CodecAttack perturbs audio in codec latent space with multi-bitrate EoT to achieve 85.5% average ASR on Opus-compressed Audio LLMs versus under 26% for waveform baselines, with transfer to MP3 and AAC.
Neural Drone Localization Exploiting Signal Synthesis of Real- World Audio Data
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NDR-SHKF replaces the static forgetting factor in Sage-Husa Kalman Filters with a learned vector-valued memory attenuation policy from a bifurcated recurrent network trained end-to-end on whitened innovations to minimize estimation error.
GOTabPFN combines GO-LR ordering (equivalent to weighted minimum linear arrangement) and NSC compression to enable practical TabPFN-style prediction on HDLSS tabular data under tight token budgets, improving stability and accuracy.
A-THENA improves averaged IoT intrusion detection accuracy by 3.69-6.88 percentage points over baselines on three datasets using time-aware hybrid encoding and network-specific augmentation, with near-zero false alarms and real-time deployment on Raspberry Pi Zero 2 W.
Pretraining on broad sound events plus on-the-fly augmentations improves out-of-domain true-positive rates for acoustic drone detection at fixed low false-positive rates.
citing papers explorer
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Codec-Robust Attacks on Audio LLMs
CodecAttack perturbs audio in codec latent space with multi-bitrate EoT to achieve 85.5% average ASR on Opus-compressed Audio LLMs versus under 26% for waveform baselines, with transfer to MP3 and AAC.
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Learned Memory Attenuation in Sage-Husa Kalman Filters for Robust UAV State Estimation
NDR-SHKF replaces the static forgetting factor in Sage-Husa Kalman Filters with a learned vector-valued memory attenuation policy from a bifurcated recurrent network trained end-to-end on whitened innovations to minimize estimation error.
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GOTabPFN: From Feature Ordering to Compact Tokenization for Tabular Foundation Models on High-Dimensional Data
GOTabPFN combines GO-LR ordering (equivalent to weighted minimum linear arrangement) and NSC compression to enable practical TabPFN-style prediction on HDLSS tabular data under tight token budgets, improving stability and accuracy.
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A-THENA: Early Intrusion Detection for IoT with Time-Aware Hybrid Encoding and Network-Specific Augmentation
A-THENA improves averaged IoT intrusion detection accuracy by 3.69-6.88 percentage points over baselines on three datasets using time-aware hybrid encoding and network-specific augmentation, with near-zero false alarms and real-time deployment on Raspberry Pi Zero 2 W.
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Improving acoustic drone detection generalization through pretraining and data augmentation
Pretraining on broad sound events plus on-the-fly augmentations improves out-of-domain true-positive rates for acoustic drone detection at fixed low false-positive rates.