Application of the 0-1 Programming Model for Cost-Effective Regression Test

Abstract-This paper reports an application of the 0-1 programming model to the regression testing plan for an industrial software. The key idea is to formulate a testing plan as a 0-1 programming problem (Knapsack problem). The empirical study shows that the 0-1 programming method can produce a cost-effective testing plan in which all potential regressions are found at only 22% of the cost of running all test cases

Novel scotoma detection method using time required for fixation to the random targets

We developed a novel scotoma detection system using time required for fixation to the random targets, or the” eye-guided scotoma detection method “. In order to verify the” eye-guided scotoma detection method “, we measured 78 eyes of 40 subjects, and examined the measurement results in comparison with the results of measurement by Humphrey perimetry. The results were as follows: (1) Mariotte scotomas were detected in 100% of the eyes tested; (2) The false-negative rate (the percentage of cases where a scotoma was evaluated as a non-scotoma) was less than 10%; (3) The positive point distribution in the low-sensitivity eyes was well matched. These findings suggested that the novel scotoma detection method in the current study will pave the way for the realization of mass screening to detect pathological scotoma earlier.[Author summary] Conventional perimeters, such as the Goldmann perimeter and Humphrey perimeter, require experienced examiners and space occupying. With either perimeter, subjects’ eye movements need to be strictly fixed to the fixation target of the device. Other perimeters can monitor fixation and automatically measure the visual field. With the eye-guided scotoma detection method proposed in the current study, subjects feel less burdened since they do not have to fixate on the fixation target of the device and can move their eyes freely. Subjects simply respond to visual targets on the display; then, scotomas can be automatically detected. The novel method yields highly accurate scotoma detection through an algorithm that separates scotomas from non-scotomas

Multi Proxy Anchor Loss and Effectiveness of Deep Metric Learning Performance Metrics

Deep metric learning (DML) learns the mapping, which maps into embedding space in which similar data is near and dissimilar data is far. However, conventional proxy-based losses for DML have two problems: gradient problems and applying the real-world dataset with multiple local centers. Besides, DML performance metrics also have some issues have stability and flexibility. This paper proposes multi-proxies anchor (MPA) loss and normalized discounted cumulative gain (nDCG@k) metric. This study contributes three following: (1) MPA loss is able to learn the real-world dataset with multi-local centers. (2) MPA loss improves the training capacity of a neural network owing to solving the gradient issues. (3) nDCG@k metric encourages complete evaluation for various datasets. Finally, we demonstrate MPA loss's effectiveness, and MPA loss achieves higher accuracy on two datasets for fine-grained images