Multi-objective optimization of multicast overlays for collaborative applications

Current real-time collaborative application implementations use dedicated infrastructure to carry out all communication and synchronization activities. Specifically, they require all end nodes to communicate directly with and through the central server. In this paper, we investigate scenarios in which the most resource intensive functionality of continuous communication among collaborators to disseminate changes is decentralized, utilizing the end users themselves as relays. We observe that communication characteristics of real-time collaboration makes use of existing multicast mechanisms unsuitable. As collaborative editing sessions are typically long and repeated, it is possible to gather and leverage node behavior (their instabilities and frequency of sending updates) and communication links (latencies and average costs). Several criteria to determine the quality of a multicast tree can be identified: cost, latency and instability. In this paper, we analyze the complexity of the problem of finding optimal communication topologies, and propose approximate algorithms to optimize the same. We also consider the multiobjective problem in which we search for a topology that provides good trade-off between these sometimes conflicting measures. Validation of our proposed algorithms on numerous graphs shows that it is important to consider the multiobjective problem, as optimal solutions for one performance measure can be far from optimal for the other metrics. Finally, we briefly present an implementation of a communication library that uses the proposed algorithms to periodically adjust the dissemination tree.     

Trends in youth’s videogame playing, overall computer use, and communication technology use: The impact of self-esteem and the Big Five personality factors

The objective of the current research is to model trends in video game playing, overall computer use, and communication technology use in a longitudinal sample of youths, aged 11-16 over a 3-year interval. In addition, individual difference characteristics that may be predictive of these trends were included, namely, socio-demographic characteristics (gender, ethnicity, and parental income) and personality characteristics (self-esteem, the Big Five personality factors). Findings suggested that youth increased their overall computer and communication technology use but decreased their videogame playing over time. Many individual differences predicted mean levels of these technologies with fewer predicting slopes. Conclusions, implications, and limitations are discussed.     

A hybrid capacitive pressure and temperature sensor fabricated by adhesive bonding technique for harsh environment of kraft pulp digesters

We have developed a compensated capacitive pressure and temperature sensor for kraft pulp digesters (pH 13.5, temperatures 25–175°C reaching a local maximum of 180°C and pressures up to 2 MPa). The gauge capacitive pressure sensor was fabricated by bonding silicon and Pyrex chips using a high temperature, low viscosity UV (ultraviolent) adhesive as the gap-controlling layer and heat curing adhesive as the bonding agent. A simple chip bonding technique, involving insertion of the adhesive into the gap between two chips was developed. A platinum thin-film wire was patterned on top of a silicon chip to form a resistance temperature detector (RTD) with a nominal resistance of 1,500 Ω. A silicon dioxide layer and a thin layer of Parylene were deposited to passivate the pressure sensor diaphragm and the sensors were embedded into epoxy for protection against the caustic environment in kraft digesters. The sensors were tested up to 2 MPa and 170°C in an environment chamber. The maximum thermal error of ±1% (absolute value of ±20 kPa) full scale output (FSO) and an average sensitivity of 0.554 fF/kPa were measured. Parylene-coated silicon chips were tested for a full kraft pulping cycle with no signs of corrosion.     

What happens next? Predicting other road users' behaviour as a function of driving experience and processing time

Hazard perception is one of the most important facets of driving and if the appropriate diagnostic tool is used it can discriminate between novice and experienced drivers. In this study video clips of actual driving scenarios were shown to novice and experienced drivers. The clips were stopped just prior to hazard onset and either the screen went black or the final still image stayed on the screen. Participants were then asked five questions about what happened next. This variant of the hazard perception test allowed the influence of processing time to be included and the level of situation awareness to be measured. Experienced drivers significantly anticipated more correct hazardous outcomes than novice drivers when the screen went black. Novice drivers benefited from the extra processing time afforded by the image remaining on the screen and significantly anticipated more hazards when the image remained on the screen than when it went black. The findings indicate that when processing time is manipulated, hazard perception accuracy reveals experiential differences. These differences are discussed with reference to hazard perception and situation awareness. This research informs the current controversy over whether hazard perception is a good diagnostic tool for driving performance. It identifies potential confounds in previous work and demonstrates that experiential differences can be found if the appropriate tests are used. Further, it suggests improvements for new hazard perception tests.     

