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International Conference on Artificial Intelligence and Virtual Reality最新文献

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Better Pretrained Embedding with Convolutional Neural Networks for Morphological Stemming 基于卷积神经网络的更好的形态学词干预训练嵌入
International Conference on Artificial Intelligence and Virtual Reality Pub Date : 2019-07-27 DOI: 10.1145/3348488.3348499
Y. Oo, K. Soe
{"title":"Better Pretrained Embedding with Convolutional Neural Networks for Morphological Stemming","authors":"Y. Oo, K. Soe","doi":"10.1145/3348488.3348499","DOIUrl":"https://doi.org/10.1145/3348488.3348499","url":null,"abstract":"Words are considered as independent entities without any direct relationship among morphologically related word. So, some rare words are poorly estimated and unknown words are represented only a few vectors. The process of stemming is to reduce different forms to a common morphological root. Word embedding is a good generalization to unseen words and that can capture general syntactic as well as semantic properties of word. Furthermore, deep learning approaches have become more and more prominent in NLP tasks and pre-trained embedding layers have been applied to improve the performance of neural network architectures for many NLP applications. However, word segmentation for Myanmar Language, like for most Asian Languages, is a vital task and widely-studied sequence labeling problem. Normally, stemming is considered as a separate process from segmentation. In this paper, new approach indicates segmentation boundaries when it performs stemming. This paper proposes several word representations from character and syllable level and they are corporate in convolutional neural network (CNN-based model) which jointly learns stemming and segmentation boundaries in parallel. It is also evaluated the performance of convolutional neural network that relies on different pre-trained embedding. According to the experimental results, the pre-trained embedding has a vast effect on the performance.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131784921","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
CCA Model with Training Approach to Improve Recognition Rate of SSVEP in Real Time 基于训练方法的CCA模型实时提高SSVEP识别率
International Conference on Artificial Intelligence and Virtual Reality Pub Date : 2019-07-27 DOI: 10.1145/3348488.3348498
Deep Soni, N. S. Malan, Shiru Sharma
{"title":"CCA Model with Training Approach to Improve Recognition Rate of SSVEP in Real Time","authors":"Deep Soni, N. S. Malan, Shiru Sharma","doi":"10.1145/3348488.3348498","DOIUrl":"https://doi.org/10.1145/3348488.3348498","url":null,"abstract":"Brain Computer Interfaces (BCIs) are often used to control external devices using electroencephalogram (EEG) signals. In Steady-State Visually Evoked Potentials (SSVEP) based BCIs, suboptimal Information Transfer Rate (ITR) is achieved due to false detection of SSVEP as one of the target class while the subject is not focusing on any target. To alleviate this issue, we propose a class labelling method where a classifier is trained against the non-target class. In the experiment, features are extracted using Canonical Correlation Analysis (CCA) and class labelling is performed using the proposed method. Afterwards, Linear Discriminant Analysis (LDA) has been employed for classification task. The results were compared with standard methods such as CCA and Fast Fourier Transform (FFT), implemented for the same experimental setup. The proposed method was found to be highly accurate and it successfully overcame the issues found in previous methods.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"46 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127994007","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
Motion Statistic Based Local Homography Transformation Estimation for Mismatch Removal 基于运动统计的局部单应变换估计去错
International Conference on Artificial Intelligence and Virtual Reality Pub Date : 2019-07-27 DOI: 10.1145/3348488.3348496
Songlin Du, T. Ikenaga
{"title":"Motion Statistic Based Local Homography Transformation Estimation for Mismatch Removal","authors":"Songlin Du, T. Ikenaga","doi":"10.1145/3348488.3348496","DOIUrl":"https://doi.org/10.1145/3348488.3348496","url":null,"abstract":"Accurately establishing pixel-level correspondence between images taken from same objects is an essential problem in many computer vision applications, such as 3D reconstruction, simultaneous localization and mapping (SLAM), and augmented reality (AR). Existing local feature descriptor based image matching approaches are unable to avoid mismatches which cause negative effects to the above mentioned applications. This paper proposes a motion statistic based local homography transformation estimation method for removing mismatches. The proposed method estimates local homography transformations between the grids in a pair of images and then classifies each match as correct or incorrect by checking whether it is consisting with the corresponding local homography transformation or not. Experimental results on the widely used Oxford affine image dataset show that the proposed approach finds out more potential correct matches than the existing state-of-the-art method.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126905148","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
