Time series (TSs) are usually represented numerically; however, there are many situations where a linguistic description is preferable. Granular linguistic model of phenomena (GLMP) is a paradigm used in the generation of linguistic descriptions of static situations without considering the temporal relationship of data present, for example, in TSs. This paper presents a new method to generate linguistic descriptions of TSs with an operation similar to petri nets (PNs) and inspired by GLMP. The presented approach maintains the operation of PNs, adding a mechanism to generate linguistic descriptions based on GLMP. The main components of GLMP are added to places and transitions of PNs. This extension is called linguistic PNs (LPNs) and is a language that can be used to generate linguistic descriptions of systems. GLMP is a method that can be used by experts to design how the linguistic descriptions are synthesized and generated. So, LPNs also allow incorporating expert knowledge and combining descriptions in an appropriate way. The experimental part is focused on showing how LPNs can be used to linguistically describe TSs, including the occurrence of maxima or minima, and trends.

The generation of text reports from numerical and symbolic data is getting the attention of many researchers. This paper presents an R package useful to develop computational systems able to generate linguistic descriptions of complex phenomena. It generates text reports from numerical and symbolic data related to the phenomena under consideration. This is an implementation of our previous research work that is supported by the computational theory of perceptions grounded in the fuzzy sets theory. Developing open source software while we follow the key issues (novelty, usability, interoperability, and relevance) will facilitate the adoption of this new discipline in the research community and industry. We present illustrative examples that show how to use this new R package. The examples reveal that the package is ready to become a relevant tool in the research field of text generation from data.

Monitoring and analysis of complex phenomena attract the attention of both academy and industry.Dealing with data produced by complex phenomena requires the use of advance computational intelligence techniques.Namely, linguistic description of complex phenomena constitutes a mature research line.It is supported by the Computational Theory of Perceptions grounded on the Fuzzy Sets Theory.Its aim is the development of computational systems with the ability to generate vague descriptions of the world in a similar way how humans do.This is a human-centric and multi-disciplinary research work.Moreover, its success is a matter of careful design; thus, developers play a key role.The rLDCP R package was designed to facilitate the development of new applications.This demo introduces the use of rLDCP, for both beginners and advance developers, in practical use cases.

Quality evaluation is a fundamental problem in the field of linguistic description of data. In this work, we analyze the concept of quality and study different approaches to measure quality. Although most of the approaches considered focused on time series data, that are one of the most frequent datasets in real application domains, they can be used for quality assessment of linguistic descriptions generated for any type of data.

Due to imprecise or incomplete information, ordinary reasoning often only allows us to reach conjectures rather than logical consequences. In previous papers, conjectures from a set of premises were mathematically characterized and classified into logical consequences, hypothesis, and speculations. The goal of this paper is to introduce a numerical value, or weight, providing a measure of up to which extent a conjecture is supported by the premises. Several examples are shown.

Virtual agents could be a solution for companies that deal with maintaining personalized contact with high volumes of customers. Nevertheless these virtual agents must show certain emotional empathy when interacting with each different customer in order to enrich the communication of information. In previous research, we have developed several instances of computational systems able to generate relevant linguistic descriptions of data. In this paper, in order to increase the effectiveness and acceptance of the generated texts, we improve the advice messages to customers by including certain emotional content. We describe a project together with an energy supplier company where we have developed a prototype that demonstrates the viability of our approach.

This paper describes the use of the granular linguistic model of a phenomenon (GLMP) to model the assessment of integer arithmetic learning and implement the automated generation of a formative criterion-based assessment report in natural language, as well as a numerical grade. The report is generated based exclusively on the objective scores automatically generated by Moodle quizzes. GLMP is based on fuzzy logic and the computational theory of perceptions, and uses inference systems based on linguistic rules.

In this paper, we present a contribution to solve the problem of organizing the representation of experience in computational systems which are able to generate relevant linguistic descriptions of data for specific users and contexts. We claim, that, typically, the expert knowledge modeled in these systems is limited to one of the dimensions of the meaning of natural language. Here, we model the experience base distinguishing among three types of meaning, namely, Ideational meaning concerning with the technical, impersonal description of the specific phenomenon; Interpersonal meaning concerning with the role of the partners involved in the communication process and Textual meaning concerning with the contribution to the meaning of the specific realization with natural language of both previous types of meaning. In order to organize these types of meaning (also called components of experience base) in a practical computational representation, we have built an ontology that will help designers to model their experience in the application domain. Using this ontology, the computational system is able of identifying the most suitable linguistic descriptions for describing the input data. Our approach is presented with the support of a practical example in the domain of the maintenance of comfort in a room.

