Outcome evaluation of HIV/AIDS outreach project at Malmesbury prison in South Africa

The HIV/AIDS pandemic has struck the prison systems with exceptional voracity. While some prisons have attempted to address the problem with educational programming and case management, Malmsebury prison in South Africa has incorporated a restorative justice component to add to the offender\u27s sense of efficacy and leadership skills once released back into the community. This study also examined the outcomes of Ithemba Project, also referred to as the Group of Hope, an HIV/AIDS Community Outreach Project, at the prison In Cape Town, South Africa

Scare Tactics: Evaluating Problem Decompositions Using Failure Scenarios

Our interest is in the design of multi-agent problem-solving systems, which we refer to as composite systems. We have proposed an approach to composite system design by decomposition of problem statements. An automated assistant called Critter provides a library of reusable design transformations which allow a human analyst to search the space of decompositions for a problem. In this paper we describe a method for evaluating and critiquing problem decompositions generated by this search process. The method uses knowledge stored in the form of failure decompositions attached to design transformations. We suggest the benefits of our critiquing method by showing how it could re-derive steps of a published development example. We then identify several open issues for the method

The Benefits of Time: Characterizing Intra-and Inter-Annual Variability in Oregon Wetland Ecosystems Using the Landsat Spectral-Temporal Domain

The use of Landsat data has historically been constrained to spectral and spatial information derived from a carefully selected image or set of images. However, free and open access to Landsat imagery combined with advances in data storage and computing are revolutionizing how the Landsat temporal domain is used to map and monitor land surface properties and land cover change. Since the opening of the USGS archives in 2008, many different time series analysis approaches have been developed without a unified framework for characterizing information extracted from dense time series of Landsat imagery. In Chapter 1, we define Spectral-Temporal Features (STFs) as discrete or continuous features derived from time series of remotely sensed observations. Like spectral indices, STFs represent a transformation of the original image data and can provide new information about land surface properties and other biophysical parameters. STFs offer a number of improvements over conventional spectral or spatial inputs, including seamless coverage over large extents, more consistent and stable feature sets for classification through time, and new information on both spectral and temporal variability in reflectance that can be related to biophysical parameters. To demonstrate how STFs can be applied in practice, we present a series of case studies spanning a range of geographic locations within different ecosystem types, and study objectives. These case studies illustrate relationships between different STFs and various biophysical parameters and yield insight into the specific ecological metrics that can be discovered and characterized with the spectral-temporal domain. With the release of collection-style Landsat products and continued advances in pre-processing algorithms, as well as availability of tiled Analysis Ready Data and improved access to cloud- and cluster-based computing resources such as Google Earth Engine, the Australian Data Cube, and Sentinel Hub, time series approaches are becoming increasingly prevalent. We argue that STFs provide new information on both spectral and temporal variability in reflectance in different ecosystems that can be related to biophysical parameters. Thus, there is a critical need to continue to review and standardize the discussion and application of STFs for locally-accurate mapping and monitoring of forested ecosystem dynamics. In Chapter 2, we test the utility of primary STFs derived from time series of all available Landsat TM/ETM+ observations, including Global Surface Water (GSW) features, for discriminating among wetlands at different categorical resolutions within the National Wetlands Inventory (NWI) classification taxonomy. We examine two key types of primary STFs, 1) reflectance STFs, which characterize reflectance values of spectral indices used, and 2) day of year (DOY) STFs, which quantify the timing of their associated reflectance STFs. As an exploratory measure, we also abstract and evaluate spatial-temporal climate features, such as the per-pixel annual maximum of daily maximum temperature, to yield insight into potential drivers of wetland characterization. Using an NWI reference dataset from two Oregon ecoregions in distinctly different eco-hydrological climate zones, we test classification agreement and examine relative performance of different classification inputs across ecoregions and wetland categories. We test an array of classifications that use consistent training and testing datasets, but vary the features and feature-sets used as model inputs. Beyond classification performance, we also explore categorical-level agreement and the importances of different features for differentiation within different wetland categories. Our aim is not to estimate the accuracy of the reference NWI map or monitor wetland change over time and space, but rather to build a framework for multi-level wetland classification across climate gradients. We found that STFs feature-sets consistently produced high overall accuracies and were able to accurately delineate wetland habitats across climate gradients and wetland categorical resolutions even further when combined with other features. Additionally, accuracies decreased with increasing categorical resolution in both energy-and water-limited ecosystems. Evaluation of individual feature importance for distinguishing between different wetland habitats showed that different features are more important for different climate gradients and categorical resolutions. However, GSW Occurrence was consistently valuable for both ecoregions across all categorical resolutions, exemplifying the value of utilizing the GSW dataset for wetland classification. Further, although not all types of features were found to be important in overall classification, in quantifying correlations between individual features and individual wetland habitat classification probabilities, we found that all feature types were had and strong positive and negative correlations with individual habitats. This indicates the importance of using the various features as inputs for wetland classification. In Chapter 3, we use all available Landsat imagery from 1985 - 2017 to explore how Pacific Northwest wetland ecosystems are changing over time in different climate zones and at varying categorical resolutions. Additionally, we investigate the long term changes in abstracted Landsat spectral-temporal features that are closely associated with different aspects of wetland hydro-ecological processes. We found that our annual classification model built from Landsat spectral-temporal features, climate-temporal features, and ancillary datasets performs well in showing change in wetland habitat. Individual STFs also display distinct changes in intra-annual wetland dynamics in the context of wetland land use change. In terms of long term wetland change, Willamette Valley wetlands are trending toward more non-vegetated wetlands, fewer vegetated wetlands, and extreme annual-conditions with the lower extrema occurring earlier in the year. In addition to other drivers, this change may be attributed to increased precipitation and increased temperature. In contrast North Basin wetlands are trending towards more vegetated wetlands, fewer non-vegetated wetlands, and extreme annual-conditions with the lower extrema occurring earlier in the year, except for max TCW which is trending towards later annual occurrence. Timing and persistence are key for wetland habitats and this study begins the work to examine change in both occurrence of wetland habitat type and timing of key hydrologic and phenological features and ecosystem drivers

