Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning最新文献

Improved Convolutional Neural Networks by Integrating High-frequency Information for Image Classification 基于高频信息的图像分类改进卷积神经网络

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590082

Chengyuan Zhuang, Xiaohui Yuan, Xuan Guo, Zhenchun Wei, Juan Xu, Yuqi Fan

引用次数: 0

SSGAR: A Genetic-based Routing Solution for Aeronautical Networks aided by Software Defined Satellite Network 基于软件定义卫星网络的航空网络遗传路由解决方案

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590069

Kaixuan Sun, Ketong Wu, Wenke Yuan, Guangyuan Wei, Huasen He

{"title":"SSGAR: A Genetic-based Routing Solution for Aeronautical Networks aided by Software Defined Satellite Network","authors":"Kaixuan Sun, Ketong Wu, Wenke Yuan, Guangyuan Wei, Huasen He","doi":"10.1145/3590003.3590069","DOIUrl":"https://doi.org/10.1145/3590003.3590069","url":null,"abstract":"In the next generation network, both the satellite network layer and aeronautical network layer will play significant roles, leading the world into the era of global interconnectivity. However, the large-scale and high-mobility characteristics of aircraft networks greatly challenge the application of traditional routing algorithms. Therefore, this paper aims to solve this challenge by exploiting a Software Defined Satellite Network (Sat-SDN) to facilitate the routing in aeronautical networks. By centrally controlling aeronautical routing through satellites, the computation and communication overhead for aeronautical networks are relieved, since frequent packet flooding and broadcasting for synchronizing the rapidly-fluctuating topology of aeronautical networks can be avoided. To extend the aeronautical networking and transmission mechanism to a global scale, a multi-domain extension mechanism is proposed, while the concept of dynamic inter-domain telescope nodes is induced to greatly simplify the network topology. A Sat-SDN aided Genetic-based Aeronautical Routing (SSGAR) algorithm is further designed to solve the problem of huge routing calculation space and long convergence time in large-scale multi-node network scenarios. Moreover, experiments and simulations are conducted using real aircraft data, which demonstrate that our proposed SSGAR algorithm can effectively reduce communication costs and improve transmission quality compared to existing solutions.","PeriodicalId":340225,"journal":{"name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125920727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detecting Respiratory Events with End-to-End ConvNet 端到端卷积神经网络检测呼吸事件

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590098

Yanping Shuai, Zhangbo Li, Xingjun Wang, Hanrong Cheng

引用次数: 0

Robust Hypergraph-Augmented Graph Contrastive Learning for Graph Self-Supervised Learning 图自监督学习的鲁棒超图-增广图对比学习

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590053

Zeming Wang, Xiaoyang Li, Rui Wang, Changwen Zheng

{"title":"Robust Hypergraph-Augmented Graph Contrastive Learning for Graph Self-Supervised Learning","authors":"Zeming Wang, Xiaoyang Li, Rui Wang, Changwen Zheng","doi":"10.1145/3590003.3590053","DOIUrl":"https://doi.org/10.1145/3590003.3590053","url":null,"abstract":"Graph contrastive learning has emerged as a promising method for self-supervised graph representation learning. The traditional framework conventionally imposes two graph views generated by leveraging graph data augmentations. Such an approach focuses on leading the model to learn discriminative information from graph local structures, which brings up an intrinsic issue that the model partially fails to obtain sufficient discriminative information contained by the graph global information. To this end, we propose a hypergraph-augmented view to empower the self-supervised graph representation learning model to better capture the global information from nodes and corresponding edges. In the further exploration of the graph contrastive learning, we discover a principal challenge undermining conventional contrastive methods: the false negative sample problem, i.e., specific negative samples actually belong to the same category of the anchor sample. To address this issue, we take the neighbors of nodes into consideration and propose the robust graph contrastive learning. In practice, we empirically observe that the proposed hypergraph-augmented view can further enhance the robustness of graph contrastive learning by adopting our framework. Based on these improvements, we propose a novel method called Robust Hypergraph-Augmented Graph Contrastive Learning (RH-GCL). We conduct various experiments in the settings of both transductive and inductive node classification. The results demonstrate that our method achieves the state-of-the-art (SOTA) performance on different datasets. Specifically, the accuracy of node classification on Cora dataset is 84.4%, which is 1.1% higher than that of GRACE. We also perform the ablation study to verify the effectiveness of each part of our proposed method.","PeriodicalId":340225,"journal":{"name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129792753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved YOLOv5 UAV Target Detection Algorithm by Fused Attention Mechanism 基于融合注意机制的改进YOLOv5无人机目标检测算法

