BiLSTM
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CLSP-REQA: A Real-Time Quality-Aware Closed-Loop Seizure Prediction Framework with Mamba-BiLSTM and Confidence-Gated Intervention
arXiv:2606.00074v1 Announce Type: cross Abstract: Reliable seizure prediction is a prerequisite for closed-loop neurostimulation therapy, yet existing methods rarely account for the variability in EEG signal quality encountered in real-world deployment, and the overwhelming majority adopt non-strict evaluation protocols that overestimate generalisation performance. We propose CLSP-REQA (Closed-Loop Seizure Prediction with Real-time EEG Quality Assessment), a unified framework that embeds a...
ProSarc: Prosody-Aware Sarcasm Recognition Framework via Temporal Prosodic Incongruity
Announce Type: new Abstract: We present ProSarc, an audio-only framework that detects sarcasm by modelling temporal prosodic incongruity, that is, the mismatch between local prosodic dynamics and the utterance-level emotional baseline. Dual encoding paths, a Global Emotion Encoder and a Temporal Prosody Encoder (BiLSTM + multi-head attention), feed a Prosodic Incongruity Analyzer that produces a scalar incongruity score for classification. Monte Carlo dropout provides uncertainty estimates,...
MSTN: A Lightweight and Fast Model for General TimeSeries Analysis
arXiv:2511.20577v5 Announce Type: replace Abstract: Real-world time series often exhibit strong non-stationarity, complex nonlinear dynamics, and behavior expressed across multiple temporal scales, from rapid local fluctuations to slow-evolving long-range trends. However, many contemporary architectures impose rigid, fixed-scale structural priors such as patch-based tokenization, predefined receptive fields, or frozen backbone encoders - which can over-regularize temporal dynamics and limit...
Privacy-Preserving Smart Surveillance with Cross-Dataset Violence Detection and Decentralized Evidence Governance
Announce Type: new Abstract: AI-enabled surveillance can accelerate public-safety response, yet most systems still leave recorded evidence under centralized administrative control. This paper proposes a privacy-preserving smart surveillance framework that separates incident detection from evidence disclosure. A lightweight MobileNetV2-based video classifier detects violent clips, while each recorded incident segment is immediately encrypted and made accessible only through threshold-based...
GuardNet: Ensemble Strategies of Shallow Neural Networks for Robust Prompt Injection and Jailbreak Detection
arXiv:2606.05566v1 Announce Type: new Abstract: Large Language Models (LLMs) have transformed natural language processing, but they remain vulnerable to Prompt Injection (PI) and Jailbreak (JB) attacks. In addition, benchmark evaluations may be affected by contamination and partial information leakage, compromising performance estimates.
Multi-View Speech Representation Learning for Parkinson's Disease Detection Using Context-guided Cross-modal Attention
arXiv:2606.09271v1 Announce Type: new Abstract: Parkinson's disease (PD) is a progressive neurodegenerative disorder that frequently causes speech impairments associated with hypokinetic dysarthria. As speech production relies on the precise coordination of complex neuromuscular mechanisms, speech analysis has emerged as a promising non-invasive and cost-effective biomarker for early PD detection. Recent deep learning approaches have shown encouraging results; however, most existing methods...
ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection
Announce Type: new Abstract: Time series anomaly detection is a crucial task in various domains, including finance, healthcare, and industry. However, existing methods often struggle to generalize across different datasets, especially when anomalies are subtle or context-dependent. To solve this issue, we introduce ChronosAD, a novel architecture for anomaly detection that uses a time series foundation model as a feature extractor.
FDM: A Framework for Decision-making to build ML-based Malware detection systems
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