This paper presents a novel dynamic analysis approach to software plagiarism detection. Such an approach is inherently more resilient to code obfuscation techniques such as renaming of program entities, reordering of statements, etc. We develop our technique in the context of a dynamic analysis and visualization system for Java, called JIVE, but the techniques are applicable to other object-oriented languages. Our analyses are based on the execution traces of Java programs (produced by JIVE), and our experimental results confirm that this approach is both efficient and effective in detecting plagiarism of Java programs when their source codes are not available.

There has been a wide interest in applying Deep Learning (DL) algorithms for automated binary and multi class classification of colour fundus images affected with Diabetic Retinopathy (DR). These algorithms have shown high sensitivity and specificity for detecting DR in non-clinical setup. Transfer learning has been successfully tested in many medical imaging applications like skin cancer detection, pulmonary nodule detection, Alzheimer’s disease etc. This paper experiments with the different DL architectures such as VGG19, InceptionV3, ResNet50, MobileNet and NASNet for automated DR classification (binary and multi class) on Messidor dataset. The dataset is publicly available, and comprises of 1200 retinal fundus images. The images belong to four different classes of DR namely, normal (class 0), mild (class 1), moderate (class 2) and severe (class 3), graded based on the severity level of DR. In our experiment, we have enhanced the quality of input images by applying algorithms like CLAHE (Contrast Limited Adaptive Histogram Equalisation) algorithm and Powerlaw transformation as pre-processing techniques, which work on the small image patches with high accuracy, contrast limiting and image sharpening. Hyper parameter tuning on pretrained InceptionV3 architecture, resulted in enhancing the accuracy of the model. Both binary and multi class results were analysed considering inter class (one class with another class) accuracies. We achieved an accuracy of 78% between class 0 and class 1, the accuracy between class 0 and class 2 further reduced to 69%, while class 1 and class 2 showed an accuracy of 61%. Moreover, the interclass class accuracy between class 1 and class 3 was 62%, class 2 and class 3 further reduced to 49%. The accuracy further diminished between class 0 and class 3 to 32%. These experiments suggest that the pretrained models provided better results in classifying normal and mild, but they were not that much efficient in classifying moderate-severe and normal-severe binary classifications.

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Computational retinal imaging and vision based techniques have been assisting ophthalmologists for efficient and rapid screening and diagnostics, like other healthcare areas. Human retina is the only place in the human circulatory systems that can be seen using non-invasive techniques like fundus imaging, Optical Coherence Tomography (OCT) and invasive technique like Fluorescein angiograms. Attempts have been progressing towards developing an automatic detection and analysis of many eye related complications like diabetic retinopathy; age-related macular degeneration and glaucoma. There have been many survey papers of algorithms for pre-processing, segmentation and classification involving Fundus and OCT images. It has been observed that fundus fluorescein angiograms (FFA) play an important role in the detection and analysis of diabetic maculopathy which has become one of the major complications arising due to prolonged diabetes. It is also considered as severe as it affects the central vision of the human eye. Here we propose to perform a systematic review of the developed algorithms for processing FFA images of patients suffering from diabetic maculopathy.