Journal articles on the topic 'Weighted Visibility Network'

The visibility graph algorithm proves to be a simple and efficient method to transform time series into complex network and has been widely used in time series analysis because it can inherit the dynamic characteristics of original time series in topological structure. Now, visibility graph analysis of univariate time series has become mature gradually. However, most of complex systems in real world are multi-dimensional, so the univariate analysis is difficult to describe the global characteristics when applied to multi-dimensional series. In this paper, a novel method of analyzing the multivariate time series is proposed. For patients with myocardial infarction and healthy subjects, the 12-lead electrocardiogram signals of each individual are considered as a multivariate time series, which is transformed into a multiplex visibility graph through visibility graph algorithm and then mapped to fully connected complex network. Each node of the network corresponds to a lead, and the inter-layer mutual information between visibility graphs of two leads represents the weight of edges. Owing to the fully connected network of different groups showing an identical topological structure, the dynamic characteristics of different individuals cannot be uniquely represented. Therefore, we reconstruct the fully connected network according to inter-layer mutual information, and when the value of inter-layer mutual information is less than the threshold we set, the edge corresponding to the inter-layer mutual information is deleted. We extract average weighted degree and average weighted clustering coefficient of reconstructed networks for recognizing the 12-lead ECG signals of healthy subjects and myocardial infarction patients. Moreover, multiscale weighted distribution entropy is also introduced to analyze the relation between the length of original time series and final recognition result. Owing to higher average weighted degree and average weighted clustering coefficient of healthy subjects, their reconstructed networks show a more regular structure, higher complexity and connectivity, and the healthy subjects can be distinguished from patients with myocardial infarction, whose reconstructed networks are sparser. Experimental results show that the identification accuracy of both parameters, average weighted degree and average weighted clustering coefficient, reaches 93.3%, which can distinguish between the 12-lead electrocardiograph signals of healthy people and patients with myocardial infarction, and realize the automatic detection of myocardial infarction.

Emotion recognition, as a challenging and active research area, has received considerable awareness in recent years. In this study, an attempt was made to extract complex network features from electroencephalogram (EEG) signals for emotion recognition. We proposed a novel method of constructing forward weighted horizontal visibility graphs (FWHVG) and backward weighted horizontal visibility graphs (BWHVG) based on angle measurement. The two types of complex networks were used to extract network features. Then, the two feature matrices were fused into a single feature matrix to classify EEG signals. The average emotion recognition accuracies based on complex network features of proposed method in the valence and arousal dimension were 97.53% and 97.75%. The proposed method achieved classification accuracies of 98.12% and 98.06% for valence and arousal when combined with time-domain features.

In urethra-sparing radiation therapy, prostatic urinary tract visualization is important in decreasing the urinary side effect. A methodology has been developed to visualize the prostatic urinary tract using post-urination magnetic resonance imaging (PU-MRI) without a urethral catheter. This study investigated whether the combination of PU-MRI and super-resolution (SR) deep learning models improves the visibility of the prostatic urinary tract. We enrolled 30 patients who had previously undergone real-time-image-gated spot scanning proton therapy by insertion of fiducial markers. PU-MRI was performed using a non-contrast high-resolution two-dimensional T2-weighted turbo spin-echo imaging sequence. Four different SR deep learning models were used: the enhanced deep SR network (EDSR), widely activated SR network (WDSR), SR generative adversarial network (SRGAN), and residual dense network (RDN). The complex wavelet structural similarity index measure (CW-SSIM) was used to quantitatively assess the performance of the proposed SR images compared to PU-MRI. Two radiation oncologists used a 1-to-5 scale to subjectively evaluate the visibility of the prostatic urinary tract. Cohen’s weighted kappa (k) was used as a measure of agreement of inter-operator reliability. The mean CW-SSIM in EDSR, WDSR, SRGAN, and RDN was 99.86%, 99.89%, 99.30%, and 99.67%, respectively. The mean prostatic urinary tract visibility scores of the radiation oncologists were 3.70 and 3.53 for PU-MRI (k = 0.93), 3.67 and 2.70 for EDSR (k = 0.89), 3.70 and 2.73 for WDSR (k = 0.88), 3.67 and 2.73 for SRGAN (k = 0.88), and 4.37 and 3.73 for RDN (k = 0.93), respectively. The results suggest that SR images using RDN are similar to the original images, and the SR deep learning models subjectively improve the visibility of the prostatic urinary tract.

A complex network has been widely used to reveal the rule of a complex system. How to convert the stock data into a network is an open issue since the stock data are so large and their random volatility is strong. In this paper, a seasonal trend decomposition procedure based on the loess ([Formula: see text]) method is applied to convert the stock time series into a directed and weighted symbolic network. Three empirical stock datasets, including the closing price of Shanghai Securities Composite Index, S&P 500 Index, and Nikkei 225 Index, are considered. The properties of these stock time series are revealed from the topological characteristics of corresponding symbolic networks. The results show that: (1) both the weighted indegree and outdegree distributions obey the power-law distribution well; (2) fluctuations of stock closing price are revealed by related network topological properties, such as weighting degree, betweenness, pageranks, and clustering coefficient; and (3) stock closing price, in particular, periods such as financial crises, can be identified by modularity class of the symbolic networks. Moreover, the comparison between the [Formula: see text] method and the visibility graph further highlights the advantages of the [Formula: see text] method in terms of the time complexity of the algorithm. Our method offers a new idea to study the network conversion of stock time series.

Tall buildings have become an integral part of cities despite all their pros and cons. Some current tall buildings have several problems because of their unsuitable location; the problems include increasing density, imposing traffic on urban thoroughfares, blocking view corridors, etc. Some of these buildings have destroyed desirable views of the city. In this research, different criteria have been chosen, such as environment, access, social-economic, land-use, and physical context. These criteria and sub-criteria are prioritized and weighted by the analytic network process (ANP) based on experts’ opinions, using Super Decisions V2.8 software. On the other hand, layers corresponding to sub-criteria were made in ArcGIS 10.3 simultaneously, then via a weighted overlay (map algebra), a locating plan was created. In the next step seven hypothetical tall buildings (20 stories), in the best part of the locating plan, were considered to evaluate how much of theses hypothetical buildings would be visible (fuzzy visibility) from the street and open spaces throughout the city. These processes have been modeled by MATLAB software, and the final fuzzy visibility plan was created by ArcGIS. Fuzzy visibility results can help city managers and planners to choose which location is suitable for a tall building and how much visibility may be appropriate. The proposed model can locate tall buildings based on technical and visual criteria in the future development of the city and it can be widely used in any city as long as the criteria and weights are localized.

A new measure for the characterization of interconnected dynamical systems coupling is proposed. The method is based on the representation of time series as weighted cross-visibility networks. The weights are introduced as the metric distance between connected nodes. The structure of the networks, depending on the coupling strength, is quantified via the entropy of the weighted adjacency matrix. The method has been tested on several coupled model systems with different individual properties. The results show that the proposed measure is able to distinguish the degree of coupling of the studied dynamical systems. The original use of the geodesic distance on Gaussian manifolds as a metric distance, which is able to take into account the noise inherently superimposed on the experimental data, provides significantly better results in the calculation of the entropy, improving the reliability of the coupling estimates. The application to the interaction between the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole and to the influence of ENSO on influenza pandemic occurrence illustrates the potential of the method for real-life problems.

