资源分配是规划建筑项目的最关键问题之一，因为它对成本，时间和质量的直接影响。根据项目目标，通常有特定的分配方法用于自动资源管理。但是，在整个建筑组织中利用资源的综合计划和优化是稀缺的。这项研究的目的是为建筑公司提供自动资源分配结构，以深入强化学习（DRL），可在各种情况下使用。在这种结构中，数据收集（DH）收集了分布式物联网（IoT）传感器设备的资源信息，这些传感器设备将在自主资源管理方法中采用的各个公司项目中。然后，将覆盖资源分配（CRA）与从DH获得的信息进行比较，其中自动资源管理（ARM）确定了感兴趣的项目。同样，具有类似模型的双重Q-NETWORKS（DDQN）在基于公司的结构化资源信息的两种不同的分配情况下进行了培训，以平衡目标与资源约束。本文中建议的技术可以通过将投资组合信息与采用的单个项目信息相结合来有效地适应大型资源管理系统。此外，详细分析了重要信息处理参数对资源分配绩效的影响。此外，提出了管理方法的普遍性结果，这表明当情况变量发生变化时，不需要额外的培训。

这项研究提出了一个可靠的模型，用于识别具有最高精度的不同建筑材料，该模型被利用为用于广泛的施工应用（例如自动进度监控）的有利工具。在这项研究中，一种称为视觉变压器（VIT）的新型深度学习结构用于检测和分类建筑材料。使用不同的图像数据集评估了所采用方法的鲁棒性。为此，对模型进行了训练和测试，并在两个大型不平衡数据集上进行了测试，即建筑材料库（CML）和建筑材料数据集（BMD）。还通过组合CML和BMD来创建更不平衡的数据集并评估使用方法的功能来生成第三个数据集。所达到的结果揭示了评估指标的精度为100％，例如三个不同数据集的每个材料类别的准确性，精度，召回率和F1得分。据信，建议的模型实现了用于检测和分类不同材料类型的强大工具。迄今为止，许多研究试图自动对仍然存在一些错误的各种建筑材料进行分类。这项研究将解决上述缺点，并提出一个模型以更高的精度检测材料类型。所采用的模型也能够被推广到不同的数据集。

Minimising the longest travel distance for a group of mobile robots with interchangeable goals requires knowledge of the shortest length paths between all robots and goal destinations. Determining the exact length of the shortest paths in an environment with obstacles is challenging and cannot be guaranteed in a finite time. We propose an algorithm in which the accuracy of the path planning is iteratively increased. The approach provides a certificate when the uncertainties on estimates of the shortest paths become small enough to guarantee the optimality of the goal assignment. To this end, we apply results from assignment sensitivity assuming upper and lower bounds on the length of the shortest paths. We then provide polynomial-time methods to find such bounds by applying sampling-based path planning. The upper bounds are given by feasible paths, the lower bounds are obtained by expanding the sample set and leveraging knowledge of the sample dispersion. We demonstrate the application of the proposed method with a multi-robot path-planning case study.

Large language models show improved downstream task performance when prompted to generate step-by-step reasoning to justify their final answers. These reasoning steps greatly improve model interpretability and verification, but objectively studying their correctness (independent of the final answer) is difficult without reliable methods for automatic evaluation. We simply do not know how often the stated reasoning steps actually support the final end task predictions. In this work, we present ROSCOE, a suite of interpretable, unsupervised automatic scores that improve and extend previous text generation evaluation metrics. To evaluate ROSCOE against baseline metrics, we design a typology of reasoning errors and collect synthetic and human evaluation scores on commonly used reasoning datasets. In contrast with existing metrics, ROSCOE can measure semantic consistency, logicality, informativeness, fluency, and factuality - among other traits - by leveraging properties of step-by-step rationales. We empirically verify the strength of our metrics on five human annotated and six programmatically perturbed diagnostics datasets - covering a diverse set of tasks that require reasoning skills and show that ROSCOE can consistently outperform baseline metrics.

Machine Learning (ML) technologies have been increasingly adopted in Medical Cyber-Physical Systems (MCPS) to enable smart healthcare. Assuring the safety and effectiveness of learning-enabled MCPS is challenging, as such systems must account for diverse patient profiles and physiological dynamics and handle operational uncertainties. In this paper, we develop a safety assurance case for ML controllers in learning-enabled MCPS, with an emphasis on establishing confidence in the ML-based predictions. We present the safety assurance case in detail for Artificial Pancreas Systems (APS) as a representative application of learning-enabled MCPS, and provide a detailed analysis by implementing a deep neural network for the prediction in APS. We check the sufficiency of the ML data and analyze the correctness of the ML-based prediction using formal verification. Finally, we outline open research problems based on our experience in this paper.

