Abstract

Abstract

Abstract
Nowadays, with the exponential growth of data stream sources (e.g., Internet of Things [IoT], social networks, crowdsourcing platforms, and personal mobile devices), data stream processing has become indispensable for online classification, recommendation, and evaluation. Its main goal is to maintain dynamic models updated, holding the captured patterns, to make accurate predictions. The foundations of data streams algorithms are incremental processing, in order to reduce the computational resources required to process large quantities of data, and relevance model updating. This article addresses data stream knowledge processing, covering classification, recommendation, and evaluation; describing existing algorithms/techniques; and identifying open challenges.

Abstract
We present SyVMO, an algorithmic extension of the Variable Markov Oracle algorithm, to model and predict multi-part dependencies from symbolic music manifestations. Our model has been implemented as a software application named INCITe for computer-assisted algorithmic composition. It learns variable amounts of musical data from style-agnostic music represented as multiple viewpoints. To evaluate the SyVMO model within INCITe, we adopted the Creative Support Index survey and semi-structured interviews. Four expert composers participated in the evaluation using both personal and exogenous music corpus of variable size. The results suggest that INCITe shows great potential to support creative music tasks, namely in assisting the composition process. The use of SyVMO allowed the creation of polyphonic music suggestions from style-agnostic sources while maintaining a coherent melodic structure. © 2021, Springer Nature Switzerland AG.

Abstract

Abstract
Software for robotic systems is becoming progressively more complex despite the existence of established software ecosystems like ROS, as the problems we delegate to robots become more and more challenging. Ensuring that the software works as intended is a crucial (but not trivial) task, although proper quality assurance processes are rarely seen in the open-source robotics community. This paper explains how we analyzed and improved a specialized path planner for steep-slope vineyards regarding its software dependability. The analysis revealed previously unknown bugs in the system, with a relatively low property specification effort. We argue that the benefits of similar quality assurance processes far outweigh the costs and should be more widespread in the robotics domain.