Computing with words

Computing with words enhances system resilience by using natural language to model and operate linguistic uncertainty, allowing dependable operation in real-world environments. Moving from simply measuring to understanding shifts the focus from data (how much) to meaning (what is). This transition allows to interpret the world rather than just record it. By understanding this shift, the systems gain the resilience to adapt to disruptions instead of simply reacting to fixed rules.
Computing with words represents the shift in computational intelligence, transitioning from manipulation of numbers to manipulation of perceptions expressed in natural language. We utilize it to address the inherent vagueness and subjectivity of our human conversations, where words serve as granules of information characterized by our elastic constraints. With our projects, we move from mere probabilities to viable possibilities, hence enriching summative structures with human-centric, intuitive ones.
  • Computing with Words and Perceptions

    Computing with Words and Perceptions is a relatively new method of computing for computer systems. As words occur in the “natural language” and not in the numerical format, they are imprecise and cannot be processed by traditional systems. Because of this, a lot of relevant information gets lost. In order to change that, the team of the Human-ist examines linguistic computing and tries to create intelligent and smart computer systems. With the help of Computing with Words and Perceptions, new ways of computing should be developed which make the transformation of imprecise information into computer-readable data possible so that they can subsequently be applied in computations. It should be possible to include all necessary information in decision-making processes. It is thus the aim to make today’s processing of information more efficient such that the interaction between humans and computer systems can be optimized.

    Human-IST Collaborators: Edy Portmann, Sara D’Onofrio

    External Collaborators: Swiss Post

    Start date: September 2017

  • An explainable framework for predicting lung cancer genetic mutations using fuzzy few shot guessing

    Its research proposes a framework for predicting and explaining lung cancer prognosis and genetic mutations using a few high-quality histologic images and a fuzzy few shot learning algorithm based on fuzzy clustering methods. The framework includes a Web Ontology Language (OWL 2.0) to create a tree of fuzzy and crisp relationships between the classes of lung cancers, histologic pattern growth, and genetic mutations. The results are explained to specialized physicians. The framework is benchmarked with state-of-the-art solutions like standard few shot learning and deep learning, comparing them in terms of images required for training, accuracy and speed of prediction, extrapolation to genetic mutations, and explainability of the results.

    Human-IST collaborators: Servio Lima, Luis Terán

    Partners/ External collaborators (companies): SOLCA Ecuador, NVIDIA

    Publications:

    ABSTRACT published 2019: Explainable neuro-fuzzy recurrent neural network to predict colo-rectal cancer with different timeframe data (EUSFLAT Chequia 2019) PAPER published 2020: A Proposal for an Explainable Fuzzy-based Deep Learning System for Skin Cancer Prediction (ICEDEG Argentina APRIL 2020)

    WORKING PAPERS:

    • Fuzzy membership as an alternative method to similarity functions applied to few shot learning
    • An explainable fuzzy ontology web language applied to lung cancer histologic patterns
  • Neuro-fuzzy explainable systems for medicine

    One of the reasons why deep neural networks haven´t been adopted pervasively in medicine is the lack of explainability that helps physicians to know why an algorithm yielded a prediction. It is well known that deep learning relies on data to learn, so if data is biased or with errors, prediction will be also with errors. Moreover, modern attacks aimed towards deep neural networks make these systems more unreliable. This is why the field of medicine needs explainable systems that help physicians determine that the reasons of the predictions were correct. Neuro-fuzzy systems are based on fuzzy logic for interpreting the predictions of neural networks using fuzzy rules. These rules can be converted to natural language by means of several techniques. Both, the fuzzy rules and the natural language can be analyzed by a human who can use his expert criteria to decide if those rules or natural language explanations are correct. A good consequence of this, is that the whole system can be maintained and corrected by expert criteria, while preserving the accuracy of deep neural systems.

    Human-IST collaborators: Servio Lima, Luis Terán, Edy Portmann

    Partners/ External collaborators (companies): Doctor Online

  • Fuzzy explainable self-deep learning recommender algorithms for social networks

    In mathematical modelling we come across two inconveniences. The first is caused by the excessive complexity of the situation being modeled, which leads to very complex models to be used in practice or NP-Complete problems, where there is no known way to find a solution quickly. The second inconvenience consists of the indeterminacy, which is represented by vagueness of its semantics and our capability to work with vague notions. Fuzzy mathematics applied to machine learning in the context of explainable systems for recommender systems, can bring a better approach to model mathematically complex situations in a more human understandable way, reducing the complexity and leading to a better understanding of the reasoning behind the algorithm decisions.

    This project aims to model mathematically, using our unique Fuzzy Recommender - Machine Learning approach, the human interaction in different social networks under the Beekeeper platform across several industries. Where those models are tolerant of imprecision, uncertainty, partial truth and approximation.

    Human-IST collaborators: Jose Mancera, Luis Terán, Edy Portmann
    Partners/ External collaborators (companies): Beekeeper