Abstract

Abstract

Abstract
The developments occurring in recent years in the Information and Communication Technologies led inevitably a great effect on the operation, structure and strategy of organizations around the world, and obviously also in the tourist sector organizations. Through the Information and Communication Technologies, it is possible to reduce communication costs and operation and may increase the flexibility, interactivity, efficiency, productivity and competitiveness. Information and Communication Technologies appear one of the area most critical to the success of tourism in the future and the way it will promote the tourist destinations. This paper aims to analyze the relevance given by the various Portuguese institutions of higher education to Information and Communication Technologies in their degrees. The analysis carried out was done in degree courses operating in this school year, 2012/2013, in Portuguese universities and polytechnics, public and private. © 2013 AISTI.

Abstract

Abstract
The proposed spam filtering architecture for MTA1 servers is a component based architecture that allows distributed processing and centralized knowledge. This architecture allows heterogeneous systems to coexist and benefit from a centralized knowledge source and filtering rules. MTA servers in the infrastructure contribute to a common knowledge, allowing for a more rational resource usage. The architecture is fully scalable, ranging from all-in-one system with minimal components instances, to multiple components instances distributed across multiple systems. Filtering rules can be implemented as independent modules that can be added, removed or modified without impact on MTA servers operation. A proof-of-concept solution was developed. Most of spam is filtered due to a grey-listing effect from the architecture itself. Using simple filters as Domain Name System black and white lists, and Sender Policy Framework validation, it is possible to guarantee a spam filtering effective, efficient and virtually without false positives. © IFIP International Federation for Information Processing 2013

Abstract
Long recordings of heart rate variability (HRV) display non-stationary characteristics and exhibit long- and short-range correlations. The nonparametric methodology detrended fluctuation analysis (DFA) has become a widely used technique for the detection of long-range correlations in non-stationary HRV data. Recently, we have proposed an alternative approach based on fractional integrated autoregressive moving average (ARFIMA) modelling. These models are an extension of the AR models usual in HRV analysis and have special interest for applications because of their ability for modelling both short- and long-term behaviour of a time series. In this work, DFA is used to assess also short-range scales, further characterizing the data. The methods are applied to 24 h HRV recordings from the Noltisalis database, collected from healthy subjects, patients suffering from congestive heart failure and heart transplanted patients. The analysis of short-range scales leads to a better discrimination between the different groups. © 2013, Springer-Verlag Berlin Heidelberg.