Browsing by Subject "Attributes"
Now showing 1 - 5 of 5
Results Per Page
Sort Options
Item Characterization of VTI media with PS[subscript v] AVO attributes(2014-12) Gustie, Patrick John; Tatham, R. H. (Robert H.), 1943-Amplitude variation with offset (AVO) signatures in vertically transverse isotropic (VTI) media vary as the degree of the anisotropy contrast between layers varies. When the contrasts in two parameters (δ and ε) that quantify the VTI elastic anisotropy are varied, the fraction of energy that reflects from a given layer interface as a mode converted shear wave (R[subscript PS]) also varies for specified angles of incidence. Mode-converted (PS[subscript V]) AVO crossplots may potentially be used to map stratigraphic layers exhibiting intrinsic VTI anisotropy with the moderate to high degrees of weak elastic anisotropy that are often attributed to shale formations. Calculated values of reflected, mode-converted energy as a function of angle of incidence (R[subscript PS](i)) are plotted to determine what mode-converted seismic data indicate about the degree of VTI weak elastic anisotropy present in a given layer. These computations involve varying the degree of weak elastic anisotropy, in this case contrasts in Thomsen’s δ and ε parameters, so that the relationship between these parameters and the amplitude variation with offset (AVO) signature can be quantified. Once this relationship is understood, it may be possible to delineate sweet spot areas of shale formations in seismic data according to how the representative points plot on an AVO crossplot. For such crossplots, the y-intercepts of the reflectivity curves in a particular parameterized space are plotted on the x-axis while the slopes of the parameterized reflectivity curves in this parameterized space are plotted on the y-axis. The grouping of points on the mode-converted AVO crossplots according to the contrast in Thomsen’s δ and ε parameters for weak elastic anisotropy is encouraging. This grouping implies that it may indeed be possible to use an AVO attribute map to characterize a given organic shale formation according to its degree of intrinsic VTI anisotropy. This attribute map would be calibrated to known production data in the locality in order to locate which areas of the mode-converted AVO crossplot predict a likely production sweet spot.Item Comparison of the Perception of Facility Managers on Green Roofs Attributes and Barriers to their Implementation(2014-07-24) Ferrer Garcia, Eduardo RThis study compares perceptions of facility managers on green roof attributes and barriers for their implementation. The population under study were the four IFMA chapters of the State of Texas (Austin, Dallas-Fort-Worth, Houston and San Antonio). A questionnaire containing 21 statements related to green roof attributes and 14 statements related to green roof barriers for their implementation was used and responses were measured on a five-point Likert scale. Two types of questionnaires were used to collect responses. An online questionnaire that was distributed through the chapter?s members list, and face to face responses were obtained on IFMA chapters meetings. The response rate for the questionnaire was 7.7%. The nonparametric statistic method of Kruskal-Wallis was used to check for differences among the four chapters with respect to perceptions on a given statement. The responses suggest that facility managers generally agreed with the majority of the statements regarding benefits that green roofs can provide. Similarly, the majority of facility managers tended to agree with the statements regarding barriers for green roofs implementation. The results of the investigation for ?=0.05 and a p-value=7.815 showed that no significant differences were found for any of the 35 statements with respect to the facility managers perceptions.Item Faculty perceptions about attributes and barriers impacting the adoption and diffusion of Web-Based Educational Technologies (WBETs) at the University of Cape Coast and the University of Ghana, Legon(Texas A&M University, 2006-10-30) Yakah, Jemima AbenaThe purpose of this study was to determine faculty perceptions about factors impacting the adoption and diffusion of Web-Based Educational Technologies (WBETs) at the University of Cape Coast and the University of Ghana, Legon. This study, based on Rogers?????? theory of adoption and diffusion, is a modified replication of a study by Li (2004), in the context of Ghana. Data were collected with a modified instrument created by Li (2004), from 61 teaching faculty out of a target accessible population of 200. The instrument comprised of four sections: The first, was used to collect data about faculty stage in the innovation development process. The second was used to collect data describing five attributes (relative advantage, compatibility, complexity, trialability, and observability) impacting the adoption and diffusion of WBETs. The third was used to collect data about ten barriers (concerns about time, concerns about incentives, program credibility, financial concerns, planning issues, conflict with traditional education, fear of technology, technical expertise, administrative support, and infrastructure) impacting the adoption and diffusion of WBETs. The fourth section was used to collect data on personal characteristics of the faculty. Descriptive, correlational and regression analyses were used to examine relationships between faculty personal characteristics, stage in the innovation-decision process, and perceptions of attributes and barriers impacting the adoption and diffusion of WBETs. From the descriptive results, respondents perceived ??????relative advantage?????? and ??????observability?????? as the two most important attributes that impact the adoption and diffusion of WBETs. Infrastructure, financial concerns, and technical expertise were perceived as posing moderate to strong barriers to the adoption and diffusion of WBETs. Only compatibility (attribute) and technical expertise (barrier) had statistically significant correlations with faculty stage in the innovation decision process. The attributes and barriers altogether explained only 10.6% and 17.3% respectively of faculty stage in the innovation-decision process. Of the eight personal characteristics examined, only ??????experience with WBETs?????? had a statistically significant correlation with faculty stage in the innovation-decision process. Recommendations to administrators and policy makers include allocating investments and resources that promote attributes and eliminate barriers, and conduct further research into factors that affect the adoption and diffusion of WBETs.Item Interactive image search with attributes(2014-08) Kovashka, Adriana Ivanova; Grauman, Kristen Lorraine, 1979-An image retrieval system needs to be able to communicate with people using a common language, if it is to serve its user's information need. I propose techniques for interactive image search with the help of visual attributes, which are high-level semantic visual properties of objects (like "shiny" or "natural"), and are understandable by both people and machines. My thesis explores attributes as a novel form of user input for search. I show how to use attributes to provide relevance feedback for image search; how to optimally choose what to seek feedback on; how to ensure that the attribute models learned by a system align with the user's perception of these attributes; how to automatically discover the shades of meaning that users employ when applying an attribute term; and how attributes can help learn object category