BCS (Body Condition Score) is a method to estimate body fat reserves and accumulated energy balance of cows, placing estimations (or BCS values) in a scale of 1 to 5. Periodically rating BCS of dairy cows is very important since BCS values are associated with milk production, reproduction, and health of cows. However, in practice, obtaining BCS values is a time-consuming and subjective task performed visually by expert scorers. There have been several efforts to automate BCS of dairy cows by using image analysis and machine learning techniques. In a previous work, an automatic system to estimate BCS values was proposed, which is based on Convolutional Neural Networks (CNNs). In this paper we significantly extend the techniques exploited by that system via using transfer learning and ensemble modeling techniques to further improve BCS estimation accuracy. The improved system has achieved good estimations results in comparison with the base system. Overall accuracy of BCS estimations within 0.25 units of difference from true values has increased 4% (up to 82%), while overall accuracy within 0.50 units has increased 3% (up to 97%).

Discovering services acquires importance as Service-Oriented Computing (SOC) becomes an adopted paradigm. SOC’s most popular materializations, namely Web Services technologies, have different challenges related to service discovery and, in turn, many approaches have been proposed. As these approaches are different, one solution may be better than another according to certain requirements. In consequence, choosing a service discovery system is a hard task. To alleviate this task, this paper proposes eight criteria, based on the requirements for discovering services within common service-oriented environments, allowing the characterization of discovery systems. These criteria cover functional and non-functional aspects of approaches to service discovery. The results of the characterization of 22 contemporary approaches and potential research directions for the area are also shown.