Pub Date : 2020-06-01DOI: 10.1109/iccia49625.2020.00006
{"title":"ICCIA 2020 Committees","authors":"","doi":"10.1109/iccia49625.2020.00006","DOIUrl":"https://doi.org/10.1109/iccia49625.2020.00006","url":null,"abstract":"","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"50 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":"117276226","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}
Pub Date : 2020-06-01DOI: 10.1109/iccia49625.2020.00052
{"title":"ICCIA 2020 Index","authors":"","doi":"10.1109/iccia49625.2020.00052","DOIUrl":"https://doi.org/10.1109/iccia49625.2020.00052","url":null,"abstract":"","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"631 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":"122125884","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}
Pub Date : 2020-06-01DOI: 10.1109/ICCIA49625.2020.00017
Javeria Almas, Usman Qamar
Latent Semantic Analysis uses Singular Value Decomposition (SVD) to effectively retrieve relevant information from the information corpus. However, LSA has a high computational cost. In order to address this aspect, it is proposed to filter only those words carrying high semantic importance. The aim is to improve the execution time of semantic space construction and dimensionality reduction. We present how the use of data filter can effectively meet the proposed goals in comparison to baseline method of performing recommendations. The proposed system was assessed over a dataset of 80 articles (Titles and Abstracts). The results of the experiments show that the proposed system performed better in terms of elapsed time with an average precision of 85.54% (78.64% for baseline method) and an average recall of 92.96% (89.70% for baseline method).
{"title":"Affect of Data Filter on Performance of Latent Semantic Analysis based Research Paper Recommender System","authors":"Javeria Almas, Usman Qamar","doi":"10.1109/ICCIA49625.2020.00017","DOIUrl":"https://doi.org/10.1109/ICCIA49625.2020.00017","url":null,"abstract":"Latent Semantic Analysis uses Singular Value Decomposition (SVD) to effectively retrieve relevant information from the information corpus. However, LSA has a high computational cost. In order to address this aspect, it is proposed to filter only those words carrying high semantic importance. The aim is to improve the execution time of semantic space construction and dimensionality reduction. We present how the use of data filter can effectively meet the proposed goals in comparison to baseline method of performing recommendations. The proposed system was assessed over a dataset of 80 articles (Titles and Abstracts). The results of the experiments show that the proposed system performed better in terms of elapsed time with an average precision of 85.54% (78.64% for baseline method) and an average recall of 92.96% (89.70% for baseline method).","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"32 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":"129877882","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}
Pub Date : 2020-06-01DOI: 10.1109/ICCIA49625.2020.00024
Huimin Yang, Hangbin Wu
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.
{"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}
Pub Date : 2020-06-01DOI: 10.1109/ICCIA49625.2020.00042
Fangce Guo, Tiandeng Wu, Xinyu Jin
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.
{"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}
Pub Date : 2020-06-01DOI: 10.1109/ICCIA49625.2020.00033
Bo Lv, Feng Pan, Xinyu Miao, Changjun Hu
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.
{"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}
Pub Date : 2020-06-01DOI: 10.1109/ICCIA49625.2020.00037
Wenping Liu, Haoyu Jin
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.
{"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}
Pub Date : 2020-06-01DOI: 10.1109/ICCIA49625.2020.00020
Chenggong Zhai, Dinghai Wang, Heng Zhang
With the continuous development of big data technology and the deepening of the information construction of military supplies, the position and role of information technology in the support of military supplies are more and more prominent. This paper introduces the challenge of big data to the information security of quartermaster, puts forward the architecture design of the information security of Quartermaster based on big data, and deeply analyzes how to ensure the information security of Quartermaster under the background of big data.
{"title":"Challenge and Countermeasure of Big Data to Army Information Security","authors":"Chenggong Zhai, Dinghai Wang, Heng Zhang","doi":"10.1109/ICCIA49625.2020.00020","DOIUrl":"https://doi.org/10.1109/ICCIA49625.2020.00020","url":null,"abstract":"With the continuous development of big data technology and the deepening of the information construction of military supplies, the position and role of information technology in the support of military supplies are more and more prominent. This paper introduces the challenge of big data to the information security of quartermaster, puts forward the architecture design of the information security of Quartermaster based on big data, and deeply analyzes how to ensure the information security of Quartermaster under the background of big data.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"113 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":"121146309","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}
Pub Date : 2020-06-01DOI: 10.1109/ICCIA49625.2020.00027
Chao Song, Bin Wang, Jia-tuo Xu
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.
{"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}
Pub Date : 2020-06-01DOI: 10.1109/ICCIA49625.2020.00019
Guohui Song, Yongbin Wang
An obstacle of conducting investigation on paraphrase generation is short of high-quality, publicly-available labeled dataset of sentential paraphrases, which is particularly serious for Chinese paraphrase generation research. Therefore, the study in Chinese paraphrase generation is the starting stage. This paper aimed to use a novel way to create Chinese paraphrase dataset, which contains 8K sentences pairs. The data source comes from a bank QA dataset, in which there are several sentences to express each problem. By calculating the similarity between the same semantic sentences, we can obtain paraphrase pairs to create Chinese paraphrase dataset. Then, we achieve paraphrase generation task by leveraging a classical Seq2Sseq model with attention mechanism. Following previous work and evaluate paraphrase generation result on our Chinese dataset. Experimental results not only show that the dataset is suitable for Chinese paraphrase generation task, but also provides a benchmark for further research on this research area.
{"title":"Paraphrase Generation with Chinese Short Text Dataset","authors":"Guohui Song, Yongbin Wang","doi":"10.1109/ICCIA49625.2020.00019","DOIUrl":"https://doi.org/10.1109/ICCIA49625.2020.00019","url":null,"abstract":"An obstacle of conducting investigation on paraphrase generation is short of high-quality, publicly-available labeled dataset of sentential paraphrases, which is particularly serious for Chinese paraphrase generation research. Therefore, the study in Chinese paraphrase generation is the starting stage. This paper aimed to use a novel way to create Chinese paraphrase dataset, which contains 8K sentences pairs. The data source comes from a bank QA dataset, in which there are several sentences to express each problem. By calculating the similarity between the same semantic sentences, we can obtain paraphrase pairs to create Chinese paraphrase dataset. Then, we achieve paraphrase generation task by leveraging a classical Seq2Sseq model with attention mechanism. Following previous work and evaluate paraphrase generation result on our Chinese dataset. Experimental results not only show that the dataset is suitable for Chinese paraphrase generation task, but also provides a benchmark for further research on this research area.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"99 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":"129154594","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}
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