Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494508
L. Magdalena, D. Gómez, L. Garmendia, J. Montero
Computable aggregation operators can be seen as a generalization of aggregation operators where the mathematical function is replaced by a program that performs the aggregation process. This extension allows the introduction of new aggregation processes not feasible under the classical framework. Particularly interesting are some non-deterministic processes widely considered to merge information. However, especially in non-deterministic processes, the extension of some of the well-known concepts for aggregation operators such as monotony, is needed. In this work, a new concept of monotonicity is proposed, from a probabilistic perspective, for non-deterministic computable aggregation operators. To be consistent, the concept coincides with the classical definition in the deterministic case. In addition, some cases of interest are analysed.
{"title":"Population Monotonicity of Non-deterministic Computable Aggregations","authors":"L. Magdalena, D. Gómez, L. Garmendia, J. Montero","doi":"10.1109/FUZZ45933.2021.9494508","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494508","url":null,"abstract":"Computable aggregation operators can be seen as a generalization of aggregation operators where the mathematical function is replaced by a program that performs the aggregation process. This extension allows the introduction of new aggregation processes not feasible under the classical framework. Particularly interesting are some non-deterministic processes widely considered to merge information. However, especially in non-deterministic processes, the extension of some of the well-known concepts for aggregation operators such as monotony, is needed. In this work, a new concept of monotonicity is proposed, from a probabilistic perspective, for non-deterministic computable aggregation operators. To be consistent, the concept coincides with the classical definition in the deterministic case. In addition, some cases of interest are analysed.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124418373","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494525
A. K. Panda, B. Kosko
A random rule foam grows and combines several independent fuzzy rule-based systems by randomly sampling input-output data from a trained deep neural classifier. The random rule foam defines an interpretable proxy system for the sampled black-box classifier. The random foam gives the complete Bayesian posterior probabilities over the foam subsystems that contribute to the proxy system's output for a given pattern input. It also gives the Bayesian posterior over the if-then fuzzy rules in each of these constituent foams. The random foam also computes a conditional variance that describes the uncertainty in its predicted output given the random foam's learned rule structure. The mixture structure leads to bootstrap confidence intervals around the output. Using the Bayesian posterior probabilities to prune or discard low-probability sub-foams improves the system's classification accuracy. Simulations used the MNIST image data set of 60,000 gray-scale images of ten hand-written digits. Dropping the lowest-probability foams per input pattern brought the pruned random foam's classification accuracy nearly to that of the neural classifier. Posterior pruning outperformed simple accuracy pruning of a random foam and outperformed a random forest trained on the same neural classifier.
{"title":"Bayesian Pruned Random Rule Foams for XAI","authors":"A. K. Panda, B. Kosko","doi":"10.1109/FUZZ45933.2021.9494525","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494525","url":null,"abstract":"A random rule foam grows and combines several independent fuzzy rule-based systems by randomly sampling input-output data from a trained deep neural classifier. The random rule foam defines an interpretable proxy system for the sampled black-box classifier. The random foam gives the complete Bayesian posterior probabilities over the foam subsystems that contribute to the proxy system's output for a given pattern input. It also gives the Bayesian posterior over the if-then fuzzy rules in each of these constituent foams. The random foam also computes a conditional variance that describes the uncertainty in its predicted output given the random foam's learned rule structure. The mixture structure leads to bootstrap confidence intervals around the output. Using the Bayesian posterior probabilities to prune or discard low-probability sub-foams improves the system's classification accuracy. Simulations used the MNIST image data set of 60,000 gray-scale images of ten hand-written digits. Dropping the lowest-probability foams per input pattern brought the pruned random foam's classification accuracy nearly to that of the neural classifier. Posterior pruning outperformed simple accuracy pruning of a random foam and outperformed a random forest trained on the same neural classifier.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127717681","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494516
Adrià Torrens Urrutia, M. Dolores Jiménez-López, Antoni Brosa-Rodríguez
One of the currently biggest challenges in NLP is to develop multilingual language technology. Lack of data in low-resources languages poses great difficulty to NLP researchers and limits NLP technology's availability to a small number of resource-rich languages. It has been shown that linguistic typology and the knowledge of language universals can help NLP in the development of multilingual resources. To contribute to this research area, we present a fuzzy approach to language universals. Our proposal combines a constraint-based formalism with fuzzy logic to define a fuzzy-gradient model to characterize linguistic universals. This model will allow us to evaluate linguistic universals and to define a universal grammar. This universal grammar will be integrated into an automatic technique to infer from linguistic data the particular grammar of any understudied natural language.
