Liyan Zhang, Hao Zhou, Juan Wang, Lei Wang, Cheng-yi Xia
Over the past two decades, magnetic resonance imaging (MRI) has been widely applied into the diagnosis of knee joint diseases. Due to the complexity and diversity of MRI data, traditional feature extraction requires manual searching for features to segment meniscus, and the final segmentation results still need to be further filtered. Therefore, it is necessary to design a novel method to automatically extract features directly from images. In this study, we develop a framework to implement this goal by using a mask region-based convolution neural network (Mask R-CNN) without manual intervention. In order to highlight the proportion of meniscus, we first preprocess the original image data so that it is reduced to about 1/8 of the original size, and then input the preprocessed image data into the trained Mask R-CNN. Afterwards, transfer learning is used to generate the weight of our network. By testing 1000 images, the mean intersection over union (IOU) and dice similarity coefficient (DSC) are up to 83.68% and 91.13%, respectively. The current results demonstrate that our approach is feasible and has a potential significance in clinical practice.
{"title":"Automatic segmentation for meniscus magnetic resonance images of knee joint based on Mask region-based convolution neural network","authors":"Liyan Zhang, Hao Zhou, Juan Wang, Lei Wang, Cheng-yi Xia","doi":"10.1145/3507548.3507556","DOIUrl":"https://doi.org/10.1145/3507548.3507556","url":null,"abstract":"Over the past two decades, magnetic resonance imaging (MRI) has been widely applied into the diagnosis of knee joint diseases. Due to the complexity and diversity of MRI data, traditional feature extraction requires manual searching for features to segment meniscus, and the final segmentation results still need to be further filtered. Therefore, it is necessary to design a novel method to automatically extract features directly from images. In this study, we develop a framework to implement this goal by using a mask region-based convolution neural network (Mask R-CNN) without manual intervention. In order to highlight the proportion of meniscus, we first preprocess the original image data so that it is reduced to about 1/8 of the original size, and then input the preprocessed image data into the trained Mask R-CNN. Afterwards, transfer learning is used to generate the weight of our network. By testing 1000 images, the mean intersection over union (IOU) and dice similarity coefficient (DSC) are up to 83.68% and 91.13%, respectively. The current results demonstrate that our approach is feasible and has a potential significance in clinical practice.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124078459","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}
Cross-modal retrieval is to achieve flexible query between different modalities. Many approaches solve the problem by learning a common feature space under to separate the multimodal instances from different categories. But it is challenge to design an effective projecting function. In this paper, we propose a novel cross-modal retrieval method, called Adaptive Margin Ranking for Supervised Cross-modal Retrieval (AMRS). In the solution, we design a neural network as the nonlinear mapping function. To maximize the discrimination of multimodal feature in common representation space, we keep away the samples with different semantic by an adaptive margin, and jointly force the modality invariance to eliminate cross-modal discrepancy. Experimental results on widely used benchmark datasets show that the proposed method is effective in cross-modal learning.
{"title":"Adaptive Margin Ranking for Supervised Cross-modal Retrieval","authors":"Tianyuan Xu, Xueliang Liu","doi":"10.1145/3507548.3507599","DOIUrl":"https://doi.org/10.1145/3507548.3507599","url":null,"abstract":"Cross-modal retrieval is to achieve flexible query between different modalities. Many approaches solve the problem by learning a common feature space under to separate the multimodal instances from different categories. But it is challenge to design an effective projecting function. In this paper, we propose a novel cross-modal retrieval method, called Adaptive Margin Ranking for Supervised Cross-modal Retrieval (AMRS). In the solution, we design a neural network as the nonlinear mapping function. To maximize the discrimination of multimodal feature in common representation space, we keep away the samples with different semantic by an adaptive margin, and jointly force the modality invariance to eliminate cross-modal discrepancy. Experimental results on widely used benchmark datasets show that the proposed method is effective in cross-modal learning.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121385688","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}
Deep Convolutional neural networks are widely used in computer vision research because of their good feature extraction ability, which can often show good performance in related tasks. Performance of deep convolution network models is not only related to its own architecture design, but also closely related to training data. When training model with large dataset and the images are clear and noise in images is less, it can get good result. But in the case of small dataset and low image quality, it is easy to appear that the model can fit the data in the training process and perform badly in testing, that is, overfitting problem. Our work proposes random polygon cover algorithm to simulate the possible damage object and partial content loss in training dataset, which is also a data augmentation technique. We'll use experiments to prove the effectiveness of this approach, while trying to reveal how data augmentation works and how our method differs from dropout.
