无人驾驶飞行器（UAV）是支持各种服务，包括通信的技术突破之一。UAV将在提高无线网络的物理层安全方面发挥关键作用。本文定义了窃听地面用户与UAV之间的链路的问题，该联接器用作空中基站（ABS）。提出了加强学习算法Q - 学习和深Q网络（DQN），用于优化ABS的位置和传输功率，以增强地面用户的数据速率。如果没有系统了解窃听器的位置，这会增加保密容量。与Q-Learnch和基线方法相比，仿真结果显示了拟议DQN的快速收敛性和最高保密能力。

人工智能（AI）将在蜂窝网络部署，配置和管理中发挥越来越多的作用。本文研究了AI驱动的6G无线电访问网络（RANS）的安全含义。尽管6G标准化的预期时间表仍在数年之外，但与6G安全有关的预标准化工作已经在进行中，并且将受益于基本和实验研究。Open Ran（O-Ran）描述了一个以行业为导向的开放式体系结构和用于使用AI控制的下一代架设的接口。考虑到这种体系结构，我们确定了对数据驱动网络和物理层元素，相应的对策和研究方向的关键威胁。

Compliance in actuation has been exploited to generate highly dynamic maneuvers such as throwing that take advantage of the potential energy stored in joint springs. However, the energy storage and release could not be well-timed yet. On the contrary, for multi-link systems, the natural system dynamics might even work against the actual goal. With the introduction of variable stiffness actuators, this problem has been partially addressed. With a suitable optimal control strategy, the approximate decoupling of the motor from the link can be achieved to maximize the energy transfer into the distal link prior to launch. However, such continuous stiffness variation is complex and typically leads to oscillatory swing-up motions instead of clear launch sequences. To circumvent this issue, we investigate decoupling for speed maximization with a dedicated novel actuator concept denoted Bi-Stiffness Actuation. With this, it is possible to fully decouple the link from the joint mechanism by a switch-and-hold clutch and simultaneously keep the elastic energy stored. We show that with this novel paradigm, it is not only possible to reach the same optimal performance as with power-equivalent variable stiffness actuation, but even directly control the energy transfer timing. This is a major step forward compared to previous optimal control approaches, which rely on optimizing the full time-series control input.

Person re-identification is a challenging task because of the high intra-class variance induced by the unrestricted nuisance factors of variations such as pose, illumination, viewpoint, background, and sensor noise. Recent approaches postulate that powerful architectures have the capacity to learn feature representations invariant to nuisance factors, by training them with losses that minimize intra-class variance and maximize inter-class separation, without modeling nuisance factors explicitly. The dominant approaches use either a discriminative loss with margin, like the softmax loss with the additive angular margin, or a metric learning loss, like the triplet loss with batch hard mining of triplets. Since the softmax imposes feature normalization, it limits the gradient flow supervising the feature embedding. We address this by joining the losses and leveraging the triplet loss as a proxy for the missing gradients. We further improve invariance to nuisance factors by adding the discriminative task of predicting attributes. Our extensive evaluation highlights that when only a holistic representation is learned, we consistently outperform the state-of-the-art on the three most challenging datasets. Such representations are easier to deploy in practical systems. Finally, we found that joining the losses removes the requirement for having a margin in the softmax loss while increasing performance.

A recent explosion of research focuses on developing methods and tools for building fair predictive models. However, most of this work relies on the assumption that the training and testing data are representative of the target population on which the model will be deployed. However, real-world training data often suffer from selection bias and are not representative of the target population for many reasons, including the cost and feasibility of collecting and labeling data, historical discrimination, and individual biases. In this paper, we introduce a new framework for certifying and ensuring the fairness of predictive models trained on biased data. We take inspiration from query answering over incomplete and inconsistent databases to present and formalize the problem of consistent range approximation (CRA) of answers to queries about aggregate information for the target population. We aim to leverage background knowledge about the data collection process, biased data, and limited or no auxiliary data sources to compute a range of answers for aggregate queries over the target population that are consistent with available information. We then develop methods that use CRA of such aggregate queries to build predictive models that are certifiably fair on the target population even when no external information about that population is available during training. We evaluate our methods on real data and demonstrate improvements over state of the art. Significantly, we show that enforcing fairness using our methods can lead to predictive models that are not only fair, but more accurate on the target population.

