Network navigation, encompassing both spatial and temporal cooperation to locate mobile agents, is a key enabler for numerous emerging location-based applications. In such cooperative networks, the positional information ...

Time series classification is an increasing research topic due to the vast amount of time series data that is being created over a wide variety of fields. The particularity of the data makes it a challenging task and ...

Crowdsourcing is widely used nowadays in machine learning for data labeling. Although in the traditional case annotators are asked to provide a single label for each instance, novel approaches allow annotators, in case ...

The statistical assessment of the empirical comparison of algorithms is an essential step in heuristic optimization. Classically, researchers have relied on the use of statistical tests. However, recently, concerns about ...

Estimation of distribution algorithms have already demonstrated their utility when solving a broad range of combinatorial problems. However, there is still room for methodological improvements when approaching constrained ...

This paper deals with the problem of detecting sand dunes from remotely sensed images of the surface of Mars. We build on previous approaches that propose methods to extract informative features for the classification of ...

The demand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements. Conventional range-based localization approaches rely on ...

In the field of evolutionary computation, it is usual to generate artificial benchmarks of instances that are used as a test-bed to determine the performance of the algorithms at hand. In this context, a recent work on ...

Cloud infrastructures provide computing resources to applications in the form of Virtual Machines (VMs). Many applications deployed in cloud resources have an elastic behavior, that is, they change the number of servers ...

Majority voting is a popular and robust strategy to aggregate different opinions in learning from crowds, where each worker labels examples ac- cording to their own criteria. Although it has been extensively studied in the ...