Jan 2027· Journal of Engineering, Project, and Production Management· 0 citations
Abstract
Digital technologies such as Building Information Modeling (BIM), Artificial Intelligence (AI), simulation platforms, and virtual and augmented reality are reshaping civil engineering education, yet their influence on sustainability awareness and decision-making is not well understood. This study surveyed 73 students enrolled in civil engineering courses at a public university in the southeastern United States. The instrument comprised 36 Likert-scale statements, grouped into eight constructs covering technology use in the curriculum, professional mindset, sustainability knowledge and lifecycle and environmental awareness. They also covered understanding of water resources, cognitive ability, emotional intelligence, and problem-solving. It also includes three decision scenarios in which a digital tool output conflicted with human input or practical constraints. Students reported favorable attitudes toward digital tools, along with strong conceptual sustainability awareness, but two applied-skill gaps stood out: only 32.9% felt able to interpret hydrologic models, and only 28.8% felt they could transfer simulation-based lessons to real world settings. In the scenarios, at most 12.3% of respondents accepted the digital output at face value, and 83.6% never did so across all three scenarios. Breadth of tool exposure was unrelated to perceptions or decision style, whereas the small group of students reporting exposure to hydrologic modeling rated their model interpretation ability substantially higher, suggesting that relevant depth matters more than the number of tools encountered. The findings support curricula that pair earlier and deeper BIM and hydrologic modeling instruction with lifecycle content and that train verification and interpretation skills alongside tool operation.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9