2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)最新文献_第8页

Line Average Pooling: A Better Way to Handle Feature Maps on CNN for Skin Cancer Classification 线平均池化:处理CNN上用于皮肤癌分类的特征图的更好方法

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00094

Zipei Chen, Yifei Du, Teoh Teik Toe

引用次数: 0

Semi-supervised Crowd Counting based on Patch Crowds Statistics 基于补丁人群统计的半监督人群计数

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00130

Sifan Peng, B. Yin, Yinfeng Xia, Qianqian Yang, Luyang Wang

{"title":"Semi-supervised Crowd Counting based on Patch Crowds Statistics","authors":"Sifan Peng, B. Yin, Yinfeng Xia, Qianqian Yang, Luyang Wang","doi":"10.1109/CACML55074.2022.00130","DOIUrl":"https://doi.org/10.1109/CACML55074.2022.00130","url":null,"abstract":"Crowd counting has been widely applied in various fields including social security, urban planning, and intelligent monitoring. A series of excellent fully-supervised crowd counting methods spring up and achieve great performance. Nevertheless, all of the fully-supervised methods deeply depend on large quantities of annotated crowd density maps. Collecting and annotating crowd images is time-consuming and expensive especially for highly dense crowds. In contrast, unlabeled crowd images can be acquired without having to make a great effort. However, it is challenging to effectively exploit unlabeled data for crowd counting. To this end, we propose a semi-supervised crowd counting method that aims to optimize the crowd counting models via exploiting large amounts of unlabeled crowd images. Firstly, we design an effective proxy task based on image patch counts statistics. Then, we present an end-to-end iterative learning strategy to train our semi-supervised framework. To prove the effectiveness of our semi-supervised method, we conducted various experiments on three benchmark crowd counting datasets. Experimental results demonstrate that our semi-supervised algorithm achieves competitive performance compared with the the-state-of-art semi-supervised crowd counting approaches. Furthermore, experimental results show that our method performs well on cross-dataset.","PeriodicalId":137505,"journal":{"name":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132449525","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

DETGAN: GAN for Arbitrary-oriented Object Detection in Remote Sensing Images DETGAN:用于遥感图像中任意方向目标检测的GAN

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00063

Siyuan Cheng, Ping Yao, Kai Deng, Li Fu

{"title":"DETGAN: GAN for Arbitrary-oriented Object Detection in Remote Sensing Images","authors":"Siyuan Cheng, Ping Yao, Kai Deng, Li Fu","doi":"10.1109/CACML55074.2022.00063","DOIUrl":"https://doi.org/10.1109/CACML55074.2022.00063","url":null,"abstract":"Object detection in remote sensing images has be-come a research focus in recent years with the development of deep learning. However, due to objective reasons such as weather, cost, etc., we can hardly obtain abundant high-quality remote sensing images, especially for specific targets, which severely limits the training of the object detector, leading to poor detection performance. Thus for the first time, this paper introduces the Generative Adversarial Networks(GANs) for arbitrary-oriented object detection in remote sensing images, by augmenting the dataset to improve the performance of detectors. We construct DETGAN with two-layer self-attention modules to capture long-distance dependence for high-quality image generation. To solve the mismatch between generated slices and the samples for detectors, we propose the GAN-to-Detection transfer strategy, in which the slices are inserted into a background with the same size as the samples for detectors and then added to the training set. Experiments show that the performance of ship detectors is successfully improved with the transfer strategy, and demonstrate that GAN is an effective way to alleviate the problem of data insufficiency in remote sensing image object detection.","PeriodicalId":137505,"journal":{"name":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125162509","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

Fusion of Global and Local Features for Text Classification 基于全局和局部特征融合的文本分类

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00075

Yifan Hou, Ge Cheng, Yun Zhang, Dongliang Zhang

引用次数: 0

Dynamic Combination Model of Cloud Manufacturing Equipment for Multi-variety and Small-batch 多品种小批量云制造设备动态组合模型

