Does the dataset meet your expectations? Explaining sample representation in image data

Since the behavior of a neural network model is adversely affected by a lack of diversity in training data, we present a method that identifies and explains such deficiencies. When a dataset is labeled,we n ote that annotations alone are capable of providing a human interpretable summary of sample diversity. This allows explaining any lack of diversity as the mismatch found when comparing the actual distribution of annotations in the dataset with an expected distribution of annotations, specified manually to capture essential label diversity. While, in many practical cases, labeling (samples → annotations) is expensive, its inverse, simulation (annotations → samples) can be cheaper. By mapping the expected distribution of annotations into test samples using parametric simulation, we present a method that explains sample representation using the mismatch in diversity between simulated and collected data.We then apply the method to examine a dataset of geometric shapes to qualitatively and quantitatively explain sample representation in termsof comprehensible aspects such as size, position, and pixel brightness

Does the dataset meet your expectations? Explaining sample representation in image data

Since the behavior of a neural network model is adversely affected by a lack of diversity in training data, we present a method that identifies and explains such deficiencies. When a dataset is labeled,we n ote that annotations alone are capable of providing a human interpretable summary of sample diversity. This allows explaining any lack of diversity as the mismatch found when comparing the actual distribution of annotations in the dataset with an expected distribution of annotations, specified manually to capture essential label diversity. While, in many practical cases, labeling (samples → annotations) is expensive, its inverse, simulation (annotations → samples) can be cheaper. By mapping the expected distribution of annotations into test samples using parametric simulation, we present a method that explains sample representation using the mismatch in diversity between simulated and collected data.We then apply the method to examine a dataset of geometric shapes to qualitatively and quantitatively explain sample representation in termsof comprehensible aspects such as size, position, and pixel brightness

Opinion Mining for Software Development: A Systematic Literature Review

Opinion mining, sometimes referred to as sentiment analysis, has gained increasing attention in software engineering (SE) studies. SE researchers have applied opinion mining techniques in various contexts, such as identifying developers’ emotions expressed in code comments and extracting users’ critics toward mobile apps. Given the large amount of relevant studies available, it can take considerable time for researchers and developers to figure out which approaches they can adopt in their own studies and what perils these approaches entail. We conducted a systematic literature review involving 185 papers. More specifically, we present 1) well-defined categories of opinion mining-related software development activities, 2) available opinion mining approaches, whether they are evaluated when adopted in other studies, and how their performance is compared, 3) available datasets for performance evaluation and tool customization, and 4) concerns or limitations SE researchers might need to take into account when applying/customizing these opinion mining techniques. The results of our study serve as references to choose suitable opinion mining tools for software development activities, and provide critical insights for the further development of opinion mining techniques in the SE domain