Lightweight peer-to-peer secure multi-party VoIP protocol

Multi-party voice-over-IP (MVoIP) services provide economical and convenient group communication mechanisms for many emerging applications such as distance collaboration systems, on-line meetings and Internet gaming. In this paper, we present a light peer-to-peer (P2P) protocol to provide MVoIP services on small platforms like mobile phones and PDAs. Unlike other proposals, our solution is fully distributed and self-organizing without requiring specialized servers or IP multicast support.

Improving kNN multi-label classification in Prototype Selection scenarios using class proposals

Prototype Selection (PS) algorithms allow a faster Nearest Neighbor classification by keeping only the most profitable prototypes of the training set. In turn, these schemes typically lower the performance accuracy. In this work a new strategy for multi-label classifications tasks is proposed to solve this accuracy drop without the need of using all the training set. For that, given a new instance, the PS algorithm is used as a fast recommender system which retrieves the most likely classes. Then, the actual classification is performed only considering the prototypes from the initial training set belonging to the suggested classes. Results show that this strategy provides a large set of trade-off solutions which fills the gap between PS-based classification efficiency and conventional kNN accuracy. Furthermore, this scheme is not only able to, at best, reach the performance of conventional kNN with barely a third of distances computed, but it does also outperform the latter in noisy scenarios, proving to be a much more robust approach.