人类在需要快速传达对象信息的游戏中显示出高级的抽象功能。他们将消息内容分解为多个部分，并以可解释的协议将它们传达。为了为机器提供这种功能，我们提出了基于原始的草图抽象任务，其目标是在预算影响下使用一组固定的绘图原始图表示草图。为了解决这项任务，我们的原始匹配网络（PMN）以自我监督的方式学习了草图的可解释抽象。具体而言，PMN将草图的每个笔划都映射到给定集中最相似的原始性，预测了仿射转换将所选原始词与目标冲程对齐的仿射转换。我们学习了端到端的这一笔触至关重要的映射，当原始草图精确地用预测的原语重建时，距离转换损失是最小的。我们的PMN抽象在经验上取得了素描识别和基于草图的图像检索的最高性能，同时也是高度可解释的。这为草图分析打开了新的可能性，例如通过提取定义对象类别的最相关的原始图来比较草图。代码可在https://github.com/explainableml/sketch-primitives上找到。

这项工作使用来自建设性模拟的可靠数据比较了监督的机器学习方法，以估算空袭期间发射导弹的最有效时刻。我们采用了重采样技术来改善预测模型，分析准确性，精度，召回和F1得分。的确，我们可以根据决策树以及其他算法对重采样技术的显着敏感性来确定模型的显着性能。最佳F1分数的模型的值分别为0.379和0.465，而没有重新采样技术，这一值分别增加了22.69％。因此，如果理想，重新采样技术可以改善模型的召回率和F1得分，而准确性和精确度略有下降。因此，通过通过建设性模拟获得的数据，可以根据机器学习模型开发决策支持工具，从而可以提高BVR空中战斗的飞行质量，从而提高进攻任务的有效性以达到特定目标。

通常，基于生物谱系的控制系统可能不依赖于各个预期行为或合作适当运行。相反，这种系统应该了解未经授权的访问尝试的恶意程序。文献中提供的一些作品建议通过步态识别方法来解决问题。这些方法旨在通过内在的可察觉功能来识别人类，尽管穿着衣服或配件。虽然该问题表示相对长时间的挑战，但是为处理问题的大多数技术存在与特征提取和低分类率相关的几个缺点，以及其他问题。然而，最近的深度学习方法是一种强大的一组工具，可以处理几乎任何图像和计算机视觉相关问题，为步态识别提供最重要的结果。因此，这项工作提供了通过步态认可的关于生物识别检测的最近作品的调查汇编，重点是深入学习方法，强调他们的益处，暴露出弱点。此外，它还呈现用于解决相关约束的数据集，方法和体系结构的分类和表征描述。

这项工作调查了使用深神经网络（DNN）来执行武器接触区域（WEZ）最大发射范围的估计。韦茨允许飞行员识别空域，其中可用导弹具有更大的成功参与特定目标的概率，即围绕着对手易受射击群体的飞机的假设区域。我们提出了一种方法来确定使用50,000个变化条件下的模拟发射的给定导弹的韦茨。这些模拟用于训练当飞机在不同的烧制条件下发现自身时，可以预测韦茨的DNN，其测定系数为0.99。它提供了另一种关于前面研究的程序，因为它采用了非离散化模型，即，它立即考虑了WEZ的所有方向，以前尚未完成。此外，所提出的方法使用实验设计，允许较少的模拟运行，提供更快的模型训练。

这项工作旨在在防御柜台（DCA）任务的背景下提供超出视觉范围（BVR）空战的参与决策支持工具。在BVR AIR作战中，接合判决是指通过假设令人反感的姿态和执行相应的演示来选择导频的时刻。为了模拟这一决定，我们使用巴西空军航空航天仿真环境（\ {Ambiente de Simula \ C {C} \〜a \〜a \〜ao ao aeroispacial - Asa}在葡萄牙语中，它产生了3,729个建设性模拟，每个建设性模拟持续12分钟，总共10,316场比赛。我们通过称为DCA指数的操作性标准分析了所有样本，这些标准基于主题专家的经验，这类使命的成功程度代表。该公制考虑了同一团队和对方团队的飞机的距离，对抗空气巡逻的点以及所使用的导弹数。通过在整个参与过程中开始和DCA指数的平均值之前定义参与状态，我们创建了一个监督的学习模型，以确定新的参与的质量。一种基于决策树的算法，与XGBoost库一起使用，提供了一种回归模型，以预测具有接近0.8的确定系数的DCA索引和0.05的根均方误差，可以为BVR飞行员提供参数以决定是否或不要搞。因此，使用通过仿真获得的数据，这项工作通过基于BVR Air战斗的机器学习构建决策支持系统而有贡献。