Space Vector Generation for 3D Scenes from Text Descriptions 空间矢量生成从文本描述的3D场景
International Conference on Artificial Intelligence and Virtual Reality Pub Date : 2019-07-27 DOI: 10.1145/3348488.3348497
Chunhua Wang, Baihui Tang, Sanxing Cao
{"title":"Space Vector Generation for 3D Scenes from Text Descriptions","authors":"Chunhua Wang, Baihui Tang, Sanxing Cao","doi":"10.1145/3348488.3348497","DOIUrl":"https://doi.org/10.1145/3348488.3348497","url":null,"abstract":"3D scene generation using voice and text is convenient and low cost of learning. In the previous text-to-3D scene system, the user must use a fixed format for text input. In this paper, we present a newly system that generates space vector for 3D scenes from text natural language input. It is intended to benefit 3D scene producer and applications by generates space vector from text descriptions. This system consists of three parts: text processing, computational spatial relationships, and spatial vector generation. It computes the space vector by processing the text and then using the neural network to find the spatial relationships in the statement. The space vector is used to represent the spatial relationship between different objects in the text. This system eliminates the need for the user to control 3D scenes in fixed format text from the text to the 3D scene. The system is mainly used to process Chinese text, which can be used as a reference when dealing with other languages.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123042105","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
End-to-End Deep Learning for Autonomous Longitudinal and Lateral Control based on Vehicle Dynamics 基于车辆动力学的自主纵向和横向控制的端到端深度学习
International Conference on Artificial Intelligence and Virtual Reality Pub Date : 2018-11-23 DOI: 10.1145/3293663.3293677
Tsung-Ming Hsu, Cheng-Hsien Wang, Yu-Rui Chen
{"title":"End-to-End Deep Learning for Autonomous Longitudinal and Lateral Control based on Vehicle Dynamics","authors":"Tsung-Ming Hsu, Cheng-Hsien Wang, Yu-Rui Chen","doi":"10.1145/3293663.3293677","DOIUrl":"https://doi.org/10.1145/3293663.3293677","url":null,"abstract":"An end to end method predicting decisions by using deep learning method to mimic driving behaviors from observed images information is one of the famous methods for developing an autonomous self-driving car. In this paper, we investigate the end to end method based on the deep convolution neural network by considering the vehicle dynamic to mimic decisions of human drivers such as steering angle, acceleration, and deceleration. The effect due to the vehicle dynamics of host car by ignoring previous states is investigated through the comparison of predicted accurate and variation by collecting real data in a simulation study.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127183665","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: 10
Social Computing Through Business-based: Tamu Gadang Portal 基于商业的社会计算:Tamu Gadang门户
International Conference on Artificial Intelligence and Virtual Reality Pub Date : 2018-11-23 DOI: 10.1145/3293663.3293664
Aslina Baharum, S. Wahab, Rozita Ismail, Nur Shahida Ab Fatah, Noor Fazlinda Fabeil, N. A. M. Noor
{"title":"Social Computing Through Business-based: Tamu Gadang Portal","authors":"Aslina Baharum, S. Wahab, Rozita Ismail, Nur Shahida Ab Fatah, Noor Fazlinda Fabeil, N. A. M. Noor","doi":"10.1145/3293663.3293664","DOIUrl":"https://doi.org/10.1145/3293663.3293664","url":null,"abstract":"Currently, there is a continual growth in the application of social within a breadth of business domains. Social computing is based on creating or recreating social conventions and social context through the use of software and technology. Due to the limited number of business-based website that applies social computing values, the Tamu Gadang Portal is developed by using Dynamic Systems Development method to evaluate the relation between the social computing.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122788345","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
Evaluation and Analysis of Capacity Scheduler and Fair Scheduler in Hadoop Framework on Big Data Technology 基于大数据技术的Hadoop框架中容量调度和公平调度的评价与分析
International Conference on Artificial Intelligence and Virtual Reality Pub Date : 2018-11-23 DOI: 10.1145/3293663.3293680
Muhammad Salman, Diyanatul Husna, Adhitya Wicaksono, A. A. P. Ratna
