我们提出了一种自适应学习智能辅导系统，该系统使用基于模型的强化学习形式，将学习活动分配给学生。该模型经过数千名学生的轨迹培训，以最大程度地提高其运动完成率并继续在线学习，并自动调整自己的新活动。与学生进行的随机对照试验表明，与其他方法相比，我们的模型可提高较高的完成率，并显着改善学生的参与度。我们的方法是完全自动解锁学习经验个性化的新机会。

在智能辅导系统中生成提示的现有工作（ITS）主要集中在手动和非个人反馈上。在这项工作中，我们探索了ITS中的个性化反馈作为个性化反馈。我们的个性化反馈可以在学生答案中查明正确，错误或缺失的短语，并通过提出自然语言问题来指导他们正确答案。我们的方法结合了因果分析，以使用基于文本相似性的NLP变压器模型来分解学生答案，以识别正确和不正确或缺失的零件。我们培训了一些弹药的神经问题生成和问题重新排序模型，以显示解决学生答案中缺少的组件的问题，这些组件使学生朝着正确的答案迈进。在基于真实对话的ITS测试时，我们的模型在学生学习的增长方面大大优于简单和强大的基线。最后，我们表明我们个性化的纠正反馈系统有可能改善生成的问答系统。

The general translator formalism and computing specific implementations are proposed. The implementation of specific elements necessary to process the source and destination information within the translators are presented. Some common directives or instructions, such as classes and procedures, were unified and generalized in order to allow general translations implementations. In order to cover general cases, two levels of processing are required, related to the source and destination information appropriate transformations, with the related control and processing instructions. The proposed general translator elements are useful for processing natural or artificial information described through any types of languages or systems.

Scale-invariance is an open problem in many computer vision subfields. For example, object labels should remain constant across scales, yet model predictions diverge in many cases. This problem gets harder for tasks where the ground-truth labels change with the presentation scale. In image quality assessment (IQA), downsampling attenuates impairments, e.g., blurs or compression artifacts, which can positively affect the impression evoked in subjective studies. To accurately predict perceptual image quality, cross-resolution IQA methods must therefore account for resolution-dependent errors induced by model inadequacies as well as for the perceptual label shifts in the ground truth. We present the first study of its kind that disentangles and examines the two issues separately via KonX, a novel, carefully crafted cross-resolution IQA database. This paper contributes the following: 1. Through KonX, we provide empirical evidence of label shifts caused by changes in the presentation resolution. 2. We show that objective IQA methods have a scale bias, which reduces their predictive performance. 3. We propose a multi-scale and multi-column DNN architecture that improves performance over previous state-of-the-art IQA models for this task, including recent transformers. We thus both raise and address a novel research problem in image quality assessment.

Document images are a ubiquitous source of data where the text is organized in a complex hierarchical structure ranging from fine granularity (e.g., words), medium granularity (e.g., regions such as paragraphs or figures), to coarse granularity (e.g., the whole page). The spatial hierarchical relationships between content at different levels of granularity are crucial for document image understanding tasks. Existing methods learn features from either word-level or region-level but fail to consider both simultaneously. Word-level models are restricted by the fact that they originate from pure-text language models, which only encode the word-level context. In contrast, region-level models attempt to encode regions corresponding to paragraphs or text blocks into a single embedding, but they perform worse with additional word-level features. To deal with these issues, we propose MGDoc, a new multi-modal multi-granular pre-training framework that encodes page-level, region-level, and word-level information at the same time. MGDoc uses a unified text-visual encoder to obtain multi-modal features across different granularities, which makes it possible to project the multi-granular features into the same hyperspace. To model the region-word correlation, we design a cross-granular attention mechanism and specific pre-training tasks for our model to reinforce the model of learning the hierarchy between regions and words. Experiments demonstrate that our proposed model can learn better features that perform well across granularities and lead to improvements in downstream tasks.

