30 citations · 35 across the 7 of their papers we have counts for
9 papers
BotSCL: Heterophily-aware Social Bot Detection with Supervised Contrastive Learning
Qi Wu, Yingguang Yang, Buyun He +3
Detecting ever-evolving social bots has become increasingly challenging. Advanced bots tend to interact more with humans as a camouflage to evade detection. While graph-based detec…
Last Week with ChatGPT: A Weibo Study on Social Perspective Regarding ChatGPT for Education and Beyond
Yao Tian, Chengwei Tong, Lik-Hang Lee +3
The application of AI-powered tools has piqued the interest of many fields, particularly in the academic community. This study uses ChatGPT, currently the most powerful and popular…
Spatiotemporal and Semantic Zero-inflated Urban Anomaly Prediction
Yao Lu, Pengyuan Zhou, Yong Liao +1
Urban anomaly predictions, such as traffic accident prediction and crime prediction, are of vital importance to smart city security and maintenance. Existing methods typically use…
FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot Detection
Yingguang Yang, Renyu Yang, Hao Peng +4
Social bot detection is of paramount importance to the resilience and security of online social platforms. The state-of-the-art detection models are siloed and have largely overloo…
TKN: Transformer-based Keypoint Prediction Network For Real-time Video Prediction
Haoran Li, XiaoLu Li, Yihang Lin +4
Video prediction is a complex time-series forecasting task with great potential in many use cases. However, traditional methods prioritize accuracy and overlook slow prediction spe…
Mitigating Backdoors in Federated Learning with FLD
Yihang Lin, Pengyuan Zhou, Zhiqian Wu +1
Federated learning allows clients to collaboratively train a global model without uploading raw data for privacy preservation. This feature, i.e., the inability to review participa…
UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction
Wei Tang, Benfeng Xu, Yuyue Zhao +4
Relational triple extraction is challenging for its difficulty in capturing rich correlations between entities and relations. Existing works suffer from 1) heterogeneous representa…
Celeritas: Fast Optimizer for Large Dataflow Graphs
Hengwei Xu, Yong Liao, Haiyong Xie +1
The rapidly enlarging neural network models are becoming increasingly challenging to run on a single device. Hence model parallelism over multiple devices is critical to guarantee…
Towards User-Centered Metrics for Trustworthy AI in Immersive Cyberspace
Pengyuan Zhou, Benjamin Finley, Lik-Hang Lee +3
AI plays a key role in current cyberspace and future immersive ecosystems that pinpoint user experiences. Thus, the trustworthiness of such AI systems is vital as failures in these…