Summary form only given. Information granules and their computing, which give rise to the framework of Granular Computing, deliver interesting opportunities to endow processing with an important facet of human-centricity. This facet directly implies that the underlying processing supports non-numeric data inherently associated with the variable perception of humans and generates results being seamlessly comprehended by users. Given that systems, which are quite commonly become distributed and hierarchical, managing granular information in hierarchical and distributed architectures is of growing interest, especially when invoking mechanisms of knowledge generation and knowledge sharing. The outstanding feature of human centricity of Granular Computing along with essential fuzzy set-based constructs constitutes the crux of our study. We elaborate on some new directions of knowledge elicitation and quantification realized in the setting of fuzzy sets. With this regard, we concentrate on an idea of knowledge-based clustering, which aims at the seamless realization of the data-expertise design of information granules. It is also emphasized that collaboration and reconciliation of locally available knowledge give rise to the concept of higher type information granules. The other interesting directions enhancing human centricity of computing with fuzzy sets, deals with non-numeric, semi-qualitative characterization of information granules (fuzzy sets) as well as inherent evolving capabilities of associated human-centric systems. We discuss a suite of algorithms facilitating a qualitative assessment of fuzzy sets, formulate a series of associated optimization tasks guided by well-formulated performance indexes, and discuss the underlying essence of the resulting solutions.
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