Search Results for author:
Found 25 papers, 10 papers with code
Defending LVLMs Against Vision Attacks through Partial-Perception Supervision
Instead of directly using responses from partial images for voting, we investigate using them to supervise the LVLM's responses to the original images.
AEGIS: An Agent-based Framework for General Bug Reproduction from Issue Descriptions
Manually writing reproduction scripts is a time-consuming task with high requirements for developers.
Detecting and Explaining Anomalies Caused by Web Tamper Attacks via Building Consistency-based Normality
Web applications are crucial infrastructures in the modern society, which have high demand of reliability and security.
LLM-based Abstraction and Concretization for GUI Test Migration
Then, we propose a concretization technique that utilizes the general test logic to guide an LLM in generating the corresponding GUI test case (including events and assertions) for the target app.
SpreadsheetLLM: Encoding Spreadsheets for Large Language Models
Finally, we propose Chain of Spreadsheet for downstream tasks of spreadsheet understanding and validate in a new and demanding spreadsheet QA task.
Contribution Evaluation of Heterogeneous Participants in Federated Learning via Prototypical Representations
Our core idea is the construction and application of the class contribution momentum indicator from individual, relative, and holistic perspectives, thereby achieving an effective and efficient contribution evaluation of heterogeneous participants without relying on an auxiliary test dataset.
Towards Large Language Model Aided Program Refinement
Moreover, the opaque procedure from specification to code provided by LLM is an uncontrolled black box.
PaCoST: Paired Confidence Significance Testing for Benchmark Contamination Detection in Large Language Models
We validate the effectiveness of PaCoST and apply it on popular open-source models and benchmarks.
A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Models
With the successful application of deep learning in communications systems, deep neural networks are becoming the preferred method for signal classification.
Towards Transferable Attacks Against Vision-LLMs in Autonomous Driving with Typography
To further explore the risk in AD systems and the transferability of practical threats, we propose to leverage typographic attacks against AD systems relying on the decision-making capabilities of Vision-LLMs.
Automated Similarity Metric Generation for Recommendation
Given the critical role of similarity metrics in recommender systems, existing methods mainly employ handcrafted similarity metrics to capture the complex characteristics of user-item interactions.
CoCoST: Automatic Complex Code Generation with Online Searching and Correctness Testing
Large Language Models have revolutionized code generation ability by converting natural language descriptions into executable code.
Rogue Emitter Detection Using Hybrid Network of Denoising Autoencoder and Deep Metric Learning
Rogue emitter detection (RED) is a crucial technique to maintain secure internet of things applications.
Semi-Supervised Specific Emitter Identification Method Using Metric-Adversarial Training
Specifically, pseudo labels are innovatively introduced into metric learning to enable semi-supervised metric learning (SSML), and an objective function alternatively regularized by SSML and virtual adversarial training (VAT) is designed to extract discriminative and generalized semantic features of radio signals.
Soft-Labeled Contrastive Pre-training for Function-level Code Representation
In this paper, we present \textbf{SCodeR}, a \textbf{S}oft-labeled contrastive pre-training framework with two positive sample construction methods to learn functional-level \textbf{Code} \textbf{R}epresentation.
ASTF: Visual Abstractions of Time-Varying Patterns in Radio Signals
A time-frequency diagram is a commonly used visualization for observing the time-frequency distribution of radio signals and analyzing their time-varying patterns of communication states in radio monitoring and management.
Ultra Lite Convolutional Neural Network for Fast Automatic Modulation Classification in Low-Resource Scenarios
Automatic modulation classification (AMC) is a key technique for designing non-cooperative communication systems, and deep learning (DL) is applied effectively to AMC for improving classification accuracy.
Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning
Thus, we focus on few-shot SEI (FS-SEI) for aircraft identification via automatic dependent surveillance-broadcast (ADS-B) signals, and a novel FS-SEI method is proposed, based on deep metric ensemble learning (DMEL).
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective
For those reasons, there is a high demand for trustworthy and rigorous methods to verify the robustness of neural network models.
A Real-World Radio Frequency Signal Dataset Based on LTE System and Variable Channels
However, most of these datasets are collected from 2. 4G WiFi devices and through similar channel environments.
A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement
Our attack technique targets the inherent vulnerabilities of NLP models, allowing us to generate samples even without interacting with the victim NLP model, as long as it is based on pre-trained language models (PLMs).
DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training
Moreover, our case study shows that our visual solution can well reflect the characteristics of various training scenarios, showing good potential of DVI as a debugging tool for analyzing deep learning training processes.
Generalizing Neural Networks by Reflecting Deviating Data in Production
However, inputs may deviate from the training dataset distribution in real deployments.
sFuzz: An Efficient Adaptive Fuzzer for Solidity Smart Contracts
In this work, we present an adaptive fuzzer for smart contracts on the Ethereum platform called sFuzz.
Software Engineering
SR2CNN: Zero-Shot Learning for Signal Recognition
This paper proposes a ZSL framework, signal recognition and reconstruction convolutional neural networks (SR2CNN), to address relevant problems in this situation.
