All papers

Presents an open-source security probing framework to evaluate language models against adversarial attacks and identify vulnerabilities.

Presents an open-source security probing framework to evaluate language models against adversarial attacks and identify vulnerabilities.

Presents an open-source security probing framework to evaluate language models against adversarial attacks and identify vulnerabilities.

Presents an open-source security probing framework to evaluate language models against adversarial attacks and identify vulnerabilities.

Presents an open-source security probing framework to evaluate language models against adversarial attacks and identify vulnerabilities.

Presents an open-source security probing framework to evaluate language models against adversarial attacks and identify vulnerabilities.

Surveys physical and media access control layer enhancements in the IEEE 802.11bn Wi-Fi 8 standard for ultra-high reliability applications.

Combines YOLO architectures, attention mechanisms, and classical computer vision features to detect defects on printed circuit boards.

Argues that statistical methods cannot practically quantify the reliability of life-critical software due to required testing volume.

Simulates classroom interactions using multiple language model agents configured to support student learning in computer networks education.

Surveys and categorizes prompt engineering patterns used to improve large language model performance in software engineering tasks.

Injects model-predictive control disturbances into expert robot demonstrations to train imitation learning policies that recover safely from errors.

Injects model-predictive control disturbances into expert robot demonstrations to train imitation learning policies that recover safely from errors.

Evaluates handwritten exam scripts using vision-language models and dynamic response fragmentation rather than traditional text recognition.

Specializes compact video segmentation models on live camera streams by intermittently distilling knowledge from a larger teacher model.

Specializes compact video segmentation models on live camera streams by intermittently distilling knowledge from a larger teacher model.

The authors introduce a training loop where multimodal models use text and visual abilities alongside program execution to generate their own training data.

The authors introduce a training loop where multimodal models use text and visual abilities alongside program execution to generate their own training data.

The authors introduce a training loop where multimodal models use text and visual abilities alongside program execution to generate their own training data.

The authors introduce a training loop where multimodal models use text and visual abilities alongside program execution to generate their own training data.

The authors introduce a training loop where multimodal models use text and visual abilities alongside program execution to generate their own training data.

The authors introduce a training loop where multimodal models use text and visual abilities alongside program execution to generate their own training data.

The authors introduce a training loop where multimodal models use text and visual abilities alongside program execution to generate their own training data.

The paper proposes a modular template matching method for estimating surface mount component poses during circuit board assembly.

The text contains only publisher page metadata for a paper on visual place recognition using feature aggregation and reranking.

The text contains only publisher storefront metadata for a paper on wireless biomedical sensor networks for healthcare.

The paper introduces an interactive web-based tool for analyzing and visualizing execution trajectories of software engineering agents.

The paper introduces an interactive web-based tool for analyzing and visualizing execution trajectories of software engineering agents.

The paper analyzes 33,000 GitHub pull requests to examine why AI coding agent contributions succeed or fail.

The paper introduces a method to reduce redundant external knowledge retrieval by estimating what a language model already knows.

The paper introduces a method to reduce redundant external knowledge retrieval by estimating what a language model already knows.

The paper introduces a method to reduce redundant external knowledge retrieval by estimating what a language model already knows.

The paper introduces a method to reduce redundant external knowledge retrieval by estimating what a language model already knows.

The paper introduces a method to reduce redundant external knowledge retrieval by estimating what a language model already knows.

The paper introduces a method to reduce redundant external knowledge retrieval by estimating what a language model already knows.

This survey paper reviews attacks, defenses, and evaluation methods across 135 studies on secure retrieval-augmented generation systems.

This survey paper reviews attacks, defenses, and evaluation methods across 135 studies on secure retrieval-augmented generation systems.

This survey reviews in-band telemetry methods and assesses their practical feasibility for monitoring service performance across broadband networks.

The paper applies the sequential minimal optimization algorithm to detect SQL injection attacks using network traffic data.

The paper evaluates ten models for detecting Indonesian AI-generated text and examines how paraphrasing affects detection accuracy.

The paper presents a flexible triple-band rectenna designed for ambient radio-frequency energy harvesting to power passive Internet of Things devices.

The paper introduces an improved A-star algorithm to optimize serverless resource allocation and job scheduling for MapReduce workloads.

The paper evaluates and compares the power consumption and resource utilization of five 64-bit multiplier architectures implemented on an FPGA platform.

The paper proposes a noise-enrichment data augmentation methodology and curated dataset to improve bird sound recognition models under limited data conditions.

The paper presents an efficient two-pass encoding scheme using low-complexity spatial and temporal features for adaptive live video streaming.

The paper proposes an improved fast Gauss transform algorithm for efficient kernel density estimation in computer vision applications.

The paper examines how along-path evaporation duct variations affect parabolic equation path-loss predictions using maritime radio measurements.

The paper provides a formal definition and an evaluation framework for concept map mining techniques.

The paper studies electrical impedance spectroscopy using an AC nanopore method to evaluate the dielectric properties of single nanoparticles.

The paper introduces an approximation method using Lagrange interpolation to derive closed-form solutions for continuous-time neural networks.

The paper presents a high-resolution analog-to-digital converter architecture using a sigma-delta modulator for ultrasound color Doppler imaging.

The paper surveys artificial intelligence techniques for detecting and mitigating jamming and interference in 5G and 6G wireless communication networks.

The paper investigates the gap between raw obfuscated email source and recipient-visible text, evaluating lightweight classifiers under different obfuscations.

This paper introduces an adaptive framework to detect radio jamming attacks within machine learning integrated 5G Open RAN architectures.

This paper introduces an adaptive framework to detect radio jamming attacks within machine learning integrated 5G Open RAN architectures.

This paper introduces an adaptive framework to detect radio jamming attacks within machine learning integrated 5G Open RAN architectures.

This paper introduces an adaptive framework to detect radio jamming attacks within machine learning integrated 5G Open RAN architectures.

This paper uses Bayesian inference combined with machine learning models to detect and classify jamming attacks in 5G networks.

This paper presents a convolutional neural network model to detect and classify facial acne across four areas of the face.

This paper implements a lightweight binary neural network on a RISC-V processor to detect attacks on vehicle CAN bus networks.