Today, handheld devices can accommodate a large amount of different resources. Thus, a considerable effort is often required to mobile users in order to search for the resources suitable for the specific circumstance. Further, this effort rarely brings to a satisfactory result. To ease this work, resource recommenders have been proposed in the last years. Typically, the recommendation is based on recognizing the current situations of the users and suggesting them the appropriate resources for those situations. The recognition task is performed by exploiting contextual information and preferably without using any explicit input from the user. To this aim, we propose to adopt a collaborative scheme based on an emergent paradigm. The underlying idea is that simple individual actions can lead to an emergent collective behavior that represents an implicit form of contextual information. We show how this behavior can be extracted by using a multi-agent scheme, where agents do not directly communicate amongst themselves, but rather through the environment. The multi-agent scheme is structured into three levels of information processing. The first level is based on a stigmergic paradigm, in which marking agents leave marks in the environment in correspondence to the position of the user. The accumulation of such marks enables the second level, a fuzzy information granulation process, in which relevant events can emerge and are captured by means of event agents. Finally, in the third level, a fuzzy inference process, managed by situation agents, deduces the user situations from the underlying events. The proposed scheme is evaluated on a set of representative real scenarios related to meeting events. In all the scenarios, the collaborative situation-aware scheme promptly recognizes the correct situations, except for one case, thus proving its effectiveness

Situation awareness is a computing paradigm which allows applications to sense parameters in the environment, comprehend their meaning and project their status in the next future. In collaborative situation awareness, a challenging area in the field of Ambient Intelligence applications, situation patterns emerge from users collective behavior. In this paper we introduce a multi-agent system that exploits positioning information coming from mobile devices to detect the occurrence of user0 s situations related to social events. In the functional view of the system, the first level of information processing is managed by marking agents which leave marks in the environment in correspondence to the users0 positions. The accumulation of marks enables a stigmergic cooperation mechanism, generating short-term memory structures in the local environment. Information provided by such structures is granulated by event agents which associate a certainty degree with each event. Finally, an inference level, managed by situation agents, deduces user situations from the underlying events by exploiting fuzzy rules whose parameters are generated automatically by a neuro-fuzzy approach. Fuzziness allows the system to cope with the uncertainty of the events. In the architectural view of the system, we adopt semantic web standards to guarantee structural interoperability in an open application environment. The system has been tested on different real-world scenarios to show the effectiveness of the proposed approach.

The purpose of this paper is to develop a diagnostic tool that can analyze light microscope images of human oocytes and derive a description of the oocyte cytoplasm that is useful for quality assessment in assisted insemination. The proposed approach includes three main phases: 1) segmentation; 2) feature extraction; and 3) clustering. In the segmentation phase, a region of interest inside the cytoplasm is extracted through morphological operators and the Hough transform. In the second phase, regions that result from segmentation are processed through a multiresolution texture analysis to extract a set of features that describe different levels of cytoplasm granularity. To this aim, we evaluate some statistics in the Haar wavelet transform domain. Finally, the extracted features are used to cluster oocytes according to different levels of granularity. This approach is made by fuzzy clustering. Experimental results on a collection of microscope images of oocytes are reported to show the effectiveness of the proposed approach. In addition, comparison with alternative methods for feature extraction and clustering is performed.

The approach combining image analysis techniques and artificial neural networks is proposed here for automatic classification of mineral inclusions and pores in archaeological potsherds using optical digital images. Particularly, the automatic identification of quartz, calcareous aggregates and secondary porosity is considered. A collection of both plane and cross polarised light images acquired via a digital camera connected to optical microscopy in transmitted light is used. Images concern Holocene potsherds (8900 e4200 years BP) from Takarkori rock shelter archaeological site (SW Libya, Central Sahara). The adopted methodology involves different phases. Firstly, image segmentation is carried out to isolate regions corresponding to the interested mineral inclusions and pores. A segmentation procedure based on mathematical operators is customized for each type of inclusions and for pores. Secondly, numerical features are extracted from each segmented region, thus collecting data to perform automatic classification. A modular classifier is considered for classification, which is based on a combination of three two-layer feed-forward neural networks that are trained separately to recognise each class. Experimental results show that the created modular classifier provides high classification accuracy for both inclusions and pores. The classifier was finally applied absent the image analysis phase on new samples to show the effectiveness of the proposed methodology.

