Journal of Intelligent & Fuzzy Systems - Volume 42, issue 5

Authors: Ivanov, Vladimir | Solovyev, Valery

Article Type: Research Article

Abstract: Concrete/abstract words are used in a growing number of psychological and neurophysiological research. For a few languages, large dictionaries have been created manually. This is a very time-consuming and costly process. To generate large high-quality dictionaries of concrete/abstract words automatically one needs extrapolating the expert assessments obtained on smaller samples. The research question that arises is how small such samples should be to do a good enough extrapolation. In this paper, we present a method for automatic ranking concreteness of words and propose an approach to significantly decrease amount of expert assessment. The method has been evaluated on a large …test set for English. The quality of the constructed dictionaries is comparable to the expert ones. The correlation between predicted and expert ratings is higher comparing to the state-of-the-art methods. Show more

Keywords: Concrete words, abstract words, word embeddings, fastText, ELMo, BERT, machine extrapolation

DOI: 10.3233/JIFS-219240

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4513-4521, 2022

Authors: García-Mendoza, Juan-Luis | Villaseñor-Pineda, Luis | Orihuela-Espina, Felipe | Bustio-Martínez, Lázaro

Article Type: Research Article

Abstract: Distant Supervision is an approach that allows automatic labeling of instances. This approach has been used in Relation Extraction. Still, the main challenge of this task is handling instances with noisy labels (e.g., when two entities in a sentence are automatically labeled with an invalid relation). The approaches reported in the literature addressed this problem by employing noise-tolerant classifiers. However, if a noise reduction stage is introduced before the classification step, this increases the macro precision values. This paper proposes an Adversarial Autoencoders-based approach for obtaining a new representation that allows noise reduction in Distant Supervision. The representation obtained using …Adversarial Autoencoders minimize the intra-cluster distance concerning pre-trained embeddings and classic Autoencoders. Experiments demonstrated that in the noise-reduced datasets, the macro precision values obtained over the original dataset are similar using fewer instances considering the same classifier. For example, in one of the noise-reduced datasets, the macro precision was improved approximately 2.32% using 77% of the original instances. This suggests the validity of using Adversarial Autoencoders to obtain well-suited representations for noise reduction. Also, the proposed approach maintains the macro precision values concerning the original dataset and reduces the total instances needed for classification. Show more

Keywords: Noise reduction, adversarial autoencoders, distant supervision

DOI: 10.3233/JIFS-219241

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4523-4529, 2022

Authors: García, Alfredo | González, Juan M. | Palomino, Amparo D.

Article Type: Research Article

Abstract: In the current world, the need to know instantaneous information that helps people to know their current physical and intellectual conditions has become paramount, each time new systems that provide information to the user in real time are incorporated in portable devices. This information indicates different health parameters of the user, it can be obtained through their physiological variables such as: number of steps, heart rate, oxygenation level in the blood and other ones. One of the most requested intellectual conditions to be known by the user is: the level of attention reached when the user executes a task. This …work describes a methodology and the experimentation to know the level of attention of people through a test to identify colors also are shown the development and the application of a system (hardware and software) to measure the level of attention of people using two input signals: corporal posture and brain waves. The mathematical analysis to find the correlation between the corporal posture and the level of attention is shown in this paper. The results obtained indicate that the corporal posture influences on the level of attention of people directly. Show more

Keywords: Attention level, corporal posture, cognitive process, feedback system, brain waves

DOI: 10.3233/JIFS-219242

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4531-4540, 2022

Authors: Kostiuk, Yevhen | Lukashchuk, Mykola | Gelbukh, Alexander | Sidorov, Grigori

