Pub Date : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851593
H. Florez, M. Argáez
We present a wavelet-based model-order reduction method (MOR) that provides an alternative subspace when Proper Orthogonal Decomposition (POD) is not a choice. We thus compare the wavelet- and POD-based approaches for reducing high-dimensional nonlinear transient and steady-state continuation problems. We also propose a line-search regularized Petrov-Galerkin (PG) Gauss-Newton (GN) algorithm that includes a regularization procedure and a globalization strategy. Numerical results included herein indicate that wavelet-based method is competitive with POD for compression ratios below 25% while POD achieves up to 90%. Full-order-model (FOM) results demonstrate that the proposed PGGN algorithm outperforms the standard GN method.
{"title":"Applications and comparison of model-order reduction methods based on wavelets and POD","authors":"H. Florez, M. Argáez","doi":"10.1109/NAFIPS.2016.7851593","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851593","url":null,"abstract":"We present a wavelet-based model-order reduction method (MOR) that provides an alternative subspace when Proper Orthogonal Decomposition (POD) is not a choice. We thus compare the wavelet- and POD-based approaches for reducing high-dimensional nonlinear transient and steady-state continuation problems. We also propose a line-search regularized Petrov-Galerkin (PG) Gauss-Newton (GN) algorithm that includes a regularization procedure and a globalization strategy. Numerical results included herein indicate that wavelet-based method is competitive with POD for compression ratios below 25% while POD achieves up to 90%. Full-order-model (FOM) results demonstrate that the proposed PGGN algorithm outperforms the standard GN method.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133491895","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}
Pub Date : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851574
M. Ceberio, V. Kreinovich
The Organizing Committee of NAFIPS’2016 welcomes you to El Paso, Texas. We hope that you will enjoy both the scientific part of the conference – an exciting exchange of ideas related to fuzzy logic and soft computing – and the beauty of our Southwestern border city of El Paso. This conference would not be possible without hard work of many people all over the world. We are very thankful to all of these people. We want to thank the plenary speakers: • Ildar Batyrshin, Centro de Investigación en Computación, Instituto Politechnico Nacional, Mexico City, Mexico • Piero Bonissone, Piero P. Bonissone Analytics, LLC, CEO, USA • Samir Abou-Samra, DigiPen Institute of Technology, USA • Martine De Cock, Institute of Technology of the University of Washington Tacoma, USA & Ghent University, Belgium • Christian Servin, El Paso Community College, El Paso, Texas, USA • Dongrui Wu, DataNova LLC, USA for taking the time to come and present their fascinating research results. We want to thank organizers of special sessions – some of which are, in fact, mini-conferences – who helped organize these focused parts of our meeting. The special sessions this year are as follows: • Computational Intelligence in Biomedical Applications, organized by Phuong Nguyen; • Computing with Words and Beyond, organized by Victor Raskin and Julia Taylor; • Fuzzy Logic Applications in Construction Engineering and Management, organized by Aminah Robinson Fayek and Chrysostomos Stylios; • Fuzzy Pattern Recognition with High Uncertainty, organized by Mohammad H. Fazel Zarandi, I. Burhan Turksen, Behshad Lahijanian; • High Level Fuzzy Social Networks and Social Media, organized by Susan Bastani, Mohammad H. Fazel Zarandi, I. Burhan Turksen, and Mansoureh Naderipour; and • Inter-relation between interval and fuzzy techniques, organized by Martine Ceberio and Vladik Kreinovich. We want to thank all the authors who contributed their interesting papers on various applications of fuzzy techniques and on various theoretical aspects of these techniques. We want to thank the chair of the program committee, Hung T. Nguyen, and the members of the program committee, as well as the many anonymous referees who helped the authors improve the clarity of their results. We also want to thank our sponsors: NAFIPS, IEEE, and the University of Texas at El Paso. We want to thank NAFIPS Board of Directors for their active help. We are especially thankful to NAFIPS President William Melek. We want to thank the University of Texas at El Paso staff, especially Ms. Alexandra Garcia and Ms. Lourdes Chee, for their continuous support with conference logistics. And, last but not the least, we want to thank the students. The students are our future. We want to thank the students who submitted their papers, the students who came to listen and learn, and especially the student helpers Cristian Ayub, Phillip Hassoun, Miguel Zamudio for their hard work; many thanks to Angel Garcia and Leobardo Valera: they were essential
