arXiv subjects

Riya Jain

Publications and source records attributed to Riya Jain.

4 recordsLinked to original sources

arXiv2025

Dual-Branch Convolutional Framework for Spatial and Frequency-Based Image Forgery Detection

With a very rapid increase in deepfakes and digital image forgeries, ensuring the authenticity of images is becoming increasingly challenging. This report introduces a forgery detection framework that combines spatial and frequency-based features for detecting forgeries. We propose a dual branch convolution neural network that operates on features extracted from spatial and frequency domains. Features from both branches are fused and compared within a Siamese network, yielding 64 dimensional embeddings for classification. When benchmarked on CASIA 2.0 dataset, our method achieves an accuracy of 77.9%, outperforming traditional statistical methods. Despite its relatively weaker performance compared to larger, more complex forgery detection pipelines, our approach balances computational complexity and detection reliability, making it ready for practical deployment. It provides a strong methodology for forensic scrutiny of digital images. In a broader sense, it advances the state of the art in visual forensics, addressing an urgent requirement in media verification, law enforcement and digital content reliability.

cs.LG↗

arXiv2025

STRAPSim: A Portfolio Similarity Metric for ETF Alignment and Portfolio Trades

Accurately measuring portfolio similarity is critical for a wide range of financial applications, including Exchange-traded Fund (ETF) recommendation, portfolio trading, and risk alignment. Existing similarity measures often rely on exact asset overlap or static distance metrics, which fail to capture similarities among the constituents (e.g., securities within the portfolio) as well as nuanced relationships between partially overlapping portfolios with heterogeneous weights. We introduce STRAPSim (Semantic, Two-level, Residual-Aware Portfolio Similarity), a novel method that computes portfolio similarity by matching constituents based on semantic similarity, weighting them according to their portfolio share, and aggregating results via residual-aware greedy alignment. We benchmark our approach against Jaccard, weighted Jaccard, as well as BERTScore-inspired variants across public classification, regression, and recommendation tasks, as well as on corporate bond ETF datasets. Empirical results show that our method consistently outperforms baselines in predictive accuracy and ranking alignment, achieving the highest Spearman correlation with return-based similarity. By leveraging constituent-aware matching and dynamic reweighting, portfolio similarity offers a scalable, interpretable framework for comparing structured asset baskets, demonstrating its utility in ETF benchmarking, portfolio construction, and systematic execution.

q-fin.ST↗

arXiv2021

CLARINET: A RISC-V Based Framework for Posit Arithmetic Empiricism

Many engineering and scientific applications require high precision arithmetic. IEEE~754-2008 compliant (floating-point) arithmetic is the de facto standard for performing these computations. Recently, posit arithmetic has been proposed as a drop-in replacement for floating-point arithmetic. The posit\texttrademark data representation and arithmetic claim several absolute advantages over the floating-point format and arithmetic, including higher dynamic range, better accuracy, and superior performance-area trade-offs. However, there does not exist any accessible, holistic framework that facilitates the validation of these claims of posit arithmetic, especially when the claims involve long accumulations (quire). In this paper, we present a consolidated general-purpose processor-based framework to support posit arithmetic empiricism. The end-users of the framework have the liberty to seamlessly experiment with their applications using posit and floating-point arithmetic since the framework is designed for the two number systems to coexist. Melodica is a posit arithmetic core that implements parametric fused operations that uniquely involve the quire data type. Clarinet is a Melodica-enabled processor based on the RISC-V ISA. To the best of our knowledge, this is the first-ever integration of quire with a RISC-V core. To show the effectiveness of the Clarinet platform, we perform an extensive application study and benchmark some of the common linear algebra and computer vision kernels. We emulate Clarinet on a Xilinx FPGA and present utilization and timing data. Clarinet and Melodica remain actively under development and is available in open-source for posit arithmetic empiricism.

cs.AR↗

arXiv2021

GupShup: An Annotated Corpus for Abstractive Summarization of Open-Domain Code-Switched Conversations

Code-switching is the communication phenomenon where speakers switch between different languages during a conversation. With the widespread adoption of conversational agents and chat platforms, code-switching has become an integral part of written conversations in many multi-lingual communities worldwide. This makes it essential to develop techniques for summarizing and understanding these conversations. Towards this objective, we introduce abstractive summarization of Hindi-English code-switched conversations and develop the first code-switched conversation summarization dataset - GupShup, which contains over 6,831 conversations in Hindi-English and their corresponding human-annotated summaries in English and Hindi-English. We present a detailed account of the entire data collection and annotation processes. We analyze the dataset using various code-switching statistics. We train state-of-the-art abstractive summarization models and report their performances using both automated metrics and human evaluation. Our results show that multi-lingual mBART and multi-view seq2seq models obtain the best performances on the new dataset

cs.CL↗