With the prevalence of software systems adopting neural network models, the quality assurance of these systems has become crucial. Hence, various studies have proposed repairing methods for neural network models so far to improve the quality of the models. While these methods are evaluated by researchers, it is difficult to tell whether they succeed in all models and datasets (i.e., all developers’ environments). Because these methods require many resources, such as execution times, failing to repair neural networks would cost developers their resources. Hence, if developers can know whether repairing methods succeed before adopting them, they could avoid wasting their resources. This paper proposes prediction models that predict whether repairing methods succeed in repairing neural networks using a small resource. Our prediction models predict repairs and side-effects of repairing methods, respectively. We evaluated our prediction models on a state-of-the-art repairing method Arachne on three datasets, Fashion-MNIST, CIFAR-10, and GTSRB, and found our prediction models achieved high performance, an average ROC-AUC of 0.931 and an average f1score of 0.880 for the side-effects and an average ROC-AUC of 0.768 and an average f1-score of 0.725 for the repairs.

Energy consumption is a concern in the data-center and at the edge, on mobile devices such as smartphones. Software that consumes too much energy threatens the utility of the end-user's mobile device. Energy consumption is fundamentally a systemic kind of performance and hence it should be addressed at design time via a software architecture that supports it, rather than after release, via some form of refactoring. Unfortunately developers often lack knowledge of what kinds of designs and architectures can help address software energy consumption. In this paper we show that some simple design choices can have significant effects on energy consumption. In particular we examine the Model-View-Controller architectural pattern and demonstrate how converting to Model-View-Presenter with bundling can improve the energy performance of both benchmark systems and real world applications. We show the relationship between energy consumption and bundled and delayed view updates: bundling events in the presenter can often reduce energy consumption by 30%.

In this paper, we propose a novel type of 3D scanning system named 'climbing sensor'. This system has been designed for scanning narrow and vertically stalky spaces, which are hard or extremely inefficient to scan by commercial laser range scanners due to their dimensions and limitation of FOVs. The climbing sensor equips a platform with two line scanners on a lift, and they scan through the whole target while the lift moves downwards along a ladder. One scanner is for scanning the target, which scans horizontally as the lift moves vertically, and the other scanner is for localizing the platform, which scans vertically. By using spatio-temporal range image acquired from the vertical scanning, we can accurately calculate the speed of the moving platform, with which a correct 3D model can be constructed from horizontal scans. We applied this scanning system to the Bayon Temple in Cambodia as a part of our digital archiving project of cultural assets. The scanning results proved that the system gives a sufficiently accurate 3D model and the effectiveness of our proposed system and speed estimating process.