Monitoring compliance status by an organization has been historically difficult due to the growing number of compliance requirements being imposed by various standards, frameworks, and regulatory requirements. Existing practices by organizations even with the assistance of security tools and appliances is mostly manual in nature as there is still a need for a human expert to interpret and map the reports generated by various solutions to actual requirements as stated in various compliance documents. As the number of requirements increases, this process is becoming either too costly or impractical to manage by the organization. Aside from the numerous requirements, multiple of these documents actually overlap in terms of domains and actual requirements. However, since current tools do not directly map and highlight overlaps as well as generate detailed gap reports, an organization would perform compliance activities redundantly across multiple requirements thereby increasing cost as well. In this paper, we present an approach that attempts to provide an end-to-end solution from compliance document requirements to actual verification and validation of implementation for audit purposes with the intention of automating compliance status monitoring as well as providing the ability to have continuous compliance monitoring as well as reducing the redundant efforts that an organization embarks on for multiple compliance requirements. This research thru enhancing existing security ontologies to model compliance documents and applying information extraction practices would allow for overlapping requirements to be identified and gaps to be clearly explained to the organization. Thru the use of secure systems development lifecycle, and heuristics the research also provide a mechanism to automate the technical validation of compliance statuses thereby allowing for continuous monitoring as well as mapping to the enhanced ontology to allow reusability via conceptual mapping of multiple standards and requirements. Practices such as unit testing and continuous integration from secure systems development life cycle are incorporated to allow for flexibility of the automation process while at the same time using it to support the mapping between compliance requirements.
Advancement in ambient intelligence is driving the trend towards innovative interaction with computing systems. In this paper, we present our efforts towards the development of the ambient intelligent space TALA, which has the concept of empathy in cognitive science as its architecture's backbone to guide its human-system interactions. We envision TALA to be capable of automatically identifying its occupant, modeling his/her affective states and activities, and providing empathic responses via changes in ambient settings. We present here the empirical results and analyses we obtained for the first two of this three-fold capability. We constructed face and voice datasets for identity and affect recognition and an activity dataset. Using a multimodal approach, specifically, applying a decision level fusion of independent face and voice models, we obtained accuracies of 88% and 79% for identity and affect recognition, respectively. For activity recognition, classification is 80% accurate even without employing any fusion technique.
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