Singapore University of Social Sciences
Recent publications
Electroencephalography (EEG) signal is an important physiological signal commonly used in machine learning to decode brain activities, including imagined words and sentences. We aimed to develop an automated lightweight EEG signal-based sentence classification model using a novel dynamic-sized binary pattern (DSBP) textural feature extractor and iterative multi-classifiers based majority voting (IMCMV) algorithm for iterative voting of results calculated using different classifiers for multi-channel EEG signal inputs. A new Turkish sentence EEG(TSEEG) was prospectively acquired. It comprised of 15-second 14-channel EEG signals recorded when 40 volunteers (for each dataset, we collected EEG signals from 20 participants) were either shown or read corresponding to demonstration or listening modes, respectively. Hence, 20 standardized commonly used sentences were obtained in their native Turkish language. The developed sentence classification model extracted 5,400 multilevel deep features from each channel EEG signal segment using the novel DSBP, statistical features, and multilevel discrete wavelet transform (MDWT). 512 features were then chosen using the neighborhood component analysis selection function. k-nearest neighbor and support vector machine classifiers were used to calculate two prediction vectors from the selected features using tenfold cross-validation, i.e., 28 vectors were generated for each 14-channel EEG recording. Finally, the best general voted results were determined for increasing numbers of iteratively calculated prediction vectors using the novel IMCMV algorithm. Channel-wise and voted results were found to be excellent for sentence classification for the TSEEG dataset in both demonstration and listening modes. The DSBP-IMCMV-based model attained the best general classification rates of 98.81% and 98.19% in the demonstration and listening modes, respectively.
Introduction/context Healthcare workers (HCWs) play an important role in fighting against the COVID-19 pandemic. However, they have been exposed to mixed public responses more significantly during the COVID-19 pandemic, which have potentially affected their work and life. Aim We aim to study what public responses toward HCWs existed, how and why such public responses impacted HCW’s work engagement and well-being, and how Human Resource (HR) professionals navigate these impacts. These understandings are important for improving HCWs’ work and life quality. Methods We adopted a mixed approach including both quantitative and qualitative methods to investigate how the public responses impact HCWs’ work engagement and well-being and how human resource management (HRM) shall intervene. Our quantitative study enables us to collect and analyze a large amount of public responses toward HCWs from the social media platform during the COVID-19 pandemic globally, and uncover the sentiments and topics of these pubic responses via big data and AI technologies. Our qualitative study allows us to understand how and why these public responses impact HCWs’ work engagement and well-being via interviews and further identify how HR professionals shall navigate these impacts. Results The sentiment analysis showed that 55.9% of the discussions toward HCWs were positive, 27.2% were neutral, and 16.9% were negative. The topic modeling analysis indicated that the commonly identified topics were related to fear (the negative responses) and gratitude (the positive responses). The interviews with 18 HCWs revealed that HCWs’ work engagement and well-being were decreased by negative public responses through experiencing tension or disappointment due to social and physical ostracism, rejection, discrimination, and criticism. On the other hand, positive public responses in terms of encouragement, recognition, and tangible donations increased their work engagement and well-being. The analysis also suggested that occupational calling served as a mechanism that explained why public responses had such impacts on HCWs. The interview results also highlighted the significance of HRM in bridging positive public responses toward HCWs and revealed problems with communication from HRM during the pandemic. This research provides practical implications about how to improve HCWs work engagement and well-being during the pandemic via public and HRM efforts.
The aim of this practice report is to discuss the implementation of a service-learning module developed to support the psychological wellbeing of postgraduate students and older adults in the community, with a view to fostering their connection and tackling loneliness in both populations. The module, ‘Self-Identity, Intergenerational and Intercultural Learning’ (SIIL), was offered to students enrolled in the Mental Health Studies Master of Science at the Institute of Psychiatry, Psychology & Neuroscience, King’s College London. The module included lectures on the scientific underpinnings of wellbeing and ageism positionally within intercultural and intergenerational contexts. Students were introduced to qualitative research with a focus on autoethnography. They engaged with older adults through phone conversations and undertook wellbeing-promoting experiential exercises and self-reflection. The interactions provided students the opportunity to learn at an academic and personal level, while allowing older adults to share their experiences during the COVID-19 pandemic. These lessons learnt will inform future practice. Future directions for further developments of this methodology in other disciplines are also discussed in this practice report.
