Search Results for author:
Found 16 papers, 4 papers with code
FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter Update
This approach ensures that a portion of the local model remains personalized, thereby enhancing the model's robustness against biased parameters from other clients.
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.
FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy
Under the orchestration of a server, edge devices (also called clients in FL) collaboratively train a global deep-learning model without sharing any local data.
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.
Deep Edge Intelligence: Architecture, Key Features, Enabling Technologies and Challenges
DEI employs Deep Learning, Artificial Intelligence, Cloud and Edge Computing, 5G/6G networks, Internet of Things, Microservices, etc.
CSSR: A Context-Aware Sequential Software Service Recommendation Model
We propose a novel software service recommendation model to help users find their suitable repositories in GitHub.
Privacy-Aware Identity Cloning Detection based on Deep Forest
We propose a novel method to detect identity cloning of social-sensor cloud service providers to prevent the detrimental outcomes caused by identity deception.
Conflict Detection in IoT-based Smart Homes
Conflicts may arise during interactions between the resident and IoT services in smart homes.
Heuristics based Mosaic of Social-Sensor Services for Scene Reconstruction
We propose a heuristics-based social-sensor cloud service selection and composition model to reconstruct mosaic scenes.
Subjective Metrics-based Cloud Market Performance Prediction
The support vector machine showed the best prediction results compared to the other models.
A Knowledge Graph based Approach for Mobile Application Recommendation
With the rapid prevalence of mobile devices and the dramatic proliferation of mobile applications (apps), app recommendation becomes an emergent task that would benefit both app users and stockholders.
Enabling Edge Cloud Intelligence for Activity Learning in Smart Home
We propose a novel activity learning framework based on Edge Cloud architecture for the purpose of recognizing and predicting human activities.
Social-Sensor Composition for Tapestry Scenes
The extensive use of social media platforms and overwhelming amounts of imagery data creates unique opportunities for sensing, gathering and sharing information about events.
