人类开发人员可以使用网络安全缺陷生产代码。可以新兴'智能'代码完成工具有助于修复这些缺点吗？在这项工作中，我们研究了对零拍摄漏洞修复的代码（如Openai的Codex和AI21的侏罗纪J-1）使用大型语言模型（如Openai的Codex和AI21的J-1）。我们调查设计方面的挑战，提示将Coax LLMS进入生成不安全代码的修复版本。由于许多方法来短语和句法 - 具有自然语言，这很困难。通过对四个商业，黑盒子，“现成的”典型的模型进行大规模研究，以及局部训练的模型，在合成，手工制作和现实世界的安全错误场景的混合中，我们的实验表明，LLMS可以共同修复100％的综合生成和手工制作的情景，以及58％的脆弱性，在真实的开源项目中的历史错误中选择。

在设计基于AI的系统中，有蓬勃发展的兴趣，以帮助人类设计计算系统，包括自动生成计算机代码的工具。这些最值得注意的是，以第一个自我描述的“Ai对程序员”，GitHub Copilot，一种在开源GitHub代码上培训的语言模型。但是，代码通常包含错误 - 因此，鉴于Copilot处理的大量未曝避代码，肯定是语言模型将从可利用的错误代码中学到。这提出了对Copilot代码捐助的安全的担忧。在这项工作中，我们系统地调查了可能导致Github CopIlot推荐不安全代码的普遍存在和条件。为了执行此分析，我们提示CopIlot在与高风险CWE相关的方案中生成代码（例如，从吉利的“前25名”列表中的方案）。我们探索了三个不同代码生成轴上的Copilot的表现 - 检查它如何表现为特定的弱点多样性，提示的多样性以及域的多样性。总共生产89个不同的Copilot方案，以完成，生产1,689个计划。其中，我们发现大约40％的脆弱。

In this work we introduce reinforcement learning techniques for solving lexicographic multi-objective problems. These are problems that involve multiple reward signals, and where the goal is to learn a policy that maximises the first reward signal, and subject to this constraint also maximises the second reward signal, and so on. We present a family of both action-value and policy gradient algorithms that can be used to solve such problems, and prove that they converge to policies that are lexicographically optimal. We evaluate the scalability and performance of these algorithms empirically, demonstrating their practical applicability. As a more specific application, we show how our algorithms can be used to impose safety constraints on the behaviour of an agent, and compare their performance in this context with that of other constrained reinforcement learning algorithms.

A reduced order model of a generic submarine is presented. Computational fluid dynamics (CFD) results are used to create and validate a model that includes depth dependence and the effect of waves on the craft. The model and the procedure to obtain its coefficients are discussed, and examples of the data used to obtain the model coefficients are presented. An example of operation following a complex path is presented and results from the reduced order model are compared to those from an equivalent CFD calculation. The controller implemented to complete these maneuvers is also presented.

Force modulation of robotic manipulators has been extensively studied for several decades. However, it is not yet commonly used in safety-critical applications due to a lack of accurate interaction contact modeling and weak performance guarantees - a large proportion of them concerning the modulation of interaction forces. This study presents a high-level framework for simultaneous trajectory optimization and force control of the interaction between a manipulator and soft environments, which is prone to external disturbances. Sliding friction and normal contact force are taken into account. The dynamics of the soft contact model and the manipulator are simultaneously incorporated in a trajectory optimizer to generate desired motion and force profiles. A constrained optimization framework based on Alternative Direction Method of Multipliers (ADMM) has been employed to efficiently generate real-time optimal control inputs and high-dimensional state trajectories in a Model Predictive Control fashion. Experimental validation of the model performance is conducted on a soft substrate with known material properties using a Cartesian space force control mode. Results show a comparison of ground truth and real-time model-based contact force and motion tracking for multiple Cartesian motions in the valid range of the friction model. It is shown that a contact model-based motion planner can compensate for frictional forces and motion disturbances and improve the overall motion and force tracking accuracy. The proposed high-level planner has the potential to facilitate the automation of medical tasks involving the manipulation of compliant, delicate, and deformable tissues.

