IADIS-INTERNATIONAL JOURNAL ON COMPUTER SCIENCE AND INFORMATION SYSTEMS

1-15

Authors: John L. Gibbs

The task of color grading (or color correction) for film and video is significant and complex, involving aesthetic and technical decisions that require a trained operator and a good deal of time. In order to determine whether deep neural networks are capable of learning this complex aesthetic task, we compare two network frameworks—a classification network, and a conditional generative adversarial network, or cGAN—examining the quality and consistency of their output as potential automated solutions to color correction. Results are very good for both networks, though each exhibits problem areas. The classification network has issues with generalizing due to the need to collect and especially to label all data being used to train it. The cGAN on the other hand can use unlabeled data, which is much easier to collect. While the classification network does not directly affect images, only identifying image problems, the cGAN, creates a new image, introducing potential image degradation in the process; thus multiple adjustments to the network need to be made to create high quality output. We find that the data labeling issue for the classification network is a less tractable problem than the image correction and continuity issues discovered with the cGAN method, which have direct solutions. Thus we conclude the cGAN is the more promising network with which to automate color correction and grading.

30-46

Authors: Felix Sanchez-Puchol, Joan A. Pastor-Collado, and Baptista Borrell

Enterprise Reference Architectures (ERAs) and Reference Models (RMs) have emerged over the last years as relevant instruments for improving the quality and effectiveness of enterprise architecture (EA) practice. Whilst a wide variety of different ERAs and RMs have been proposed for different industries and types of business, only few of them have been devoted to the Higher Education (HE) sector. In this paper, we propose an in-depth analysis process which we then critically apply to review, compare and classify 20 existing ERAs and RMs targeted to the HE domain. Our process uses a common set of 12 definitional attributes. In so doing, we contribute to the existing body of knowledge by providing a unified, structured and comprehensive analysis process and catalog of these abstract EA artifacts. With this we aim to create awareness on their potential practical utility and to increase their visibility, transparency and opportunity for their reusability by different HE stakeholders. Hence, the proposed process and catalog is expected to be useful both for practitioners and researchers by providing a panoramic view of more or less ready-to-use existing ERAs and RMs for HE, as well as a structure way to regard them. Moreover, and by specifying their main scope, coverage and extend of knowledge captured, the process and catalog might become a valuable tool for providing guidance to HE stakeholders on making better-informed decisions on the selection of suitable architectural artifacts for being conveniently adapted or applied in different EA practices conducted at their respective institutions.

62-75

Authors: André Langer, Christoph Göpfert ,and Martin Gaedke

Using appropriate entity URIs is a crucial factor for the success of semantic-enabled applications for data management and data retrieval. Especially data applications that collect data to build knowledge graphs rely on correct concept identifiers in favor of ambiguous literals. This collection involves human interaction in the web frontend without annoying the user. But appropriate user interfaces for this task are still a challenge. In this article, we focus on the design of form elements that unobtrusively allow input data both for human and machine interaction from a semantic point of view. Motivated by web-based scholarly document-submission systems, we first present a brief current-state analysis on the support of semantic input operations, investigate how these usersinput interfaces can be improved for concept linking purposes with an auto-suggestion behavior and finally evaluate with a proof-of concept implementation and user survey the advantages and acceptance of our approach.

93-108

Authors: Mathew Nicho, Hussein Fakhry,and Uche Egbue

Spear phishing emails pose great danger to employees of organizations due to the inherent weakness of the employees in identifying the threat from spear phishing cues, as well as the spear phisher’s skill in crafting contextually convincing emails. This raises the main question of which construct (user vulnerabilities or phisher skills) has a greater influence on the vulnerable user. Researchers have provided enough evidence of user vulnerabilities, namely the desire for monetary gain, curiosity of the computer user, carelessness on the part of the user, the trust placed in the purported sender by the user, and a lack of awareness on the part of the computer user. However, there is a lack of research on the magnitude of each of these factors in influencing an unsuspecting user to fall for a phishing or spear phishing attack which we explored in this paper. While user vulnerabilities pose major risk, the effect of the spear phisher’s ability in skillfully crafting convincing emails (using fear appeals, urgency of action, and email contextualization) to trap even skillful IT security personnel is an area that needs to be explored. Therefore, we explored the relationships between the two major constructs namely ‘user vulnerabilities’ and ‘email contextualization’, through the theory of planned behavior with the objective to find out the major factors that lead to computer users biting the phishers’ bait. In this theoretical version of the paper, we provided the resulting two constructs that needed to be tested.