Abstract
This paper addresses the scenario of multi-release anonymization of datasets. We consider dynamic datasets where data can be inserted and deleted, and view this scenario as a case where each release is a small subset of the dataset corresponding, for example, to the results of a query. Compared to multiple releases of the full database, this has the obvious advantage of faster anonymization. We present an algorithm for post-processing anonymized queries that prevents anonymity attacks using multiple released queries. This algorithm can be used with several distinct protection principles and anonymization algorithms, which makes it generic and flexible. We give an experimental evaluation of the algorithm and compare it to m-invariance both in terms of efficiency and data quality. To this end, we propose two data quality metrics based on Shannon's entropy, and show that they can be seen as a refinement of existing metrics. © 2012 ACM.

Abstract
We introduce a simple agent construct associated to a named local database and a "Twitter-style"weak inheritance mechanism between local agents. On top of a remote predicate call layer, connecting distributed agent spaces, we build a replication mechanism allowing agents "visiting" remote spaces to expose their computational capabilities to non-local followers. The resulting protocol has the remarkable property that only updates to the state of the agents are sent over the network through transactional remote predicate calls guaranteed to always terminate, and therefore spawning of multiple threads can be avoided. At the same time, calls to a visiting agent's code by its followers are always locally executed, resulting in performance gains and reduced communication efforts. Copyright 2012 ACM.

Abstract
In this paper we propose a hybrid music recommender system, which combines usage and content data. We describe an online evaluation experiment performed in real time on a commercial music web site, specialised in content from the very long tail of music content. We compare it against two stand-alone recommenders, the first system based on usage and the second one based on content data. The results show that the proposed hybrid recommender shows advantages with respect to usage- and content-based systems, namely, higher user absolute acceptance rate, higher user activity rate and higher user loyalty. Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Abstract
Hydro energy represents a priority in the energy policy of Portugal, with the aim of decreasing the dependence on fossil fuels. In this context, optimal hydro scheduling acquires added significance in moving towards a sustainable environment. A mixed-integer nonlinear programming approach is considered to enable optimal hydro scheduling for the short-term time horizon, including the effect of head on power production, start-up costs related to the units, multiple regions of operation, and constraints on discharge variation. As new contributions to the field, market uncertainty is introduced in the model via price scenarios and risk management is included using Conditional Value-at-Risk to limit profit volatility. Moreover, plant scheduling and pool offering by the hydro power producer are simultaneously considered to solve a realistic cascaded hydro system.

Abstract
Cryptographic software development is a challenging field: high performance must be achieved, while ensuring correctness and compliance with low-level security policies. CAO is a domain specific language designed to assist development of cryptographic software. An important feature of this language is the design of a novel type system introducing native types such as predefined sized vectors, matrices and bit strings, residue classes modulo an integer, finite fields and finite field extensions, allowing for extensive static validation of source code. We present the formalisation, validation and implementation of this type system. © 2012 Springer-Verlag.

Abstract
In this paper, an approach is presented that identifies music samples which are difficult for current state-of-the-art beat trackers. In order to estimate this difficulty even for examples without ground truth, a method motivated by selective sampling is applied. This method assigns a degree of difficulty to a sample based on the mutual disagreement between the output of various beat tracking systems. On a large beat annotated dataset we show that this mutual agreement is correlated with the mean performance of the beat trackers evaluated against the ground truth, and hence can be used to identify difficult examples by predicting poor beat tracking performance. Towards the aim of advancing future beat tracking systems, we demonstrate how our method can be used to form new datasets containing a high proportion of challenging music examples.