In this paper, to improve the slow processing speed of the rule-based visible and NIR (near-infrared) image synthesis method, we present a fast image fusion method using DenseFuse, one of the CNN (convolutional neural network)-based image synthesis methods. The proposed method applies a raster scan algorithm to secure visible and NIR datasets for effective learning and presents a dataset classification method using luminance and variance. Additionally, in this paper, a method for synthesizing a feature map in a fusion layer is presented and compared with the method for synthesizing a feature map in other fusion layers. The proposed method learns the superior image quality of the rule-based image synthesis method and shows a clear synthesized image with better visibility than other existing learning-based image synthesis methods. Compared with the rule-based image synthesis method used as the target image, the proposed method has an advantage in processing speed by reducing the processing time to three times or more.

In this paper, we present a powerful underwater image dehazing technique that exploits two image characteristics—RGB color channels and image features. In using RGB color channels, each color channel is decomposed into two units based on the similarities via the k-mean. This markedly improves the adaptability and identification of similar pixels, and thus reduces pixels with a weak correlation, leaving only pixels with a higher correlation. We use an infinite impulse response (IIR) in the triple-dual and parallel interaction structure to suppress hazed pixels via a pixel comparison and amplification to increase the visibility of even very minor features. This improves the visual perception of the final image, thus improving the overall usefulness and quality of the image. The softmax-weighted fusion is finally used to fuse the output color channel features to attain the final image. This preserves the color, leaving our proposed method’s output very true to the original scene’s. This is accomplished by taking advantage of adaptive learning based on the confidence levels of the pixel contribution variation in each color channel during subsequent fuses. The proposed technique both visually and objectively outperforms the existing methods in several rigorous tests.

Cities are among the best examples of complex systems. The adaptive components of a city, such as its people, firms, institutions, and physical structures, form intricate and often non-intuitive interdependencies with one another. These interdependencies can be quantified and represented as links of a network that give visibility to otherwise cryptic structural elements of urban systems. Here, we use aspects of information theory to elucidate the interdependence network among labor skills, illuminating parts of the hidden economic structure of cities. Using pairwise interdependencies we compute an aggregate, skills-based measure of system “tightness” of a city’s labor force, capturing the degree of integration or internal connectedness of a city’s economy. We find that urban economies with higher tightness tend to be more productive in terms of higher GDP per capita. However, related work has shown that cities with higher system tightness are also more negatively affected by shocks. Thus, our skills-based metric may offer additional insights into a city’s resilience. Finally, we demonstrate how viewing the web of interdependent skills as a weighted network can lead to additional insights about cities and their economies.

During rainy times, the impact of outdoor vision systems gets considerably decreased owing to the visibility barrier, distortion, and blurring instigated by raindrops. So, it is essential to eradicate it from the rainy images for ensuring the reliability of outdoor vision system. To achieve this, several rain removal studies have been performed in recent days. In this view, this paper presents a new Faster Region Convolutional Neural Network (Faster RCNN) with Optimal Densely Connected Networks (DenseNet)-based rain removal technique called FRCNN-ODN. The presented involves weighted mean filtering (WMF) is applied as a denoising technique, which helps to boost the quality of the input image. In addition, Faster RCNN technique is used for rain detection that comprises region proposal network (RPN) and Fast RCNN model. The RPN generates high quality region proposals that are exploited by the Faster RCNN to detect rain drops. Also, the DenseNet model is utilized as a baseline network to generate the feature map. Moreover, sparrow search optimization algorithm (SSOA) is applied to choose the hyperparameters of the DenseNet model namely learning rate, batch size, momentum, and weight decay. An extensive experimental validation process is performed to highlight the effectual outcome of the FRCNN-ODN model and investigated the results with respect to several dimensions. The FRCNN-ODN method produced a higher UIQI of 0.981 for the applied image 1. Furthermore, on the applied image 2, the FRCNN-ODN model achieved a maximum UIQI of 0.982. Furthermore, the FRCNN-ODN algorithm produced a higher UIQI of 0.998 on the applied image 3. The simulation outcome showcased the superior outcome of the FRCNN-ODN (Optimal Densely Connected Networks) model with existing methods in terms of distinct measures.

Purpose: To investigate the feasibility of using deep learning methods to differentiate benign from malignant breast lesions in ultrafast MRI with both temporal and spatial information. Methods: A total of 173 single breasts of 122 women (151 examinations) with lesions above 5 mm were retrospectively included. A total of 109 out of 173 lesions were benign. Maximum intensity projection (MIP) images were generated from each of the 14 contrast-enhanced T1-weighted acquisitions in the ultrafast MRI scan. A 2D convolutional neural network (CNN) and a long short-term memory (LSTM) network were employed to extract morphological and temporal features, respectively. The 2D CNN model was trained with the MIPs from the last four acquisitions to ensure the visibility of the lesions, while the LSTM model took MIPs of an entire scan as input. The performance of each model and their combination were evaluated with 100-times repeated stratified four-fold cross-validation. Those models were then compared with models developed with standard DCE-MRI which followed the same data split. Results: In the differentiation between benign and malignant lesions, the ultrafast MRI-based 2D CNN achieved a mean AUC of 0.81 ± 0.06, and the LSTM network achieved a mean AUC of 0.78 ± 0.07; their combination showed a mean AUC of 0.83 ± 0.06 in the cross-validation. The mean AUC values were significantly higher for ultrafast MRI-based models than standard DCE-MRI-based models. Conclusion: Deep learning models developed with ultrafast breast MRI achieved higher performances than standard DCE-MRI for malignancy discrimination. The improved AUC values of the combined models indicate an added value of temporal information extracted by the LSTM model in breast lesion characterization.

This paper focuses on the exploitation of fifth generation (5G) centimetre-wave (cmWave) and millimetre-wave (mmWave) transmissions for high-accuracy positioning, in order to complement the availability of Global Navigation Satellite Systems (GNSS) in harsh environments, such as urban canyons. Our goal is to present a representative methodology to simulate and assess their hybrid positioning capabilities over outdoor urban, suburban and rural scenarios. A novel scenario definition is proposed to integrate the network density of 5G deployments with the visibility masks of GNSS satellites, which helps to generate correlated scenarios of both technologies. Then, a generic and representative modeling of the 5G and GNSS observables is presented for snapshot positioning, which is suitable for standard protocols. The simulations results indicate that GNSS drives the achievable accuracy of its hybridisation with 5G cmWave, because non-line-of-sight (NLoS) conditions can limit the cmWave localization accuracy to around 20 m. The 5G performance is significantly improved with the use of mmWave positioning with dominant line-of-sight (LoS) conditions, which can even achieve sub-meter localization with one or more base stations. Therefore, these results show that NLoS conditions need to be weighted in 5G localization, in order to complement and outperform GNSS positioning over urban environments.

