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2022 14th International Conference on Machine Learning and Computing (ICMLC)最新文献

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Real-Time Deep Compressed Sensing Reconstruction for Electrocardiogram Signals 心电图信号的实时深度压缩感知重构
2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529896
Weibin Cao, Jun Zhang
{"title":"Real-Time Deep Compressed Sensing Reconstruction for Electrocardiogram Signals","authors":"Weibin Cao, Jun Zhang","doi":"10.1145/3529836.3529896","DOIUrl":"https://doi.org/10.1145/3529836.3529896","url":null,"abstract":"The rapid development of wearable device technology provides an efficient way of data acquisition for remote ECG monitoring and identification. However, existing iteration based signal recovery methods have high latency, while the deep learning based method have a shortcoming that large increase in parameters makes training more difficult as the signal length increases. In this paper, we combine compressed sensing and generative adversarial networks to propose a signal recovery method based on dilated convolution. The proposed model can accept more prior information from compressed long signal without increasing parameters and achieve feature domain self-adaptation by fitting the distribution of reconstructed and original signals. Experiments result on MIT-BIH and PTB datasets demonstrate that the proposed method achieves comparable or better results in reconstruction accuracy and reconstruction time when compared to some existing iteration-based methods and some deep learning based methods. For example, reconstructing a 2s signal takes only 0.013s, which is a 50% improvement over other deep learning methods.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134633958","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
Characterizing phenotypes for Mental Health Disorders with Wrapper-typed Feature selection techniques:: Comparison of RF-RFE and Fuzzy Forest 用包装型特征选择技术表征精神健康障碍的表型::RF-RFE和模糊森林的比较
2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529837
Jiemiao Chen
{"title":"Characterizing phenotypes for Mental Health Disorders with Wrapper-typed Feature selection techniques:: Comparison of RF-RFE and Fuzzy Forest","authors":"Jiemiao Chen","doi":"10.1145/3529836.3529837","DOIUrl":"https://doi.org/10.1145/3529836.3529837","url":null,"abstract":"Random Forest is a popular feature selection method suitable for handling “small n, large p” problem but lacking capability of dealing collinearity. To compensate the gap of removing highly correlated features, wrapper-typed feature selection methods: Fuzzy Forest (FF) and Random Forest- Recursive Feature Elimination (RF-RFE) have been developed. These two methods are similar in multiple ways but implement different strategies to deal with features. Meanwhile, the field of clinical psychiatry is changing in the way it characterizes mental health disorders. Thus, the aims of our paper are to compare the impact of FF and RF-RFE and to study phenotypic features relevant to three mental health disorders: schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder (ADHD). We specify the classification problem as “one versus rest” (OVR) and implement phenotype data from Consortium for Neuropsychiatric Phenomics. FF and RF-RFE are applied to select the optimal feature subsets separately, which are then evaluated by Support Vector Machines (SVM) and Extreme Learning Machines (ELM) classifiers respectively. The evaluation criteria include precision, recall, accuracy, F-measure and Area under the curve. As a result, RF-RFE showed superior feature selection performance over FF. Also, we found that the features of the original data are informative in diseases from most to least: schizophrenia, ADHD and bipolar disorder.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114207170","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
Optimization algorithm of manhole recognition based on YOLOv2 基于YOLOv2的人孔识别优化算法
2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529852
Mengzi Yin, Xuebin Yan, Shuqi Yin, Xingxing Liu
{"title":"Optimization algorithm of manhole recognition based on YOLOv2","authors":"Mengzi Yin, Xuebin Yan, Shuqi Yin, Xingxing Liu","doi":"10.1145/3529836.3529852","DOIUrl":"https://doi.org/10.1145/3529836.3529852","url":null,"abstract":"Aiming at the current situation that there is a lack of methods other than manual investigation when there are problems such as settlement, damage, and missing of manholes, a custom YOLOv2 network model algorithm based on the ResNet-50 feature extraction network was proposed. The original algorithm is optimized from the aspects of detection classes, learning methods, pre-training model, anchor boxes’ estimation and parameter configuration. The pre-trained convolutional neural network ResNet-50 was uesd as the feature extraction network combined with the YOLOv2 original network to create a detection network, and the preprocessed training set data was trained to obtain target detector. By running the target detector on the input test set data, the detection of manholes is realized. Compared with the original YOLOv2 algorithm, the training time is respectively shortened by 47%, the recall rate and F1 are increased by 9 times and 5 times, and the accuracy and detection scores are respectively maintained at 98% and 50%. The improved algorithm can detect manholes efficiently and accurately in reality.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132385024","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
A Community Detection Algorithm Based on Correlation Analysis of Connection Pattern 一种基于连接模式相关性分析的社区检测算法
2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529949
Xinhong Yin, Shiyan Zhao, Xianbo Li, Hailong Su
