Related papers: Autark: A Serverless Toolkit for Prototyping Urban…
Developing Visual Analytics (VA) applications requires integrating complex machine learning models with expressive interactive interfaces. Developers face a stark trade-off: building tightly-coupled monoliths plagued by fragile…
The paper presents a prototype of the accelerator commissioning and simulation application toolkit based on the Unified Accelerator Libraries (UAL) framework. The existing UAL 1.x environment has been implemented as an open collection of…
Visual Simultaneous Localization and Mapping (vSLAM) is a prevailing technology for many emerging robotic applications. Achieving real-time SLAM on mobile robotic systems with limited computational resources is challenging because the…
Software intensive systems play a crucial role in most, if not all, aspects of modern society. As such, both their sustainability and their role in supporting sustainable processes, must be realized by design. To this aim, the architecture…
In the task abstraction phase of the visualization design process, including in "design studies", a practitioner maps the observed domain goals to generalizable abstract tasks using visualization theory in order to better understand and…
Agentic UAVs represent a new frontier in autonomous aerial intelligence, integrating perception, decision-making, memory, and collaborative planning to operate adaptively in complex, real-world environments. Driven by recent advances in…
The evolution of decentralized microservice-based systems is challenging. These challenges are classified into static and dynamic categories. Regarding the static perspective, documenting and visualizing the fluid application topology is…
The combination of LLM agents with external tools enables models to solve complex tasks beyond their knowledge base. Human-designed tools are inflexible and restricted to solutions within the scope of pre-existing tools created by experts.…
The increasing integration of machine learning across various domains has underscored the necessity for accessible systems that non-experts can utilize effectively. To address this need, the field of automated machine learning (AutoML) has…
Ambiguity, an information state where multiple interpretations are plausible, is a common challenge in visual analytics (VA) systems. We discuss lessons learned from a case study designing VA tools for Canadian avalanche forecasters.…
Many scientific disciplines have traditionally advanced by iterating over hypotheses using labor-intensive trial-and-error, which is a slow and expensive process. Recent advances in computing, digitalization, and machine learning have…
Autonomous racing demands safe control of vehicles at their physical limits for extended periods of time, providing insights into advanced vehicle safety systems which increasingly rely on intervention provided by vehicle autonomy.…
In human-robot collaboration domains, augmented reality (AR) technologies have enabled people to visualize the state of robots. Current AR-based visualization policies are designed manually, which requires a lot of human efforts and domain…
This paper presents the ARCAD simulator for the rapid development of Unmanned Aerial Systems (UAS), including underactuated and fully-actuated multirotors, fixed-wing aircraft, and Vertical Take-Off and Landing (VTOL) hybrid vehicles. The…
As machine learning (ML) systems become increasingly widespread, it is necessary to audit these systems for biases prior to their deployment. Recent research has developed algorithms for effectively identifying intersectional bias in the…
Many data analytic systems have adopted a newly emerging compute resource, serverless (SL), to handle data analytics queries in a timely and cost-efficient manner, i.e., serverless data analytics. While these systems can start processing…
Understanding risk in autonomous driving requires not only perception and prediction, but also high-level reasoning about agent behavior and context. Current Vision Language Model (VLM)-based methods primarily ground agents in static images…
While artificial intelligence (AI) models have achieved human or even superhuman performance in many well-defined applications, they still struggle to show signs of broad and flexible intelligence. The Abstraction and Reasoning Corpus…
Agentic workflows in large language model systems integrate retrieval, reasoning, and memory, but existing frameworks suffer from scalability and reproducibility limitations due to fragmented data orchestration, serialization overhead, and…
The context of this paper is the creation of large uniform archaeological datasets from heterogeneous published resources, such as find catalogues - with the help of AI and Big Data. The paper is concerned with the challenge of consistent…