{"title":"A Fuzzy Approach to Language Universals for NLP","authors":"Adrià Torrens Urrutia, M. Dolores Jiménez-López, Antoni Brosa-Rodríguez","doi":"10.1109/FUZZ45933.2021.9494516","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494516","url":null,"abstract":"One of the currently biggest challenges in NLP is to develop multilingual language technology. Lack of data in low-resources languages poses great difficulty to NLP researchers and limits NLP technology's availability to a small number of resource-rich languages. It has been shown that linguistic typology and the knowledge of language universals can help NLP in the development of multilingual resources. To contribute to this research area, we present a fuzzy approach to language universals. Our proposal combines a constraint-based formalism with fuzzy logic to define a fuzzy-gradient model to characterize linguistic universals. This model will allow us to evaluate linguistic universals and to define a universal grammar. This universal grammar will be integrated into an automatic technique to infer from linguistic data the particular grammar of any understudied natural language.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132634779","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494582
Adam Kiersztyn, Paweł Karczmarek, Krystyna Kiersztyn, R. Lopucki, S. Grzegórski, W. Pedrycz
With the advent of research into Granular Computing, in particular information granules, the way of thinking about data has changed gradually. Researchers and practitioners do not consider only their specific properties, but also try to look at the data in a more general way, closer to the way people think. This kind of knowledge representation is expressed particularly in approaches based on linguistic modeling or fuzzy techniques such as fuzzy clustering, but also newer approaches related to the explanation of how artificial intelligence works on these data (so-called explainable artificial intelligence). Therefore, especially important from the point of view of the methodology of data research is an attempt to understand their potential as information granules. Such a kind of approach to data presentation and analysis may introduce considerations of a higher, more general level of abstraction, while at the same time reliably describing the network of relationships between the data and the observed information granules. In this study, we tackle this topic with particular emphasis on the problem of choosing a predictive model. In a series of numerical experiments based on both artificially generated data, ecological data on changes in bird arrival dates in the context of climate change, and COVID-19 infections data we demonstrate the effectiveness of the proposed approach built with a novel application of information potential granules.
{"title":"The Concept of Granular Representation of the Information Potential of Variables","authors":"Adam Kiersztyn, Paweł Karczmarek, Krystyna Kiersztyn, R. Lopucki, S. Grzegórski, W. Pedrycz","doi":"10.1109/FUZZ45933.2021.9494582","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494582","url":null,"abstract":"With the advent of research into Granular Computing, in particular information granules, the way of thinking about data has changed gradually. Researchers and practitioners do not consider only their specific properties, but also try to look at the data in a more general way, closer to the way people think. This kind of knowledge representation is expressed particularly in approaches based on linguistic modeling or fuzzy techniques such as fuzzy clustering, but also newer approaches related to the explanation of how artificial intelligence works on these data (so-called explainable artificial intelligence). Therefore, especially important from the point of view of the methodology of data research is an attempt to understand their potential as information granules. Such a kind of approach to data presentation and analysis may introduce considerations of a higher, more general level of abstraction, while at the same time reliably describing the network of relationships between the data and the observed information granules. In this study, we tackle this topic with particular emphasis on the problem of choosing a predictive model. In a series of numerical experiments based on both artificially generated data, ecological data on changes in bird arrival dates in the context of climate change, and COVID-19 infections data we demonstrate the effectiveness of the proposed approach built with a novel application of information potential granules.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128129823","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494481
Ettore Mariotti, J. M. Alonso, R. Confalonieri
We introduce a novel framework to deal with fairness, accountability and explainability of intelligent systems. This framework puts together several tools to deal with bias at the level of data, algorithms and human cognition. The framework makes use of intelligent classifiers endowed with fuzzy-grounded linguistic explainability. As a result, it facilitates the exhaustive comparison of (white/grey/black)-box modelling techniques in combination with different strategies for handling missing values and unbalanced datasets. The proposal is evaluated on a realworld dataset in the context of banking services and reported results are encouraging.
