最近已经设计了一些轻巧的卷积神经网络（CNN）模型，用于遥感对象检测（RSOD）。但是，他们中的大多数只是用可分离的卷积代替了香草卷积，这可能是由于很多精确损失而无法有效的，并且可能无法检测到方向的边界框（OBB）。同样，现有的OBB检测方法很难准确限制CNN预测的对象的形状。在本文中，我们提出了一个有效的面向轻质对象检测器（LO-DET）。具体而言，通道分离聚集（CSA）结构旨在简化可分开的卷积的复杂性，并开发了动态的接收场（DRF）机制，以通过自定义卷积内核及其感知范围来保持高精度，以保持高精度。网络复杂性。 CSA-DRF组件在保持高精度的同时优化了效率。然后，对角支撑约束头（DSC-Head）组件旨在检测OBB，并更准确，更稳定地限制其形状。公共数据集上的广泛实验表明，即使在嵌入式设备上，拟议的LO-DET也可以非常快地运行，具有检测方向对象的竞争精度。

任意为导向的对象检测（AOOD）在遥感方案中的图像理解起着重要作用。现有的AOOD方法面临歧义和高成本的挑战。为此，提出了由粗粒角分类（CAC）和细粒角回归（FAR）组成的多透明角度表示（MGAR）方法。具体而言，设计的CAC避免了通过离散角编码（DAE）避免角度预测的歧义，并通过使DAE的粒度变形来降低复杂性。基于CAC，FAR的开发是为了优化角度预测，成本比狭窄的DAE粒度要低得多。此外，与IOU指导的自适应重新加权机制相交，旨在提高角度预测的准确性（IFL）。在几个公共遥感数据集上进行了广泛的实验，这证明了拟议的MGAR的有效性。此外，对嵌入式设备进行的实验表明，拟议的MGAR也对轻型部署也很友好。

任意为导向的对象检测（AOOD）已被广泛应用于在遥感图像中以不同方向的方式定位和分类对象。但是，AOOD模型中本地化和分类任务的不一致特征可能会导致歧义和低质量的对象预测，从而限制了检测性能。在本文中，提出了一种称为任务采样卷积（TS-CONV）的AOOD方法。 TS-CONV适应从各个敏感区域进行任务特征，并将这些特征映射为对齐方式，以指导动态标签分配以获得更好的预测。具体而言，TS-CONV中定位卷积的采样位置由与空间坐标相关的定向边界框（OBB）预测监督。尽管分类卷积的采样位置和卷积内核设计为根据不同方向进行自适应调整，以改善特征的方向鲁棒性。此外，制定了动态任务感知标签分配（DTLA）策略来选择最佳候选位置，并根据从TS-CONV获得的排名的任务吸引分数动态分配标签。在涵盖多个场景，多模式图像和多个对象的几个公共数据集上进行了广泛的实验，证明了所提出的TS-CONV的有效性，可伸缩性和出色性能。

最近，已经提出了许多任意定向的物体检测（AOOD）方法并在许多领域中引起了广泛的关注。然而，它们中的大多数基于锚箱或标准高斯热手套。这种标签分配策略不仅可以反映任意取向对象的形状和方向特征，而且还具有高参数调整工作。本文提出了一种称为通用高斯热爱标记（GGH1）的新型Aood方法。具体地，提出了一种无锚性对象适应标签分配（OLA）策略以基于二维（2-D）定向的高斯热手段来定义正面候选物，其反映了任意取向对象的形状和方向特征。基于OLA，开发了定向边界盒（OBB）表示组分（ORC）以指示OBBS并通过神经网络学习适应地调整高斯中心以适应不同对象的特征。此外，具有面积标准化和动态置信度加权的关节优化损耗（JOL）旨在优化不同子特设的错位最佳结果。公共数据集的广泛实验表明，所提出的GGHL具有低参数调整和时间成本的良好性能。此外，通常适用于大多数Aood的方法，以提高其性能，包括嵌入式平台上的轻量级模型。

尽管Yolov2方法在对象检测时非常快，但由于其骨干网络的性能较低和多尺度区域特征的缺乏，其检测准确性受到限制。因此，在本文中提出了一种基于Yolov2的Yolo（DC）Yolo（DC-SPP-YOLO）方法的密集连接（DC）和空间金字塔池（SPP）方法。具体而言，在Yolov2的骨干网络中采用了卷积层的密集连接，以增强特征提取并减轻消失的梯度问题。此外，引入了改进的空间金字塔池以池并加入多尺度区域特征，以便网络可以更全面地学习对象功能。 DC-SPP-YOLO模型是根据由MSE（均方误差）损耗和跨透镜损失组成的新损失函数建立和训练的。实验结果表明，DC-SPP-Yolo的地图（平均平均精度）高于Pascal VOC数据集和UA-Detrac数据集上的Yolov2。提出了DC-SPP-Yolo方法的有效性。

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.

