随着我们远离数据，预测不确定性应该增加，因为各种各样的解释与鲜为人知的信息一致。我们引入了远距离感知的先验（DAP）校准，这是一种纠正训练域之外贝叶斯深度学习模型过度自信的方法。我们将DAPS定义为模型参数的先验分布，该模型参数取决于输入，通过其与训练集的距离度量。DAP校准对后推理方法不可知，可以作为后处理步骤进行。我们证明了其在各种分类和回归问题中对几个基线的有效性，包括旨在测试远离数据的预测分布质量的基准。

Computational units in artificial neural networks follow a simplified model of biological neurons. In the biological model, the output signal of a neuron runs down the axon, splits following the many branches at its end, and passes identically to all the downward neurons of the network. Each of the downward neurons will use their copy of this signal as one of many inputs dendrites, integrate them all and fire an output, if above some threshold. In the artificial neural network, this translates to the fact that the nonlinear filtering of the signal is performed in the upward neuron, meaning that in practice the same activation is shared between all the downward neurons that use that signal as their input. Dendrites thus play a passive role. We propose a slightly more complex model for the biological neuron, where dendrites play an active role: the activation in the output of the upward neuron becomes optional, and instead the signals going through each dendrite undergo independent nonlinear filterings, before the linear combination. We implement this new model into a ReLU computational unit and discuss its biological plausibility. We compare this new computational unit with the standard one and describe it from a geometrical point of view. We provide a Keras implementation of this unit into fully connected and convolutional layers and estimate their FLOPs and weights change. We then use these layers in ResNet architectures on CIFAR-10, CIFAR-100, Imagenette, and Imagewoof, obtaining performance improvements over standard ResNets up to 1.73%. Finally, we prove a universal representation theorem for continuous functions on compact sets and show that this new unit has more representational power than its standard counterpart.

The text-to-image model Stable Diffusion has recently become very popular. Only weeks after its open source release, millions are experimenting with image generation. This is due to its ease of use, since all it takes is a brief description of the desired image to "prompt" the generative model. Rarely do the images generated for a new prompt immediately meet the user's expectations. Usually, an iterative refinement of the prompt ("prompt engineering") is necessary for satisfying images. As a new perspective, we recast image prompt engineering as interactive image retrieval - on an "infinite index". Thereby, a prompt corresponds to a query and prompt engineering to query refinement. Selected image-prompt pairs allow direct relevance feedback, as the model can modify an image for the refined prompt. This is a form of one-sided interactive retrieval, where the initiative is on the user side, whereas the server side remains stateless. In light of an extensive literature review, we develop these parallels in detail and apply the findings to a case study of a creative search task on such a model. We note that the uncertainty in searching an infinite index is virtually never-ending. We also discuss future research opportunities related to retrieval models specialized for generative models and interactive generative image retrieval. The application of IR technology, such as query reformulation and relevance feedback, will contribute to improved workflows when using generative models, while the notion of an infinite index raises new challenges in IR research.

The connectivity-aware path design is crucial in the effective deployment of autonomous Unmanned Aerial Vehicles (UAVs). Recently, Reinforcement Learning (RL) algorithms have become the popular approach to solving this type of complex problem, but RL algorithms suffer slow convergence. In this paper, we propose a Transfer Learning (TL) approach, where we use a teacher policy previously trained in an old domain to boost the path learning of the agent in the new domain. As the exploration processes and the training continue, the agent refines the path design in the new domain based on the subsequent interactions with the environment. We evaluate our approach considering an old domain at sub-6 GHz and a new domain at millimeter Wave (mmWave). The teacher path policy, previously trained at sub-6 GHz path, is the solution to a connectivity-aware path problem that we formulate as a constrained Markov Decision Process (CMDP). We employ a Lyapunov-based model-free Deep Q-Network (DQN) to solve the path design at sub-6 GHz that guarantees connectivity constraint satisfaction. We empirically demonstrate the effectiveness of our approach for different urban environment scenarios. The results demonstrate that our proposed approach is capable of reducing the training time considerably at mmWave.

In this paper, we elaborate on the design and discuss the results of a multi-agent simulation that we have developed using the PSI cognitive architecture. We demonstrate that imbuing agents with intrinsic needs for group affiliation, certainty and competence will lead to the emergence of social behavior among agents. This behavior expresses itself in altruism toward in-group agents and adversarial tendencies toward out-group agents. Our simulation also shows how parameterization can have dramatic effects on agent behavior. Introducing an out-group bias, for example, not only made agents behave aggressively toward members of the other group, but it also increased in-group cohesion. Similarly, environmental and situational factors facilitated the emergence of outliers: agents from adversarial groups becoming close friends. Overall, this simulation showcases the power of psychological frameworks, in general, and the PSI paradigm, in particular, to bring about human-like behavioral patterns in an emergent fashion.