{"title":"Random Polygon Cover for Oracle Bone Character Recognition","authors":"Liu Dazheng","doi":"10.1145/3507548.3507569","DOIUrl":"https://doi.org/10.1145/3507548.3507569","url":null,"abstract":"Deep Convolutional neural networks are widely used in computer vision research because of their good feature extraction ability, which can often show good performance in related tasks. Performance of deep convolution network models is not only related to its own architecture design, but also closely related to training data. When training model with large dataset and the images are clear and noise in images is less, it can get good result. But in the case of small dataset and low image quality, it is easy to appear that the model can fit the data in the training process and perform badly in testing, that is, overfitting problem. Our work proposes random polygon cover algorithm to simulate the possible damage object and partial content loss in training dataset, which is also a data augmentation technique. We'll use experiments to prove the effectiveness of this approach, while trying to reveal how data augmentation works and how our method differs from dropout.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127217877","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}
The daily audience data and static movie attributes are both important factors that influence the movie's succeeding box offices. This paper proposes the first framework that utilizes both daily audience data and static movie attributes to accurately the performance of movies’ box-office prediction. To use the daily audience data dynamics, we utilized the recent proposed rank pooling strategy to encode multi-scale audience data dynamics. Meanwhile, we also consider 15 static movie attributes. Both static and dynamic features are combined in a multi-stream residual network for box-office prediction. The experiments conducted on the dataset that contains 120 movies’ daily audience data show that the proposed multi-scale dynamic encoding achieved promising results in prediction the next one- or two-days’ box office while the static-dynamic fusion model achieved the best performance under all conditions
{"title":"Deep Learning Static and Dynamic Movie Attributes for Box Office Prediction","authors":"Linxi Chen","doi":"10.1145/3507548.3507610","DOIUrl":"https://doi.org/10.1145/3507548.3507610","url":null,"abstract":"The daily audience data and static movie attributes are both important factors that influence the movie's succeeding box offices. This paper proposes the first framework that utilizes both daily audience data and static movie attributes to accurately the performance of movies’ box-office prediction. To use the daily audience data dynamics, we utilized the recent proposed rank pooling strategy to encode multi-scale audience data dynamics. Meanwhile, we also consider 15 static movie attributes. Both static and dynamic features are combined in a multi-stream residual network for box-office prediction. The experiments conducted on the dataset that contains 120 movies’ daily audience data show that the proposed multi-scale dynamic encoding achieved promising results in prediction the next one- or two-days’ box office while the static-dynamic fusion model achieved the best performance under all conditions","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126437496","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}
In order to realize the accumulation of sea temperature, conductivity, pressure, depth, salinity, density and sound profile observation data, a ocean observation data storage system based on database is designed. For efficiently realizing accumulation of ocean observation data, the system makes full use of the advantages of MYSQL database management platform, and orderly stores tens of thousands ofConductivity-Temperature-Depth(CTD) profile observation data. At the same time, the data quality control will be applied to the received hydrological observation data, which improves the quality of the data stored in the database and enhances the data usability. After the system was developed, a simulation environment was set up to test the system. The results show that the system hasrational function design and stable operation, which can well realize the accumulation of CTD profile observation data. The realization of ocean hydrologic profile observation data accumulation provides reliable data source for the subsequent in-depth data-mining and utilization of ocean hydrologic observation data.
{"title":"Design and implementation of CTD profile observation data accumulation system based on MySQL","authors":"Xing-min Li, Tao Dong, Xin-peng Wang, Li-Shan Ma","doi":"10.1145/3507548.3507590","DOIUrl":"https://doi.org/10.1145/3507548.3507590","url":null,"abstract":"In order to realize the accumulation of sea temperature, conductivity, pressure, depth, salinity, density and sound profile observation data, a ocean observation data storage system based on database is designed. For efficiently realizing accumulation of ocean observation data, the system makes full use of the advantages of MYSQL database management platform, and orderly stores tens of thousands ofConductivity-Temperature-Depth(CTD) profile observation data. At the same time, the data quality control will be applied to the received hydrological observation data, which improves the quality of the data stored in the database and enhances the data usability. After the system was developed, a simulation environment was set up to test the system. The results show that the system hasrational function design and stable operation, which can well realize the accumulation of CTD profile observation data. The realization of ocean hydrologic profile observation data accumulation provides reliable data source for the subsequent in-depth data-mining and utilization of ocean hydrologic observation data.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126479519","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}
Faced with the problem of text recommendation with massive data on the Internet, the use of a recommendation method based on deep learning combined with semantic information will improve the accuracy of the recommendation results. Therefore, we propose a HyReB (Hybrid Recommendation algorithm combining BERT and CNN network). The algorithm HyReB uses the BERT word vector as the input of the CNN network and incorporates external semantic information in features extraction and topic classification. Then we combine BERT and TextRank algorithms to extract text keywords and calculate the BERT word vector similarity of topic word. Finally, we do the weighted calculation of the label proportion of the recommended text and the similarity of the topic word vector to make the text top-N recommendation. The HyReB algorithm makes user interest extraction more refined and incorporates BERT semantic information into the text recommendation. Experiments show that the feature extraction of HyReB is more accurate and has a better recommendation effect when performing small-scale accurate text recommendation.
