Performing massive data mining experiments with multiple datasets and methods is a common task faced by most bioinformatics and computational biology laboratories. WEKA is a machine learning package designed to facilitate this task by providing tools
that allow researchers to select from several classification methods and specific test strategies. Despite its popularity, the current WEKA environment for batch experiments, namely Experimenter, has four limitations that impact its usability: the selection of value ranges for methods options lacks flexibility and is not intuitive; there is no support for parallelisation when running large-scale data mining tasks; the XML schema is difficult to read, necessitating the use of the Experimenters graphical user interface for generation and modification; and robustness is limited by the fact that results are not saved until the last test has concluded. FlexDM implements an interface to WEKA to run batch processing tasks in a simple and intuitive way. In a short and easy-to-understand XML file, one can define hundreds of tests to be performed on several datasets. FlexDM also allows those tests to be executed asynchronously in parallel to take advantage of multi-core processors, significantly increasing usability and productivity. Results are saved incrementally for better robustness and reliability. FlexDM is implemented in Java and runs on Windows, Linux and OSX. As we encourage other researchers to explore and adopt our software, FlexDM is made available as a pre-configured bootable reference environment. All code, supporting documentation and usage examples are also available for download at http://sourceforge.net/projects/flexdm.
Computer vision plays a major role in the robotics industry, where vision data is frequently used for navigation and high-level decision making. Although there is significant research in algorithms and functional requirements, there is a comparative
lack of emphasis on how best to map these abstract concepts onto an appropriate software architecture. In this study, we distinguish between the functional and non-functional requirements of a computer vision system. Using a RoboCup humanoid robot system as a case study, we propose and develop a software architecture that fulfills the latter criteria. The modifiability of the proposed architecture is demonstrated by detailing a number of feature detection algorithms and emphasizing which aspects of the underlying framework were modified to support their integration. To demonstrate portability, we port our vision system (designed for an application-specific DARwIn-OP humanoid robot) to a general-purpose, Raspberry Pi computer. We evaluate performance on both platforms and compare them to a vision system optimised for functional requirements only. The architecture and implementation presented in this study provide a highly generalisable framework for computer vision system design that is of particular benefit in research and development, competition and other environments in which rapid system evolution is necessary.
