事实证明，变质测试对于许多领域中的测试案例生成和故障检测有效。它是一种软件测试策略，它使用程序的输入输出对之间的某些关系，称为变质关系。这种方法与自主系统域相关，因为它在可能难以确定的给定测试输入结果的情况下有助于。因此，在本文中，我们提供了变质测试以及自主系统域中的实现概述。我们在使用GNC API的自动无人机中实施了障碍物检测和回避任务，并在凉亭中的模拟旁边实现了障碍物。特别是，我们描述了对有效变质关系发展至关重要的特性和最佳实践。我们还展示了两种用于单态和多个无人机的变质测试的变质关系。我们的关系揭示了鉴于变质测试，实施和回避算法的几个属性和一些弱点。结果表明，变质测试在自主系统领域具有巨大的潜力，应考虑在该领域的质量保证。

背景：当使用深度学习模型时，存在许多可能的漏洞，一些最令人担忧的是对抗性输入，这可能会导致错误的决策。因此，作为解决这些输入脆弱性的软件测试过程的一部分，有必要针对对抗输入进行重新训练。此外，对于节能测试和再培训，数据科学家需要支持，这是最佳的指导指标和最佳数据集配置。目的：我们检查了四个指导指标，用于重新卷积神经网络和三个重新培训配置。我们的目标是在图像分类的背景下，从数据科学家的角度来看，针对有关准确性，资源利用率和时间的对抗性输入的模型。方法：我们在两个数据集中进行了一项实证研究，以进行图像分类。我们探索：（a）通过订购由四个不同的指导指标设置的新培训（神经元覆盖，基于可能性的惊喜充足性，基于距离的惊喜充足性和随机性）来设置的新培训，通过订购新的培训来重新卷积神经网络的准确性，资源利用和时间，（b），（b），（b）具有三种不同配置的卷积神经网络（从头开始和增强数据集，使用权重和增强数据集）以及使用权重和仅使用对抗性输入的三种不同配置的卷积神经网络的准确性和资源利用）。结果：我们揭示了从原始权重的对抗性输入和以惊喜充足度指标的订购为最佳型号W.R.T.进行重新训练。使用的指标。结论：尽管需要更多的研究，但我们建议数据科学家使用上述配置和指标来应对深度学习模型的对抗性输入的脆弱性，因为它们可以在不使用许多输入的情况下针对对抗性输入来改善模型。

高数据质量对于当今基于AI的系统至关重要。但是，尽管数据质量一直是研究的对象，但显然缺乏对潜在数据质量问题的研究（例如，模棱两可的，无关的价值）。这些问题本质上是潜在的，因此通常不明显。然而，它们可能与基于AI的系统（例如技术债务，数据引起的故障）的未来问题的风险增加有关。作为软件工程中代码气味的对应物，我们指的是数据气味的问题。本文概念化了数据的气味，并在基于AI的系统的背景下的原因，后果，检测和使用。此外，出现了36个数据气味的目录，分为三类（即可信度的气味，可理解的气味，一致性的气味）。此外，该文章概述了用于检测数据气味的工具支持，并提出了240多个现实世界数据集中初始气味检测的结果。

协作AI系统（CAISS）旨在与共同空间中的人类合作，实现共同目标。这一关键环境产生可能危害人类的危险情况。因此，建立具有符合要求，具体域标准和法规的强保证的这些系统具有最大的重要性。到目前为止，迄今为止仅报告了一些规模的影响，因为许多工作仍有待管理可能的风险。我们在这方面确定了新出现的问题，然后我们向我们的愿景报告，以及我们的多学科研究团队组成的软件/系统和机电一体化工程师的进展，以开发才能开发风险驱动的保证程序。

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

We present a dynamic path planning algorithm to navigate an amphibious rotor craft through a concave time-invariant obstacle field while attempting to minimize energy usage. We create a nonlinear quaternion state model that represents the rotor craft dynamics above and below the water. The 6 degree of freedom dynamics used within a layered architecture to generate motion paths for the vehicle to follow and the required control inputs. The rotor craft has a 3 dimensional map of its surroundings that is updated via limited range onboard sensor readings within the current medium (air or water). Path planning is done via PRM and D* Lite.

While the capabilities of autonomous systems have been steadily improving in recent years, these systems still struggle to rapidly explore previously unknown environments without the aid of GPS-assisted navigation. The DARPA Subterranean (SubT) Challenge aimed to fast track the development of autonomous exploration systems by evaluating their performance in real-world underground search-and-rescue scenarios. Subterranean environments present a plethora of challenges for robotic systems, such as limited communications, complex topology, visually-degraded sensing, and harsh terrain. The presented solution enables long-term autonomy with minimal human supervision by combining a powerful and independent single-agent autonomy stack, with higher level mission management operating over a flexible mesh network. The autonomy suite deployed on quadruped and wheeled robots was fully independent, freeing the human supervision to loosely supervise the mission and make high-impact strategic decisions. We also discuss lessons learned from fielding our system at the SubT Final Event, relating to vehicle versatility, system adaptability, and re-configurable communications.

We present Muse, a text-to-image Transformer model that achieves state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Muse is trained on a masked modeling task in discrete token space: given the text embedding extracted from a pre-trained large language model (LLM), Muse is trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality etc. Our 900M parameter model achieves a new SOTA on CC3M, with an FID score of 6.06. The Muse 3B parameter model achieves an FID of 7.88 on zero-shot COCO evaluation, along with a CLIP score of 0.32. Muse also directly enables a number of image editing applications without the need to fine-tune or invert the model: inpainting, outpainting, and mask-free editing. More results are available at https://muse-model.github.io

The visual dimension of cities has been a fundamental subject in urban studies, since the pioneering work of scholars such as Sitte, Lynch, Arnheim, and Jacobs. Several decades later, big data and artificial intelligence (AI) are revolutionizing how people move, sense, and interact with cities. This paper reviews the literature on the appearance and function of cities to illustrate how visual information has been used to understand them. A conceptual framework, Urban Visual Intelligence, is introduced to systematically elaborate on how new image data sources and AI techniques are reshaping the way researchers perceive and measure cities, enabling the study of the physical environment and its interactions with socioeconomic environments at various scales. The paper argues that these new approaches enable researchers to revisit the classic urban theories and themes, and potentially help cities create environments that are more in line with human behaviors and aspirations in the digital age.

Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.