We discuss a platform that has both software and hardware components, and whose purpose is to support research into characterizing and mitigating the sim-to-real gap in robotics and vehicle autonomy engineering. The software is operating-system independent and has three main components: a simulation engine called Chrono, which supports high-fidelity vehicle and sensor simulation; an autonomy stack for algorithm design and testing; and a development environment that supports visualization and hardware-in-the-loop experimentation. The accompanying hardware platform is a 1/6th scale vehicle augmented with reconfigurable mountings for computing, sensing, and tracking. Since this vehicle platform has a digital twin within the simulation environment, one can test the same autonomy perception, state estimation, or controls algorithms, as well as the processors they run on, in both simulation and reality. A demonstration is provided to show the utilization of this platform for autonomy research. Future work will concentrate on augmenting ART/ATK with support for a full-sized Chevy Bolt EUV, which will be made available to this group in the immediate future.

我们介绍了Sparrow，这是一个寻求信息的对话代理，与提示的语言模型基线相比，训练有素，更有帮助，正确和无害。我们使用从人类反馈中的强化学习来培训我们的模型，以帮助人类评估者判断代理人的行为。首先，为了使我们的代理人更有帮助和无害，我们将良好对话的要求分解为代理人应遵循的自然语言规则，并分别向评估者询问每个规则。我们证明，这种崩溃使我们能够收集对代理行为的更多针对性的人类判断，并允许更有效的规则条件奖励模型。其次，我们的代理商在收集对模型声明的偏好判决时提供了支持事实主张的来源的证据。对于事实问题，麻雀提供的证据支持了78％的时间。比基线比基线更享受麻雀，同时对人类的对抗性探测更具弹性，在探测时只有8％的时间违反了我们的规则。最后，我们进行了广泛的分析，表明尽管我们的模型学会遵守我们的规则，但它可以表现出分布偏见。

在本文中，我们研究了视觉和热图像的性能和公平性，并将评估扩展到掩盖的合成图像。使用SeadyFace和Thermal掩码数据集，我们提出了一个过程来评估真实图像的公平性，并显示如何将同一过程应用于合成图像。随机猜测的人口统计差异为1.59，当识别性能提高到99.99 \％时，人口统计学差异为1.59。我们表明，固有的偏见数据集可以深深影响任何生物识别系统的公平性。偏见数据集的主要原因是由于数据收集过程而导致的类不平衡。为了解决不平衡的数据集，可以使用合成图像来增强样品的较少类，以生成更平衡的数据集，从而在训练机器学习系统时产生较小的偏见。对于支持生物特征的系统，公平性至关重要，而相关的公平，多样性和包容性（EDI）的相关概念非常适合生物识别技术公平性的概括，我们专注于3个最常见的人口统计组年龄，性别和种族。

鉴于生成对抗网络（GAN）的多功能性，我们试图了解使用现有的gan从现有的gan增强模拟图像并减少SIM卡之间的差距所带来的好处。我们在模拟机器人性能和基于图像的感知的背景下进行分析。具体而言，我们量化了GAN减少机器人技术图像感知差异的能力。使用语义细分，我们使用名义上和增强的城市环境模拟来分析训练和测试中的SIM对差异。作为次要应用，我们考虑使用GAN来增强室内环境。对于此应用，对象检测用于分析训练和测试的增强。提出的结果量化了使用GAN时SIM到真实差距的减少，并说明了其使用的好处。

连续的软件工程在许多领域已变得司空见惯。但是，在调节需要考虑其他问题的密集部门时，通常认为很难采用连续的开发方法，例如DevOps。在本文中，我们提出了一种将拉力请求用作设计控件的方法，并将这种方法应用于认证的医疗系统中的机器学习，这是一种新颖的技术，这是一种新颖的技术，旨在为机器学习系统增加解释性，作为监管审核跟踪。我们以前曾使用过一种工业系统来证明这种方法，以证明如何以连续的方式开发医疗系统。