跌倒在不断增加的全球老龄化人口中非常普遍，可能会对他们的健康，福祉和生活质量产生各种负面影响，包括限制他们进行日常生活活动（ADL）的能力，这对于这对于对此至关重要，这对一个人的寄托。跌倒期间的及时协助是非常必要的，这涉及跟踪老年人在与ADL相关的多样化导航模式中的室内位置，以检测跌倒的精确位置。随着全球护理人员人数的减少，重要的是，智能生活环境的未来可以在ADL期间发现下降，同时能够跟踪老年人在现实世界中的室内位置。为了应对这些挑战，这项工作为环境辅助生活系统提出了一种具有成本效益和简单的设计范式，该系统可以在ADL期间捕获用户行为的多模式组成部分，这是在现实世界中同时以现实世界的方式执行秋季检测和室内定位所必需的。 。提出了来自现实世界实验的概念证明，以维护系统的有效工作。还提出了两项​​与先前作品的比较研究的发现，以维护这项工作的新颖性。第一个比较研究表明，在其软件设计和硬件设计的有效性方面，提出的系统在室内定位和跌倒检测领域中如何优于先前的验证领域。第二项比较研究表明，与这些领域的先前作品相比，该系统的开发成本最少，这些领域涉及下划线系统的现实开发，从而维护其具有成本效益的性质。

本文提出了一个多功能的跨学科框架，为个性化的环境辅助生活做出了四项科学贡献，其特定重点是满足智能生活环境未来各种衰老人群的不同和动态需求。首先，它提出了一种基于概率推理的数学方法，以对这些环境中多个用户的用户多样性产生的任何活动建模所有可能的用户交互形式。其次，它提出了一种系统，该系统将这种方法与机器学习方法一起使用，以建模单个用户配置文件和特定用户的用户交互，以检测每个特定用户的动态室内位置。第三，为了满足开发高度准确的室内本地化系统以增加信任，依赖和无缝的用户接受，该框架引入了一种新颖的方法，其中两种增强方法梯度增强和Adaboost算法都集成并用于基于决策树的基于决策树学习模型以执行室内定位。第四，该框架引入了两个新型功能，以在检测每个用户的特定地点的位置以及跟踪特定用户是否位于基于多层室内的特定空间区域内还是外部，以提供室内本地化的语义上下文。环境。这些新型框架的新功能是在与本地化相关的大数据数据集中测试的，这些数据集从18个不同的用户收集的数据集中，这些用户在3个建筑物中导航，该建筑物由5层和254个室内空间区域组成。结果表明，与对普通用户建模的传统方法相比，对每个特定用户建模的个性化AAL的这种室内定位方法始终达到更高的准确性。

The lack of any sender authentication mechanism in place makes CAN (Controller Area Network) vulnerable to security threats. For instance, an attacker can impersonate an ECU (Electronic Control Unit) on the bus and send spoofed messages unobtrusively with the identifier of the impersonated ECU. To address the insecure nature of the system, this thesis demonstrates a sender authentication technique that uses power consumption measurements of the electronic control units (ECUs) and a classification model to determine the transmitting states of the ECUs. The method's evaluation in real-world settings shows that the technique applies in a broad range of operating conditions and achieves good accuracy. A key challenge of machine learning-based security controls is the potential of false positives. A false-positive alert may induce panic in operators, lead to incorrect reactions, and in the long run cause alarm fatigue. For reliable decision-making in such a circumstance, knowing the cause for unusual model behavior is essential. But, the black-box nature of these models makes them uninterpretable. Therefore, another contribution of this thesis explores explanation techniques for inputs of type image and time series that (1) assign weights to individual inputs based on their sensitivity toward the target class, (2) and quantify the variations in the explanation by reconstructing the sensitive regions of the inputs using a generative model. In summary, this thesis (https://uwspace.uwaterloo.ca/handle/10012/18134) presents methods for addressing the security and interpretability in automotive systems, which can also be applied in other settings where safe, transparent, and reliable decision-making is crucial.

Warning: this paper contains content that may be offensive or upsetting. In the current context where online platforms have been effectively weaponized in a variety of geo-political events and social issues, Internet memes make fair content moderation at scale even more difficult. Existing work on meme classification and tracking has focused on black-box methods that do not explicitly consider the semantics of the memes or the context of their creation. In this paper, we pursue a modular and explainable architecture for Internet meme understanding. We design and implement multimodal classification methods that perform example- and prototype-based reasoning over training cases, while leveraging both textual and visual SOTA models to represent the individual cases. We study the relevance of our modular and explainable models in detecting harmful memes on two existing tasks: Hate Speech Detection and Misogyny Classification. We compare the performance between example- and prototype-based methods, and between text, vision, and multimodal models, across different categories of harmfulness (e.g., stereotype and objectification). We devise a user-friendly interface that facilitates the comparative analysis of examples retrieved by all of our models for any given meme, informing the community about the strengths and limitations of these explainable methods.

