Deciding and reserving appropriate resources in the Cloud, is a basic initial step for adopters when employing an Infrastructure as a Service to host their application. However, the size and number of Virtual Machines used, along with the expected application workload, will highly influence its operation, in terms of the observed Quality of Service. This paper proposes a machine learning approach, based on Artificial Neural Networks, for mapping Quality of Service required levels and (expected) application workload to concrete resource demands. The presented solution is evaluated through a comercial Customer Relationship Management application, generating a training set of realistic workload and Quality of Service measurements in order to illustrate the effectiveness of the proposed technique in a real-world scenario.

We present a technology which enables wide-scale deployment of a unique and powerful data access and search tool. This tool makes it possible for content owners on the Internet to offer end-users the ability to search, assess, or otherwise examine their nondisclosable private data. The end-users may use arbitrary, distrusted assessment algorithms and receive the results which are produced. These results take the form of a meaningful digest, e.g., a useful statistic, a relevance assessment, etc. There exists some controllable danger of information theft which, however, is quantitatively dependent on system parameters and can be tuned accordingly. We consider a broad class of practical applications where brute force attacks exist which are easier and cheaper to implement than an attack through appropriately configured PIVOTS, making our system, effectively, very trustworthy. The actual creation of the search algorithm may be performed by the end-users themselves in specialized applications, or by third-party search-engine operators who offer their services to end-users. Sophisticated data mining technologies which are very often not directly available to content owners can thus be exploited to mine even private data collections. Users discovering interesting items in private data sets may purchase this specific data, but other desirable exploitation possibilities are also analyzed. A novel business model is presented and its potential for great impact on e-commerce is explored. Three diverse and important real-world applications are presented in conclusion.

The goal of this paper is to present the work carried out up to now for the development of the Greek Text-To-Speech (GRTTS) system by NTUA. The system under consideration is based on the method of concatenative synthesis and follows the Bell Labs approach to this technique. In order that the input text to the GRTTS is translated into continuous synthetic speech the following modules have already been studied and implemented: (i) module for the linguistic analysis of the input text; (ii) the acoustic inventory module. On the same time it is under development the duration module of the GRTTS, for the computation of the appropriate temporal structure of synthesized speech. The objectives of the above studies, in combination with the concatenative synthesis technique, which is one of the simplest methods for speech synthesis, are to bypass most of the problems encountered by other synthesis methods such as articulatory and formant synthesis systems. The major objective is to minimize abrupt discontinuities and thus maximize the naturalness of the synthesized utterances.