NAFIPS 2016组委会欢迎您来到德克萨斯州埃尔帕索。我们希望你会喜欢会议的科学部分——一个关于模糊逻辑和软计算的激动人心的思想交流——以及我们西南边境城市埃尔帕索的美丽。这次会议的召开，离不开世界各国人民的共同努力。我们非常感谢所有这些人。我们要感谢全体会议发言人:•Ildar Batyrshin, Centro de Investigación en Computación，墨西哥墨西哥城国立理工学院•Piero Bonissone, Piero P. Bonissone Analytics, LLC，首席执行官，美国•Samir Abou-Samra，美国DigiPen理工学院•Martine de Cock，美国塔科马华盛顿大学理工学院和比利时根特大学•Christian Servin，埃尔帕索社区学院，美国德克萨斯州埃尔帕索•吴东睿，DataNova LLC感谢美国花时间来展示他们迷人的研究成果。我们要感谢特别会议的组织者- -其中一些实际上是小型会议- -他们帮助组织了我们会议的这些重点部分。今年的特别会议如下:•由Phuong Nguyen组织的生物医学应用中的计算智能;•由维克多·拉斯金(Victor Raskin)和朱莉娅·泰勒(Julia Taylor)组织的“文字计算及超越”;•模糊逻辑在建筑工程和管理中的应用，由Aminah Robinson Fayek和Chrysostomos Stylios组织;•高不确定性模糊模式识别，由Mohammad H. Fazel Zarandi, I. Burhan Turksen, Behshad Lahijanian组织;•高级模糊社交网络和社交媒体，由Susan Bastani, Mohammad H. Fazel Zarandi, I. Burhan Turksen和Mansoureh Naderipour组织;•区间和模糊技术之间的相互关系，由Martine Ceberio和Vladik Kreinovich组织。我们要感谢所有在模糊技术的各种应用和这些技术的各种理论方面贡献了有趣论文的作者。我们要感谢项目委员会主席Hung T. Nguyen和项目委员会成员，以及许多匿名审稿人，他们帮助作者提高了结果的清晰度。我们还要感谢我们的赞助商:NAFIPS, IEEE和德克萨斯大学埃尔帕索分校。我们要感谢NAFIPS董事会的积极帮助。我们特别感谢NAFIPS主席William Melek。我们要感谢德克萨斯大学埃尔帕索分校的工作人员，特别是亚历山德拉·加西亚女士和卢德斯·奇女士，感谢他们在会议后勤方面的持续支持。最后但同样重要的是，我们要感谢学生们。学生是我们的未来。我们要感谢提交论文的同学们，感谢前来聆听和学习的同学们，特别是学生助手Cristian Ayub, Phillip Hassoun, Miguel Zamudio的辛勤工作;非常感谢安吉尔·加西亚和莱奥巴多·瓦莱拉:他们在整个过程中发挥了至关重要的作用。谢谢大家!
{"title":"Greetings from NAFIPS 2016 organizing committee chairs","authors":"M. Ceberio, V. Kreinovich","doi":"10.1109/NAFIPS.2016.7851574","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851574","url":null,"abstract":"The Organizing Committee of NAFIPS’2016 welcomes you to El Paso, Texas. We hope that you will enjoy both the scientific part of the conference – an exciting exchange of ideas related to fuzzy logic and soft computing – and the beauty of our Southwestern border city of El Paso. This conference would not be possible without hard work of many people all over the world. We are very thankful to all of these people. We want to thank the plenary speakers: • Ildar Batyrshin, Centro de Investigación en Computación, Instituto Politechnico Nacional, Mexico City, Mexico • Piero Bonissone, Piero P. Bonissone Analytics, LLC, CEO, USA • Samir Abou-Samra, DigiPen Institute of Technology, USA • Martine De Cock, Institute of Technology of the University of Washington Tacoma, USA & Ghent University, Belgium • Christian Servin, El Paso Community College, El Paso, Texas, USA • Dongrui Wu, DataNova LLC, USA for taking the time to come and present their fascinating research results. We want to thank organizers of special sessions – some of which are, in fact, mini-conferences – who helped organize these focused parts of our meeting. The special sessions this year are as follows: • Computational Intelligence in Biomedical Applications, organized by Phuong Nguyen; • Computing with Words and Beyond, organized by Victor Raskin and Julia Taylor; • Fuzzy Logic Applications in Construction Engineering and Management, organized by Aminah Robinson Fayek and Chrysostomos Stylios; • Fuzzy Pattern Recognition with High Uncertainty, organized by Mohammad H. Fazel Zarandi, I. Burhan Turksen, Behshad Lahijanian; • High Level Fuzzy Social Networks and Social Media, organized by Susan Bastani, Mohammad H. Fazel Zarandi, I. Burhan Turksen, and Mansoureh Naderipour; and • Inter-relation between interval and fuzzy techniques, organized by Martine Ceberio and Vladik Kreinovich. We want to thank all the authors who contributed their interesting papers on various applications of fuzzy techniques and on various theoretical aspects of these techniques. We want to thank the chair of the program committee, Hung T. Nguyen, and the members of the program committee, as well as the many anonymous referees who helped the authors improve the clarity of their results. We also want to thank our sponsors: NAFIPS, IEEE, and the University of Texas at El Paso. We want to thank NAFIPS Board of Directors for their active help. We are especially thankful to NAFIPS President William Melek. We want to thank the University of Texas at El Paso staff, especially Ms. Alexandra Garcia and Ms. Lourdes Chee, for their continuous support with conference logistics. And, last but not the least, we want to thank the students. The students are our future. We want to thank the students who submitted their papers, the students who came to listen and learn, and especially the student helpers Cristian Ayub, Phillip Hassoun, Miguel Zamudio for their hard work; many thanks to Angel Garcia and Leobardo Valera: they were essential","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127391859","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}
Pub Date : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851590
Dalila Koulougli, A. Hadjali, Idir Rassoul
