FCTA 2022 Abstracts

Full Papers Short Papers
Full Papers
Paper Nr: 2
Title:

An Ontology-based Possibilistic Framework for Extracting Relevant Terms from Job Advertisements
Authors:

Albeiro Espinal, Yannnis Haralambous, Dominique Bedart and John Puentes
Abstract: In a traditional recruitment process, large amounts of resumes and job postings are often handled manually, which is very time-consuming. Existing machine learning techniques for automatic resume ranking lack accuracy in accessing relevant information in job offers, which is crucially needed in order to ensure the pertinence of resumes. We present a context-driven possibilistic framework for extracting such information from job postings, in the form of relevant terms. In our process, after considering the recruiters’ specific organizational context, we analyze their term relevance evaluation strategies in job advertisements. By interviewing a group of recruiters and analyzing their behavior, we have derived a first set of textual relevance markers. Existing term-extraction methods from the literature were also applied to extract such textual relevance markers. We have evaluated all markers using cognitive uncertainty measures and we have integrated them into an ontology-based Belief-Desire-Intention architecture. Doing this, we have improved the F1 score and recall measures of existing state-of-the-art term extraction approaches by 20% and 29% respectively. Besides, our framework is open-ended: it is possible to add new textual markers at any time as nodes of a fuzzy decision tree, the calculation of which depends on the context and domain of job offers.
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Paper Nr: 7
Title:

Evolving Analog Electronic Circuits for Fuzzy Membership Functions Generation
Authors:

P. G. Coelho, J. F. M. do Amaral, Y. C. Bacelar, E. N. Da Rocha and M. C. Bentes
Abstract: Recent research advances in fuzzy systems applications as controllers of increasingly complex systems motivate the consideration of analog circuits capable of implementing fuzzy logic. The purpose of this paper is to evolve the component values of known topologies of analog circuits to generate membership functions. In order to accomplish that, a hybrid model is used for the evolution of electronic circuits, based on genetic algorithms, using a fuzzy system to evaluate multiple objectives. The traditional fitness assessment of genetic algorithms is modified, so that a fuzzy system is effectively responsible for the assessment, thus being able to aggregate the different objectives of the electronic design and generating a fitness value for each circuit in the population. The proposed model presents a simpler and more interpretable way of inserting preferences and specifications, as it uses fuzzy logic. Such specifications are inserted before the evolution of the circuit, ensuring that it is guided in the desired direction, preventing the designer from having to choose the most appropriate solution at the end of the process. An implementation based purely on simulation of circuit models was chosen, providing a flexible environment.
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Paper Nr: 9
Title:

Rough Real Functions and Intuitionistic L-fuzziness
Authors:

Zoltán E. Csajbók
Abstract: This work has been motivated by developing tools to manage rough real functions. Rough real function is a real function attached to a special Cartesian coordinate system. Its values are categorized via the x and y axes. Some papers establish a connection between the rough real functions and the intuitionistic fuzzy sets to achieve the set goal. Until now, rough real functions could only take values from the unit interval. This paper presents the possible extension of the previous methods to more realistic rough real functions. However, care must be taken to ensure that the selected tools are semantically consistent with the nature of the rough real functions.
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Short Papers
Paper Nr: 1
Title:

Data Driven Level Set Fuzzy Modeling for Cryptocurrencies Price Forecasting
Authors:

Leandro Maciel, Rosangela Ballini and Fernando Gomide
Abstract: The paper develops a data-driven fuzzy modeling procedure based on level set to forecast cryptocurrencies prices. Data-driven level set is a novel fuzzy modeling method that differs from linguistic and functional fuzzy models in how the fuzzy rules are built and processed. The level set-based model outputs the weighted average of output functions associated with the fuzzy rules. Output functions map the activation levels of the fuzzy rules directly in the model outputs. Computational experiments are done to evaluate the level set method to forecast the closing prices of Bitcoin, Ethereum, Litecoin and Ripple. Comparisons are made with ARIMA, ETS, MLP and naı̈ve random walk. The results suggest that the random walk outperforms most methods addressed in this paper, but it is surpassed by the level set model for Ethereum. When performance is measured by the direction of price change, the level set-based fuzzy modeling performs best amongst the remaining methods.
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Paper Nr: 4
Title:

