2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)最新文献_第4页

Developing a Statistical Turkish Sign Language Translation System for Primary School Students 开发土耳其语小学生手语统计翻译系统

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778246

Buse Buz, Tunga Güngör

引用次数: 3

An Implementation of Vision Based Deep Reinforcement Learning for Humanoid Robot Locomotion 基于视觉的人形机器人运动深度强化学习的实现

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778209

Recen Özaln, Çağrı Kaymak, Özal Yıldırım, A. Uçar, Y. Demir, C. Güzelı̇ş

引用次数: 11

PokerBot: Hand Strength Reinforcement Learning PokerBot:手部力量强化学习

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778267

Angela Ramirez, Solomon Reinman, Narges Norouzi

引用次数: 1

Face Recognition Using Dynamic Features Extracted from Smile Videos 从微笑视频中提取动态特征的人脸识别

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778400

M. Taskiran, Mehmet Killioglu, N. Kahraman, Ç. Erdem

引用次数: 6

Offline Signature Identification and Verification Using Capsule Network 基于胶囊网络的离线签名识别与验证

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778228

Dilara Gumusbas, T. Yıldırım

{"title":"Offline Signature Identification and Verification Using Capsule Network","authors":"Dilara Gumusbas, T. Yıldırım","doi":"10.1109/INISTA.2019.8778228","DOIUrl":"https://doi.org/10.1109/INISTA.2019.8778228","url":null,"abstract":"In offline signature identification and verification systems, hand -crafted feature extraction methods, such as local binary patterns, have recently been set aside for automatic feature extraction methods such as convolutional neural networks (CNN). Although these CNN-based algorithms often obtain satisfying results, they require either many samples to find the best data representations or pre-trained network weights. To obviate the necessity of many samples as well as pre-trained weights, Capsule Network has recently claimed to achieve the best data representation using only a limited amount of data. This network not only obtains many variations of limited input samples via affine transformations in the algorithm but also uses hierarchical layers to select the most informative features without losing the exact informational position of the others. It is from this point of view that this paper first aims to evaluate performances of Capsule Network and the CNN-based equivalent model for the signature identification task. This evaluation is done under two lower resolutions than is usual to understand whether texture patterns are still staying as informative as they usually are for both algorithms. While Capsule Network achieves 98,8% and 98,6 % accuracies for 64×64 and 32×32 input resolutions, respectively, CNN obtains 55,4% and 54,7% accuracies. The second aim of the paper is to generalize the capability of Capsule Network concerning the verification task. Through this evaluation, the capability of Capsule Network is shown to obtain better feature extraction and classification results compared to the CNN-based equivalent model for the verification task as well as the identification task.","PeriodicalId":262143,"journal":{"name":"2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127645081","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: 16

An Efficient and Affordable Web-based Prototyping and Testing Platform for Analog Sensors 一种高效、经济的基于网络的模拟传感器原型和测试平台

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778323

Jiannan Zhai, Chancey Kelly, J. Hallstrom

引用次数: 0

Improved Background Subtraction-based Moving Vehicle Detection by Optimizing Morphological Operations using Machine Learning 利用机器学习优化形态学操作改进的基于背景减除的移动车辆检测

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-03 DOI: 10.1109/INISTA.2019.8778263

Zakaria Charouh, M. Ghogho, Z. Guennoun

引用次数: 16

Sentiment Analysis through Transfer Learning for Turkish Language 基于迁移学习的土耳其语情感分析

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-01 DOI: 10.1109/INISTA.2019.8778305

S. Akin, Tuğba Yıldız

引用次数: 0

A Method for Detecting and Analyzing the Sentiment of Tweets Containing Fuzzy Sentiment Phrases 包含模糊情感短语的推文情感检测与分析方法

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-01 DOI: 10.1109/INISTA.2019.8778360

H. Phan, N. Nguyen, Van Cuong Tran, D. Hwang

引用次数: 11

A Probabilistic Multi-Agent System Architecture for Reasoning in Smart Homes 用于智能家居推理的概率多智能体系统架构

2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) Pub Date : 2019-07-01 DOI: 10.1109/INISTA.2019.8778306

Dagmawi Neway Mekuria, Paolo Sernani, Nicola Falcionelli, A. Dragoni

{"title":"A Probabilistic Multi-Agent System Architecture for Reasoning in Smart Homes","authors":"Dagmawi Neway Mekuria, Paolo Sernani, Nicola Falcionelli, A. Dragoni","doi":"10.1109/INISTA.2019.8778306","DOIUrl":"https://doi.org/10.1109/INISTA.2019.8778306","url":null,"abstract":"Uncertainty is inevitable in ambient assisted living (AAL) environments as sensors may read inaccurate data or due to the existence of unobserved variables for privacy reasons. Furthermore, the dynamic nature of the home environment and vague human communications may result in ambiguous, incomplete and inconsistent contextual information, which ultimately lead the smart home system into uncertainty. This paper aims to tackle some of these challenges, in particular, uncertainty due to vague human communication and missing information in ambient environments. For this, we proposed a probabilistic multi-agent system architecture for reasoning in smart homes by utilizing the notion of multiagent systems (MAS) technologies and probabilistic logic programming techniques. Accordingly, this study shows how the probabilistic reasoning technique enables the agents to reason under uncertainty. Furthermore, it discusses how the intelligent agents enhance their decision-making process by exchanging information about missing data or unobservable variables using agent interaction protocols. In general, the study demonstrates that the combination of MAS technologies and probabilistic logic programming can help in building a reasoning system, which is capable of performing well under vague inhabitant commands and missing information in a partially observable environment.","PeriodicalId":262143,"journal":{"name":"2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123628907","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