Abstract

We present a Spiral Visualization that facilitates users to visually comprehend large graphs. Spiral Visualization is a representation that highlights key aspects of networks, including the number, size, and density of communities, important or central nodes within communities, centrality distribution within communities, connections between communities, and connections between nodes. To facilitate analysis and comprehension of networks using various interaction techniques, such as zooming, tooltip, and highlight, we have implemented a Spiral Visualization dashboard. We conducted a qualitative user study incorporating observation, think-aloud protocols, and participant rating of confidence and easiness to assess the usability and suitability of our visualization. The findings suggest that our visualization is appropriate for the network tasks evaluated. However, tasks requiring color comparison, such as identifying the densest community and comparing community densities were found to be more challenging to perform.

Abstract

This paper addresses the challenges faced by Indian languages in leveraging deep learning for natural language processing (NLP) due to limited resources, annotated datasets, and Transformer-based architectures. We specifically focus on Telugu and aim to construct a Telugu morph analyzer dataset comprising 10,000 sentences. Furthermore, we assess the performance of established multilingual Transformer models (m-Bert, XLMR, IndicBERT) and mono-lingual Transformer model BERT-Te (trained from scratch on an extensive Telugu corpus comprising 80,15,588 sentences). Our findings demonstrate the efficacy of Transformer-based representations pre-trained on Telugu data improved the performance of the Telugu morph analyzer, surpassing existing multi-lingual approaches. This highlights the necessity of developing dedicated corpora, annotated datasets, and machine learning models in a mono-lingual setting. Using our dataset, we present benchmark results for the Telugu morph analyzer achieved through simple fine-tuning on BERT-Te. The morph analyzer dataset 1 and codes are open-sourced and available here.

Abstract

Brain stroke has become a significant burden on global health and thus we need remedies and prevention strategies to overcome this challenge. For this, the immediate identification of stroke and risk stratification is the primary task for clinicians. To aid expert clinicians, automated segmentation models are crucial. In this work, we consider the publicly available dataset ATLAS v2.0 to benchmark various end-to-end supervised U-Net style models. Specifically, we have benchmarked models on both 2D and 3D brain images and evaluated them using standard metrics. We have achieved the highest Dice score of 0.583 on the 2D transformer-based model and 0.504 on the 3D residual U-Net respectively. We have conducted the Wilcoxon test for 3D models to correlate the relationship between predicted and actual stroke volume.

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Abstract: Ashish Rajadhyakshya’s recent book John-Ghatak-Tarkovsky: Citizens, Filmmakers, Hackers is the latest in a recent surge in interest in the politically provocative cinema emerging from the New wave film movement in India. This paper looks at some of the ways in which the language of the cinema of the New wave, Ritwik Ghatak in particular, makes available a re-located political field in which the local idioms of revolution and protest that marked these films finds a new modality. This new relocation can be mapped, on the one hand, onto films that emerge from and speak to these cinematic traditions, and on the other, in the intellectual interest these films have generated outside of their reformist social-realist mandate. This paper considers the stakes of the filmic afterlives and re-territorialized audiences the regional cinemas of the Indian New Wave have spawned. I suggest that as the state changes the technologies of its control, these films provide a language of resistance that simultaneously evokes a shared past and animates present day strategies of resistance.

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Folk Horror and Angry Witches This paper looks at some recent film texts, in particular, Pari (Fairy, Prosit Roy 2018), Tumbbad (Rahi Anil Barve, Anand Gandhi 2018) and Bulbul (Nightingale, Anvita Dutta, Netflix 2020), widely regarded as folk-horror (in multiple Indian languages) critiques of gender-patriarchy and capital. A re-tooling of our understanding of film grammar maps an image of the non-human anthropo-body of excess across the three texts. This body, in keeping with genre expectations, gathers its essence in the Gothic tradition of the supernatural ephemeral human shape. I propose that this image under pressure yields the spectre of an ecological unworlding – the untenability of the human. The folk-horror provides a syntax that assumes to render intelligible radically interrupted and refashioned natural processes. I contend that the push toward this genre in the last decade results in an aesthetic mediation between a fantasy of a pre-capital world and an apocalyptic climate crisis. This recasting of folk-horror allows the Anthropocene to be recast as moral failure, not what Jean Luc Nancy calls a “civilisational catastrophe”… and “philosophic earthquake.”

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We study communication models for channels with erasures in which the erasure pattern can be controlled by an adversary with partial knowledge of the transmitted codeword. In particular, we design block codes for channels with binary inputs with an adversary who can erase a fraction p of the transmitted bits. We consider causal adversaries, who must choose to erase an input bit using knowledge of that bit and previously transmitted bits, and myopic adversaries, who can choose an erasure pattern based on observing the transmitted codeword through a binary erasure channel with random erasures. For both settings we design efficient (polynomial time) encoding and decoding algorithms that use randomization at the encoder only. Our constructions achieve capacity for the causal and "sufficiently myopic" models. For the "insufficiently myopic" adversary, the capacity is unknown, but existing converses show the capacity is zero for a range of parameters. For all parameters outside of that range, our construction achieves positive rates.

Abstract

AI is about learning algorithms and huge amounts of data and are drivers of economic growth -- what does this mean for the field of development studies? Can we re-orient to twin AI studies and development theory and practice to generate how development challenges are identified and researched? To do this a good grasp is needed of AI internal mechanisms and outcomes in addressing development issues -- this argument will be developed through a case study of the ADAS [Advanced Driver Assistance System] deployment in India. Over and above discussing the ADAS we bring an anthropological lens to understand the social context that surrounds the system. Focusing on bus drivers, we offer findings from a qualitative and ethnographic study of drivers in a collaborative effort to achieve road safety by deploying AI-driven technology and empowering stakeholders in the transport industry in India especially, bus drivers as critical actors in the city transport network.

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The proposed work endeavors to support the preservation of tribal arts in their original art form with their unique colors, shapes, and objects. Tribal arts of each region of India is a ‘message’ of their life, traditions, and history, requiring preservation in its pristine, unique form. For example, dotted patterns in Bhil, geometric figures in Warli, dashes in Gond, deities’ expressions in Pichwai paintings, etc., are some notably diverse visual patterns that depict the profound stories of their wholesome culture. In this preliminary work, a dataset is collected by mining the websites, which are direct outlets of tribal arts, thereby ensuring the authenticity of the images included. The collected dataset includes 863 paintings across five different classes of tribal arts viz Warli, Bhil, Gond, Pichwai, and Kalighat, as described in Figure 1. Furthermore, image classification baselines on the collected dataset are developed by fine-tuning the pre-trained ResNet50, InceptionV3, and EfficientNetB0 models. The classification accuracy for the aforementioned baselines is reported in Table 1. Among the baselines, the best testing accuracy of 91.57 % is observed for EfficientNetB0; the confusion matrix for which is shown in Figure 2. The associated F1-scores for this baseline model across all five classes, namely Warli, Bhil, Gond, Pichwai, and Kalighat, were found to be 0.958, 0.882, 0.875, 0.909, and 0.958, respectively which suggests that the dataset is balanced in nature. Furthermore, class activation maps are generated for each image of the collected dataset across all baselines to ascertain the alignment of results generated with the ground truth, which offers a means to comprehend and compare the regions of focus in decision-making. Figure 3 provides an example of a class activation map revealing the salient focus areas, such as human figures represented by triangles and circles in a Warli painting. This analysis can also be used by artists for designing the artwork. The extension of the classification work is to detect counterfeit tribal paintings and quantify the extent of deviation from the original art form, ultimately contributing towards tribal arts preservation and safeguarding their invaluable cultural heritage.