{"title":"A Framework for Analyzing Fairness, Accountability, Transparency and Ethics: A Use-case in Banking Services","authors":"Ettore Mariotti, J. M. Alonso, R. Confalonieri","doi":"10.1109/FUZZ45933.2021.9494481","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494481","url":null,"abstract":"We introduce a novel framework to deal with fairness, accountability and explainability of intelligent systems. This framework puts together several tools to deal with bias at the level of data, algorithms and human cognition. The framework makes use of intelligent classifiers endowed with fuzzy-grounded linguistic explainability. As a result, it facilitates the exhaustive comparison of (white/grey/black)-box modelling techniques in combination with different strategies for handling missing values and unbalanced datasets. The proposal is evaluated on a realworld dataset in the context of banking services and reported results are encouraging.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132207957","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494477
Kutay Bölat, T. Kumbasar
Working with unlabeled data carries the burden of uncertainties especially when the data are high-dimensional. Clustering is not an exception in this aspect and it requires special treatment. In this study, we propose to cope with the uncertainties which occur during clustering high-dimensional data with Interval Type-2 (IT2) Fuzzy Sets (FSs) and Deep Learning (DL) methods. Generation of the IT2-FSs is done with different cluster similarity functions parameterized with Interval Valued Parameters (IVPs). These parameters are introduced as the representations of the uncertainty in cluster assignments. As the backbone of the proposed method, Deep Embedding Clustering (DEC) is employed. The resulting IT2 fuzzy clustering inference is integrated into DEC so that both the inference and the training of the proposed model are operational in popular DL frameworks. Therefore, for a straightforward deployment, the constraints on IT2-FSs are redefined by introducing parameterization tricks upon IVPs. The presented comparative results indicate that coping with the uncertainties through IT2-FSs is superior to their baseline type-1 counterparts.
{"title":"Integrating Interval Type-2 Fuzzy Sets into Deep Embedding Clustering to Cope with Uncertainty","authors":"Kutay Bölat, T. Kumbasar","doi":"10.1109/FUZZ45933.2021.9494477","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494477","url":null,"abstract":"Working with unlabeled data carries the burden of uncertainties especially when the data are high-dimensional. Clustering is not an exception in this aspect and it requires special treatment. In this study, we propose to cope with the uncertainties which occur during clustering high-dimensional data with Interval Type-2 (IT2) Fuzzy Sets (FSs) and Deep Learning (DL) methods. Generation of the IT2-FSs is done with different cluster similarity functions parameterized with Interval Valued Parameters (IVPs). These parameters are introduced as the representations of the uncertainty in cluster assignments. As the backbone of the proposed method, Deep Embedding Clustering (DEC) is employed. The resulting IT2 fuzzy clustering inference is integrated into DEC so that both the inference and the training of the proposed model are operational in popular DL frameworks. Therefore, for a straightforward deployment, the constraints on IT2-FSs are redefined by introducing parameterization tricks upon IVPs. The presented comparative results indicate that coping with the uncertainties through IT2-FSs is superior to their baseline type-1 counterparts.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134639287","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494572
R. Guillaume, A. Kasperski, P. Zieliński
This paper deals with a linear optimization problem with uncertain objective function coefficients modeled by possibility distributions. The fuzzy robust optimization framework is applied to compute a solution. Namely, the necessity degree that the objective value is lower than a given threshold is maximized. The aim of this paper is to take the knowledge on dependencies between the objective coefficients into account by means of a family of copula functions. It is shown that this new approach limits the conservatism of fuzzy robust optimization, better evaluates possibility distributions for the values of the objective function and do not increase the complexity of the problem.
