Measurement of hard-life experiences through the psychological sense: the SEM approach

Autor: Bui Thi Thanh Mai, Tran Manh Dung, Vu Tuan Anh

Publikováno v: Journal of International Economics and Management, Vol 23, Iss 2This paper aims to develop a scale for hard-life experiences through the psychological sense. Past hardships can impact individuals' cognition, stress levels, and resilience. This scale can be applied in various research areas, such as psychology, health, behavior, and decision-making in organizations. Learning about hard-life experiences is one aspect of longitudinal studies on the historical life of key leaders, who are business owners, crucial managers, and entrepreneurs focusing on the traumatic feeling during lifetime living from childhood to adulthood. A total of 149 key leaders who are business owners, crucial managers, and entrepreneurs in Vietnam responded to a survey. Structural equation modeling investigated the validity and reliability of hard-life experience measurement scales with two coherent constructs. The findings of this research discovered two sub-scales chronic and acute hard-life experiences to measure key leaders’ hardship during their lifetime, equivalenting for the period of childhood up to adolescence, and the adult period of individual key leaders essentially. This study makes a significant contribution by introducing a new benchmark to the existing literature on the experiences of business owners, crucial managers, and entrepreneurs. Scholars can later use this scale to investigate relationships between hard-life experience factors and others by quantitative methods., Pp 1-22 (2023)

This paper aims to develop a scale for hard-life experiences through the psychological sense. Past hardships can impact individuals' cognition, stress levels, and resilience. This scale can be applied in various research areas, such as psychology, he

Externí odkaz: https://doaj.org/article/dec3d85d8a1b4e349dbf3c1a6f1aea6b

Towards Transferable Attacks Against Vision-LLMs in Autonomous Driving with Typography

Autor: Chung, Nhat, Gao, Sensen, Vu, Tuan-Anh, Zhang, Jie, Liu, Aishan, Lin, Yun, Dong, Jin Song, Guo, Qing

Vision-Large-Language-Models (Vision-LLMs) are increasingly being integrated into autonomous driving (AD) systems due to their advanced visual-language reasoning capabilities, targeting the perception, prediction, planning, and control mechanisms. Ho

Externí odkaz: http://arxiv.org/abs/2405.14169

Vision-Aware Text Features in Referring Image Segmentation: From Object Understanding to Context Understanding

Autor: Nguyen-Truong, Hai, Nguyen, E-Ro, Vu, Tuan-Anh, Tran, Minh-Triet, Hua, Binh-Son, Yeung, Sai-Kit

Referring image segmentation is a challenging task that involves generating pixel-wise segmentation masks based on natural language descriptions. The complexity of this task increases with the intricacy of the sentences provided. Existing methods hav

Externí odkaz: http://arxiv.org/abs/2404.08590

Exploring Boundary of GPT-4V on Marine Analysis: A Preliminary Case Study

Autor: Zheng, Ziqiang, Chen, Yiwei, Zhang, Jipeng, Vu, Tuan-Anh, Zeng, Huimin, Tim, Yue Him Wong, Yeung, Sai-Kit

Large language models (LLMs) have demonstrated a powerful ability to answer various queries as a general-purpose assistant. The continuous multi-modal large language models (MLLM) empower LLMs with the ability to perceive visual signals. The launch o

Externí odkaz: http://arxiv.org/abs/2401.02147

Test-Time Augmentation for 3D Point Cloud Classification and Segmentation

Autor: Vu, Tuan-Anh, Sarkar, Srinjay, Zhang, Zhiyuan, Hua, Binh-Son, Yeung, Sai-Kit

Data augmentation is a powerful technique to enhance the performance of a deep learning task but has received less attention in 3D deep learning. It is well known that when 3D shapes are sparsely represented with low point density, the performance of

Externí odkaz: http://arxiv.org/abs/2311.13152