Epidemiological studies report high levels of anxiety and depression amongst adolescents. These psychiatric conditions and complex interplays of biological, social and environmental factors are important risk factors for suicidal behaviours and suicide, which show a peak in late adolescence and early adulthood. Although deaths by suicide have fallen globally in recent years, suicide deaths are increasing in some countries, such as the US. Suicide prevention is a challenging global public health problem. Currently, there aren’t any validated clinical biomarkers for suicidal diagnosis, and traditional methods exhibit limitations. Artificial intelligence (AI) is budding in many fields, including in the diagnosis of medical conditions. This review paper summarizes recent studies (past 8 years) that employed AI tools for the automated detection of depression and/or anxiety disorder and discusses the limitations and effects of some modalities. The studies assert that AI tools produce promising results and could overcome the limitations of traditional diagnostic methods. Although using AI tools for suicidal ideation exhibits limitations, these are outweighed by the advantages. Thus, this review article also proposes extracting a fusion of features such as facial images, speech signals, and visual and clinical history features from deep models for the automated detection of depression and/or anxiety disorder in individuals, for future work. This may pave the way for the identification of individuals with suicidal thoughts.
This chapter contributes to this volume on teachers' lives, work, and professional preparation in two ways. First, it provides a brief summary of the contemporary literature concerned with research and practice in professional development in early childhood education. Second, it offers a critique of this literature, specifically in the context of a globally mobile early childhood education workforce. Such critique is essential if educational institutions serving our youngest citizens are to recognize and value the diverse knowledges and cultural forms of all children and their families.
Digital citizenship in the school curriculum has typically focused on digital competence and e-safety, and rarely on Internet-mediated social engagement and participation. On the other hand, social media use to rally support and coordination of place-based protests have blossomed across the globe. Students, including tweens and teens, empowered through easy information access (including fake ones) and extensive connectivity, have participated actively in on- and off-campus protests. Children and youth have the right to protection and participation. Digital citizenship education, as a system level sociotechnical regime, needs to prepare students for digitally mediated participation conducive to personal and social wellbeing.
In this digitalized era, Internet addiction has been a severe problem that needs imperative solutions derived from the same mechanism that leads to its addiction. To uncover a more nuanced mechanism for Internet addiction in association with decision-making focus and emotions and thus generate e􀀀ective interventions, we conducted three experiments to investigate how various forms of emotion priming a􀀀ect intertemporal choice among Internet addicts and normal Internet users. We divided the emotions into three categories, namely emotional valence (negative and positive emotions), expected emotion type (expected regret, expected joy), and current emotion type (current regret, current joy). In experiment one, we examined the e􀀀ect of two participant types (Internet addicts and normal Internet users) with three emotion valences (positive, negative, and neutral). In experiment two, we examined the e􀀀ect of two participant types (Internet addicts and normal Internet users) with three current emotion types (current regret, joy, and neutral). In experiment three, we examined the e􀀀ect of two participant types (Internet addicts and normal Internet users) with two expected emotion types (expected regret and expected joy). We conducted a completely randomized experimental design in each experiment and used subjective value as the dependent variable index of intertemporal choice. The results showed that the subjective value of Internet addicts was significantly lower than that of normal Internet users across three studies. The subjective value of individuals primed with positive emotions was significantly higher than those primed with negative emotions, no matter whether they were normal Internet users or addicts (experiment one). The subjective value of individuals primed with expected joy was significantly higher than those primed with expected regret, no matter whether they were normal Internet users or addicts (experiment three). When primed with current joy, however, the Internet addicts’ subjective value was significantly lower than when primed with current regret, but this did not apply to normal Internet users (experiment two). These results suggest positive emotions and expected joy enhanced long-term goals and greater rewards focus on intertemporal decision-making compared to negative emotions and expected regret. However, current joy facilitated short-term goals, andsmaller rewards focus on intertemporal decision-making compared to current regret. The theoretical and practical implications for Internet addiction are also discussed in this paper.
Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy.
Chronotype is linked to adverse health measures and may have important associations with obstructive sleep apnea and blood pressure, but data are limited. This study aimed to determine the separate and combined associations of chronotype with obstructive sleep apnea and blood pressure in a middle-aged community population. Adults (n = 811) from the Raine Study (female = 59.2%; age mean [range] = 56.6 [42.1-76.6] years) were assessed for chronotype (Morningness-Eveningness Questionnaire), blood pressure and hypertension (doctor diagnosed or systolic blood pressure ≥ 140 mmHg and/or diastolic ≥ 90 mmHg), and obstructive sleep apnea at different in-laboratory apnea-hypopnea index thresholds (5, 10, 15 events per hr). Linear and logistic regression models examined relationships between chronotype and the presence and severity of obstructive sleep apnea, blood pressure, hypertension, and blood pressure stratified by obstructive sleep apnea severity at above-mentioned apnea-hypopnea index thresholds. Covariates included age, sex, body mass index, alcohol consumption, smoking, physical activity, sleep duration, anti-hypertensive medication, insomnia, and depressive symptoms. Most participants were categorised as morning (40%) or intermediate (43%), with 17% meeting criteria for evening chronotypes. Participants with apnea-hypopnea index ≥ 15 events per hr and morning chronotype had higher systolic (9.9 mmHg, p < 0.001) and a trend for higher diastolic blood pressure (3.4 mmHg, p = 0.07) compared with those with an evening chronotype, and higher systolic blood pressure compared with those with an intermediate chronotype (4.8 mmHg, p = 0.03). Across chronotype categories, no differences in systolic or diastolic blood pressure or odds of hypertension were found at apnea-hypopnea index thresholds of ≥ 5 or ≥ 10 events per hr. Among participants with apnea-hypopnea index ≥ 15 events per hr, systolic blood pressure is higher in those with a morning chronotype than evening and intermediate chronotypes. Assessment for morning chronotype may improve risk stratification for hypertension in patients with obstructive sleep apnea.
A logo reflects a brand's face and is an indicator of the brand’s personality and identity. Therefore, its importance has been long established among marketers. This article develops marketers' understanding on how logo size preference can be affected by consumers' psychological power and self-construal. Through six studies, we show that larger brand logos are preferred by low-power (vs. high-power) consumers when an independent self-construal is salient because of a greater tendency to engage in self-deceptive enhancement—the tendency to see oneself in exaggerated and glorified terms. In contrast, a smaller brand logo is preferred by low-power (vs. high-power) consumers when an interdependent self-construal is salient because of a greater tendency to engage in impression management—the tendency to be modest and normatively appropriate. These findings provide meaningful insights for managers in terms of logo size decisions and marketing communications.
Interpreted as ‘unity in diversity’, Indonesia's national politicians appropriated the slogan Bhinneka Tunggal Ika from a fourteenth century poem to legitimise one nation‐state for the diverse archipelago. As Indonesia becomes a rapidly urbanising country, the concepts of ‘unity’ and ‘diversity’ intertwine with changing landscapes and societies. With the growth of cities as centres of population and economic activities, the intensity of development in the city has transformed urban spaces, social interactions, economies and aspirations. Much of these urban transformations have affected urban kampung, early forms of urban settlement. Urban kampung has a historical role in the making of ‘unity in diversity’ as a national concept, but official discourses rarely associate kampung with the slogan, even when concerns on tolerance and diversity increase in urbanising Indonesia. Using urban kampung as a viewpoint in city‐ and nation‐building, we conduct archival and ethnographic research to interpret everyday practices of Bhinneka Tunggal Ika. To what extent does the slogan play a role in the social construction of city neighbourhoods? How do everyday realities in an urban kampung relate to the national slogan? As kampung remains relatively autonomous but also stigmatised in the city, taking kampung as a viewpoint allows insights into city‐ and nation‐building knowledge that integrate everyday practice and conceptualization of the city and the nation.
