arXiv subjects

Ayushi Sharma

Publications and source records attributed to Ayushi Sharma.

7 recordsLinked to original sources

arXiv2026

An Empirical Study of the TianoCore Community

We investigate the software security and maintenance practices adopted by stakeholders in the TianoCore community and identify opportunities to improve firmware development workflows. We conduct a survey and a limited interview study with participants representing independent firmware vendors, original equipment manufacturers, security experts, firmware developers, and academic researchers. This open-source development community maintains a reference implementation for the core of the UEFI firmware. We highlight important gaps in the current state of firmware development within the TianoCore ecosystem and identify key areas in which improved security practices, greater adoption of memory-safe technologies, and increased automation of manual processes could strengthen the maintenance and security of the UEFI firmware.

cs.SE↗

arXiv2026

Landseer: Exploring the Machine Learning Defense Landscape

Machine learning systems face diverse threats that undermine robustness, privacy, and fairness. Although many defenses have been proposed, each typically addresses a single risk in isolation. Real-world deployments, however, require these defenses to be composed to meet multiple guarantees simultaneously. The process of composing defenses is complex and not well understood, and its impact on performance and security remains unclear. We present Landseer, a modular framework for integrating machine learning (ML) defenses into the ML lifecycle and systematically evaluating their composition. Landseer encapsulates defenses as containerized modules, allowing existing and new techniques to be plugged in with minimal effort. Its evaluation engine automates experiments across multiple metrics, supporting the study of defenses both individually and in combination. In a preliminary study, we identified 35 state-of-the-art machine learning defenses. After filtering for reproducibility, we analyzed their performance using Landseer's unified evaluation process. Our findings reveal gaps in replicability across defense families and provide insights into the challenges and opportunities in integrating multiple defenses, establishing a foundation for improving the reliability of machine learning systems.

cs.CR↗

arXiv2026

deadtrees.earth-aerial: A Multi-Resolution Aerial Image Dataset for Tree Cover and Mortality Detection

Forests worldwide are increasingly threatened by climate change and disturbances such as fire, pests, and pathogens, creating an urgent need for scalable monitoring of tree cover and tree mortality. Aerial imagery from drones and aircraft is a key data source for detailed and large-scale mapping of tree crowns and mortality. However, related progress is limited by the lack of globally representative, harmonized datasets for joint segmentation of tree cover and mortality. We introduce two novel, open, machine-learning-ready datasets to enable joint segmentation of tree cover and tree mortality from centimeter-scale aerial imagery for the first time at global scales. With DTE-aerial-train, we provide a training dataset comprising 385K image patches of size 1024x1024 pixels, with resolutions ranging from 2.5 to 20 cm. It includes multi-class expert-annotated and -audited pseudo-labels for tree cover and mortality. With DTE-aerial-bench, we provide a geographically balanced benchmark test set of 25 globally distributed orthoimages totaling 525 patches with high-quality expert annotations for both tree cover and mortality. Both the training and benchmark datasets span tropical, temperate, boreal, and dryland biomes and cover a wide range of forest structures and mortality patterns. Using the benchmark test set for evaluation, we establish strong reference baselines that improve mortality segmentation across all biomes and scales with significant gains in challenging regions, such as boreal forests, where the F1 score increases from 0.40 to 0.58 with around 45% relative improvement. All data, models, and code will be publicly released under permissive open-source licenses. An interactive visualization of the benchmark dataset is available at deadtrees.earth/releases/dte-aerial-bench.

cs.CV↗

arXiv2026

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning

In hierarchical multi-label classification, a persistent challenge is enabling model predictions to reach deeper levels of the hierarchy for more detailed or fine-grained classifications. This difficulty partly arises from the natural rarity of certain classes (or hierarchical nodes) and the hierarchical constraint that ensures child nodes are almost always less frequent than their parents. To address this, we propose a weighted loss objective for neural networks that combines node-wise imbalance weighting with focal weighting components, the latter leveraging modern quantification of ensemble uncertainties. By emphasizing rare nodes rather than rare observations (data points), and focusing on uncertain nodes for each model output distribution during training, we observe improvements in recall by up to a factor of five on benchmark datasets, along with statistically significant gains in $F_{1}$ score. We also show our approach aids convolutional networks on challenging tasks, as in situations with suboptimal encoders or limited data.

cs.LG↗

arXiv2025

PlantTraitNet: An Uncertainty-Aware Multimodal Framework for Global-Scale Plant Trait Inference from Citizen Science Data

Global plant maps of plant traits, such as leaf nitrogen or plant height, are essential for understanding ecosystem processes, including the carbon and energy cycles of the Earth system. However, existing trait maps remain limited by the high cost and sparse geographic coverage of field-based measurements. Citizen science initiatives offer a largely untapped resource to overcome these limitations, with over 50 million geotagged plant photographs worldwide capturing valuable visual information on plant morphology and physiology. In this study, we introduce PlantTraitNet, a multi-modal, multi-task uncertainty-aware deep learning framework that predictsfour key plant traits (plant height, leaf area, specific leaf area, and nitrogen content) from citizen science photos using weak supervision. By aggregating individual trait predictions across space, we generate global maps of trait distributions. We validate these maps against independent vegetation survey data (sPlotOpen) and benchmark them against leading global trait products. Our results show that PlantTraitNet consistently outperforms existing trait maps across all evaluated traits, demonstrating that citizen science imagery, when integrated with computer vision and geospatial AI, enables not only scalable but also more accurate global trait mapping. This approach offers a powerful new pathway for ecological research and Earth system modeling.

cs.CV↗

arXiv2024

Rust for Embedded Systems: Current State, Challenges and Open Problems (Extended Report)

Embedded software is used in safety-critical systems such as medical devices and autonomous vehicles, where software defects, including security vulnerabilities, have severe consequences. Most embedded codebases are developed in unsafe languages, specifically C/C++, and are riddled with memory safety vulnerabilities. To prevent such vulnerabilities, RUST, a performant memory-safe systems language, provides an optimal choice for developing embedded software. RUST interoperability enables developing RUST applications on top of existing C codebases. Despite this, even the most resourceful organizations continue to develop embedded software in C/C++. This paper performs the first systematic study to holistically understand the current state and challenges of using RUST for embedded systems. Our study is organized across three research questions. We collected a dataset of 2,836 RUST embedded software spanning various categories and 5 Static Application Security Testing ( SAST) tools. We performed a systematic analysis of our dataset and surveys with 225 developers to investigate our research questions. We found that existing RUST software support is inadequate, SAST tools cannot handle certain features of RUST embedded software, resulting in failures, and the prevalence of advanced types in existing RUST software makes it challenging to engineer interoperable code. In addition, we found various challenges faced by developers in using RUST for embedded systems development.

cs.CR↗

arXiv2023

MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization

Hyperparameter optimization (HPO) is a powerful technique for automating the tuning of machine learning (ML) models. However, in many real-world applications, accuracy is only one of multiple performance criteria that must be considered. Optimizing these objectives simultaneously on a complex and diverse search space remains a challenging task. In this paper, we propose MO-DEHB, an effective and flexible multi-objective (MO) optimizer that extends the recent evolutionary Hyperband method DEHB. We validate the performance of MO-DEHB using a comprehensive suite of 15 benchmarks consisting of diverse and challenging MO problems, including HPO, neural architecture search (NAS), and joint NAS and HPO, with objectives including accuracy, latency and algorithmic fairness. A comparative study against state-of-the-art MO optimizers demonstrates that MO-DEHB clearly achieves the best performance across our 15 benchmarks.

cs.LG↗