A hybrid positioning system for technology‐independent location‐aware computing

Location‐aware computing is a form of context‐aware mobile computing that refers to the ability of providing users with services that depend on their position. Locating the user terminal, often called positioning, is essential in this form of computing. Towards this aim, several technologies exist, ranging from personal area networking, to indoor, outdoor, and up to geographic area systems. Developers of location‐aware software applications have to face with a number of design choices, that typically depend on the chosen technology. This work addresses the problem of easing the development of pull location‐aware applications, by allowing uniform access to multiple heterogeneous positioning systems. Towards this aim, the paper proposes an approach to structure location‐aware mobile computing systems in a way independent of positioning technologies. The approach consists in structuring the system into a layered architecture, that provides application developers with a standard Java Application Programming Interface (JSR‐179 API), and encapsulates location data management and technology‐specific positioning subsystems into lower layers with clear interfaces. In order to demonstrate the proposed approach we present the development of HyLocSys. It is an open hybrid software architecture designed to support indoor/outdoor applications, which allows the uniform (combined or separate) use of several positioning technologies. HyLocSys uses a hybrid data model, which allows the integration of different location information representations (using symbolic and geometric coordinates). Moreover, it allows support to handset‐ and infrastructure‐based positioning approaches while respecting the privacy of the user. The paper presents a prototypal implementation of HyLocSys for heterogeneous scenarios. It has been implemented and tested on several platforms and mobile devices. Copyright © 2009 John Wiley & Sons, Ltd.     

Pool Evolution: A Parallel Pattern for Evolutionary and Symbolic Computing

We introduce a new parallel pattern derived from a specific application domain and show how it turns out to have application beyond its domain of origin. The pool evolution pattern models the parallel evolution of a population subject to mutations and evolving in such a way that a given fitness function is optimized. The pattern has been demonstrated to be suitable for capturing and modeling the parallel patterns underpinning various evolutionary algorithms, as well as other parallel patterns typical of symbolic computation. In this paper we introduce the pattern, we discuss its implementation on modern multi/many core architectures and finally present experimental results obtained with FastFlow and Erlang implementations to assess its feasibility and scalability.     

Designing wrapper components for e-services in integrating heterogeneous systems

Component-based approaches are becoming more and more popular to support Internet-based application development. Different component modeling approaches, however, can be adopted, obtaining different abstraction levels (either conceptual or operational). In this paper we present a component-based architecture for the design of e-applications, and discuss the concept of wrapper components as building blocks for the development of e-services, where these services are based on legacy systems. We discuss their characteristics and their applicability in Internet-based application development. Received: 30 October 2000 / Accepted: 9 January 2001 Published online: 28 June 2001     

Temporal Kohonen Map and the Recurrent Self-Organizing Map: Analytical and Experimental Comparison

This paper compares two Self-Organizing Map (SOM) based models for temporal sequence processing (TSP) both analytically and experimentally. These models, Temporal Kohonen Map (TKM) and Recurrent Self-Organizing Map (RSOM), incorporate leaky integrator memory to preserve the temporal context of the input signals. The learning and the convergence properties of the TKM and RSOM are studied and we show analytically that the RSOM is a significant improvement over the TKM, because the RSOM allows simple derivation of a consistent learning rule. The results of the analysis are demonstrated with experiments.     

Feedforward networks in financial predictions: the future that modifies the present

The main goal of this paper is to show how relatively minor modifications of well-known algorithms (in particular, back propagation) can dramatically increase the performance of an artificial neural network (ANN) for time series prediction. We denote our proposed sets of modifications as the 'self-momentum', 'Freud' and 'Jung' rules. In our opinion, they provide an example of an alternative approach to the design of learning strategies for ANNs, one that focuses on basic mathematical conceptualization rather than on formalism and demonstration. The complexity of actual prediction problems makes it necessary to experiment with modelling possibilities whose inherent mathematical properties are often not well understood yet. The problem of time series prediction in stock markets is a case in point. It is well known that asset price dynamics in financial markets are difficult to trace, let alone to predict with an operationally interesting degree of accuracy. We therefore take financial prediction as a meaningful test bed for the validation of our techniques. We discuss in some detail both the theoretical underpinnings of the technique and our case study about financial prediction, finding encouraging evidence that supports the theoretical and operational viability of our new ANN specifications. Ours is clearly only a preliminary step. Further developments of ANN architectures with more and more sophisticated 'learning to learn' characteristics are now under study and test.     

Multimodal support to group dynamics

The complexity of group dynamics occurring in small group interactions often hinders the performance of teams. The availability of rich multimodal information about what is going on during the meeting makes it possible to explore the possibility of providing support to dysfunctional teams from facilitation to training sessions addressing both the individuals and the group as a whole. A necessary step in this direction is that of capturing and understanding group dynamics. In this paper, we discuss a particular scenario, in which meeting participants receive multimedia feedback on their relational behaviour, as a first step towards increasing self-awareness. We describe the background and the motivation for a coding scheme for annotating meeting recordings partially inspired by the Bales’ Interaction Process Analysis. This coding scheme was aimed at identifying suitable observable behavioural sequences. The study is complemented with an experimental investigation on the acceptability of such a service.

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Segmentation of news videos based on audio-video information

In this paper, we propose an innovative architecture to segment a news video into the so-called “stories” by both using the included video and audio information. Segmentation of news into stories is one of the key issues for achieving efficient treatment of news-based digital libraries. While the relevance of this research problem is widely recognized in the scientific community, we are in presence of a few established solutions in the field. In our approach, the segmentation is performed in two steps: first, shots are classified by combining three different anchor shot detection algorithms using video information only. Then, the shot classification is improved by using a novel anchor shot detection method based on features extracted from the audio track. Tests on a large database confirm that the proposed system outperforms each single video-based method as well as their combination.

Efficient matching and indexing of graph models in content-basedretrieval

In retrieval from image databases, evaluation of similarity, based both on the appearance of spatial entities and on their mutual relationships, depends on content representation based on attributed relational graphs. This kind of modeling entails complex matching and indexing, which presently prevents its usage within comprehensive applications. In this paper, we provide a graph-theoretical formulation for the problem of retrieval based on the joint similarity of individual entities and of their mutual relationships and we expound its implications on indexing and matching. In particular, we propose the usage of metric indexing to organize large archives of graph models, and we propose an original look-ahead method which represents an efficient solution for the (sub)graph error correcting isomorphism problem needed to compute object distances. Analytic comparison and experimental results show that the proposed lookahead improves the state-of-the-art in state-space search methods and that the combined use of the proposed matching and indexing scheme permits for the management of the complexity of a typical application of retrieval by spatial arrangement