AI Communications最新文献_第7页

Decision-making under uncertainty for multi-robot systems 多机器人系统的不确定决策

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-09-01 DOI: 10.3233/aic-220118

Bruno Lacerda, Anna Gautier, Alex Rutherford, A. Stephens, Charlie Street, N. Hawes

引用次数: 1

Person re-identification based on multi-scale global feature and weight-driven part feature 基于多尺度全局特征和权重驱动部分特征的人物再识别

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-08-24 DOI: 10.3233/aic-210258

Qingwei Tang, Pu Yan, Jie Chen, Hui Shao, Fuyu Wang, G. Wang

引用次数: 0

Cross-form efficient attention pyramidal network for semantic image segmentation 面向语义图像分割的十字形高效注意力金字塔网络

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-08-09 DOI: 10.3233/aic-210266

Anamika Maurya, S. Chand

引用次数: 1

Deep Reinforcement Learning for Multi-Agent Interaction 多智能体交互的深度强化学习

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-08-02 DOI: 10.48550/arXiv.2208.01769

I. Ahmed, Cillian Brewitt, Ignacio Carlucho, Filippos Christianos, Mhairi Dunion, Elliot Fosong, Samuel Garcin, Shangmin Guo, Balint Gyevnar, Trevor A. McInroe, Georgios Papoudakis, Arrasy Rahman, Lukas Schafer, Massimiliano Tamborski, Giuseppe Vecchio, Cheng Wang, Stefano V. Albrecht

引用次数: 6

Perspectives on the System-level Design of a Safe Autonomous Driving Stack 安全自动驾驶堆栈系统级设计展望

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-07-29 DOI: 10.48550/arXiv.2208.00096

Majd Hawasly, Jonathan Sadeghi, Morris Antonello, Stefano V. Albrecht, John Redford, S. Ramamoorthy

引用次数: 1

An argumentative approach for handling inconsistency in prioritized Datalog± ontologies 处理优先数据本体论中不一致的论证方法

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-07-18 DOI: 10.3233/aic-220087

Loan Ho, S. Arch-int, Erman Acar, S. Schlobach, N. Arch-int

{"title":"An argumentative approach for handling inconsistency in prioritized Datalog± ontologies","authors":"Loan Ho, S. Arch-int, Erman Acar, S. Schlobach, N. Arch-int","doi":"10.3233/aic-220087","DOIUrl":"https://doi.org/10.3233/aic-220087","url":null,"abstract":"Prioritized Datalog ± is a well-studied formalism for modelling ontological knowledge and data, and has a success story in many applications in the (Semantic) Web and in other domains. Since the information content on the Web is both inherently context-dependent and frequently updated, the occurrence of a logical inconsistency is often inevitable. This phenomenon has led the research community to develop various types of inconsistency-tolerant semantics over the last few decades. Although the study of query answering under inconsistency-tolerant semantics is well-understood, the problem of explaining query answering under such semantics took considerably less attention, especially in the scenario where the facts are prioritized. In this paper, we aim to fill this gap. More specifically, we use Dung’s abstract argumentation framework to address the problem of explaining inconsistency-tolerant query answering in Datalog ± KB where facts are prioritized, or preordered. We clarify the relationship between preferred repair semantics and various notions of extensions for argumentation frameworks. The strength of such argumentation-based approach is the explainability; users can more easily understand why different points of views are conflicting and why the query answer is entailed (or not) under different semantics. To this end we introduce the formal notion of a dialogical explanation, and show how it can be used to both explain showing why query results hold and not hold according to the known semantics in inconsistent Datalog ± knowledge bases.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"6 1","pages":"243-267"},"PeriodicalIF":0.8,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81898610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Improved YOLOv3 detection method for PCB plug-in solder joint defects based on ordered probability density weighting and attention mechanism 基于有序概率密度加权和注意机制的PCB插件焊点缺陷YOLOv3检测方法的改进

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-07-15 DOI: 10.3233/aic-210245

