2020 5th International Conference on Computational Intelligence and Applications (ICCIA)最新文献

ICCIA 2020 Committees

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/iccia49625.2020.00006

引用次数: 0

ICCIA 2020 Index

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/iccia49625.2020.00052

引用次数: 0

Affect of Data Filter on Performance of Latent Semantic Analysis based Research Paper Recommender System 数据过滤对基于潜在语义分析的论文推荐系统性能的影响

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00017

Javeria Almas, Usman Qamar

引用次数: 0

Intelligent classification of point clouds for indoor components based on dimensionality reduction 基于降维的室内构件点云智能分类

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00024

Huimin Yang, Hangbin Wu

{"title":"Intelligent classification of point clouds for indoor components based on dimensionality reduction","authors":"Huimin Yang, Hangbin Wu","doi":"10.1109/ICCIA49625.2020.00024","DOIUrl":"https://doi.org/10.1109/ICCIA49625.2020.00024","url":null,"abstract":"With the wide application of LiDAR, RGBD cameras and other sensors in computer vision, intelligent robotics, indoor positioning and navigation, the processing of point clouds of indoor scene components has been a difficult problem in these fields. Due to the disorder, sparsity, and limited information of point clouds, it is a challenge to consume point cloud directly. This paper proposes an intelligent classification method based on the disordered point clouds of indoor components. First, a deep learning network is employed to extract high-dimensional features. Then the features are divided into different clusters using two algorithms: t-distributed stochastic neighbor embedding (t-SNE) and density-based spatial clustering with applications of noises (DBSCAN). Finally, the classical iterative closest point (ICP) is used to match the laser point clouds with the model point clouds whose semantic labels are known in the model dataset. As a result, the method has a good performance on the classification of indoor point clouds, and the accuracy of classification is 98.6%, which can realize the intelligent classification of indoor components point clouds.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128517674","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

An Efficient Method Based on Region-adjacent Embedding for Text Classification of Chinese Electronic Medical Records 基于区域邻域嵌入的中文电子病历文本分类方法

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00042

Fangce Guo, Tiandeng Wu, Xinyu Jin

{"title":"An Efficient Method Based on Region-adjacent Embedding for Text Classification of Chinese Electronic Medical Records","authors":"Fangce Guo, Tiandeng Wu, Xinyu Jin","doi":"10.1109/ICCIA49625.2020.00042","DOIUrl":"https://doi.org/10.1109/ICCIA49625.2020.00042","url":null,"abstract":"In the field of natural language processing (NLP), word-embedding-based models have been widely applied in many tasks with great success, which are believed to make significant promotion to the development of text classification. We propose the region-adjacent embedding (RAE) to construct an effective model in this paper. RAE makes use of the context weight unit (CWU) combining adjacent words from different region to capture shalow-level context information and adds a self-attention unit (SAU) to learn deep-level semantic understandings. Our RAE model has two characteristics. First, RAE utilizes a lightweight network to regionalize the embeddings. Second, we pay attention to regionalization of embeddings without neglecting the connection with local embeddings. Based on this, we can connect the proposed RAE model acting as a bridge to the traditional word embeddings and downstream neural networks which are capable of deeper feature extraction. In this paper, we introduce RAE to the classification task on Chinese electronic medical records. The experiments show that structures with our method perform better than the plain structures themselves.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121781835","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

Optimization Algorithm of Time Synchronization Network Monitoring Based on Variational Autoencoder 基于变分自编码器的时间同步网络监控优化算法

