A Survey of Fuzzy Systems Software: Taxonomy, Current Research Trends, and Prospects

Fuzzy systems have been used widely thanks to their ability to successfully solve a wide range of problems in different application fields. However, their replication and application require a high level of knowledge and experience. Furthermore, few researchers publish the software and/or source code associated with their proposals, which is a major obstacle to scientific progress in other disciplines and in industry. In recent years, most fuzzy system software has been developed in order to facilitate the use of fuzzy systems. Some software is commercially distributed, but most software is available as free and open-source software, reducing such obstacles and providing many advantages: quicker detection of errors, innovative applications, faster adoption of fuzzy systems, etc. In this paper, we present an overview of freely available and open-source fuzzy systems software in order to provide a well-established framework that helps researchers to find existing proposals easily and to develop well-founded future work. To accomplish this, we propose a two-level taxonomy, and we describe the main contributions related to each field. Moreover, we provide a snapshot of the status of the publications in this field according to the ISI Web of Knowledge. Finally, some considerations regarding recent trends and potential research directions are presentedThis work was supported in part by the Spanish Ministry of Economy and Competitiveness under Grants TIN2014-56633-C3-3-R and TIN2014-57251-P, the Andalusian Government under Grants P10-TIC-6858 and P11-TIC-7765, and the GENIL program of the CEI BioTIC GRANADA under Grant PYR-2014-2S

A half century of progress towards a unified neural theory of mind and brain with applications to autonomous adaptive agents and mental disorders

Invited article for the book Artificial Intelligence in the Age of Neural Networks and Brain Computing R. Kozma, C. Alippi, Y. Choe, and F. C. Morabito, Eds. Cambridge, MA: Academic PressThis article surveys some of the main design principles, mechanisms, circuits, and architectures that have been discovered during a half century of systematic research aimed at developing a unified theory that links mind and brain, and shows how psychological functions arise as emergent properties of brain mechanisms. The article describes a theoretical method that has enabled such a theory to be developed in stages by carrying out a kind of conceptual evolution. It also describes revolutionary computational paradigms like Complementary Computing and Laminar Computing that constrain the kind of unified theory that can describe the autonomous adaptive intelligence that emerges from advanced brains. Adaptive Resonance Theory, or ART, is one of the core models that has been discovered in this way. ART proposes how advanced brains learn to attend, recognize, and predict objects and events in a changing world that is filled with unexpected events. ART is not, however, a “theory of everything” if only because, due to Complementary Computing, different matching and learning laws tend to support perception and cognition on the one hand, and spatial representation and action on the other. The article mentions why a theory of this kind may be useful in the design of autonomous adaptive agents in engineering and technology. It also notes how the theory has led to new mechanistic insights about mental disorders such as autism, medial temporal amnesia, Alzheimer’s disease, and schizophrenia, along with mechanistically informed proposals about how their symptoms may be ameliorated

Kirjoitetut tunnisteet peruskoulun luonnontieteiden diagrammeissa: kielelliset rakenteet ja diskurssisuhteet

Communication, by nature, is multimodal: it uses various forms (modes) of communication, such as spoken language, written language, illustrations, and many others to create meaning. Multimodality research is the study of communicative situations that rely on such various modes and their combinations. One form of multimodality very commonly seen in everyday life comes in diagrams, which can convey very complex concepts by combining visual expressive resources (such as illustrations or photographs), written language, and diagrammatic elements such as lines and arrows. The primary aim of my thesis is to establish whether the linguistic structures of written labels – that is, textual elements – in diagrams can inform the decomposition of visual expressive resources. Put simply, I seek to find if said visual elements can more accurately be divided into further, more granular units in accordance with linguistic patterns in their accompanying textual elements. To answer my main research question, I posit three sub-questions. First, if certain diagram types (macro-structures), such as tables, cycles, or cross-sections co-occur with specific linguistic patterns; second, if different rhetorical functions found in diagrams employ different structures in their written labels as well; and third, if these functions are signaled by other means in tandem with written language. Answering these questions can help in designing future multimodal corpora and their annotation schemata, increasing annotation accuracy and possibilities for their processing. The theoretical framework used in this thesis synthesizes theories from multimodality theory, discourse studies, and diagrams research. I approach diagrams from the perspective of multimodality, highlighting them as discursive artefacts. This is enabled by the diagrammatic mode, which establishes how discourse semantics can function in the context of diagrams and how their interpretation is dynamic; that is, each element or combination of multiple elements can in turn contextualize or be a part of other elements and their combinations on a different scale. I also discuss the discourse-semantic concepts of coherence and cohesion as they relate to multimodal artefacts: different elements, even if not linguistic, can combine to create semantically meaningful connections between constituents in such an artefact. To exemplify this, I also apply Rhetorical Structure Theory (RST), which seeks to formalize how units of discourse are interconnected and work towards a shared communicative goal. RST employs rhetorical relations such as ELABORATION and IDENTIFICATION to describe how units and their combinations relate to other parts of a text (or other communicative whole). The data I use consists of two interrelated and complementary multimodal corpora: AI2D and AI2D-RST. AI2D is a collection of primary-school textbook science diagrams, annotated for blobs (visual expressive resources), labels, and diagrammatic elements, created for question-answering purposes. It also contains the linguistic data in each of the corpus’s diagrams. AI2D-RST contains a subset of the diagrams in AI2D, expanding them with additional annotation layers for information on macro-structures, visual connectivity, and RST, describing each element’s rhetorical relation in the diagram. I computationally find each rhetorical relation containing a label in AI2D-RST, noting its type, the type of the diagram it appears in, and fetching the labels’ linguistic content from AI2D. I then process each label’s contents with spaCy, a library for natural language processing, for linguistic elements such as phrase types, part-of-speech patterns, and average word counts. The output contains data on each label’s rhetorical relation, the possible macro-structure it is contained in, and said linguistic structures. The results show that there are indeed some differences in how distinct rhetorical relations and macro-groups use language: for example, cycles contain the most verb phrases and highest word count, indicating the use of written language to explicate certain processes to viewers. As linguistic patterns differ across these classes and are contextualized by surrounding diagrammatic elements, approaching diagrams from a discursive standpoint may be beneficial for future empirical multimodality research as well as designing annotation schemata to be more intuitive for annotators. With larger datasets and further research, precise sets of rules containing linguistic structures and layout information may be developed to increase accuracy in probability-based computational analysis of diagrams

