This book provides a solution to the control and motion planning design for an octocopter system. It includes a particular choice of control and motion planning algorithms which is based on the authors' previous research work, so it can be used as a reference design guidance for students, researchers as well as autonomous vehicles hobbyists. The control is constructed based on a fault tolerant approach aiming to increase the chances of the system to detect and isolate a potential failure in order to produce feasible control signals to the remaining active motors. The used motion planning algorithm is risk-aware by means that it takes into account the constraints related to the fault-dependant and mission-related maneuverability analysis of the octocopter system during the planning stage. Such a planner generates only those reference trajectories along which the octocopter system would be safe and capable of good tracking in case of a single motor fault and of majority of double motor fault scenarios. The control and motion planning algorithms presented in the book aim to increase the overall reliability of the system for completing the mission.

In this work, we introduce a hypergraph representation learning framework called Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a set of hyperedge-dependent embeddings for each node in the hypergraph. HNN derives multiple embeddings per node in the hypergraph where each embedding for a node is dependent on a specific hyperedge of that node. Notably, HNN is accurate, data-efficient, flexible with many interchangeable components, and useful for a wide range of hypergraph learning tasks. We evaluate the effectiveness of the HNN framework for hyperedge prediction and hypergraph node classification. We find that HNN achieves an overall mean gain of 7.72% and 11.37% across all baseline models and graphs for hyperedge prediction and hypergraph node classification, respectively.

Learning fair graph representations for downstream applications is becoming increasingly important, but existing work has mostly focused on improving fairness at the global level by either modifying the graph structure or objective function without taking into account the local neighborhood of a node. In this work, we formally introduce the notion of neighborhood fairness and develop a computational framework for learning such locally fair embeddings. We argue that the notion of neighborhood fairness is more appropriate since GNN-based models operate at the local neighborhood level of a node. Our neighborhood fairness framework has two main components that are flexible for learning fair graph representations from arbitrary data: the first aims to construct fair neighborhoods for any arbitrary node in a graph and the second enables adaption of these fair neighborhoods to better capture certain application or data-dependent constraints, such as allowing neighborhoods to be more biased towards certain attributes or neighbors in the graph.Furthermore, while link prediction has been extensively studied, we are the first to investigate the graph representation learning task of fair link classification. We demonstrate the effectiveness of the proposed neighborhood fairness framework for a variety of graph machine learning tasks including fair link prediction, link classification, and learning fair graph embeddings. Notably, our approach achieves not only better fairness but also increases the accuracy in the majority of cases across a wide variety of graphs, problem settings, and metrics.

Breast cancer is the second most common type of cancer in women in Canada and the United States, representing over 25% of all new female cancer cases. Neoadjuvant chemotherapy treatment has recently risen in usage as it may result in a patient having a pathologic complete response (pCR), and it can shrink inoperable breast cancer tumors prior to surgery so that the tumor becomes operable, but it is difficult to predict a patient's pathologic response to neoadjuvant chemotherapy. In this paper, we investigate the efficacy of leveraging learnt volumetric deep features from a newly introduced magnetic resonance imaging (MRI) modality called synthetic correlated diffusion imaging (CDI$^s$) for the purpose of pCR prediction. More specifically, we leverage a volumetric convolutional neural network to learn volumetric deep radiomic features from a pre-treatment cohort and construct a predictor based on the learnt features using the post-treatment response. As the first study to explore the utility of CDI$^s$ within a deep learning perspective for clinical decision support, we evaluated the proposed approach using the ACRIN-6698 study against those learnt using gold-standard imaging modalities, and found that the proposed approach can provide enhanced pCR prediction performance and thus may be a useful tool to aid oncologists in improving recommendation of treatment of patients. Subsequently, this approach to leverage volumetric deep radiomic features (which we name Cancer-Net BCa) can be further extended to other applications of CDI$^s$ in the cancer domain to further improve prediction performance.

基于方面的情感分析（ABSA）是一个自然语言处理问题，需要分析用户生成的评论以确定：a）审查的目标实体，b）其所属的高级方面，c）对目标和方面表达的情绪。 ABSA的许多但分散的语料库使研究人员很难快速识别最适合特定ABSA子任务的Corpora。这项研究旨在介绍一个可用于培训和评估自动级ABSA系统的语料库数据库。此外，我们还概述了有关各种ABSA及其子任务的主要语料库，并突出了研究人员在选择语料库时应考虑的几个语料库功能。我们得出结论，需要进一步的大规模ABSA语料库。此外，由于每个语料库的构建方式都不同，因此研究人员在许多语料库上尝试一种新颖的ABSA算法，并且通常只采用一个或几个语料库，这是耗时的。该领域将从ABSA CORPORA的数据标准协议中受益。最后，我们讨论当前收集方法的优势和缺点，并为将来的ABSA数据集收集提出建议。

在本文中，我们介绍了对非对称确定点处理（NDPP）的在线和流媒体地图推断和学习问题，其中数据点以任意顺序到达，并且算法被约束以使用单次通过数据以及子线性存储器。在线设置有额外要求在任何时间点维护有效的解决方案。为了解决这些新问题，我们提出了具有理论担保的算法，在几个真实的数据集中评估它们，并显示它们对最先进的离线算法提供了可比的性能，该算法将整个数据存储在内存中并采取多次传递超过它。

Graphs的许多模型属于边缘无关的点产品型号的框架。这些模型输出所有节点之间存在的边缘的概率，并且两个节点之间的链路的概率随与节点相关联的矢量的点乘积而增加。最近的工作表明，这些模型无法捕获实际图中的关键结构，特别是异种结构，其中在不同节点之间发生链接。我们提出了一种独立的图形生成模型，它足以捕捉到异源性，B）产生非负嵌入物，这允许在社区方面解释的链接预测，C）有效地在具有梯度的真实图中优化跨熵损失下降。我们的理论结果展示了我们模型的表现力，其能够使用最大程度的线性的多个簇进行准确地重建图表，以及其在数据中捕获异常和精梳性的能力。此外，我们的实验展示了我们模型对多种重要应用任务等多个重要应用程序任务的有效性，例如多标签聚类和链路预测。

In security-sensitive applications, the success of machine learning depends on a thorough vetting of their resistance to adversarial data. In one pertinent, well-motivated attack scenario, an adversary may attempt to evade a deployed system at test time by carefully manipulating attack samples. In this work, we present a simple but effective gradientbased approach that can be exploited to systematically assess the security of several, widely-used classification algorithms against evasion attacks. Following a recently proposed framework for security evaluation, we simulate attack scenarios that exhibit different risk levels for the classifier by increasing the attacker's knowledge of the system and her ability to manipulate attack samples. This gives the classifier designer a better picture of the classifier performance under evasion attacks, and allows him to perform a more informed model selection (or parameter setting). We evaluate our approach on the relevant security task of malware detection in PDF files, and show that such systems can be easily evaded. We also sketch some countermeasures suggested by our analysis.