Crowdsourcing is defined as an emerging computation paradigm, where the power of crowds is utilized to facilitate large scale tasks that are costly or time consuming with traditional methods. One of the most important technical challenges of crowdsourcing is quality control of workers' responses. Human factors play a key role in achieving high quality answers in crowdsourcing-based solving tasks. The most major factor is pertained to the uncertainty of workers about the responses that they provide to resolve the task at hand. On the other hand, workers may have diverse levels of expertise and skill. It is then important to take into account both the degrees of uncertainty and expertise to return the most correct reliable answer. In this paper, we propose a belief functions-based approach to achieve this goal. We conduct also some comprehensive experiments to validate the effectiveness of our proposal.
{"title":"Handling query answering in crowdsourcing systems: A belief function-based approach","authors":"Dalila Koulougli, A. Hadjali, Idir Rassoul","doi":"10.1109/NAFIPS.2016.7851590","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851590","url":null,"abstract":"Crowdsourcing is defined as an emerging computation paradigm, where the power of crowds is utilized to facilitate large scale tasks that are costly or time consuming with traditional methods. One of the most important technical challenges of crowdsourcing is quality control of workers' responses. Human factors play a key role in achieving high quality answers in crowdsourcing-based solving tasks. The most major factor is pertained to the uncertainty of workers about the responses that they provide to resolve the task at hand. On the other hand, workers may have diverse levels of expertise and skill. It is then important to take into account both the degrees of uncertainty and expertise to return the most correct reliable answer. In this paper, we propose a belief functions-based approach to achieve this goal. We conduct also some comprehensive experiments to validate the effectiveness of our proposal.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121971219","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}
Pub Date : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851581
Ondřej Vaverka
We present an initial study of the relation-valued attributes in rank-aware databases. We introduce the grouping and ungrouping operations and show their basic properties. We use a model that generalizes the Codd model of data by considering tuples in relations annotated by scores indicating a degree to which tuples match queries. We utilize the complete residuated lattices as the structures of degrees. We argue that relation-valued attributes are a useful concept and play an irreplaceable role in rank-aware databases as they form a bridge between the rank-aware and classical model.
{"title":"Relation-valued attributes in rank-aware databases and related concepts","authors":"Ondřej Vaverka","doi":"10.1109/NAFIPS.2016.7851581","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851581","url":null,"abstract":"We present an initial study of the relation-valued attributes in rank-aware databases. We introduce the grouping and ungrouping operations and show their basic properties. We use a model that generalizes the Codd model of data by considering tuples in relations annotated by scores indicating a degree to which tuples match queries. We utilize the complete residuated lattices as the structures of degrees. We argue that relation-valued attributes are a useful concept and play an irreplaceable role in rank-aware databases as they form a bridge between the rank-aware and classical model.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"1203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122299888","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}
Pub Date : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851612
Denise M. Case, C. Stylios
Project management is a complex process impacted by numerous factors either from the external environment and/or internal factors completely or partially under the project manager's control. Managing projects successfully involves a complex amalgamation of comprehensive, informed planning, dynamic assessment and analysis of changes in external and internal factors, and the development and communication of updated strategies over the life of the project. Project management involves the interaction and analysis of many systems and requires the continuous integration and evaluation of large amounts of information. Fuzzy Cognitive Maps (FCM) allow us to encode project management knowledge and experiential results to create a useful model of the interacting systems. This paper covers the representation and development of a construction project management FCM that provides an integrated view of the most important concepts affecting construction project management and risk management. This paper then presents the soft computing approach of FCM to project management (PM) modeling and analysis. The resulting PM-FCM models the interaction of internal and external factors and offers an abstract conceptual model of interacting concepts for construction project management application.
