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.

Abstract
The literature on FDI entry modes and corruption tends to convey the idea that corruption leads to a choice between low equity modes, i.e. joint ventures with local partners, and non-equity modes, namely exports and contracting, in order to avoid contact with corrupt state officials. Recently, some studies have argued that despite corruption, linguistic and historical ties between home and host countries lead MNCs to prefer high-equity modes. Focusing on a rather unexplored setting, the African countries, most specifically the Portuguese-speaking ones (PALOP - Paises Africanos de LIngua Oficial Portuguesa), which include countries where levels of corruption are very high (e.g., Guinea-Bissau and Angola), high (e. g., Mozambique, Sao Tome and Principe), and intermediate (e.g., Cape Verde), maintaining also close linguistic and historical ties with Portugal, we found that the FDI entry mode is associated with the less corrupt markets. Thus, our results do not support the recent contention that cultural and historical links are likely to perform a mediating role, by fostering foreign direct investment, in supporting African countries to overcome the dismal growth some have been facing in the last few decades. On the contrary, our findings highlight the pressing need for these countries to combat corruption if higher economic growth via FDI attraction is envisioned.

Abstract
This paper proposes a method for automating the detection of lanes in Thin-Layer Chromatography images. Our approach includes a preprocessing step to detect the image region of interest, followed by background estimation and removal. This image is then projected onto the horizontal direction to integrate the information into a one-dimensional profile. A smoothing filter is applied to this profile and the outcome is the input of the lane detection process, which is performed in three phases. The first one aims at obtaining an initial set of candidate lanes that are further validated or removed in the second phase. The last phase is a refinement step that allows the inclusion of lanes that are not clearly distinguishable in the profile and that were not included in the initial set. The method was evaluated in 66 chromatography images and achieved values of recall, precision and F-beta-measure of 97.0%, 99.4% and 98.2%, respectively.