{"title":"Robust Possibilistic Optimization with Copula Function","authors":"R. Guillaume, A. Kasperski, P. Zieliński","doi":"10.1109/FUZZ45933.2021.9494572","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494572","url":null,"abstract":"This paper deals with a linear optimization problem with uncertain objective function coefficients modeled by possibility distributions. The fuzzy robust optimization framework is applied to compute a solution. Namely, the necessity degree that the objective value is lower than a given threshold is maximized. The aim of this paper is to take the knowledge on dependencies between the objective coefficients into account by means of a family of copula functions. It is shown that this new approach limits the conservatism of fuzzy robust optimization, better evaluates possibility distributions for the values of the objective function and do not increase the complexity of the problem.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132113056","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494400
Wen He, Rosa M. Rodríguez, Bapi Dutta, Luis Martínez
In a group decision making (GDM) problem, the information is fused to obtain a collective result, which helps to choose the best solution/s to the problem. Recently, a new representation model called Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT), which extends the representation of the comparative linguistic expressions (CLEs) to a continuous domain combining the advantages of the hesitant fuzzy linguistic term sets and 2-tuple linguistic representation model has been proposed to model experts' preferences. Due to the need of fusing experts' preferences in GDM processes, it is convenient to have enough flexible aggregation operators for such processes. However, so far, only two aggregation operators have been introduced to aggregate ELICIT information in GDM problems, the fuzzy arithmetic mean operator and the Bonferroni mean operator. Thus, it seems necessary to define new aggregation operators with different features to model wide range of decision-making scenarios. One widely used operator to aggregate preferences in decision making is the OWA operator. The key issue to apply the OWA operator is the reordering process of the arguments. However, the ELICIT information does not have an inherent order because it is represented by a fuzzy number. Therefore, the aim of this contribution is to define the type-1 ELICIT OWA operator by using crisp and fuzzy weights, particularly interval weights, and define a multi-criteria group decision making model which applies the type-1 ELICIT OWA operator to fuse the information. Additionally, an experimental study is introduced to demonstrate the feasibility of the proposed aggregation operator.
在群体决策(GDM)问题中，信息融合得到一个集体的结果，这有助于选择问题的最佳解决方案。近年来，人们提出了一种新的表征模型——带符号翻译的扩展比较语言表达(Extended Comparative Linguistic Expressions with Symbolic Translation，简称ELICIT)，将比较语言表达(CLEs)的表征扩展到一个连续域，结合犹豫模糊语言术语集和二元组语言表征模型的优点，对专家的偏好进行建模。由于GDM过程需要融合专家的偏好，因此为该过程提供足够灵活的聚合算子是很方便的。然而，到目前为止，在GDM问题中只引入了两种聚合算子来聚合引出信息，即模糊算术均值算子和Bonferroni均值算子。因此，似乎有必要定义具有不同特征的新聚合操作符来模拟广泛的决策场景。在决策过程中，一个广泛用于聚合首选项的操作符是OWA操作符。应用OWA操作符的关键问题是参数的重新排序过程。然而，引出信息没有固有的顺序，因为它是由模糊数表示的。因此，本文的目的是通过使用清晰和模糊的权重，特别是区间权重来定义1型引出OWA算子，并定义一个多准则群体决策模型，该模型应用1型引出OWA算子来融合信息。此外，还介绍了实验研究，以验证所提出的聚合算子的可行性。
{"title":"Exploiting the type-1 OWA operator to fuse the ELICIT information","authors":"Wen He, Rosa M. Rodríguez, Bapi Dutta, Luis Martínez","doi":"10.1109/FUZZ45933.2021.9494400","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494400","url":null,"abstract":"In a group decision making (GDM) problem, the information is fused to obtain a collective result, which helps to choose the best solution/s to the problem. Recently, a new representation model called Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT), which extends the representation of the comparative linguistic expressions (CLEs) to a continuous domain combining the advantages of the hesitant fuzzy linguistic term sets and 2-tuple linguistic representation model has been proposed to model experts' preferences. Due to the need of fusing experts' preferences in GDM processes, it is convenient to have enough flexible aggregation operators for such processes. However, so far, only two aggregation operators have been introduced to aggregate ELICIT information in GDM problems, the fuzzy arithmetic mean operator and the Bonferroni mean operator. Thus, it seems necessary to define new aggregation operators with different features to model wide range of decision-making scenarios. One widely used operator to aggregate preferences in decision making is the OWA operator. The key issue to apply the OWA operator is the reordering process of the arguments. However, the ELICIT information does not have an inherent order because it is represented by a fuzzy number. Therefore, the aim of this contribution is to define the type-1 ELICIT OWA operator by using crisp and fuzzy weights, particularly interval weights, and define a multi-criteria group decision making model which applies the type-1 ELICIT OWA operator to fuse the information. Additionally, an experimental study is introduced to demonstrate the feasibility of the proposed aggregation operator.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133814847","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494428
Mahmudul Hasan, Mosarrat Jahan, Shaily Kabir, Christian Wagner
Trust estimation of vehicles is vital for the correct functioning of Vehicular Ad Hoc Networks (VANETs) as it enhances their security by identifying reliable vehicles. However, accurate trust estimation still remains distant as existing works do not consider all malicious features of vehicles, such as dropping or delaying packets, altering content, and injecting false information. Moreover, data consistency of messages is not guaranteed here as they pass through multiple paths and can easily be altered by malicious relay vehicles. This leads to difficulty in measuring the effect of content tampering in trust calculation. Further, unreliable wireless communication of VANETs and unpredictable vehicle behavior may introduce uncertainty in the trust estimation and hence its accuracy. In this view, we put forward three trust factors - captured by fuzzy sets to adequately model malicious properties of a vehicle and apply a fuzzy logic-based algorithm to estimate its trust. We also introduce a parameter to evaluate the impact of content modification in trust calculation. Experimental results reveal that the proposed scheme detects malicious vehicles with high precision and recall and makes decisions with higher accuracy compared to the state-of-the-art.
