arose as a synthesis of insights into human-centred information processing by Zadeh in the late ’90s and the Granular Computing name was coined, at this early stage, by T.Y Lin. Although the name is now in widespread use, or perhaps because of it, there are calls for a clarification of the distinctiveness of Granular Computing against the background of other human-centred information processing paradigms. This study examines the basic motivation for information granulation and casts Granular Computing as a structured combination of algorithmic and non-algorithmic information processing that mimics human, intelligent synthesis of knowledge from information.

Services for mobile and pervasive computing should extensively exploit contextual information both to adapt to user needs and to enable autonomic behavior. This raises the problem of how to represent, organize, aggregate, and make available such data to services so as to have it become meaningful and usable knowledge. In this paper, we identify the key software engineering challenges introduced by the need of accessing and exploiting huge amount of heterogeneous contextual information. Following, we survey the relevant proposals in the area of context-aware pervasive computing, data mining and granular computing discussing their potentials and limitations with regard to their adoption in the development of context-aware pervasive services. On these bases, we propose the W4 model for contextual data and show how it can represent a simple yet effective model to enable flexible general-purpose management of contextual knowledge by pervasive services. A summarizing discussion and the identification of current limitations and open research directions conclude the paper.

This paper presents an integrated image fusion and match score fusion of multispectral face images. The fusion of visible and long wave infrared face images is performed using 2ν-Granular SVM which uses multiple SVMs to learn both the local and global properties of the multispectral face images at different granularity levels and resolution. The 2ν-GSVM performs accurate classification which is subsequently used to dynamically compute the weights of visible and infrared images for generating a fused face image. 2D log polar Gabor transform and local binary pattern feature extraction algorithms are applied to the fused face image to extract global and local facial features respectively. The corresponding match scores are fused using Dezert Smarandache theory of fusion which is based on plausible and paradoxical reasoning. The efficacy of the proposed algorithm is validated using the Notre Dame and Equinox databases and is compared with existing statistical, learning, and evidence theory based fusion algorithms. Key words:

Abstract. Reading and literature exploration are important tasks of scientific research. However, conventional retrieval systems provide limited support for these tasks by concentrating on identifying relevant materials. New generation systems should provide additional support functionality by focusing on analyzing and organizing the retrieved materials. A framework of literature exploration support systems is proposed. Techniques of granular computing are used to construct granular knowledge structures from the contents, structures, and usages of scientific documents. The granular knowledge structures provide a high level understanding of scientific literature and hints regarding what has been done and what needs to be done. As a demonstration, we examine granular knowledge structures obtained from an analysis of papers from two rough sets related conferences.

Granular computing, as an emerging research field, provides a conceptual framework for studying many issues in data mining. This paper examines some of those issues, including data and knowledge representation and processing. It is demonstrated that one of the fundamental tasks of data mining is searching for the right level of granularity in data and knowledge representation. 1.

While many data mining models concentrate on automation and efficiency, interactive data mining models focus on adaptive and effective communications between human users and computer systems. User views, preferences, strategies and judgements play the most important roles in human-machine interactivities, guide the selection of target knowledge representations, operations, and measurements. Practically, user views, preferences and judgements also decide strategies of abnormal situation handling, and explanations of mined patterns. In this paper, we discuss these fundamental issues. 1.

granule representation, granule relation, literature analysis. Knowledge plays a central role in human and artificial intelligence. One of the key characteristics of knowledge is its structured organization. Knowledge can be and should be presented in multiple levels and multiple views to meet people’s needs in different levels of granularities and from different perspectives. In this paper, we stand on the view point of granular computing and provide our understanding on multi-level and multi-view of knowledge through granular knowledge structures (GKS). Representation of granular knowledge structures, operations for building granular knowledge structures and how to use them are investigated. As an illustration, we provide some examples through results from an analysis of proceeding papers. Results show that granular knowledge structures could help users get better understanding of the knowledge source from set theoretical, logical and visual point of views. One may consider using them to meet specific needs or solve certain kinds of problems. 1

Abstract. Considering the time constraints and Web scale data, it is impossible to achieve absolutely complete reasoning results. Plus, the same results may not meet the diversity of user needs since their expectations may differ a lot. One of the major solutions for this problem is to unify search and reasoning. From the perspective of granularity, this paper provides various strategies of unifying search and reasoning for effective problem solving on the Web. We bring the strategies of multilevel, multiperspective, starting point from human problem solving to Web scale reasoning to satisfy a wide variety of user needs and to remove the scalability barriers. Concrete methods such as network statistics based data selection and ontology supervised hierarchical reasoning are applied to these strategies. The experimental results based on an RDF dataset shows that the proposed strategies are potentially effective. 1

Abstract—This paper formulates an evidence-theoretic multimodal unification approach using belief functions that takes into account the variability in biometric image characteristics. While processing non-ideal images the variation in the quality of features at different levels of abstraction may cause individual classifiers to generate conflicting genuine-impostor decisions. Existing fusion approaches are non-adaptive and do not always guarantee optimum performance improvements. We propose a contextual unification framework to dynamically select the most appropriate evidence-theoretic fusion algorithm for a given scenario. In the first approach, the unification framework uses deterministic rules to select the most appropriate fusion algorithm; while in the second approach, the framework intelligently learns from the input evidences using a 2ν-granular support vector machine. The effectiveness of our unification approach is experimentally validated by fusing match scores from level-2 and level-3 fingerprint features. Compared to existing fusion algorithms, the proposed unification approach is computationally efficient, and the verification accuracy is not compromised even when conflicting decisions are encountered.

Abstract — Granular Computing is a paradigm destined to study how to compute with granules of knowledge that are collective objects formed from individual objects by means of a similarity measure. The idea of granulation was put forth by Lotfi Zadeh: granulation is inculcated in fuzzy set theory by the very definition of a fuzzy set and inverse values of fuzzy membership functions are elementary forms of granules. Similarly, rough sets admit granules defined naturally as classes of indiscernibility relations; the search for more flexible granules has led to granules based on blocks (Grzymala–Busse), templates (H.S.Nguyen), rough inclusions (Polkowski, Skowron), and tolerance or similarity relations, and more generally, binary relations (T.Y. Lin, Y. Y. Yao). Rough inclusions establish a form of similarity relations