Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence最新文献_第2页

A Comparative Study of Speculative Retrieval for Multi-Modal Data Trails: Towards User-Friendly Human-Vehicle Interactions 多模态数据路径推测检索的比较研究:面向用户友好的人机交互

Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence Pub Date : 2020-04-23 DOI: 10.1145/3404555.3404617

Yaohua Wang, Zhengtao Huang, Rongze Li, Xinyu Yin, Min Luo, Zheng Zhang, Xu Sun

{"title":"A Comparative Study of Speculative Retrieval for Multi-Modal Data Trails: Towards User-Friendly Human-Vehicle Interactions","authors":"Yaohua Wang, Zhengtao Huang, Rongze Li, Xinyu Yin, Min Luo, Zheng Zhang, Xu Sun","doi":"10.1145/3404555.3404617","DOIUrl":"https://doi.org/10.1145/3404555.3404617","url":null,"abstract":"In the era of growing developments in Autonomous Vehicles, the importance of Human-Vehicle Interaction has become apparent. However, the requirements of retrieving in-vehicle drivers' multi-modal data trails, by utilizing embedded sensors, have been consid- ered user unfriendly and impractical. Hence, speculative designs, for in-vehicle multi-modal data retrieval, has been demanded for future personalized and intelligent Human-Vehicle Interaction. In this paper, we explore the feasibility to utilize facial recog- nition techniques to build in-vehicle multi-modal data retrieval. We first perform a comprehensive user study to collect relevant data and extra trails through sensors, cameras and questionnaire. Then, we build the whole pipeline through Convolution Neural Net- works to predict multi-model values of three particular categories of data, which are Heart Rate, Skin Conductance and Vehicle Speed, by solely taking facial expressions as input. We further evaluate and validate its effectiveness within the data set, which suggest the promising future of Speculative Designs for Multi-modal Data Retrieval through this approach.","PeriodicalId":220526,"journal":{"name":"Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123304423","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

Learning Temporal Structure of Videos for Action Recognition Using Pattern Theory 基于模式理论的动作识别视频时间结构学习

Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence Pub Date : 2020-04-23 DOI: 10.1145/3404555.3404628

Xiaoyu Zhang

引用次数: 0

A Multiobjective Evolutionary Approach for Influence Maximization in Multilayer Networks 多层网络中影响最大化的多目标进化方法

Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence Pub Date : 2020-04-23 DOI: 10.1145/3404555.3404568

Qipeng Lu, Zhan Bu, Yuyao Wang

{"title":"A Multiobjective Evolutionary Approach for Influence Maximization in Multilayer Networks","authors":"Qipeng Lu, Zhan Bu, Yuyao Wang","doi":"10.1145/3404555.3404568","DOIUrl":"https://doi.org/10.1145/3404555.3404568","url":null,"abstract":"Influence Maximization (IM) is one key algorithmic problems in information diffusion research; it aims to select a set of users from a social network and, by following a specific model, maximize the number of users influenced (the influence spread). Yet despite its immense potential, relatively little research is dedicated to IM for multilayer networks. Conversely, most existing IM studies that rely on a greedy algorithm strategy only obtain a single solution that provides limited insights on the target networks' core organization. With that in mind, we focus on studying the Influence Maximization Problem (IMP) in multilayer networks. Specifically, we define novel concepts, such as the pairwise reciprocal length and pairwise influence, with respect to the information-diffusion process in multilayer networks. Then we formulate the IM in multilayer networks as a multiobjective optimization problem and employ the classic Nondominated Sorting Genetic Algorithm II (NSGA-II) to find a set of Pareto-optimal solutions that provide a wide range of options for decision makers. To maintain population diversity and accelerate the algorithm's convergence, we combine a heuristic population initialization strategy and an efficient two-point crossover operation. Extensive experiments show that our approach has competitive performance when compared to off-the-shelf IM algorithms with regard to influence spread and running time.","PeriodicalId":220526,"journal":{"name":"Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126089641","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

A Novel Hybrid Algorithm for Sentiment Analysis via Classifier Ensembles for Online Shops User Using User Generated Contents and Review 一种基于分类器集成的基于用户生成内容和评论的在线商店用户情感分析混合算法

Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence Pub Date : 2020-04-23 DOI: 10.1145/3404555.3404645

Fereshteh Ghorbanian, Mehrdad Jalali

引用次数: 1

An Improved Text Classification Model Based on Memory Convolution Neural Network 一种基于记忆卷积神经网络的改进文本分类模型

Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence Pub Date : 2020-04-23 DOI: 10.1145/3404555.3404595

Yiyao Wang, Lihua Tian, Chen Li

引用次数: 3

Bond Recommendation Based on Heterogeneous Network Embedding 基于异构网络嵌入的债券推荐

Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence Pub Date : 2020-04-23 DOI: 10.1145/3404555.3404560

Jiazhe Zhang, Cui Zhu, Wenjun Zhu

{"title":"Bond Recommendation Based on Heterogeneous Network Embedding","authors":"Jiazhe Zhang, Cui Zhu, Wenjun Zhu","doi":"10.1145/3404555.3404560","DOIUrl":"https://doi.org/10.1145/3404555.3404560","url":null,"abstract":"Bond financing has become the main way of external financing. However, few studies have addressed recommendations for financial products to financial institutions. In the case of bonds, financial institutions often need multiple types of data to back up their marketing of bonds to companies. However, it is difficult to collect data and has a large amount of analysis. Therefore, this article based on issuance of historical data, simplifying the model data needed, rely on the company recommended study on the relationship between the issuance of bonds. Bonds contain a variety of heterogeneous characteristics, which contain a wealth of information. Therefore, this paper adopts the recommendation method based on HIN. This paper improves from three aspects. First, a meaningful meta-path is designed and a constraint condition is added to the random walk strategy to make it conform to the application scenario in the financial field. Secondly, the generation strategy is designed to generate isomorphic sequence of node of target type. Thirdly, based on the same industry bond recommendation, this method solves the cold start problem of the company. This paper conducts experiments on real data sets, and experimental results show the effectiveness of this method, which will assist account managers to find business opportunities.","PeriodicalId":220526,"journal":{"name":"Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124927046","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

A Study of Population Diversity Using an Enhanced Brain Storm Optimization 利用增强的头脑风暴优化方法研究种群多样性

Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence Pub Date : 2020-04-23 DOI: 10.1145/3404555.3404606

S. N. Kofie, S. H. Sackey

{"title":"A Study of Population Diversity Using an Enhanced Brain Storm Optimization","authors":"S. N. Kofie, S. H. Sackey","doi":"10.1145/3404555.3404606","DOIUrl":"https://doi.org/10.1145/3404555.3404606","url":null,"abstract":"In evolutionary algorithm, population diversity is a vital factor for solving problems. To avoid the premature convergence, it is imperative to preserve the population diversity during the evolution. The population diversity ensures avoiding the premature convergence. Population diversity is a measure that has been used extensively in studies to measure premature convergence. Population diversity is convenient for measuring and dynamically adjusting an algorithm's ability of exploration or exploitation. Brain Storm Optimization (BSO) is a new kind of swarm intelligence method inspired by the cooperative behavior of human beings in the problem-solving process. BSO suffers the premature convergence which happens partly due to the solutions getting clustered. The solution set clustered after a few iterations which indicate that the diversity level decreases rapidly during the search. In order to enhance the computational efficacy of the original BSO algorithm and maintain the population diversity, we propose two ways to re-cluster the original BSO. We introduce a BSO in objective space with a Cauchy distribution with the knowledge that Cauchy distribution infers a faster rate. In addition, we introduce a new step size equation as a parameter to balance the exploration and exploitation in the search space. A good algorithm maintains population diversity in both spaces (objective and parameter spaces). Our goal is to investigate why the enhanced BSO performs efficiently from the perspective of population diversity on a set of five benchmark functions. Experimental figures show that the performance of the proposed algorithms performs better from the population diversity measurement.","PeriodicalId":220526,"journal":{"name":"Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123335331","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

FCDnet

Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence Pub Date : 2020-04-23 DOI: 10.1145/3404555.3404638

Yichang Liu, Huiling Gen

引用次数: 0

Humanoid Robot Control System Based on AR-SSVEP 基于AR-SSVEP的仿人机器人控制系统

Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence Pub Date : 2020-04-23 DOI: 10.1145/3404555.3404625

Yao Wang, Xiang Zhang, Kun Li, Jinhai Wang, Xiaogang Chen

引用次数: 6

Kernel-Based Relocation Siamese Network for Real-Time Visual Object Tracking 基于核的重定位Siamese网络实时视觉目标跟踪

Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence Pub Date : 2020-04-23 DOI: 10.1145/3404555.3404596

Bohao Shen

引用次数: 0