The advent of storing images on cloud platforms has introduced serious privacy concerns. The images are routinely scanned by machine learning algorithms to determine the contents. Usually the scanning is for marketing purposes but more malevolent purposes include criminal activity and government surveillance. The images are automatically analysed by machine learning algorithms. Notably, deep convolutional neural networks perform very well at identifying image classes. Obviously, the images could be encrypted before storing to cloud platforms and then decrypted after downloading. This would certainly obfuscate the images. However, many users prefer to be able to peruse the images on the cloud platform. This creates a difficult problem in which users prefer images stored in a way so that a human can understand them but machine learning algorithms cannot. This paper proposes a novel technique, termed seam doppelganger, for formatting images using seam carving to identify seams for replacement. The approach degrades typical image classification performance in order to provide privacy while leaving the image human-understandable. Furthermore, the technique can be largely reversed providing a reasonable facsimile of the original image. Using the ImageNet database for birds, we show how the approach degrades a state-of-the-art residual network (ResNet50) for various amounts of seam replacements.

Java is arguably today’s most popular and widely used object-oriented programming language. Learning Java is a daunting task for students, and teaching it is a challenging undertaking for instructors. To assess students’ object-oriented programming skills with Java, we developed the “Department-Employee” project. In this article, we review the history of object-oriented programming, provide an overview of object-oriented programming with Java, and present a summary of existing Java projects and their limitations. We also provide the project specifications as well as the course background, grading rubric, and score reports. Survey data are presented on students’ backgrounds, as well as students’ perceptions regarding the project. Results from the instructor score reports, correlation of the project score and the final course score, and student perceptions show that the “Department-Employee” project is effective in assessing students’ object-oriented programming skills with Java.

With the emergence of wireless sensor networks and pervasive computing, innovative location-aware technologies and services are being investigated. Several iterative approaches employing distributed computations over sensors have been proposed in the literature for locating all the sensor nodes in the network. Due to their iterative nature these techniques are inefficient in terms of power, a very precious resource in sensor networks. This paper presents a novel power efficient approach aimed at identifying the locations of all the nodes in a sensor network given the location of a small subset of nodes. The technique, using evolution strategies, is independent of the ranging method used to estimate distances between nodes and involves sink nodes in the computation. The proposed approach provides substantial energy savings over existing techniques while providing comparable accuracy, and requires the presence of at least one neighbor for each sensor node compared to at least 3 neighbors for most of the existing techniques

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