26545586
Spatial decision support for assisted housing mobility counseling
This paper presents a prototype spatial decision support system (SDSS) that enables clients in the Housing Choice Voucher Program to make better decisions about neighborhoods in which to search for housing and specific units to evaluate for occupancy. Application requirements are based on field research establishing limitations of housing counselors to provide detailed assistance to clients, and the capability of clients to use a spatial decision support system. Decision opportunities are identified using value-focused thinking and spatial analysis. Specific destination alternatives are ranked using a destination choice algorithm based on multicriteria decision models that incorporates alternative relocation strategies.
206862419
A spatial decision support system prototype for housing mobility program planning
Abstract. This paper presents a GIS-based decision support system prototype intended for use by public housing authority (PHA) administrators and planners designing policy for housing mobility programs. Housing mobility programs enable low-income families, many of whom live in government-operated public housing, to move to more desirable private-market rentals via rent subsidies. Unfortunately, housing authority planners have limited ability to visualize alternative relocation schemes of cohorts of low-income families or the impacts associated with these relocation policies. Thus, they are often not able to give highest-quality advice to clients regarding places to search for private-market rental housing. Housing Location Planner assists PHA planners in three ways: it analyzes spatial, demographic and housing market characteristics of the study area, selects certain portions of the study area for input to an optimization model which generates alternative family allocations, and displays optimization model results in a way that links decision variable values and objective function values. Housing Location Planner is seen as a first step in the development of even more sophisticated multi-stakeholder spatial decision support systems for subsidized housing planning in which one or more alternative allocations of families across a study area is chosen as a basis for policy initiatives.
29097804
Spatial decision support system for home-delivered services
Abstract. We present a spatial decision support system for the non-profit sector, designed to assist planning in the area of home-delivered services such as meals on wheels. Using data collected from existing programs, current and forecasted demographic data, and a set of algorithmic tools, we provide a system for evaluating current meals on wheels facilities, and for making incremental facility location decisions that satisfy coverage and equity requirements.
10445058
Time series classification using Gaussian mixture models of reconstructed phase spaces
A new signal classification approach is presented that is based upon modeling the dynamics of a system as they are captured in a reconstructed phase space. The modeling is done using full covariance Gaussian mixture models of time domain signatures, in contrast with current and previous work in signal classification that is typically focused on either linear systems analysis using frequency content or simple nonlinear machine learning models such as artificial neural networks. The proposed approach has strong theoretical foundations based on dynamical systems and topological theorems, resulting in a signal reconstruction, which is asymptotically guaranteed to be a complete representation of the underlying system, given properly chosen parameters. The algorithm automatically calculates these parameters to form appropriate reconstructed phase spaces, requiring only the number of mixtures, the signals, and their class labels as input. Three separate data sets are used for validation, including motor current simulations, electrocardiogram recordings, and speech waveforms. The results show that the proposed method is robust across these diverse domains, significantly outperforming the time delay neural network used as a baseline.
104744
Sentence-Internal Prosody Does not Help Parsing the Way Punctuation Does
This paper investigates the usefulness of sentence-internal prosodic cues in syntactic parsing of transcribed speech. Intuitively, prosodic cues would seem to provide much the same information in speech as punctuation does in text, so we tried to incorporate them into our parser in much the same way as punctuation is. We compared the accuracy of a statistical parser on the LDC Switchboard treebank corpus of transcribed sentence-segmented speech using various combinations of punctuation and sentence-internal prosodic information (duration, pausing, and f0 cues). With no prosodic or punctuation information the parser’s accuracy (as measured by F-score) is 86.9%, and adding punctuation increases its F-score to 88.2%. However, all of the ways we have tried of adding prosodic information decrease the parser’s F-score to between 84.8% to 86.8%, depending on exactly which prosodic information is added. This suggests that for sentence-internal prosodic information to improve speech transcript parsing, either different prosodic cues will have to used or they will have be exploited in the parser in a way different to that used currently.
8126535
An adaptive approach for texture modelling
Markov random fields (MRFs) continue to be an important and useful representation for modelling textured images. Standard methods for MRF image modeling make use of the equivalent Gibbs distribution (GD) to express the joint probabilities of groups of neighboring pixels. The authors investigate a new approach to the use of the GD in image modeling. Specifically, they develop an adaptive approach to the formation of clique potential functions for the distribution. Traditional tools, such as the multi-level logistic (MLL) model, have been based on the use of a predetermined and identical set of potential functions. In the present paper it is shown that by incorporating additional parameters into the model in order to control the shape of these functions, it is possible to arrive at a more complete parametrization of the image. A simple model based on this concept is described and implemented, and image simulations using the well-known Gibbs sampler algorithm are constructed to demonstrate the usefulness of an adaptive set of potential functions.<<ETX>>
18721179
Some requirements for architectural support of software debugging
Architectural support of high-level, symbolic debugging is described at three levels of abstraction: the user's view of desired debugging functionality, the debugger implementor's view of architectural requirements that support the functionality, and the computer architect's view of architectural features or attributes that implement the requirements. References are made where possible to computing systems that meet the requirements. The paper is written from the viewpoint of debugger implementors, and is addressed primarily to computer architects.
13391708
Early Deletion of Fillers In Processing Conversational Speech
This paper evaluates the benefit of deleting fillers (e.g. you know, like) early in parsing conversational speech. Readability studies have shown that disfluencies (fillers and speech repairs) may be deleted from transcripts without compromising meaning (Jones et al., 2003), and deleting repairs prior to parsing has been shown to improve its accuracy (Charniak and Johnson, 2001). We explore whether this strategy of early deletion is also beneficial with regard to fillers. Reported experiments measure the effect of early deletion under in-domain and out-of-domain parser training conditions using a state-of-the-art parser (Charniak, 2000). While early deletion is found to yield only modest benefit for in-domain parsing, significant improvement is achieved for out-of-domain adaptation. This suggests a potentially broader role for disfluency modeling in adapting text-based tools for processing conversational speech.