💻 Category

cs

3031 papers

💻 computer science

OrganSegBench: Bridging the Translational Gap for Medical Segmentation Foundation Models Through Principled Model Synergy

To address the translational gap in medical segmentation foundation models caused by overoptimistic benchmarks and the accuracy-fairness trade-off, this paper introduces OrganSegBench, a rigorous multi-dimensional evaluation framework that demonstrates how principled model synergy through ensemble strategies outperforms monolithic models in achieving safe, equitable, and clinically trustworthy AI.

Qing Li, Yizhe Zhang, Xin Guo, Haosen Zhang, Mo Yang, Mengting Sun, Longyu Sun, Haoyang Zhang, Junhong Liu, Yan Li, Yaji (…)2026-06-25
💻 computer science

A Hybrid Retrieval Augmented Generation Approach for Explainable Clinical Decision Support Using Vector Databases and Best Matching 25

This paper presents a hybrid Retrieval Augmented Generation framework that integrates BM25 and BioBERT-based semantic search with Reciprocal Rank Fusion to enhance explainable, personalized clinical decision support and early disease risk prediction, achieving superior retrieval performance on a dataset of 182,000 medical records compared to traditional baselines.

Bodireddy Mahalakshmi, Smita Khairnar, Shilpa Gite, Anurag Lengure, Anuj Loharkar, Swaraj Mahadik, Soham Mahajan2026-06-25
💻 computer science

An Explainable Hybrid of Classical and Quantum Support Vector Machine Models for Maternal Mental Health Risk Prediction Using Multicountry Data

This study proposes an explainable Hybrid Classical-Quantum Support Vector Machine model that leverages multicountry data to achieve superior maternal mental health risk prediction performance (99.86% accuracy) while identifying key clinical predictors through SHAP analysis.

Shallon Ahimbisibwe, Emmanuel Ahishakiye, Samuel Maling, Simon Kawuma, Richard Ntwari, Boaz Twinamasiko, Fred Kaggwa2026-06-25
💻 computer science

Runtime Detection of Attacks and Misconfigurations in Cloud-Native Kubernetes Environments Using eBPF Network Telemetry

This paper demonstrates that Hubble/eBPF network telemetry, when combined with carefully constructed ground-truth labels, can effectively detect runtime attacks and misconfigurations in Kubernetes environments, revealing that destination-side identity attributes are the strongest indicators of threats despite a trade-off between the high precision of rule-based detectors and the higher recall of Random Forest classifiers.

Andrew Oppong-Asante, Bernard Kyiewu, Clinton Amponsah, Linda Bessa-Simons, Caleb Boakye Yiadom2026-06-25
💻 computer science

ST-HAE: Spatio-Temporal Anomaly Detection inHeterogeneous IoT Networks Under Realistic Class Imbalance

This paper introduces ST-HAE, an unsupervised spatio-temporal hybrid autoencoder that achieves high-precision anomaly detection in heterogeneous IoT networks under realistic class imbalance by training exclusively on benign traffic and utilizing bidirectional temporal encoding to significantly reduce false alarm rates and variance compared to existing methods.

Vamsi Krishna Reddy, Nageswara Rao kuda2026-06-25
💻 computer science

Detection of AI-Generated Product Main Images in Cross-Border E-Commerce Based on Multi-Feature Fusion

This paper addresses the challenge of detecting AI-generated product images in cross-border e-commerce by introducing the CBEC-AIGC-Bench dataset and proposing MFF-EComDet, a multi-feature fusion network that combines spatial semantic and frequency artifact branches with a cross-attention mechanism to effectively distinguish generation artifacts from promotional text interference.

lili huang, zhidan hui2026-06-25
💻 computer science

FluxGraph: Synergizing Dynamic Graph Attention and Flux Memory for Unsupervised Time Series Anomaly Detection

FluxGraph is an unsupervised framework for time series anomaly detection that synergizes dynamic graph attention with flux memory and bidirectional feature flow to effectively identify subtle masquerade anomalies and gradual baseline drifts in industrial and automotive systems, outperforming existing methods through a novel multi-residual fusion approach.

Yunya Xiong, Tao Xu, Guozhi Song2026-06-25
💻 computer science

A Multi-Layer Framework for Hallucination Detection and Mitigation in Large Language Models Using Retrieval Grounding and Small Language Model Verification

This paper proposes a multi-layer framework that enhances the factual reliability of large language models by decomposing responses into atomic claims, grounding them with retrieval, and verifying them through a small language model and neuro-symbolic reasoning, achieving significant performance gains on HotpotQA and SciFact datasets while maintaining interpretability and computational efficiency.

Md. Mahmudul Hasan, Israk Kayum Chowdhury, Md. Eliyas Akondo, Tanvir Ahammad, Khandaker Mohammad Mohi Uddin2026-06-25