Human gait modeling consists of studying the biomechanics of this human movement. Its importance lies in the fact that its analysis can help in the diagnosis of walking and movement disorders or rehabilitation programs, among other medical situations. Fuzzy finite state machines can be used to model the temporal evolution of this type of phenomenon. Nevertheless, the definition of details of the model in each particular case is a complex task for experts. In this paper, we present an automatic method to learn the model parameters that are based on the hybridization of fuzzy finite state machines and genetic algorithms leading to genetic fuzzy finite state machines. This new genetic fuzzy system automatically learns the fuzzy rules and membership functions of the fuzzy finite state machine, while an expert defines the possible states and allowed transitions. Our final goal is to obtain a specific model for each person's gait in such a way that it can generalize well with different gaits of the same person. The obtained model must become an accurate and human friendly linguistic description of this phenomenon, with the capability to identify the relevant phases of the process. A complete experimentation is developed to test the performance of the new proposal when dealing with datasets of 20 different people, comprising a detailed analysis of results, which shows the advantages of our proposal in comparison with some other classical and computational intelligence techniques.

The amount of data provided by computers about objects in our environment increases. Nevertheless, the ability to extract relevant and understandable knowledge from this information remains limited. On-Line Analytical Processing (OLAP) is a well known paradigm used to help users to navigate by the information stored in databases. In the research line of Computational Theory of Perceptions, we have created the Granular Linguistic Model of a Phenomenon. It is a data structure that allows computational systems to generate linguistic descriptions of input data. In this paper, we explore the possibilities of using OLAP to navigate in this structure of information. Inspired in the way humans use NL, we adapt the typical operations in OLAP, namely, drilling, rolling and slicing/dicing, to navigate by hierarchical granular structures of fuzzy perceptions. The long term aim is to create a new type of human computer interface that will assist users in the analysis of the likely huge amount of available information about relevant phenomena. Obtained results show the viability of this approach including a practical demonstration of concept.

Satellites in the orbit of Mars planet provide thousands of images of its surface. Typically these images are analyzed by experts that select relevant features and generate textual reports containing the result of their observations. Nevertheless, the database of images has grown up and currently this procedure is not effective enough. As a result of our research on Computational Theory of Perceptions, we describe a computational application able to generate simple linguistic descriptions of circular structures on the Mars' surface. We include several examples and analysis of the obtained results.

There is a clear need for computational systems able to produce automatic linguistic descriptions of data about phenomena. Linguistic summarization represents an attempt to describe by means of linguistic expressions patterns emerging in data. Generating data summaries can be seen as a very complex and non-trivial data mining task. Language is the unique meta-language to describe and understand various complex phenomena and humans use it for multi-modal data fusion in their brains. Our work is devoted to advance towards the development of a framework for the fusion of heterogenous information using linguistic descriptions based on NL. This application paper deals with the development of a computational system capable of automatically generating linguistic descriptions of driving activity from vehicle simulator data. Based on Fuzzy Logic, and as a contribution towards the development of Computational Theory of Perceptions, the proposed solution is part of our research on granular linguistic models of phenomena. We will generate a set of valid sentences describing a phenomenon through a granular linguistic model of a phenomenon. Then a relevancy analysis will be performed, in order to choose the most suitable sequence of clauses to each specific input data. We have used real time-series data from a vehicle simulator to evaluate the performance of our approach.

We describe how to use a Granular Linguistic Model of a Phenomenon (GLMP) to assess e-learning processes. We apply this technique to evaluate algorithm learning using the GRAPHs learning environment.

This work presents a general framework for people indoor activity recognition. Firstly, a Wireless Fidelity (WiFi) localization system implemented as a Fuzzy Rule-based Classifier (FRBC) is used to obtain an approximate position at the level of discrete zones (office, corridor, meeting room, etc). Secondly, a Fuzzy Finite State Machine (FFSM) is used for human body posture recognition (seated, standing upright or walking). Finally, another FFSM combines both WiFi localization and posture recognition to obtain a robust, reliable, and easily understandable activity recognition system (working in the desk room, crossing the corridor, having a meeting, etc). Each user carries with a personal digital agenda (PDA) or smart-phone equipped with a WiFi interface for localization task and accelerometers for posture recognition. Our approach does not require adding new hardware to the experimental environment. It relies on the WiFi access points (APs) widely available in most public and private buildings. We include a practical experimentation where good results were achieved.

