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397 results on '"Leong, Hong Va"'

1. Understanding Impacts of Electromagnetic Signal Injection Attacks on Object Detection

Author

Zhang, Youqian, Yang, Chunxi, Fu, Eugene Y., Jiang, Qinhong, Yan, Chen, Chau, Sze-Yiu, Ngai, Grace, Leong, Hong-Va, Luo, Xiapu, and Xu, Wenyuan

Subjects
Computer Science - Cryptography and Security, Computer Science - Computer Vision and Pattern Recognition
Abstract

Object detection can localize and identify objects in images, and it is extensively employed in critical multimedia applications such as security surveillance and autonomous driving. Despite the success of existing object detection models, they are often evaluated in ideal scenarios where captured images guarantee the accurate and complete representation of the detecting scenes. However, images captured by image sensors may be affected by different factors in real applications, including cyber-physical attacks. In particular, attackers can exploit hardware properties within the systems to inject electromagnetic interference so as to manipulate the images. Such attacks can cause noisy or incomplete information about the captured scene, leading to incorrect detection results, potentially granting attackers malicious control over critical functions of the systems. This paper presents a research work that comprehensively quantifies and analyzes the impacts of such attacks on state-of-the-art object detection models in practice. It also sheds light on the underlying reasons for the incorrect detection outcomes., Comment: 2024 IEEE International Conference on Multimedia and Expo (ICME), July 15 - July 19, 2024, Niagra Falls, Ontario, Canada

Published
2024

2. Using Attention-Based Neural Networks for Predicting Student Learning Outcomes in Service-Learning

Author

Fu, Eugene Yujun, Ngai, Grace, Leong, Hong Va, Chan, Stephen C. F., and Shek, Daniel T. L.

Abstract

As a high-impact educational practice, service-learning has demonstrated success in positively influencing students' overall development, and much work has been done on investigating student learning outcomes from service-learning. A particular direction is to model students' learning outcomes in the context of their learning experience, i.e., the various student, course, and pedagogical elements. It contributes to a better understanding of the learning process, a more accurate prediction of students' attainments on the learning outcomes, and improvements in the design of learning activities to maximize student learning. However, most of the existing work in this area relies on statistical analysis that makes assumptions about attribute independence or simple linear dependence, which may not accurately reflect real-life scenarios. In contrast, the study described in this paper adopted a neural network-based approach to investigate the impact of students' learning experience on different service-learning outcomes. A neural network with attention mechanisms was constructed to predict students' service-learning outcomes by modeling the contextual information from their various learning experiences. In-depth evaluation experiments on a large-scale dataset collected from more than 10,000 students showed that this proposed model achieved better accuracy on predicting service-learning outcomes. More importantly, it could capture the interdependence between different aspects of student learning experience and the learning outcomes. We believe that this framework can be extended to student modeling for other types of learning activities.

Published
2023
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3. Comparison of Workload and Academic Performances of Transfer and Native Students in an Asian Educational Context

Author

Cheung, Kin, Chan, Eric S. W., Ng, Jeremy, Tsang, Hilda, and Leong, Hong-va

Abstract

Post-secondary admission and community college (CC) transfer are two common routes of entry to baccalaureate study. Previous studies comparing the academic performances of native and transfer students have generated inconsistent findings and, furthermore, they were largely conducted in Western educational contexts. This study compared the workloads, grade point averages (GPA) and attrition rates of native and transfer students in an Asian educational context. Various measures related to GPA, credit load and attrition rate were collected from the institutional dataset of a Hong Kong university in 2019; this dataset contained records of 14,141 native students and 7308 transfer students enrolled between 2012 and 2018. Results show that the transfer students experienced transfer shock and had heavier study loads but lighter non-academic loads than the native students. Despite the transfer shock, they attained a higher award GPA and had a lower attrition rate than their native counterparts. Discussion and implications are presented, in relation to transfer shock and transfer student success. In particular, this article argues that CC education does not necessarily produce academically inferior degree-seeking students and that CC transfer is a viable pathway to baccalaureate study.

Published
2022
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6. Moment-to-Moment Detection of Internal Thought from Eye Vergence Behaviour

Author

Huang, Michae Xuelin, Li, Jiajia, Ngai, Grace, Leong, Hong Va, and Bulling, Andreas

Subjects
Computer Science - Human-Computer Interaction
Abstract

Internal thought refers to the process of directing attention away from a primary visual task to internal cognitive processing. Internal thought is a pervasive mental activity and closely related to primary task performance. As such, automatic detection of internal thought has significant potential for user modelling in intelligent interfaces, particularly for e-learning applications. Despite the close link between the eyes and the human mind, only a few studies have investigated vergence behaviour during internal thought and none has studied moment-to-moment detection of internal thought from gaze. While prior studies relied on long-term data analysis and required a large number of gaze characteristics, we describe a novel method that is computationally light-weight and that only requires eye vergence information that is readily available from binocular eye trackers. We further propose a novel paradigm to obtain ground truth internal thought annotations that exploits human blur perception. We evaluate our method for three increasingly challenging detection tasks: (1) during a controlled math-solving task, (2) during natural viewing of lecture videos, and (3) during daily activities, such as coding, browsing, and reading. Results from these evaluations demonstrate the performance and robustness of vergence-based detection of internal thought and, as such, open up new directions for research on interfaces that adapt to shifts of mental attention.