{"title":"Fuzzy Cognitive Map to model project management problems","authors":"Denise M. Case, C. Stylios","doi":"10.1109/NAFIPS.2016.7851612","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851612","url":null,"abstract":"Project management is a complex process impacted by numerous factors either from the external environment and/or internal factors completely or partially under the project manager's control. Managing projects successfully involves a complex amalgamation of comprehensive, informed planning, dynamic assessment and analysis of changes in external and internal factors, and the development and communication of updated strategies over the life of the project. Project management involves the interaction and analysis of many systems and requires the continuous integration and evaluation of large amounts of information. Fuzzy Cognitive Maps (FCM) allow us to encode project management knowledge and experiential results to create a useful model of the interacting systems. This paper covers the representation and development of a construction project management FCM that provides an integrated view of the most important concepts affecting construction project management and risk management. This paper then presents the soft computing approach of FCM to project management (PM) modeling and analysis. The resulting PM-FCM models the interaction of internal and external factors and offers an abstract conceptual model of interacting concepts for construction project management application.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128028099","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}
Pub Date : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851617
O. Kosheleva, V. Kreinovich, Martha Osegueda Escobar, Kimberly Kato
Experts often describe their estimates by using words from natural language, i.e., in effect, sorted labels. To efficiently represent the corresponding expert knowledge in a computer-based system, we need to translate these labels into a computer-understandable language, i.e., into numbers. There are many ways to translate labels into numbers. In this paper, we propose to select a translation which is the most robust, i.e., which preserves the order between the corresponding numbers under the largest possible deviations from the original translation. The resulting formulas are in good accordance with the translation coming from the Laplace's principle of sufficient reason, and - somewhat surprisingly - with the current estimates of the proportion of dark matter and dark energy in our Universe.
{"title":"Towards the most robust way of assigning numerical degrees to ordered labels, with possible applications to dark matter and dark energy","authors":"O. Kosheleva, V. Kreinovich, Martha Osegueda Escobar, Kimberly Kato","doi":"10.1109/NAFIPS.2016.7851617","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851617","url":null,"abstract":"Experts often describe their estimates by using words from natural language, i.e., in effect, sorted labels. To efficiently represent the corresponding expert knowledge in a computer-based system, we need to translate these labels into a computer-understandable language, i.e., into numbers. There are many ways to translate labels into numbers. In this paper, we propose to select a translation which is the most robust, i.e., which preserves the order between the corresponding numbers under the largest possible deviations from the original translation. The resulting formulas are in good accordance with the translation coming from the Laplace's principle of sufficient reason, and - somewhat surprisingly - with the current estimates of the proportion of dark matter and dark energy in our Universe.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131476113","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}
Pub Date : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851619
A. Pownuk, V. Kreinovich
In many practical situations, we know the exact form of the objective function, and we know the optimal decision corresponding to each value of the corresponding parameter x. What should we do if we do not know the exact value of x, and instead, we only know x with uncertainty - e.g., with interval uncertainty? In this case, a reasonable idea is to select one value from the given interval, and to use the optimal decision corresponding to the selected value. But which value should we choose? In this paper, we provide a solution to this problem for the situation in the simplest 1-D case. Somewhat surprisingly, it turns out the usual practice of selecting the midpoint is rarely optimal, a better selection is possible.
{"title":"Which point from an interval should we choose?","authors":"A. Pownuk, V. Kreinovich","doi":"10.1109/NAFIPS.2016.7851619","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851619","url":null,"abstract":"In many practical situations, we know the exact form of the objective function, and we know the optimal decision corresponding to each value of the corresponding parameter x. What should we do if we do not know the exact value of x, and instead, we only know x with uncertainty - e.g., with interval uncertainty? In this case, a reasonable idea is to select one value from the given interval, and to use the optimal decision corresponding to the selected value. But which value should we choose? In this paper, we provide a solution to this problem for the situation in the simplest 1-D case. Somewhat surprisingly, it turns out the usual practice of selecting the midpoint is rarely optimal, a better selection is possible.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130997379","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}
Pub Date : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851600
V. Cross
Ontologies have become a focal point in the advancement of the Semantic Web especially in the biological and biomedical domains which have a wealth of ontologies such as those found in BioPortal. Computing the degree of semantic similarity between ontological concepts has been a significant function for their use in various applications. Semantic similarity measures that utilize the information content (IC) of an ontological concept have become more and more standard since they have been widely studied and evaluated. The meaning of information content and its calculation, however, have seen numerous interpretations and formulations. Just recently a method of calculating IC incorporates belief function and plausibility theory into the early corpus-based IC method. The argument is that humans intuitively use inductive inference, and, therefore, plausibility should be incorporated when calculating IC. Various approaches to determine IC measures and the role of the ontology structure has played in IC measures are reviewed. The recent inductive inference approach, which considers both the ontology structure and corpus frequency, is analyzed and compared to other existing IC measures. The analysis and comparison is motivated by the assumptions made in the construction of these IC measures and provides insights into factors to be considered in assessing the IC of an ontological concept.