Search and rescue, wildfire monitoring, and flood/hurricane impact assessment are mission-critical services for recent IoT networks. Communication synchronization, dependability, and minimal communication jitter are major simulation and system issues for the time-based physics-based ROS simulator, event-based network-based wireless simulator, and complex dynamics of mobile and heterogeneous IoT devices deployed in actual environments. Simulating a heterogeneous multi-robot system before deployment is difficult due to synchronizing physics (robotics) and network simulators. Due to its master-based architecture, most TCP/IP-based synchronization middlewares use ROS1. A real-time ROS2 architecture with masterless packet discovery synchronizes robotics and wireless network simulations. A velocity-aware Transmission Control Protocol (TCP) technique for ground and aerial robots using Data Distribution Service (DDS) publish-subscribe transport minimizes packet loss, synchronization, transmission, and communication jitters. Gazebo and NS-3 simulate and test. Simulator-agnostic middleware. LOS/NLOS and TCP/UDP protocols tested our ROS2-based synchronization middleware for packet loss probability and average latency. A thorough ablation research replaced NS-3 with EMANE, a real-time wireless network simulator, and masterless ROS2 with master-based ROS1. Finally, we tested network synchronization and jitter using one aerial drone (Duckiedrone) and two ground vehicles (TurtleBot3 Burger) on different terrains in masterless (ROS2) and master-enabled (ROS1) clusters. Our middleware shows that a large-scale IoT infrastructure with a diverse set of stationary and robotic devices can achieve low-latency communications (12% and 11% reduction in simulation and real) while meeting mission-critical application reliability (10% and 15% packet loss reduction) and high-fidelity requirements.

Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc. In observational studies, de-confounding is a fundamental problem of individualised treatment effects (ITE) estimation. This paper proposes disentangled representations with adversarial training to selectively balance the confounders in the binary treatment setting for the ITE estimation. The adversarial training of treatment policy selectively encourages treatment-agnostic balanced representations for the confounders and helps to estimate the ITE in the observational studies via counterfactual inference. Empirical results on synthetic and real-world datasets, with varying degrees of confounding, prove that our proposed approach improves the state-of-the-art methods in achieving lower error in the ITE estimation.

我们解决了在室内环境中对于具有有限感应功能和有效载荷/功率限制的微型航空车的高效3-D勘探问题。我们开发了一个室内探索框架，该框架利用学习来预测看不见的区域的占用，提取语义特征，样本观点，以预测不同探索目标的信息收益以及计划的信息轨迹，以实现安全和智能的探索。在模拟和实际环境中进行的广泛实验表明，就结构化室内环境中的总路径长度而言，所提出的方法的表现优于最先进的勘探框架，并且在勘探过程中的成功率更高。

批次归一化被广泛用于深度学习以使中间激活归一化。深层网络臭名昭著地增加了训练的复杂性，要​​求仔细的体重初始化，需要较低的学习率等。这些问题已通过批归一化解决（\ textbf {bn}）来解决，通过将激活的输入归功于零平均值和单位标准偏差。使培训过程的批归归量化部分显着加速了非常深网络的训练过程。一个新的研究领域正在进行研究\ textbf {bn}成功背后的确切理论解释。这些理论见解中的大多数试图通过将其对优化，体重量表不变性和正则化的影响来解释\ textbf {bn}的好处。尽管\ textbf {bn}在加速概括方面取得了不可否认的成功，但分析的差距将\ textbf {bn}与正则化参数的效果相关联。本文旨在通过\ textbf {bn}对正则化参数的数据依赖性自动调整，并具有分析证明。我们已将\ textbf {bn}提出为对非 - \ textbf {bn}权重的约束优化，通过该优化，我们通过它演示其数据统计信息依赖于正则化参数的自动调整。我们还为其在嘈杂的输入方案下的行为提供了分析证明，该方案揭示了正则化参数的信号与噪声调整。我们还通过MNIST数据集实验的经验结果证实了我们的主张。

离线手写数学表达识别（HMER）是数学表达识别领域的主要领域。与在线HMER相比，由于缺乏时间信息和写作风格的可变性，离线HMER通常被认为是一个更困难的问题。在本文中，我们目的是使用配对对手学习的编码器模型。语义不变的特征是从手写数学表达图像及其编码器中的印刷数学表达式中提取的。学习语义不变的特征与Densenet编码器和变压器解码器相结合，帮助我们提高了先前研究的表达率。在Crohme数据集上进行了评估，我们已经能够将最新的Crohme 2019测试集结果提高4％。

在此演示论文中，我们设计和原型Rhythmedge是一种低成本，基于深度学习的无接触系统，用于常规的HR监控应用。通过促进无接触性质，实时/离线操作，廉价和可用的传感组件以及计算设备，节奏对现有方法的好处。我们的Rhythmedge系统是可移植的，可以轻松部署，以在中等控制的室内或室外环境中可靠的人力资源估计。 Rhythmedge通过检测面部视频（远程光摄影学； RPPG）的血量变化来测量人力资源，并使用现成的市售资源可限制的边缘平台和摄像机进行即时评估。我们通过将Rhythmedge的可伸缩性，灵活性和兼容性部署到不同的体系结构的三个资源约束平台上（Nvidia Jetson Nano，Google Coral Development Board，Raspberry Pi）和三个异质摄像机，可与不同的体系结构进行部署，并证明了Rhythmedge的可伸缩性和兼容性。摄像头，动作摄像头和DSLR）。 Rhythmedge进一步存储纵向心血管信息，并为用户提供即时通知。我们通过分析其运行时，内存和功率使用情况来彻底测试三个边缘计算平台的原型稳定性，延迟和可行性。