2020 5th International Conference on Computational Intelligence and Applications (ICCIA)最新文献_第3页

Human Action Recognition Based on Improved Fusion Attention CNN and RNN 基于改进融合注意力CNN和RNN的人体动作识别

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00028

Han Zhao, Xinyu Jin

引用次数: 4

Modeling Price and Risk in Chinese Financial Derivative Market with Deep Neural Network Architectures 基于深度神经网络的中国金融衍生品市场价格与风险建模

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00010

Chenyu Wang, Zhongchen Miao, Yuefeng Lin, Hang Jiang, Jian Gao, Jidong Lu, Guangwei Shi

{"title":"Modeling Price and Risk in Chinese Financial Derivative Market with Deep Neural Network Architectures","authors":"Chenyu Wang, Zhongchen Miao, Yuefeng Lin, Hang Jiang, Jian Gao, Jidong Lu, Guangwei Shi","doi":"10.1109/ICCIA49625.2020.00010","DOIUrl":"https://doi.org/10.1109/ICCIA49625.2020.00010","url":null,"abstract":"As rapid growth, Chinese financial derivative market is holding increasingly large proportions in entire domestic capital market as well as in global shares. To the nature of derivative instruments, plenty of market data features (such as prices and trading volumes) and off-market factors (such as financial news and policies) can directly impact on the price and risk in Chinese financial derivative markets, which is becoming more and more infeasible to model by using only traditional financial models and hand-crafted features. To alleviate the issue, in this paper we introduce some state-of-art deep neural network architectures and model two significant futures market price and risk indicators that are widely used by Chinese regulators, which are turn-over ratio (ratio of daily trading volumes and daily open interest volumes) and price basis (gap between futures price and corresponding spot product price). The extensive experimental results show that deep learning methods perform better prediction accuracy than traditional methods, among which convolutional LSTM achieves better results in most cases as it can capture local time-variant patterns. In addition, we also propose methods to exploit alternative off-market features (such as social media emotions and Baidu Search Index) with DNN models, which are proven beneficial to the price and risk prediction by rendering extra information than only market data.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"331 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":"116235462","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

Artificial Neural Network for Forecasting of Daily Reservoir Inflow: Case Study of the Kotmale Reservoir in Sri Lanka 人工神经网络在水库日来水预测中的应用——以斯里兰卡Kotmale水库为例

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00009

U. Dampage, Yasiru Gunaratne, Ovindi Bandara, S. Silva, Vinushi Waraketiya

{"title":"Artificial Neural Network for Forecasting of Daily Reservoir Inflow: Case Study of the Kotmale Reservoir in Sri Lanka","authors":"U. Dampage, Yasiru Gunaratne, Ovindi Bandara, S. Silva, Vinushi Waraketiya","doi":"10.1109/ICCIA49625.2020.00009","DOIUrl":"https://doi.org/10.1109/ICCIA49625.2020.00009","url":null,"abstract":"The knowledge of water inflow figures is paramount in decision making on the allocation for consumption for numerous purposes; irrigation, hydropower, domestic and industrial usage, and flood control. The understanding on how reservoir inflows are affected by different climatic and hydrological conditions is crucial to enable effective water management and downstream flood control. In this research, we propose a method using a Long Short -Term Memory (LSTM) Artificial Neural Network (ANN) to assist the aforesaid decision-making process. The Kotmale reservoir which is the uppermost reservoir in the Mahaweli reservoir complex in Sri Lanka was used as the test bed for this research. The ANN uses the runoff in the Kotmale reservoir catchment area and the effect of Sea Surface temperatures (SST) to make a forecast for seven days ahead. Three types of ANN are tested; Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN) and LSTM. The extensive field trials and validation endeavors found that the LSTM ANN provides superior performance in the aspects of accuracy and latency.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"84 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":"124342615","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

