{"title":"WSAI 2020 Title Page","authors":"","doi":"10.1109/wsai49636.2020.9143277","DOIUrl":"https://doi.org/10.1109/wsai49636.2020.9143277","url":null,"abstract":"","PeriodicalId":346385,"journal":{"name":"2020 2nd World Symposium on Artificial Intelligence (WSAI)","volume":"102 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":"122616415","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}
{"title":"A Spectral Clustering-Based Community Detection Algorithm for RDF Graphs","authors":"Gustaph Sanga, Xingxing Hao","doi":"10.1109/WSAI49636.2020.9143285","DOIUrl":"https://doi.org/10.1109/WSAI49636.2020.9143285","url":null,"abstract":"Community detection is a widely explored research area in complex networks. Communities are a set of components with highly intra-community similarities and little inter-community similarities. Detecting community structure in complex directed and labeled graphs is an inter-disciplinary research area with great importance in real-world knowledge domains. This paper provides a precise description of a new complex directed and labeled graph, termed as Resource Description Framework (RDF) Graph. These graphs are solely based on specific semantic web knowledge bases. Furthermore, we propose a new community detection algorithm based on spectral clustering, termed as RDF-SCA, to detect patterns and communities in RDF Graphs. Finally, we present findings obtained from firing an RDF graph using our proposed RDF-SCA community detection algorithm.","PeriodicalId":346385,"journal":{"name":"2020 2nd World Symposium on Artificial Intelligence (WSAI)","volume":"27 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":"122378504","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}
Yexiu Zhong, Senhui Qiu, Xiaoshu Luo, Zhiming Meng, Junxiu Liu
{"title":"Facial Expression Recognition Based on Optimized ResNet","authors":"Yexiu Zhong, Senhui Qiu, Xiaoshu Luo, Zhiming Meng, Junxiu Liu","doi":"10.1109/WSAI49636.2020.9143287","DOIUrl":"https://doi.org/10.1109/WSAI49636.2020.9143287","url":null,"abstract":"Facial expression recognition (FER) is a very challenging task for machines to understand the emotional changes in human beings. Recently, using deep learning methods has greatly improved the accuracy of FER. However, considering the requirements of deploying deep learning algorithms in embedded systems, the size of neural networks needs to be further optimized on the basis of ensuring the high accuracy. In order to reduce the model parameters and maintain the high FER accuracy, inspired by the advances that Residual Network (ResNet) have achieved in image recognition and classification, we propose a simplified and efficient neural network based on ResNet and Squeeze-and-Excitation (SENet).Experiments show that the proposed algorithm obtains recognition accuracy of 74.143% and 95.253% on the FFE2013 and CK+ databases, respectively. Compared with state-of-the-art methods using Visual Geometry Group (VGG) or other deep neural networks, the proposed algorithm not only improves the accuracy, but also reduces the size of the model, which is competitive with existing methods in terms of size of the model parameters and recognition accuracy. Furthermore, this work also shows that only increasing the network layers hardly improves the recognition accuracy on the FFE2013 and CK+ databases.","PeriodicalId":346385,"journal":{"name":"2020 2nd World Symposium on Artificial Intelligence (WSAI)","volume":"15 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":"127852642","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}
{"title":"Adaptive Dynamic Programming Based on Multi-dimensional Taylor Network for Time-Delay Nonlinear System with Uncertainties","authors":"Zheng-Yi Duan, Hong-sen Yan","doi":"10.1109/WSAI49636.2020.9143315","DOIUrl":"https://doi.org/10.1109/WSAI49636.2020.9143315","url":null,"abstract":"For the uncertain time-delay system, this paper investigates a novel robust adaptive dynamic programming (ADP) to guarantee the stability and performance of the system. By devising a novel cost function which integrates the effects of time delay and uncertainties, the uncertain time-delay system is transformed into a control problem of the nominal system through ADP. By applying the control policy designed for the nominal system, the uncertain system is guaranteed to be asymp-totically stable. Besides, in order to increase the computation efficiency, multi-dimensional Taylor network is utilized as the approximating architecture to estimate the optimal value function and optimal control. A simulation example is provided to verify the effectiveness of the presented control approach.","PeriodicalId":346385,"journal":{"name":"2020 2nd World Symposium on Artificial Intelligence (WSAI)","volume":"97 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":"115706980","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}
{"title":"Subspace Clustering Based on Latent Low Rank Representation with Schatten-p Norm","authors":"Jiangzhong Cao, Yilan Fu, Xiaodong Shi, B. Ling","doi":"10.1109/WSAI49636.2020.9143313","DOIUrl":"https://doi.org/10.1109/WSAI49636.2020.9143313","url":null,"abstract":"Clustering analysis is one of the key technologies in the field of data mining. Among them, high-dimensional data clustering is the core and the most challenging task in clustering analysis. Subspace clustering is an effective clustering method for high-dimensional data. Subspace clustering with Latent Low-Rank Representation (LatLRR) is promising because it can solves the problem of Low-Rank Representation (LRR) of insufficient samples. However, the nuclear norm is usually used to approximate the rank in LatLRR since finding the low rank solution is NP-hard. In order to obtain a better low rank matrix and take into account the insufficiency of samples, this paper proposes a LatLRR model based on Schatten-p norm which introduces the Schatten-p norm to approximate the rank function and an Lp norm constraint error term to improve the robustness. Experimental results show that the algorithm can effectively improve the subspace clustering performance.","PeriodicalId":346385,"journal":{"name":"2020 2nd World Symposium on Artificial Intelligence (WSAI)","volume":"3 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131686545","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}
{"title":"GPU Based Angular Radial Transform for Image Analysis","authors":"Mingkai Tang, Zhuozhang Li, Zhuo Yang, Jian-Wei Zhu, Yinwei Zhan","doi":"10.1109/WSAI49636.2020.9143278","DOIUrl":"https://doi.org/10.1109/WSAI49636.2020.9143278","url":null,"abstract":"Angular radial transform (ART) is a shape descriptor in MPEG-7 for image representation and image analysis. For real world applications, execution efficiency is always a significant challenge. With widespread use of Graphics Processing Unit (GPU) in image processing tasks, this paper presents GPU based angular radial transform (GART). Proposed parallel GART is based on mathematical properties of ART and optimization techniques of GPU. Optimal parameter selections for GPU execution are also evaluated. In our experiments, with same computation result GART is over 1800 times faster than ART. Wide range of applications that using ART will inspired from this work.","PeriodicalId":346385,"journal":{"name":"2020 2nd World Symposium on Artificial Intelligence (WSAI)","volume":"134 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":"132637953","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}