Using knowledge structures to redesign an instructor-operator station

Frequently, user interface (UI) designers must choose between modifying an established, but suboptimal and familiar, UI or to avoid such changes. Changing the UI’s, organization may frustrate users who have become familiar with the original design, whereas failing to make changes may force users to perform at an unsatisfactory level. This paper presents two studies that investigate whether users familiar with a poorly designed UI would find items faster, and prefer a reorganized UI that conformed to domain expert knowledge, or would their familiarity with the original UI yield faster performance and higher satisfaction.This paper describes activities to redesign a menu structure in a simulator instructor–operator station (IOS) using hierarchical card sorting and cluster analysis (Romesburg, 2004). This analysis was used to reorganize the menu structure to reflect the knowledge representations of domain experts in accordance with the principle of proximity compatibility (Wickens and Carswell, 1995, Rothrock et al., 2006). The new design was validated with a separate set of users by a reaction time experiment and preference selection.     

Using visual design experts in critique-based evaluation of 2D vector visualization methods

We describe an experiment in which art and illustration experts evaluated six 2D vector visualization methods. We found that these expert critiques mirrored previously recorded experimental results; these findings support that using artists, visual designers and illustrators to critique scientific visualizations can be faster and more productive than quantitative user studies. Our participants successfully evaluated how well the given methods would let users complete a given set of tasks. Our results show a statistically significant correlation with a previous objective study: designers' subjective predictions of user performance by these methods match users measured performance. The experts improved the evaluation by providing insights into the reasons for the effectiveness of each visualization method and suggesting specific improvements.     

A multi-level cache model for run-time optimization of remote visualization

Remote visualization is an enabling technology aiming to resolve the barrier of physical distance. While many researchers have developed innovative algorithms for remote visualization, previous work has focused little on systematically investigating optimal configurations of remote visualization architectures. In this paper, we study caching and prefetching, an important aspect of such architecture design, in order to optimize the fetch time in a remote visualization system. Unlike a processor cache or web cache, caching for remote visualization is unique and complex. Through actual experimentation and numerical simulation, we have discovered ways to systematically evaluate and search for optimal configurations of remote visualization caches under various scenarios, such as different network speeds, sizes of data for user requests, prefetch schemes, cache depletion schemes, etc. We have also designed a practical infrastructure software to adaptively optimize the caching architecture of general remote visualization systems, when a different application is started or the network condition varies. The lower bound of achievable latency discovered with our approach can aid the design of remote visualization algorithms and the selection of suitable network layouts for a remote visualization system.     

Integral-based filtering of continuous glucose sensor measurements for glycaemic control in critical care

Hyperglycaemia is prevalent in critical illness and increases the risk of further complications and mortality, while tight control can reduce mortality up to 43%. Adaptive control methods are capable of highly accurate, targeted blood glucose regulation using limited numbers of manual measurements due to patient discomfort and labour intensity. Therefore, the option to obtain greater data density using emerging continuous glucose sensing devices is attractive. However, the few such systems currently available can have errors in excess of 20-30%. In contrast, typical bedside testing kits have errors of approximately 7-10%. Despite greater measurement frequency larger errors significantly impact the resulting glucose and patient specific parameter estimates, and thus the control actions determined creating an important safety and performance issue. This paper models the impact of the continuous glucose monitoring system (CGMS, Medtronic, Northridge, CA) on model-based parameter identification and glucose prediction. An integral-based fitting and filtering method is developed to reduce the effect of these errors. A noise model is developed based on CGMS data reported in the literature, and is slightly conservative with a mean Clarke Error Grid (CEG) correlation of R=0.81 (range: 0.68-0.88) as compared to a reported value of R=0.82 in a critical care study. Using 17 virtual patient profiles developed from retrospective clinical data, this noise model was used to test the methods developed. Monte-Carlo simulation for each patient resulted in an average absolute 1-h glucose prediction error of 6.20% (range: 4.97-8.06%) with an average standard deviation per patient of 5.22% (range: 3.26-8.55%). Note that all the methods and results are generalizable to similar applications outside of critical care, such as less acute wards and eventually ambulatory individuals. Clinically, the results show one possible computational method for managing the larger errors encountered in emerging continuous blood glucose sensors, thus enabling their more effective use in clinical glucose regulation studies.