Abstract. Obtaining continuous aerosol-optical-depth (AOD) measurements is a difficult task due to the cloud-cover problem. With the main motivation of overcoming this problem, an AOD-predicting model is proposed. In this study, the optical properties of aerosols in Penang, Malaysia were analyzed for four monsoonal seasons (northeast monsoon, pre-monsoon, southwest monsoon, and post-monsoon) based on data from the AErosol RObotic NETwork (AERONET) from February 2012 to November 2013. The aerosol distribution patterns in Penang for each monsoonal period were quantitatively identified according to the scattering plots of the Ångström exponent against the AOD. A new empirical algorithm was proposed to predict the AOD data. Ground-based measurements (i.e., visibility and air pollutant index) were used in the model as predictor data to retrieve the missing AOD data from AERONET due to frequent cloud formation in the equatorial region. The model coefficients were determined through multiple regression analysis using selected data set from in situ data. The calibrated model coefficients have a coefficient of determination, R2, of 0.72. The predicted AOD of the model was generated based on these calibrated coefficients and compared against the measured data through standard statistical tests, yielding a R2 of 0.68 as validation accuracy. The error in weighted mean absolute percentage error (wMAPE) was less than 0.40% compared with the real data. The results revealed that the proposed model efficiently predicted the AOD data. Performance of our model was compared against selected LIDAR data to yield good correspondence. The predicted AOD can enhance measured short- and long-term AOD and provide supplementary information for climatological studies and monitoring aerosol variation.

In this paper, a tone mapping algorithm is presented to map real-world luminance into displayed luminance. Our purpose is to reveal the local contrast of real-world scenes on a conventional monitor. Around this point, we propose a three-stage algorithm to visualize high dynamic range images. All pixels of high dynamic range images are classified into three groups. For the first stage, we introduce piecewise linear mapping as the global tone mapping operator to map the luminance of the first group, which provides overall impressions of luminance. For the second stage, the luminance of the second group is determined by the weighted average of its neighborhood pixels, which are derived from the first group’s pixels. For the third stage, the luminance of the third group is determined by the weighted average of its neighborhood pixels, which are derived from the second group’s pixels. Experimental results on several real-world images and the TMQI database show that our algorithm can improve the visibility of real-world scenes with about 12% and 9% higher scores of mean opinion score and tone-mapped image quality index than the closest competitive tone mapping methods. Compared to the existing tone mapping methods, our algorithm produces visually compelling results without halo artifacts and loss of detail.

With the rapid access convenience of content brought by 5G technology, the integrity protection of content becomes more important. The reversible visible watermarking algorithm has attracted more attention due to its effective content protection. In this paper, a novel improved reversible visible image watermarking scheme based on gradient-weighted class activation mapping (Grad-CAM) and the just noticeable difference (JND) model has been presented. The proposed region of interest (ROI) selection strategy is used to locate the main protected body of images for watermark embedding. Divide the watermark and ROI into nonoverlapping blocks in the same way and then embed the classified two types of watermark blocks into corresponding ROI blocks with the JND model. The optimal bit positions for watermark embedding can be selected adaptively with JND threshold and achieve the tradeoff between the watermark visibility and watermarked image quality. For lossless image recovery and watermark extraction, the recovery information is reversibly hidden into watermarked image. In the experiments, the same process of grayscale images is used to each channel separately for color images watermarking. Besides, there are six aspects in this paper to estimate the proposed scheme; with the comparison to other reversible visible watermarking schemes, experimental results demonstrate the effectiveness of our proposed scheme.

In the second half of the 19th century, Buddhist bells from Japan began to arrive in Switzerland. The fact that these were objects listed in the so-called ethnographic collections is not surprising and the history of collecting has been a subject of postcolonial research. However, remarkably, the travel route of these bells, some of which weighed over a ton, could not be documented. Until now, the way how the bells were imported into Switzerland as unknown, and the problem of their provenance unsolved. This article argues that a global history approach provides new insights in two respects: The consideration of materiality allows a new nderstanding of the objects, while the activities of local collectors, seen from a micro-global point of view, reveal the local imprints of the global. Within this rationale, a history of individual bells in the possession of individual art lovers and museums translates into a history of scrap metal trade, allows to consider the disposal of disliked objects at their place of origin, and opens up a global framing of local history. Using global history as a concept, the historicity of the global gains visibility as we look at the intersection of materiality and the local involvement of global networks. Ultimately, as we follow the journey of the bells, reinterpreting scrap metal into art has formed a striking way in which local history assimilates the global.

High-dynamic-range (HDR) imaging is a digital image processing technique that enhances an image’s visibility by modifying its color and contrast ranges. Generative adversarial networks (GANs) have proven to be potent deep learning models for HDR imaging. However, obtaining a sufficient volume of training image pairs is difficult. This problem has been solved using CycleGAN, but the result of the use of CycleGAN for converting a low-dynamic-range (LDR) image to an HDR image exhibits problematic color distortion, and the intensity of the output image only slightly changes. Therefore, we propose a GAN training optimization model for converting LDR images into HDR images. First, a gamma shift method is proposed for training the GAN model with an extended luminance range. Next, a weighted loss map trains the GAN model for tone compression in the local area of images. Then, a regional fusion training model is used to balance the training method with the regional weight map and the restoring speed of local tone training. Finally, because the generated module tends to show a good performance in bright images, mean gamma tuning is used to evaluate image luminance channels, which are then fed into modules. Tests are conducted on foggy, dark surrounding, bright surrounding, and high-contrast images. The proposed model outperforms conventional models in a comparison test. The proposed model complements the performance of an object detection model even in a real night environment. The model can be used in commercial closed-circuit television surveillance systems and the security industry.

Thermal infrared (TIR) target tracking is a challenging task as it entails learning an effective model to identify the target in the situation of poor target visibility and clutter background. The sparse representation, as a typical appearance modeling approach, has been successfully exploited in the TIR target tracking. However, the discriminative information of the target and its surrounding background is usually neglected in the sparse coding process. To address this issue, we propose a mask sparse representation (MaskSR) model, which combines sparse coding together with high-level semantic features for TIR target tracking. We first obtain the pixel-wise labeling results of the target and its surrounding background in the last frame, and then use such results to train target-specific deep networks using a supervised manner. According to the output features of the deep networks, the high-level pixel-wise discriminative map of the target area is obtained. We introduce the binarized discriminative map as a mask template to the sparse representation and develop a novel algorithm to collaboratively represent the reliable target part and unreliable target part partitioned with the mask template, which explicitly indicates different discriminant capabilities by label 1 and 0. The proposed MaskSR model controls the superiority of the reliable target part in the reconstruction process via a weighted scheme. We solve this multi-parameter constrained problem by a customized alternating direction method of multipliers (ADMM) method. This model is applied to achieve TIR target tracking in the particle filter framework. To improve the sampling effectiveness and decrease the computation cost at the same time, a discriminative particle selection strategy based on kernelized correlation filter is proposed to replace the previous random sampling for searching useful candidates. Our proposed tracking method was tested on the VOT-TIR2016 benchmark. The experiment results show that the proposed method has a significant superiority compared with various state-of-the-art methods in TIR target tracking.

AbstractTemporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems on how to: (1) Create appropriate networks to reflect the characteristics of biological time series. (2) Detect characteristic dynamic patterns or events as network temporal communities. General community detection methods use metrics comparing the connectivity within a community to random models, or are based on the betweenness centrality of edges or nodes. However, such methods were not designed for network representations of time series. We introduce a visibility-graph-based method to build networks from time series and detect temporal communities within these networks. To characterize unevenly sampled time series (typical of biological experiments), and simultaneously capture events associated to peaks and troughs, we introduce the Weighted Dual-Perspective Visibility Graph (WDPVG). To detect temporal communities in individual signals, we first find the shortest path of the network between start and end nodes, identifying high intensity nodes as the main stem of our community detection algorithm that act as hubs for each community. Then, we aggregate nodes outside the shortest path to the closest nodes found on the main stem based on the closest path length, thereby assigning every node to a temporal community based on proximity to the stem nodes/hubs. We demonstrate the validity and effectiveness of our method through simulation and biological applications.

AbstractThis paper proposes a new method for converting a time-series into a weighted graph (complex network), which builds on electrostatics in physics. The proposed method conceptualizes a time-series as a series of stationary, electrically charged particles, on which Coulomb-like forces can be computed. This allows generating electrostatic-like graphs associated with time-series that, additionally to the existing transformations, can be also weighted and sometimes disconnected. Within this context, this paper examines the structural similarity between five different types of time-series and their associated graphs that are generated by the proposed algorithm and the visibility graph, which is currently the most popular algorithm in the literature. The analysis compares the source (original) time-series with the node-series generated by network measures (that are arranged into the node-ordering of the source time-series), in terms of a linear trend, chaotic behaviour, stationarity, periodicity, and cyclical structure. It is shown that the proposed electrostatic graph algorithm generates graphs with node-measures that are more representative of the structure of the source time-series than the visibility graph. This makes the proposed algorithm more natural rather than algebraic, in comparison with existing physics-defined methods. The overall approach also suggests a methodological framework for evaluating the structural relevance between the source time-series and their associated graphs produced by any possible transformation.

From its origins in social media usage, the term “sharing” has become an umbrella term for socio-technological competency. Even though sharing is a multifaceted construct – influenced by user motivations, expectations, and particularly the intrinsic and extrinsic social benefits to be gained – there are few definitions that approach it around these terms. We analyze these benefits in terms of what we call sociability affordances - i.e., social network site (SNS) affordances that arise outside either the merely material or imagined affordances - which enable individuals to take social actions toward social integration and interaction. Semi-structured interviews with young Chileans from three representative social media use typologies showed that each were closely linked with the performance of sharing actions for intrinsic and extrinsic social integration and interaction gains. Coding of interviews found four main sociability affordances from sharing practices: social visibility; audience engagement; social strengthening and enduring intra-personal communication. Based on U&G and self-determination theories, and rooted in an affordance approach, this study finds that online sharing behaviors are carefully weighed upon to achieve either factual or imagined social gains.