Published
2019

7. Curvable Image Markers: Toward Trackable Markers for Every Surface

Author

Sin, Zackary P. T., Ng, Peter H. F., Leong, Hong Va, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Delir Haghighi, Pari, editor, Khalil, Ismail, editor, and Kotsis, Gabriele, editor

Published
2022
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8. Event Cube for Suicidal Event Analysis: A Case Study

Author

Li, Qing, Yan, Zhihan, Li, Jun, Yang, Zhenguo, Lin, Zehang, Leong, Hong Va, Chen, Lei, Yu, Nancy Xiaonan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhang, Wenjie, editor, Zou, Lei, editor, Maamar, Zakaria, editor, and Chen, Lu, editor

Published
2021
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9. Suicide Ideation Detection on Social Media During COVID-19 via Adversarial and Multi-task Learning

Author

Li, Jun, Yan, Zhihan, Lin, Zehang, Liu, Xingyun, Leong, Hong Va, Yu, Nancy Xiaonan, Li, Qing, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, U, Leong Hou, editor, Spaniol, Marc, editor, Sakurai, Yasushi, editor, and Chen, Junying, editor

Published
2021
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13. Transferring Object Layouts from Virtual to Physical Rooms: Towards Adapting a Virtual Scene to a Physical Scene for VR

Author

Sin, Zackary P. T., Ng, Peter H. F., Shiu, Simon C. K., Chung, Fu-lai, Leong, Hong Va, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Gavrilova, Marina, editor, Chang, Jian, editor, Thalmann, Nadia Magnenat, editor, Hitzer, Eckhard, editor, and Ishikawa, Hiroshi, editor

Published
2019
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14. Multi-level Motion-Informed Approach for Video Generation with Key Frames

Author

Sin, Zackary P. T., Ng, Peter H. F., Shiu, Simon C. K., Chung, Fu-lai, Leong, Hong Va, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Gavrilova, Marina, editor, Chang, Jian, editor, Thalmann, Nadia Magnenat, editor, Hitzer, Eckhard, editor, and Ishikawa, Hiroshi, editor

Published
2019
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24. Quantifying Reviewer Credibility in Online Tourism

Author

Wang, Yuanyuan, Chan, Stephen Chi Fai, Ngai, Grace, Leong, Hong-Va, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Decker, Hendrik, editor, Lhotská, Lenka, editor, Link, Sebastian, editor, Basl, Josef, editor, and Tjoa, A Min, editor

Published
2013
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27. Discovering User Interface Requirements of Search Results for Mobile Clients by Contextual Inquiry

Author

Chan, David L., Luk, Robert W. P., Leong, Hong Va, Ho, Edward K. S., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Salvendy, Gavriel, editor, and Smith, Michael J., editor

Published
2009
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28. Designing I*CATch: A Multipurpose, Education-Friendly Construction Kit for Physical and Wearable Computing

Author

Ngai, Grace, Chan, Stephen C. F., Leong, Hong Va, and Ng, Vincent T. Y.

Abstract

This article presents the design and development of i*CATch, a construction kit for physical and wearable computing that was designed to be scalable, plug-and-play, and to provide support for iterative and exploratory learning. It consists of a standardized construction interface that can be adapted for a wide range of soft textiles or electronic boards, a set of functional components, and an easy-to-use hybrid text-graphical integrated development environment. The objective was to design an easily usable, manufacturable and extensible construction kit that can be used in a wide range of teaching tasks for a wide variety of student demographic profiles. We present detailed specifications of our construction kit and explain some of the major design decisions. Experiences in using the kit in multiple teaching environments, ranging from elementary school to postgraduate, demonstrate that the design objectives have been achieved.

Published
2013
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29. Aqua: An Adaptive QUery-Aware Location Updating Scheme for Mobile Objects

Author

Zhou, Jing, Leong, Hong Va, Lu, Qin, Lee, Ken C. K., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Zhou, Lizhu, editor, Ooi, Beng Chin, editor, and Meng, Xiaofeng, editor

Published
2005
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30. Semantic Role Labeling Using Maximum Entropy

Author

Lan, Kwok Cheung, Ho, Kei Shiu, Luk, Robert Wing Pong, Leong, Hong Va, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Zhang, Jun, editor, He, Ji-Huan, editor, and Fu, Yuxi, editor

Published
2005
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33. Leadership Maintenance in Group-Based Location Management Scheme

Author

Lam, Gary Hoi Kit, Leong, Hong Va, Chan, Stephen Chi Fai, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Meersman, Robert, editor, and Tari, Zahir, editor

Published
2004
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45. Neural Proxy: Empowering Neural Volume Rendering for Animation

