No abstract

Overdeveloped land, misused resources, limited and fast development landscape in urban Taiwan, are problems and challenges government continuously facing and avoiding; Re-utilizing underdeveloped and limited landscape in Taiwan is an important subject. Taiwan Kaohsiung obtains and preserves the largest Military Dependents’ Village (MDV) landscape in Taiwan; an important, unique, and valuable historic cultural landscape affiliate historical, military and ethnic group living culture village. This study reviewed sustainable development and village preserve literature, and propose re-development structural framework of MDV through two-stage experts’ questionnaire survey. The first stage utilized the Fuzzy Delphi Method, which focuses on impact factors, and the second, the AHP Method, deals with performance factors. The results indicate that the key impacts on the cultural landscape sustainability redevelopment strategies for Huangpu MDV were its cultural value, historic site, and maintenance management.

We present a case study of a pilot project that was developed to evaluate the use of data mining in audit selection for the Minnesota Department of Revenue (DOR). The Internal Revenue Service (IRS) estimated the gap between revenue owed and revenue collected for 2001 to be approximately $345 billion, of which they were able to recover only $55 billion, and the estimated gap for 2006 was approximately $450 billion, of which the IRS was able to recover only $65 billion. It is critical for the government to reduce the gap and the fundamental process for doing so is audit selection. We present a data mining based approach that was used to improve the audit selection process at the DOR. We describe the manual audit selection process used at the time of the pilot project for Sales and Use taxes, discuss the data from various sources, address issues regarding feature selection, and explain the data mining techniques used. Results from the pilot project revealed that the data mining based approach can increase efficiency in the audit selection process. We also report results from actual field audits performed by auditors at the DOR, and results validated the usefulness of the data mining based approach for audit selection. The impact of the pilot project would be a refinement of the manual audit selection process and tax assessment procedures for other types of taxes.

No abstract

Inspired by the group decision making process, ensembles or combinations of classifiers have been found favorable in a wide variety of application domains. Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid ensemble. Why does such an ensemble work? The question remains. Following the concept of diversity, which is one of the fundamental elements of the success of ensembles, we conduct a theoretical analysis of why hybrid ensembles work, connecting using different algorithms to accuracy gain. We also conduct experiments on classification performance of hybrid ensembles of classifiers created by decision tree and naïve Bayes classification algorithms, each of which is a top data mining algorithm and often used to create non-hybrid ensembles. Therefore, through this paper, we provide a complement to the theoretical foundation of creating and using hybrid ensembles.

The extreme weather conditions that are increasingly affecting Taiwan require urgent solutions, especially as land-use pressures and intensive urban development are triggering new types of vulnerability to natural disasters. Green infrastructure is an especially promising means of enhancing the resilience of urban environments, as well as their residents’ quality of life. However, due to the indirect nature of green investment, the economic value of green infrastructure is not adequately reflected in market prices, and novel methods of economic valuation are needed to ascertain their value. To fulfill that need, this study conducts a cost–benefit analysis of investment in green infrastructure related to urban renewal and identifies economic factors that could directly and indirectly increase environmental quality and promote sustainable development. The main finding of this work is that the increased cost of a green approach for a particular urban-renewal infrastructure project in Taiwan could be recouped in approximately eight years. Specifically, version of the plan based on green infrastructure would cost an additional US $9.2 million up front, but its positive impact would be greater than the non-green version by US $1.2 million per year.

No abstract

Abstract-The rise of smartphones can be connected to the large number of applications, or apps, which can be installed and run by users on smartphones. It becomes important for researchers, smartphone designers, and application developers to know how users use apps on their smartphones. The aim of this paper is to present our work where data mining is applied to smartphone app usage log data with a focus on data preparation. The methods include association rule mining and sequential pattern mining. The results are the discovered rules and patterns that reflect users' real-life app-using behaviors; they can lead to improved user interfaces, and they can also be used to reconstruct the context in which a user used his or her smartphone. We demonstrate an application of data mining that can have an impact on the smartphone industry, and we also demonstrate a data-driven approach that can help us know more about how users use apps on their smartphones.Index Terms-Association rule mining, sequential pattern mining, smartphone app usage log.