Tootfinder

Analysis of User Experience Evaluation Methods for Deaf users: A Case Study on a mobile App
A. E. Fuentes-Cort\'azar, A. Rivera-Hern\'andez, J. R. Rojano-C\'aceres
https://arxiv.org/abs/2507.22455

Analysis of User Experience Evaluation Methods for Deaf users: A Case Study on a mobile App
User Experience (UX) evaluation methods that are commonly used with hearing users may not be functional or effective for Deaf users. This is because these methods are primarily designed for users with hearing abilities, which can create limitations in the interaction, perception, and understanding of the methods for Deaf individuals. Furthermore, traditional UX evaluation approaches often fail to address the unique accessibility needs of Deaf users, resulting in an incomplete or biased assessme…

An analysis of the top 100 trending TikTok videos under #mentalhealthtips finds 52 contain misinformation, including misused language and quick-fix methods (The Guardian)

An empirical comparison of some outlier detection methods with longitudinal data
Marcello D'Orazio
https://arxiv.org/abs/2507.21203 https://arxiv.org/p…

An empirical comparison of some outlier detection methods with longitudinal data
This note investigates the problem of detecting outliers in longitudinal data. It compares well-known methods used in official statistics with proposals from the fields of data mining and machine learning that are based on the distance between observations or binary partitioning trees. This is achieved by applying the methods to panel survey data related to different types of statistical units. Traditional methods are quite simple, enabling the direct identification of potential outliers, but t…

Principled Curriculum Learning using Parameter Continuation Methods
Harsh Nilesh Pathak, Randy Paffenroth
https://arxiv.org/abs/2507.22089 https://arxiv.or…

Principled Curriculum Learning using Parameter Continuation Methods
In this work, we propose a parameter continuation method for the optimization of neural networks. There is a close connection between parameter continuation, homotopies, and curriculum learning. The methods we propose here are theoretically justified and practically effective for several problems in deep neural networks. In particular, we demonstrate better generalization performance than state-of-the-art optimization techniques such as ADAM for supervised and unsupervised learning tasks.

An analysis of the top 100 trending TikTok videos under #mentalhealthtips finds 52 contain misinformation, including misused language and quick-fix methods (The Guardian)

Iterative Methods for the Projected Solutions of Quasi-equilibrium Problems
Didier Aussel, Jauny, Asrifa Sultana, Shivani Valecha
https://arxiv.org/abs/2506.22080

Iterative Methods for the Projected Solutions of Quasi-equilibrium Problems
Aussel et al. (J Optim Theory Appl 170 818-837 2016) introduced the concept of projected solutions for the quasi-variational inequalities with a non-self constraint map, that is, the case where the constraint map may take values outside the feasible set. This paper presents two iterative methods to determine the projected solutions for quasi-equilibrium problems (QEP). We prove the convergence of the sequence generated by iterative methods to a projected solution of the QEP under some suitable …

Image-Guided Shape-from-Template Using Mesh Inextensibility Constraints
Thuy Tran, Ruochen Chen, Shaifali Parashar
https://arxiv.org/abs/2507.22699 https://

Image-Guided Shape-from-Template Using Mesh Inextensibility Constraints
Shape-from-Template (SfT) refers to the class of methods that reconstruct the 3D shape of a deforming object from images/videos using a 3D template. Traditional SfT methods require point correspondences between images and the texture of the 3D template in order to reconstruct 3D shapes from images/videos in real time. Their performance severely degrades when encountered with severe occlusions in the images because of the unavailability of correspondences. In contrast, modern SfT methods use a c…

ODE Methods for Computing One-Dimensional Self-Motion Manifolds
Dominic Guri, George Kantor
https://arxiv.org/abs/2507.21957 https://arxiv.org/pdf/2507.219…

ODE Methods for Computing One-Dimensional Self-Motion Manifolds
Redundant manipulators are well understood to offer infinite joint configurations for achieving a desired end-effector pose. The multiplicity of inverse kinematics (IK) solutions allows for the simultaneous solving of auxiliary tasks like avoiding joint limits or obstacles. However, the most widely used IK solvers are numerical gradient-based iterative methods that inherently return a locally optimal solution. In this work, we explore the computation of self-motion manifolds (SMMs), which repre…

SAEL: Leveraging Large Language Models with Adaptive Mixture-of-Experts for Smart Contract Vulnerability Detection
Lei Yu, Shiqi Cheng, Zhirong Huang, Jingyuan Zhang, Chenjie Shen, Junyi Lu, Li Yang, Fengjun Zhang, Jiajia Ma
https://arxiv.org/abs/2507.22371

SAEL: Leveraging Large Language Models with Adaptive Mixture-of-Experts for Smart Contract Vulnerability Detection
With the increasing security issues in blockchain, smart contract vulnerability detection has become a research focus. Existing vulnerability detection methods have their limitations: 1) Static analysis methods struggle with complex scenarios. 2) Methods based on specialized pre-trained models perform well on specific datasets but have limited generalization capabilities. In contrast, general-purpose Large Language Models (LLMs) demonstrate impressive ability in adapting to new vulnerability pa…

Genuinely multi-dimensional stationarity preserving global flux Finite Volume formulation for nonlinear hyperbolic PDEs
Wasilij Barsukow, Mirco Ciallella, Mario Ricchiuto, Davide Torlo
https://arxiv.org/abs/2506.21700

Genuinely multi-dimensional stationarity preserving global flux Finite Volume formulation for nonlinear hyperbolic PDEs
Classical Finite Volume methods for multi-dimensional problems include stabilization (e.g. via a Riemann solver), that is derived by considering several one-dimensional problems in different directions. Such methods therefore ignore a possibly existing balance of contributions coming from different directions, such as the one characterizing multi-dimensional stationary states. Instead being preserved, they are usually diffused away by such methods. Stationarity preserving methods use a better s…