As artificial intelligence (AI) becomes a prominent part of modern life, AI literacy is becoming important for all citizens, not just those in technology careers. Previous research in AI education materials has largely focused on the introduction of terminology as well as AI use cases and ethics, but few allow students to learn by creating their own machine learning models. Therefore, there is a need for enriching AI educational tools with more adaptable and flexible platforms for interested educators with any level of technical experience to utilize within their teaching material. As such, we propose the development of an open-source tool (Build-a-Bot) for students and teachers to not only create their own transformer-based chatbots based on their own course material, but also learn the fundamentals of AI through the model creation process. The primary concern of this paper is the creation of an interface for students to learn the principles of artificial intelligence by using a natural language pipeline to train a customized model to answer questions based on their own school curriculums. The model uses contexts given by their instructor, such as chapters of a textbook, to answer questions and is deployed on an interactive chatbot/voice agent. The pipeline teaches students data collection, data augmentation, intent recognition, and question answering by having them work through each of these processes while creating their AI agent, diverging from previous chatbot work where students and teachers use the bots as black-boxes with no abilities for customization or the bots lack AI capabilities, with the majority of dialogue scripts being rule-based. In addition, our tool is designed to make each step of this pipeline intuitive for students at a middle-school level. Further work primarily lies in providing our tool to schools and seeking student and teacher evaluations.

我们介绍了一种新的算法，基于回归的监督学习（RSL），用于每个实例神经网络（NN）为经典计划问题定义的启发式功能。RSL使用回归来选择与目标不同距离的相关状态集。然后，RSL制定了一个监督的学习问题，以获取定义NN启发式的参数，并使用标记为目标状态的精确或估计距离的选定状态。我们的实验研究表明，RSL在覆盖范围内优于先前的经典计划NN启发式功能，同时需要减少两个数量级的训练时间。

人们普遍认为，人工智能（AI）系统，尤其是使用机器学习（ML）的系统，应该能够“解释”其行为。不幸的是，关于什么构成“解释”几乎没有共识。这引起了系统为可解释的人工智能（XAI）提供的解释与用户和其他受众真正需要的解释之间的解释，这些解释应由全部功能角色，受众，受众和解释能力的全部范围定义。在本文中，我们探讨了解释的特征以及如何使用这些功能评估其实用性。我们专注于根据其功能角色定义的解释要求，试图理解它们的用户的知识状态以及生成它们所需的信息的可用性。此外，我们讨论了XAI对系统的信任的风险，而无需建立他们的信任度，并为XAI领域建立指标以指导和基础系统生成的解释的实用性定义了关键的下一步。

This paper considers doing quantile regression on censored data using neural networks (NNs). This adds to the survival analysis toolkit by allowing direct prediction of the target variable, along with a distribution-free characterisation of uncertainty, using a flexible function approximator. We begin by showing how an algorithm popular in linear models can be applied to NNs. However, the resulting procedure is inefficient, requiring sequential optimisation of an individual NN at each desired quantile. Our major contribution is a novel algorithm that simultaneously optimises a grid of quantiles output by a single NN. To offer theoretical insight into our algorithm, we show firstly that it can be interpreted as a form of expectation-maximisation, and secondly that it exhibits a desirable `self-correcting' property. Experimentally, the algorithm produces quantiles that are better calibrated than existing methods on 10 out of 12 real datasets.

排名和分数是判断使用的两个常见数据类型，以表达对象集合中对质量的偏好和/或质量的看法。存在许多模型以单独研究每种类型的数据，但没有统一的统计模型同时捕获两个数据类型，而不首先执行数据转换。我们提出了Mallows-Binomial模型来缩短这种差距，它通过量化的参数来与二项式分数模型相结合，这些差距通过量化的参数来量化对象质量，共识等级和法官之间的共识水平。我们提出了一种有效的树搜索算法来计算模型参数的精确MLE，分析和通过模拟研究模型的统计特性，并通过模拟将我们的模型应用于来自授予面板审查的实例，从而将其分数和部分排名的拨款。 。此外，我们展示了如何使用模型输出来排序对象的信心。拟议的模型被证明是从分数和排名中明智地结合信息，以量化对象质量并衡量具有适当统计不确定性的相互达成的共识。