Deep neural networks (DNNs) are currently being deployed as machine learning technology
in a wide range of important real-world applications. DNNs consist of a huge number of …

Data science projects represent a greater challenge than software engineering for
organizations pursuing their adoption. The diverse stakeholders involved emphasize the …

In recent years, the landscape of computing paradigms has witnessed a gradual yet
remarkable shift from monolithic computing to distributed and decentralized paradigms such …

AI applications powered by deep learning inference are increasingly run natively on edge
devices to provide better interactive user experience. This often necessitates fitting a model …

Widespread adoption of mobile augmented reality (AR) and virtual reality (VR) applications
depends on their smoothness and immersiveness. Modern AR applications applying …

Deep learning has been widely applied in solving many tasks, such as image recognition,
speech recognition, and natural language processing. It requires a high-quality dataset …

Partitioning and distributing deep neural networks (DNNs) across end-devices, edge
resources and the cloud has a potential twofold advantage: preserving privacy of the input …

The last decade has seen the emergence of Deep Neural Networks (DNNs) as the de facto
algorithm for various computer vision applications. In intelligent edge devices, sensor data …

Visual place recognition (VPR) is a robot's ability to determine whether a place was visited
before using visual data. While conventional handcrafted methods for VPR fail under …

Indoor location-based services constitute an important part of our daily lives, providing
position and direction information about people or objects in indoor spaces. These systems …