Zheng Wang, Wenbin Chen, Taifu Li, Shaolin Zhang, Rui Xiong

{"title":"Improved YOLOv3 detection method for PCB plug-in solder joint defects based on ordered probability density weighting and attention mechanism","authors":"Zheng Wang, Wenbin Chen, Taifu Li, Shaolin Zhang, Rui Xiong","doi":"10.3233/aic-210245","DOIUrl":"https://doi.org/10.3233/aic-210245","url":null,"abstract":"Printed Circuit Board (PCB) is the heart component of electronic products, and its defect detection is the basic requirement of PCB quality control in the production process. Traditional visual detection methods need artificial design features, so their detection accuracy is poor, and the rate of missed and false detection is high. To solve the above problems, this paper proposes an improved YOLOv3 (You Only Look Once) detection method for PCB plug-in solder spot defects based on the combination of the ordered probability density weighting and the attention mechanism. First, in order to obtain a higher priority priori box, the ordered probability density weighting (OWA) method is used to optimize the multiple sets of a priori boxes generated by K-means. Then, to get more effective feature information, the Squeeze-and-Excitation mechanism (SE) is added to the backbone network. In the feature detection network, the Convolutional Block Attention Module (CBAM) attention mechanism is joined, at the same time in the inspection network output layer three layer feature are fusions. Finally, in order to accelerate the convergence speed of model and improve the accuracy of the model, the network loss function was improved by using the generalized joint generalized intersection over union (GIoU), and the COCO data model was applied to PCB solder spot defect training by transfer learning method. After testing, the average detection accuracy of improved network is improved from 84.35% to 96.69%, and the improved network has better convergence than the original network. The study shows that the improved method based on YOLOv3 is more suitable for industrial application of PCB plug-in solder spot defect detection.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"58 1","pages":"171-186"},"PeriodicalIF":0.8,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90254002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Highlights of AI Research in Europe 欧洲人工智能研究的亮点

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-07-14 DOI: 10.3233/aic-229002

S. Schockaert, R. Peñaloza

引用次数: 0

Channel attention and multi-scale graph neural networks for skeleton-based action recognition 基于骨架的动作识别的通道关注和多尺度图神经网络

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-07-13 DOI: 10.3233/aic-210250

Ronghao Dang, Chengju Liu, Meilin Liu, Qi Chen

{"title":"Channel attention and multi-scale graph neural networks for skeleton-based action recognition","authors":"Ronghao Dang, Chengju Liu, Meilin Liu, Qi Chen","doi":"10.3233/aic-210250","DOIUrl":"https://doi.org/10.3233/aic-210250","url":null,"abstract":"3D skeleton data has been widely used in action recognition as the skeleton-based method has achieved good performance in complex dynamic environments. The rise of spatio-temporal graph convolutions has attracted much attention to use graph convolution to extract spatial and temporal features together in the field of skeleton-based action recognition. However, due to the huge difference in the focus of spatial and temporal features, it is difficult to improve the efficiency of extracting the spatiotemporal features. In this paper, we propose a channel attention and multi-scale neural network (CA-MSN) for skeleton-based action recognition with a series of spatio-temporal extraction modules. We exploit the relationship of body joints hierarchically through two modules, i.e., a spatial module which uses the residual GCN network with the channel attention block to extract the high-level spatial features, and a temporal module which uses the multi-scale TCN network to extract the temporal features at different scales. We perform extensive experiments on both the NTU-RGBD60 and NTU-RGBD120 datasets to verify the effectiveness of our network. The comparison results show that our method achieves the state-of-the-art performance with the competitive computing speed. In order to test the application effect of our CA-MSN model, we design a multi-task tandem network consisting of 2D pose estimation, 2D to 3D pose regression and skeleton action recognition model. The end-to-end (RGB video-to-action type) recognition effect is demonstrated. The code is available at https://github.com/Rh-Dang/CA-MSN-action-recognition.git.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"106 1","pages":"187-205"},"PeriodicalIF":0.8,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82457126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Multiple refinement and integration network for Salient Object Detection 显著目标检测的多重细化集成网络

IF 0.8 4区计算机科学

AI Communications Pub Date : 2022-05-10 DOI: 10.3233/aic-210273

Chao Dai, Chen Pan, W. He, Hanqi Sun

{"title":"Multiple refinement and integration network for Salient Object Detection","authors":"Chao Dai, Chen Pan, W. He, Hanqi Sun","doi":"10.3233/aic-210273","DOIUrl":"https://doi.org/10.3233/aic-210273","url":null,"abstract":"The purpose of the salient object detection (SOD) task is to suppress the background noise and segment the salient foreground regions. Some previous methods considered the strategies of background suppression and multi-level feature fusion. Other methods encountered the problem that single-scale convolution features are difficult to capture the correct object size. This paper reconsiders the above problems and proposes a comprehensive solution to achieve SOD for improving the detection performance and ensuring relatively fewer parameters. First, it is difficult to achieve a better refinement effect through only one refinement operation. To this end, a multi-scale denoising module (MSDM) and multi-pooling refinement module (MPRM) are proposed to jointly complete the refinement task of multi-level features. Besides, it is difficult to fully integrate complementary features through only one feature integration operation. Mutual learning module (MLM) is proposed to preliminarily integrate multi-level features. To reduce information redundancy, multi-attention (MA) mechanism is used to assist further integration. The proposed algorithm is called multiple refinement and integration network (MRINet). Experimental results on five benchmark datasets show that MRINet outperforms state-of-the-art methods on multiple evaluation metrics. Moreover, our ResNet-based algorithm only contains 25.202 million parameters, which is less than other ResNet-based algorithms and can run at over 37 fps on a single GPU. The code will be available at https://github.com/dc3234/MRINet.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":"247 1","pages":"31-44"},"PeriodicalIF":0.8,"publicationDate":"2022-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76756365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1