Numerous works use word embedding-based metrics to quantify societal biases and stereotypes in texts. Recent studies have found that word embeddings can capture semantic similarity but may be affected by word frequency. In this work we study the effect of frequency when measuring female vs. male gender bias with word embedding-based bias quantification methods. We find that Skip-gram with negative sampling and GloVe tend to detect male bias in high frequency words, while GloVe tends to return female bias in low frequency words. We show these behaviors still exist when words are randomly shuffled. This proves that the frequency-based effect observed in unshuffled corpora stems from properties of the metric rather than from word associations. The effect is spurious and problematic since bias metrics should depend exclusively on word co-occurrences and not individual word frequencies. Finally, we compare these results with the ones obtained with an alternative metric based on Pointwise Mutual Information. We find that this metric does not show a clear dependence on frequency, even though it is slightly skewed towards male bias across all frequencies.

This report summarizes the work carried out by the authors during the Twelfth Montreal Industrial Problem Solving Workshop, held at Universit\'e de Montr\'eal in August 2022. The team tackled a problem submitted by CBC/Radio-Canada on the theme of Automatic Text Simplification (ATS).

Feature acquisition algorithms address the problem of acquiring informative features while balancing the costs of acquisition to improve the learning performances of ML models. Previous approaches have focused on calculating the expected utility values of features to determine the acquisition sequences. Other approaches formulated the problem as a Markov Decision Process (MDP) and applied reinforcement learning based algorithms. In comparison to previous approaches, we focus on 1) formulating the feature acquisition problem as a MDP and applying Monte Carlo Tree Search, 2) calculating the intermediary rewards for each acquisition step based on model improvements and acquisition costs and 3) simultaneously optimizing model improvement and acquisition costs with multi-objective Monte Carlo Tree Search. With Proximal Policy Optimization and Deep Q-Network algorithms as benchmark, we show the effectiveness of our proposed approach with experimental study.

360-degree panoramic videos have gained considerable attention in recent years due to the rapid development of head-mounted displays (HMDs) and panoramic cameras. One major problem in streaming panoramic videos is that panoramic videos are much larger in size compared to traditional ones. Moreover, the user devices are often in a wireless environment, with limited battery, computation power, and bandwidth. To reduce resource consumption, researchers have proposed ways to predict the users' viewports so that only part of the entire video needs to be transmitted from the server. However, the robustness of such prediction approaches has been overlooked in the literature: it is usually assumed that only a few models, pre-trained on past users' experiences, are applied for prediction to all users. We observe that those pre-trained models can perform poorly for some users because they might have drastically different behaviors from the majority, and the pre-trained models cannot capture the features in unseen videos. In this work, we propose a novel meta learning based viewport prediction paradigm to alleviate the worst prediction performance and ensure the robustness of viewport prediction. This paradigm uses two machine learning models, where the first model predicts the viewing direction, and the second model predicts the minimum video prefetch size that can include the actual viewport. We first train two meta models so that they are sensitive to new training data, and then quickly adapt them to users while they are watching the videos. Evaluation results reveal that the meta models can adapt quickly to each user, and can significantly increase the prediction accuracy, especially for the worst-performing predictions.

This paper presents a corpus annotated for the task of direct-speech extraction in Croatian. The paper focuses on the annotation of the quotation, co-reference resolution, and sentiment annotation in SETimes news corpus in Croatian and on the analysis of its language-specific differences compared to English. From this, a list of the phenomena that require special attention when performing these annotations is derived. The generated corpus with quotation features annotations can be used for multiple tasks in the field of Natural Language Processing.