Adaptive Capacity of the Water Management Systems of Two Medieval Khmer Cities, Angkor and Koh Ker

abstract: Understanding the resilience of water management systems is critical for the continued existence and growth of communities today, in urban and rural contexts alike. In recent years, many studies have evaluated long-term human-environmental interactions related to water management across the world, highlighting both resilient systems and those that eventually succumb to their vulnerabilities. To understand the multitude of factors impacting resilience, scholars often use the concept of adaptive capacity. Adaptive capacity is the ability of actors in a system to make adaptations in anticipation of and in response to change to minimize potential negative impacts. In this three-paper dissertation, I evaluate the adaptive capacity of the water management systems of two medieval Khmer cities, located in present-day Cambodia, over the course of centuries. Angkor was the capital of the Khmer Empire for over 600 years (9 th -15 th centuries CE), except for one brief period when the capital was relocated to Koh Ker (921 – 944 CE). These cities both have massive water management systems that provide a comparative context for studying resilience; while Angkor thrived for hundreds of years, Koh Ker was occupied as the capital of the empire for a relatively short period. In the first paper, I trace the chronological and spatial development of two types of settlement patterns (epicenters and lower-density temple-reservoir settlement units) at Angkor in relation to state-sponsored hydraulic infrastructure. In the second and third papers, I conduct a diachronic analysis using empirical data for the adaptive capacity of the water management systems at both cities. The results suggest that adaptive capacity is useful for identifying causal factors in the resilience and failures of systems over the long term. The case studies also demonstrate the importance and warn of the danger of large centralized water management features.Dissertation/ThesisDoctoral Dissertation Anthropology 201

Active Learning for Text Classification

Text classification approaches are used extensively to solve real-world challenges. The success or failure of text classification systems hangs on the datasets used to train them, without a good dataset it is impossible to build a quality system. This thesis examines the applicability of active learning in text classification for the rapid and economical creation of labelled training data. Four main contributions are made in this thesis. First, we present two novel selection strategies to choose the most informative examples for manually labelling. One is an approach using an advanced aggregated confidence measurement instead of the direct output of classifiers to measure the confidence of the prediction and choose the examples with least confidence for querying. The other is a simple but effective exploration guided active learning selection strategy which uses only the notions of density and diversity, based on similarity, in its selection strategy. Second, we propose new methods of using deterministic clustering algorithms to help bootstrap the active learning process. We first illustrate the problems of using non-deterministic clustering for selecting initial training sets, showing how non-deterministic clustering methods can result in inconsistent behaviour in the active learning process. We then compare various deterministic clustering techniques and commonly used non-deterministic ones, and show that deterministic clustering algorithms are as good as non-deterministic clustering algorithms at selecting initial training examples for the active learning process. More importantly, we show that the use of deterministic approaches stabilises the active learning process. Our third direction is in the area of visualising the active learning process. We demonstrate the use of an existing visualisation technique in understanding active learning selection strategies to show that a better understanding of selection strategies can be achieved with the help of visualisation techniques. Finally, to evaluate the practicality and usefulness of active learning as a general dataset labelling methodology, it is desirable that actively labelled dataset can be reused more widely instead of being only limited to some particular classifier. We compare the reusability of popular active learning methods for text classification and identify the best classifiers to use in active learning for text classification. This thesis is concerned using active learning methods to label large unlabelled textual datasets. Our domain of interest is text classification, but most of the methods proposed are quite general and so are applicable to other domains having large collections of data with high dimensionality

Design and Analysis of the WCCI 2010 Active Learning Challenge

We organized a data mining challenge on “active learning” for IJCNN/WCCI 2010, addressing machine learning problems where labeling data is expensive, but large amounts of unlabeled data are available at low cost. Examples include handwriting and speech recognition, document classification, vision tasks, drug design using recombinant molecules and protein engineering. Such problems might be tackled from different angles: learning from unlabeled data or active learning. In the former case, the algorithms must satisfy themselves with the limited amount of labeled data and capitalize on the unlabeled data with semi-supervised learning methods. Several challenges have addressed this problem in the past. In the latter case, the algorithms may place a limited number of queries to get new sample labels. The goal in that case is to optimize the queries and the problem is referred to as active learning. While the problem of active learning is of great importance, organizing a challenge in that area is non trivial. This is the problem we have addressed, and we describe our approach in this paper. The “active learning” challenge is part of the WCCI 2010 competition program (http://www.wcci2010. org/competition-program). The website of the challenge remains open for submission of new methods beyond the termination of the challenge as a resource for students and researchers (http://clopinet.com/al)