Author

Sin, Zackary P. T., Ng, Peter H. F., and Leong, Hong Va

Subjects
Computer graphics, Machine learning, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Computing methodologies, ComputingMethodologies_COMPUTERGRAPHICS
Abstract

Achieving photo-realistic result is an enticing proposition for the computer graphics community. Great progress has been achieved in the past decades, but the cost of human expertise has also grown. Neural rendering is a promising candidate for reducing this cost as it relies on data to construct the scene representation. However, one key component for adapting neural rendering for practical use is currently missing: animation. There seems to be a lack of discussion on how to enable neural rendering works for synthesizing frames for unseen animations. To fill this research gap, we propose neural proxy, a novel neural rendering model that utilizes animatable proxies for representing photo-realistic targets. Via a tactful combination of components from neural volume rendering and neural texture, our model is able to render unseen animations without any temporal learning. Experiment results show that the proposed model significantly outperforms current neural rendering works., Pacific Graphics Short Papers, Posters, and Work-in-Progress Papers, Neural Rendering and 3D Models, 31, 36, Zackary P. T. Sin, Peter H. F. Ng, and Hong Va Leong, CCS Concepts: Computing methodologies --> Computer graphics; Machine learning

Published
2021
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49. Fully Automatic Intervertebral Disc Segmentation Using Multimodal 3D U-Net

Author

Wang, Chuanbo, Guo, Ye, Chen, Wei, Yu, Zeyun, Getov, Vladimir, Gaudiot, Jean-Luc, Yamai, Nariyoshi, Cimato, Stelvio, Chang, Morris, Teranishi, Yuuichi, Yang, Ji-Jiang, Leong, Hong Va, Shahriar, Hossian, Takemoto, Michiharu, Towey, Dave, Takakura, Hiroki, Elci, Atilla, Takeuchi, Susumu, Puri, Satish, and Graduate School

Subjects
FOS: Computer and information sciences, Computer science, business.industry, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, Pattern recognition, Intervertebral disc, 02 engineering and technology, Image segmentation, Electrical Engineering and Systems Science - Image and Video Processing, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, medicine.anatomical_structure, 0202 electrical engineering, electronic engineering, information engineering, Medical imaging, medicine, FOS: Electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Segmentation, Artificial intelligence, business
Abstract

Intervertebral discs (IVDs), as small joints lying between adjacent vertebrae, have played an important role in pressure buffering and tissue protection. The fully-automatic localization and segmentation of IVDs have been discussed in the literature for many years since they are crucial to spine disease diagnosis and provide quantitative parameters in the treatment. Traditionally hand-crafted features are derived based on image intensities and shape priors to localize and segment IVDs. With the advance of deep learning, various neural network models have gained great success in image analysis including the recognition of intervertebral discs. Particularly, U-Net stands out among other approaches due to its outstanding performance on biomedical images with a relatively small set of training data. This paper proposes a novel convolutional framework based on 3D U-Net to segment IVDs from multi-modality MRI images. We first localize the centers of intervertebral discs in each spine sample and then train the network based on the cropped small volumes centered at the localized intervertebral discs. A detailed comprehensive analysis of the results using various combinations of multi-modalities is presented. Furthermore, experiments conducted on 2D and 3D U-Nets with augmented and non-augmented datasets are demonstrated and compared in terms of Dice coefficient and Hausdorff distance. Our method has proved to be effective with a mean segmentation Dice coefficient of 89.0% and a standard deviation of 1.4%.

Published
2020
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50. Round-Eye: A system for tracking nearest surrounders in moving object environments

Author

Lee, Ken C.K., Schiffman, Josh, Zheng, Baihua, Lee, Wang-Chien, and Leong, Hong Va

Abstract

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jss.2007.03.007 Byline: Ken C.K. Lee (a), Josh Schiffman (a), Baihua Zheng (b), Wang-Chien Lee (a), Hong Va Leong (c) Keywords: Moving objects; Nearest surrounder; Pervasive computing Abstract: This paper presents "Round-Eye", a system for tracking nearest surrounding objects (or nearest surrounders) in moving object environments. This system provides a platform for surveillance applications. The core part of this system is continuous nearest surrounder (NS) query that maintains views of the nearest objects at distinct angles from query points. This query differs from conventional spatial queries such as range queries and nearest neighbor queries as NS query considers both distance and angular aspects of objects with respect to a query point at the same time. In our system framework, a centralized server is dedicated (1) to collect location updates of both objects and queries, (2) to determine which NS queries are invalidated in presence of object/query location changes and corresponding result changes if any, and (3) to refresh the affected query answers. To enhance the system performance in terms of processing time and network bandwidth consumption, we propose various techniques, namely, safe region, partial query reevaluation, and incremental query result update. Through simulations, we evaluate our system with the proposed techniques over a wide range of settings. Author Affiliation: (a) Department of Computer Science and Engineering, Pennsylvania State University, PA, USA (b) School of Information Systems, Singapore Management University, Singapore (c) Department of Computing, The Hong Kong Polytechnic University, Hong Kong

Published
2007

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