Abstract
Advertisers struggle to reach effectively and efficiently to their customers, continuously seeking to influence them and simultaneously reduce the overall publicity costs. Business areas like the mobile devices industry, together with wireless technologies and interactive environments, bring an huge opportunity for marketing purposes. supporting the chance to turn advertising into a convenient and easily accessible source of information by letting marketers communicate with costumers in a more direct, personal and contextualized way. This paper presents some foundations for the development of a system that will allow context-aware personalized profile-based advertising delivery, by using Bluetooth technology to identify and communicate with customers in a given geographic area, through their mobile devices.

Abstract

Abstract
In the predictive modeling tasks, a clear distinction is often made between learning problems that are supervised or unsupervised, the first involving only labeled data (training patterns with known category labels) while the latter involving only unlabeled data. There is a growing interest in a hybrid setting, called semi-supervised learning, in semi-supervised classification, the labels of only a small portion of the training data set are available. The unlabeled data, instead of being discarded, are also used in the learning process. Motivated by a breast cancer application, in this work we address a new learning task, in-between classification and semi-supervised classification. Each example is described using two different feature sets, not necessarily both observed for a given example. If a single view is observed, then the class is only due to that feature set, if both views are present the observed class label is the maximum of the two values corresponding to the individual views. We propose new learning methodologies adapted to this learning paradigm and experimentally compare them with baseline methods from the conventional supervised and unsupervised settings. The experimental results verify the usefulness of the proposed approaches. © 2011 IEEE.

Abstract
This paper aims to explore the performance alignment in collaborative network environments. It has been stated that performance management based on data collection and the evaluation of key performance indicators (KPIs) may not be effective due to the different indicators and measurement systems in place for the various participants in a collaborative network (CN). Therefore, measuring the strategic and inter-organizational alignment based on key alignment indicators (KAIs) can be an excellent alternative to improve performance evaluation systems. This approach has led to the exploration of the performance prediction paradigm and develops tools to estimate a performance and evaluate the degree of alignment by creating instances of a future performance in collaborative networks.

Abstract
In this paper, two novel hybrid multimode/single mode fiber Fabry-Perot (FP) cavities were compared. The cavities fabricated by chemical etching are presented as high temperature and strain sensors. In order to produce this FP cavity a single mode fiber was spliced to a graded index multimode fiber with 62.5 mu m core diameter. The multimode fiber was cut approximately 150 mu m away from the splice. Then the tip of the fiber containing the multimode fiber segment was dipped into a solution of 48% of HF during 8 minutes, creating a concavity due to the fact that the reaction between HF and the germanium doped fiber core is much faster than the reaction between HF and the pure silica cladding. By this method a concavity of approximately 100 mu m deep was created at the fiber tip. Two different FP cavities can be fabricated. The first cavity is obtained when a spliced with an identical tip concavity fiber (Sensor A) and the second is created when a tip concavity is spliced to a single mode fiber (Sensor B). The Fabry-Perot cavities were tested as a high temperature sensor in the range between room temperature and 800 degrees C and as strain sensors. A reversible shift of the interferometric peaks with temperature allowed to estimate a sensitivity of 0.75 +/- 0.03 pm/degrees C and 0.98 +/- 0.04 pm/degrees C for the sensor A and B respectively. For strain measurement sensor A demonstrated a sensitivity of 1.85 +/- 0.07 pm/mu epsilon and sensor B showed a sensitivity of 3.14 +/- 0.05 pm/mu epsilon. The sensors demonstrated the feasibility of low cost fiber optic sensors for high temperature and strain.

Abstract