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00033

Bo Lv, Feng Pan, Xinyu Miao, Changjun Hu

{"title":"Optimization Algorithm of Time Synchronization Network Monitoring Based on Variational Autoencoder","authors":"Bo Lv, Feng Pan, Xinyu Miao, Changjun Hu","doi":"10.1109/ICCIA49625.2020.00033","DOIUrl":"https://doi.org/10.1109/ICCIA49625.2020.00033","url":null,"abstract":"In this paper an optimization algorithm for time synchronization in telecommunication network is proposed based on VAE(Variational Auto Encoder)framework. Firstly features are represented in latent space under proposed framework while performance of synchronization network is measured and evaluated. Secondly optimization algorithm is further designed with which feature of abnormal samples and benchmark are adaptively merged for smooth adjustment with low risk in practical network operation. Meanwhile considering the characteristics as domain knowledge of synchronization network, a novel metric is adopted to reduce the fluctuation of adjustment. The simulation results verified that performance of synchronization network is significantly improved by optimization templates reconstructed through decoding part of VAE model. It is implied that prior knowledge of synchronization in latent space is introduced with certain interpret-ability for assessment of monitoring performance while optimization adjustment can be properly operated through novel metric proposed in this algorithm.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124286376","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

Risk analysis of a closed-loop artificial pancreas based on generalized predictive control 基于广义预测控制的闭环人工胰腺风险分析

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00037

Wenping Liu, Haoyu Jin

{"title":"Risk analysis of a closed-loop artificial pancreas based on generalized predictive control","authors":"Wenping Liu, Haoyu Jin","doi":"10.1109/ICCIA49625.2020.00037","DOIUrl":"https://doi.org/10.1109/ICCIA49625.2020.00037","url":null,"abstract":"An improved generalized predictive control (GPC) algorithm with two adaptive strategies, namely, an adaptive reference glucose trajectory (AT) and an adaptive softening factor (AF), was proposed for artificial pancreas systems (AP) in our previous research. Tests with the UVA/Padova type 1 diabetes mellitus simulator (T1DMS), approved by the US Food and Drug Administration, showed that it realized an effective control of the blood glucose concentrations (BGCs) of adult and adolescent patients with type 1 diabetes. Here, risk analysis was further performed for the GPC controllers with 20 in-silico subjects (10 adults and 10 adolescents). Two indexes provided by the UVA/Padova T1DMS, including low blood glucose index (LBGI) and high blood glucose index (HBGI), were used to analyze the long-term risks for hypoglycemia and hyperglycemia of the GPC controllers. Results showed that both adult and adolescent groups had minimal risks for hypoglycemia and hyperglycemia with our GPC controllers. Moreover, AT strategy played a better role in preventing hypoglycemia and AF strategy played a better role in preventing hyperglycemia. Thus, the GPC+AT+AF controller is effective and safe, and it could be potentially applied in the AP systems.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125685778","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

Challenge and Countermeasure of Big Data to Army Information Security 大数据对军队信息安全的挑战与对策

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00020

Chenggong Zhai, Dinghai Wang, Heng Zhang

引用次数: 0

Classifying Tongue Images using Deep Transfer Learning 使用深度迁移学习对舌头图像进行分类

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00027

Chao Song, Bin Wang, Jia-tuo Xu

{"title":"Classifying Tongue Images using Deep Transfer Learning","authors":"Chao Song, Bin Wang, Jia-tuo Xu","doi":"10.1109/ICCIA49625.2020.00027","DOIUrl":"https://doi.org/10.1109/ICCIA49625.2020.00027","url":null,"abstract":"Traditional Chinese Medicine (TCM) believes that the tongue image is closely related to the health of the human organs and tongues’ visual features can provide valuable clues for disease diagnosis. Applying tongue image analysis technique for automatic disease diagnosis is an active research filed in the modernization of TCM. Although deep learning has advantages over traditional methods in automatic extraction of high-dimensional features, it needs large training samples, which limits its application in medical image analysis, especially in tongue image, because it is difficult to collect enough labeled images. In this paper, we make the first attempt to use deep transfer learning for tongue image analysis. First, we extract the tongue features through the pre-trained networks (ResNet and Inception_v3), and then rewrite the output layer of the original network with global average pooling and full-connected layer to output classification results. A dataset of 2245 tongue images we collected from specialized TCM medical institutions is used for classification performance evaluation. The experimental results demonstrate that the proposed method achieves the better classification accuracy than the existing deep learning methods which proves the effectiveness of the proposed deep transfer learning for tongue image classification.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130889482","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: 7

Paraphrase Generation with Chinese Short Text Dataset 中文短文本数据集释义生成

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00019

Guohui Song, Yongbin Wang

引用次数: 0