Narrow Results

This paper proposes universal coarse-grained reconfigurable computing architecture for hardware implementation of decision trees (DTs), artificial neural networks (ANNs), and support vector machines (SVMs), suitable for both field programmable gate arrays (FPGA) and application specific integrated circuits (ASICs) implementation. Using this universal architecture, two versions of DTs (functional DT and axis-parallel DT), two versions of SVMs (with polynomial and radial kernel) and two versions of ANNs (multi layer perceptron ANN and radial basis ANN) machine learning classifiers, have been implemented in FPGA. Experimental results, based on 18 benchmark datasets of standard UCI machine learning repository database, show that FPGA implementation provides significant improvement (1–2 orders of magnitude) in the average instance classification time, in comparison with software implementations based on R project.

This paper presents a systematic review of empirical research on cybersecurity issues. 14 empirical articles about cybersecurity, published in the two top IS journals, MISQ (12) and ISR (2), between 2008 and 2017, were selected and analyzed, classified into three categories: individual level (non-work setting), employee level (work setting), and organization level (policy/regulation environment). This paper provides a holistic picture of cybersecurity issues, for instance, fundamental theories, impressive research methods, and influencing factors. More importantly, for the first time an integrative framework was developed by R Project, which potentially text-mines end-users’ behaviors and decision-making processes toward cybersecurity under the circumstance of security breach. Some explanations of extant empirical study and potential research are addressed and discussed as well.