Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems最新文献

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Hybrid High-order in Graph Attention Layer 混合高阶图注意层
E. Haihong, Di Zeng, Meina Song
{"title":"Hybrid High-order in Graph Attention Layer","authors":"E. Haihong, Di Zeng, Meina Song","doi":"10.1145/3372422.3372442","DOIUrl":"https://doi.org/10.1145/3372422.3372442","url":null,"abstract":"As a result of approximating the Eigenbasis of the graph Laplacian proposed by GC-layer of Kipf & Welling [5], the convolution operation is efficiently applied from Euclidean domain to graph domain, and the end-to-end deep graph neural network is widely used and developed. However, fixed neighborhood limits the learning ability of the model, and GAT [7] models global node pairs to avoid information loss. In the form, this modeling is equivalent to only considering the first-order proximity relation of the network, which leads to the indirect and lossy transmission of the higher-order information of the network, even if the multi-layer attention mechanism is used to expand the order of the network. In order to avoid the above situation and obtain higher-order information better, this paper tries to establish the concept of higher-order neighborhood mixed learning of graphs. In our work, unlike the implicit propagation of neighborhood information through activation functions in the past, our model called H-GAT explicitly obtain the information of high-order neighborhood of nodes, and use attention mechanism to model different weights between high-step nodes.","PeriodicalId":118684,"journal":{"name":"Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132036552","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
Convolutional Neural Network using Stacked Frames for Video Classification 基于堆叠帧的卷积神经网络视频分类
Itthisak Phueaksri, S. Sinthupinyo
{"title":"Convolutional Neural Network using Stacked Frames for Video Classification","authors":"Itthisak Phueaksri, S. Sinthupinyo","doi":"10.1145/3372422.3372434","DOIUrl":"https://doi.org/10.1145/3372422.3372434","url":null,"abstract":"We propose a Convolutional Neural Network model with stacked frame images for video classification. In this research, one of the challenges is that each video is more than 3,600 seconds long. We extracted ordered frames by skipping some frames from each video. Under our assumption, it is not practical to train each video with all the extracted frames as input because it can cause building a huge model. Hence, we created a median filter layer for reducing the number of frames before training. In the experiment, we extracted 500 frame images from each video. In the reduction process with a median filter layer, we were able to reduce the number of frames from 500 frame images to 50 median images. In the training process, we trained our model that used binary classification to classify seven classes of TV programs. With 1,092 video clips of TV programs, our model achieved 79.38% average accuracy.","PeriodicalId":118684,"journal":{"name":"Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115339158","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
Adversarial Multi-task Label Embedding for Text Classification 文本分类的对抗性多任务标签嵌入
Kai Shuang, Menghan Xu, Wentao Zhang, Zhixuan Zhang
{"title":"Adversarial Multi-task Label Embedding for Text Classification","authors":"Kai Shuang, Menghan Xu, Wentao Zhang, Zhixuan Zhang","doi":"10.1145/3372422.3372433","DOIUrl":"https://doi.org/10.1145/3372422.3372433","url":null,"abstract":"Multi-task learning makes use of the potential correlation among related tasks to perform well in text classification. However, in the most multi-task works, labels are converted to meaningless one-hot vectors, which cause the loss of label semantics closely related to text semantics. Besides, shared and private features captured by previous shared-private multi-task learning framework are usually confused by the fact that the shared unit simply shares the parameters. In this paper, we propose the Adversarial Multi-task Label Embedding model. In this model, we integrate label semantics and improve the performance of multi-task learning. We introduce adversarial training and orthogonal constraints into the multi-task learning framework to prevent shared features and private features from interacting with each other. Extensive experimental results on six benchmark datasets demonstrate that our proposed approach is superior to the state-of-the-art multi-task text classification methods.","PeriodicalId":118684,"journal":{"name":"Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114624184","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
Effectiveness of Haar-like Features and ViBe Algorithm for Detecting Jaywalkers Haar-like特征和ViBe算法检测行人的有效性
J. P. Tomas, Shaina Nicole V. Jocsing, James Kirk L. Guanzon, Chielo Jane A. Matias
