2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)最新文献_第8页

E-assessment using image processing in ∞Exams 在∞考试中使用图像处理的电子评估

2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 1900-01-01 DOI: 10.1109/SAMI.2017.7880322

Ádám Tóth, I. Vajda, Z. Vámossy

引用次数: 3

Moving toward an intelligent interactive social engagement framework for information gathering 朝着信息收集的智能交互式社会参与框架迈进

2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 1900-01-01 DOI: 10.1109/SAMI.2017.7880307

Cindy L. Bethel, Zachary Henkel, D. Eakin, D. May, Melinda Pilkinton

{"title":"Moving toward an intelligent interactive social engagement framework for information gathering","authors":"Cindy L. Bethel, Zachary Henkel, D. Eakin, D. May, Melinda Pilkinton","doi":"10.1109/SAMI.2017.7880307","DOIUrl":"https://doi.org/10.1109/SAMI.2017.7880307","url":null,"abstract":"The objective of this research is to investigate the use of robots as intermediaries to gather sensitive information from children. The research is multidisciplinary in nature. The goals will be accomplished through the development of an integrated robotic framework that includes a novel architecture and an interactive user interface to gather information using methodologies recommended for forensic interviews with children. The Interactive Social Engagement Architecture (ISEA) is designed to integrate behavior-based robotics, human behavior models, cognitive architectures, and expert user input to increase social engagement between a human and system (e.g., robot, avatar, etc.). ISEA provides for the autonomous generation of robot behaviors for self-preservation and to convey social intelligence. The framework is designed to be modular and adaptable to different applications and domains; however for this project, the focus is on social engagement for information gathering. The interactive user interface provides interviewers with the ability to use a robot as an intermediary for gathering this information. The interface and framework have been iteratively improved through observations from user studies conducted to date with 186 children ages 8–12. This project compares the effectiveness of robot versus human interviewers to gather sensitive information from children using situations in which this would commonly occur — cases of child eyewitness memory and child reports of bullying. This research has the potential to transform how sensitive information is gathered as it relates to criminal investigations and proceedings.","PeriodicalId":105599,"journal":{"name":"2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"146 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120862612","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

Application of fixed point transformation to classical model identification using new tuning rule 不动点变换在经典模型辨识中的应用

2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 1900-01-01 DOI: 10.1109/SAMI.2017.7880315

A. Dineva, J. Tar, A. Várkonyi-Kóczy, V. Piuri

{"title":"Application of fixed point transformation to classical model identification using new tuning rule","authors":"A. Dineva, J. Tar, A. Várkonyi-Kóczy, V. Piuri","doi":"10.1109/SAMI.2017.7880315","DOIUrl":"https://doi.org/10.1109/SAMI.2017.7880315","url":null,"abstract":"Up to now the fundamental tool of adaptive nonlinear control design is Lyapunov's 2nd or \"Direct\" Method. Recently the Sigmoid Generated Fixed Point Transformation (SGFPT) has been introduced for evading the application of the Lyapunov technique. This systematic method has been presented for the generation of whole families of Fixed Point Transformations and has been extended from Single Input Single Output (SISO) to Multiple Input Multiple Output (MIMO) systems. Few studies have been revealed that the original Robust Fixed Point Transformation (RFPT) can be successfully combined with some modification of the classical methods, such as the Modified Adaptive Inverse Dynamic Robot Controller (MAIDRC) and the Modified Adaptive Slotine-Li Robot Controller (MADSLRC). This paper presents that the SGFPT can also well coexist with the MAIDRC control design. Additionally, a novel, even more simplified tuning technique is proposed that also applies fixed point transformation-based tuning rule for parameter identification. The theoretical considerations are validated by numerical simulations made for a 2 Degree of Freedom (DoF) paradigm, in the adaptive control of two coupled mass-points with simultaneous parameter identification.","PeriodicalId":105599,"journal":{"name":"2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116229228","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

Hybridized krill herd algorithm for large-scale optimization problems 大规模优化问题的杂交磷虾群算法

2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 1900-01-01 DOI: 10.1109/SAMI.2017.7880356

Ivana Stromberger, N. Bačanin, M. Tuba

引用次数: 4

Twitter data analysis and visualizations using the R language on top of the Hadoop platform 在Hadoop平台上使用R语言进行Twitter数据分析和可视化

2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 1900-01-01 DOI: 10.1109/SAMI.2017.7880327

M. Sarnovský, P. Butka, Andrea Huzvarova

引用次数: 5

From perceptrons to deep neural networks 从感知器到深度神经网络

2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 1900-01-01 DOI: 10.1109/SAMI.2017.7880296

P. Lacko

引用次数: 2

The analysis of participatory computing in dynamic web environments 动态网络环境下参与式计算的分析

2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 1900-01-01 DOI: 10.1109/SAMI.2017.7880277

László Viktor Jánoky, P. Ekler

引用次数: 1

Simulation-based analysis of mixed traffic flow using VISSIM environment 基于VISSIM环境的混合交通流仿真分析

2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 1900-01-01 DOI: 10.1109/SAMI.2017.7880332

Zsuzsanna Bede, B. Németh, P. Gáspár

引用次数: 5

Simple understandable analysis of medical data to support the diagnostic process 简单易懂的医疗数据分析，以支持诊断过程

2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 1900-01-01 DOI: 10.1109/SAMI.2017.7880293

F. Babič, M. Vadovský, M. Muchová, Ján Paralič, L. Majnarić

引用次数: 5

Clustering-based fuzzy knowledgebase reduction in the FRIQ-learning 基于聚类的模糊知识库约简在friq学习中的应用

2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) Pub Date : 1900-01-01 DOI: 10.1109/SAMI.2017.7880302

T. Tompa, S. Kovács

{"title":"Clustering-based fuzzy knowledgebase reduction in the FRIQ-learning","authors":"T. Tompa, S. Kovács","doi":"10.1109/SAMI.2017.7880302","DOIUrl":"https://doi.org/10.1109/SAMI.2017.7880302","url":null,"abstract":"This paper introduces a fuzzy knowledgebase reduction method with applying a clustering technique in the Fuzzy Rule Interpolation-based Q-learning (FRIQ-learning). The FRIQ-learning method stars with an empty knowledgebase, which is a fuzzy rule-base filled only with rules defining the boundaries of the problem space. Then the system builds the rule-base incrementally episode by episode, based on a properly defined reward function. The FRIQ-learning method is finished, when its terminating conditions become true. This case we get the final rule-base as a solution for the given problem. But the constructed final rule-base may contain redundant rules, which can be automatically omitted from the rule-base by reduction methods. The main goal of the paper is to introduce a new, clustering based reduction method, which is suitable for eliminating the unnecessary rules of the rule-base and hence decrease the size of the fuzzy knowledgebase. For demonstrating the benefits of the suggested clustering based fuzzy knowledgebase reduction method, application examples of the \"cart pole\" and the \"mountain car\" benchmarks are also discussed briefly in the paper.","PeriodicalId":105599,"journal":{"name":"2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI)","volume":"262 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132753787","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: 13