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590074

Yan He, Yanni Zhao, Hongfei Nie

引用次数: 0

Construction of Scene Library System for Commercial Vehicle Products Based on Multidimensional Terminal 基于多维终端的商用车产品场景库系统构建

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590025

Yunshuang Zheng, Shilan Hu, Nannan Xue

{"title":"Construction of Scene Library System for Commercial Vehicle Products Based on Multidimensional Terminal","authors":"Yunshuang Zheng, Shilan Hu, Nannan Xue","doi":"10.1145/3590003.3590025","DOIUrl":"https://doi.org/10.1145/3590003.3590025","url":null,"abstract":"Scene-based product design is an effective way to improve user experience, and static scene library is an important basis for commercial vehicle product planners to carry out their design and planning. This paper explores the characteristics of commercial vehicle's dynamic scene and static scene, as well as the current situation and problems of commercial vehicle's static scene library. Furthermore, using the research methods of combinatorial design and task analysis, a digital solution for the construction of commercial vehicle's static scene library is proposed in this paper with reference to the construction process of vehicle's dynamic scene library. This paper innovatively proposes the combination of scene library and questionnaire system to solve the problem of scene information collection and scene creation scale by means of structured questionnaire, which provides a reference for the construction of commercial vehicle's static scene library.This research helps automobile enterprises systematically manage the survey results, expand the application scope of the survey data, extend the application time of the survey results, and reduce the enterprise research costs.","PeriodicalId":340225,"journal":{"name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115947625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discrimination of seismic and non-seismic signal using SCOUTER 基于SCOUTER的地震与非地震信号识别

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590045

Kang Wang, Ji Zhang, J. Zhang

引用次数: 0

Image Generation Model Applying PCA on Latent Space 基于PCA的隐空间图像生成模型

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590080

Myungseo Song, Asim Niaz, K. Choi

引用次数: 0

Deep Vision Network Based CT Image Detection for Aiding Lumbar Herniated Disc Diagnosis 基于深度视觉网络的CT图像检测辅助腰椎间盘突出症诊断

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590092

W. Xie, Fei-wei Qin, Yanli Shao

{"title":"Deep Vision Network Based CT Image Detection for Aiding Lumbar Herniated Disc Diagnosis","authors":"W. Xie, Fei-wei Qin, Yanli Shao","doi":"10.1145/3590003.3590092","DOIUrl":"https://doi.org/10.1145/3590003.3590092","url":null,"abstract":"Recently, artificial intelligence (AI) technologies have applied in the field of clinical medicine widely. And some of researches try to use AI to assist the diagnosis of spinal disease. In this study, HerniationDet: an automatic lumbar disc herniation detection method based on two stage detection framework (e.g. R-CNN, Fast R-CNN, Faster R-CNN, etc.) is presented. Firstly, after comparing the performance of various backbone networks such as VGG, ResNet, EfficientNet, etc., a feature extractor based on VGG16 is constructed to automatically and efficiently extract the necessary feature information from medical images. Secondly, we use region proposal network (RPN) to generate region proposals and provide them to the part of Fast R-CNN for classification and regression. After precisely studying the image of disc herniation, we adjust the scale and radio of the anchor, to make them more in line with the characteristics of the lumbar disc image dataset. Finally, the object detection algorithm is first used on CT images which achieved 89.50% mAP, and then applied to MR images of the lumbar disc to achieve the goal of automatically identifying lumbar disc herniation with or without calcification. Hence, artificial intelligence assisted diagnosis of calcified lumbar disc herniation on MR images can be achieved with 81.24% mAP, by further using a multi-modal learning strategy.","PeriodicalId":340225,"journal":{"name":"Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127124341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance evaluation of agricultural logistics enterprises based on GA algorithm 基于遗传算法的农产品物流企业绩效评价

Proceedings of the 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning Pub Date : 2023-03-17 DOI: 10.1145/3590003.3590068

Bin Ye

引用次数: 0