2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media)最新文献_第4页

PassSmell: Using Olfactory Media for Authentication PassSmell:使用嗅觉媒介进行身份验证

2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media) Pub Date : 2019-08-01 DOI: 10.1109/Ubi-Media.2019.00063

Anas Ali Alkasasbeh, G. Ghinea, Wu-Yuin Hwang

{"title":"PassSmell: Using Olfactory Media for Authentication","authors":"Anas Ali Alkasasbeh, G. Ghinea, Wu-Yuin Hwang","doi":"10.1109/Ubi-Media.2019.00063","DOIUrl":"https://doi.org/10.1109/Ubi-Media.2019.00063","url":null,"abstract":"In currently used verification systems, different senses are being used as inputs or outputs, such as touch and sight. In a similar way, olfactory media (sense of smell) could be used to take part in the verification method. In this study, an empirical investigation was conducted to study the impact of olfactory data as a data channel on user performance and Quality of Experience (QoE). The olfactory data were used with words in our verification model (PassSmell). To this end, we developed two different versions of applications, namely enhanced-olfactory and none-olfactory, for which a database of words with/without scents were used. Twenty-eight participants were invited to take part in our experiment, evenly split into a control and experimental group. Time and number of failed/successful attempts were recorded. A significant difference was found, in terms of time taken, between the experimental and the control groups, as determined by independent sample t-test. Similar results were found with respect to average scores and number of successful attempts. Regarding user QoE, having olfactory data with words instead of passwords influenced the users positively, which resulted in their being attracted to using this kind of application in the future.","PeriodicalId":259542,"journal":{"name":"2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121597433","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

Assistive Robot with Action Planner and Schedule for Family 具有家庭行动计划和日程安排的辅助机器人

2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media) Pub Date : 2019-08-01 DOI: 10.1109/Ubi-Media.2019.00041

Supanut Konngern, Naphssorn Kaibutr, Nawaporn Konru, T. Tantidham, Chih-Lin Hu, Tipajin Thaipisutikul, T. Shih, P. Mongkolwat

{"title":"Assistive Robot with Action Planner and Schedule for Family","authors":"Supanut Konngern, Naphssorn Kaibutr, Nawaporn Konru, T. Tantidham, Chih-Lin Hu, Tipajin Thaipisutikul, T. Shih, P. Mongkolwat","doi":"10.1109/Ubi-Media.2019.00041","DOIUrl":"https://doi.org/10.1109/Ubi-Media.2019.00041","url":null,"abstract":"In the present day, the world is moving quickly, making the modern live of people to be more complex. Some people may have less time to manage their schedules, which could make them forget to do something that may be important. Especially, a family may not be able to manage their time well because they are caught up in their busy works. We have developed an application to help members in a family to manage their schedule. This application is a scheduling application with reminder, a user can add tasks for other family members and specify an activity, a place to remind each activity, and a person who is going to be reminded or do the activity. Multiple tasks can be complied together to become a plan, a plan specifies the date and time that the defined tasks must be done. In reminder part, we use a personal robot assistant called Zenbo from ASUS opened for developers. Zenbo has capabilities to speak, move, detect persons and much more. It works with the application to remind members in family to perform an activity at a preset time. Zenbo retrieves the information of tasks that stored on the application database. It can go to the specified place to notify a person to do the task. Users can view the history of the previous Because people are too busy and may not manage their time well, we developed a schedule and reminder application that works with Zenbo, to help people in family manage their activities and time effectively.","PeriodicalId":259542,"journal":{"name":"2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129914417","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

Facial Expression Recognition: A Comparison of Bottleneck Feature Extraction 面部表情识别:瓶颈特征提取的比较

2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media) Pub Date : 2019-08-01 DOI: 10.1109/Ubi-Media.2019.00039

Prasitthichai Naronglerdrit

{"title":"Facial Expression Recognition: A Comparison of Bottleneck Feature Extraction","authors":"Prasitthichai Naronglerdrit","doi":"10.1109/Ubi-Media.2019.00039","DOIUrl":"https://doi.org/10.1109/Ubi-Media.2019.00039","url":null,"abstract":"This paper compares the performance of bottleneck feature extraction based on two different architectures, the first, Convolutional Neural Network (CNN) based bottleneck feature extraction and the second, Deep Neural Network (DNN) based bottleneck feature extraction. Both of CNN and DNN based bottleneck feature extraction network were trained for 200 epochs to perform a feature extraction task. The input of bottleneck network is the same as the output which is the preprocessed images. From the bottleneck network, after training, the layers after the bottleneck layer were cut-off and set the bottleneck layer as an output layer. The result of the bottleneck feature extraction is that it can reduce the dimension of the images from 4096 to 128 to be used as a feature vectors for a classification process. In the classification process, it was performed by Artificial Neural Network (ANN) with three fully-connected layers, and trained for 500 epochs. In order to evaluate the performance, the 10-flod cross-validation was applied to the networks. The result is that the CNN based bottleneck feature extraction performs a better performance than DNN based which are 99.54% and 98.91% respectively.","PeriodicalId":259542,"journal":{"name":"2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116490667","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

