arXiv subjects

Mihaela Cardei

Publications and source records attributed to Mihaela Cardei.

4 recordsLinked to original sources

arXiv2026

Channel-Aware Selection of Folded Bloom Filters for Distributed Systems

Periodic Bloom-filter transmission can impose substantial overhead in communication-constrained distributed systems. Lossless compression preserves membership behavior but provides a single transmission size, whereas established OR folding produces smaller representations with higher false-positive rates (FPRs) while preserving the no-false-negative property. This paper investigates channel-aware selection among OR-folded representations. The sender retains an unchanged canonical filter, constructs a catalog satisfying a maximum FPR, and selects the FPR-qualified representation with the largest retained length supported by the communication resources available at each reporting opportunity. Unlike folding driven principally by cardinality and false-positive constraints, selection is driven by time-varying communication conditions. Using two phishing URL datasets, the framework is evaluated under Five-State Markov Capacity, Gilbert--Elliott burst-error, and Rayleigh block-fading models. Channel-aware folding improves communication efficiency and receiver freshness relative to complete-filter and lossless-compression baselines when communication opportunities vary substantially. Under the more favorable Gilbert--Elliott model, it remains competitive in efficiency while maintaining the freshest receiver state. These results show that FPR-qualified folded views provide useful transmission operating points when a recent lower-fidelity update is preferable to delaying a larger representation.

cs.IT↗

arXiv2026

Entropy-Punctured Bloom Filters for Memory-Efficient Machine Learning

Memory-efficient feature representations are increasingly important in machine learning settings where storage, transmission cost, bandwidth, or privacy constraints limit access to raw data. Bloom Filter (BF) encodings provide compact probabilistic representations of engineered features, but their behavior under structural compression and their applicability to regression tasks remain underexplored. In this work, we propose entropy-punctured Bloom Filters, a memory-aware encoding strategy that removes low-variability bit positions identified using empirical entropy. Starting from fixed-length BF encodings of quantized features, the proposed approach produces reduced representations that preserve predictive structure while improving predictive efficiency relative to encoded representation size. We evaluate the approach on diverse regression datasets, comparing raw features, Principal Component Analysis (PCA), Random Projection (RP), and Bloom Filter variants under leakage-free evaluation protocols and approximately matched representation sizes. Performance is assessed using ridge regression, XGBoost, and neural networks, with predictive efficiency measured as R2 relative to encoded representation size per sample. Results show that Bloom Filter encodings remain competitive with classical compressed representations while achieving substantial storage savings. Entropy-based puncturing further reduces representation size with minimal loss in predictive fidelity, yielding improved predictive efficiency. These findings demonstrate that entropy-punctured Bloom Filters provide an effective representation-level compression approach for memory-constrained machine learning.

cs.LG↗

arXiv2026

Bloom Filter Encoding for Machine Learning

We present a method that uses a Bloom filter transform to preprocess data for machine learning. Each sample is encoded into a compact bit-array representation using hash-based encoding, producing a fixed-length feature space that reduces memory usage and obfuscates original feature values. The encoding does not rely on keyed hashing; however, a key can optionally be used to control the mapping and would be required to reproduce the representation. We evaluate the approach on six datasets spanning text, time-series, tabular, and image domains: SMS Spam Collection, ECG200, Adult 50K, CDC Diabetes, MNIST, and Fashion MNIST. Four classifiers are considered: Extreme Gradient Boosting, Deep Neural Networks, Convolutional Neural Networks, and Logistic Regression. Results show that models trained on Bloom filter encodings achieve performance comparable to models trained on raw data or standard dimensionality reduction techniques across several datasets, while providing consistent memory savings. These findings suggest that Bloom filter encodings can serve as an efficient, general-purpose pre-processing representation that preserves useful similarity structure for learning tasks while providing a degree of data obfuscation.

cs.LG↗

arXiv2026

Predicting Student Success with Heterogeneous Graph Deep Learning and Machine Learning Models

Early identification of student success is crucial for enabling timely interventions, reducing dropout rates, and promoting on time graduation. In educational settings, AI powered systems have become essential for predicting student performance due to their advanced analytical capabilities. However, effectively leveraging diverse student data to uncover latent and complex patterns remains a key challenge. While prior studies have explored this area, the potential of dynamic data features and multi category entities has been largely overlooked. To address this gap, we propose a framework that integrates heterogeneous graph deep learning models to enhance early and continuous student performance prediction, using traditional machine learning algorithms for comparison. Our approach employs a graph metapath structure and incorporates dynamic assessment features, which progressively influence the student success prediction task. Experiments on the Open University Learning Analytics (OULA) dataset demonstrate promising results, achieving a 68.6% validation F1 score with only 7% of the semester completed, and reaching up to 89.5% near the semester's end. Our approach outperforms top machine learning models by 4.7% in validation F1 score during the critical early 7% of the semester, underscoring the value of dynamic features and heterogeneous graph representations in student success prediction.

cs.LG↗