尽管在现代的机器学习算法的最新进展，其内在机制的不透明仍是采用的障碍。在人工智能系统灌输信心和信任，解释的人工智能已成为提高现代机器学习算法explainability的响应。归纳逻辑程序（ILP），符号人工智能的子场中，起着产生，因为它的直观的逻辑驱动框架的可解释的解释有希望的作用。 ILP有效利用绎推理产生从实例和背景知识解释的一阶分句理论。然而，在发展中通过ILP需要启发方法的几个挑战，在实践中他们的成功应用来解决。例如，现有的ILP系统通常拥有广阔的解空间，以及感应解决方案是对噪声和干扰非常敏感。本次调查总结在ILP的最新进展和统计关系学习和神经象征算法的讨论，其中提供给ILP协同意见。继最新进展的严格审查，我们划定观察的挑战，突出对发展不言自明的人工智能系统进一步ILP动机研究的潜在途径。

Autonomous vehicle (AV) algorithms need to be tested extensively in order to make sure the vehicle and the passengers will be safe while using it after the implementation. Testing these algorithms in real world create another important safety critical point. Real world testing is also subjected to limitations such as logistic limitations to carry or drive the vehicle to a certain location. For this purpose, hardware in the loop (HIL) simulations as well as virtual environments such as CARLA and LG SVL are used widely. This paper discusses a method that combines the real vehicle with the virtual world, called vehicle in virtual environment (VVE). This method projects the vehicle location and heading into a virtual world for desired testing, and transfers back the information from sensors in the virtual world to the vehicle. As a result, while vehicle is moving in the real world, it simultaneously moves in the virtual world and obtains the situational awareness via multiple virtual sensors. This would allow testing in a safe environment with the real vehicle while providing some additional benefits on vehicle dynamics fidelity, logistics limitations and passenger experience testing. The paper also demonstrates an example case study where path following and the virtual sensors are utilized to test a radar based stopping algorithm.

The simple idea that not all things are equally difficult has surprising implications when applied in a fairness context. In this work we explore how "difficulty" is model-specific, such that different models find different parts of a dataset challenging. When difficulty correlates with group information, we term this difficulty disparity. Drawing a connection with recent work exploring the inductive bias towards simplicity of SGD-trained models, we show that when such a disparity exists, it is further amplified by commonly-used models. We quantify this amplification factor across a range of settings aiming towards a fuller understanding of the role of model bias. We also present a challenge to the simplifying assumption that "fixing" a dataset is sufficient to ensure unbiased performance.

With water quality management processes, identifying and interpreting relationships between features, such as location and weather variable tuples, and water quality variables, such as levels of bacteria, is key to gaining insights and identifying areas where interventions should be made. There is a need for a search process to identify the locations and types of phenomena that are influencing water quality and a need to explain why the quality is being affected and which factors are most relevant. This paper addresses both of these issues through the development of a process for collecting data for features that represent a variety of variables over a spatial region, which are used for training and inference, and analysing the performance of the features using the model and Shapley values. Shapley values originated in cooperative game theory and can be used to aid in the interpretation of machine learning results. Evaluations are performed using several machine learning algorithms and water quality data from the Dublin Grand Canal basin.

With the boom of digital educational materials and scalable e-learning systems, the potential for realising AI-assisted personalised learning has skyrocketed. In this landscape, the automatic generation of educational questions will play a key role, enabling scalable self-assessment when a global population is manoeuvring their personalised learning journeys. We develop EduQG, a novel educational question generation model built by adapting a large language model. Our initial experiments demonstrate that EduQG can produce superior educational questions by pre-training on scientific text.

We present a data-driven framework to automate the vectorization and machine interpretation of 2D engineering part drawings. In industrial settings, most manufacturing engineers still rely on manual reads to identify the topological and manufacturing requirements from drawings submitted by designers. The interpretation process is laborious and time-consuming, which severely inhibits the efficiency of part quotation and manufacturing tasks. While recent advances in image-based computer vision methods have demonstrated great potential in interpreting natural images through semantic segmentation approaches, the application of such methods in parsing engineering technical drawings into semantically accurate components remains a significant challenge. The severe pixel sparsity in engineering drawings also restricts the effective featurization of image-based data-driven methods. To overcome these challenges, we propose a deep learning based framework that predicts the semantic type of each vectorized component. Taking a raster image as input, we vectorize all components through thinning, stroke tracing, and cubic bezier fitting. Then a graph of such components is generated based on the connectivity between the components. Finally, a graph convolutional neural network is trained on this graph data to identify the semantic type of each component. We test our framework in the context of semantic segmentation of text, dimension and, contour components in engineering drawings. Results show that our method yields the best performance compared to recent image, and graph-based segmentation methods.

