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Artificial intelligence (AI) raises expectations of substantial increases in rates of technological and scientific progress, but such anticipations are often not connected to detailed ground-level studies of AI use in innovation processes.…
Artificial Intelligence (AI) systems for online recruitment markets have the potential to significantly enhance the efficiency and effectiveness of job placements and even promote fairness or inclusive hiring practices. Neglecting Diversity…
Recent developments in Artificial Intelligence (AI) provide unprecedented automation opportunities in the Architecture, Engineering, and Construction (AEC) industry. However, despite the enthusiasm regarding the use of AI, 85% of current…
Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering. However, most…
Recent advancements in large language models (LLMs) and AI systems have led to a paradigm shift in the design and optimization of complex AI workflows. By integrating multiple components, compound AI systems have become increasingly adept…
The automation system for Course of Action (CoA) planning is an essential element in future warfare. As maneuver speeds increase, surveillance ranges extend, and weapon ranges grow, the operational area expands, making traditional…
Onboarding newcomers is vital for the sustainability of open-source software (OSS) projects. To lower barriers and increase engagement, OSS projects have dedicated experts who provide guidance for newcomers. However, timely responses are…
The ability to update a path plan is a required capability for autonomous mobile robots navigating through uncertain environments. This paper proposes a re-planning strategy using a multilayer planning and control framework for cases where…
Assessing the effects of the energy transition and liberalization of energy markets on resource adequacy is an increasingly important and demanding task. The rising complexity in energy systems requires adequate methods for energy system…
Autonomous vehicles (AVs) are now operating on public roads, which makes their testing and validation more critical than ever. Simulation offers a safe and controlled environment for evaluating AV performance in varied conditions. However,…
The potential for negative impacts of AI has rapidly become more pervasive around the world, and this has intensified a need for responsible AI governance. While many regulatory bodies endorse risk-based approaches and a multitude of risk…
In healthcare, AI techniques are widely used for tasks like risk assessment and anomaly detection. Despite AI's potential as a valuable assistant, its role in complex medical data analysis often oversimplifies human-AI collaboration…
Information gathering in large-scale or time-critical scenarios (e.g., environmental monitoring, search and rescue) requires broad coverage within limited time budgets, motivating the use of multi-agent systems. These scenarios are commonly…
As AI systems increasingly shape decision making in creative design contexts, understanding how humans engage with these tools has become a critical challenge for interactive intelligent systems research. This paper contributes a challenge…
The design of alloys is a multi-scale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process…
Artificial Intelligence (AI) and Machine Learning have enormous potential to transform businesses and disrupt entire industry sectors. However, companies wishing to integrate algorithmic decisions into their face multiple challenges: They…
Many generative AI systems as well as decision-support systems (DSSs) provide operators with predictions or recommendations. Various studies show, however, that people can mistakenly adopt the erroneous results presented by those systems.…
Despite the increasing development of Artificial Intelligence (AI) systems, Requirements Engineering (RE) activities face challenges in this new data-intensive paradigm. We identified a lack of support for problem discovery within AI…
Developing a reliable parametric cost model at the conceptual stage of the project is crucial for projects managers and decision-makers. Existing methods, such as probabilistic and statistical algorithms have been developed for project cost…
As the artificial intelligence community advances into the era of large models with billions of parameters, distributed training and inference have become essential. While various parallelism strategies-data, model, sequence, and…