This article reflects on part of a three-year battle over the redevelopment of an iconic Melbourne music venue, the Palace-Metro Nightclub (the Palace), involving the tactical use of Facebook Page data at trial. We were invited by the Save the Palace group, Melbourne City Council and the National Trust of Australia to provide Facebook Page data analysis as evidence of the social value of the venue at an appeals trial heard at the Victorian Civil Administration Tribunal (VCAT) in 2016. We take a reflexive ethnographic approach here to explore the data production, collection and analysis processes as these represent and constitute a “data public”.Although the developers won the appeal and were able to re-develop the site, the court accepted the validity of social media data as evidence of the building’s social value (Jinshan Investment Group Pty Ltd v Melbourne CC [2016] VCAT 626, 117; see also Victorian Planning Reports). Through the case, we elaborate on the concept of data publics by considering the “affordising” (Pollock) processes at play when extracting, analysing and visualising social media data. Affordising refers to the designed, deliberate and incidental effects of datafication and highlights the need to attend to the capacities for data collection and processing as they produce particular analytical outcomes. These processes foreground the compositional character of data publics, and the unevenness of data literacies (McCosker “Data Literacies”; Gray et al.) as a factor of the interpersonal and institutional capacity to read and mobilise data for social outcomes.We begin by reconsidering the often-assumed connection between social media data and their publics. Taking onboard theoretical accounts of publics as problem-oriented (Dewey) and dynamically constituted (Kelty), we conceptualise data publics through the key elements of a) consequentiality, b) sufficient connection over time, c) affective or emotional qualities of connection and interaction with the events. We note that while social data analytics may be a powerful tool for public protest, it equally affords use against public interests and introduces risks in relation to a lack of transparency, access or adequate data literacy.Urban Protest and Data Publics There are many examples globally of the use of social media to engage publics in battles over urban development or similar issues (e.g. Fredericks and Foth). Some have asked how social media might be better used by neighborhood organisations to mobilise protest and save historic buildings, cultural landmarks or urban sites (Johnson and Halegoua). And we can only note here the wealth of research literature on social movements, protest and social media. To emphasise Gerbaudo’s point, drawing on Mattoni, we “need to account for how exactly the use of these media reshapes the ‘repertoire of communication’ of contemporary movements and affects the experience of participants” (2). For us, this also means better understanding the role that social data plays in both aiding and reshaping urban protest or arming third sector groups with evidence useful in social institutions such as the courts.New modes of digital engagement enable forms of distributed digital citizenship, which Meikle sees as the creative political relationships that form through exercising rights and responsibilities. Associated with these practices is the transition from sanctioned, simple discursive forms of social protest in petitions, to new indicators of social engagement in more nuanced social media data and the more interactive forms of online petition platforms like change.org or GetUp (Halpin et al.). These technical forms code publics in specific ways that have implications for contemporary protest action. That is, they provide the operational systems and instructions that shape social actions and relationships for protest purposes (McCosker and Milne).All protest and social movements are underwritten by explicit or implicit concepts of participatory publics as these are shaped, enhanced, or threatened by communication technologies. But participatory protest publics are uneven, and as Kelty asks: “What about all the people who are neither protesters nor Twitter users? In the broadest possible sense this ‘General Public’ cannot be said to exist as an actual entity, but only as a kind of virtual entity” (27). Kelty is pointing to the porous boundary between a general public and an organised public, or formal enterprise, as a reminder that we cannot take for granted representations of a public, or the public as a given, in relation to Like or follower data for instance.If carefully gauged, the concept of data publics can be useful. To start with, the notions of publics and publicness are notoriously slippery. Baym and boyd explore the differences between these two terms, and the way social media reconfigures what “public” is. Does a Comment or a Like on a Facebook Page connect an individual sufficiently to an issues-public? As far back as the 1930s, John Dewey was seeking a pragmatic approach to similar questions regarding human association and the pluralistic space of “the public”. For Dewey, “the machine age has so enormously expanded, multiplied, intensified and complicated the scope of the indirect consequences [of human association] that the resultant public cannot identify itself” (157). To what extent, then, can we use data to constitute a public in relation to social protest in the age of data analytics?There are numerous well formulated approaches to studying publics in relation to social media and social networks. Social network analysis (SNA) determines publics, or communities, through links, ties and clustering, by measuring and mapping those connections and to an extent assuming that they constitute some form of sociality. Networked publics (Ito, 6) are understood as an outcome of social media platforms and practices in the use of new digital media authoring and distribution tools or platforms and the particular actions, relationships or modes of communication they afford, to use James Gibson’s sense of that term. “Publics can be reactors, (re)makers and (re)distributors, engaging in shared culture and knowledge through discourse and social exchange as well as through acts of media reception” (Ito 6). Hashtags, for example, facilitate connectivity and visibility and aid in the formation and “coordination of ad hoc issue publics” (Bruns and Burgess 3). Gray et al., following Ruppert, argue that “data publics are constituted by dynamic, heterogeneous arrangements of actors mobilised around data infrastructures, sometimes figuring as part of them, sometimes emerging as their effect”. The individuals of data publics are neither subjugated by the logics and metrics of digital platforms and data structures, nor simply sovereign agents empowered by the expressive potential of aggregated data (Gray et al.).Data publics are more than just aggregates of individual data points or connections. They are inherently unstable, dynamic (despite static analysis and visualisations), or vibrant, and ephemeral. We emphasise three key elements of active data publics. First, to be more than an aggregate of individual items, a data public needs to be consequential (in Dewey’s sense of issues or problem-oriented). Second, sufficient connection is visible over time. Third, affective or emotional activity is apparent in relation to events that lend coherence to the public and its prevailing sentiment. To these, we add critical attention to the affordising processes – or the deliberate and incidental effects of datafication and analysis, in the capacities for data collection and processing in order to produce particular analytical outcomes, and the data literacies these require. We return to the latter after elaborating on the Save the Palace case.Visualising Publics: Highlighting Engagement and IntensityThe Palace theatre was built in 1912 and served as a venue for theatre, cinema, live performance, musical acts and as a nightclub. In 2014 the Heritage Council decided not to include the Palace on Victoria’s heritage register and hence opened the door for developers, but Melbourne City Council and the National Trust of Australia opposed the redevelopment on the grounds of the building’s social significance as a music venue. Similarly, the Save the Palace group saw the proposed redevelopment as affecting the capacity of Melbourne CBD to host medium size live performances, and therefore impacting deeply on the social fabric of the local music scene. The Save the Palace group, chaired by Rebecca Leslie and Michael Raymond, maintained a 36,000+ strong Facebook Page and mobilised local members through regular public street protests, and participated in court proceedings in 2015 and February 2016 with Melbourne City Council and National Trust Australia. Joining the protesters in the lead up to the 2016 appeals trial, we aimed to use social media engagement data to measure, analyse and present evidence of the extent and intensity of a sustained protest public. The evidence we submitted had to satisfy VCAT’s need to establish the social value of the building and the significance of its redevelopment, and to explain: a) how social media works; b) the meaning of the number of Facebook Likes on the Save The Palace Page and the timing of those Likes, highlighting how the reach and Likes pick up at significant events; and c) whether or not a representative sample of Comments are supportive of the group and the Palace Theatre (McCosker “Statement”). As noted in the case (Jinshan, 117), where courts have traditionally relied on one simple measure for contemporary social value – the petition – our aim was to make use of the richer measures available through social media data, to better represent sustained engagement with the issues over time.Visualising a protest public in this way raises two significant problems for a workable concept of data publics. The first involves the “affordising” (Pollock) work of both the platform and our data analysis. This concerns the role played by data access and platform affordances for data capture, along with methodological choices made to best realise or draw out