Building Footprint Extraction in Dense Areas using Super Resolution and Frame Field Learning

Despite notable results on standard aerial datasets, current state-of-the-arts fail to produce accurate building footprints in dense areas due to challenging properties posed by these areas and limited data availability. In this paper, we propose a framework to address such issues in polygonal building extraction. First, super resolution is employed to enhance the spatial resolution of aerial image, allowing for finer details to be captured. This enhanced imagery serves as input to a multitask learning module, which consists of a segmentation head and a frame field learning head to effectively handle the irregular building structures. Our model is supervised by adaptive loss weighting, enabling extraction of sharp edges and fine-grained polygons which is difficult due to overlapping buildings and low data quality. Extensive experiments on a slum area in India that mimics a dense area demonstrate that our proposed approach significantly outperforms the current state-of-the-art methods by a large margin.Comment: Accepted at The 12th International Conference on Awareness Science and Technolog

What Shapes Undergraduate Students’ Satisfaction in Unstable Learning Contexts?

This paper investigates what determinants, and to what extent, they influence students’ satisfaction in unstable learning contexts. Using a national-scaled sample of Vietnamese HEIs with a sound theoretical background, we find that regardless of instabilities from external shocks, the key factors that shape students’ satisfaction are fixed by traditional norms (self-efficacy, infrastructure, lecturer) rather than occasional factors occurring from each event. We find in particular that self-efficacy is the most influential factor for students’ satisfaction and friendship is the most prominent element that enhances students’ self- efficacy. Overall, this paper enriched the literature on student satisfaction, especially during unstable contexts. Thus, it has important implications for educators and HEIs stakeholders in management planning in the time to come

Attitudes toward Self-Disclosure on Facebook: A Review of Perception, Emotion and Behavior in University Students

A number of social networking platforms have emerged as a result of the development of information and communication technology which have become increasingly user-friendly and full with valuable features. The social networking site with most users is Facebook. Teenagers, particularly college students use Facebook most frequently to study, gain information, entertain themselves and connect with others through self-disclosing personal information on the Facebook profile page. This quantitative study aimed to analyze the attitude of pedagogical students regarding self-disclosure on Facebook as represented through cognition, emotion and behavior concerning academic achievement. The survey was completed by 535university student’s majority in pedagogy. There were 41 students who used it for less than three years between three to five years by 218 students and 276 students who use it more than five years. The questionnaire was self-reported by participants to assess university students' attitudes toward self-disclosure on Facebook. The results indicate that pedagogical students with excellent academic achievement and more than five years of Facebook experience had the highest-level attitude toward self-disclosure on Facebook. The results indicate a positive relationship between cognition, emotion and influence factors students' Facebook attitudes. Future research on methods that enhance student positive disclosure can benefit from this study. Future research should examine how self-disclosure on Facebook relates to other aspects, such as Facebook usage time, financial state and perception of advantages and its disadvantages of Facebook in order to evaluate students' attitudes objectively

Shape Gradient for Isogeometric Structural Design

International audienceThe transfer of geometrical data from CAD (Computer Aided Design) to FEA (Finite-Element Analysis) is a bottleneck of automated design optimization procedures, yielding a loss of accuracy and cumbersome software couplings. Isogeometric analysis methods propose a new paradigm, that allows one to overcome these di fficulties by using a unique geometrical representation, yielding a direct relationship between geometry and analysis. In this study, in the framework of linear elasticity problems, we investigate its use for sensitivity analysis and, more speci cally, shape gradient computations

Shape Gradient Computation in Isogeometric Analysis for Linear Elasticity

The transfer of geometrical data from CAD (Computer Aided Design) to FEA (Finite-Element Analysis) is a bottleneck of automated design optimization procedures, yielding a loss of accuracy and cumbersome software couplings. Isogeometric analysis methods propose a new paradigm that allows to overcome these difficulties by using a unique geometrical representation that yields a direct relationship between geometry and analysis. In this study, we investigate its use for sensitivity analysis and more specifically shape gradient computations, in the framework of linear elasticity problems. The potential of isogeometric analysis methods for shape gradient computations is demonstrated for two- and three-dimensional design problems

Open-Vocabulary Affordance Detection in 3D Point Clouds

Affordance detection is a challenging problem with a wide variety of robotic applications. Traditional affordance detection methods are limited to a predefined set of affordance labels, hence potentially restricting the adaptability of intelligent robots in complex and dynamic environments. In this paper, we present the Open-Vocabulary Affordance Detection (OpenAD) method, which is capable of detecting an unbounded number of affordances in 3D point clouds. By simultaneously learning the affordance text and the point feature, OpenAD successfully exploits the semantic relationships between affordances. Therefore, our proposed method enables zero-shot detection and can be able to detect previously unseen affordances without a single annotation example. Intensive experimental results show that OpenAD works effectively on a wide range of affordance detection setups and outperforms other baselines by a large margin. Additionally, we demonstrate the practicality of the proposed OpenAD in real-world robotic applications with a fast inference speed (~100ms). Our project is available at https://openad2023.github.io.Comment: Accepted to The 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023

Language-driven Scene Synthesis using Multi-conditional Diffusion Model

Scene synthesis is a challenging problem with several industrial applications. Recently, substantial efforts have been directed to synthesize the scene using human motions, room layouts, or spatial graphs as the input. However, few studies have addressed this problem from multiple modalities, especially combining text prompts. In this paper, we propose a language-driven scene synthesis task, which is a new task that integrates text prompts, human motion, and existing objects for scene synthesis. Unlike other single-condition synthesis tasks, our problem involves multiple conditions and requires a strategy for processing and encoding them into a unified space. To address the challenge, we present a multi-conditional diffusion model, which differs from the implicit unification approach of other diffusion literature by explicitly predicting the guiding points for the original data distribution. We demonstrate that our approach is theoretically supportive. The intensive experiment results illustrate that our method outperforms state-of-the-art benchmarks and enables natural scene editing applications. The source code and dataset can be accessed at https://lang-scene-synth.github.io/.Comment: Accepted to NeurIPS 202

Impairment of Assets Under Perspectives of International and Vietnamese Accounting

This study investigates the impairment of assets on financial reportings of three separate entities - Vietnam Prosperity Joint-Stock Commercial Bank (VPBank), The Bank of East Asia (BEA) and The Nestlé Group - in the financial year of 2017. By bringing out the differences between international accounting system of asset impairment (IAS 36) and Vietnamese accounting system (VAS), this research has determined the gap between two sets of financial statements under IFRS and VAS in conveying the business performance and financial position. The empirical results show Vietnam’s necessary for a convergence in asset impairment with international accounting system. Therefore, we provide some recommendations for Vietnam in applying asset impairment regarding to IAS 36 partly or completely. Keywords: Asset impairment, International accounting, Vietnamese accounting. DOI: 10.7176/RJFA/10-12-07 Publication date:June 30th 2019

A Research on the Quality of Public Transportation Services by Bus in Vietnam

This study was conducted to assess the status of the quality of public passenger transport services by bus in Hanoi. Data were collected from regular passengers using buses as a means of transportation in the city, including passengers standing at stations, waiting shelters and on vehicles to make trips and students of some universities who use buses as a means of transportation. We employ descriptive statistics and hierarchical analysis to learn about the topic of research. The results indicate that the quality of public transport services by buses in Hanoi, which was judged by passengers quite well. In particular, the safety level, convenience, security and hygiene is up to 70%, which was higher than the highest quality level. Quality of fast level and reliability are low. Keywords: quality of services, public passenger transport, buses, Vietnam. DOI: 10.7176/RJFA/10-13-04 Publication date:July 31st 201