Conventional methods for measuring cardiovascular parameters use skin contact techniques requiring a measuring device to be worn by the user. To avoid discomfort of contact devices, camera-based techniques using photoplethysmography have been recently introduced. Nevertheless, these solutions are typically expensive and difficult to be used daily at home. In this work, we propose an innovative solution for monitoring cardiovascular parameters that is low cost and can be easily integrated within any common home environment. The proposed system is a contact-less device composed of a see-through mirror equipped with a camera that detects the person’s face and processes video frames using photoplethysmography in order to estimate the heart rate, the breath rate and the blood oxygen saturation. In addition, the color of lips is automatically detected via clustering-based color quantization. The estimated parameters are used to predict a risk of cardiovascular disease by means of fuzzy inference rules integrated in the mirror-based monitoring system. Comparing our system to a contact device in measuring vital parameters on still or slightly moving subjects, we achieve measurement errors that are within acceptable margins according to the literature. Moreover, in most cases, the response of the fuzzy rule-based system is comparable with that of the clinician in assessing a risk level of cardiovascular disease.

Granular computing is a problem solving paradigm based on information granules, which are conceptual entities derived through a granulation process. Solving a complex problem, via a granular computing approach, means splitting the problem into information granules and handling each granule as a whole. This leads to a multi-level view of information granulation, which permeates human reasoning and has a significant impact in any field involving both human-oriented and machine- oriented problem solving. In this chapter we examine a view of granular computing as a paradigm of human-inspired problem solving and information processing with multiple levels of granularity, with special focus on fuzzy information granulation. To support the importance of granulation with multiple levels, we present a multi-level approach for extracting well-defined and semantically sound fuzzy information granules from numerical data.

In this work we propose an approach based on shape clustering for image retrieval. Firstly, shapes of objects contained into images are represented by means of Fourier descriptors. Then, a fuzzy clustering process is applied to automatically discover a set of shape prototypes representative of a number of semantic categories. The adopted fuzzy clustering algorithm is equipped with a mechanism of partial supervision that enables identification of shape categories by taking advantage of some domain knowledge expressed in terms of a set of labeled shapes. Successively, the derived shape prototypes are exploited in order to retrieve shapes similar to a shape query submitted by a user. The suitability of the proposed approach is shown through an experimental comparison on a benchmark dataset in terms of retrieval accuracy.

Recommender systems are systems capable of assisting users by quickly providing them with relevant resources according to their interests or preferences. The efficacy of a recommender system is strictly connected with the possibility of creating meaningful user profiles, including information about user preferences, interests, goals, usage data and interactive behavior. In particular, analysis of user preferences is important to predict user behaviors and make appropriate recommendations. In this paper, we present a fuzzy framework to represent, learn and update user profiles. The representation of a user profile is based on a structured model of user cognitive states, including a competence profile, a preference profile and an acquaintance profile. The strategy for deriving and updating profiles is to record the sequence of accessed resources by each user, and to update preference profiles accordingly, so as to suggest similar resources at next user accesses. The adaption of the preference profile is performed continuously, but in earlier stages it is more sensitive to updates (plastic phase) while in later stages it is less sensitive (stable phase) to allow resource recommendation. Simulation results are reported to show the effectiveness of the proposed approach.

In Magnetic Resonance (MR) brain image analysis, segmentation is commonly used for detecting, measuring and analyzing the main anatomical structures of the brain and eventually identifying pathological regions. Brain image segmentation is of fundamental importance since it helps clinicians and researchers to concentrate on specific regions of the brain in order to analyze them. However, segmentation of brain images is a difficult task due to high similarities and correlations of intensity among different regions of the brain image. Among various methods proposed in the literature, clustering algorithms prove to be successful tools for image segmentation. In this paper, we present a framework for image segmentation that is devoted to support the expert in identifying different brain regions for further analysis. The framework includes different clustering methods to perform segmentation of MR images. Furthermore, it enables easy comparison of different segmentation results by providing a quantitative evaluation using an entropy-based measure as well as other measures commonly used to evaluate segmentation results. To show the potential of the framework, the implemented clustering methods are compared on simulated T1-weighted MR brain images from the Internet Brain Segmentation Repository (IBSR database) provided with ground truth segmentation.

In the era of the Web, there is urgent need for developing systems able to personalize the online experience of Web users on the basis of their needs. Web recommendation is a promising technology that attempts to predict the interests of Web users, by providing them with information and/or services that they need without explicitly asking for them. In this paper we propose NEWER, a usage-based Web recommendation system that exploits the potential of Computational Intelligence techniques to dynamically suggest interesting pages to users according to their preferences. NEWER employs a neuro-fuzzy approach in order to determine categories of users sharing similar interests and to discover a recommendation model as a set of fuzzy rules expressing the associations between user categories and relevances of pages. The discovered model is used by a online recommendation module to determine the list of links judged relevant for users. The results obtained on both synthetic and real-world data show that NEWER is effective for recommendation, leading to a quality of the generated recommendations comparable and often significantly better than those of other approaches employed for the comparison.