Fairness-aware mining of massive data streams is a growing and challenging concern in the contemporary domain of machine learning. Many stream learning algorithms are used to replace humans at critical decision-making points e.g., hiring staff, assessing credit risk, etc. This calls for handling massive incoming information with minimum response delay while ensuring fair and high quality decisions. Recent discrimination-aware learning methods are optimized based on overall accuracy. However, the overall accuracy is biased in favor of the majority class; therefore, state-of-the-art methods mainly diminish discrimination by partially or completely ignoring the minority class. In this context, we propose a novel adaptation of Na\"ive Bayes to mitigate discrimination embedded in the streams while maintaining high predictive performance for both the majority and minority classes. Our proposed algorithm is simple, fast, and attains multi-objective optimization goals. To handle class imbalance and concept drifts, a dynamic instance weighting module is proposed, which gives more importance to recent instances and less importance to obsolete instances based on their membership in minority or majority class. We conducted experiments on a range of streaming and static datasets and deduced that our proposed methodology outperforms existing state-of-the-art fairness-aware methods in terms of both discrimination score and balanced accuracy.

神经科学方面的巨大努力正在努力绘制许多新物种的连接群，包括果蝇果蝇的接近完成。重要的是要问这些模型是否可以使人工智能受益。在这项工作中，我们提出了两个基本问题：（1）生物连接组可以在机器学习中提供的何处以及何时提供使用，（2）哪些设计原理对于提取连接组的良好表示是必要的。为此，我们将秀丽隐杆线虫线虫的运动电路转化为以不同水平的生物物理现实主义水平的人工神经网络，并评估了这些网络在运动和非运动行为任务上训练这些网络的结果。我们证明，生物物理现实主义不必维持使用生物回路的优势。我们还确定，即使没有保留确切的接线图，建筑统计数据也提供了有价值的先验。最后，我们表明，虽然秀丽隐杆线虫运动电路对运动问题提供了强大的感应偏见，但其结构可能会阻碍与运动无关的任务（例如视觉分类问题）。

尽管在时间序列重建的深度学习方法中取得了长足的进步，但由于其对优化损失的贡献可忽略不计，因此没有设计现有的方法来揭示具有微小信号强度的本地活动。但是，这种局部活动可以表示生理系统中重要的异常事件，例如额外的焦点触发心脏电波异常的传播。我们讨论了一种重建这种本地活动的新技术，尽管信号强度很小，但它是随后具有较大信号强度的全球活动的原因。我们的中心创新是通过明确建模并解开系统潜在的潜在隐藏内部干预措施的影响来解决此问题。在状态空间模型（SSM）的新型神经公式中，我们首先通过分别描述的相互作用的神经ODES系统引入潜在动力学的因果效应建模1）内部干预的连续时间动力学； 2）它对系统本地状态轨迹的影响。因为不能直接观察干预措施，而必须与观察到的后续效果脱离，所以我们整合了对系统的无天然干预动态的知识，并通过假设它是对实际观察到的差异来推断隐藏干预措施的推断和假设的无干预动态。我们证明了对重建异位焦点的提出框架的概念证明，从而破坏了从远程观察到正常心脏电气传播的过程。

基于深度学习的图生成方法具有显着的图形数据建模能力，从而使它们能够解决广泛的现实世界问题。使这些方法能够在生成过程中考虑不同的条件，甚至通过授权它们生成满足所需标准的新图形样本来提高其有效性。本文提出了一种条件深图生成方法，称为SCGG，该方法考虑了特定类型的结构条件。具体而言，我们提出的SCGG模型采用初始子图，并自动重新收获在给定条件子结构之上生成新节点及其相应的边缘。 SCGG的体系结构由图表表示网络和自动回归生成模型组成，该模型是端到端训练的。使用此模型，我们可以解决图形完成，这是恢复缺失的节点及其相关的部分观察图的猖and固有的困难问题。合成数据集和现实世界数据集的实验结果证明了我们方法的优势与最先进的基准相比。

尽管大量研究专门用于变形检测，但大多数研究都无法推广其在训练范式之外的变形面。此外，最近的变体检测方法非常容易受到对抗攻击的影响。在本文中，我们打算学习一个具有高概括的变体检测模型，以对各种形态攻击和对不同的对抗攻击的高度鲁棒性。为此，我们开发了卷积神经网络（CNN）和变压器模型的合奏，以同时受益于其能力。为了提高整体模型的鲁棒精度，我们采用多扰动对抗训练，并生成具有高可传递性的对抗性示例。我们详尽的评估表明，提出的强大合奏模型将概括为几个变形攻击和面部数据集。此外，我们验证了我们的稳健集成模型在超过最先进的研究的同时，对几次对抗性攻击获得了更好的鲁棒性。