Knowledge graph embedding (KGE) that maps entities and relations into ve...
Variational autoencoders (VAE) are powerful generative models that learn...
Recent advances in edge computing have pushed cloud-based data caching
s...
Since the traffic conditions change over time, machine learning models t...
Fine-tuning attacks are effective in removing the embedded watermarks in...
Supply chain management plays an essential role in our economy, as evide...
Knowledge graph completion (KGC) can predict missing links and is crucia...
Mobile edge computing has become an effective and fundamental paradigm f...
Semantic code search technology allows searching for existing code snipp...
Federated learning (FL) is experiencing a fast booming with the wave of
...
Continual learning (CL) refers to a machine learning paradigm that using...
Federated Learning aims to learn machine learning models from multiple
d...
Context: Decentralized applications on blockchain platforms are realized...
With the development of blockchain technologies, the number of smart
con...
With the fast evolving of cloud computing and artificial intelligence (A...
Federated learning (FL) utilizes edge computing devices to collaborative...
Current annotation for plant disease images depends on manual sorting an...
Numerous resource-limited smart objects (SOs) such as sensors and actuat...
The Scholarly Document Processing (SDP) workshop is to encourage more ef...
Obtaining training data for multi-document summarization (MDS) is time
c...
Matrix factorization (MF) has been widely applied to collaborative filte...
Non-negative tensor factorization models enable predictive analysis on c...
Emerging technologies like the Internet of Things (IoT) require latency-...