2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)最新文献_第10页

Pedestrian Attribute Recognition Based on Association Rules 基于关联规则的行人属性识别

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016404

Diwei Xie, Heqian Qiu, Linfeng Xu

引用次数: 0

A Multimodal Fusion Scene Graph Generation Method Based on Semantic Description 基于语义描述的多模态融合场景图生成方法

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016416

Liwen Ma, Weifeng Liu, Yaning Wang

引用次数: 0

Deep Text Retrieval Models based on DNN, CNN, RNN and Transformer: A review 基于DNN、CNN、RNN和Transformer的深度文本检索模型综述

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016379

Jianping Liu, Xintao Chu, Yingfei Wang, Meng Wang

{"title":"Deep Text Retrieval Models based on DNN, CNN, RNN and Transformer: A review","authors":"Jianping Liu, Xintao Chu, Yingfei Wang, Meng Wang","doi":"10.1109/ccis57298.2022.10016379","DOIUrl":"https://doi.org/10.1109/ccis57298.2022.10016379","url":null,"abstract":"The development of deep learning technology provides a new development direction for text retrieval. Researchers have applied deep learning techniques to different information retrieval objects and carried out rich studies on them, such as web pages, scientific literature, and scientific data. This paper selects 40 research papers on related topics in the past 10 years through a step-by-step selection and conducts a review on the dimensions of model input, model structure, and its performance. Firstly, according to the differences in methods, we divided the deep learning text retrieval model into four categories: DNN-based, CNN-based, RNN-based, and Transformer-based, and analyzed the classical model structure and retrieval effect of each category. Secondly, we analyzed and compared the application scenarios of different types of models, and summarized some classic retrieval datasets. Finally, we discussed the main challenges and future research trends of deep text retrieval. This review is expected to provide basic knowledge and effective research entry points for scholars engaged in deep learning text retrieval.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125500701","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

SPNet: Utilizing Subspace Projection to Achieve Feature Interaction for Click-Through Rate SPNet:利用子空间投影实现点击率的特征交互

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016313

Xu Zhang, Z. Ou, Meina Song

引用次数: 0

An optimization r-cnn method for Ovarian cyst detection 优化r-cnn方法检测卵巢囊肿

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016426

Jiade Li, Qili Chen

引用次数: 0

Magnetic target localization method based on mobile platform 基于移动平台的磁目标定位方法

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016375

Pengfei Jiang, X. Pang, Hui Yang

引用次数: 0

A Joint Learning Sentiment Analysis Method Incorporating Emoji-Augmentation 结合表情符号增强的联合学习情感分析方法

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016405

Jie Chen, Luping Luo, Bojing Ji, Shu Zhao, Yanping Zhang

{"title":"A Joint Learning Sentiment Analysis Method Incorporating Emoji-Augmentation","authors":"Jie Chen, Luping Luo, Bojing Ji, Shu Zhao, Yanping Zhang","doi":"10.1109/CCIS57298.2022.10016405","DOIUrl":"https://doi.org/10.1109/CCIS57298.2022.10016405","url":null,"abstract":"Social media is the platform for most people to share their opinions, emojis are also widely used to express moods, emotions, and feelings on social media. There have been many researched on emojis and sentiment analysis. However, existing methods mainly face two limitations. First, since deep learning relies on large amounts of labeled data, the training samples of emoji are not enough to achieve the training effect. Second, they consider the sentiment of emojis and texts separately, not fully exploring the impact of emojis on the sentiment polarity of texts. In this paper, we propose a joint learning sentiment analysis method incorporating emoji-augmentation, and the method has two advantages compared with the existing work. First, We optimize the easy data augmentation method so that the newly generated sentences can also preserve the semantic information of emojis, which relieves the problem of insufficient training data with emojis. Second, it fuses emojis and text features to allow the model to better learn the mutual emotional semantics between text and emojis, jointly training emojis and words to obtain the sentence representations containing more semantic information of both emojis and text. Our experimental results show that the proposed method can significantly improve the performance compared with several baselines on two datasets.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128286191","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

