Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications

Social Virtual Reality based Learning Environments (VRLEs) such as vSocial render instructional content in a three-dimensional immersive computer experience for training youth with learning impediments. There are limited prior works that explored attack vulnerability in VR technology, and hence there is a need for systematic frameworks to quantify risks corresponding to security, privacy, and safety (SPS) threats. The SPS threats can adversely impact the educational user experience and hinder delivery of VRLE content. In this paper, we propose a novel risk assessment framework that utilizes attack trees to calculate a risk score for varied VRLE threats with rate and duration of threats as inputs. We compare the impact of a well-constructed attack tree with an adhoc attack tree to study the trade-offs between overheads in managing attack trees, and the cost of risk mitigation when vulnerabilities are identified. We use a vSocial VRLE testbed in a case study to showcase the effectiveness of our framework and demonstrate how a suitable attack tree formalism can result in a more safer, privacy-preserving and secure VRLE system.Comment: Tp appear in the CCNC 2019 Conferenc

Disaster recovery best practices for Dominican Republic’s contact center

Contact Centers are an important growing industry in the Dominican Republic which provides employment, foreign exchange and exposition of the country on the international market. The international competition requires standardization of the systems and processes to be in compliance with international requirements. One of the important elements to be validated is Disaster Recovery planning because of the exposures of the Dominican Republic to events that may affect the continuity of service that Contact Centers must provide to customers and clients. In this paper an analysis is elaborated about the risks that the Dominican Republic encounters for disastrous events and a set of best practices are summarized to help a Contact Center to be prepared for incidents and design their own Disaster Recovery Plan

Depth estimation of inner wall defects by means of infrared thermography

There two common methods dealing with interpreting data from infrared thermography: qualitatively and quantitatively. On a certain condition, the first method would be sufficient, but for an accurate interpretation, one should undergo the second one. This report proposes a method to estimate the defect depth quantitatively at an inner wall of petrochemical furnace wall. Finite element method (FEM) is used to model multilayer walls and to simulate temperature distribution due to the existence of the defect. Five informative parameters are proposed for depth estimation purpose. These parameters are the maximum temperature over the defect area (Tmax-def), the average temperature at the right edge of the defect (Tavg-right), the average temperature at the left edge of the defect (Tavg-left), the average temperature at the top edge of the defect (Tavg-top), and the average temperature over the sound area (Tavg-so). Artificial Neural Network (ANN) was trained with these parameters for estimating the defect depth. Two ANN architectures, Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) network were trained for various defect depths. ANNs were used to estimate the controlled and testing data. The result shows that 100% accuracy of depth estimation was achieved for the controlled data. For the testing data, the accuracy was above 90% for the MLP network and above 80% for the RBF network. The results showed that the proposed informative parameters are useful for the estimation of defect depth and it is also clear that ANN can be used for quantitative interpretation of thermography data

Operating and maintenance cost reduction using probabilistic risk assessment (PRA)

"January 1992."Includes bibliographical references (pages 129-132)Final report, "Operating and maintenance cost reduction using probabilistic risk assessment (PRA)"This study quantifies the change in one measure of plant risk, the frequency of loss of long-term decay heat removal, due to changes in maintenance at the James A. Fitzpatrick (JAF) plant. Quantification is accomplished in two steps. First, the effects of maintenance are quantified in terms of changes in: a) the frequency of common cause failure of residual heat removal (RHR) pumps and b) the frequency with which operators fail to correctly restore the RHR system following maintenance. These parameters are selected as the result of an importance analysis for the plant. Second, the changes in these two parameters are propagated through a simple plant model to obtain the associated change in plant risk. Based on this study's assessment of the current maintenance program at JAF, it appears that the potential for significant risk reduction due to improved maintenance is not extremely large; an optimal program might lead to an 80% reduction. The optimal program would place a stronger emphasis on predictive maintenance, and would employ improved procedures for RHR pump maintenance. There is potential for significant risk increase (around a factor of 70) if the maintenance program is significantly degraded (e.g., if post-maintenance is deemphasized). This study shows how, at a simple level, maintenance program changes can be quantified without explicit modeling of the details of a plant's management and organizational structure. However, such modeling may be required: a) to more strongly justify the quantitative factors used in the analysis and b) to quantify the effect of other program changes not yet treated (e.g., the strengthening of program elements ensuring feedback of information to organization). In addition, failure data specific to the JAF plant are also needed to increase the confidence in the quantitative results of this study.Sponsored by New York Power Authority, White Plains, NY under contract no. S-90-0019

An assessment of technology alternatives for telecommunications and information management for the space exploration initiative

On the 20th anniversary of the Apollo 11 lunar landing, President Bush set forth ambitious goals for expanding human presence in the solar system. The Space Exploration Initiative (SEI) addresses these goals beginning with Space Station Freedom, followed by a permanent return to the Moon, and a manned mission to Mars. A well designed, adaptive Telecommunications, Navigation, and Information Management (TNIM) infrastructure is vital to the success of these missions. Utilizing initial projections of user requirements, a team under the direction of NASA's Office of Space Operations developed overall architectures and point designs to implement the TNIM functions for the Lunar and Mars mission scenarios. Based on these designs, an assessment of technology alternatives for the telecommunications and information management functions was performed. This technology assessment identifies technology developments necessary to meet the telecommunications and information management system requirements for SEI. Technology requirements, technology needs and alternatives, the present level of technology readiness in each area, and a schedule for development are presented

Analysis and modeling of human performance in nuclear power plants

This work investigates two important areas in human reliability engineering: models for human performance analysis, and reliable data for these models. In the first area, the author addresses the two modeling techniques THERP and SAINT showing that the flexibility and nuclear relevance of the second give it the merit to be used in modeling and analyzing human tasks in complex man-machine systems such as nuclear systems. A case study is investigated and simulated by the SAINT to explore its dynamic capabilities;In the second area, the author evaluates all the available human data sources, develops a new description format for human related events with all the information matrices needed for human reliability analysis. A complete operations data bank for sixty-five commercial power plants in the U.S.A. over a period of ten years is constructed;Data analysis, with qualitative and quantitative evaluations using the information matrices provided in the operations data bank, is presented. Operator error probability computations are made for some operational tasks in selected nuclear engineered safety feature systems. Moreover, the assessment of the role of the performance shaping factors is discussed