{"title":"A Community Detection Algorithm Based on Correlation Analysis of Connection Pattern","authors":"Xinhong Yin, Shiyan Zhao, Xianbo Li, Hailong Su","doi":"10.1145/3529836.3529949","DOIUrl":"https://doi.org/10.1145/3529836.3529949","url":null,"abstract":"A large number of complex systems in the real world can be abstractly expressed as complex networks. However, the existing network in the real society contains some information of link pattern and there is some correlation between the vertices. Therefore, based on the idea of the correlation analysis between the link pattern of vertices, we propose a community detection algorithm based on correlation analysis of link pattern, named CCP algorithm. The algorithm firstly obtains the link pattern of the vertex, then calculates the correlation coefficient to obtain the correlation among the connection nodes and obtains the must-link and the cannot-link pairwise constraints. Secondly, expands the must-link and the cannot-link according to the transferability of the must-link. Then, according to the expanded cannot-link set cooperation seed node, the skeleton of the community structure is attracted to the must-link. Finally, the nodes that are not classified into the community is divided into corresponding communities by minimum spanning tree, and the final community structure is obtained. In order to verify the performance of the proposed method, experiments are carried out on the actual network data sets and synthetic network data sets. The experimental results show that the proposed algorithm can extract high quality community structures from the network.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126454360","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
Network Abnormality Location Algorithm Based on Greedy Monte Carlo Tree 基于贪心蒙特卡罗树的网络异常定位算法
2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529947
Chunlin Wang, Neng Yang, Jianyong Sun, Wanjin Xu, Xiaolin Chen
{"title":"Network Abnormality Location Algorithm Based on Greedy Monte Carlo Tree","authors":"Chunlin Wang, Neng Yang, Jianyong Sun, Wanjin Xu, Xiaolin Chen","doi":"10.1145/3529836.3529947","DOIUrl":"https://doi.org/10.1145/3529836.3529947","url":null,"abstract":"The cloud service providers manage very large data centers all over the world. When an abnormality occurs, various types of alarm information are triggered. The operation engineers need to quickly discover and locate all the abnormalities. To solve the problems of computing-intensive, non-real-time, and inaccurate abnormal detection and location algorithm, we propose to improve the Monte Carlo Tree Search (MCTS) based on the greedy algorithm by: 1) improving the selection of the next node in MCTS by using greedy algorithm and searching the best node with depth-first method; 2) adopting the sparse matrix to store the record of the 5-dimensional log files, then employing the subscript file to record the subscript of the 5-dimensional array, and using the subscript to access the sparse matrix to save memory space and searching time; 3) reducing calculations by pruning some branches based on the observation that the optimal node combination of present layer must belong to the search space of the best layer combination of the previous layer. The experimental results show that GMCTS algorithm reduces 40% computation time than the HotSpot in 5D data, and the correct positioning efficiency is up to 96.1%.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128935434","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
A highly efficient, confidential, and continuous federated learning backdoor attack strategy 一种高效、保密和持续的联邦学习后门攻击策略
2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529845
Jiarui Cao, Liehuang Zhu
{"title":"A highly efficient, confidential, and continuous federated learning backdoor attack strategy","authors":"Jiarui Cao, Liehuang Zhu","doi":"10.1145/3529836.3529845","DOIUrl":"https://doi.org/10.1145/3529836.3529845","url":null,"abstract":"Federated learning is a kind of distributed machine learning. Researchers have conducted extensive research on federated learning's security defences and backdoor attacks. However, most studies are based on the assumption federated learning participant's data obey iid (independently identically distribution). This paper will evaluate the security issues of non-iid federated learning and propose a new attack strategy. Compared with the existing attack strategy, our approach has three innovations. The first one, we conquer foolsgold [1] defences through the attacker's negotiation. In the second one, we propose a modified gradient upload strategy for fedsgd backdoor attack, which significantly improves the backdoor attack's confidentiality on the original basis. Finally, we offer a bit Trojan method to realize continuous on non-iid federated learning. We conduct extensive experiments on different datasets to illustrate our backdoor attack strategy is highly efficient, confidential, and continuous on non-iid federated learning.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116988403","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: 3
Faster R-CNN with Generative Adversarial Occlusion Network for Object Detection 基于生成对抗遮挡网络的更快R-CNN目标检测
2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529854
Feng Li, Jiehui Li, Yancong Deng