{"title":"Effectiveness of Haar-like Features and ViBe Algorithm for Detecting Jaywalkers","authors":"J. P. Tomas, Shaina Nicole V. Jocsing, James Kirk L. Guanzon, Chielo Jane A. Matias","doi":"10.1145/3372422.3372436","DOIUrl":"https://doi.org/10.1145/3372422.3372436","url":null,"abstract":"Despite many attempts, common techniques used in pedestrian detection still encounter problems such as high miss rate and false detection rates with images and videos. Several studies have been conducted in the field of object detection, and there are still other existing gaps such as, detection hampered due to lighting, object size distortion caused by angle and perspective projection while taking the video footage, and ghosting in certain frames due to sudden movements of static objects. This study focuses in devising a model that detects and classifies pedestrians crossing from other moving objects within a given region of interest (ROI). The proponents utilized ViBe algorithm that covered the process of segmenting the foreground objects (pedestrians crossing) from the background, while to process the segmented images, Haar-like features was utilized that focused on the characteristics of the different human body parts which determined if the segmented moving objects are pedestrians. The scope of this study did not include detection of pedestrians that are using skateboards, wheelchairs, canes, and other walking aids that are utilized while crossing the road. This study determines the volume of people who are really crossing in the designated pedestrian lane. A Region of Interest (ROI) which is the pedestrian lane of any design, was considered in quantifying the number of people crossing inside and outside the said ROI.","PeriodicalId":118684,"journal":{"name":"Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems","volume":"197 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134093874","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
Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems 2019年第二届计算智能与智能系统国际会议论文集
{"title":"Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems","authors":"","doi":"10.1145/3372422","DOIUrl":"https://doi.org/10.1145/3372422","url":null,"abstract":"","PeriodicalId":118684,"journal":{"name":"Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124893831","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
Investigating Methods of Determining Number of Hidden Units in Deep Learning for Taxi Recommender System 出租车推荐系统深度学习中隐藏单元数量确定方法的研究
Undarmaa Chinzorig, H. Song, Jun Park
{"title":"Investigating Methods of Determining Number of Hidden Units in Deep Learning for Taxi Recommender System","authors":"Undarmaa Chinzorig, H. Song, Jun Park","doi":"10.1145/3372422.3372446","DOIUrl":"https://doi.org/10.1145/3372422.3372446","url":null,"abstract":"With modern ubiquitous computing environments, recommender systems have become a major part of modern intelligent services. Taxi recommender system allows both passengers and drivers to minimize waiting times and obtain the current location of each other. In this paper, we present a taxi recommender system based on deep learning for catching taxis. In deep learning technique, random selections of hyperparameters lead to overfitting or underfitting problems in prediction or classification. In particular, determining number of hidden units is one of the critical issues facing research. Therefore, we investigated how hidden units affect the performance of deep learning for taxi recommender system and compared the results of these existing methods for determining number of hidden units. Deep Learning algorithms, such as Deep Neural Network (DNN), which have been successfully used, were employed for classifying road segments. Finally, our proposed system can spot regions, where a passenger can catch a taxi within walkable distance.","PeriodicalId":118684,"journal":{"name":"Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117017364","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
Variable Neighborhood Search for Optimal Railway Station Location 最优火车站位置的变邻域搜索
Ornurai Sangsawang, Sunarin Chanta
{"title":"Variable Neighborhood Search for Optimal Railway Station Location","authors":"Ornurai Sangsawang, Sunarin Chanta","doi":"10.1145/3372422.3372453","DOIUrl":"https://doi.org/10.1145/3372422.3372453","url":null,"abstract":"This research aims to find optimal railway station locations. The objective is to maximize the covered the number of expected passengers that can be covered. The problem is formulated as integer programming model based on the capacitated single p-hub maximal covering location problem. Two types of coverage are considered to reflect passengers' satisfaction, which are the condition on time to go to a railway station and the condition on total travelling time. Since the problem is complex, we developed Variable Neighborhood Search with three different neighborhood structures. The proposed algorithm was applied to solve the case study of high-speed railway station location. The results showed that the proposed algorithm found the optimal solutions for all cases within a few seconds.","PeriodicalId":118684,"journal":{"name":"Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115164896","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 Comparative Study on GA-based Scheduling on Cloud Computing 基于遗传算法的云计算调度比较研究
