arXiv subjects

Garima Sharma

Publications and source records attributed to Garima Sharma.

3 recordsLinked to original sources

arXiv2026

Age Matters: Analyzing Age-Related Discussions in App Reviews

In recent years, mobile applications have become indispensable tools for managing various aspects of life. From enhancing productivity to providing personalized entertainment, mobile apps have revolutionized people's daily routines. Despite this rapid growth and popularity, gaps remain in how these apps address the needs of users from different age groups. Users of varying ages face distinct challenges when interacting with mobile apps, from younger users dealing with inappropriate content to older users having difficulty with usability due to age-related vision and cognition impairments. Although there have been initiatives to create age-inclusive apps, a limited understanding of user perspectives on age-related issues may hinder developers from recognizing specific challenges and implementing effective solutions. In this study, we explore age discussions in app reviews to gain insights into how mobile apps should cater to users across different age groups.We manually curated a dataset of 4,163 app reviews from the Google Play Store and identified 1,429 age-related reviews and 2,734 non-age-related reviews. We employed eight machine learning, deep learning, and large language models to automatically detect age discussions, with RoBERTa performing the best, achieving a precision of 92.46%. Additionally, a qualitative analysis of the 1,429 age-related reviews uncovers six dominant themes reflecting user concerns.

cs.SE↗

arXiv2023

Emolysis: A Multimodal Open-Source Group Emotion Analysis and Visualization Toolkit

Automatic group emotion recognition plays an important role in understanding complex human-human interaction. This paper introduces, Emolysis, a Python-based, standalone open-source group emotion analysis toolkit for use in different social situations upon getting consent from the users. Given any input video, Emolysis processes synchronized multimodal input and maps it to group level emotion, valence and arousal. Additionally, the toolkit supports major mobile and desktop platforms (Android, iOS, Windows). The Emolysis platform also comes with an intuitive graphical user interface that allows users to select different modalities and target persons for more fine-grained emotion analysis. Emolysis is freely available for academic research and encourages application developers to extend it to application specific environments on top of the existing system. We believe that the extension mechanism is quite straightforward. Our code models and interface are available at https://github.com/ControlNet/emolysis.

cs.HC↗

arXiv2020

Speak2Label: Using Domain Knowledge for Creating a Large Scale Driver Gaze Zone Estimation Dataset

Labelling of human behavior analysis data is a complex and time consuming task. In this paper, a fully automatic technique for labelling an image based gaze behavior dataset for driver gaze zone estimation is proposed. Domain knowledge is added to the data recording paradigm and later labels are generated in an automatic manner using Speech To Text conversion (STT). In order to remove the noise in the STT process due to different illumination and ethnicity of subjects in our data, the speech frequency and energy are analysed. The resultant Driver Gaze in the Wild (DGW) dataset contains 586 recordings, captured during different times of the day including evenings. The large scale dataset contains 338 subjects with an age range of 18-63 years. As the data is recorded in different lighting conditions, an illumination robust layer is proposed in the Convolutional Neural Network (CNN). The extensive experiments show the variance in the dataset resembling real-world conditions and the effectiveness of the proposed CNN pipeline. The proposed network is also fine-tuned for the eye gaze prediction task, which shows the discriminativeness of the representation learnt by our network on the proposed DGW dataset. Project Page: https://sites.google.com/view/drivergazeprediction/home

cs.CV↗