160 research outputs found
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
Towards an architectural framework for intelligent virtual agents using probabilistic programming
We present a new framework called KorraAI for conceiving and building
embodied conversational agents (ECAs). Our framework models ECAs' behavior
considering contextual information, for example, about environment and
interaction time, and uncertain information provided by the human interaction
partner. Moreover, agents built with KorraAI can show proactive behavior, as
they can initiate interactions with human partners. For these purposes, KorraAI
exploits probabilistic programming. Probabilistic models in KorraAI are used to
model its behavior and interactions with the user. They enable adaptation to
the user's preferences and a certain degree of indeterminism in the ECAs to
achieve more natural behavior. Human-like internal states, such as moods,
preferences, and emotions (e.g., surprise), can be modeled in KorraAI with
distributions and Bayesian networks. These models can evolve over time, even
without interaction with the user. ECA models are implemented as plugins and
share a common interface. This enables ECA designers to focus more on the
character they are modeling and less on the technical details, as well as to
store and exchange ECA models. Several applications of KorraAI ECAs are
possible, such as virtual sales agents, customer service agents, virtual
companions, entertainers, or tutors
Animacy, agency and causality in Korean voice and diathesis: A cognitive-semiotic usage-based perspective.
Adopting a usage-based construction grammar approach, the thesis proposes a radically revised account of the Korean voice system with two main oppositions: ACTIVE ~ INACTIVE and ENDOACTIVE ~ EXOACTIVE. These are marked on the verb, but voice categories are primarily semantic and equally basic. The attendant clause structures are only weakly determined by the predicate's voice status and instead inherited from a systemically independent diathesis system. The thesis first demonstrates the inherent Indoeuropean biases and asymmetries in the Standard Voice Model that underlies the traditional active-passive-causative account. It then turns to the Korean system and its central features: inchoative-passive conflation in a single INACTIVE voice, voice-marking paradigm proliferation with equipollency and complex correspondences to voice categories, causative and passive usage of unmarked basic verbs, and animacy, agency and causality differentiation in the diathesis system. The thesis then details animacy-related effects in the oblique argument system. The choice of Inanimate and Animate Locational patterns is conditioned not by ontological animacy but by utterance-specific situational animacy and agency. And the variety of Korean agent-phrase-like patterns reflects differentiations along the situational animacy, agency and causality dimensions that correlate with animacy and agency constraints on diathesis selection. Finally, the thesis investigates the lexical spread and usage of 'morphological' and 'analytic passive' verbs. It shows that inchoative usage and inchoative-passive ambivalences are so widespread that they must be considered a central feature of a single INACTIVE category. And animacy and agency differentiation drives a systemic alignment of non-interpersonal actions, weakly agentive situations and inanimate causation with spontaneous situations. In conclusion, the thesis proposes that inchoative-passive conflation may be due to the fact that the ANIMATE ~ INANIMATE and AGENTIVE ~ NON-AGENTIVE dichotomies push the organisation and frequency distribution in the Korean diathesis system towards alignment of non-agentive causation with spontaneous situation-dynamics
An outline of English lexicology
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Language Matters in Predicting Meme Success: A Feedforward Connectionist Network
The challenge of predicting meme success has gained attention from researchers, largely due to the increased availability of social media data. Many models focus on structural features of online social networks as predictors of meme success. The current work takes a different approach, predicting meme success from linguistic features.We propose predictive power is gained by grounding memes in theories of working memory, emotion, memory, and psycholinguistics. The linguistic content of several memes were analyzed with linguistic analysis tools. These features were then trained with a multilayer supervised backpropagation network. A set of new memes was used to test the generalization of the network. Results indicated the network was able to generalize the linguistic features in order to predict success at greater than chance levels (80% accuracy). Linguistic features appear to be enough to predict meme transmission success without any information about social network structure
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