Camera-based autonomous systems that emulate human perception are
increa...
Visual object tracking has seen significant progress in recent years.
Ho...
Existing approaches for autonomous control of pan-tilt-zoom (PTZ) camera...
Although organizations are continuously making concerted efforts to hard...
A variety of explanation methods have been proposed in recent years to h...
Recent efforts in interpretable deep learning models have shown that
con...
The advancements in machine learning opened a new opportunity to bring
i...
Generative models such as the variational autoencoder (VAE) and the
gene...
Personalized IoT adapts their behavior based on contextual information, ...
In this paper, we present an approach to Complex Event Processing (CEP) ...
Recently, there has been a large amount of work towards fooling
deep-lea...
We present an experimentation platform for coalition situational
underst...
Future coalition operations can be substantially augmented through agile...
Training a model to detect patterns of interrelated events that form
sit...
Human attention is a scarce resource in modern computing. A multitude of...
Tuning hyperparameters for machine learning algorithms is a tedious task...
The increasing ubiquity of low-cost wireless sensors in smart homes and
...
Deep neural networks have achieved state-of-the-art performance on vario...
Deep Neural Networks (DNNs) deliver state-of-the-art performance in many...
We consider a sensing application where the sensor nodes are wirelessly
...
Deep neural networks (DNNs) are vulnerable to adversarial examples, even...
Deep neural networks (DNNs) are vulnerable to adversarial examples,
pert...
Time awareness is critical to a broad range of emerging applications – i...
Research evidence in Cyber-Physical Systems (CPS) shows that the introdu...
Speech is a common and effective way of communication between humans, an...
Through the last decade, we have witnessed a surge of Internet of Things...
The performance of a distributed network state estimation problem depend...
State-of-the-art convolutional neural networks are enormously costly in ...