Roaming in the Mobile Internet: when coverage sharing agreements call for regulation

Revised: 2006-06

The reverse self-dual serial cost-sharing rule

In this study we define a cost sharing rule for cost sharing problems. This rule is related to the serial cost-sharing rule defined by Moulin and Shenker (1992). We give some formulas and axiomatic characterizations for the new rule. The axiomatic characterizations are related to some previous ones provided by Moulin and Shenker (1994) and Albizuri (2010).

Intermediate serial cost-sharing rules

In this paper we give a generalization of the serial cost-sharing rule defined by Moulin and Shenker (1992) for cost sharing problems. According to the serial cost sharing rule, agents with low demands of a good pay cost increments associated with low quantities in the production process of that good. This fact might not always be desirable for those agents, since those cost increments might be higher than others, for example with concave cost functions. In this paper we give a family of cost sharing rules which allocates cost increments in all the possible places in the production process. And we characterize axiomatically each of them by means of an axiomatic characterization related to the one given for the serial cost-sharing rule by Moulin and Shenker (1994).

The minimal overlap cost sharing rule

In this paper we introduce a new cost sharing rule-the minimal overlap cost sharing rule-which is associated with the minimal overlap rule for claims problems defined by O'Neill (1982). An axiomatic characterization is given by employing a unique axiom: demand separability. Variations of this axiom enable the serial cost sharing rule (Moulin and Shenker, 1992) and the rules of a family (Albizuri, 2010) that generalize the serial cost sharing rule to be characterized. Finally, a family that includes the minimal overlap cost sharing rule is defined and obtained by means of an axiomatic characterization.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.