Verification of Knowledge Assets in Electronic Repositories: Expert- and Community-Governance

Two mechanisms that verify knowledge contributions in electronic repositories are expert-governance and community-governance. Our goal is to examine repository users' perceptions of the conditions under which these mechanisms verify knowledge contributions. Qualitative data show that perceived credibility of experts, perceived ownership of content, and experts' (meticulous) execution of governance functions are salient for expert-governance, and the perceived involvement of community members, and community members' (continuous and collective) execution of governance functions are important for community-governance.     

Laminated paper-based analytical devices (LPAD): fabrication, characterization, and assays

Paper-based microfluidic devices have recently garnered an increasing interest in the literature. The majority of these devices were produced by patterning hydrophobic zones in hydrophilic paper via photoresist or wax. Others were created by cutting paper using a laser. Here, we present a fabrication method for producing devices by simple craft-cutting and lamination, in a way similar to making an identification (ID) card. The method employs a digital craft cutter and roll laminator to produce laminated paper-based analytical devices (LPAD). Lamination with a plastic backing provides the mechanical strength for a paper device. The approach of using a craft cutter and laminator makes it possible to rapid-prototype LPAD with no more difficulty than producing a typical ID card, at very low cost. Devices constructed using this method have been exploited for simultaneous detection of bovine serum albumin (BSA) and glucose in synthetic urine with colorimetric assays. Both BSA and glucose are detectable at clinically relevant concentrations, with the detection limit at 2.5 μM for BSA and 0.5 mM for glucose.     

An ontological engineering approach for automating inspection and quarantine at airports

Customs and quarantine departments are applying information systems to automate their inspection processes and improve their inspection efficiency and accuracy. The product codes from the Harmonized System (HS codes) are the essential elements of the system's integration, automation and intelligence. The identified HS codes are well-accepted and precise product references used by customs authorities, to match applicable policies to the products being inspected and taxed. Domain ontology for importing and exporting industry can be used to acquire HS codes for given products, and is a prerequisite for an integrated and intelligent automated inspection system. The authors have proposed and implemented an importing and exporting domain ontology. The ontology is composed of an integrated and comprehensive knowledge base derived from static dictionaries and the HS specification, and dynamic processing data. Based on this ontology, a reasoning engine is developed to generate HS codes intelligently for the given product names. Information systems can use the engine to get HS codes for submitted products and find applicable policies automatically. The ontology and the engine have been implemented in a Java-based platform and published as a HS Web service. In this paper, knowledge structure, reasoning mechanism and implementation details for the domain ontology and reasoning engine are presented. A test bed in the application environment has been conducted and experimental results have been obtained. The ontology and the service have the potential to be widely used by authorities and international traders of importing and exporting industry around the world.     

A superscalar simulation employing poisson distributed stalls

This paper presents a statistical approach to estimating the performance of a superscalar processor. Traditional trace-driven simulators can take a large amount time to conduct a performance evaluation of a machine, especially as the number of instructions increases. The result of this type of simulation is typically tied to the particular trace that was run. Elements such as dependencies, delays, and stalls are all a direct result of the particular trace being run, and can differ from trace to trace. This paper describes a model designed to separate simulation results from a specific trace. Rather than running a trace-driven simulation, a statistical model is employed, more specifically a Poisson distribution, to predict how these types of delay affects performance. Through the use of this statistical model, a performance evaluation can be conducted using a general code model, with specific stall rates, rather than a particular code trace. This model allows simulations to quickly run tens of millions of instructions and evaluate the performance of a particular micro-architecture while at the same time, allowing the flexibility to change the structure of the architecture.     

Using multiple endmember spectral mixture analysis to retrieve subpixel fire properties from MODIS

The Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors on NASA's Terra and Aqua satellites image most of the Earth multiple times each day, providing useful data on fires that cannot be practically acquired using other means. Unfortunately, current fire products from MODIS and other sensors leave large uncertainties in measurements of fire sizes and temperatures, which strongly influence how fires spread, the amount and chemistry of their gas and aerosol emissions, and their impacts on ecosystems. In this study, we use multiple endmember spectral mixture analysis (MESMA) to retrieve subpixel fire sizes and temperatures from MODIS, possibly overcoming some limitations of existing methods for characterizing fire intensities such as estimating the fire radiative power (FRP). MESMA is evaluated using data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to assess the performance of FRP and MESMA retrievals of fire properties from a simultaneously acquired MODIS image, for a complex of fires in Ukraine from August 21, 2002. The MESMA retrievals of fire size described in this paper show a slightly stronger correlation than FRP does to fire pixel counts from the coincident ASTER image. Prior to this work, few studies, if any, had used MESMA for retrieving fire properties from a broad-band sensor like MODIS, or compared MESMA to higher-resolution fire data or other measures of fire properties like FRP. In the future, MESMA retrievals could be useful for fire spread modeling and forecasting, reducing hazards that fires pose to property and health, and enhancing scientific understanding of fires and their effects on ecosystems and atmospheric composition.     

Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data

In this study we implemented a comprehensive analysis to validate the MODIS and GOES satellite active fire detection products (MOD14 and WFABBA, respectively) and characterize their major sources of omission and commission errors which have important implications for a large community of fire data users. Our analyses were primarily based on the use of 30 m resolution ASTER and ETM+ imagery as our validation data. We found that at the 50% true positive detection probability mark, WFABBA requires four times more active fire area than is necessary for MOD14 to achieve the same probability of detection, despite the 16× factor separating the nominal spatial resolutions of the two products. Approximately 75% and 95% of all fires sampled were omitted by the MOD14 and WFABBA instantaneous products, respectively; whereas an omission error of 38% was obtained for WFABBA when considering the 30-minute interval of the GOES data. Commission errors for MOD14 and WFABBA were found to be similar and highly dependent on the vegetation conditions of the areas imaged, with the larger commission errors (approximately 35%) estimated over regions of active deforestation. Nonetheless, the vast majority (> 80%) of the commission errors were indeed associated with recent burning activity where scars could be visually confirmed in the higher resolution data. Differences in thermal dynamics of vegetated and non-vegetated areas were found to produce a reduction of approximately 50% in the commission errors estimated towards the hours of maximum fire activity (i.e., early-afternoon hours) which coincided with the MODIS/Aqua overpass. Lastly, we demonstrate the potential use of temporal metrics applied to the mid-infrared bands of MODIS and GOES data to reduce the commission errors found with the validation analyses.     

Selective sampling for approximate clustering of very large data sets

A key challenge in pattern recognition is how to scale the computational efficiency of clustering algorithms on large data sets. The extension of non‐Euclidean relational fuzzy c‐means (NERF) clustering to very large (VL = unloadable) relational data is called the extended NERF (eNERF) clustering algorithm, which comprises four phases: (i) finding distinguished features that monitor progressive sampling; (ii) progressively sampling from a N × N relational matrix R_N to obtain a n × n sample matrix R_n; (iii) clustering R_n with literal NERF; and (iv) extending the clusters in R_n to the remainder of the relational data. Previously published examples on several fairly small data sets suggest that eNERF is feasible for truly large data sets. However, it seems that phases (i) and (ii), i.e., finding R_n, are not very practical because the sample size n often turns out to be roughly 50% of n, and this over‐sampling defeats the whole purpose of eNERF. In this paper, we examine the performance of the sampling scheme of eNERF with respect to different parameters. We propose a modified sampling scheme for use with eNERF that combines simple random sampling with (parts of) the sampling procedures used by eNERF and a related algorithm sVAT (scalable visual assessment of clustering tendency). We demonstrate that our modified sampling scheme can eliminate over‐sampling of the original progressive sampling scheme, thus enabling the processing of truly VL data. Numerical experiments on a distance matrix of a set of 3,000,000 vectors drawn from a mixture of 5 bivariate normal distributions demonstrate the feasibility and effectiveness of the proposed sampling method. We also find that actually running eNERF on a data set of this size is very costly in terms of computation time. Thus, our results demonstrate that further modification of eNERF, especially the extension stage, will be needed before it is truly practical for VL data. © 2008 Wiley Periodicals, Inc.     

RoadCompass: following rural roads with vision + ladar using vanishing point tracking

We present a vision- and ladar-based approach to autonomous driving on rural and desert roads that has been tested extensively in a closed-loop system. The vision component uses Gabor wavelet filters for texture analysis to find ruts and tracks from which the road vanishing point can be inferred via Hough-style voting, yielding a direction estimate for steering control. The ladar component projects detected obstacles along the road direction onto the plane of the front of the vehicle and tracks the 1-D obstacle “gap” presumed due to the road to yield a lateral offset estimate. Several image- and state-based tests to detect failure conditions such as off-road poses (i.e., there is no road to follow) and poor lighting due to sun glare or distracting shadows are also explained. The system’s efficacy is demonstrated with analysis of diverse logged data including from the 2005 DARPA Grand Challenge, as well as tests with full control of a vehicle over 15 km of difficult roads at up to 37 km/h with no waypoints.