Supervised Question Answering systems (QA systems) rely on domain-specific human-labeled data for training. Unsupervised QA systems generate their own question-answer training pairs, typically using secondary knowledge sources to achieve this outcome. Our approach (called PIE-QG) uses Open Information Extraction (OpenIE) to generate synthetic training questions from paraphrased passages and uses the question-answer pairs as training data for a language model for a state-of-the-art QA system based on BERT. Triples in the form of <subject, predicate, object> are extracted from each passage, and questions are formed with subjects (or objects) and predicates while objects (or subjects) are considered as answers. Experimenting on five extractive QA datasets demonstrates that our technique achieves on-par performance with existing state-of-the-art QA systems with the benefit of being trained on an order of magnitude fewer documents and without any recourse to external reference data sources.

Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale labeled dataset (e.g., ImageNet) for achieving satisfactory performance, which is usually unavailable for medical images. Additionally, due to the gap between medical and natural images, the improvement generated by the ImageNet pretrained weights significantly degrades while transferring the weights to medical image processing tasks. In this paper, we propose Bootstrap Own Latent of Transformer (BOLT), a self-supervised learning approach specifically for medical image classification with the Transformer backbone. Our BOLT consists of two networks, namely online and target branches, for self-supervised representation learning. Concretely, the online network is trained to predict the target network representation of the same patch embedding tokens with a different perturbation. To maximally excavate the impact of Transformer from limited medical data, we propose an auxiliary difficulty ranking task. The Transformer is enforced to identify which branch (i.e., online/target) is processing the more difficult perturbed tokens. Overall, the Transformer endeavours itself to distill the transformation-invariant features from the perturbed tokens to simultaneously achieve difficulty measurement and maintain the consistency of self-supervised representations. The proposed BOLT is evaluated on three medical image processing tasks, i.e., skin lesion classification, knee fatigue fracture grading and diabetic retinopathy grading. The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.

Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs often contain incomplete triples that are difficult to inductively infer by KGEs. To address this challenge, we resort to analogical inference and propose a novel and general self-supervised framework AnKGE to enhance KGE models with analogical inference capability. We propose an analogical object retriever that retrieves appropriate analogical objects from entity-level, relation-level, and triple-level. And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding. In order to combine inductive inference capability from the original KGE model and analogical inference capability enhanced by AnKGE, we interpolate the analogy score with the base model score and introduce the adaptive weights in the score function for prediction. Through extensive experiments on FB15k-237 and WN18RR datasets, we show that AnKGE achieves competitive results on link prediction task and well performs analogical inference.

Digital engineering transformation is a crucial process for the engineering paradigm shifts in the fourth industrial revolution (4IR), and artificial intelligence (AI) is a critical enabling technology in digital engineering transformation. This article discusses the following research questions: What are the fundamental changes in the 4IR? More specifically, what are the fundamental changes in engineering? What is digital engineering? What are the main uncertainties there? What is trustworthy AI? Why is it important today? What are emerging engineering paradigm shifts in the 4IR? What is the relationship between the data-intensive paradigm and digital engineering transformation? What should we do for digitalization? From investigating the pattern of industrial revolutions, this article argues that ubiquitous machine intelligence (uMI) is the defining power brought by the 4IR. Digitalization is a condition to leverage ubiquitous machine intelligence. Digital engineering transformation towards Industry 4.0 has three essential building blocks: digitalization of engineering, leveraging ubiquitous machine intelligence, and building digital trust and security. The engineering design community at large is facing an excellent opportunity to bring the new capabilities of ubiquitous machine intelligence and trustworthy AI principles, as well as digital trust, together in various engineering systems design to ensure the trustworthiness of systems in Industry 4.0.