{"title":"Evaluation and Analysis of Capacity Scheduler and Fair Scheduler in Hadoop Framework on Big Data Technology","authors":"Muhammad Salman, Diyanatul Husna, Adhitya Wicaksono, A. A. P. Ratna","doi":"10.1145/3293663.3293680","DOIUrl":"https://doi.org/10.1145/3293663.3293680","url":null,"abstract":"Apache Hadoop is an open source framework that implements MapReduce. It is scalable, reliable, and fault tolerant. Scheduling is an important process in Hadoop MapReduce. It is because scheduling has responsibility to allocate resources for running applications based on resource capacity, queues, running tasks, and the number of users. Changing single node to multi node Hadoop cluster can optimize HDFS, but quite costly. Scheduler performs the function of scheduling based on resource requirements, such as memory, CPU, disk, and network. The most general purpose of scheduling algorithm is minimizing the time of completing a task. Hadoop Scheduling is an independent module where users are able to design their own scheduler based on the application's actual need. So it can fulfill the specific need of the business in accordance with the desired result. This research will analyze the characteristic of Capacity Scheduler and Fair Scheduler.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116339925","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
Interference Coordination Based Resource Allocation in Ultra-Dense Networks 基于干扰协调的超密集网络资源分配
International Conference on Artificial Intelligence and Virtual Reality Pub Date : 2018-11-23 DOI: 10.1145/3293663.3293679
Xiaorong Zhu, Xiaoyi Zhang, Zhen Wang
{"title":"Interference Coordination Based Resource Allocation in Ultra-Dense Networks","authors":"Xiaorong Zhu, Xiaoyi Zhang, Zhen Wang","doi":"10.1145/3293663.3293679","DOIUrl":"https://doi.org/10.1145/3293663.3293679","url":null,"abstract":"In this paper, we propose an energy efficiency maximization resource allocation algorithm based on interference coordination. Firstly, the intra and inter cluster interference are coordinated by coloring theory and partial information interaction. Then the joint channel and power allocation problem is divided into two sub-problems to resolve. We first propose a novel maximum-minimum algorithm based on interference coordination for channel allocation and then introduce damped motion and fitness variance to modify particle swarm optimization (PSO) for power allocation. Simulation results show that our proposed algorithm has great improvement in suppressing interference as well as maximizing energy efficiency.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117115125","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
Machine Learning Methods for Septic Shock Prediction 感染性休克预测的机器学习方法
International Conference on Artificial Intelligence and Virtual Reality Pub Date : 2018-11-23 DOI: 10.1145/3293663.3293673
Aiman Darwiche, Sumitra Mukherjee
{"title":"Machine Learning Methods for Septic Shock Prediction","authors":"Aiman Darwiche, Sumitra Mukherjee","doi":"10.1145/3293663.3293673","DOIUrl":"https://doi.org/10.1145/3293663.3293673","url":null,"abstract":"Sepsis is an organ dysfunction life-threatening disease that is caused by a dysregulated body response to infection. Sepsis is difficult to detect at an early stage, and when not detected early, is difficult to treat and results in high mortality rates. Developing improved methods for identifying patients in high risk of suffering septic shock has been the focus of much research in recent years. This paper develops an improved method for septic shock prediction. Using the data from the MMIC-III database, an ensemble classifier is trained to identify high-risk patients. A robust prediction model is built by obtaining a risk score from fitting the Cox Hazard model on multiple input features. The score is added to the list of features and the Random Forest ensemble classifier is trained to produce the model. The Cox Enhanced Random Forest (CERF) proposed method is evaluated by comparing its predictive accuracy to those of extant methods.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128247481","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: 12
Further Experiments on A Combination of Linear SVM Weight and ReliefF for Dimensionality Reduction 线性支持向量机加权与浮雕结合降维的进一步实验
International Conference on Artificial Intelligence and Virtual Reality Pub Date : 2018-11-23 DOI: 10.1145/3293663.3293682
W. Buathong, Pita Jarupunphol
{"title":"Further Experiments on A Combination of Linear SVM Weight and ReliefF for Dimensionality Reduction","authors":"W. Buathong, Pita Jarupunphol","doi":"10.1145/3293663.3293682","DOIUrl":"https://doi.org/10.1145/3293663.3293682","url":null,"abstract":"This research further investigated how dimensional data could be efficiently downsized using a multilayered technique based on a combination of two major feature selections, including Linear SVM Weight and ReliefF together with classifier namely Support Vector Machine (SVM). Two datasets, including SRBCT and USPS, were used for the experiment. The results show that the proposed technique is more efficient than using either Linear SVM Weight or ReliefF alone for dimensionality reduction. The dimensional data could be downsized from 2,308 to 8 attributes where the accuracy rate could reach 100 percent in SRBCT. The experimental result of SBRCT was also consistent with that of USPS in which the dimensional data could be downsized from 256 to 55 attributes with the accuracy of 95.76 percent.","PeriodicalId":420290,"journal":{"name":"International Conference on Artificial Intelligence and Virtual Reality","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114159938","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
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