A key component of fact verification is thevevidence retrieval, often from multiple documents. Recent approaches use dense representations and condition the retrieval of each document on the previously retrieved ones. The latter step is performed over all the documents in the collection, requiring storing their dense representations in an index, thus incurring a high memory footprint. An alternative paradigm is retrieve-and-rerank, where documents are retrieved using methods such as BM25, their sentences are reranked, and further documents are retrieved conditioned on these sentences, reducing the memory requirements. However, such approaches can be brittle as they rely on heuristics and assume hyperlinks between documents. We propose a novel retrieve-and-rerank method for multi-hop retrieval, that consists of a retriever that jointly scores documents in the knowledge source and sentences from previously retrieved documents using an autoregressive formulation and is guided by a proof system based on natural logic that dynamically terminates the retrieval process if the evidence is deemed sufficient. This method is competitive with current state-of-the-art methods on FEVER, HoVer and FEVEROUS-S, while using $5$ to $10$ times less memory than competing systems. Evaluation on an adversarial dataset indicates improved stability of our approach compared to commonly deployed threshold-based methods. Finally, the proof system helps humans predict model decisions correctly more often than using the evidence alone.

Current technological advances open up new opportunities for bringing human-machine interaction to a new level of human-centered cooperation. In this context, a key issue is the semantic understanding of the environment in order to enable mobile robots more complex interactions and a facilitated communication with humans. Prerequisites are the vision-based registration of semantic objects and humans, where the latter are further analyzed for potential interaction partners. Despite significant research achievements, the reliable and fast registration of semantic information still remains a challenging task for mobile robots in real-world scenarios. In this paper, we present a vision-based system for mobile assistive robots to enable a semantic-aware environment perception without additional a-priori knowledge. We deploy our system on a mobile humanoid robot that enables us to test our methods in real-world applications.

Google，Amazon和Microsoft等提供商提供的商业ML API已在许多应用程序中大大简化了ML的采用。许多公司和学者都为使用ML API用于对象检测，OCR和情感分析等任务。处理相同任务的不同ML API可能具有非常异构的性能。此外，API的基础模型也随着时间的推移而发展。随着ML API迅速成为一个有价值的市场，并且是消耗机器学习的广泛方式，因此系统地研究和比较不同的API并表征API随时间变化的方式至关重要。但是，由于缺乏数据，目前该主题目前没有被忽视。在本文中，我们介绍了HAPI（API的历史），该数据集由1,761,417个商业ML API应用程序（涉及来自亚马逊，Google，IBM，Microsoft和其他提供商的API），包括图像标签，文本识别和文本识别和文本识别和文本，从2020年到2022年的挖掘。每个实例都由API的查询输入（例如图像或文本）以及API的输出预测/注释和置信分数组成。 HAPI是ML API使用情况的第一个大型数据集，并且是研究ML-AS-A-Service（MLAAS）的独特资源。作为HAPI启用的分析类型的示例，我们表明ML API的性能会随着时间的流逝而大幅变化 - 在特定基准数据集上删除了几个API的精度。即使API的汇总性能保持稳定，其误差模式也可以在2020年至2022年之间在不同的数据子类型中转移。这种更改可能会大大影响使用某些ML API作为组件的整个分析管道。随着时间的流逝，我们进一步使用HAPI研究人口亚组的商业API绩效差异。 HAPI可以刺激MLAA的不断发展领域的更多研究。

这项研究是有关阿拉伯历史文档的光学特征识别（OCR）的一系列研究的第二阶段，并研究了不同的建模程序如何与问题相互作用。第一项研究研究了变压器对我们定制的阿拉伯数据集的影响。首次研究的弊端之一是训练数据的规模，由于缺乏资源，我们的3000万张图像中仅15000张图像。另外，我们添加了一个图像增强层，时间和空间优化和后校正层，以帮助该模型预测正确的上下文。值得注意的是，我们提出了一种使用视觉变压器作为编码器的端到端文本识别方法，即BEIT和Vanilla Transformer作为解码器，消除了CNNs以进行特征提取并降低模型的复杂性。实验表明，我们的端到端模型优于卷积骨架。该模型的CER为4.46％。

机器学习（ML）研究通常集中在模型上，而最突出的数据集已用于日常的ML任务，而不考虑这些数据集对基本问题的广度，困难和忠诚。忽略数据集的基本重要性已引起了重大问题，该问题涉及现实世界中的数据级联以及数据集驱动标准的模型质量饱和，并阻碍了研究的增长。为了解决此问题，我们提出Dataperf，这是用于评估ML数据集和数据集工作算法的基准软件包。我们打算启用“数据棘轮”，其中培训集将有助于评估相同问题的测试集，反之亦然。这种反馈驱动的策略将产生一个良性的循环，该循环将加速以数据为中心的AI。MLCommons协会将维护Dataperf。