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00065

Zhaoyang Bai, Shuhan Liu, Lin Xiong, Qiyang Huang, Shijian Bao, Hui Tang

{"title":"Dynamic Combination Model of Cloud Manufacturing Equipment for Multi-variety and Small-batch","authors":"Zhaoyang Bai, Shuhan Liu, Lin Xiong, Qiyang Huang, Shijian Bao, Hui Tang","doi":"10.1109/CACML55074.2022.00065","DOIUrl":"https://doi.org/10.1109/CACML55074.2022.00065","url":null,"abstract":"In the cloud manufacturing environment, the sources of manufacturing tasks and resources are more extensive. The balanced utilization of manufacturing resources is conducive to the timely completion of tasks and the good operation of the cloud system platform. Based on the network graph theory, this paper firstly constructed the cloud manufacturing process network graph based on product manufacturing BOM, described the selection constraint relationship between equipment in each process of the product, and formed the matching relationship between product processing process and manufacturing equipment. When new cloud manufacturing tasks and cloud manufacturing resources are added, or the processed and pending tasks and cloud manufacturing resources change, the cloud manufacturing dynamic matching network is updated synchronously. Secondly, considering the load of manufacturing resources, a task load queue centered on manufacturing resources was constructed. The nonlinear programming model was used to build a workshop equipment scheduling model aiming at minimizing the total processing time and cost of products. Finally, genetic algorithm is used to solve and verify the validity and accuracy of the model.","PeriodicalId":137505,"journal":{"name":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116733666","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

Classification for diabetic retinopathy by using staged convolutional neural network 分阶段卷积神经网络在糖尿病视网膜病变分类中的应用

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00045

Hongqiu Wang, Yingxue Sun, Yunjian Cao, G. Ouyang, Xin Wang, Shaozhi Wu, Miao Tian

{"title":"Classification for diabetic retinopathy by using staged convolutional neural network","authors":"Hongqiu Wang, Yingxue Sun, Yunjian Cao, G. Ouyang, Xin Wang, Shaozhi Wu, Miao Tian","doi":"10.1109/CACML55074.2022.00045","DOIUrl":"https://doi.org/10.1109/CACML55074.2022.00045","url":null,"abstract":"Diabetic retinopathy (DR) is the leading cause of permanent blindness in the working-age population, which is one of the common complications of diabetes. DR grading is crucial in determining the relevant treatment to reduce vision loss. Automatic grading approaches of DR are very significant for helping ophthalmologists design adequate treatment to patients. However, DR grading is challenging due to the facts of intra-class variations and inter-class similarities. The key point of solving DR grading is to find abundant discriminative lesions corresponding to subtle visual differences, such as microaneurysms, soft exudates and hemorrhages. To solve the problem, we proposed a two-stage classification process to firstly classify the presence or absence of DR based on the characteristics of fundus images of DR patients. Then for fundus images with DR, we proposed a novel lesion attention module to perceive and capture lesion features for fine-grained classification. Comprehensive experiments are conducted on DDR dataset to evaluate the effectiveness of the proposed DR grading method. Our method achieves the state-of-the-art results on DDR dataset.","PeriodicalId":137505,"journal":{"name":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127075303","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: 2

Cross-Domain Defect Detection Network 跨域缺陷检测网络

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00053

Zhen Zhou, Chuwen Lan, Zehua Gao

{"title":"Cross-Domain Defect Detection Network","authors":"Zhen Zhou, Chuwen Lan, Zehua Gao","doi":"10.1109/CACML55074.2022.00053","DOIUrl":"https://doi.org/10.1109/CACML55074.2022.00053","url":null,"abstract":"Nowadays, defect detection in industrial field has been rapidly developed and applied, but it also faces some problems. First, it is difficult and expensive to collect defect images, leading to the problem of small samples. Networks lack the ability of generalization. Second, current neural networks are limited to specific industrial scenarios for learning and training and hard to be applied on a new domain, thus lack the cross-domain migration capability. Based on the above problems, we propose the concept of cross-domain data joint learning and a two-stage Cross-Domain Defect Detection Network(C3DN) based on segmentation network and classification network, trying to mine the hidden value in cross-domain data. The segmentation part can effectively extract defects from different material textures and locate them while the classification part can focus on the defects with attentional mechanism and tell whether there are defects. In order to verify the feasibility of cross-domain data joint learning, we organized and re-annotated the public datasets of various industrial fields to form a new cross-domain dataset. C3DN had a strong performance in both validation set and test set, showing its good generalization ability. Through the cross-domain defect detection confusion matrix, the excellent performance of C3DN in different industrial fields was compared and verified, showing its good cross-domain migration ability.","PeriodicalId":137505,"journal":{"name":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128149814","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: 1