{"title":"Constructing a measure of information content for an ontological concept","authors":"V. Cross","doi":"10.1109/NAFIPS.2016.7851600","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851600","url":null,"abstract":"Ontologies have become a focal point in the advancement of the Semantic Web especially in the biological and biomedical domains which have a wealth of ontologies such as those found in BioPortal. Computing the degree of semantic similarity between ontological concepts has been a significant function for their use in various applications. Semantic similarity measures that utilize the information content (IC) of an ontological concept have become more and more standard since they have been widely studied and evaluated. The meaning of information content and its calculation, however, have seen numerous interpretations and formulations. Just recently a method of calculating IC incorporates belief function and plausibility theory into the early corpus-based IC method. The argument is that humans intuitively use inductive inference, and, therefore, plausibility should be incorporated when calculating IC. Various approaches to determine IC measures and the role of the ontology structure has played in IC measures are reviewed. The recent inductive inference approach, which considers both the ontology structure and corpus frequency, is analyzed and compared to other existing IC measures. The analysis and comparison is motivated by the assumptions made in the construction of these IC measures and provides insights into factors to be considered in assessing the IC of an ontological concept.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131897306","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}
Pub Date : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851587
M. Kwiatkowska, F. Pouw
With the recent advancements in data mining and availability of large data repositories, a vast amount of collected data are reused as secondary data sources. Although the use of secondary data provides many new opportunities for knowledge discoveries, it requires a careful analysis of the primary research process, namely, the original purpose, conceptualization and operationalization of the variables, and the specific context of data collection. This paper focuses on the interpretation of the secondary data as the evidence of existence or non-existence of real-world phenomena. Our discussion is based on the extended fuzzy logic approach, FLe, proposed by Lotfi Zadeh for the modeling of real-world problems. We demonstrate the necessity of an explicit model for the conceptualization and operationalization process using real-life examples from ecological and medical research.
{"title":"The role of conceptualization and operationalization in the use of secondary data","authors":"M. Kwiatkowska, F. Pouw","doi":"10.1109/NAFIPS.2016.7851587","DOIUrl":"https://doi.org/10.1109/NAFIPS.2016.7851587","url":null,"abstract":"With the recent advancements in data mining and availability of large data repositories, a vast amount of collected data are reused as secondary data sources. Although the use of secondary data provides many new opportunities for knowledge discoveries, it requires a careful analysis of the primary research process, namely, the original purpose, conceptualization and operationalization of the variables, and the specific context of data collection. This paper focuses on the interpretation of the secondary data as the evidence of existence or non-existence of real-world phenomena. Our discussion is based on the extended fuzzy logic approach, FLe, proposed by Lotfi Zadeh for the modeling of real-world problems. We demonstrate the necessity of an explicit model for the conceptualization and operationalization process using real-life examples from ecological and medical research.","PeriodicalId":208265,"journal":{"name":"2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114301683","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}
Pub Date : 2016-10-01DOI: 10.1109/NAFIPS.2016.7851598
M. Argáez, H. Florez, O. Méndez
A global regularized Gauss-Newton method is proposed to obtain a zero residual for square nonlinear problems on an affine subspace. The affine subspace is characterized by using wavelets which enable us to solve the problem without making simulations before solving it. We pose the problem as a zero-overdetermined nonlinear composite function where the inside function provided the solution we are seeking. A Gauss-Newton method is presented together with its standard Newton's assumptions that guarantee to retain the q-quadratic rate of convergence. To avoid the singularity and the high-nonlinearity a regularized strategy is presented which preserves the fast rate of convergence. A line-search method is included for global convergence. We rediscover that the Petrov-Galerkin (PG) inexact directions for the Newton method are the Gauss-Newton (GN) directions for the composite function. The results obtained in a set of large-scale problems show the capability of the method for reproducing their essential features while reducing the computational cost associated with high-dimensional problems by a substantial order of magnitude.
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