Currently, there are quite a number of computational systems that attend to humans automatically, e.g., using natural language. However, the interaction with these machines is still too artificial. Usually, these machines are insensitive to the emotional content being expressed by the communication partner, as well as incapable of expressing emotional content. This paper presents the architecture of a computational system to simulate emotional states. The simulator is split up in three modules that provide to the designer a great number of possibilities to model different aspects of a simulated emotional personality. The model of transition between different emotions is based on a Fuzzy Finite States Machine. This allows the system to be in several emotional states simultaneously with certain degree. As a demonstration of concept, we present a prototype where the emotional content is based on the environmental conditions of light and temperature.

The development of Internet technologies makes it possible to obtain data in near real time about the financial state of companies. Moreover, tools such as XBRL have been developed to deal with the automatic generation of business reports. However the available tools are not suitable to support the current tendency towards the so called, Continuous Reporting. Here, for a specific purpose, the wealth of available information should be summarized in short reports where the relevant information is remarked while the irrelevant remains hidden. Using a combination of Zadeh's Computational Theory of Perceptions and Semantic Web techniques, we propose a first approach to a computational framework to generate short reports that consist of few but meaningful Natural Language sentences. These sentences are designed to solve the user needs in a specific context of meaning defined with the help of an ontology. A practical demonstration of the computational framework is included.

The linguistic description of a physical phenomenon is a summary of the available information where certain relevant aspects are remarked while other irrelevant aspects remain hidden. This paper deals with the development of computational systems capable to generate linguistic descriptions from images captured by a video camera. The problem of linguistically labeling images in a database is a challenge where still much work remains to be done. In this paper, we contribute to this field using a model of the observed phenomenon that allows us to interpret the content of images. We build the model by combining techniques from Computer Vision with ideas from the Zadeh's Computational Theory of Perceptions. We include a practical application consisting of a computational system capable to provide a linguistic description of the behavior of traffic in a roundabout.

In this paper we present a new method to analyze quasi-periodic signals. This method consists of modeling these signals using a Fuzzy Finite State Machine as a particular case of a Linguistic Fuzzy Model of a dynamical system. This model defines states and transitions using a priori knowledge of the signal we want to analyze. The model is represented using fuzzy rules that make it easily comprehensible. We include a practical example analyzing quasi-periodic signals of acceleration measured during the human gait cycle where good results were achieved.

In this paper a method is described to automatically generate linguistic summaries of real world time series data provided by a utility company. The methodology involves the following main steps: partitioning of time series into fuzzy intervals, calculation of statistical indicators for the partitions, generation of summarising sentences and determination of the truth-fullness of these sentences, and finally selection of relevant sentences from the generated set of sentences.

The idea of linguistic variable in fuzzy logic allows the representation of the subjective meaning of a measure at a given instant of time. This paper explores possibilities of extending the concept of linguistic term to create the linguistic description of the temporal evolution of a signal. We focus our effort in signals that, having potentially an infinite duration, present more or less repetitive patterns. We introduce the concept of fuzzy alphabet as an extension of the concept of alphabet in automata theory. The fuzzification of the sequence of sampled inputs is used to create an initial description of the signal. Then we use a fuzzy finite state machine transducer to create a linguistic description of the signal temporal evolution made with concepts of a higher level of abstraction. A simple experiment monitoring the accelerations produced while driving an automobile has delivered promising results: we have automatically generated a linguistic report describing the perceptions obtained in the car caused by the physical characteristics of the road traveled and the driver's style of driving.

Manipulability is a measure of performance for mechanical manipulators. Intuitively, Manipulability at a point of a manipulator workspace measures how easy is to reach the neighbour points. Currently, this value is obtained using an algebraic formula based on the Jacobian of the manipulator that is not very intuitive. Also, this formulation defines Manipulability as a continuous function of the workspace coordinates; however, due to natural restrictions for the mechanical implementation, actual devices include physical limits where this algebraic formulation produces incoherent Manipulability values. This work presents a new formulation based on fuzzy rules that provides a more intuitive definition of Manipulability. This formulation solves the boundary problem, while remains consistent with the algebraic one in those spatial zones where this works correctly. In order to demonstrate the advantage of this new approach, the Manipulability of a well known commercial haptic device has been studied comparing both formulations