Tootfinder

"I know it's not right, but that's what it said to do": Investigating Trust in AI Chatbots for Cybersecurity Policy
Brandon Lit, Edward Crowder, Daniel Vogel, Hassan Khan
https://arxiv.org/abs/2510.08917

"I know it's not right, but that's what it said to do": Investigating Trust in AI Chatbots for Cybersecurity Policy
AI chatbots are an emerging security attack vector, vulnerable to threats such as prompt injection, and rogue chatbot creation. When deployed in domains such as corporate security policy, they could be weaponized to deliver guidance that intentionally undermines system defenses. We investigate whether users can be tricked by a compromised AI chatbot in this scenario. A controlled study (N=15) asked participants to use a chatbot to complete security-related tasks. Without their knowledge, the ch…

Refactoring Towards Microservices: Preparing the Ground for Service Extraction
Rita Peixoto, Filipe F. Correia, Thatiane Rosa, Eduardo Guerra, Alfredo Goldman
https://arxiv.org/abs/2510.03050

Refactoring Towards Microservices: Preparing the Ground for Service Extraction
As organizations increasingly transition from monolithic systems to microservices, they aim to achieve higher availability, automatic scaling, simplified infrastructure management, enhanced collaboration, and streamlined deployments. However, this migration process remains largely manual and labour-intensive. While existing literature offers various strategies for decomposing monoliths, these approaches primarily focus on architecture-level guidance, often overlooking the code-level challenges …

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields
Rui-Yang Zhang, Henry B. Moss, Lachlan Astfalck, Edward Cripps, David S. Leslie
https://arxiv.org/abs/2509.26005

BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields
We introduce a formal active learning methodology for guiding the placement of Lagrangian observers to infer time-dependent vector fields -- a key task in oceanography, marine science, and ocean engineering -- using a physics-informed spatio-temporal Gaussian process surrogate model. The majority of existing placement campaigns either follow standard `space-filling' designs or relatively ad-hoc expert opinions. A key challenge to applying principled active learning in this setting is that Lagra…