{"title":"Faster R-CNN with Generative Adversarial Occlusion Network for Object Detection","authors":"Feng Li, Jiehui Li, Yancong Deng","doi":"10.1145/3529836.3529854","DOIUrl":"https://doi.org/10.1145/3529836.3529854","url":null,"abstract":"Abstract: Performing object detection on partially occluded objects is a challenging task due to the amount of variation in location, scale, and ratio present in real-world occlusion. A typical solution to this problem is to provide a large enough dataset with ample occluded samples for feature learning. However, this is rather costly given the amount of time and effort involved in the data collection process. In addition, even with such a dataset, there is no guarantee that it covers all possible cases of common occlusion in the real world. In this paper, we propose an alternate approach that utilizes the power of adversarial learning to reinforce the training of common object detection models. More specifically, we propose a Generative Adversarial Occlusion Network (GAON) capable of generating partially shaded training samples that are challenging for the object detector to classify. We demonstrate the efficacy of such an approach by conducting experiments on the Faster R-CNN detector, and the results indicate the superiority of our approach in improving the model's performance on occluded inputs.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124449503","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
DeepGCNMIL: Multi-head Attention Guided Multi-Instance Learning Approach for Whole-Slide Images Survival Analysis Using Graph Convolutional Networks DeepGCNMIL:基于图卷积网络的全幻灯片图像生存分析的多头注意引导多实例学习方法
2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529942
Fei Wu, Pei Liu, Bo Fu, Feng Ye
{"title":"DeepGCNMIL: Multi-head Attention Guided Multi-Instance Learning Approach for Whole-Slide Images Survival Analysis Using Graph Convolutional Networks","authors":"Fei Wu, Pei Liu, Bo Fu, Feng Ye","doi":"10.1145/3529836.3529942","DOIUrl":"https://doi.org/10.1145/3529836.3529942","url":null,"abstract":"∗Analyzing giga-pixel Whole-Slide Images (WSIs) has difficulty in expanding to large-scale data set due to labor intensive patchlevel annotation. Current multi-instance learning (MIL) frameworks guided by attention mechanism have successfully built the relation between giga-pixel WSI and survival, which is suitable for large-scale data analysis. However, the simple aggregation of patchlevel features may not comprehensively characterize WSI, since it ignores the internal connection between patches. To address this problem, this paper proposes a graph convolutional networks-based MIL framework, named as DeepGCNMIL. We firstly cluster patches into several phenotypes based on their similarity, then build graphs for these clusters to consider internal connections among patches through node edges and exploit a three-layer graph convolutional network (GCN) to learn representation of each phenotype. Moreover, we introduce multi-head attention to aggregate phenotype features into WSI representation for prognostic risk assessment. Our method achieves a C-index of 0.673 (± 0.053) on the NLST dataset (0.035 ahead of the second place) and 0.632 (± 0.065) on the TCGA_BRCA dataset (0.018 ahead of the second place), which show that for large-scale prognostic modeling of Giga-pixel digital pathological images, our method outperforms similar WSI survival prediction models. This novel MIL framework could be effectively utilized to assess the prognosis risk of individual patients and help provide personalized medicine.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130820150","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: 4
A Research on liquidity risk management under the framework of COSO-ERM COSO-ERM框架下的流动性风险管理研究
2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529945
Siran Liu, Lan Wang
{"title":"A Research on liquidity risk management under the framework of COSO-ERM","authors":"Siran Liu, Lan Wang","doi":"10.1145/3529836.3529945","DOIUrl":"https://doi.org/10.1145/3529836.3529945","url":null,"abstract":"Previous research on liquidity risk management are mainly concentrated on companies such as commercial banks, securities companies, insurance companies, and group financial companies. Some traditional companies follow internal control management, but attach less importance to the overall risk management of the company. The article focus on the issue of liquidity risk management based on the company's cash flow status and key financial indicators. According to the five elements of the COSO-ERM framework, five suggestions are provided for the cash flow crisis management.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"231 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131644999","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
A Swin Transformer based Framework for Shape Recognition 基于Swin变压器的形状识别框架
2022 14th International Conference on Machine Learning and Computing (ICMLC) Pub Date : 2022-02-18 DOI: 10.1145/3529836.3529894
Tianyang Gu, Ruipeng Min
{"title":"A Swin Transformer based Framework for Shape Recognition","authors":"Tianyang Gu, Ruipeng Min","doi":"10.1145/3529836.3529894","DOIUrl":"https://doi.org/10.1145/3529836.3529894","url":null,"abstract":"Shape recognition is a fundamental problem in the field of computer vision, which aims to classify various shapes. The current mainstream network architecture is convolutional neural network (CNN), however, CNN offers limited ability to extract valuable information from simple shapes for shape classification. To address this problem, this paper proposes a deep learning model based on self-attention and Vision Transformers structure (ViT) to achieve shape recognition. Compared with the traditional CNN structure, ViT considers the long-distance relationship and reduces the loss of information between layers. The model utilizes a shifted-window hierarchical vision transformer (Swin Transformer) structure and an all-scale shape representation to improve the performance of the model. Experimental results show that the proposed model achieves superior accuracy compared to other methods, achieving an accuracy of 93.82% on the animal dataset, while the performance of state-of-the-art VGG-based method is only 90.02%.","PeriodicalId":285191,"journal":{"name":"2022 14th International Conference on Machine Learning and Computing (ICMLC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126510271","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
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