Degradation in the quality of images that are captured in the hazy environment is mainlydue to 1) different weather conditions and 2) the attenuation in reflected light. These factors introduce a severe color distortion ...

This paper studies a fundamental algorithmic problem related to the design of demand-aware networks: networks whose topologies adjust toward the traffic patterns they serve, in an online manner. The goal is to strike a ...

Sensor nodes attached with the human body continuously sense and transfer real-time raw data to the coordinator node. The continuous process of sensing generally produces redundant data, which drains energy of the nodes. ...

Limited availability of annotated medical imaging data poses a challenge for deep learning algorithms. Although transfer learning minimizes this hurdle in general, knowledge transfer across disparate domains is shown to ...

Fog Computing extends traditional cloud based services to the edge of the network, close to where the data is generated. This technology fills a performance void in the cloud-to-thing architecture. By leveraging Fog ...

An important issue faced during software development is to identify defects and the properties of those defects, if found, in a given source file. Determining defectiveness of source code assumes significance due to its ...

Knowledge Graphs have increasingly become the preferred approach to complex problems that involve low-level business data. It is well known that Google, LinkedIn, Facebook, Twitter – all have a knowledge graph at their ...

We present GAZED- eye GAZe-guided EDiting for videos captured by a solitary, static, wide-angle and high-resolution camera. Eye-gaze has been effectively employed in computational applications as a cue to capture interesting ...

In this paper, we have proposed a novel feature descriptors combining color and texture information collectively. In our proposed color descriptor component, the inter-channel relationship between Hue (H) and Saturation ...

In this paper, we present a novel unsupervised domain adaptation framework, Multi-Partition Feature Alignment Network, that learns a deep neural model for the target domain without the need for any supervision. Recent ...