297 citations · 545 across the 13 of their papers we have counts for
13 papers
Generalizing Neural Networks by Reflecting Deviating Data in Production
Yan Xiao, Yun Lin, Ivan Beschastnikh +3
Trained with a sufficiently large training and testing dataset, Deep Neural Networks (DNNs) are expected to generalize. However, inputs may deviate from the training dataset distri…
Self-Checking Deep Neural Networks in Deployment
Yan Xiao, Ivan Beschastnikh, David S. Rosenblum +4
The widespread adoption of Deep Neural Networks (DNNs) in important domains raises questions about the trustworthiness of DNN outputs. Even a highly accurate DNN will make mistakes…
Directed Graph Convolutional Network
Zekun Tong, Yuxuan Liang, Changsheng Sun +2
Graph Convolutional Networks (GCNs) have been widely used due to their outstanding performance in processing graph-structured data. However, the undirected graphs limit their appli…
Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics
Yuxuan Liang, Kun Ouyang, Yiwei Wang +4
Citywide crowd flow analytics is of great importance to smart city efforts. It aims to model the crowd flow (e.g., inflow and outflow) of each region in a city based on historical…
Fine-Grained Urban Flow Inference
Kun Ouyang, Yuxuan Liang, Ye Liu +4
The ubiquitous deployment of monitoring devices in urban flow monitoring systems induces a significant cost for maintenance and operation. A technique is required to reduce the num…
MMKG: Multi-Modal Knowledge Graphs
Ye Liu, Hui Li, Alberto Garcia-Duran +3
We present MMKG, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KG…
UrbanFM: Inferring Fine-Grained Urban Flows
Yuxuan Liang, Kun Ouyang, Lin Jing +5
Urban flow monitoring systems play important roles in smart city efforts around the world. However, the ubiquitous deployment of monitoring devices, such as CCTVs, induces a long-l…
Efficient Decentralized LTL Monitoring Framework Using Tableau Technique
Omar Al-Bataineh, David Rosenblum
This paper presents a novel framework for decentralized monitoring of Linear Temporal Logic (LTL), under the situation where processes are synchronous, uniform (i.e. all processes…
Computing Maximal Expected Termination Time of Probabilistic Timed Automata
Omar Al-Bataineh, Michael Fisher, David Rosenblum
The paper addresses the problem of computing maximal expected time to termination of probabilistic timed automata (PTA) models, under the condition that the system will, eventually…
An Interval-Based Bayesian Generative Model for Human Complex Activity Recognition
Li Liu, Yongzhong Yang, Lakshmi Narasimhan Govindarajan +4
Complex activity recognition is challenging due to the inherent uncertainty and diversity of performing a complex activity. Normally, each instance of a complex activity has its ow…
Action2Activity: Recognizing Complex Activities from Sensor Data
Ye Liu, Liqiang Nie, Lei Han +2
As compared to simple actions, activities are much more complex, but semantically consistent with a human's real life. Techniques for action recognition from sensor generated data…
Predicting Urban Water Quality with Ubiquitous Data
Ye Liu, Yuxuan Liang, Shuming Liu +2
Urban water quality is of great importance to our daily lives. Prediction of urban water quality help control water pollution and protect human health. However, predicting the urba…
Asymptotic Bounds for Quantitative Verification of Perturbed Probabilistic Systems
Guoxin Su, David S. Rosenblum
The majority of existing probabilistic model checking case studies are based on well understood theoretical models and distributions. However, real-life probabilistic systems usual…