Abstract
Hardcore, jungle, and drum and bass (HJDB) are fast-paced electronic dance music genres that often employ resequenced breakbeats or drum samples from jazz and funk percussionist solos. We present a style-specific method for downbeat detection specifically designed for HJDB. The presented method combines three forms of metrical information in the prediction of downbeats: low-level onset event information; periodicity information from beat tracking; and high-level information from a regression model trained with classic breakbeats. In an evaluation using 206 HJDB pieces, we demonstrate superior accuracy of our style specific method over four general downbeat detection algorithms. We present this result to motivate the need for style-specific knowledge and techniques for improved downbeat detection. © 2012 International Society for Music Information Retrieval.

Abstract
This work presents an optimized version of XMuSer, an ILP based framework suitable to explore temporal patterns available in multi-relational databases. XMuSer's main idea consists of exploiting frequent sequence mining, an efficient method to learn temporal patterns in the form of sequences. XMuSer framework efficiency is grounded on a new coding methodology for temporal data and on the use of a predictive sequence miner. The frameworks selects and map the most interesting sequential patterns into a new table, the sequence relation. In the last step of our framework, we use an ILP algorithm to learn a classification theory on the enlarged relational database that consists of the original multi-relational database and the new sequence relation. We evaluate our framework by addressing three classification problems and map each one of three different types of sequential patterns: frequent, closed or maximal. The experiments show that our ILP based framework gains both from the descriptive power of the ILP algorithms and the efficiency of the sequential miners. © 2012 Springer-Verlag Berlin Heidelberg.

Abstract
Information and knowledge sharing within collaborative networks stills being a challenging problem. Particularly in self governed or mediated networks the information/collaboration deadlock is likely to occur if there are not instrumental methods, socially accepted, that foster usable and useful patterns of collaborative information management. This paper describes how the vision for a solution to this problem was developed using the design science frameworks and the concept of technological rules. The result is materialised in the concept collaborative spaces as pivoting collaborative structures in the network enabling locally shared information to feed the network global level.

Abstract
Content based image retrieval (CBIR) is employed in medicine to improve radiologists' diagnostic performance. Today effective medical CBIR systems are limited to specific applications, as to reduce the amount of medical knowledge to model. Although supervised approaches could ease the incorporation of medical expertise, its application is not common due to the scarce number of available user annotations. This paper introduces the application of radiology reports to supervise CBIR systems. The concept is to make use of the textual distances between reports to build a transformation in image space through a manifold learning algorithm. A comparison was made between the presented approach and non-supervised CBIR systems, using a Leave-One-Patient-Out evaluation in a database of computer tomography scans of interstitial lung diseases. Supervised CBIR augmented the mean average precision consistently with an increase between 3 to 8 points, which suggests supervision by radiology reports increases CBIR performance.

Abstract

Abstract
In this paper we establish a threshold for perceptually acceptable beat tracking based on the mutual agreement of a committee of beat trackers. In the first step we use an existing annotated dataset to show that mutual agreement can be used to select one committee member as the most reliable beat tracker for a song. Then we conduct a listening test using a subset of the Million Song Dataset to establish a threshold which results in acceptable quality of the chosen beat output. For both datasets, we obtain a percentage of trackable music of about 73%, and we investigate which data tags are related to acceptable and problematic beat tracking. The results indicate that current datasets are biased towards genres which tend to be easy for beat tracking. The proposed methods provide a means to automatically obtain a confidence value for beat tracking in non-annotated data and to choose between a number of beat tracker outputs. © 2012 International Society for Music Information Retrieval.