Scoreboard Architectural Pattern and Integration of Emotion Recognition Results

This paper proposes a new design pattern, named
Scoreboard
, dedicated for applications solving complex, multi-stage, non-deterministic problems. The pattern provides a computational framework for the design and implementation of systems that integrate a large number of diverse specialized modules that may vary in accuracy, solution level, and modality. The
Scoreboard
is an extension of
Blackboard
design pattern and comes under behavioral type. The pattern allows for an integration of multimodal results, employing early, and/or late fusion paradigms. Additionally, it provides a framework for the evaluation of the modules, dealing with inconsistency and low accuracy. In this paper, the
Scoreboard
design pattern is described with the standard meta-data model, followed by a sample implementation. This paper also provides the evaluation results based on experiments and a case study. The evaluation results confirmed the robustness, modularization, ease of integration, efficiency, and adaptability of the solutions with the
Scoreboard
pattern in comparison with the
Blackboard
pattern and “no pattern” condition. This paper provides also a case study of
Scoreboard
application in an integration of emotion recognition results. There are certain complex problems in modern software engineering which require multi-stage, multi-party, multi-modal solutions, and non-deterministic control strategies. Among those are natural language processing, image processing, and emotion recognition, to name just a few. The proposed
Scoreboard
pattern might be used in the software addressing the problems, especially in research systems that explore large solution spaces and require runtime decisions on execution order.