Prosody and Kinesics Based Co-analysis Towards Continuous Gesture Recognition

The aim of this study is to develop a multimodal co-analysis framework for continuous gesture recognition by exploiting prosodic and kinesics manifestation of natural communication. Using this framework, a co-analysis pattern between correlating components is obtained. The co-analysis pattern is clustered using K-means clustering to determine how well the pattern distinguishes the gestures. Features of the proposed approach that differentiate it from the other models are its less susceptibility to idiosyncrasies, its scalability, and simplicity. The experiment was performed on Multimodal Annotated Gesture Corpus (MAGEC) that we created for research on understanding non-verbal communication community, particularly the gestures

Fuzzy Logic in Decision Support: Methods, Applications and Future Trends

During the last decades, the art and science of fuzzy logic have witnessed significant developments and have found applications in many active areas, such as pattern recognition, classification, control systems, etc. A lot of research has demonstrated the ability of fuzzy logic in dealing with vague and uncertain linguistic information. For the purpose of representing human perception, fuzzy logic has been employed as an effective tool in intelligent decision making. Due to the emergence of various studies on fuzzy logic-based decision-making methods, it is necessary to make a comprehensive overview of published papers in this field and their applications. This paper covers a wide range of both theoretical and practical applications of fuzzy logic in decision making. It has been grouped into five parts: to explain the role of fuzzy logic in decision making, we first present some basic ideas underlying different types of fuzzy logic and the structure of the fuzzy logic system. Then, we make a review of evaluation methods, prediction methods, decision support algorithms, group decision-making methods based on fuzzy logic. Applications of these methods are further reviewed. Finally, some challenges and future trends are given from different perspectives. This paper illustrates that the combination of fuzzy logic and decision making method has an extensive research prospect. It can help researchers to identify the frontiers of fuzzy logic in the field of decision making

Pysafe: An interdisciplinary approach to interface design

The purpose of this project is two fold : examine the prevailing wisdom of the interaction design field over the past forty years and look at emerging perspectives, then apply the established principles along with new trends in a practical application. Includes source code

PRIVAFRAME: A Frame-Based Knowledge Graph for Sensitive Personal Data

The pervasiveness of dialogue systems and virtual conversation applications raises an important theme: the potential of sharing sensitive information, and the consequent need for protection. To guarantee the subject’s right to privacy, and avoid the leakage of private content, it is important to treat sensitive information. However, any treatment requires firstly to identify sensitive text, and appropriate techniques to do it automatically. The Sensitive Information Detection (SID) task has been explored in the literature in different domains and languages, but there is no common benchmark. Current approaches are mostly based on artificial neural networks (ANN) or transformers based on them. Our research focuses on identifying categories of personal data in informal English sentences, by adopting a new logical-symbolic approach, and eventually hybridising it with ANN models. We present a frame-based knowledge graph built for personal data categories defined in the Data Privacy Vocabulary (DPV). The knowledge graph is designed through the logical composition of already existing frames, and has been evaluated as background knowledge for a SID system against a labeled sensitive information dataset. The accuracy of PRIVAFRAME reached 78%. By comparison, a transformer-based model achieved 12% lower performance on the same dataset. The top-down logical-symbolic frame-based model allows a granular analysis, and does not require a training dataset. These advantages lead us to use it as a layer in a hybrid model, where the logical SID is combined with an ANNs SID tested in a previous study by the authors

A Systematic Literature Review of the Use of Computational Text Analysis Methods in Intimate Partner Violence Research

Purpose: Computational text mining methods are proposed as a useful methodological innovation in Intimate Partner Violence (IPV) research. Text mining can offer researchers access to existing or new datasets, sourced from social media or from IPV-related organisations, that would be too large to analyse manually. This article aims to give an overview of current work applying text mining methodologies in the study of IPV, as a starting point for researchers wanting to use such methods in their own work. Methods This article reports the results of a systematic review of academic research using computational text mining to research IPV. A review protocol was developed according to PRISMA guidelines, and a literature search of 8 databases was conducted, identifying 22 unique studies that were included in the review. Results: The included studies cover a wide range of methodologies and outcomes. Supervised and unsupervised approaches are represented, including rule-based classification (n = 3), traditional Machine Learning (n = 8), Deep Learning (n = 6) and topic modelling (n = 4) methods. Datasets are mostly sourced from social media (n = 15), with other data being sourced from police forces (n = 3), health or social care providers (n = 3), or litigation texts (n = 1). Evaluation methods mostly used a held-out, labelled test set, or k-fold Cross Validation, with Accuracy and F1 metrics reported. Only a few studies commented on the ethics of computational IPV research. Conclusions: Text mining methodologies offer promising data collection and analysis techniques for IPV research. Future work in this space must consider ethical implications of computational approaches