A Proposed Perspective Shift: Viewing Specification Design as a Planning Problem

16 pagesWe argue that in certain problem domains, AI planning can be viewed as a foundation for generation, critiquing, and elaboration of a specification. Two specification design projects in our group are used as a focus of discussion

Landsat-based monitoring of annual wetland change in the main-stem Willamette River floodplain of Oregon, USA from 1972 to 2012

Despite holding substantial ecological value, wetlands in the United States have experienced a significant decline in both area and function over the past century with the majority of freshwater wetland loss attributed to agricultural conversion. Agriculture is the second largest industry in the State of Oregon and the State places substantial emphasis in its land use planning goals on the preservation of agricultural land. Oregon’s Willamette Valley accounts for the majority of agricultural output with 53% of the valley bottom classified as agricultural land. Additionally, the valley houses 70% of the state's population. The valley was once comprised of extensive wet prairies and abundant riparian forests along the Willamette River floodplain, but native ecosystems have been reduced to a fraction of their original distribution since Euro-American settlement in the mid 1800s. The few wetlands that remain are at high risk to loss and degradation from agricultural activity. Following national wetland conservation policies, Oregon has since attempted to monitor and regulate losses due to disturbance and modification of the State's remaining wetlands through a "no-net-loss" policy aiming to decrease wetland losses and replace disturbed wetlands through mitigation. The National Wetlands Inventory (NWI) was designed to produce detailed maps and status reports of the characteristics and extent of the nation's wetlands and help determine the efficacy of no-net-loss policy implementation on the nation’s wetlands. In some cases, the NWI has been found to have low categorical and spatial accuracy and coarse temporal resolution, with some maps over two decades old. Although Landsat satellite imagery was originally found to lack the needed spatial resolution for classification detail and wetness designation that aerial photography provided, Landsat has 40 years of freely available, high quality annual imagery and should be explored for use in annual wetland change detection. Our objectives were to: (1) Quantify and characterize spatial and ecological trends in annual wetland change through gain, loss, and conversion in the Willamette Valley; (2) Evaluate the effect of the no-net-loss federal wetland conservation policy change enacted in 1990 on trends in net wetland area; and (3) Describe a new methodology that reaches back through the over 40-year Landsat archive to map fine scale wetland and related land-use changes from 1972-2012. We used annual Landsat MSS and TM/ETM+ images from 1972 to 2012 to manually interpret loss, gain, and type conversion of wetland area in the two-year inundation floodplain of the Main-Stem Willamette River using TimeSync, Google Earth, and ArcMap. By creating Tasseled Cap Brightness, Greenness, and Wetness indices for MSS data that visually match TM/ETM+ Tasseled Cap images, we were able to construct a complete and consistent annual time series and utilize the entire Landsat archive. Additionally, with an extended time series, we were able to compare trends of annual net change in wetland area before and after the no-net-loss policy established under Section 404 of the Clean Water Act in 1990. We found that wetlands experienced annual loss, gain, and type conversion across the entire study period. Vegetated wetlands (emergent and riparian wetlands) experienced a 314 ha net loss of wetland area across the 40 year study period whereas non-vegetated wetlands (lacustrine and riverine wetlands) experienced a 393 ha net gain. All wetland types combined saw a 79 ha net increase in wetland area across the full study period. The majority of both gain and loss in the study area was attributed to and from agricultural conversion followed by urban land use. Time series analysis of the rate of change of net wetland area was calculated using the Theil-Sen (TS) Slope estimate analysis. For annual change of wetland area before and after 1990 no-net-loss policy implementations, the rate of annual wetland area lost slowed for riparian wetlands and reversed into trends of annual net gain in area of emergent wetlands. The rate of annual net area gained for lacustrine wetlands was slowed post-policy. Accuracy assessment of land use change polygons in the field was only able to capture 12% of our interpretations due to access restrictions associated with private land. In spite of a low sample size (n=45), overall accuracy of land use classification through wetland change polygons was at 80%. This accuracy increased to 91.1% when land use classes were aggregated to either wetland or upland categories, indicating that our methodology was more accurate at distinguishing between general upland and wetland than finer categorical classes

