arXiv subjects

Moumita Sen Sarma

Publications and source records attributed to Moumita Sen Sarma.

3 recordsLinked to original sources

arXiv2026

From Field Data to Global Food Systems Intelligence: A Semantic Graph Framework for Sustainable Wheat Production

In response to the growing need for structured, interoperable agricultural data, this paper presents the Sustainable Wheat Production Datahub, a modular, graph-based framework that brings diverse wheat production datasets together into a single, queryable store. Using the Knowledge Acquisition and Representation Methodology (KNARM), with domain experts in the loop, we developed ontologies for nutrient management and disease management, two of the areas that most affect wheat yield and its sustainability, covering practices such as nitrogen fertilization and fungicide-based disease control. The two ontologies are federated, meaning they are maintained as separate but connected modules, joined by a bridging layer of cross-domain links, and together they form the schema of a knowledge graph (KG). We construct this KG by populating the ontologies with data from diverse sources, including field trials, expert knowledge, and environmental descriptors. We validate the ontologies, showing the KG accurately answers practical agronomic questions expressed in SPARQL. We demonstrate that the KG derives facts entailed by the ontologies beyond those explicitly stored, and that a single query can draw across independently sourced datasets. The framework also has practical implications for wheat research and extension programs, since it makes data from different sources easier to find, combine, and reuse. Designed to expand toward the full wheat lifecycle, from farm to table, this work establishes the foundation for a scalable, semantically rich global food systems datahub.

cs.AI↗

arXiv2026

Do CNNs Internally Represent Real and Fake Images Differently? A Hidden-Layer Analysis

Fake/synthetic images are increasingly prevalent, but it remains unclear whether Convolutional Neural Networks (CNNs) process real and fake images in the same internal manner. This work examines the hypothesis that CNNs represent real and fake images differently, such that fake images induce different hidden-layer activation patterns even when semantic content is preserved. The hypothesis is evaluated in scene recognition settings using trained CNN models. Dense-layer activations are extracted, and neurosymbolic methods assign semantic labels to selected neurons. For each real test image, corresponding fake images are generated with similar semantic content using object-label-guided text-to-image and image-to-image generation based on Stable Diffusion variants. Paired real-fake activation patterns are then compared statistically. Additional experiments with another dataset, CNN architecture, generative model, and JPEG/blur degradation analysis assess robustness. Results suggest that fake images evoke different hidden-neuron activations, and these differences are not explained only by simple image degradation. Overall, the findings indicate that real and fake images differ in CNN hidden-layer activation behavior at least in some settings, which opens the door for follow-up work on making use of this different behavior to improve fake image detection.

cs.CV↗

arXiv2026

A Case Study on Concept Induction for Neuron-Level Interpretability in CNN

Deep Neural Networks (DNNs) have advanced applications in domains such as healthcare, autonomous systems, and scene understanding, yet the internal semantics of their hidden neurons remain poorly understood. Prior work introduced a Concept Induction-based framework for hidden neuron analysis and demonstrated its effectiveness on the ADE20K dataset. In this case study, we investigate whether the approach generalizes by applying it to the SUN2012 dataset, a large-scale scene recognition benchmark. Using the same workflow, we assign interpretable semantic labels to neurons and validate them through web-sourced images and statistical testing. Our findings confirm that the method transfers to SUN2012, showing its broader applicability.

cs.CV↗