Tootfinder

AI-Driven Tools in Modern Software Quality Assurance: An Assessment of Benefits, Challenges, and Future Directions
Ihor Pysmennyi, Roman Kyslyi, Kyrylo Kleshch
https://arxiv.org/abs/2506.16586

AI-Driven Tools in Modern Software Quality Assurance: An Assessment of Benefits, Challenges, and Future Directions
Traditional quality assurance (QA) methods face significant challenges in addressing the complexity, scale, and rapid iteration cycles of modern software systems and are strained by limited resources available, leading to substantial costs associated with poor quality. The object of this research is the Quality Assurance processes for modern distributed software applications. The subject of the research is the assessment of the benefits, challenges, and prospects of integrating modern AI-orient…

MAPF-World: Action World Model for Multi-Agent Path Finding
Zhanjiang Yang, Meng Li, Yang Shen, Yueming Li, Lijun Sun
https://arxiv.org/abs/2508.12087 https://

MAPF-World: Action World Model for Multi-Agent Path Finding
Multi-agent path finding (MAPF) is the problem of planning conflict-free paths from the designated start locations to goal positions for multiple agents. It underlies a variety of real-world tasks, including multi-robot coordination, robot-assisted logistics, and social navigation. Recent decentralized learnable solvers have shown great promise for large-scale MAPF, especially when leveraging foundation models and large datasets. However, these agents are reactive policy models and exhibit limi…

SAVOR: Skill Affordance Learning from Visuo-Haptic Perception for Robot-Assisted Bite Acquisition
Zhanxin Wu, Bo Ai, Tom Silver, Tapomayukh Bhattacharjee
https://arxiv.org/abs/2506.02353

SAVOR: Skill Affordance Learning from Visuo-Haptic Perception for Robot-Assisted Bite Acquisition
Robot-assisted feeding requires reliable bite acquisition, a challenging task due to the complex interactions between utensils and food with diverse physical properties. These interactions are further complicated by the temporal variability of food properties-for example, steak becomes firm as it cools even during a meal. To address this, we propose SAVOR, a novel approach for learning skill affordances for bite acquisition-how suitable a manipulation skill (e.g., skewering, scooping) is for a …

Proceedings 18th Interaction and Concurrency Experience
Cl\'ement Aubert (Augusta University), Cinzia Di Giusto (Universit\'e C\^ote d'Azur, CNRS, I3SSophia Antipolis), Simon Fowler (University of Glasgow School of Computing Science), Violet Ka I Pun (Western Norway University of Applied Sciences)
https://arxiv.org/abs/2508.123…

Proceedings 18th Interaction and Concurrency Experience
This volume contains the proceedings of ICE'25, the 18th Interaction and Concurrency Experience, which was held on Friday 20th June 2025 at the École National Supérieure des Arts et Métiers in Lille, France, as a satellite workshop of DisCoTec 2025. The ICE workshop series features a distinguishing review and selection procedure: PC members are encouraged to interact, anonymously, with authors. The 2025 edition of ICE received 7 submissions, each reviewed by three PC members, and about 75 co…

A Humanoid Social Robot as a Teaching Assistant in the Classroom
Thomas Sievers
https://arxiv.org/abs/2508.05646 https://arxiv.org/pdf/2508.05646

A Humanoid Social Robot as a Teaching Assistant in the Classroom
Although innovation and the support of new technologies are much needed to ease the burden on the education system, social robots in schools to help teachers with educational tasks are rare. Child-Robot Interaction (CRI) could support teachers and add an embodied social component to modern multi-modal and multi-sensory learning environments already in use. The social robot Pepper, connected to the Large Language Model (LLM) ChatGPT, was used in a high school classroom to teach new learning cont…

Properties of slow magneto-acoustic waves observed simultaneously using Hi-C 2.1 and AIA
Suraj K. Tripathy, S. Krishna Prasad, D. Banerjee
https://arxiv.org/abs/2506.06126

Properties of slow magneto-acoustic waves observed simultaneously using Hi-C 2.1 and AIA
Propagating slow magneto-acoustic waves are commonly observed in different coronal structures but are most prominent in active region fan loops. Their rapid damping with damping lengths of the order of a wavelength has been investigated in the past by several authors. Although different physical mechanisms have been proposed, significant discrepancies between the theory and observations remain. Recent high-resolution observations captured simultaneously by two different instruments reveal disti…

Quantitative multi-metabolite imaging of Parkinson's disease using AI boosted molecular MRI
Hagar Shmuely (School of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel), Michal Rivlin (School of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel), Or Perlman (School of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel, Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel)

Quantitative multi-metabolite imaging of Parkinson's disease using AI boosted molecular MRI
Traditional approaches for molecular imaging of Parkinson's disease (PD) in vivo require radioactive isotopes, lengthy scan times, or deliver only low spatial resolution. Recent advances in saturation transfer-based PD magnetic resonance imaging (MRI) have provided biochemical insights, although the image contrast is semi-quantitative and nonspecific. Here, we combined a rapid molecular MRI acquisition paradigm with deep learning based reconstruction for multi-metabolite quantification of gluta…

End-to-end Acoustic-linguistic Emotion and Intent Recognition Enhanced by Semi-supervised Learning
Zhao Ren, Rathi Adarshi Rammohan, Kevin Scheck, Sheng Li, Tanja Schultz
https://arxiv.org/abs/2507.07806

End-to-end Acoustic-linguistic Emotion and Intent Recognition Enhanced by Semi-supervised Learning
Emotion and intent recognition from speech is essential and has been widely investigated in human-computer interaction. The rapid development of social media platforms, chatbots, and other technologies has led to a large volume of speech data streaming from users. Nevertheless, annotating such data manually is expensive, making it challenging to train machine learning models for recognition purposes. To this end, we propose applying semi-supervised learning to incorporate a large scale of unlab…

This https://arxiv.org/abs/2504.00420 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation
Building a lifelong robot that can effectively leverage prior knowledge for continuous skill acquisition remains significantly challenging. Despite the success of experience replay and parameter-efficient methods in alleviating catastrophic forgetting problem, naively applying these methods causes a failure to leverage the shared primitives between skills. To tackle these issues, we propose Primitive Prompt Learning (PPL), to achieve lifelong robot manipulation via reusable and extensible primi…

Rapid MRI-Based Synthetic CT Simulations for Precise tFUS Targeting
Hengyu Gao, Shaodong Ding, Ziyang Liu, Jiefu Zhang, Bolun Li, Zhiwu An, Li Wang, Jing Jing, Tao Liu, Yubo Fan, Zhongtao Hu
https://arxiv.org/abs/2507.08688

Rapid MRI-Based Synthetic CT Simulations for Precise tFUS Targeting
Accurate targeting is critical for the effectiveness of transcranial focused ultrasound (tFUS) neuromodulation. While CT provides accurate skull acoustic properties, its ionizing radiation and poor soft tissue contrast limit clinical applicability. In contrast, MRI offers superior neuroanatomical visualization without radiation exposure but lacks skull property mapping. This study proposes a novel, fully CT free simulation framework that integrates MRI-derived synthetic CT (sCT) with efficient …