Article Type: Research Article

Abstract: Probabilistic Bayesian methods are widely used in the machine learning domain. Variational Autoencoder (VAE) is a common architecture for solving the Language Modeling task in a self-supervised way. VAE consists of a concept of latent variables inside the model. Latent variables are described as a random variable that is fit by the data. Up to now, in the majority of cases, latent variables are considered normally distributed. The normal distribution is a well-known distribution that can be easily included in any pipeline. Moreover, the normal distribution is a good choice when the Central Limit Theorem (CLT) holds. It makes it …effective when one is working with i.i.d. (independent and identically distributed) random variables. However, the conditions of CLT in Natural Language Processing are not easy to check. So, the choice of distribution family is unclear in the domain. This paper studies the priors selection impact of continuous distributions in the Low-Resource Language Modeling task with VAE. The experiment shows that there is a statistical difference between the different priors in the encoder-decoder architecture. We showed that family distribution hyperparameter is important in the Low-Resource Language Modeling task and should be considered for the model training. Show more

Keywords: Bayesian model, low-resource language modeling, NLP, priors, RNN, VAE, Variational Autoencoder

DOI: 10.3233/JIFS-219243

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4541-4549, 2022

Authors: Rebollar, Fernando | Aldeco-Perez, Rocio | Ramos, Marco A.

Article Type: Research Article

Abstract: The general population increasingly uses digital services, meaning services which are delivered over the internet or an electronic network, and events such as pandemics have accelerated the need of using new digital services. Governments have also increased their number of digital services, however, these digital services still lack of sufficient information security, particularly integrity. Blockchain uses cryptographic techniques that allow decentralization and increase the integrity of the information it handles, but it still has disadvantages in terms of efficiency, making it incapable of implementing some digital services where a high rate of transactions are required. In order to increase its …efficient, a multi-layer proposal based on blockchain is presented. It has four layers, where each layer specializes in a different type of information and uses properties of public blockchain and private blockchain. An statistical analysis is performed and the proposal is modeled showing that it maintains and even increases the integrity of the information while preserving the efficiency of transactions. Besides, the proposal can be flexible and adapt to different types of digital services. It also considers that voluntary nodes participate in the decentralization of information making it more secure, verifiable, transparent and reliable. Show more

Keywords: Blockchain, digital services, trust, smart contracts

DOI: 10.3233/JIFS-219244

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4551-4562, 2022

Authors: Gutiérrez-Soto, Claudio | Gutiérrez-Bunster, Tatiana | Fuentes, Guillermo

Article Type: Research Article

Abstract: Big Data is a generic term that involves the storing and processing of a large amount of data. This large amount of data has been promoted by technologies such as mobile applications, Internet of Things (IoT), and Geographic Information Systems (GIS). An example of GIS is a Spatio-Temporal Database (STDB). A complex problem to address in terms of processing time is pattern searching on STDB. Nowadays, high information processing capacity is available everywhere. Nevertheless, the pattern searching problem on STDB using traditional Data Mining techniques is complex because the data incorporate the temporal aspect. Traditional techniques of pattern searching, such …as time series, do not incorporate the spatial aspect. For this reason, traditional algorithms based on association rules must be adapted to find these patterns. Most of the algorithms take exponential processing times. In this paper, a new efficient algorithm (named Minus-F1) to look for periodic patterns on STDB is presented. Our algorithm is compared with Apriori, Max-Subpattern, and PPA algorithms on synthetic and real STDB. Additionally, the computational complexities for each algorithm in the worst cases are presented. Empirical results show that Minus-F1 is not only more efficient than Apriori, Max-Subpattern, and PAA, but also it presents a polynomial behavior. Show more

Keywords: Pattern searching, Association rule algorithms, spatio-temporal databases

DOI: 10.3233/JIFS-219245

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4563-4572, 2022

Authors: Carreón-Díaz de León, Carlos Leopoldo | Vergara-Limon, Sergio | Vargas-Treviño, María Aurora D. | González-Calleros, Juan Manuel