Synthesis of an Evolutionary Fuzzy Multi-objective Energy Management System for an Electric Boat
Authors:

Antonino Capillo, Enrico De Santis, Fabio F. Mascioli and Antonello Rizzi
Abstract: Even though it is known that Renewable Energy Sources (RESs) are necessary to face Climate Change and pollution, technology is still in a developement phase, aiming at improving energy exploitation from RESs, as these type of sources suffer from low energy density and variability over time. Thus, proper ICT infrastructures equipped with a robust software, i.e., Energy Management System (EMS), are needed to ensure that Renewable Energy (RE) does not go to waste. Relatively small local electrical grids called Microgrids (MGs) represent the EMS ecosystem, since their main features are the proximity between generation and loads and the presence of Energy Storage Systems (ESSs) adopted to recover surplus energy. The Vehicle-to-Grid (V2G) paradigm helps to realize the Smart City, which in substance is an interconnection of MGs hosting electrical vehicles for an efficient energy management at a larger scale. In this context, e-boats have only recently been considered. Hence, in this work a Multi-Objective (MO) EMS is synthesized for an e-boat docked in a small Microgrid (PV generator and ESS) with the aim of maximizing the charging time of the e-boat ESS and spending as little as possible both for energy purchase and also in terms of ESS wear. A Fuzzy Inference System - Hierarchical Genetic Algorithm (FIS-HGA) is used to achieve the Pareto Front, with the HGA that is in charge of optimizing the FIS parameters. Results laid to a balanced trade-off between the two objectives, since the e-boat ESS is almost fully charged in a reasonable time and with a low cost, compatible with people transportation. Last but not least, the inference process of a FIS is easily interpretable, in the perspective of an Explainable AI.
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Paper Nr: 6
Title:

A Novel Approach to Weighted Fuzzy Rules for Positive Samples
Authors:

Martina Daňková
Abstract: In this contribution, we propose a novel approach to automated fuzzy rule base generation based on underlying observational data. The core of this method lies in adding information to a particular fuzzy rule in the form of attached weight given as a value extracted from a relational data model. In particular, we blend two approaches to receive particular models that overcome their specific drawbacks.
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Paper Nr: 8
Title:

Fuzzy and Evidential Contribution to Multilevel Clustering
Authors:

Martin Cabotte, Pierre-Alexandre Hébert and Émilie Poisson-Caillault
Abstract: Clustering algorithms based on split-and-merge concept, divisive or agglomerative process are widely developed to extract patterns with different shapes, sizes and densities. Here a multilevel approach is considered in order to characterise general patterns up to finer shapes. This paper focus on the contribution of both fuzzy and evidential models to build a relevant divisive clustering. Algorithms and both a priori and a posteriori split criteria are discussed and evaluated. Basic crisp/fuzzy/evidential algorithms are compared to cluster four datasets within a multilevel approach. Finally, same framework is also applied in embedded spectral space in order to give an overall comparison.
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Paper Nr: 10
Title:

Hybrid Fuzzy Classification Algorithm with Modifed Initialization and Crossover
Authors:

Tatiana Pleshkova and Vladimir Stanovov
Abstract: The article proposes two modifications of initialization and crossover operations for the design of a genetic fuzzy system. A fuzzy logic system is used to solve data classification problems and is automatically generated by a genetic algorithm. The paper uses a genetic algorithm to encode of several fuzzy granulations into a single rule, while each individual encodes a rule base. The proposed algorithm uses several training objects of the same class to create a single rule during initialization. The modified crossover creates a new rule base from most efficient rules selected from parents. To evaluate the effectiveness of the modification, the computational experiments were carried out on several datasets, followed by verification using Mann-Whitney U test. The proposed initialization modification allows reducing the number of rules in a fuzzy rule base and increasing the accuracy and F-score on some datasets. The crossover modification shows higher efficiency only on one dataset.
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