The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems. To accelerate the research on AI security, the Artificial Intelligence Security Competition (AISC) was organized by the Zhongguancun Laboratory, China Industrial Control Systems Cyber Emergency Response Team, Institute for Artificial Intelligence, Tsinghua University, and RealAI as part of the Zhongguancun International Frontier Technology Innovation Competition (https://www.zgc-aisc.com/en). The competition consists of three tracks, including Deepfake Security Competition, Autonomous Driving Security Competition, and Face Recognition Security Competition. This report will introduce the competition rules of these three tracks and the solutions of top-ranking teams in each track.

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.

学习多样化的技能是机器人技术的主要挑战之一。为此，模仿学习方法取得了令人印象深刻的结果。这些方法需要明确标记的数据集或采用一致的技能执行，以使学习和积极控制单个行为，从而限制其适用性。在这项工作中，我们提出了一种合作的对抗方法，用于从未标记的数据集中获得可控技能的单一多功能策略，该数据集包含各种状态过渡模式，通过最大化其可区分性。此外，我们表明，通过在生成的对抗性模仿学习框架中利用无监督的技能发现，新颖而有用的技能随着成功的任务实现而出现。最后，在示威中编码的各种技能的忠实复制中，对获得的多功能策略进行了测试，并呈现了忠实的复制。

大多数当前的多模式摘要方法遵循级联的方式，在该方式中，首先使用现成的对象检测器来提取视觉特征，然后将这些功能与语言表示融合在一起，以使用编码器模型生成摘要。级联的方式无法捕获图像和段落之间的语义一致性，这对于确切的摘要至关重要。在本文中，我们向vil-sum提出了段落级级\ textbf {vi} sion- \ textbf {l} arnguage语义对齐和多模式\ textbf {sum} marization。 VIL-SUM的核心是一个联合多模式编码器，具有两个精心设计的任务，图像重新排序和图像选择。联合多模式编码器捕获了模式之间的交互，重新排序任务指导该模型学习段落级别的语义对齐，而选择任务指导模型在最终摘要中将模型指向所选摘要相关的图像。实验结果表明，我们提出的VIL-SUM显着优于当前最新方法。在进一步的分析中，我们发现两个精心设计的任务和联合多模式编码器可以有效地指导模型学习合理的段落图像和摘要图像关系。

学习敏捷技能是机器人技术的主要挑战之一。为此，加强学习方法取得了令人印象深刻的结果。这些方法需要根据奖励功能或可以在模拟中查询的专家来提供明确的任务信息，以提供目标控制输出，从而限制其适用性。在这项工作中，我们提出了一种生成的对抗方法，用于从部分和潜在的物理不兼容的演示中推断出奖励功能，以成功地获得参考或专家演示的成功技能。此外，我们表明，通过使用Wasserstein gan公式和从以粗糙和部分信息为输入的示范中进行过渡，我们能够提取强大的策略并能够模仿证明的行为。最后，在一个名为Solo 8的敏捷四倍的机器人上测试了所获得的技能，例如后空飞弹，并对手持人类示范的忠实复制进行了测试。

Autonomous vehicles are suited for continuous area patrolling problems. However, finding an optimal patrolling strategy can be challenging for many reasons. Firstly, patrolling environments are often complex and can include unknown and evolving environmental factors. Secondly, autonomous vehicles can have failures or hardware constraints such as limited battery lives. Importantly, patrolling large areas often requires multiple agents that need to collectively coordinate their actions. In this work, we consider these limitations and propose an approach based on a distributed, model-free deep reinforcement learning based multi-agent patrolling strategy. In this approach, agents make decisions locally based on their own environmental observations and on shared information. In addition, agents are trained to automatically recharge themselves when required to support continuous collective patrolling. A homogeneous multi-agent architecture is proposed, where all patrolling agents have an identical policy. This architecture provides a robust patrolling system that can tolerate agent failures and allow supplementary agents to be added to replace failed agents or to increase the overall patrol performance. This performance is validated through experiments from multiple perspectives, including the overall patrol performance, the efficiency of the battery recharging strategy, the overall robustness of the system, and the agents' ability to adapt to environment dynamics.