Attention based Long Short-Term Memory Network for Coastal Visibility Forecast 基于注意的长短期记忆网络海岸带能见度预报

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/CCIS57298.2022.10016374

Rui Min, Ming Wu, Mengqiu Xu, Xun Zu, Xun Zhu

{"title":"Attention based Long Short-Term Memory Network for Coastal Visibility Forecast","authors":"Rui Min, Ming Wu, Mengqiu Xu, Xun Zu, Xun Zhu","doi":"10.1109/CCIS57298.2022.10016374","DOIUrl":"https://doi.org/10.1109/CCIS57298.2022.10016374","url":null,"abstract":"Visibility prediction in coastal areas has always been an important issue affecting the safety of residents and the efficiency of urban transportation. The visibility prediction methods currently used by meteorological centers are mainly based on the statistical forecast with relatively low prediction accuracy and high computational complexity. These methods cannot work well with large amounts of data. However, with the rapid development of deep learning technology, the use of deep learning has become a primary trend. In this paper, we propose our visibility prediction model based on (Long Short-Term Memory) LSTM network and self-attention mechanism. The model takes Medium-range Forecasts Data from European Centre for Mediumrange Weather Forecasting (ECMWF) which we use EC data to refer it for simplicity and observatory visibility data as input to predict and uses the LSTM network as the backbone to extract time series information. We also use self-attention mechanism to process the input data before the data is input to the model to let the model better focus on the valuable information for prediction. Compared with the predicted visibility in EC data, our proposed method improved the 3-hour prediction accuracy by 20%, 1.5 times, and 8 times for high-range, medium-range, and low-range visibility, respectively. We also find the data imbalance will greatly affect the prediction accuracy for low-visibility data and use the weighted-loss and mix-up data augmentation strategy model in our model training. We improved the accuracy of low-visibility data by 1.2 times while the prediction results of high-visibility and medium-visibility data remained almost the same. In addition, we conduct several experiments to verify the effectiveness of our model design and the rationality of data augmentation.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128484444","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

High-precision visual navigation and localization method 高精度视觉导航与定位方法

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016389

Z. Peng, Dongsheng Li, Longtao Cai

引用次数: 0

Contrastive Semantic Similarity Learning for Multi-Hop Question Answering over Event-Centric Knowledge Graphs 基于事件中心知识图的多跳问答对比语义相似学习

2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS) Pub Date : 2022-11-26 DOI: 10.1109/ccis57298.2022.10016335

Wei Tang, Qingchao Kong, W. Mao, Xiaofei Wu

{"title":"Contrastive Semantic Similarity Learning for Multi-Hop Question Answering over Event-Centric Knowledge Graphs","authors":"Wei Tang, Qingchao Kong, W. Mao, Xiaofei Wu","doi":"10.1109/ccis57298.2022.10016335","DOIUrl":"https://doi.org/10.1109/ccis57298.2022.10016335","url":null,"abstract":"Question answering in natural languages provides an intuitive and efficient way to help people access the rich information stored in various kinds of knowledge graphs (KGs). One of the key challenges for question answering over knowledge graphs (KGQA) is to learn a semantic representation of the input question and candidate relation chains over KGs and accurately measure the similarity between them. However, existing methods often failed to capture the semantic similarity for complex question answering, e.g., multi-hop and temporal constrained situations. In addition, existing KGQA related research mostly concentrates on entities while often ignores the events which contain a large portion of the world knowledge. To solve this issue, we propose a Contrastive Semantic Similarity Learning (CSSL) method for multi-hop question answering over event-centric KGs. In this method, for candidate relation chains generation, the retrieval subgraph is first constructed by identifying the topic event or entity in the question. To better accommodate complex questions, we introduce the contrastive learning framework to learn a common semantic space, where the similarity score is finally calculated to select the final answer. The experimental results on the EventQA dataset show that the proposed method achieves superior performances compared to the state-of-the-art baselines.","PeriodicalId":374660,"journal":{"name":"2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)","volume":"167 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131513754","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