Giving machines the ability to imagine possible new objects or scenes from linguistic descriptions and produce their realistic renderings is arguably one of the most challenging problems in computer vision. Recent advances in deep generative models have led to new approaches that give promising results towards this goal. In this paper, we introduce a new method called DiCoMoGAN for manipulating videos with natural language, aiming to perform local and semantic edits on a video clip to alter the appearances of an object of interest. Our GAN architecture allows for better utilization of multiple observations by disentangling content and motion to enable controllable semantic edits. To this end, we introduce two tightly coupled networks: (i) a representation network for constructing a concise understanding of motion dynamics and temporally invariant content, and (ii) a translation network that exploits the extracted latent content representation to actuate the manipulation according to the target description. Our qualitative and quantitative evaluations demonstrate that DiCoMoGAN significantly outperforms existing frame-based methods, producing temporally coherent and semantically more meaningful results.

焊接联合检查（SJI）是生产印刷电路板（PCB）的关键过程。在SJI期间发现焊料错误非常具有挑战性，因为焊接接头的尺寸很小，并且可能需要各种形状。在这项研究中，我们首先表明焊料的特征多样性低，并且可以作为精细颗粒的图像分类任务执行SJI，该任务侧重于难以固定的对象类。为了提高细粒度的分类精度，发现通过最大化熵来惩罚自信模型预测，在文献中很有用。与此信息内联，我们建议使用{\ alpha} -skew Jensen-Shannon Divergence（{\ alpha} -js）来惩罚模型预测的信心。我们将{\ alpha} -js正则化与现有基于熵指定的方法和基于注意机制，分割技术，变压器模型和特定损耗函数的方法进行比较。我们表明，在细化的焊料联合分类任务中，所提出的方法可以达到不同模型的F1得分和竞争精度。最后，我们可视化激活图，并表明，凭借熵的规范化，更精确的类歧视区域是局部的，这也更适合噪声。接受代码将在这里接受。

全球定位系统（GPS）已成为我们日常生活的一部分，其主要目标是提供地理位置服务。对于无人驾驶系统（UAS），地理定位能力是极为重要的必要性，使用惯性导航系统（INS）伴随着GPS的心脏而实现。没有地理位置服务，UAS将无法飞往目的地或回家。不幸的是，GPS信号可能会被堵塞，并在Urban Canyons中遇到多路径问题。我们的目标是提出一种替代方法，以降级或拒绝GPS信号时地理位置化UA。考虑到UAS在其平台上具有下降摄像头，可以在平台飞行时获得实时图像，因此我们将现代深度学习技术应用于地理定位。特别是，我们执行图像匹配，以在UAS获得的图像和卫星正尾之间建立潜在特征共轭物。特征匹配的典型应用遭受高层建筑物和该领域的新结构的影响，这些建筑物将不确定性引入同型估算中，因此导致地理定位性能差。取而代之的是，我们将GIS信息从OpenStreetMap（OSM）提取到语义段匹配的功能中，以纳入建筑物和地形类。 GIS掩码在选择语义匹配的功能时可以作为过滤器，从而增强了Coplanarity条件和UAS地理定位精度。发表论文后，我们的代码将在https://github.com/osupcvlab/ubiheredrone2021上公开获得。

本文介绍了一种新颖的端到端无人空中系统（UAS）导航方法，用于现实世界中的远程视觉导航。受到人类本能的双过程视觉导航系统的启发：环境理解和地标识别，我们将UAS导航任务分为两个相同的阶段。我们的系统结合了增强学习（RL）和图像匹配方法。首先，代理在指定环境中使用RL学习导航策略。为了实现这一目标，我们为培训过程设计了一个交互式的UASNAV环境。一旦代理商学习了导航政策，这意味着“熟悉环境”，我们就让UAS在现实世界中飞行，以使用图像匹配方法识别地标，并根据知识渊博的政策采取行动。在导航过程中，UAS嵌入单个相机作为唯一的视觉传感器。我们证明，UAS可以学习在现实世界中最短的道路上距离起点几百米的目的地。