Revision control is a vital component in collaborative development of artifacts such as software code and multimedia. While revision control has been widely deployed for text files, very few attempts to control the versioning of binary files can be found in the literature. This can be inconvenient for graphics applications that use a significant amount of binary data, such as images, videos, meshes, and animations. Existing strategies such as storing whole files for individual revisions or simple binary deltas, respectively consume significant storage and obscure semantic information. To overcome these limitations, in this paper we present a revision control system for digital images that stores revisions in form of graphs. Besides, being integrated with Git, our revision control system also facilitates artistic creation processes in common image editing and digital painting workflows. A preliminary user study demonstrates the usability of the proposed system.

Image annotation is an important and challenging task when managing large image collections. In this paper, a fuzzy shape annotation approach for semi-automatic image annotation is presented. A fuzzy clustering process guided by partial supervision is applied to shapes represented by Fourier descriptors in order to derive a set of shape prototypes representative of a number of semantic categories. Next, prototypes are manually annotated by attaching textual labels related to semantic categories. Based on the labeled proto-types, a new shape is automatically labeled by associating a fuzzy set that provides membership degrees of the shape to all semantic categories. The proposed annotation approach provides an innovative indexing method for shape-based image retrieval. Indeed, shape prototypes represent an inter-mediate indexing level that allows a faster retrieval process since a query is matched against prototypes, instead of the whole shape database, resulting in a speed up of the retrieval. The proposed approach is tested on synthetic and real-word images in order to show its suitability.

In this work we propose the use of partially supervised fuzzy clustering to create a two-level indexing structure useful for enabling efficient shape retrieval. Similar shapes are grouped by a fuzzy clustering algorithm that embeds a partial supervision mechanism exploiting domain knowledge expressed in terms of a set of labeled shapes. After clustering, a set of prototypes representative of shape clusters is derived and used as indexing mechanism for retrieval. A shape query is matched against prototypes, instead of the whole shape database, and then shapes belonging to clusters for which prototype similarity is higher are returned. Experimental results obtained on two different datasets are presented to show the effectiveness of the proposed approach.

Automatic image annotation is an important and challenging task when managing large image collections. In this paper, we present an incremental approach for shape labeling, which is useful to image annotation when new sets of images are available during time. Every time new shape images are available, a semi-supervised fuzzy clustering algorithm is used to group shapes into a number of clusters by exploiting knowledge about classes expressed as a set of pre-labeled shapes. Each cluster is represented by a prototype that is manually labeled and used to annotate shapes. To capture the evolution of the image set, the previously discovered prototypes are added as pre-labeled shapes to the current shape set before clustering. The performance of the proposed incremental approach is evaluated on an image dataset from the fish domain, which is divided into chunks of data to simulate the progressive availability of shapes during time.

The incidence of melanoma in situ (MIS) is growing significantly. Detection at the MIS stage provides the highest cure rate for melanoma, but reliable detection of MIS with dermoscopy alone is not yet possible. Adjunct dermoscopic instrumentation using digital image analysis may allow more accurate detection of MIS. Gray areas are a critical component of MIS diagnosis, but automatic detection of these areas remains difficult because similar gray areas are also found in benign lesions. This paper proposes a novel adaptive thresholding technique for automatically detecting gray areas specific to MIS. The proposed model uses only MIS dermoscopic images to precisely determine gray area characteristics specific to MIS. To this aim, statistical histogram analysis is employed in multiple color spaces. It is demonstrated that skew deviation due to an asymmetric histogram distorts the color detection process. We introduce a skew estimation technique that enables histogram asymmetry correction facilitating improved adaptive thresholding results. These histogram statistical methods may be extended to detect any local image area defined by histograms.

The heterogeneous nature of the Web combined with the rapid diffusion of Web-based applications have made Web browsing an intricate activity for users. This has given rise to an urgent need for developing systems capable to assist and guide users during their navigational activity in the Web. Web Usage Mining (WUM) refers to the application of Data Mining techniques for the automatic discovery of meaningful usage patterns characterizing the browsing behavior of users, starting from access data collected from interactions of users with sites. The discovered patterns may be conveniently exploited in order to implement functionalities offering useful assistance to users. This chapter is mainly intended to provide an overview of the different stages involved in a general WUM process. As an example, a WUM approach is presented which is based on the use of fuzzy clustering to discovery user categories starting from usage patterns.