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527 results on '"Cheong, Kang Hao"'

54. Cross-Issue Solidarity and Truth Convergence in Opinion Dynamics

Author

Tan, Zong Xuan and Cheong, Kang Hao

Subjects
Physics - Physics and Society
Abstract

How do movements and coalitions which engage with multiple social issues succeed in cross-issue solidarity, and when do they instead become fragmented? To address this, the mechanisms of cross-issue interaction have to be understood. Prior work on opinion dynamics and political disagreement has focused on single-issue consensus and polarization. Inspired by practices of cross-issue movement building, we have developed a general model of multi-issue opinion dynamics where agreement on one issue can promote greater inclusivity in discussing other issues, thereby avoiding the pitfalls of exclusivist interaction, where individuals engage only if they agree sufficiently on every issue considered. Our model shows that as more issues come into play, consensus and solidarity can only be maintained if inclusivity towards differing positions is increased. We further investigate whether greater inclusivity and compromise across issues lead people towards or away from normative truth, thereby addressing concerns about the non-ideal nature of political consensus.

Published
2017
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59. A bio-functional polymer that prevents retinal scarring through modulation of NRF2 signalling pathway

Author

Parikh, Bhav Harshad, Liu, Zengping, Blakeley, Paul, Lin, Qianyu, Singh, Malay, Ong, Jun Yi, Ho, Kim Han, Lai, Joel Weijia, Bogireddi, Hanumakumar, Tran, Kim Chi, Lim, Jason Y. C., Xue, Kun, Al-Mubaarak, Abdurrahmaan, Yang, Binxia, R, Sowmiya, Regha, Kakkad, Wong, Daniel Soo Lin, Tan, Queenie Shu Woon, Zhang, Zhongxing, Jeyasekharan, Anand D., Barathi, Veluchamy Amutha, Yu, Weimiao, Cheong, Kang Hao, Blenkinsop, Timothy A., Hunziker, Walter, Lingam, Gopal, Loh, Xian Jun, and Su, Xinyi

Published
2022
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72. Using opinion dynamics to identify groups for targeted intervention in lifelong learning: A case study of SkillsFuture in Singapore.

Author

Lim, Zhi Yong, Munshi, Tahir, Yap, Jun Hong, Mishra, Ankit, Lai, Joel Weijia, Yeo, Darren J., and Cheong, Kang Hao

Subjects
GROUP dynamics, ATTITUDE change (Psychology), LEARNING, CO-sleeping, WALKING speed
Abstract

In this paper, we explore the use of opinion dynamics models to examine changes in opinions of lifelong learning, and to identify groups of people for targeted intervention to improve their reception to lifelong learning. This method has a multitude of potential applications related to clustering and segmentation. Singapore's initiative for lifelong learning, SkillsFuture, serves as a case study for the application of the technique. We first found a positive correlation between how motivated people are to learn and a negative correlation between how much people know about SkillsFuture opportunities, with respect to their reception to SkillsFuture courses. Then, we used an opinion dynamics model to model the evolution of opinions to produce clusters of people whose opinions will converge. The clusters were then grouped into three groups: those with the largest increase, largest decrease and a middle group. Further examining the covariates of the people in each cluster (e.g., age, last drawn salary, etc.), we propose organizing sharing sessions for the group with the largest increase, intervention for the group with the largest decrease, and gathering more information about the motivations of the middle group. [ABSTRACT FROM AUTHOR]

Published
2024
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87. Nonsingular Predefined Time Adaptive Dynamic Surface Control for Quantized Nonlinear Systems

Author

Xu, Hao, Yu, Dengxiu, Wang, Zhen, Cheong, Kang Hao, and Chen, C. L. Philip

Abstract

This article focuses on the singularity-free predefined time control design problem of the quantized nonstrict feedback (NSF) nonlinear systems. Radial basis function neural networks (NNs) are introduced to model the unknown nonlinear dynamics. With the property of the NN basis function, the algebraic loop problem posed by the NSF control structures is addressed. The input quantization is addressed by using the nonlinear decomposition technique. A nonlinear filter with predefined time stability is constructed to decrease the computational complexity. Further, by introducing the designed predefined time filter into the backstepping recursive framework, a predefined time dynamic surface control algorithm is developed, in which the improved adding power integration technique is introduced to prevent control singularities. The Lyapunov theory demonstrates the predefined time stability of the closed-loop quantized nonlinear systems. By the developed control algorithm, the controlled system can effectively track the specified command signal. Both the tracking and filtering errors can reach a small neighborhood around zero within the preset time. The validity and superiority of the proposed control algorithm are illustrated by an actual system simulation.

Published
2024
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88. Application of local configuration pattern for automated detection of schizophrenia with electroencephalogram signals.

Author

WeiKoh, Joel En, Rajinikanth, Venkatesan, Vicnesh, Jahmunah, Pham, The‐Hanh, Oh, Shu Lih, Yeong, Chai Hong, Sankaranarayanan, Meena, Kamath, Aditya, Bairy, Giliyar Muralidhar, Barua, Prabal Datta, and Cheong, Kang Hao

Subjects
ELECTROENCEPHALOGRAPHY, FEATURE extraction, SCHIZOPHRENIA, BRAIN abnormalities, COMPUTER software, MENTAL illness
Abstract

Recently, a mix of traditional and modern approaches have been proposed to detect brain abnormalities using bio‐signal/bio‐image‐assisted methods. In hospitals, most of the initial/scheduled assessments consider the bio‐signal‐based appraisal, due to its non‐invasive nature and low cost. Further, brain bio‐signal scans can be recorded using a single/multi‐channel electrode setup, which is further evaluated by an experienced doctor, as well as computer software, to identify the nature and severity of abnormality. In this paper, we describe the development of a system for computer supported detection (CSD) of schizophrenia using the electroencephalogram (EEG) signal collected with a 19‐channel electrode array. Schizophrenia is a mental illness that interferes with the way an individual thinks and behaves. It is characterised by psychotic symptoms such as hallucinations or delusions, negative symptoms such as decreased motivation or a lack of interest in daily activities and cognitive symptoms such challenges in processing information to make informed decisions or staying focused. This research has utilized 1142 EEGs (516 normal and 626 schizophrenia) with a frame length of 25 s (6250 samples) for investigation. The work initially converts the EEG signals to images using a spectrogram. Local configuration pattern features were extracted from the images thereafter, and 10‐fold validation technique was used wherein Student's t‐test and z‐score standardization were computed per fold. The highest accuracy of 97.20% was achieved with the K‐nearest neighbour (KNN) classifier. The results obtained confirm that the KNN classifier is helpful in the rapid detection of schizophrenia. This work is one of the first studies to extract local configuration pattern features from spectrogram images, yielding a high accuracy of 97.20%, with reduced computational complexity. [ABSTRACT FROM AUTHOR]

Published
2024
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