models. I use attributes to provide a channel on which the user of an image retrieval system can communicate her information need precisely and with as little effort as possible. One-shot retrieval is generally insufficient, so interactive retrieval systems seek feedback from the user on the currently retrieved results, and adapt their relevance ranking function accordingly. In traditional interactive search, users mark some images as "relevant" and others as "irrelevant", but this form of feedback is limited. I propose a novel mode of feedback where a user directly describes how high-level properties of retrieved images should be adjusted in order to more closely match her envisioned target images, using relative attribute feedback statements. For example, when conducting a query on a shopping website, the user might state: "I want shoes like these, but more formal." I demonstrate that relative attribute feedback is more powerful than traditional binary feedback. The images believed to be most relevant need not be most informative for reducing the system's uncertainty, so it might be beneficial to seek feedback on something other than the top-ranked images. I propose to guide the user through a coarse-to-fine search using a relative attribute image representation. At each iteration of feedback, the user provides a visual comparison between the attribute in her envisioned target and a "pivot" exemplar, where a pivot separates all database images into two balanced sets. The system actively determines along which of multiple such attributes the user's comparison should next be requested, based on the expected information gain that would result. The proposed attribute search trees allow us to limit the scan for candidate images on which to seek feedback to just one image per attribute, so it is efficient both for the system and the user. No matter what potentially powerful form of feedback the system offers the user, search efficiency will suffer if there is noise on the communication channel between the user and the system. Therefore, I also study ways to capture the user's true perception of the attribute vocabulary used in the search. In existing work, the underlying assumption is that an image has a single "true" label for each attribute that objective viewers could agree upon. However, multiple objective viewers frequently have slightly different internal models of a visual property. I pose user-specific attribute learning as an adaptation problem in which the system leverages any commonalities in perception to learn a generic prediction function. Then, it uses a small number of user-labeled examples to adapt that model into a user-specific prediction function. To further lighten the labeling load, I introduce two ways to extrapolate beyond the labels explicitly provided by a given user. While users differ in how they use the attribute vocabulary, there exist some commonalities and groupings of users around their attribute interpretations. Automatically discovering and exploiting these groupings can help the system learn more robust personalized models. I propose an approach to discover the latent factors behind how users label images with the presence or absence of a given attribute, from a sparse label matrix. I then show how to cluster users in this latent space to expose the underlying "shades of meaning" of the attribute, and subsequently learn personalized models for these user groups. Discovering the shades of meaning also serves to disambiguate attribute terms and expand a core attribute vocabulary with finer-grained attributes. Finally, I show how attributes can help learn object categories faster. I develop an active learning framework where the computer vision learning system actively solicits annotations from a pool of both object category labels and the objects' shared attributes, depending on which will most reduce total uncertainty for multi-class object predictions in the joint object-attribute model. Knowledge of an attribute's presence in an image can immediately influence many object models, since attributes are by definition shared across subsets of the object categories. The resulting object category models can be used when the user initiates a search via keywords such as "Show me images of cats" and then (optionally) refines that search with the attribute-based interactions I propose. My thesis exploits properties of visual attributes that allow search to be both effective and efficient, in terms of both user time and computation time. Further, I show how the search experience for each individual user can be improved, by modeling how she uses attributes to communicate with the retrieval system. I focus on the modes in which an image retrieval system communicates with its users by integrating the computer vision perspective and the information retrieval perspective to image search, so the techniques I propose are a promising step in closing the semantic gap.Item Texas school board president's perspective on attributes of hispanic male superintendent(2010-05) Cervantes, Jose Alfredo; Ovando, Martha N., 1954-; Olivarez, Ruben; Cary, Lisa; Champion, Bret; Ott, BobbyPrevious research offers insights about characteristics of successful superintendents and provides generic lists of attributes (Collins, 2005 and Schleuning, 2003). However, little is known about specific characteristics of Hispanic male superintendents who have been successful in ascending to a superintendent position (Padilla, 2003, Garza, 2003 and Rueda, 2002). Given the current need to select superintendents who reflect the current population changes, further inquiry of the personal and professional attributes from a board presidents’ perspective is needed with a specific focus on male Hispanics who have been selected to serve as superintendent. The purpose of the study is to identify attributes (characteristics) that Texas school board presidents believe are important when having selected a Hispanic male superintendent. The study investigated four research questions: (a) the perceptions of Texas public school board presidents regarding the most important personal attributes when having selected a Hispanic male superintendent; (b) the perceptions of Texas public school board presidents regarding the most important professional attributes when having selected a Hispanic male superintendent; (c) the size (student enrollment) of a school district affect the perception of school board presidents regarding the important attributes; and (d) geographic location affect the perception of school board presidents regarding the important attributes? The study followed a quantitative research paradigm. A descriptive research design approach was used. Thus, a survey was used as instrumentation to collect data (Schleuning, 2003). Texas public school board presidents’ who were serving, and who selected and hired Hispanic male superintendents for 2008-2009 school year were surveyed. Data was analyzed: using descriptive statistics including means and standard deviations, one-way analyses and analyses of variance. Findings revealed that Texas public school board presidents regarding the most important personal attributes when having selected a Hispanic male superintendent are; level of education, previous experience in school administration, and years of experience in education. The most important professional attributes are; honest/fair standards, personal integrity, and visionary leadership. Findings also suggest that enrollment size and geographic location does not affect the perception of school board presidents when selecting a Hispanic male superintendent.