the affordances of the data for our purposes. The second concerns the issue of digital and data literacies in both the social acts that help to constitute a data public in the first place, and the capacity to read and write public data to represent those activities meaningfully. That is, Facebook and our analysis constitutes a data public in certain ways that includes potentially opaque decisions or processes. And citizens (protesters or casual Facebook commenters alike) along with social institutions (like the courts) have certain uneven capacity to effectively produce or read public protest-oriented data. The risk here, which we return to in the final section, lies in the potential for misrepresentation of publics through data, exclusions of access and ownership of data, and the uneven digital literacies at each stage of data production, analysis and sensemaking.Facebook captures data about individuals in intricate detail. Its data capture strategies are geared toward targeting for the purposes of marketing, although only a small subset of the data is publicly available through the Facebook Application Programming Interface (API), which is a kind of data “gateway”. The visible page data tells only part of the story. The total Page Likes in February 2016 was 36,828, representing a sizeable number of followers, mainly located in Melbourne but including 45 countries in total and 38 different languages. We extracted a data set of 268,211 engagements with the Page between February 2013 and August 2015. This included 45,393 post Likes and 9,139 Comments. Our strategy was to demarcate a structurally defined “community” (in the SNA sense of that term as delineating clusters of people, activities and links within a broader network), by visualising the interactions of Facebook users with Posts over time, and then examine elements of intensity of engagement. In other words, we “affordised” the network data using SNA techniques to most clearly convey the social value of the networked public.We used a combination of API access and Facebook’s native Insights data and analytics to extract use-data from that Page between June 2013 and December 2015. Analysis of a two-mode or bipartite network consisting of users and Posts was compiled using vosonSML, a package in the R programming language created at Australian National University (Graham and Ackland) and visualised with Gephi software. In this network, the nodes (or vertices) represent Facebook users and Facebook Posts submitted on the Page, and ties (or edges) between nodes represent whether a user has commented on and/or liked a post. For example, a user U might have liked Post A and commented on Post B. Additionally, a weight value is assigned for the Comments ties, indicating how many times a user commented on a particular post (note that users can only like Posts once). We took these actions as demonstrating sufficient connection over time in relation to an issue of common concern.Figure 1: Network visualisation of activity on the Save the Palace Facebook Page, June 2013 to December 2015. The colour of the nodes denotes which ‘community’ cluster they belong to (computed via the Infomap algorithm) and nodes are sized by out-degree (number of Likes/Comments made by users to Posts). The graph layout is computed via the Force Atlas 2 algorithm.Community detection was performed on the network using the Infomap algorithm (Rosvall and Bergstrom), which is suited to large-scale weighted and directed networks (Henman et al.). This analysis reveals two large and two smaller clusters or groups represented by colour differences (Fig. 1). Broadly, this suggests the presence of several clusters amongst a sustained network engaging with the page over the three years. Beyond this, a range of other colours denoting smaller clusters indicates a diversity of activity and actors co-participating in the network as part of a broader community.The positioning of nodes within the network is not random – the visualisation is generated by the Force Atlas 2 algorithm (Jacomy et al.) that spatially sorts the nodes through processes of attraction and repulsion according to the observed patterns of connectivity. As we would expect, the two-dimensional spatial arrangement of nodes conforms to the community clustering, helping us to visualise the network in the form of a networked public, and build a narrative interpretation of “what is going on” in this online social space.Social value for VCAT was loosely defined as a sense of connection, sentiment and attachment to the venue. While we could illustrate the extent of the active connections of those engaging with the Page, the network map does not in itself reveal much about the sentiment, or the emotional attachment to the Save the Palace cause. This kind of affect can be understood as “the energy that drives, neutralizes, or entraps networked publics” (Papacharissi 7), and its measure presents a particular challenge, but also interest, for understanding a data public. It is often measured through sentiment analysis of content, but we targeted reach and engagement events – particular moments that indicated intense interaction with the Page and associated events.Figure 2: Save the Palace Facebook Page: Organic post reach November—December 2014The affective connection and orientation could be demonstrated through two dimensions of post “reach”: average reach across the lifespan of the Page, and specific “reach-events”. Average reach illustrates the sustained engagement with the Page over time. Average un-paid reach for Posts with links (primarily news and legal updates), was 12,015 or 33% of the total follower base – a figure well above the standard for Community Page reach at that time. Reach-events indicated particular points of intensity and illustrates the Page’s ability to resonate publicly. Figure 2 points to one such event in November 2015, when news circulated that the developers were defying stop-work orders and demolishing parts of The Palace. The 100k reach indicated intense and widespread activity – Likes, Shares, Comments – in a short timeframe. We examined Comment activity in relation to specific reach events to qualify this reach event and illustrate the sense of outrage directed toward the developers, and expressions of solidarity toward those attempting to stop the redevelopment. Affordising Data Publics and the Transformative Work of AnalyticsEach stage of deriving evidence of social value through Page data, from building public visibility and online activity to analysis and presentation at VCAT, was affected by the affordising work of the protesters involved (particularly the Page Admins), civil society groups, platform features and data structures and our choices in analysis and presentation. The notion of affordising is useful here because, as Pollock defines the term, it draws attention to the transformative work of metrics, analytics, platform features and other devices that re-package social activity through modes of datafication and analysis. The Save the Palace group mobilised in a particular way so as to channel their activities, make them visible and archival, to capture the resonant effects of their public protest through a platform that would best make that public visible to itself. The growth of the interest in the Facebook Page feeds back on itself reflexively as more people encounter it and participate. Contrary to critiques of “clicktivism”, these acts combine digital-material events and activities that were to become consequential for the public protest – such as the engagement activities around the November 2015 event described in Figure 2.In addition, presenting the research in court introduced particular hurdles, in finding “the meaningful data” appropriate to the needs of the case, “visualizing social data for social purposes”, and the need to be “evocative as well as accurate” (Donath, 16). The visualisation and presentation of the data needed to afford a valid and meaningful expression of the social significance the Palace. Which layout algorithm to use? What scale do we want to use? Which community detection algorithm and colour scheme for nodes? These choices involve challenges regarding legibility of visualisations of public data (McCosker and Wilken; Kennedy et al.).The transformative actions at play in these tactics of public data analysis can inform other instances of data-driven protest or social participation, but also leave room for misuse. The interests of developers, for example, could equally be served by monitoring protesters’ actions through the same data, or by targeting disagreement or ambiguity in the data. Similarly, moves by Facebook to restrict access to Page data will disproportionately affect those without the means to pay for access. These tactics call for further work in ethical principles of open data, standardisation and data literacies for the courts and those who would benefit from use of their own public data in this way.ConclusionsWe have argued through the case of the Save the Palace protest that in order to make use of public social media data to define a data public, multiple levels of data literacy, access and affordising are required. Rather than assuming that public data simply constitutes a data public, we have emphasised: a) the consequentiality of the movement; b) sufficient connection over time; and c) affective or emotional qualities of connection and interaction with public events. This includes the activities of the core members of the Save the Palace protest group, and the tens of thousands who engaged in some way with the Page. It also involves Facebook’s data affordances as these allow for the extraction of public data, alongside our choices in analysis and visualisation, and the court’s capacity and openness to accept all of this as indicative of the social value (connections, sentiment, attachment) it sought for the case. The Senior Member and Member presiding over the case had little knowledge of Facebook or other social media platforms, did not use them, and hence themselves had limited capacity to recognise the social and cultural nuances of activities that took place through the Facebook Page. This does not exclude the use of the data but made it more difficult to present a picture of the relevance and consequence of the data for understanding the social value evident in the contested building. 