Lamisha Rawshan, Tasnim Rahman, A. Begum, Syeda Sumbul Hossain, T. Bhuiyan
{"title":"A Comparative Study on GA-based Scheduling on Cloud Computing","authors":"Lamisha Rawshan, Tasnim Rahman, A. Begum, Syeda Sumbul Hossain, T. Bhuiyan","doi":"10.1145/3372422.3372425","DOIUrl":"https://doi.org/10.1145/3372422.3372425","url":null,"abstract":"Cloud computing provides data storage and computing power based on user demand by assigning tasks to virtual resources. To deliver overall improved performance and meet challenges such as availability, resource utilization and reliability in the cloud, appropriate resource scheduling methods are needed. A number of metaheuristic optimization algorithms are used to solve the problem of resource scheduling. This work lists challenges and analyzes previous scheduling methods based on Genetic Algorithm (GA). It classifies the GA-based scheduling methods with respect to many parameters. At last, it presents the scopes of enhancement for future researchers.","PeriodicalId":118684,"journal":{"name":"Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122403047","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
Improved Weighted Learning Support Vector Machines (SVM) for High Accuracy 改进的高精度加权学习支持向量机(SVM)
Syahizul Amri Dzulkifli, M. Salleh, K. Talpur
{"title":"Improved Weighted Learning Support Vector Machines (SVM) for High Accuracy","authors":"Syahizul Amri Dzulkifli, M. Salleh, K. Talpur","doi":"10.1145/3372422.3372432","DOIUrl":"https://doi.org/10.1145/3372422.3372432","url":null,"abstract":"Support Vector machine (SVM) is a linear model designed for classification problem and popular due to a number of their attractive features such as high generalization ability and promising performance. However, the high generalization ability of SVM is only achieved by depending on a small part of the data points to determine an optimal hyperplane. During the learning process, the noise still exists to deviate severely the corresponding decision boundary from the ideal hyperplane. Two different weighted SVM such as one-step WSVM (OWSVM) and iteratively WSVM (iWSVM) has been reviewed besides the standard SVM. This method assigns relative important weights to achieve optimal margin hyperplane. In this study, an improved WSVM using moving weighted average is introduced to generate useful weighted and unweighted support vector for the optimal margin hyperplane. The methods are compared based on correctly labeled, mislabeled data within margin and classification accuracy using three datasets in KEEL repository with 20% noise. The results show that the proposed method yields better classification accuracy compared to OWSVM and iWSVM.","PeriodicalId":118684,"journal":{"name":"Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133660006","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
3D Shape Blending: Parts Swapping 3D形状混合:零件交换
Kyle Ong, K. Ng, Yih-Jian Yoong
{"title":"3D Shape Blending: Parts Swapping","authors":"Kyle Ong, K. Ng, Yih-Jian Yoong","doi":"10.1145/3372422.3372445","DOIUrl":"https://doi.org/10.1145/3372422.3372445","url":null,"abstract":"3D shape blending is getting more and more attention recently as it can yield numerous ideas to ease the artists/designers to strive for new concept for their product design. Besides, it is also widely extended to many other research fields such as animation that relies on the automated process of interpolating a 3D model based on user-defined weights or anchors. Such a task will take a lot of effort and time if it is to be done manually. In this paper, we propose to auto-segment the object into several meaningful features. The object will first be converted into 1D skeletal for obtaining the main features. Slinky-based segmentation method is applied to separate each semantic feature. The model then is partitioned in parts and is blended (swapped) from the source to the target model. The final result shows that the blended model successfully modify the parts of a model but reveal some gaps which can be patched easily by connecting the neighboring vertices of both the source and target model.","PeriodicalId":118684,"journal":{"name":"Proceedings of the 2019 2nd International Conference on Computational Intelligence and Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129254793","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
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