Few-shot Learning for Human Activity Recognition Based on CSI 基于CSI的少镜头学习人体活动识别

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00074

Sipeng Huang, Yang Chen, Dingchao Wu, Guangwei Yu, Yong Zhang

{"title":"Few-shot Learning for Human Activity Recognition Based on CSI","authors":"Sipeng Huang, Yang Chen, Dingchao Wu, Guangwei Yu, Yong Zhang","doi":"10.1109/CACML55074.2022.00074","DOIUrl":"https://doi.org/10.1109/CACML55074.2022.00074","url":null,"abstract":"Human Activity Recognition(HAR) based on Channel State Information(Csi)plays an increasingly impor-tant role in human-computer interaction. Traditional research requires a large amount of activity sample to train network model. However, collecting a great many of data causes waste of time and manpower. Some models can well identify the categories of activities in this scene, but when another scene is tested, the identification accuracy of the model will be reduced. Therefore it needs to re-collect data to retrain the model. We proposed a method which can transfer the knowledge learned from a scenario to a new scenario. It can also facilitate the model's knowledge learning from the source domain and quickly generalize to new tasks that contain only a small number of samples. Through this method, the model that maintains high accuracy and scalability to identify new category, we added attention mechanism can automatically extract features that are useful to the model and ignore some noise that negatively affects the model, meanwhile improve system stability and the effectiveness of activity recognition. We also performed the scaling and shifting(SS) transformation on the network, which could reduce the parameters of the model, improve the training speed, and avoid overfitting.","PeriodicalId":137505,"journal":{"name":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117197996","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

Cryptocurrency Price forecasting: A Comparative Study of Machine Learning Model in Short-Term Trading 加密货币价格预测:短期交易中机器学习模型的比较研究

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00054

Haoran Lyu

{"title":"Cryptocurrency Price forecasting: A Comparative Study of Machine Learning Model in Short-Term Trading","authors":"Haoran Lyu","doi":"10.1109/CACML55074.2022.00054","DOIUrl":"https://doi.org/10.1109/CACML55074.2022.00054","url":null,"abstract":"In recent years, the expansion of the cryptocurrency market has received significant attention among investors, studies of cryptocurrency price predictions have been conducted in various fields. With the enhancement of machine learning algorithms and increased computational capabilities, machine learning has proved one of the most efficient cryptocurrency prediction methods. However, most studies focused on single digital currency prediction or small-scale algorithm comparison for multiple currencies. This study aims to present a comparative performance of large-scale selected Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting time series data for a short-term trading period in ten cryptocurrencies (BTC, ETH, ADA, BNB, XRP, DOGE, LUNA, LINK, LTC, and BCH) with ten selected machine learning algorithms (Decision Tree, Linear Regression, Ridge Regression, Lasso Regression, Bayesian Regression, Random Forest, K-Nearest Neighbors, Neural Networks, Gradient Boosting, and Support Vector Machine). Our experiment results show that the Gradient Boosting with the mean square error criterion is superior in predicting most major cryptocurrencies by performing statistical analysis and data visualizations. Additionally, the Random Forest and Decision Tree model built by the Classification and Regression Tree algorithm also shows outstanding performance in certain currencies such as ETH, XRP, LUNA, and LTC. Thus, all three algorithms can help anticipate the short-term evolutions of the cryptocurrency market.","PeriodicalId":137505,"journal":{"name":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128893838","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

Comparison of Hyperbox Granular Computing Classification Algorithms by Positive Valuation Functions 基于正评价函数的Hyperbox颗粒计算分类算法比较

2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) Pub Date : 2022-03-01 DOI: 10.1109/CACML55074.2022.00047

Baoduo Su, Yinhao Zhang, Meiyao Zhu, Hongbing Liu

{"title":"Comparison of Hyperbox Granular Computing Classification Algorithms by Positive Valuation Functions","authors":"Baoduo Su, Yinhao Zhang, Meiyao Zhu, Hongbing Liu","doi":"10.1109/CACML55074.2022.00047","DOIUrl":"https://doi.org/10.1109/CACML55074.2022.00047","url":null,"abstract":"Granular Computing (GrC) is a computing paradigm derived from the human congiton of the real world, by which different granularity spaces can be converted to each other. For GrC, we have to face two issues, such as the operation between two granules and inclusion relation between two granules. In the paper, we proposed the hyperbox granular computing classification algorithms based on the fuzzy inclusion relation between granules in terms of the different positive valuation functions. The proposed positive valuation functions keep the consistency of the partial order relation between two vectors in the vector space and the partial order relation between two hyperbox granules in the granule space. The fuzzy lattice is constructed by the hyperbox granule set and the fuzzy inclusion relation induced by the proposed positive valuation functions, and used to design the algorithms which realize the transformation from the training set to the sparse granule space. Experimental results on the benchmark data set show superiority of the proposed hyperbox granular computing classification algorithms.","PeriodicalId":137505,"journal":{"name":"2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129000214","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