Search Results (3)

Soybean seed variety identification is essential for seed quality control, germplasm management, and variety authentication. However, few-shot classification remains challenging because different varieties often exhibit highly similar one-dimensional hyperspectral signatures, and labeled samples are limited in practical seed-testing scenarios. This study proposes a Residual Derivative-Guided Spectral Fusion (RDSF) module to improve spectral representation under limited-sample conditions. RDSF uses the raw spectrum and its first- and second-order derivatives to characterize global reflectance patterns, local slope variations, and spectral curvature, respectively. The three representations are processed by separate branches and combined through bounded learnable residual fusion, with the raw spectrum serving as the primary representation and the derivatives providing complementary corrections. As a plug-and-play component, RDSF was integrated into Prototypical Network (ProtoNet), Relation Network (RelationNet), and Model-Agnostic Meta-Learning (MAML). The module was evaluated using spectra from 11,000 individual soybean seeds representing 11 varieties under known-class and strict class-disjoint unseen-class protocols. Under the representative known-class 3-way 10-shot setting with 15 query samples per class, RDSF increased the meta-test accuracy of RelationNet from 0.8898 ± 0.0201 to 0.9184 ± 0.0060. Under the unseen-class protocol, RDSF consistently improved ProtoNet and RelationNet across all evaluated shot settings; the largest gain was observed for ProtoNet in the 5-shot setting, with the meta-test accuracy increasing from 0.8848 ± 0.0253 to 0.9094 ± 0.0080. In contrast, RDSF did not consistently improve MAML under this protocol, indicating that its effectiveness depended partly on the underlying meta-learning mechanism. Ablation experiments and architecture comparisons further showed the complementary contributions of the derivative branches and the advantages of bounded residual fusion over a three-channel architecture and direct feature concatenation. Overall, RDSF provides an effective spectral representation module for metric-based few-shot classification of soybean seed varieties under the evaluated known-class and unseen-class conditions. Full article

Model-Agnostic Meta-Learning (MAML) and Prototypical Networks (ProtoNet) establish the foundational paradigms for few-shot classification. However, MAML suffers from optimization instability caused by reconstructing classification boundaries for every new task. Conversely, ProtoNet lacks the internal mathematical capacity necessary for task-specific parameter adaptation under domain shifts. To reconcile these structural limitations, we introduce Metric-based Model-Agnostic Meta-Learning (M²AML). By completely excising the parameterized classification layer from the episodic adaptation sequence, our framework replaces traditional inner-loop classification with a dynamic self-exclusive geometric similarity metric. Substituting functional mappings with spatial distance optimizations efficiently resolves evaluation conflicts, thereby establishing perfectly synchronized inner and outer learning rates alongside substantially accelerated adaptation steps. Extensive experiments across mini-ImageNet, tiered-ImageNet, and CIFAR-FS validate our approach against a comprehensive array of established algorithms. To ensure strictly fair comparative evaluations, we meticulously reproduce the MAML, ProtoNet, and Proto-MAML baselines. Empirical results demonstrate that M²AML achieves state-of-the-art performance across most evaluation settings, delivering absolute accuracy improvements ranging from 0.1% to 2.1% over existing leading models. Full article

In cybersecurity, identifying and addressing vulnerabilities in source code is essential for maintaining secure IT environments. Traditional static and dynamic analysis techniques, although widely used, often exhibit high false-positive rates, elevated costs, and limited interpretability. Machine Learning (ML)-based approaches aim to overcome these limitations but encounter challenges related to scalability and adaptability due to their reliance on large labeled datasets and their limited alignment with the requirements of secure development teams. These factors hinder their ability to adapt to rapidly evolving software environments. This study proposes an approach that integrates Prototype-Based Model-Agnostic Meta-Learning(Proto-MAML) with a Question-Answer (QA) framework that leverages the Bidirectional Encoder Representations from Transformers (BERT) model. By employing Few-Shot Learning (FSL), Proto-MAML identifies and mitigates vulnerabilities with minimal data requirements, aligning with the principles of the Secure Development Lifecycle (SDLC) and Development, Security, and Operations (DevSecOps). The QA framework allows developers to query vulnerabilities and receive precise, actionable insights, enhancing its applicability in dynamic environments that require frequent updates and real-time analysis. The model outputs are interpretable, promoting greater transparency in code review processes and enabling efficient resolution of emerging vulnerabilities. Proto-MAML demonstrates strong performance across multiple programming languages, achieving an average precision of $98.49\%$, recall of $98.54\%$, F1-score of $98.78\%$, and exact match rate of $98.78\%$ in PHP, Java, C, and C++. Full article