are needed for unsupervised grammar induction, and point to the need for better text-only baselines in evaluating the need of multi-modality for the task.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Text classification is a natural language processing (NLP) task relevant to many commercial applications, like e-commerce and customer service. Naturally, classifying such excerpts accurately often represents a challenge, due to intrinsic language aspects, like irony and nuance. To accomplish this task, one must provide a robust numerical representation for documents, a process known as embedding. Embedding represents a key NLP field nowadays, having faced a significant advance in the last decade, especially after the introduction of the word-to-vector concept and the popularization of Deep Learning models for solving NLP tasks, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based Language Models (TLMs). Despite the impressive achievements in this field, the literature coverage regarding generating embeddings for Brazilian Portuguese texts is scarce, especially when considering commercial user reviews. Therefore, this work aims to provide a comprehensive experimental study of embedding approaches targeting a binary sentiment classification of user reviews in Brazilian Portuguese. This study includes from classical (Bag-of-Words) to state-of-the-art (Transformer-based) NLP models. The methods are evaluated with five open-source databases with pre-defined data partitions made available in an open digital repository to encourage reproducibility. The Fine-tuned TLMs achieved the best results for all cases, being followed by the Feature-based TLM, LSTM, and CNN, with alternate ranks, depending on the database under analysis.

Despite being responsible for state-of-the-art results in several computer vision and natural language processing tasks, neural networks have faced harsh criticism due to some of their current shortcomings. One of them is that neural networks are correlation machines prone to model biases within the data instead of focusing on actual useful causal relationships. This problem is particularly serious in application domains affected by aspects such as race, gender, and age. To prevent models from incurring on unfair decision-making, the AI community has concentrated efforts in correcting algorithmic biases, giving rise to the research area now widely known as fairness in AI. In this survey paper, we provide an in-depth overview of the main debiasing methods for fairness-aware neural networks in the context of vision and language research. We propose a novel taxonomy to better organize the literature on debiasing methods for fairness, and we discuss the current challenges, trends, and important future work directions for the interested researcher and practitioner.

Chronic pain is a multi-dimensional experience, and pain intensity plays an important part, impacting the patients emotional balance, psychology, and behaviour. Standard self-reporting tools, such as the Visual Analogue Scale for pain, fail to capture this burden. Moreover, this type of tools is susceptible to a degree of subjectivity, dependent on the patients clear understanding of how to use it, social biases, and their ability to translate a complex experience to a scale. To overcome these and other self-reporting challenges, pain intensity estimation has been previously studied based on facial expressions, electroencephalograms, brain imaging, and autonomic features. However, to the best of our knowledge, it has never been attempted to base this estimation on the patient narratives of the personal experience of chronic pain, which is what we propose in this work. Indeed, in the clinical assessment and management of chronic pain, verbal communication is essential to convey information to physicians that would otherwise not be easily accessible through standard reporting tools, since language, sociocultural, and psychosocial variables are intertwined. We show that language features from patient narratives indeed convey information relevant for pain intensity estimation, and that our computational models can take advantage of that. Specifically, our results show that patients with mild pain focus more on the use of verbs, whilst moderate and severe pain patients focus on adverbs, and nouns and adjectives, respectively, and that these differences allow for the distinction between these three pain classes.

视觉变压器体系结构已显示在计算机视觉（CV）空间中具有竞争力，在该空间中，它在几个基准测试中剥夺了基于卷积的网络。然而，卷积神经网络（CNN）仍然是强化学习中表示模块的优先体系结构。在这项工作中，我们使用几种最先进的自我监督方法研究了视觉变压器预处理，并评估了该培训框架中的数据效率收益。我们提出了一种称为TOV-VICREG的新的自我监督的学习方法，该方法通过添加时间订单验证任务来扩展Vicreg，以更好地捕获观测值之间的时间关系。此外，我们在样本效率方面通过Atari游戏评估了所得编码器。我们的结果表明，当通过TOV-VICREG进行预估计时，视觉变压器的表现优于其他自我监督的方法，但仍在努力克服CNN。尽管如此，我们在十场比赛中的两场比赛中，我们能够胜过CNN，在我们执行100k台阶评估中。最终，我们认为，深入强化学习（DRL）中的这种方法可能是实现自然语言处理和计算机视觉中所见的新表现的关键。源代码将提供：https：//github.com/mgoulao/tov-vicreg

贝叶斯优化（BO）算法在涉及昂贵的黑盒功能的应用中表现出了显着的成功。传统上，BO被设置为一个顺序决策过程，该过程通过采集函数和先前的功能（例如高斯过程）来估计查询点的实用性。然而，最近，通过密度比率估计（BORE）对BO进行重新制定允许将采集函数重新诠释为概率二进制分类器，从而消除了对函数的显式先验和提高可伸缩性的需求。在本文中，我们介绍了对孔的遗憾和算法扩展的理论分析，并提高了不确定性估计。我们还表明，通过将问题重新提交为近似贝叶斯推断，可以自然地扩展到批处理优化设置。所得算法配备了理论性能保证，并在一系列实验中对其他批处理基本线进行了评估。

神经网络（NNS）的重要性和复杂性正在增长。神经网络的性能（和能源效率）可以通过计算或内存资源约束。在内存阵列附近或内部放置计算的内存处理（PIM）范式是加速内存绑定的NNS的可行解决方案。但是，PIM体系结构的形式各不相同，其中不同的PIM方法导致不同的权衡。我们的目标是分析基于NN的性能和能源效率的基于DRAM的PIM架构。为此，我们分析了三个最先进的PIM架构：（1）UPMEM，将处理器和DRAM阵列集成到一个2D芯片中； （2）Mensa，是针对边缘设备量身定制的基于3D堆栈的PIM架构； （3）Simdram，它使用DRAM的模拟原理来执行位序列操作。我们的分析表明，PIM极大地受益于内存的NNS：（1）UPMEM在GPU需要内存过度按要求的通用矩阵 - 矢量乘数内核时提供23x高端GPU的性能； （2）Mensa在Google Edge TPU上提高了3.0倍和3.1倍的能源效率和吞吐量，用于24个Google Edge NN型号； （3）SIMDRAM在三个二进制NNS中以16.7倍/1.4倍的速度优于CPU/GPU。我们得出的结论是，由于固有的建筑设计选择，NN模型的理想PIM体系结构取决于模型的独特属性。