Subham Kumar

Subham Kumar, Prakrithi Shivaprakash, Abhishek Manoharan et al.

Automatic Speech Recognition (ASR) is increasingly used to document clinical encounters, yet its reliability in multilingual and demographically diverse Indian healthcare context remains largely unknown. In this study, we first conduct the systematic audit of ASR performance on real-world psychiatric interview data spanning Kannada, Hindi and Indian English, comparing eight state-of-the-art models including IndicWhisper, WhisperLargeV3, Sarvam, GoogleS2T, Gemma3n, OmniLingual, Vaani, and Gemini. Our results reveal substantial variability across models and languages, with some systems performing competitively in Indian English but failing in regional speech. We further fine-tune two of the best performing opensource models, i.e., Gemma3n and OmniLingual, using various methods. With this, we uncover systematic performance gaps tied to speaker role and gender, raising concerns about equitable deployment in clinical settings, which are further mitigated by fairness-aware fine-tuning. To this end, we propose SamaVaani, a unified debiasing technique that simultaneously improves ASR performance and improves fairness across demographic groups.

Bhavya Dixit, Aayushi Rajgor, Subham Kumar et al.

Unmanned aerial vehicle (UAV) swarms rely on distributed coordination and cooperative communication to support scalable operations, extended coverage, and applications such as surveillance and real-time data exchange. Wireless technologies such as radio frequency (RF) and WiFi are widely used for UAV-to-UAV and UAV-to-ground control station (GCS) communication but introduce significant security challenges. MAVLink, the predominant communication protocol in UAV systems, provides message integrity and authentication but lacks built-in encryption, leaving telemetry traffic vulnerable to eavesdropping. In our previous work, we proposed MAVShield, a lightweight encryption framework for MAVLink communications. In this paper, MAVShield, AES-CTR, Speck-CTR, ChaCha20, and Rabbit are integrated into four custom-built UAVs to establish secure communication links over RF and WiFi channels. Their performance is evaluated through flight experiments using a UAV swarm testbed. Encrypted telemetry data enable autonomous formation control and collision avoidance during flight. For collision avoidance, we develop a modified artificial potential field (APF) algorithm that computes attractive and repulsive forces directly in geodetic coordinates, eliminating Cartesian transformations and reducing trajectory oscillations while avoiding local-minimum trapping. CPU utilization, memory consumption, and packet delivery ratio (PDR) are measured for each encryption scheme. Results show that MAVShield achieves performance comparable to unencrypted communication while outperforming AES-CTR, Speck-CTR, ChaCha20, and Rabbit in overall efficiency. Algebraic cryptanalysis and Wireshark-based traffic analysis demonstrate resistance to key-recovery attacks and protection of telemetry confidentiality. The results indicate that MAVShield is an efficient and secure solution for UAV swarm communication.

Subham Kumar, Prakrithi Shivaprakash, Koustav Rudra et al.

Determining the appropriate locus of care for addiction patients is one of the most critical clinical decisions that affects patient treatment outcomes and effective use of resources. With a lack of sufficient specialized treatment resources, such as inpatient beds or staff, there is an unmet need to develop an automated framework for the same. Current decision-making approaches suffer from severe class imbalances in addiction datasets. To address this limitation, we propose a novel graph neural network (GRACE) framework that formalizes locus of care prediction as a structured learning problem. Further, we perform extensive feature engineering and propose a new approach of obtaining an unbiased meta-graph to train a GNN to overcome the class imbalance problem. Experimental results in real-world data show an improvement of 11-35% in terms of the F1 score of the minority class over competitive baselines. The codes and note embeddings are available at https://anonymous.4open.science/r/GRACE-F8E1/.

Paramansh Singh, Siraj Sandhu, Subham Kumar et al.

This paper describes our submissions to SemEval 2020 Task 11: Detection of Propaganda Techniques in News Articles for each of the two subtasks of Span Identification and Technique Classification. We make use of pre-trained BERT language model enhanced with tagging techniques developed for the task of Named Entity Recognition (NER), to develop a system for identifying propaganda spans in the text. For the second subtask, we incorporate contextual features in a pre-trained RoBERTa model for the classification of propaganda techniques. We were ranked 5th in the propaganda technique classification subtask.

Raghav Brahmadesam Venkataramaiyer, Subham Kumar, Vinay P. Namboodiri

Line art is arguably one of the fundamental and versatile modes of expression. We propose a pipeline for a robot to look at a grayscale line art and redraw it. The key novel elements of our pipeline are: a) we propose a novel task of mimicking line drawings, b) to solve the pipeline we modify the Quick-draw dataset to obtain supervised training for converting a line drawing into a series of strokes c) we propose a multi-stage segmentation and graph interpretation pipeline for solving the problem. The resultant method has also been deployed on a CNC plotter as well as a robotic arm. We have trained several variations of the proposed methods and evaluate these on a dataset obtained from Quick-draw. Through the best methods we observe an accuracy of around 98% for this task, which is a significant improvement over the baseline architecture we adapted from. This therefore allows for deployment of the method on robots for replicating line art in a reliable manner. We also show that while the rule-based vectorization methods do suffice for simple drawings, it fails for more complicated sketches, unlike our method which generalizes well to more complicated distributions.