5,750 research outputs found
ImageJ2: ImageJ for the next generation of scientific image data
ImageJ is an image analysis program extensively used in the biological
sciences and beyond. Due to its ease of use, recordable macro language, and
extensible plug-in architecture, ImageJ enjoys contributions from
non-programmers, amateur programmers, and professional developers alike.
Enabling such a diversity of contributors has resulted in a large community
that spans the biological and physical sciences. However, a rapidly growing
user base, diverging plugin suites, and technical limitations have revealed a
clear need for a concerted software engineering effort to support emerging
imaging paradigms, to ensure the software's ability to handle the requirements
of modern science. Due to these new and emerging challenges in scientific
imaging, ImageJ is at a critical development crossroads.
We present ImageJ2, a total redesign of ImageJ offering a host of new
functionality. It separates concerns, fully decoupling the data model from the
user interface. It emphasizes integration with external applications to
maximize interoperability. Its robust new plugin framework allows everything
from image formats, to scripting languages, to visualization to be extended by
the community. The redesigned data model supports arbitrarily large,
N-dimensional datasets, which are increasingly common in modern image
acquisition. Despite the scope of these changes, backwards compatibility is
maintained such that this new functionality can be seamlessly integrated with
the classic ImageJ interface, allowing users and developers to migrate to these
new methods at their own pace. ImageJ2 provides a framework engineered for
flexibility, intended to support these requirements as well as accommodate
future needs
The Artificial Intelligence Workbench: a retrospective review
Last decade, biomedical and bioinformatics researchers have been demanding advanced and user-friendly applications for real use in practice. In this context, the Artificial Intelligence Workbench, an open-source Java desktop application framework for scientific software development, emerged with the goal of provid-ing support to both fundamental and applied research in the domain of transla-tional biomedicine and bioinformatics. AIBench automatically provides function-alities that are common to scientific applications, such as user parameter defini-tion, logging facilities, multi-threading execution, experiment repeatability, work-flow management, and fast user interface development, among others. Moreover, AIBench promotes a reusable component based architecture, which also allows assembling new applications by the reuse of libraries from existing projects or third-party software. Ten years have passed since the first release of AIBench, so it is time to look back and check if it has fulfilled the purposes for which it was conceived to and how it evolved over time
RobotKube: Orchestrating Large-Scale Cooperative Multi-Robot Systems with Kubernetes and ROS
Modern cyber-physical systems (CPS) such as Cooperative Intelligent Transport
Systems (C-ITS) are increasingly defined by the software which operates these
systems. In practice, microservice architectures can be employed, which may
consist of containerized microservices running in a cluster comprised of robots
and supporting infrastructure. These microservices need to be orchestrated
dynamically according to ever changing requirements posed at the system.
Additionally, these systems are embedded in DevOps processes aiming at
continually updating and upgrading both the capabilities of CPS components and
of the system as a whole. In this paper, we present RobotKube, an approach to
orchestrating containerized microservices for large-scale cooperative
multi-robot CPS based on Kubernetes. We describe how to automate the
orchestration of software across a CPS, and include the possibility to monitor
and selectively store relevant accruing data. In this context, we present two
main components of such a system: an event detector capable of, e.g.,
requesting the deployment of additional applications, and an application
manager capable of automatically configuring the required changes in the
Kubernetes cluster. By combining the widely adopted Kubernetes platform with
the Robot Operating System (ROS), we enable the use of standard tools and
practices for developing, deploying, scaling, and monitoring microservices in
C-ITS. We demonstrate and evaluate RobotKube in an exemplary and reproducible
use case that we make publicly available at
https://github.com/ika-rwth-aachen/robotkube .Comment: 7 pages, 2 figures, 2 tables; Accepted to be published as part of the
26th IEEE International Conference on Intelligent Transportation Systems
(ITSC), Bilbao, Spain, September 24-28, 202
Abductive Design of BDI Agent-based Digital Twins of Organizations
For a Digital Twin - a precise, virtual representation of a physical counterpart - of a human-like system to be faithful and complete, it must appeal to a notion of anthropomorphism (i.e., attributing human behaviour to non-human entities) to imitate (1) the externally visible behaviour and (2) the internal workings of that system. Although the Belief-Desire-Intention (BDI) paradigm was not developed for this purpose, it has been used successfully in human modeling applications. In this sense, we introduce in this thesis the notion of abductive design of BDI agent-based Digital Twins of organizations, which builds on two powerful reasoning disciplines: reverse engineering (to recreate the visible behaviour of the target system) and goal-driven eXplainable Artificial Intelligence (XAI) (for viewing the behaviour of the target system through the lens of BDI agents). Precisely speaking, the overall problem we are trying to address in this thesis is to “Find a BDI agent program that best explains (in the sense of formal abduction) the behaviour of a target system based on its past experiences . To do so, we propose three goal-driven XAI techniques: (1) abductive design of BDI agents, (2) leveraging imperfect explanations and (3) mining belief-based explanations. The resulting approach suggests that using goal-driven XAI to generate Digital Twins of organizations in the form of BDI agents can be effective, even in a setting with limited information about the target system’s behaviour
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