Recent studies reveal that deep neural network (DNN) based object detectors are vulnerable to adversarial attacks in the form of adding the perturbation to the images, leading to the wrong output of object detectors. Most current existing works focus on generating perturbed images, also called adversarial examples, to fool object detectors. Though the generated adversarial examples themselves can remain a certain naturalness, most of them can still be easily observed by human eyes, which limits their further application in the real world. To alleviate this problem, we propose a differential evolution based dual adversarial camouflage (DE_DAC) method, composed of two stages to fool human eyes and object detectors simultaneously. Specifically, we try to obtain the camouflage texture, which can be rendered over the surface of the object. In the first stage, we optimize the global texture to minimize the discrepancy between the rendered object and the scene images, making human eyes difficult to distinguish. In the second stage, we design three loss functions to optimize the local texture, making object detectors ineffective. In addition, we introduce the differential evolution algorithm to search for the near-optimal areas of the object to attack, improving the adversarial performance under certain attack area limitations. Besides, we also study the performance of adaptive DE_DAC, which can be adapted to the environment. Experiments show that our proposed method could obtain a good trade-off between the fooling human eyes and object detectors under multiple specific scenes and objects.

语义本地化（SELO）是指使用语义信息（例如文本）在大规模遥感（RS）图像中获得最相关位置的任务。作为基于跨模式检索的新兴任务，Selo仅使用字幕级注释来实现语义级检索，这表明了其在统一下游任务方面的巨大潜力。尽管Selo已连续执行，但目前没有系统地探索并分析了这一紧急方向。在本文中，我们彻底研究了这一领域，并根据指标和测试数据提供了完整的基准，以推进SELO任务。首先，基于此任务的特征，我们提出了多个判别评估指标来量化SELO任务的性能。设计的显着面积比例，注意力转移距离和离散的注意距离可用于评估从像素级别和区域级别中产生的SELO图。接下来，为了为SELO任务提供标准评估数据，我们为多样化的，多语义的，多目标语义定位测试集（AIR-SLT）贡献。 AIR-SLT由22个大型RS图像和59个具有不同语义的测试用例组成，旨在为检索模型提供全面的评估。最后，我们详细分析了RS跨模式检索模型的SELO性能，探索不同变量对此任务的影响，并为SELO任务提供了完整的基准测试。我们还建立了一个新的范式来引用RS表达理解，并通过将其与检测和道路提取等任务相结合，证明了Selo在语义中的巨大优势。拟议的评估指标，语义本地化测试集和相应的脚本已在github.com/xiaoyuan1996/semanticlocalizationmetrics上访问。

在变异场景中已经揭示了对抗性实例的现象。最近的研究表明，精心设计的对抗性防御策略可以改善深度学习模型针对对抗性例子的鲁棒性。但是，随着国防技术的快速发展，由于现有手动设计的对抗性攻击的性能较弱，因此很难评估防御模型的鲁棒性。为了应对挑战，鉴于防御模型，需要进一步利用有效的对抗性攻击，较少的计算负担和较低的健壮精度。因此，我们提出了一种用于自动对抗攻击优化设计的多目标模因算法，该算法实现了对近乎最佳的对抗性攻击对防御模型的近乎最佳的对抗性攻击。首先，构建了自动对抗攻击优化设计的更通用的数学模型，其中搜索空间不仅包括攻击者操作，大小，迭代号和损失功能，还包括多个对抗性攻击的连接方式。此外，我们开发了一种组合NSGA-II和本地搜索以解决优化问题的多目标模因算法。最后，为了降低搜索过程中的评估成本，我们根据模型输出的每个图像的跨熵损失值的排序提出了代表性的数据选择策略。关于CIFAR10，CIFAR100和Imagenet数据集的实验显示了我们提出的方法的有效性。

在卫星布局设计中，热源布局优化（HSLO）是一种有效的技术，可降低最高温度并改善整个系统的热量管理。最近，已经提出了深度学习的替代辅助HSLO，该辅助辅助HSLO从布局到相应的温度场进行了映射，以便在优化过程中替换仿真以大大降低计算成本。但是，它面临两个主要挑战：1）特定任务的神经网络代理通常是手动设计的，这是复杂的，需要丰富的调试经验，这对工程领域的设计师来说是具有挑战性的； 2）现有的HSLO算法只能在单个优化中获得几乎最佳的解决方案，并且很容易被捕获以局部最佳限制。为了应对第一个挑战，考虑减少总参数编号并确保相似的准确性以及与特征金字塔网络（FPN）框架相结合的神经体系结构搜索（NAS）方法，以实现自动搜索小型搜索的目的深度学习的替代模型。为了应对第二项挑战，提出了一种基于多模式搜索的布局优化算法（MNSLO），该算法（MNSLO）可以单一优化同时获得更多，更好的近似最佳设计方案。最后，利用了两个典型的二维热传导优化问题来证明该方法的有效性。具有相似的精度，NAS找到了比原始FPN的参数少80％，拖失板少64％和36％的模型。此外，在自动搜索的深度学习代理人的帮助下，MNSLO可以同时实现多个接近最佳的设计方案，以为设计师提供更多的设计多样性。

物体检测中的物理对抗攻击引起了越来越受到关注。然而，最先前的作品专注于通过生成单独的对抗贴片来隐藏来自探测器的物体，该贴片仅覆盖车辆表面的平面部分并且无法在物理场景中攻击多视图，长距离和部分封闭的探测器对象。为了弥合数字攻击与物理攻击之间的差距，我们利用完整的3D车辆表面来提出坚固的全面覆盖伪装攻击（FCA）到愚弄探测器。具体来说，我们首先尝试在整个车辆表面上渲染非平面伪装纹理。为了模仿现实世界的环境条件，我们将引入转换功能，将渲染的伪装车辆转移到照片现实场景中。最后，我们设计了一个有效的损失功能，以优化伪装纹理。实验表明，全面覆盖伪装攻击不仅可以在各种测试用例下优于最先进的方法，而且还可以推广到不同的环境，车辆和物体探测器。 FCA的代码可用于：https://idrl-lab.github.io/full-coverage-camouflage -Adversarial-Attack/。