Design Issues in a Minimal Language to Support Lisp-based, Object-based, and Rule-based Programming

13 pagesIn this paper we argue for a minimalist view of language design for Expert System environments. In support of our arguments we present MIN, a minimal language which extends the less-is-better philosophy of Scheme to include both object-based and rule-based components. We discuss the strengths and weaknesses of MIN as an embodiment of the minimalist philosophy, and point to other work supporting this view of language design

Leveraging multimission satellite data for spatiotemporally coherent cyanoHAB monitoring

Cyanobacteria harmful algal blooms (cyanoHABs) present a critical public health challenge for aquatic resource and public health managers. Satellite remote sensing is well-positioned to aid in the identification and mapping of cyanoHABs and their dynamics, giving freshwater resource managers a tool for both rapid and long-term protection of public health. Monitoring cyanoHABs in lakes and reservoirs with remote sensing requires robust processing techniques for generating accurate and consistent products across local and global scales at high revisit rates. We leveraged the high spatial and temporal resolution chlorophyll-a (Chla) and phycocyanin (PC) maps from two multispectral satellite sensors, the Sentinel-2 (S2) MultiSpectral Instrument (MSI) and the Sentinel-3 (S3) Ocean Land Colour Instrument (OLCI) respectively, to study bloom dynamics in Utah Lake, United States, for 2018. We used established Mixture Density Networks (MDNs) to map Chla from MSI and train new MDNs for PC retrieval from OLCI, using the same architecture and training dataset previously proven for PC retrieval from hyperspectral imagery. Our assessment suggests lower median uncertainties and biases (i.e., 42% and -4%, respectively) than that of existing top-performing PC algorithms. Additionally, we compared bloom trends in MDN-based PC and Chla products to those from a satellite-derived cyanobacteria cell density estimator, the cyanobacteria index (CI-cyano), to evaluate their utility in the context of public health risk management. Our comprehensive analyses indicate increased spatiotemporal coherence of bloom magnitude, frequency, occurrence, and extent of MDN-based maps compared to CI-cyano and potential for use in cyanoHAB monitoring for public health and aquatic resource managers

Designing location based learning experiences for people with intellectual disabilities and additional sensory impairments

The research reported here is part of a larger project which seeks to combine serious games (or games based learning) with location based services to help people with intellectual disability and additional sensory impairments to develop work based skills. Specifically this paper reports on where these approaches are combined to scaffold the learning of new routes and ultimately independent travel to new work and educational opportunities. A phased development methodology is applied in a user sensitive manner, to ensure that user feedback drives the ongoing development process. Methods to structure this include group feedback on conceptual storyboards, expert review of prototypes using usability heuristics relating to the main system goals, and finally co-discovery methods with student pairs exploring all three modes of the system in real world contexts. Aspects of developmental and cognitive psychological theories are also reviewed and it is suggested that combining games based learning approaches with location based services is an appropriate combination of technologies for an application specifically designed to scaffold route learning for this target audience

Human-centered specification exemplars for critical infrastructure environments.

Specification models of critical infrastructure focus on parts of a larger environment. However, to consider the security of critical infrastructure systems, we need approaches for modelling the sum of these parts; these include people and activities, as well as technology. This paper presents human-centered specification exemplars that capture the nuances associated with interactions between people, technology, and critical infrastructure environments. We describe requirements each exemplar needs to satisfy, and present preliminary results in developing and evaluating them

A Goal Modeling Framework for Self-contextualizable Software

Abstract. Self-contextualizability refers to the system ability to autonomously adapt its behaviour to context in order to maintain its objectives satisfied. In this paper, we propose a modeling framework to deal with self-contextualizability at the requirements level. We use Tropos goal models to express requirements; we provide constructs to analyse and represent context at each variation point of the goal model; and we exploit the goal and context analysis to define how the system satisfies its requirements in different contexts. Tropos goal analysis provides constructs to hierarchically analyse goals and discover alternative sets of tasks the system can execute to satisfy goals; our framework extends Tropos goal model by considering context at its variation points, and provides constructs to hierarchically analyse context and discover alternative sets of facts the system has to monitor to verify a context. A self-contextualizable promotion information system scenario is used to illustrate our approach. Key words: GORE, Context Analysis, Self-Contextualization