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Found 11 papers, 2 papers with code
RITFIS: Robust input testing framework for LLMs-based intelligent software
To our knowledge, RITFIS is the first framework designed to assess the robustness of LLM-based intelligent software against natural language inputs.
A Spatial-Temporal Dual-Mode Mixed Flow Network for Panoramic Video Salient Object Detection
First, the ILA module calculates the attention between adjacent level features of consecutive frames of panoramic video to improve the accuracy of extracting salient object features from the spatial flow.
Towards Fully Decoupled End-to-End Person Search
This enables independent learning for different objectives thus fully decoupled the model for persons earch.
LEAP: Efficient and Automated Test Method for NLP Software
The widespread adoption of DNNs in NLP software has highlighted the need for robustness.
Towards Stealthy Backdoor Attacks against Speech Recognition via Elements of Sound
Motivated by these findings, we propose to exploit elements of sound ($e. g.$, pitch and timbre) to design more stealthy yet effective poison-only backdoor attacks.
VSVC: Backdoor attack against Keyword Spotting based on Voiceprint Selection and Voice Conversion
Keyword spotting (KWS) based on deep neural networks (DNNs) has achieved massive success in voice control scenarios.
PBSM: Backdoor attack against Keyword spotting based on pitch boosting and sound masking
The training of KWS is usually based on deep neural networks and requires a large amount of data.
Inflection Point: A New Perspective on Photonic Nanojets
When light propagates through the edge or middle part of microparticle's incoming interface, there is a basic rule that light converges and diverges rapidly or slowly at the output port.
Optics
Quality Assurance Technologies of Big Data Applications: A Systematic Literature Review
However, the quality of these applications is far from perfect, leading to a large amount of issues and problems.
Software Engineering
CAGFuzz: Coverage-Guided Adversarial Generative Fuzzing Testing of Deep Learning Systems
However, they have the following limitations.
