Estimating the uncertainty of modeled carbon sequestration: The GreenCert™ system

The GreenCert? system was developed to help farm and ranch owners to quantify, standardize, pool and market CO₂ emissions offset (sequestration) credits derived from improved rangeland or cropland management. It combines a user-friendly interface with the CENTURY biogeochemical model, a GIS database of soil and climate parameters, and a Monte Carlo-based uncertainty estimation methodology. This paper focuses on uncertainty treatment, discussing sources of error, parameter distributions, and the Monte Carlo randomization approach, culminating in a sensitivity analysis of model parameters.Idealized crop and grazing scenarios were used to evaluate the uncertainty of modeled soil organic carbon stocks and stock changes stemming from variability in site and management parameters. Normalized sensitivity coefficients and an integrated index for relative sensitivity of the model to the ensemble of the tested variables indicate that environmental factors are the most important in determining the actual size of the soil carbon stock, but that management is a much more important determinant of short- to medium-term carbon fluxes. GreenCert? uses the patented C-LOCK^® approach to efficiently limit uncertainty in the most critical phase of the modelling process by maximizing the use of available management information, and quantifies the remaining uncertainty in an unbiased fashion using Monte Carlo parameter randomization.     

Relevance feedback revisited: dealing with content and structure in XML documents

Relevance feedback (RF) is a technique that allows to enrich an initial query according to the user feedback. The goal is to express more precisely the user’s needs. Some open issues arise when considering semi-structured documents like XML documents. They are mainly related to the form of XML documents which mix content and structure information and to the new granularity of information. Indeed, the main objective of XML retrieval is to select relevant elements in XML documents instead of whole documents. Most of the RF approaches proposed in XML retrieval are simple adaptation of traditional RF to the new granularity of information. They usually enrich queries by adding terms extracted from relevant elements instead of terms extracted from whole documents. In this article, we describe a new approach of RF that takes advantage of two sources of evidence: the content and the structure. We propose to use the query term proximity to select terms to be added to the initial query and to use generic structures to express structural constraints. Both sources of evidence are used in different combined forms. Experiments were carried out within the INEX evaluation campaign and results show the effectiveness of our approaches.     

Driving Automotive User Interface Research

Cars offer an interesting but challenging microcosm for pervasive computing research and, in particular, for interaction with pervasive computing systems. Increasingly, researchers are looking at interactive applications in the car and investigating human-car interaction from a computer science—rather than an ergonomics or mechanical engineering—perspective. This article reports on the International Conference on Automotive User Interfaces and Interactive Vehicular Applications, wherein participants shared presentations on topics such as aesthetics, user interaction and distraction, safety, and driver monitoring.     

The Chinese Room: Visualization and Interaction to Understand and Correct Ambiguous Machine Translation

We present The Chinese Room , a visualization interface that allows users to explore and interact with a multitude of linguistic resources in order to decode and correct poor machine translations. The target users of The Chinese Room are not bilingual and are not familiar with machine translation technologies. We investigate the ability of our system to assist such users in decoding and correcting faulty machine translations. We found that by collaborating with our application, end-users can overcome many difficult translation errors and disambiguate translated passages that were otherwise baffling. We also examine the utility of our system to machine translation researchers. Anecdotal evidence suggests that The Chinese Room can help such researchers develop better machine translation systems.     

Classification of abnormal plant operation using multiple process variable trends

This paper illustrates two strategies for the detection and classification of abnormal process operating conditions in which multiple process variable trends are available. The first strategy uses a hidden Markov model (HMM) for overall process classification while the second method uses a back-propagation neural network (BPNN) to determine the overall process classification. The methods are compared in terms of their ability to detect and correctly diagnose a variety of abnormal operating conditions for a non-isothermal CSTR simulation. For the case study problem, the BPNN method resulted in better classification accuracy with a moderate increase in training time compared with the HMM approach.     

Learning and retention of associations between auditory icons and denotative referents: implications for the design of auditory warnings

OBJECTIVE: This study examined the way in which the type and preexisting strength of association between an auditory icon and a warning event affects the ease with which the icon/event pairing can be learned and retained. BACKGROUND: To be effective, an auditory warning must be audible, identifiable, interpretable, and heeded. Warnings consisting of familiar environmental sounds, or auditory icons, have potential to facilitate identification and interpretation. The ease with which pairings between auditory icons and warning events can be learned and retained is likely to depend on the type and strength of the preexisting icon/event association. METHOD: Sixty-three participants each learned eight auditory-icon/denotative-referent pairings and attempted to recall them 4 weeks later. Three icon/denotative-referent association types (direct, related, and unrelated) were employed. Participants rated the strength of the association for each pairing on a 7-point scale. RESULTS: The number of errors made while learning pairings was greater for unrelated than for either related or direct associations, whereas the number of errors made while attempting to recall pairings 4 weeks later was greater for unrelated than for related associations and for related than for direct associations. Irrespective of association type, both learning and retention performance remained at very high levels, provided the strength of the association was rated greater than 5. CONCLUSION: This suggests that strong preexisting associations are used to facilitate learning and retention of icon/denotative-referent pairings. APPLICATION: The practical implication of this study is that auditory icons having either direct or strong, indirect associations with warning events should be preferred.     

Explicit,optimal stability functionals and their application to cyclic discretization methods

The present paper contains a stability concept for discretization methods of a certain, very general classM, which is optimal (in the sense of yielding the best general, two-sided error bounds) without being more restrictive than any of the classical stability definitions. The optimal stability functional Ψ_h related to it depends on the linear part of the discretization operator, and has the important property that Ψ_h [δ] may be of orderq+1, i.e. Ψ_h [δ] = O(h ^q+1), even if the local error δ only has orderq, δ = O(h ^q). This result may be used for the construction of methods with maximum order. Its application to linear cyclic methods, for example, furnishes a new approach to the theory of linearM-cyclick-step methods of maximum order.     

Web conferencing for synchronous online tutorials: Perspectives of tutors using a new medium

This paper reports on a pilot investigation into web conferencing in a distance learning module. It focuses on the perceptions and experiences of the tutors, all of whom were new to the web conferencing environment, but were experienced in online teaching. A number of web conferencing tutorials were studied through the collection of various datasets, mostly qualitative. The data were analysed by a team of researchers, informed by key literature in this area.The main findings from the pilot indicated that tutors experienced challenges in creating social presence and in managing cognitive load when dealing with multiple tasks online. There were also technical obstacles to improvisation in response to students’ emerging needs. The findings of the pilot informed the training and support provided when web tutorials were subsequently rolled out to all tutorial groups (approximately 140) in the module. Overall, experiences from the web tutorials indicate that tutors and students reacted positively to the opportunities web conferencing provides for interactive learning and teaching.