{"title":"Text Recommendation Algorithm Fused with BERT Semantic Information","authors":"Xingyun Xie, Zifeng Ren, Yuming Gu, Chengwen Zhang","doi":"10.1145/3507548.3507582","DOIUrl":"https://doi.org/10.1145/3507548.3507582","url":null,"abstract":"Faced with the problem of text recommendation with massive data on the Internet, the use of a recommendation method based on deep learning combined with semantic information will improve the accuracy of the recommendation results. Therefore, we propose a HyReB (Hybrid Recommendation algorithm combining BERT and CNN network). The algorithm HyReB uses the BERT word vector as the input of the CNN network and incorporates external semantic information in features extraction and topic classification. Then we combine BERT and TextRank algorithms to extract text keywords and calculate the BERT word vector similarity of topic word. Finally, we do the weighted calculation of the label proportion of the recommended text and the similarity of the topic word vector to make the text top-N recommendation. The HyReB algorithm makes user interest extraction more refined and incorporates BERT semantic information into the text recommendation. Experiments show that the feature extraction of HyReB is more accurate and has a better recommendation effect when performing small-scale accurate text recommendation.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116542942","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}
∗Deep learning is a technology that uses the hierarchical structure of neural network to learn features. It allows computer models with multiple processing layers to learn and represent data like the brain’s perception and understanding of multimodal information, so as to implicitly capture complex large-scale data. The whole system of deep learning network forms a hierarchical and powerful feature representation structure, which enables it to analyze and extract useful knowledge from a large amount of data. This paper mainly introduces the development and application of supervised convolution neural network, unsupervised convolution neural network and generative countermeasure network, and analyzes the research status and challenges of deep learning network. Through the review and introduction of important papers on deep learning network, it provides researchers with accessible scientific research materials.
{"title":"Review of deep learning network","authors":"Liming Chen, Bin Xie, YingChun Chen","doi":"10.1145/3507548.3507601","DOIUrl":"https://doi.org/10.1145/3507548.3507601","url":null,"abstract":"∗Deep learning is a technology that uses the hierarchical structure of neural network to learn features. It allows computer models with multiple processing layers to learn and represent data like the brain’s perception and understanding of multimodal information, so as to implicitly capture complex large-scale data. The whole system of deep learning network forms a hierarchical and powerful feature representation structure, which enables it to analyze and extract useful knowledge from a large amount of data. This paper mainly introduces the development and application of supervised convolution neural network, unsupervised convolution neural network and generative countermeasure network, and analyzes the research status and challenges of deep learning network. Through the review and introduction of important papers on deep learning network, it provides researchers with accessible scientific research materials.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129841004","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}
Regularization is a popular technique to solve the overfitting problem of machine learning algorithms. Most regularization technique relies on parameter selection of the regularization coefficient. Plug-in method and cross-validation approach are two most common parameter selection approaches for regression methods such as Ridge Regression, Lasso Regression and Kernel Regression. Matrix factorization based recommendation system also has heavy reliance on the regularization technique. Most people select a single scalar value to regularize the user feature vector and item feature vector independently or collectively. In this paper, we prove that such approach of selecting regularization coefficient is invalid, and we provide a theoretically accurate method that outperforms the most widely used approach in both accuracy and fairness metrics.