DeepAngle is a machine learning-based method to determine the contact angles of different phases in the tomography images of porous materials. Measurement of angles in 3--D needs to be done within the surface perpendicular to the angle planes, and it could become inaccurate when dealing with the discretized space of the image voxels. A computationally intensive solution is to correlate and vectorize all surfaces using an adaptable grid, and then measure the angles within the desired planes. On the contrary, the present study provides a rapid and low-cost technique powered by deep learning to estimate the interfacial angles directly from images. DeepAngle is tested on both synthetic and realistic images against the direct measurement technique and found to improve the r-squared by 5 to 16% while lowering the computational cost 20 times. This rapid method is especially applicable for processing large tomography data and time-resolved images, which is computationally intensive. The developed code and the dataset are available at an open repository on GitHub (https://www.github.com/ArashRabbani/DeepAngle).

With a handful of demonstration examples, large-scale language models show strong capability to perform various tasks by in-context learning from these examples, without any fine-tuning. We demonstrate that in-context learning performance can be highly unstable across samples of examples, indicating the idiosyncrasies of how language models acquire information. We formulate example selection for in-context learning as a sequential decision problem, and propose a reinforcement learning algorithm for identifying generalizable policies to select demonstration examples. For GPT-2, our learned policies demonstrate strong abilities of generalizing to unseen tasks in training, with a $5.8\%$ improvement on average. Examples selected from our learned policies can even achieve a small improvement on GPT-3 Ada. However, the improvement diminishes on larger GPT-3 models, suggesting emerging capabilities of large language models.

实时动态环境感知对于拥挤空间的自动机器人至关重要。尽管流行的基于体素的映射方法可以有效地用任意复杂的形状代表3D障碍，但它们几乎无法区分静态和动态障碍，从而导致避免障碍物的性能有限。尽管在自动驾驶中存在大量基于学习的动态障碍检测算法，但四轮驱动器的有限计算资源无法使用这些方法实现实时性能。为了解决这些问题，我们为使用RGB-D摄像机提出了一个实时动态障碍物跟踪和映射系统，以避免四肢障碍物。拟议的系统首先利用带有占用体素图的深度图像来生成潜在的动态障碍区域作为建议。通过障碍区域建议，Kalman滤波器和我们的连续性过滤器将应用于跟踪每个动态障碍物。最后，使用追踪动态障碍的状态基于马尔可夫链提出了环境感知的轨迹预测方法。我们使用定制的四轮驱动器和导航计划者实施了建议的系统。仿真和物理实验表明，我们的方法可以成功地跟踪和代表动态环境中的障碍，并安全地避免障碍。

对社交媒体上的COVID-19疫苗接种的公众讨论不仅对于解决当前的Covid-19-19大流行，而且对于未来的病原体爆发而言至关重要。我们检查了一个Twitter数据集，其中包含7500万英文推文，讨论2020年3月至2021年3月的Covid-19疫苗接种。我们使用自然语言处理（NLP）技术培训了一种立场检测算法，以将推文分为“反Vax”或“ pro-Vax”或“ Pro-Vax” '，并使用主题建模技术检查话语的主要主题。虽然Pro-Vax推文（3700万）远远超过反VAX推文（1000万），但两种姿态的大多数推文（63％的反VAX和53％的Pro-Vax推文）都来自双稳定的用户，他们都发布了两者在观察期间，亲和反VAX推文。 Pro-Vax推文主要集中在疫苗开发上，而反VAX推文则涵盖了广泛的主题，其中一些主题包括真正的问题，尽管存在很大的虚假性。尽管从相反的角度讨论了这两个立场，但两种立场都是常见的。模因和笑话是最转推消息之一。尽管对反vax话语的两极分化和在线流行的担忧是毫无根据的，但针对虚假的有针对性的反驳很重要。