Abstract
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Decoupling server and client code through cloud-native domain-specific functions
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| Jose Miguel Pérez-Álvarez, Adrian Mos, Benjamin V. Hanrahan, Iyadunni J. Adenuga |
| International Conference on Automated Software Engineering (ASE), virtual event, 15-19 November, 2021 |
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Simple domain-specific graphical languages and libraries can empower a variety of users to create application behaviors and logic. However, it remains challenging to produce and maintain a heterogeneous set of client applications based on these descriptions, as each client typically requires the developers to both understand and embed the domain-specific logic. This is because application logic must be encoded to some extent in both the server and client sides.
In this paper, we propose an alternative approach, which allows the specification of application logic to reside solely on the cloud. In our system, reusable application components are assembled on the cloud in different logical chains and the client is solely concerned with how data is displayed and gathered from users. In this way, the chaining of requests and responses is done by the cloud and the client side has no knowledge of the application logic. This means that the experts in the domain build modular cloud components, arrange them in logical chains, generate a simple user interface, and later leave it to client-side developers to customize the presentation.
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VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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