InCrowd-VI: A Realistic Visual-Inertial Dataset for Evaluating Simultaneous Localization and Mapping in Indoor Pedestrian-Rich Spaces for Human Navigation.

Simultaneous localization and mapping (SLAM) techniques can be used to navigate the visually impaired, but the development of robust SLAM solutions for crowded spaces is limited by the lack of realistic datasets. To address this, we introduce InCrowd-VI, a novel visual-inertial dataset specifically designed for human navigation in indoor pedestrian-rich environments. Recorded using Meta Aria Project glasses, it captures realistic scenarios without environmental control.

Mathematical expression recognition using a new deep neural model.

In this paper, we propose a novel deep neural model for Mathematical Expression Recognition (MER). The proposed model uses encoder-decoder transformer architecture that is supported by additional pre/post-processing modules, to recognize the image of mathematical formula and convert it to a well-formed language. A novel pre-processing module based on domain prior knowledge is proposed to generate random pads around the formula's image to create more efficient feature maps and keeps all the encoder neurons active during the training process.

Recommendations for Age-Appropriate Mobile Application Design.

This paper presents recommendations for avoiding or eliminating unnecessary barriers to mobile application usage by older generations. It sets out ten areas of age-appropriate application design. The aim of this paper is to help ensure that the design of mobile applications, including assistive applications, is accessible for elderly persons.