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Biracial Phenotypes as Ambivalent SignifiersRacialisation is the process of imbuing a body with meaning (Ahmed). Rockquemore et al.’s study on American Black-White middle-class college youth emphasises the importance of phenotypes in interracial children because “physical appearance is the primary cue for racial group membership… and remains the greatest factor in how mixed-race children are classified by others” (114). Wilson’s work on British mixed race 6 to 9-year-olds argues that interracial children classify other children based on how “they locate themselves in the racial structure and how they feel about the various racial groups” (64).However, interracial children often struggle with claiming a racial identity that does not correspond to their obvious physical appearance because society is more likely to classify or perceive the child based on their corporeal manifestations than their self-identified racial master status. In instances where they are unacknowledged or rejected by homoethinc groups, interracial persons may be deemed ‘illegitimate’ trespassers within social contexts. In response, interracial bodies may selectively hyper/under-visibilise one racial identity depending on personal connotations of the social group in particular settings (Choudhry 119). Choudhry’s book on the ‘chameleon identities’ of mixed race Black-Asian and White-Asian British young people sets out four ‘interpretative repertoires’ that interracials cognitively adopt: ‘Identity in Transition’ where individuals are still coming to terms with their master status; ‘One Ethnic Identity’ where individuals always privilege one race over the other regardless of context; ‘Interethnic Identity’ where individuals consciously and equally express their dual race and parentage at all times; and ‘Situational/Chameleon-like Identity’ where individuals selectively emphasise one race over the other when it benefits them (112-116). This paper follows on a similar mode of enquiry among Malay-Chinese women in Singapore, whose racial master status is situationally-based.In ethnically heterogeneous and culturally diverse Singapore, an individual’s racial phenotype is convenient shorthand that demarcates Others’ appropriate interactions with and expectations of them. Malbon describes these brief encounters in crowded urban settings as ‘mismeetings’, in which a body’s visual markers allow for a quick assessment and situation of a person’s identity and status. A visibly racialised body thus informs Others on how to negotiate cross-cultural sensitivities and understandings with them in a shared social space. For instance, this visibility may help inform the Other of an appropriate choice of mother tongue to be adopted in conversation with a stranger, or whether to extend non-halal food to a ‘Malay-looking’ – and by extension in most parts of South East Asia, Muslim – person.Unlike previous studies, this paper is not focused on interracial individuals’ felt-race, cognitive development, or the ethnic influence in their upbringing. Instead, it concentrates on their praxis of enacting corporeal markers to enable homophilous interactions with homoethnic social groups. Some Malay-Chinese in Singapore have phenotypic features that may not distinctly reflect their ethnic diversity. Hence, they are not readily acknowledged or accepted into some homoethnic contexts and are deemed ‘illegitimate’ trespassers. It is important for Others to be able to situate them since this “brings with it privileges or deprivations that affect [their] relationships with others and [their] relation to the world” (Mohanty 109). Every day interactions that affirm or negate one’s biraciality then become micropolitics of legitimating one’s in-group status; in the words of one woman’s reactions to Malay classmates excluding her from conversations about Hari Raya, “I also Melayu ok”. These women thus find themselves under- or hyper-visibilising facets of their biracial corporeality to negotiate legitimacy and sense of belonging. Through in-depth interviews with five young Malay-Chinese women who have had to renegotiate their biraciality in educational institutions each school year, this paper seeks to document the intentional under/hyper-performativity of biraciality through visible bodily signifiers. It argues that these biracial women who are perceived as illegitimate inhabitants of social settings have agentically adopted the ambivalence others display towards them as everyday micro-actions to exercise their autonomy, and strategically reposition themselves favourably.The five women were contacted through snowball sampling among personal networks in polytechnics and universities, which are education settings where students have the liberty to dress themselves, and thus, visibilise facets of their identity. These settings were also places in which the women had to continually under/hyper-visibilise and remark their race and ethnicity in rotating tutorial and lecture groups every semester, therefore (re)constructing their identities through peer interactions (Wilson in Choudhry 112).They were aged between 18 and 23 at the time of the interview. Their state-documented ‘official’ race, self-identified religion, and state-assigned mother tongue are tabulated below. Pseudonyms are employed.Semi-structured open-ended interviews were conducted to draw out personal nuances and interpretations of their bodies as read by Others. Our face-to-face interaction proved to be especially useful when informants physically referenced bodily markers or performed verbal cues to convey their under/hyper-visibility strategies.InformantNadiaAtiqahSaraClaireWahidaSexFemaleFemaleFemaleFemaleFemaleAge2322221822‘Official’ raceMalayMalayMalayMalayChineseReligionChristianMuslimChristianChristianMuslimMother tongueMandarinMalayMandarin MandarinMalayThe Body BeingAmong primary phenotypic cues, the women acknowledged popular perceptions of Chinese as fair-skinned and Malay as darker-skinned. This shorthand has been ingrained into society through rampant media images, especially in annual national-wide initiatives based in educational institutes such as Racial Harmony Day, International Friendship Day, and National Day. These settings utilise a ‘racial colour code’ to represent the CMIO – Chinese, Malay, Indian, Others; the four racial categories all Singaporeans are officially categorised into by the state – multiracialism in Singapore. Media imagery employs four children of different skin tones clad in ethnic dress, holding hands as symbolic of unity across diversity. So normative was this image even at the level of Primary School (7-12 year-olds) that Sara found her legitimacy in Chinese lessons questioned: “I used to be quite tanned in Primary School, quite Malay-looking… during Chinese lessons, the teacher always explained [difficult things] to me in English, as if I don’t understand Mandarin. But I even took higher Chinese...”The non-congruence of Sara’s apparently Malay phenotype and Mandarin mother tongue was perceived by her teacher as incompetence; Sara was an ‘illegitimate’ pupil in Mandarin class. Despite having been qualified enough to enrol in the higher Chinese stream that she says only takes in 10% of her cohort annually, Sara felt her high performance was negated because the visual marker of her Malayness took precedence during interactions with the teacher. Instead, English was adopted as a ‘neutral’ third language for conversing.In other instances, the women reported that while their skin tone generally enabled an audience to assign them a race, closer observations of their facial features such as their eyes signposted their racial hybridity. Claire states: “People always ask if I’m mixed blood because my eyelashes are very long and thick.” Sara experienced similar questioning gazes from strangers: “… maybe it’s my big eyes, and thick eyebrows… and my double eyelids are also very ‘Malay’?"Both Claire and Sara pointed out anatomic subtleties such as the folds of their eyelids, the size of their eyes, the volume of their eyebrows, and the length of their eyelashes as markers of their racial hybridity. There also emerged a consensus based on personal experience that Malays are more likely to have double eyelids, larger eyes, thicker eyebrows, and longer lashes, than to Chinese.Visual emphases on subtle characteristics thus help audiences interpret the biraciality of these women despite the apparent ‘incongruence’ of their skin tone and facial features. However, since racial identity is “influenced by historical, cultural, and contextual factors” (Rockquemore et al. 121), corporeal indications only serve as a primary racial cue. The next segment places these women in the context of secondary cues where the body is actively engaged in performing biraciality.The Body SpeakingThe women code-switched with choice of language, mother tongue, and manner of accents and vocal inflexions to contest initial readings of their racial status. Atiqah shares: “People always think I’m Chinese, until I open my mouth and speak Malay to ‘shock’ them. After that, they just ‘get’ that I am Malay.”Atiqah’s raised vocal inflexions and increasingly enthusiastic body language – she was clenching her fist as if to symbolically convey her victory at this point of the interview – seemed to imply that she relished in the ‘shock value’ of her big racial ‘reveal’. In a setting where her racial status was misidentified, she responded by asserting her racial legitimacy by displaying her competency of the Malay language.However, this has not always had a lasting impact in her interactions. She adds that within familiar social groups where she has long asserted her racial identity, she does not always feel acknowledged. Atiqah then attempts to ‘fit in’ by quietly deciphering her peers’ verbal exchanges: “… sometimes my Chinese friends forget that I’m ‘different’ because I’m so fair. They always talk in Mandarin… and I’ll try to figure