{"title":"Theoretically Accurate Regularization Technique for Matrix Factorization based Recommender Systems","authors":"Hao Wang","doi":"10.1145/3507548.3507587","DOIUrl":"https://doi.org/10.1145/3507548.3507587","url":null,"abstract":"Regularization is a popular technique to solve the overfitting problem of machine learning algorithms. Most regularization technique relies on parameter selection of the regularization coefficient. Plug-in method and cross-validation approach are two most common parameter selection approaches for regression methods such as Ridge Regression, Lasso Regression and Kernel Regression. Matrix factorization based recommendation system also has heavy reliance on the regularization technique. Most people select a single scalar value to regularize the user feature vector and item feature vector independently or collectively. In this paper, we prove that such approach of selecting regularization coefficient is invalid, and we provide a theoretically accurate method that outperforms the most widely used approach in both accuracy and fairness metrics.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127684504","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}
It tends to become a trend of booking transportation through network equipment with the further integration of the Internet into people's life, and online car-hailing platforms have sprung up. However, a new social crisis has also come with it. Because of the need for platform expansion, most online ride-hailing drivers have not undergone strict professional ethics reviews, which increases the risk of passengers taking the car. Primarily, female users are more susceptible to abuse and harassment and even persecution by drivers due to their disadvantaged position. Unfortunately, this phenomenon is happening every day and even getting worse. Regarding this aspect of supervision, it is difficult for relevant departments to have a more direct management plan. However, it is difficult for relevant departments to give a more natural and effective management plan. Therefore, ensuring the safety of passengers (predominantly female passengers) using online car-hailing becomes particularly important. In the Chinese field, few people try to improve this problem from the perspective of natural language. This work expects to use natural language technology to evaluate the driver's language and determine the degree of potential danger and criminal tendency, thus protecting the passenger and providing evidence for the judicial authorities. We first collected many dialogues between drivers and passengers, then used back translation to expand the corpus. Finally, we adopted various BERT-based model methods to compare and analyze the performance of different variants.
{"title":"BERT-Based Detection of Sexual Harassment in Dialogues","authors":"Mingrui Yan, Xudong Luo","doi":"10.1145/3507548.3507603","DOIUrl":"https://doi.org/10.1145/3507548.3507603","url":null,"abstract":"It tends to become a trend of booking transportation through network equipment with the further integration of the Internet into people's life, and online car-hailing platforms have sprung up. However, a new social crisis has also come with it. Because of the need for platform expansion, most online ride-hailing drivers have not undergone strict professional ethics reviews, which increases the risk of passengers taking the car. Primarily, female users are more susceptible to abuse and harassment and even persecution by drivers due to their disadvantaged position. Unfortunately, this phenomenon is happening every day and even getting worse. Regarding this aspect of supervision, it is difficult for relevant departments to have a more direct management plan. However, it is difficult for relevant departments to give a more natural and effective management plan. Therefore, ensuring the safety of passengers (predominantly female passengers) using online car-hailing becomes particularly important. In the Chinese field, few people try to improve this problem from the perspective of natural language. This work expects to use natural language technology to evaluate the driver's language and determine the degree of potential danger and criminal tendency, thus protecting the passenger and providing evidence for the judicial authorities. We first collected many dialogues between drivers and passengers, then used back translation to expand the corpus. Finally, we adopted various BERT-based model methods to compare and analyze the performance of different variants.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127844424","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}
Sine cosine algorithm (SCA) has a fast convergence speed and is easy to implement. In order to overcome the evolutionary stagnation of swarm, this paper presents a novel SCA (NSCA) in which three learning strategies are used to update individuals and a selection mechanism is developed to guide each individual to choose a proper updating strategy. The selection mechanism is designed by the credit assignment method and Upper Confidence Bound (UCB). The proposed algorithm has been experimentally validated on 18 benchmark functions. Compared with SCA variants and other swarm intelligence algorithms, experimental results show NSCA is competitive in solving most functions.
{"title":"A Novel Sine Cosine Algorithm for Global Optimization","authors":"Yuan xia Shen, Chuan hua Zeng, Xiao yan Wang","doi":"10.1145/3507548.3507579","DOIUrl":"https://doi.org/10.1145/3507548.3507579","url":null,"abstract":"Sine cosine algorithm (SCA) has a fast convergence speed and is easy to implement. In order to overcome the evolutionary stagnation of swarm, this paper presents a novel SCA (NSCA) in which three learning strategies are used to update individuals and a selection mechanism is developed to guide each individual to choose a proper updating strategy. The selection mechanism is designed by the credit assignment method and Upper Confidence Bound (UCB). The proposed algorithm has been experimentally validated on 18 benchmark functions. Compared with SCA variants and other swarm intelligence algorithms, experimental results show NSCA is competitive in solving most functions.","PeriodicalId":414908,"journal":{"name":"Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence","volume":"5 6part2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120843631","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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