Abstract: An auxiliary screening system and an auxiliary screening method for a hip joint of a baby are provided. The auxiliary screening method includes: collecting plural images of the hip joint; performing an image analysis operation on each of the images of the hip joint to extract plural image features of each of the images of the hip joint and determining whether each of the images of the hip joint is a standard image according to the image features of each of the images of the hip joint; and when at least one of the images of the hip joint is determined as the standard image, plural angle parameters are calculated and the at least one of the images of the hip joint that is determined as the standard image is outputted, in which the angle parameters include values of an angle ? and an angle ?.

Abstract: Translating applications to a target language includes extracting program integrated information (PII) to be translated and creating translation context datasets based on interpretation of accessibility information associated with particular strings of PII. Translation pairs include PII and corresponding context datasets for context-based translation of application components. A two-stage index contains PII strings for first stage lookup and context datasets for distinguishing duplicate PII strings as a second stage lookup. Real-time translation is facilitated by the two-stage index, which is established by translation pairs and resulting translations.

Abstract: A method for identifying hard-coded strings in source code is disclosed. In one embodiment, such a method parses source code and associated localization resource files to identify hard-coded strings and their associated context. The method provides a confidence score for each hard-coded string that indicates whether the hard-coded string is translatable or non-translatable. Based on the confidence score for each hard-coded string, the method transforms each hard-coded string into a single equivalence word. The method then prepares training data by tagging the hard-coded strings in the source code and associated localization resource files as one of translatable and non-translatable. The method then trains a parts-of-speech (POS) tagging model using the training data. At runtime, the method fetches potential hard-coded strings and tags each hard-coded string as one of translatable and non-translatable using the POS tagging model. A corresponding system and computer program product are also disclosed.