The detection of state-sponsored trolls acting in information operations is an unsolved and critical challenge for the research community, with repercussions that go beyond the online realm. In this paper, we propose a novel AI-based solution for the detection of state-sponsored troll accounts, which consists of two steps. The first step aims at classifying trajectories of accounts' online activities as belonging to either a state-sponsored troll or to an organic user account. In the second step, we exploit the classified trajectories to compute a metric, namely "troll score", which allows us to quantify the extent to which an account behaves like a state-sponsored troll. As a study case, we consider the troll accounts involved in the Russian interference campaign during the 2016 US Presidential election, identified as Russian trolls by the US Congress. Experimental results show that our approach identifies accounts' trajectories with an AUC close to 99\% and, accordingly, classify Russian trolls and organic users with an AUC of 97\%. Finally, we evaluate whether the proposed solution can be generalized to different contexts (e.g., discussions about Covid-19) and generic misbehaving users, showing promising results that will be further expanded in our future endeavors.

该技术报告描述了在Robocup SPL（Mario）中计算视觉统计的模块化且可扩展的体系结构，该结构在Robocup 2022的SPL Open Research Challenge期间提出，该挑战在曼谷（泰国）举行。马里奥（Mario）是一个开源的，可用的软件应用程序，其最终目标是为Robocup SPL社区的发展做出贡献。Mario带有一个GUI，该GUI集成了多个机器学习和基于计算机视觉的功能，包括自动摄像机校准，背景减法，同型计算，玩家 +球跟踪和本地化，NAO机器人姿势估计和跌落检测。马里奥（Mario）被排名第一。1在开放研究挑战中。

聊天机器人用于许多应用程序中，例如自动化代理，智能家庭助理，在线游戏中的互动角色等。因此，确保他们不会以不希望的方式行事，对用户提供令人反感或有毒的反应。这并不是一项琐碎的任务，因为最先进的聊天机器人模型是在从互联网公开收集的大型公共数据集上培训的。本文提出了对聊天机器人中毒性的首次大规模测量。我们表明，公开可用的聊天机器人很容易在喂养有毒的查询时提供有毒的反应。更令人担忧的是，一些无毒的查询也会触发有毒反应。然后，我们着手设计和实验攻击，即毒性，该攻击依赖于微调的GPT-2来产生无毒的查询，使聊天机器人以有毒的方式做出反应。我们广泛的实验评估表明，我们的攻击对公共聊天机器人模型有效，并且优于先前工作提出的手动制作的恶意查询。我们还评估了针对毒性的三种防御机制，表明它们要么以影响聊天机器人的效用而降低攻击性能，要么仅有效地减轻了一部分攻击。这强调了对计算机安全和在线安全社区进行更多研究的需求，以确保聊天机器人模型不会伤害其用户。总体而言，我们有信心有毒可以用作审计工具，我们的工作将为设计更有效的聊天机器人安全防御措施铺平道路。

对网络攻击的现代防御越来越依赖于主动的方法，例如，基于过去的事件来预测对手的下一个行动。建立准确的预测模型需要许多组织的知识； las，这需要披露敏感信息，例如网络结构，安全姿势和政策，这些信息通常是不受欢迎的或完全不可能的。在本文中，我们探讨了使用联合学习（FL）预测未来安全事件的可行性。为此，我们介绍了Cerberus，这是一个系统，可以为参与组织的复发神经网络（RNN）模型进行协作培训。直觉是，FL可能会在非私有方法之间提供中间地面，在非私有方法中，训练数据在中央服务器上合并，而仅训练本地模型的较低性替代方案。我们将Cerberus实例化在从一家大型安全公司的入侵预防产品中获得的数据集上，并评估其有关实用程序，鲁棒性和隐私性，以及参与者如何从系统中贡献和受益。总体而言，我们的工作阐明了将FL执行此任务的积极方面和挑战，并为部署联合方法以进行预测安全铺平了道路。

事实核对是打击在线错误信息方面的有效解决方案之一。但是，传统的事实检查是一个需要稀缺专家人力资源的过程，因此由于要检查新内容的持续流动，因此在社交媒体上并不能很好地扩展。已经提出了基于众包的方法来应对这一挑战，因为它们可以以较小的成本进行扩展，但是尽管它们证明是可行的，但一直在受控环境中进行研究。在这项工作中，我们研究了在BirdWatch计划的Twitter启动的，在实践中部署的众包事实检查的第一个大规模努力。我们的分析表明，在某些情况下，众包可能是一种有效的事实检查策略，甚至可以与人类专家获得的结果相媲美，但不会导致其他人的一致，可行的结果。我们处理了BirdWatch计划验证的11.9k推文，并报告了i）人群和专家如何选择内容的内容的差异，ii）ii）人群和专家如何将不同的资源检索到事实检查，以及III ）与专家检查员相比，人群在事实检查可伸缩性和效率方面所显示的优势。