A partially-auxetic metamaterial is introduced, inspired by the Maltese cross. Each unit of this metamaterial consists of a pair of counter-rotating equal-armed crosses, which is interconnected to neighboring units via hinge rods and connecting rods. Based on linkage theory, the on-axes Poisson's ratio was established considering a two-fold symmetrical mechanism, while the (anti)tetrachiral mechanisms were identified for on-axes uniaxial compression. A shearing mechanism is suggested for pure shearing and diagonal loading of the metamaterial with square array. Results suggest that the approximated infinitesimal models are valid for the Poisson's ratio of the two-fold symmetrical and the (anti)tetrachiral mechanisms under on-axis tension and compression, respectively; however, the finite model is recommended for quantifying the Poisson's ratio under pure shear and off-axis loading. This metamaterial manifests microstructural trinity, in which three different loading modes result in three different groups of deformation mechanisms. Finally, suggestions are put forth for some unsolved predictive problems.
The unprecedented challenges posed by the pandemic have required the global world to adapt swiftly and cope with the demands. The pandemic, in particular, has caused severe disruptions to young children’s learning experiences, requiring a closer examination of teacher resilience. This complex, individual and context-driven quality requires educators to adapt, negotiate existing challenges and endure in adverse stress-inducing environments. Drawing on two models of teacher resilience, with a specific emphasis on protective factors, we present the empirical findings of a study that involved 284 early childhood educators from Australia, Bangladesh, India, Norway and Singapore. Participant experiences in their professional contexts indicate the role of protective factors, which include both individual and contextual factors, that facilitated quality teacher-child interactions during the pandemic. The data highlight personal strength and learning situated in their heritage cultures as crucial components of developing a strong sense of professionalism. In addition, the findings reveal the importance of professional development opportunities as an enabler of teacher resilience.
Amidst the pandemic, the work of many security personnel increased due to the additional requirements of checking vaccination records, temperature-taking, and contact-tracing procedures. There is ample research linking work hygiene and motivator factors (from Herzberg’s two-factor theory) to both job satisfaction and intentions to quit in various types of work settings. However, little is known about what keeps security officers on the job despite the exacerbated challenges posed by the pandemic. We examine how distinct hygiene and motivator factors predict intentions to quit among security officers. One thousand security officers in Singapore participated in a cross-sectional survey that assessed their current job experiences. The findings of this study revealed that job satisfaction plays a mediating effect in the positive relationship between four out of five poor hygiene factors and intentions to quit. Furthermore, the negative mediating effect of job satisfaction between all three motivator factors and intentions to quit was significant. Specifically, the intrinsic motivation for the work itself was the most significant predictor of intentions to stay. Interventions targeted at enhancing work commitment among security officers should highlight the value of security work and its role in maintaining public safety.
With modern societies becoming ever-increasingly interconnected due to technology and media, we have gained unprecedented access and exposure to other people’s lives. This has resulted in a greater desire to constantly be socially connected with the activities of others, or the fear of missing out (FoMO). While much of the present available research has established the association between FoMO and diminished emotional well-being, little has been done to identify protective factors that can help one cope with the negative psychological consequences of FoMO. Utilizing data from a 7-day diary study of a large sample of young adults ( N = 261), the current study aimed to examine the moderating role of cognitive reappraisal in attenuating diminished emotional well-being associated with FoMO. Multilevel modeling showed that cognitive reappraisal attenuated the day-to-day within-person associations between daily FoMO and indicators of daily emotional well-being such as negative affectivity, anxiety, and depressive symptoms.
Interest in the significant impact of psychological factors on innovation outcomes is growing rapidly. Our understanding of cognitive processes is, however, evolving, and research on the specific forms and role of these factors within innovation-related decisions is limited. We propose a theory of decision-making that offers consilience across research areas, is grounded in both physical and social sciences, explains the constructs already established by innovation, adoption and resistance research, and serves the needs of innovation researchers and practitioners as a pragmatic tool. Using a variety of established research tools in novel ways including semantic field and bibliometric analysis and by drawing on research from diverse disciplines, we identify evolved psychological mechanisms as influences on adoption decision processes. We conclude that Evolutionary Choice Theory, defined as the collective influence of these evolved psychological mechanisms, should be adopted by innovation practitioners and researchers and provide specific pragmatic applications to inform this adoption.
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