out what they are saying from facial expressions and gestures.”Given her fair skin tone, Atiqah finds herself hypervisiblising her Malayness by utilizing the Malay language among Malay friends, even though they often converse in English themselves. In contrast, among Chinese friends where she feels her phenotypic Chinese features are visually dominant, she appears to under-visibilise this same Malayness by not speaking up about her language barrier. Language’s potential to demarcate social boundaries thus becomes a negotiative tool for Malay-Chinese women, while they simultaneously “shift their involvement and alliances” (Choudhry 119) to exercise choice over their identity.In another instance, Wahida is a fair skinned, tudung-clad, officially documented Chinese woman who identifies more as Malay. Her apparent ‘incongruence’ is of particular concern because Wahida had been attending a Madrasah up till the age of 18. Madrasahs are Islamic learning schools which also provide full-time education from Kindergarten to Junior College level, as an alternative to the mainstream track offered by the Ministry of Education in Singapore; a vast majority of Madrasah students self-identify as Malay Muslims. The desire for a sense of belonging encouraged Wahida to undervisibilise her Chineseness when she was younger:There was once my father came to pick me up from Madrasah… I forgot why but he scolded me so loudly in Mandarin! Everybody stared at me… I was so embarrassed! I already tried so hard to hide my Chinese-ness, he ruined it.Although Wahida never spoke Mandarin in school to underplay her Chineseness, ‘passing’ as a Malay necessitated intimate Others to sustain the racial construct. In this instance, her father had broken the ‘Malay’ persona she had deliberately crafted by conversing fluently in Malay in the Madrasah.Butler’s work on ‘gender as performed’ may be applied here in that what she describes as the “sustained set of acts” or a “stylization of the body” (xv) is also necessary to enact a sustained visual signifier of one’s racial identity. Although portrayed as a natural, innate, or unquestioned heritage in CMIO media portrays for Singapore, race is in fact an intentional construction. It is the practice of a certain regime of actions that contributes to the establishment of one’s raced personality. One is not naturally ‘Malay’ or ‘Chinese’ for these identities have to be carefully rehearsed and performed in order to translate one’s hereditary race into an outward expression of visible-race as practiced. As evidenced, this constant performance of Wahida’s racial self is fragile and dialectic, especially when other actors (such as her father) do not respond favourably to her intended presentation of self.Within a supposedly neutral third language such as English, the women also demonstrated their manipulation of accents emphasising or underplaying what they deem to be Malay or Chinese intonations and syllabic stresses. Sara explains:When I’m with my Malay friends, I speak with the mat [shortened from the local colloquial term matrep which loosely stands for the Malay version of a chav or a redneck] accent. Sometimes it’s subconscious… but sometimes it’s on purpose... they all speak like that… when I speak my ‘proper’ English, I feel out of place.Sara then demonstrates that Malay-accented English nasally accentuates the ‘N’ consonant, where words such ‘morning’ and ‘action’ have weighted pronunciations as ‘mornang’ and ‘actione’. Words that begin with a ‘C’ consonant are also developed into a voiced plosive ‘K’ sound, where words such as ‘corner’ and ‘concept’ are articulated as ‘korner’ and ‘koncept’, similar to the Malay language. Claire, who demonstrated similar Malay-accented utterances, supported this.Claire also noted that within Singlish – the colloquial spoken Singaporean English – Malay-accented English also tends towards end-sentence inflexions such as “seh”, “sia”, and “siol” in place of the more Mandarin-accented English that employs the end-sentence inflexions “ba”, and “ma”.Racialising spoken English is a symbolic interaction that interracial bodies may utilise to gain recognition and acceptance into a racial group that has not yet acknowledged their ‘legitimate’ membership. This is a manifestation of Cooley’s ‘looking glass self’ where an individual’s presentation of the body is based how they think other actors’ perceive them. In doing so, biracial bodies are able to exaggerate or obscure some corporeal traits to convey their preferred racial master status.The Body DoingPhysical gestures that constitute a ‘racial code’ are mirrored and socialised among children during their upbringing, since these designate one’s bodily boundaries and limits of exchange. Thus, while unseen by outsiders, insiders of the racial group may appropriate subtle gesticulations to demarcate and legitimate each other’s membership. Atiqah contends: “We [the Malays] always salaam each other when we first meet, it’s like a signal to show that we are ‘the same’ you know, so as long as I ‘act’ Malay, then my [colour] doesn’t really matter.”The salaam is a salutation of Islamic origin, signifying ‘peace to you’. It usually involves taking the back of the hand of a senior and bringing it to one’s forehead, heart, or lips. It is commonly practiced among Malays and Muslims. However, when a body’s phenotypic markers do not adequately signify racial identity, insiders may not extend such affective body language to them. As Nadia laments:When I first came to uni, the Malay kampong [literally translates into ‘village’, but figuratively stands for a social group in which reciprocal Malay cultural relationality is attached] couldn’t tell I was one of them… when I tried to salaam one of [the boys], he asked me why I was shaking his hand!Butler illuminated the notion of bodily signifiers (skin tone) marking access and limitations of corporeal exchange (salaam). Visual signifiers on biracial bodies must thus be significant enough to signpost one’s racial master status, in order to be positively assessed, acknowledged, and legitimated by Others.Among the women, only Wahida had committed to wearing a tudung at the time of the interview. Although a religious Islamic practice (as opposed to a culturally Malay one), such ethnic dress as ethnic signifier takes precedence over one’s ambivalent bodily markers. Wahida expressed that dressing in her jubah hyper-visualised her Malayness, especially when she was schooling in a Madrasah where fellow students dressed similarly.Omar’s concept of Masuk Melayu – literally ‘to enter Malayness’ – describes non-ethnic Malays who ‘become’ Malay through converting into Islam and practising the religion. Despite Wahida’s ambivalent fair skin tone, donning a tudung publically signifies her religious inclination and signals to Other Malays her racial master status. This thus earns her legitimacy in the social group more so than other ambivalent Malay-Chinese women without such religious symbolism.Agentic IllegitimacyIn negotiating their biraciality within the setting of educational institutions, these five Malay-Chinese women expressed the body ‘being’, ‘speaking’, and ‘doing’ strategies in which selected traits more commonly associated with Malayness or Chineseness were hyper-visibilised or under-visibilised, depending on the setting in which they find themselves (Wilson), and social group in which they want to gain membership and favour. Sara recalls having to choose an ethnic dress to wear to her Primary School’s Racial Harmony Day. Her father suggested “a mix” such as “a red baju kurung” or a “green cheong sum” (in Singapore, red is associated with the festivities of Chinese New Year and green with Hari Raya) where she could express her biraciality. Owing to this childhood memory, she says she still attempts to convey her racial hybridity by dressing strategically at festive family gatherings. Atiqah similarly peppers conversations with Chinese friends with the few Mandarin phrases she knows, partly to solicit an affective response when they tease her for “trying”, and also to subtly remind them of her desire for acknowledgement and inclusivity. Despite expressing similar frustrations over their exclusion and ‘illegitimate’ status in homoethnic settings, the women reacted agentically by continuously asserting emic readings of their corporeal ambivalence, and entering into spaces that give them the opportunity to reframe Others’ readings of their visual markers through microactions. However, enacting this agentic ethnic repertoire necessitates an intimate understanding of both Malay and Chinese social markers (Choudhry 120).None of the women suggested completely dissociating themselves from either Malayness or Chineseness, although they may selectively hyper-visibilise one over the other to legitimate their group membership. Instead, they engage in a continuously dialectic repositioning that requires reflexivity, self-awareness, and an attentiveness to how they are perceived from the etic. By inculcating Malay and Chinese social cues into their repertoire, these biracial women can strategically enact their desired racial master status fluently, treating ethnic identity as fluid and in flux (Choudhry 120). In transgressing popular perceptions of CMIO imagery, Malay-Chinese women use their bodies as a sustained site for contesting visual racial stereotypes and reframe their everyday ‘illegitimacy’ into agentic ambivalence, albeit only selectively in spaces where their racial membership would be favourable.ReferencesAhmed, Sara. “Racialized Bodies.” Real Bodies: A Sociological Introduction. Ed. Mary Evans, and Ellie Lee. New York: Palgrave, 2002. 46-63.Butler, Judith. Gender Trouble: Feminism and the Subversion of Identity. New York: Routledge, 1999.Choudhry, Sultana. Multifaceted Identity of Interethnic Young People: Chameleon Identities. Farnham, England: Ashgate, 2010.Cooley, Charles. Human Nature and the Social Order. New York: Scribner's, 1902. Katz, Ilan. The Construction of Racial Identity in Children of Mixed Parentage – Mixed Metaphors. London: J. Kingsley Publishers, 1996.Malbon, Ben. “The Club. 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