Constructing Movie Domain Knowledge Graph Based on LOD 基于LOD的电影领域知识图谱构建

2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media) Pub Date : 2019-08-01 DOI: 10.1109/Ubi-Media.2019.00019

Qiuyu Lei, Yun Liu

引用次数: 1

VA Algorithm for Elderly's Falling Detection with 2D-Pose-Estimation 基于2d姿态估计的老年人跌倒检测VA算法

2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media) Pub Date : 2019-08-01 DOI: 10.1109/Ubi-Media.2019.00053

Pichayakul Jenpoomjai, Potsawat Wosri, S. Ruengittinun, Chih-Lin Hu, Chalothon Chootong

引用次数: 2

A Scheme to Create Simulated Test Items for Facilitating the Assessment in Web Security Subject 一种便于网络安全科目评估的模拟试题创建方案

2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media) Pub Date : 2019-08-01 DOI: 10.1109/Ubi-Media.2019.00067

Jun-Ming Su, Ming-Hua Cheng, Xin-Jie Wang, S. Tseng

引用次数: 1

Temporal Action Detection Based on Hierarchical Object Detection Networks 基于分层目标检测网络的时间动作检测

2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media) Pub Date : 2019-08-01 DOI: 10.1109/Ubi-Media.2019.00031

Yi-Hui Wu, Wen-Jiin Tsai, Hua-Tsung Chen

引用次数: 0

DeepBonds: A Deep Learning Approach to Predicting United States Treasury Yield 深度债券:预测美国国债收益率的深度学习方法

2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media) Pub Date : 2019-08-01 DOI: 10.1109/Ubi-Media.2019.00055

Jia-Ching Ying, Yu-Bing Wang, Chih-Kai Chang, Ching Chang, Yu-Han Chen, Yow-Shin Liou

{"title":"DeepBonds: A Deep Learning Approach to Predicting United States Treasury Yield","authors":"Jia-Ching Ying, Yu-Bing Wang, Chih-Kai Chang, Ching Chang, Yu-Han Chen, Yow-Shin Liou","doi":"10.1109/Ubi-Media.2019.00055","DOIUrl":"https://doi.org/10.1109/Ubi-Media.2019.00055","url":null,"abstract":"United State Treasury Bonds are government bonds issued by the United State Treasury through the Public Debt Bureau. The trades of U.S. Treasury Bonds have a huge influence on global economy. To analysis the trend of global economy, many economists believe U.S. Treasury Yield has the ability to predict the fluence of other financial markets such as stock market, futures market, Option market, etc. However, However, most financial prediction models focus only on predicting stock price, which is a sort of multidimensional time-series prediction. Although U.S. Treasury Yield could be viewed as a multidimensional time-series, the prediction models for predicting stock price are not able to completely satisfy the requirements for predicting U.S. Treasury Yield. Besides, most traditional machine learning methods focus only on estimation of short-term cash flow. As the result, the loss of traditional machine learning methods would significantly be increased while the period of prediction target is fluctuated. In this paper, we propose a Deep-Learning framework, DeepBonds, to build a prediction model to predict U.S. Treasury Yield with different issue period. Meanwhile, the Recurrent Neural Network with Long Short Term Memory (LSTM) architecture is utilized for effectively summarizing U.S. Treasury Yield as characteristic vectors. Based on the produced characteristic vectors, we can precisely predict future U.S. Treasury Yield with different issue period. We conduct a comprehensive experimental study based on a real dataset collected from the website of Resource Center of U.S. Department of The Treasury. The results demonstrate a significantly improved accuracy of our Deep Learning approach compared with the existing works.","PeriodicalId":259542,"journal":{"name":"2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115062639","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

Design and Implementaion of Smart Small Aquaponics System 智能小型鱼菜共生系统的设计与实现

2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media) Pub Date : 2019-08-01 DOI: 10.1109/Ubi-Media.2019.00071

Siriporn Sansri, Wu-Yuin Hwang, Theerasak Srikhumpa

引用次数: 6

A Novel Evolution-Based Recommendation System 一种新的基于进化的推荐系统

2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media) Pub Date : 2019-08-01 DOI: 10.1109/Ubi-Media.2019.00017

Yi-Cheng Chen, Yen-Lung Chu, Lin Hui, Sheng-Chih Chen, Tipajin Thaipisutikul, Kai-Ze Weng

引用次数: 1