We present a new high-quality validated image caption rating (VICR) dataset. How well a caption fits an image can be difficult to assess due to the subjective nature of caption quality. How do we evaluate whether a caption is good? We generated a new dataset to help answer this question by using our new image caption rating system, which consists of a novel robust rating scale and gamified approach to gathering human ratings. We show that our approach is consistent and teachable. 113 participants were involved in generating the dataset, which is composed of 68,217 ratings among 15,646 image-caption pairs. Our new dataset has greater inter-rater agreement than the state of the art, and custom machine learning rating predictors that were trained on our dataset outperform previous metrics. We improve over Flickr8k-Expert in Kendall's $W$ by 12\% and in Fleiss' $\kappa$ by 19\%, and thus provide a new benchmark dataset for image caption rating.

This paper presents the Java version of the AOCS framework. The AOCS framework is an object-oriented software framework for real-time satellite control systems. It provides a set of design patterns, an adaptable architecture, and a set of configurable components that support the instantiation of satellite control applications. It was originally implemented in C++ but has now been ported to Java. The paper advocates the use of framework technology as the best way to promote software reuse in the control systems domain and discusses the precautions that must be taken to use this technology with Java as an implementation language in the presence of real-time constraints. It also presents two examples of instantiations of the AOCS frameworks with two different real-time Java implementations.