An Improved Multi-objective Particle Swarm Optimization 一种改进的多目标粒子群优化算法

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00011

Shengbing Xu, Zhiping Ouyang, Jiqiang Feng

引用次数: 0

Video Prediction and Anomaly Detection Algorithm Based On Dual Discriminator 基于对偶鉴别器的视频预测与异常检测算法

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00031

Sinuo Fan, Fan-jie Meng

引用次数: 2

The application of virtual reality in empathy establishment: Foresee the future 虚拟现实在共情建立中的应用:展望未来

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00043

Yuqi Liu

{"title":"The application of virtual reality in empathy establishment: Foresee the future","authors":"Yuqi Liu","doi":"10.1109/ICCIA49625.2020.00043","DOIUrl":"https://doi.org/10.1109/ICCIA49625.2020.00043","url":null,"abstract":"The establishment of empathy is the premise and foundation for diverse innovative proposals and problem solutions. Virtual reality has provided a full range of depth and breadth for the establishment of empathy in many different types of fields due to its immersive, interactive, and imaginative characteristics. In this study, bibliometric analysis and VOSviewer software are used to cluster and visualize relevant 190 articles from the Web of Science core collection. The essay proposes a positioning of how to apply virtual reality on empathy based on two dimensions, from internal world to external world, and from business innovation to social innovation, by integrating each two of them, four application methods are summarized, which are meaning shaping, value creation, individual satisfaction, and self-realization. What’s more, using the bibliometric analysis result as a basis, the application landscape of virtual reality technology for establishing empathy has been constructed, including individual level, society level, and nature level, which reveals the existing and coming possibilities of using VR technology on building empathy in different fields. Last but not least, the paper has discussed the impact of virtual reality for empathy-building from five aspects, economy, politics, culture, society, and ecology. The efforts of this study reveal the VR tendency and have important reference significance for promoting the application of virtual reality technology in creating empathy and innovation in different fields.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"104 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":"115683110","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

Research on Service Composition Optimization Method Based on Composite Services QoS 基于组合服务QoS的业务组合优化方法研究

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00046

Chengrong Wang, Xiaodong Zhang, Dian-Hui Chu

引用次数: 1

ICCIA 2020 Opinion

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/iccia49625.2020.00005

引用次数: 0

A Hybrid Architecture for Semantic Image Similarity Learning 语义图像相似学习的混合体系结构

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00025

Oleksandr Vakhno, Long Ma

引用次数: 0

Spectral Averagely-dense Clustering Based on Dynamic Shared Nearest Neighbors 基于动态共享近邻的谱平均密集聚类

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00034

C. Yuan, L. Zhang

{"title":"Spectral Averagely-dense Clustering Based on Dynamic Shared Nearest Neighbors","authors":"C. Yuan, L. Zhang","doi":"10.1109/ICCIA49625.2020.00034","DOIUrl":"https://doi.org/10.1109/ICCIA49625.2020.00034","url":null,"abstract":"Spectral averagely-dense clustering is a clustering algorithm based on density, but it has the problem of being sensitive to the parameter ε. Aiming at the above problems, a spectral averagely-dense clustering based on dynamic shared nearest neighbors is put forward. Firstly, a similarity measures is constructed by combining self-tunning distance and shared nearest neighbors. Self-tunning distance can handle clusters of different density, and shared nearest neighbors can draw closer to the data in the same cluster and alienate the data in different clusters. Secondly, based on the sample distribution function, a method capable of self-adaptively determining the k-value of the shared nearest neighbors is proposed without setting the parameter k. Finally, the constructed similarity measure is used as the similarity measure of the fully connected graph. The ε-neighberhood graph of spectral averagely-dense clustering is replaced with the fully connected graph, which avoid setting the parameter ε. Through the experiments on artificial datasets and UCI datasets, the proposed algorithm is compared with the spectral averagelydense clustering and the standard spectral clustering. The experimental results show that the proposed algorithm not only avoids the problem of difficult selection of ε-neighberhood graph parameters, but also has better performance on the datasets.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"69 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":"133672945","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