arXiv subjects

Sanskar Singh

Publications and source records attributed to Sanskar Singh.

5 recordsLinked to original sources

arXiv2026

EduExplain: A Benchmark for Explanation-Set Retrieval and Storyboard Construction in Educational Explanation

Generating an educational explanation for a learner's query is not a single retrieval step but a pipeline: a system must first retrieve a compact set of complementary resources--passages, figures, examples, activities--that jointly support an explanation, then structure that set into an ordered, timed, role-assigned plan before rendering it as a lesson or video. Existing retrieval benchmarks evaluate neither stage at the level these applications require: they score individual documents rather than explanation sets, and provide no ground truth for how a set should be organised into an explanation. We present EduExplain, a benchmark providing human-validated ground truth for both stages. Built from secondary-level science textbooks, it comprises 6,921 typed, multimodal resources and 2,123 concepts, and pairs each of 321 learner-oriented queries with one or more human-validated explanation sets (retrieval ground truth) and 741 reference storyboards (structuring ground truth). The benchmark exposes characteristics absent from prior datasets: heterogeneous multimodal retrieval units, multiple valid explanation sets per query, and plans whose quality depends on pedagogical role composition, grounding, and timing rather than relevance alone. We describe an LLM-assisted, human-validated construction pipeline, report inter-annotator agreement for both layers, and establish baselines for each stage--lexical, dense, multimodal, and cross-encoder retrieval paired with four selection strategies, and free-form and fixed-template storyboard construction. Our results show that first-stage retrieval quality dominates retrieval-stage performance, that off-the-shelf diversity-based selection underperforms simple top-ranked selection, and that unconstrained and rigidly templated structuring both fall short of the reference plans. The benchmark is available at https://github.com/data-iitd/eduexplain.

cs.IR↗

arXiv2026

IndicContextEval: A Benchmark for Evaluating Context Utilisation in Audio Large Language Models Across 8 Indic Languages

AudioLLMs enable speech recognition conditioned on textual prompts such as domain descriptions or entity lists. However, it remains unclear whether these models genuinely utilise such context or rely on parametric knowledge learned during pretraining. Existing benchmarks cannot answer this question because they evaluate transcription under fixed prompting conditions and rarely include explicit contextual inputs. We introduce IndicContextEval, a 56-hour multilingual benchmark of natural speech from 555 speakers across 8 Indian languages and 23 professional domains. We design a 7-level prompting framework that progressively introduces contextual signals, including metadata, natural-language descriptions, entity lists in English and native script, and adversarial prompts with incorrect entities. Evaluating five models reveals substantial differences in context utilisation behaviour, highlighting the need for explicit evaluation of contextual grounding in AudioLLMs.

eess.AS↗

arXiv2025

SARCH: Multimodal Search for Archaeological Archives

In this paper, we describe a multi-modal search system designed to search old archaeological books and reports. This corpus is digitally available as scanned PDFs, but varies widely in the quality of scans. Our pipeline, designed for multi-modal archaeological documents, extracts and indexes text, images (classified into maps, photos, layouts, and others), and tables. We evaluated different retrieval strategies, including keyword-based search, embedding-based models, and a hybrid approach that selects optimal results from both modalities. We report and analyze our preliminary results and discuss future work in this exciting vertical.

cs.IR↗

arXiv2023

Adversarial Security and Differential Privacy in mmWave Beam Prediction in 6G networks

In the forthcoming era of 6G, the mmWave communication is envisioned to be used in dense user scenarios with high bandwidth requirements, that necessitate efficient and accurate beam prediction. Machine learning (ML) based approaches are ushering as a critical solution for achieving such efficient beam prediction for 6G mmWave communications. However, most contemporary ML classifiers are quite susceptible to adversarial inputs. Attackers can easily perturb the methodology through noise addition in the model itself. To mitigate this, the current work presents a defensive mechanism for attenuating the adversarial attacks against projected ML-based models for mmWave beam anticipation by incorporating adversarial training. Furthermore, as training 6G mmWave beam prediction model necessitates the use of large and comprehensive datasets that could include sensitive information regarding the user's location, differential privacy (DP) has been introduced as a technique to preserve the confidentiality of the information by purposefully adding a low sensitivity controlled noise in the datasets. It ensures that even if the information about a user location could be retrieved, the attacker would have no means to determine whether the information is significant or meaningless. With ray-tracing simulations for various outdoor and indoor scenarios, we illustrate the advantage of our proposed novel framework in terms of beam prediction accuracy and effective achievable rate while ensuring the security and privacy in communications.

cs.CR↗

arXiv2022

Video Vision Transformers for Violence Detection

Law enforcement and city safety are significantly impacted by detecting violent incidents in surveillance systems. Although modern (smart) cameras are widely available and affordable, such technological solutions are impotent in most instances. Furthermore, personnel monitoring CCTV recordings frequently show a belated reaction, resulting in the potential cause of catastrophe to people and property. Thus automated detection of violence for swift actions is very crucial. The proposed solution uses a novel end-to-end deep learning-based video vision transformer (ViViT) that can proficiently discern fights, hostile movements, and violent events in video sequences. The study presents utilizing a data augmentation strategy to overcome the downside of weaker inductive biasness while training vision transformers on a smaller training datasets. The evaluated results can be subsequently sent to local concerned authority, and the captured video can be analyzed. In comparison to state-of-theart (SOTA) approaches the proposed method achieved auspicious performance on some of the challenging benchmark datasets.

cs.CV↗