Open peer review is a growing trend in academic publications. Public access to peer review data can benefit both the academic and publishing communities. It also serves as a great support to studies on review comment generation and further to the realization of automated scholarly paper review. However, most of the existing peer review datasets do not provide data that cover the whole peer review process. Apart from this, their data are not diversified enough as they are mainly collected from the field of computer science. These two drawbacks of the currently available peer review datasets need to be addressed to unlock more opportunities for related studies. In response to this problem, we construct MOPRD, a multidisciplinary open peer review dataset. This dataset consists of paper metadata, multiple version manuscripts, review comments, meta-reviews, author's rebuttal letters, and editorial decisions. Moreover, we design a modular guided review comment generation method based on MOPRD. Experiments show that our method delivers better performance indicated by both automatic metrics and human evaluation. We also explore other potential applications of MOPRD, including meta-review generation, editorial decision prediction, author rebuttal generation, and scientometric analysis. MOPRD is a strong endorsement for further studies in peer review-related research and other applications.

Transformers are widely used in NLP tasks. However, current approaches to leveraging transformers to understand language expose one weak spot: Number understanding. In some scenarios, numbers frequently occur, especially in semi-structured data like tables. But current approaches to rich-number tasks with transformer-based language models abandon or lose some of the numeracy information - e.g., breaking numbers into sub-word tokens - which leads to many number-related errors. In this paper, we propose the LUNA framework which improves the numerical reasoning and calculation capabilities of transformer-based language models. With the number plugin of NumTok and NumBed, LUNA represents each number as a whole to model input. With number pre-training, including regression loss and model distillation, LUNA bridges the gap between number and vocabulary embeddings. To the best of our knowledge, this is the first work that explicitly injects numeracy capability into language models using Number Plugins. Besides evaluating toy models on toy tasks, we evaluate LUNA on three large-scale transformer models (RoBERTa, BERT, TabBERT) over three different downstream tasks (TATQA, TabFact, CrediTrans), and observe the performances of language models are constantly improved by LUNA. The augmented models also improve the official baseline of TAT-QA (EM: 50.15 -> 59.58) and achieve SOTA performance on CrediTrans (F1 = 86.17).

With the development and progress of science and technology, the Internet of Things(IoT) has gradually entered people's lives, bringing great convenience to our lives and improving people's work efficiency. Specifically, the IoT can replace humans in jobs that they cannot perform. As a new type of IoT vehicle, the current status and trend of research on Unmanned Aerial Vehicle(UAV) is gratifying, and the development prospect is very promising. However, privacy and communication are still very serious issues in drone applications. This is because most drones still use centralized cloud-based data processing, which may lead to leakage of data collected by drones. At the same time, the large amount of data collected by drones may incur greater communication overhead when transferred to the cloud. Federated learning as a means of privacy protection can effectively solve the above two problems. However, federated learning when applied to UAV networks also needs to consider the heterogeneity of data, which is caused by regional differences in UAV regulation. In response, this paper proposes a new algorithm FedBA to optimize the global model and solves the data heterogeneity problem. In addition, we apply the algorithm to some real datasets, and the experimental results show that the algorithm outperforms other algorithms and improves the accuracy of the local model for UAVs.

源代码对于研究人员重现方法并复制人工智能（AI）论文的结果至关重要。一些组织和研究人员手动收集具有可用源代码的AI论文，以对AI社区做出贡献。但是，手动收集是一项劳动密集型且耗时的任务。为了解决此问题，我们提出了一种方法，可以自动识别具有可用源代码的论文并提取其源代码存储库URL。通过这种方法，我们发现，从2010年到2019年发布的10个最高AI会议的常规论文中有20.5％被确定为具有可用源代码的论文，并且这些源代码存储库中有8.1％不再可访问。我们还创建了XMU NLP Lab ReadMe数据集，这是用于源代码文档研究的标记已读数文件的最大数据集。通过此数据集，我们发现了很多读书文件没有提供的安装说明或使用教程。此外，对AI会议论文的源代码的一般图片进行了大规模的综合统计分析。提出的解决方案还可以超越AI会议论文，以分析来自期刊和会议的其他科学论文，以阐明更多领域。

许多数据分析任务在很大程度上依赖对表的深入了解（多维数据）。在整个任务中，都存在表字段 /列的共同使用的元数据属性。在本文中，我们确定了四个这样的分析元数据：测量/维度二分法，公共场作用，语义场类型和默认聚集函数。尽管这些元数据面临不足的监督信号的挑战，利用现有的知识和理解分布。为了将这些元数据推理为原始表，我们提出了多任务元数据模型，该模型将现场分布和知识图信息融合到预训练的表格模型中。对于模型培训和评估，我们通过使用下游任务的各种智能监督来收集分析元数据的大型语料库（来自私人电子表格和公共表格数据集的〜582K表）。我们的最佳模型的精度= 98％，命中率在TOP-1> 67％，精度> 80％和四个分析元数据推理任务的精度= 88％。它的表现优于基于规则，传统机器学习方法和预训练的表格模型的一系列基线。分析元数据模型被部署在流行的数据分析产品中，帮助下游智能功能，例如Insights挖掘，图表 /枢轴表建议和自然语言QA ...