61 citations · 326 across the 44 of their papers we have counts for
44 papers
Exploiting Prefix-Tree in Structured Output Interfaces for Enhancing Jailbreak Attacking
Yanzeng Li, Yunfan Xiong, Jialun Zhong +3
The rise of Large Language Models (LLMs) has led to significant applications but also introduced serious security threats, particularly from jailbreak attacks that manipulate outpu…
VSD2M: A Large-scale Vision-language Sticker Dataset for Multi-frame Animated Sticker Generation
Zhiqiang Yuan, Jiapei Zhang, Ying Deng +3
As a common form of communication in social media,stickers win users' love in the internet scenarios, for their ability to convey emotions in a vivid, cute, and interesting way. Pe…
MedDiT: A Knowledge-Controlled Diffusion Transformer Framework for Dynamic Medical Image Generation in Virtual Simulated Patient
Yanzeng Li, Cheng Zeng, Jinchao Zhang +2
Medical education relies heavily on Simulated Patients (SPs) to provide a safe environment for students to practice clinical skills, including medical image analysis. However, the…
Mobius: A High Efficient Spatial-Temporal Parallel Training Paradigm for Text-to-Video Generation Task
Yiran Yang, Jinchao Zhang, Ying Deng +1
Inspired by the success of the text-to-image (T2I) generation task, many researchers are devoting themselves to the text-to-video (T2V) generation task. Most of the T2V frameworks…
Attentive Multi-Layer Perceptron for Non-autoregressive Generation
Shuyang Jiang, Jun Zhang, Jiangtao Feng +2
Autoregressive~(AR) generation almost dominates sequence generation for its efficacy. Recently, non-autoregressive~(NAR) generation gains increasing popularity for its efficiency a…
Leveraging Diverse Semantic-based Audio Pretrained Models for Singing Voice Conversion
Xueyao Zhang, Zihao Fang, Yicheng Gu +5
Singing Voice Conversion (SVC) is a technique that enables any singer to perform any song. To achieve this, it is essential to obtain speaker-agnostic representations from the sour…
Client-side Gradient Inversion Against Federated Learning from Poisoning
Jiaheng Wei, Yanjun Zhang, Leo Yu Zhang +5
Federated Learning (FL) enables distributed participants (e.g., mobile devices) to train a global model without sharing data directly to a central server. Recent studies have revea…
model-based script synthesis for fuzzing
Zian Liu, Chao Chen, Muhammad Ejaz Ahmed +2
Kernel fuzzing is important for finding critical kernel vulnerabilities. Close-source (e.g., Windows) operating system kernel fuzzing is even more challenging due to the lack of so…
SemDiff: Binary Similarity Detection by Diffing Key-Semantics Graphs
Zian Liu, Zhi Zhang, Xiaoxi Meng +8
Binary similarity detection is a critical technique that has been applied in many real-world scenarios where source code is not available, e.g., bug search, malware analysis, and c…
VulMatch: Binary-level Vulnerability Detection Through Signature
Zian Liu, Lei Pan, Chao Chen +4
Similar vulnerability repeats in real-world software products because of code reuse, especially in wildly reused third-party code and libraries. Detecting repeating vulnerabilities…
MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates
Yuchang Sun, Yuyi Mao, Jun Zhang
Federated learning (FL) is a promising framework for privacy-preserving collaborative learning, where model training tasks are distributed to clients and only the model updates nee…
Joint Activity-Delay Detection and Channel Estimation for Asynchronous Massive Random Access
Xinyu Bian, Yuyi Mao, Jun Zhang
Most existing studies on joint activity detection and channel estimation for grant-free massive random access (RA) systems assume perfect synchronization among all active users, wh…
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning
Yuchang Sun, Zehong lin, Yuyi Mao +2
Federated learning (FL) is a popular privacy-preserving distributed training scheme, where multiple devices collaborate to train machine learning models by uploading local model up…
Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data
Tailin Zhou, Zehong Lin, Jun Zhang +1
Model averaging is a widely adopted technique in federated learning (FL) that aggregates multiple client models to obtain a global model. Remarkably, model averaging in FL yields a…
Security and Privacy Problems in Voice Assistant Applications: A Survey
Jingjin Li, Chao chen, Lei Pan +3
Voice assistant applications have become omniscient nowadays. Two models that provide the two most important functions for real-life applications (i.e., Google Home, Amazon Alexa,…
In-Context Learning with Many Demonstration Examples
Mukai Li, Shansan Gong, Jiangtao Feng +4
Large pre-training language models (PLMs) have shown promising in-context learning abilities. However, due to the backbone transformer architecture, existing PLMs are bottlenecked…
Rephrasing the Reference for Non-Autoregressive Machine Translation
Chenze Shao, Jinchao Zhang, Jie Zhou +1
Non-autoregressive neural machine translation (NAT) models suffer from the multi-modality problem that there may exist multiple possible translations of a source sentence, so the r…
AutoCAD: Automatically Generating Counterfactuals for Mitigating Shortcut Learning
Jiaxin Wen, Yeshuang Zhu, Jinchao Zhang +2
Recent studies have shown the impressive efficacy of counterfactually augmented data (CAD) for reducing NLU models' reliance on spurious features and improving their generalizabili…
SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation
Hao Dong, Weihao Gu, Xianjing Zhang +5
High-definition (HD) semantic map generation of the environment is an essential component of autonomous driving. Existing methods have achieved good performance in this task by fus…
A Self-adaptive Neuroevolution Approach to Constructing Deep Neural Network Architectures Across Different Types
Zhenhao Shuai, Hongbo Liu, Zhaolin Wan +2
Neuroevolution has greatly promoted Deep Neural Network (DNN) architecture design and its applications, while there is a lack of methods available across different DNN types concer…
Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues
Jiao Ou, Jinchao Zhang, Yang Feng +1
The construction of open-domain dialogue systems requires high-quality dialogue datasets. The dialogue data admits a wide variety of responses for a given dialogue history, especia…
CAB: Comprehensive Attention Benchmarking on Long Sequence Modeling
Jun Zhang, Shuyang Jiang, Jiangtao Feng +2
Transformer has achieved remarkable success in language, image, and speech processing. Recently, various efficient attention architectures have been proposed to improve transformer…
Selecting Stickers in Open-Domain Dialogue through Multitask Learning
Zhexin Zhang, Yeshuang Zhu, Zhengcong Fei +2
With the increasing popularity of online chatting, stickers are becoming important in our online communication. Selecting appropriate stickers in open-domain dialogue requires a co…
Resource-Constrained Edge AI with Early Exit Prediction
Rongkang Dong, Yuyi Mao, Jun Zhang
By leveraging the data sample diversity, the early-exit network recently emerges as a prominent neural network architecture to accelerate the deep learning inference process. Howev…