{"title":"A Fuzzy Logic-Based Trust Estimation in Edge-Enabled Vehicular Ad Hoc Networks","authors":"Mahmudul Hasan, Mosarrat Jahan, Shaily Kabir, Christian Wagner","doi":"10.1109/FUZZ45933.2021.9494428","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494428","url":null,"abstract":"Trust estimation of vehicles is vital for the correct functioning of Vehicular Ad Hoc Networks (VANETs) as it enhances their security by identifying reliable vehicles. However, accurate trust estimation still remains distant as existing works do not consider all malicious features of vehicles, such as dropping or delaying packets, altering content, and injecting false information. Moreover, data consistency of messages is not guaranteed here as they pass through multiple paths and can easily be altered by malicious relay vehicles. This leads to difficulty in measuring the effect of content tampering in trust calculation. Further, unreliable wireless communication of VANETs and unpredictable vehicle behavior may introduce uncertainty in the trust estimation and hence its accuracy. In this view, we put forward three trust factors - captured by fuzzy sets to adequately model malicious properties of a vehicle and apply a fuzzy logic-based algorithm to estimate its trust. We also introduce a parameter to evaluate the impact of content modification in trust calculation. Experimental results reveal that the proposed scheme detects malicious vehicles with high precision and recall and makes decisions with higher accuracy compared to the state-of-the-art.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114538406","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 : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494535
Naeemeh Adel, Keeley A. Crockett, Joao Paulo Carvalho, V. Cross
The field of Computing with Words has been pivotal in the development of fuzzy semantic similarity measures. Fuzzy semantic similarity measures allow the modelling of words in a given context with a tolerance for the imprecise nature of human perceptions. In this work, we look at how this imprecision can be addressed with the use of fuzzy semantic similarity measures in the field of natural language processing. A fuzzy influence factor is introduced into an existing measure known as FUSE. FUSE computes the similarity between two short texts based on weighted syntactic and semantic components in order to address the issue of comparing fuzzy words that exist in different word categories. A series of empirical experiments investigates the effect of introducing a fuzzy influence factor into FUSE across a number of short text datasets. Comparisons with other similarity measures demonstrates that the fuzzy influence factor has a positive effect in improving the correlation of machine similarity judgments with similarity judgments of humans.
{"title":"Fuzzy Influence in Fuzzy Semantic Similarity Measures","authors":"Naeemeh Adel, Keeley A. Crockett, Joao Paulo Carvalho, V. Cross","doi":"10.1109/FUZZ45933.2021.9494535","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494535","url":null,"abstract":"The field of Computing with Words has been pivotal in the development of fuzzy semantic similarity measures. Fuzzy semantic similarity measures allow the modelling of words in a given context with a tolerance for the imprecise nature of human perceptions. In this work, we look at how this imprecision can be addressed with the use of fuzzy semantic similarity measures in the field of natural language processing. A fuzzy influence factor is introduced into an existing measure known as FUSE. FUSE computes the similarity between two short texts based on weighted syntactic and semantic components in order to address the issue of comparing fuzzy words that exist in different word categories. A series of empirical experiments investigates the effect of introducing a fuzzy influence factor into FUSE across a number of short text datasets. Comparisons with other similarity measures demonstrates that the fuzzy influence factor has a positive effect in improving the correlation of machine similarity judgments with similarity judgments of humans.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133939273","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}
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