Article Type: Research Article

Abstract: This paper presents a novel methodology to identify the dynamic parameters of a real robot with a convolutional neural network (CNN). Conventional identification methodologies use continuous motion signals. However, these signals are quantized in their amplitude and are discrete in time. Therefore, the time required to identify the parameters of a robot with a limited measurement system is related to an optimized motion trajectory performed by the robot. The proposed methodology consists of an algorithm that uses a trained CNN with the data created by the dynamical model of the case study robot. A processing technique is proposed to transform …the position, velocity, acceleration, and torque robot signals into an image whose characteristics are extracted by the CNN to determine their dynamic parameters. The proposed algorithm does not require any optimal trajectory to find the dynamic parameters. A proposed time-spectral evaluation metric is used to validate the robot data and the identification data. The validation results show that the proposed methodology identifies the parameters of a Cartesian robot in less than 1 second, exceeding 90% of the proposed evaluation metric and 98% for the simulation results. Show more

Keywords: Identification, dynamic parameters, CNN, robotics, signals

DOI: 10.3233/JIFS-219246

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4573-4586, 2022

Authors: Bahuguna, Aman | Yadav, Deepak | Senapati, Apurbalal | Saha, Baidya Nath

Article Type: Research Article

Abstract: Covid-19 braces serious mental health crisis across the world. Since a vast majority of the population exploit social media platforms such as twitter to exchange information, rapid collecting and analyzing social media data to understand personal well-being and subsequently adopting adequate measures could avoid severe socio-economic damage. Sentiment analysis on twitter data is very useful to understand and identify the mental health issues. In this research, we proposed a unified deep neuro-fuzzy approach for Covid-19 twitter sentiment classification. Fuzzy logic has been a very powerful tool for twitter data analysis where approximate semantic and syntactic analysis is more relevant because …correcting spelling and grammar in tweets are merely obnoxious. We conducted the experiment on three challenging COVID-19 twitter sentiment datasets. Experimental results demonstrate that fuzzy Sugeno integral based ensembled classifiers succeed over individual base classifiers. Show more

Keywords: Covid-19 twitter sentiment classification, deep fuzzy neural network, sugeno integral

DOI: 10.3233/JIFS-219247

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4587-4597, 2022

Authors: Crespo-Sanchez, Melesio | Lopez-Arevalo, Ivan | Aldana-Bobadilla, Edwin | Molina-Villegas, Alejandro

Article Type: Research Article

Abstract: In the last few years, text analysis has grown as a keystone in several domains for solving many real-world problems, such as machine translation, spam detection, and question answering, to mention a few. Many of these tasks can be approached by means of machine learning algorithms. Most of these algorithms take as input a transformation of the text in the form of feature vectors containing an abstraction of the content. Most of recent vector representations focus on the semantic component of text, however, we consider that also taking into account the lexical and syntactic components the abstraction of content could …be beneficial for learning tasks. In this work, we propose a content spectral-based text representation applicable to machine learning algorithms for text analysis. This representation integrates the spectra from the lexical, syntactic, and semantic components of text producing an abstract image, which can also be treated by both, text and image learning algorithms. These components came from feature vectors of text. For demonstrating the goodness of our proposal, this was tested on text classification and complexity reading score prediction tasks obtaining promising results. Show more

Keywords: Text representation, text analysis, content spectre

DOI: 10.3233/JIFS-219248

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4599-4610, 2022

Authors: Zhang, Jianfei | Rong, Wenge | Chen, Dali | Xiong, Zhang

Article Type: Research Article

Abstract: The traditional end-to-end Neural Question Generation (NQG) models tend to generate generic and bland questions, as there are two obscure points: 1) the modifications of the answer in the context can be used as the clues to the answer mentioned in the question, while they are generally not unique and can be used independently for generating diverse questions; 2) the same question content can also be asked in diverse ways, which depends on personal preference in practice. The above-mentioned two points are indeed two variables to conduct question generation, but they are not annotated in the original dataset and are …thus ignored by the traditional end-to-end models. In this paper we propose a framework that clarifies those two points through two sub-modules to better conduct question generation. We take experiments based on the GPT-2 model and the SQuAD dataset, and prove that our framework can improve the performance measured by similarity metrics, while it also provides appropriate alternatives for controllable diversity enhancement. Show more

Keywords: Question generation, external information, controllable diversity

DOI: 10.3233/JIFS-219249

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4611-4622, 2022