Abstract

This study advances Kai Katahira’s Speculation Game, an agent-based model (ABM) for financial markets, by addressing its limitation in capturing order flow imbalance, a critical indicator of herd behavior. Although the original model successfully replicated key stylized facts of financial markets, it did not account for the persistence of order imbalance observed in real-world trading. Through a comprehensive analysis of two decades of BSE Sensex data, we establish the prevalence of order imbalance and its correlation with price fluctuations. To bridge this gap, we propose an extended model, Speculation Game with Information Cascade (SGIC), which integrates Self-Organized Criticality (SOC) through a sand-pile model, enabling agents to interact within a small-world network. Our proposed model not only reproduces the stylized facts captured by the original Speculation Game, but also successfully generates the additional stylized fact of order flow imbalance. These advances enhance the realism of ABMs in financial markets, providing deeper insights into the mechanisms driving herding and market fluctuations.

Abstract

The color gamut of visual scenes can encompass a multitude of hues, but how many distinct hues are individuals aware of? We examined this question in the context of texture perception, with a grid of stimuli composed of random colors chosen from color sets varying from two to six different hues or saturations. In a behavioral experiment, participants had to discriminate between differences in the number of hues present by identifying which of four color grids included a larger number of different colors. Color number discrimination was also assessed neurally, using electroencephalogram (EEG) frequency tagging, wherein a texture with an extra hue was shown as an “oddball,” once per second, in a stream of textures presented at six images per second. In both experiments, two versus three texture contrasts were readily distinguished, yet performance fell rapidly when the set size increased further. The results suggest that sensitivity to the density of the color gamut defining a texture is very restricted.

Abstract

Motorized two-wheelers (MTW) dominate Indian road but remain underrepresented in driver behavior research. This study presents the first large-scale analysis of MTW driver gaze behavior in naturalistic, heterogeneous urban traffic, using the myEye2Wheeler dataset. A semantic segmentation pipeline (YOLOv11 + SAM2) was used to extract object-level gaze metrics under two attention modes: direct gaze (foveal overlap) and central vision (parafoveal monitoring). Results reveal a functional division: central vision supports broad monitoring, while direct gaze enables brief, selective sampling. Novice riders exhibit road-anchored scanning, returning to the road between object fixations, while experienced riders form longer chains of attention across multiple objects. The findings suggest that experience primarily refines temporal rhythm rather than altering allocation strategy and reduces object-class effects in gaze patterns. These findings offer new insight into MTW attention structures and inform future work on behavior modeling and safety systems.

Abstract

This study investigates how cinematographic techniques influence viewer perception and contribute to the objectification of women, utilizing eye-tracking data from 91 participants. They watched a sexualized music video (SV) known for objectifying portrayals and a non-sexualized music video (TV). Using dynamic Areas of Interests (AOIs)—head, torso, and lower body—gaze metrics such as fixation duration, visit count, and scan paths were recorded to assess visual attention patterns. Participants were grouped according to their average fixations on sexualized AOIs. Statistical analyses revealed significant differences in gaze behavior between the videos and among the groups, with increased attention to sexualized AOIs in SV. Additionally, data-driven group differences in fixations identified specific segments with heightened objectification that are further analyzed using scan path visualization techniques. These findings provide strong empirical evidence of camera-driven gaze objectification, demonstrating how cinematic framing implicitly shapes objectifying gaze patterns, highlighting the critical need for mindful media representation.

Abstract

This paper explores how real-world geopolitics can influence financial decision-making in virtual environments. The study primarily focuses on trading dynamics in a pandemic simulation game. The players manage resources and trade with each other to save their population. We compared two groups: one playing with made-up country names (control) and another with real country names (test). The players were not permitted to communicate outside of the resources the game provides and were finally ranked based on the number of months survived and trade volume. Our findings reveal two distinct player archetypes, one focusing on maximising trade volume and a second focusing on resource conservation. Real country names compared to color-coded labels seem to delay the perception of competition and reduce overall trade volume, suggesting increased immersion and a more conservative approach towards resource management. Observations from gameplay footage reveal instances of potential bias, with players forming diplomatic ties based on national identities. The data collected suggests that real-world geopolitics can influence financial behaviour in virtual environments as hypothesised. It also explores virtual environments to study how political narratives and dynamics affect human decision-making. The findings offer practical insights for game developers regarding fostering player engagement, mitigating bias, and creating fair economies. While parallels with real-world resource trading and allocation during pandemics were noted, further analysis would be required to explore these connections.

Abstract

Particulate matter (PM) is considered the primary contributor to air pollution and has severe implications for general health. PM concentration has high spatial variability and thus needs to be monitored locally. Traditional PM monitoring setups are bulky, expensive, and cannot be scaled for dense deployments. This paper argues for a densely deployed network of IoT-enabled PM monitoring devices using low-cost sensors, specifically focusing on PM10 and PM2.5, the most health-impacting particulates. In this work, 49 devices were deployed in a region of the Indian metropolitan city of Hyderabad, of which 43 devices were developed as part of this work, and six devices were taken off the shelf. The low-cost sensors were calibrated for seasonal variations using a precise reference sensor and were particularly adjusted to accurately measure PM10 and PM2.5 levels. A thorough analysis of data collected for 7 months has been presented to establish the need for dense deployment of PM monitoring devices. Different analyses such as mean, variance, spatial interpolation, and correlation have been employed to generate interesting insights about temporal and seasonal variations of PM10 and PM2.5. In addition, event-driven spatio-temporal analysis is done for PM2.5 and PM10 values to understand the impact of the bursting of firecrackers on the evening of the Diwali festival. A web-based dashboard is designed for real-time data visualization.

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This paper presents the myEye2Wheeler dataset, a unique resource of real-world gaze behaviour of two-wheeler drivers navigating complex Indian traffic. Most datasets are from four-wheeler drivers on well-planned roads and homoge- neous traffic. Our dataset offers a critical lens into the unique visual attention patterns and corresponding driving decision- making of Indian two-wheeler drivers. The analysis demon- strates that existing saliency models, like TASED-Net, perform less effectively on the myEye-2Wheeler dataset compared to when applied on the European 4-wheeler eye tracking datasets (DR(Eye)VE), highlighting the need for models specifically tailored to the traffic conditions. By introducing the dataset, we not only fill a significant gap in two-wheeler driver behaviour research in India but also emphasise the critical need for developing context-specific saliency models. The larger aim is to improve road safety for two-wheeler users and lane-planning for this cost-effective mode of transpor

Abstract

Indian folk paintings have a rich mosaic of symbols, colors, textures, and stories making them an invaluable repository of cultural legacy. The paper presents a novel approach to classifying these paintings into distinct art forms and tagging them with their unique salient features. A custom dataset named FolkTalent, comprising 2279 digital images of paintings across 12 different forms, has been prepared using websites that are direct outlets of Indian folk paintings. Tags covering a wide range of attributes like color, theme, artistic style, and patterns are generated using GPT4, and verified by an expert for each painting. Classification is performed employing the RandomForest ensemble technique on fine-tuned Convolutional Neural Network (CNN) models to classify Indian folk paintings, achieving an accuracy of 91.83%. Tagging is accomplished via the prominent fine-tuned CNN-based backbones with a custom classifier attached to its top to perform multi-label image classification. The generated tags offer a deeper insight into the painting, enabling an enhanced search experience based on theme and visual attributes. The proposed hybrid model sets a new benchmark in folk painting classification and tagging, significantly contributing to cataloging India’s folk-art heritage.