Abstracts of Invited and Selected Papers and Organized Symposia, WAEA Annual Meetings, San Francisco, California, July 6-8, 2005

Teaching/Communication/Extension/Profession,

Integrating cultural ecosystem services valuation into coastal wetlands restoration : a case study from South Australia

Elaborating the benefits humans receive from coastal wetlands using a Cultural Ecosystem Services assessment is an emergent and important field linking human wellbeing to ecosystem function. Translating these benefits into useable concepts for environmental policymakers, and managers is challenging yet important for supporting landscape restoration projects. This study responds to the call for Cultural Ecosystem Services case studies beyond the northern hemisphere. A household survey of residents adjacent to a peri-urban coastal wetland in South Australia and an online survey of interest groups were administered to identify co-benefits associated with a coastal restoration project in the region. A dynamic/relational cultural values framework guided the analysis. Findings reveal that visitation has a positive influence; people valued most the places with which they were familiar. The analysis confirms a mutual connection between: ‘doing’ (undertaking an activity), environmental awareness and appreciation, the formation of attachment to place, and having positive experiences. The analysis also points out that the naturalness of this coastline is highly valued. The findings here diverge from previous coastal landscape assessments based singularly on scenic value. The implication is that localised, place-based landscape assessments which include cultural values, offer a more deliberative approach to policy development and planning and will more likely incorporate what matters most to people. © 2020 The Author(s

スカイライン問合わせを利用した大規模データベースの情報選別

Conventional SQL queries take exact input and produce complete result set. However, with massive increase in data volume in different applications, the large result sets returned by traditional SQL queries are not well suited for the users to take effective decisions. Therefore, there is an increasing interest in queries like top-k queries and skyline queries those produce a more concise result set. Top-k queries rely on the scores of the objects to evaluate the usefulness of the objects. In this type of queries, users require to define their own scoring function by combining their interests. Based on the user defined scoring function, the system sorts the objects by their scores and outputs the top-k objects in the ranking list as the result. However, defining a scoring function by the users is a major draw of the top-k queries as in the large data sets where there are many conflicting criteria exist, it is very difficult for the users to define the scoring functions by themselves.……広島大学(Hiroshima University)博士(工学)Engineeringdoctora

A Low-Effort Recommendation System with High Accuracy - A New Approach with Ranked Pareto-Fronts

In recent studies on recommendation systems, the choice-based conjoint analysis has been suggested as a method for measuring consumer preferences. This approach achieves high recommendation accuracy and does not suffer from the start-up problem because it is also applicable for recommendations for new consumers or of new products. However, this method requires massive consumer input, which causes consumer reluctance. In a simulation study, we demonstrate the high accuracy, but also the high user’s effort for using a utility-based recommendation system using a choice-based conjoint analysiswith hierarchical Bayes estimation. In order to reduce the conflict between consumer effort and recommendation accuracy, we develop a novel approach that only shows Paretoefficient alternatives and ranks them according to the number of dominated attributes. We demonstrate that, in terms of the decision accuracy of the recommended products, the ranked Pareto-front approach performs better than a recommendation system that employs choice-based conjoint analysis. Furthermore, the consumer’s effort is kept low and comparable to that of simple systems that require little consumer input