This research examines consumer preferences for certification of select credence attributes by different certifying agents. Over two separate study periods, groups of Edmonton consumers participated in sessions comprised of three components a real choice experiment, a stated preference exercise and a survey designed to elicit willingness to pay for select credence attributes of eggs, certification of those attributes and establish attitudes and beliefs that may affect preferences. Results indicate that consumer shopping habits, overall trust levels and certain demographic characteristics influence the preference for an attribute as well as the preference for certification of that attribute. Survey respondents prefer certified to uncertified eggs and government is the preferred certifier. Pasteurized eggs gained the most from certification. Furthermore, an assessment of respondent knowledge about current certification practices indicates that certification schemes be accompanied by an adequate education campaign.

The importance of attribute vector ambiguity has been largely overlooked by the machine learning community. A pattern recognition problem can be solved in many ways within the scope of machine learning. Neural Networks, Decision Tree Algorithms such as C4.5, Bayesian Classifiers, and Instance Based Learning are the main algorithms. All listed solutions fail to address ambiguity in the attribute vector. The research reported shows, ignoring this ambiguity leads to problems of classifier scalability and issues with instance collection and aggregation. The Algorithm presented accounts for both ambiguity of the attribute vector and class label thus solving both issues of scalability and instance collection. The research also shows that when applied to sanitized data sets, suitable for traditional instance based learning, the presented algorithm performs equally as well.

Seismic data processing depends on mathematical and statistical tools such as convolution, crosscorrelation and stack that employ second-order statistics (SOS). Seismic signals are non-Gaussian and therefore contain information beyond SOS. One of the modern challenges of seismic data processing is reformulating algorithms e.g. migration, to utilize the extra higher order statistics (HOS) information in seismic data. The migration algorithm has two key components: the moveout correction, which corresponds to the crosscorrelation of the migration operator with the data at zero lag and the stack of the moveout-corrected data. This study reformulated the standard migration algorithm to handle the HOS information by improving the stack component, having assumed that the moveout correction is accurate. The reformulated migration algorithm outputs not only the standard form of stack, but also the variance, skewness and kurtosis of moveout-corrected data. The mean (stack) of the moveout-corrected data in this new concept is equivalent to the migration currently performed in industry. The variance of moveout-corrected data is one of the new outputs obtained from the reformulation. Though it characterizes SOS information, it is not one of the outputs of standard migration. In cases where the seismic amplitude variation with offset (AVO) response is linear, a single algorithm that outputs mean (stack) and variance combines both the standard AVO analysis and migration, thereby significantly improving the cost of seismic data processing. Furthermore, this single algorithm improves the resolution of seismic imaging, since it does not require an explicit knowledge of reflection angles to retrieve AVO information. In the reformulation, HOS information is captured by the skewness and kurtosis of moveout-corrected data. These two outputs characterize nonlinear AVO response and non-Gaussian noise (symmetric and nonsymmetric) that may be contained in the data. Skewness characterizes nonsymmetric, non-Gaussian noise, whereas kurtosis characterizes symmetric, non-Gaussian noise. These outputs also characterize any errors associated with moveout corrections. While classical seismic data processing provides a single output, HOS-related processing outputs three extra parameters i.e. the variance, skewness, and kurtosis. These parameters can better characterize geological formations and improve the accuracy of the seismic data processing performed before the application of the reformulated migration algorithm.

In recent years, the IT industry in Taiwan has gradually shifted from hardware manufacturing to software design. At the same time, conflict between IT industry and MIS graduates exists: CISA (Information Service Industry Association of China, Taipei) still claims the immense shortage of IT workers no matter how high the unemployment rate is. However, MIS graduates don¡¦t get extraordinary pay due to this situation. There are two constructs used to describe the ¡§fit¡¨: person-job fit and person-organization fit. Based on the ¡§fit¡¨ concept, the purpose of this research is to find out the gap between IT manager and MIS graduate as well as other demographic variables such as sex, area and industry. According to the purposes of this research, we have discovered some important results: a. Fit between IT manager and MIS graduate¡GFreshmen of the new generation are originative¡Benergetic¡Boptimistic but less durable for frustrations. IT managers tend to expect newcomers follow work rules and norms. b. Fit between sex¡GGeneral speaking, male tends to accept work rules and norms and prefers the value of competition and result-orientation. Female has more preferences to fringe benefits and work environment and uphold originality and respect. c. Fit between areas¡GPeople living in the North Taiwan pay more attention on ¡ureward¡vand ¡ufriendship¡vin work and agree with the value of profession and human nature. Morals and traditions are highly emphasized in the South Taiwan. d. Fit between other demographic variables¡GFirst, MIS graduates of private universities place more importance on ¡ustability¡vand ¡uperformance¡vthan that of national universities. Second, IT managers under 40 years old have more willingness to offer higher reward conditions for Freshmen and more agreement with the value of ¡ucreative humanity¡v. Finally, people in software service industry think highly of ¡ureward¡vand the value of ¡ucreative humanity¡v¡B¡umorality¡vand ¡urespect¡v.

This dissertation consists of three research papers on Preference models of decision making, all of which adopt an axiomatic approach in which preference conditions are studied so that the models in this dissertation can be verified by checking their conditions at the behavioral level. The first paper “Utility Functions Representing Preference over Interdependent Attributes” studies the problem of how to assess a two attribute utility function when the attributes are interdependent. We consider a situation where the risk aversion on one attribute could be influenced by the level of the other attribute in a two attribute decision making problem. In this case, the multilinear utility model—and its special cases the additive and multiplicative forms—cannot be applied to assess a subject’s preference because utility independence does not hold. We propose a family of preference conditions called nth degree discrete distribution independence that can accommodate a variety of dependencies among two attributes. The special case of second degree discrete distribution independence is equivalent to the utility independence condition. Third degree discrete distribution independence leads to a decomposition formula that contains many other decomposition formulas in the existing literature as special cases. As the decompositions proposed in this research is more general than many existing ones, the study provides a model of preference that has potential to be used for assessing utility functions more accurately and with relatively little additional effort. The second paper “On the Axiomatization of the Satiation and Habit Formation Utility Models” studies the axiomatic foundations of the discounted utility model that incorporates both satiation and habit formation in temporal decision. We propose a preference condition called shifted difference independence to axiomatize a general habit formation and satiation model (GHS). This model allows for a general habit formation and satiation function that contains many functional forms in the literature as special cases. Since the GHS model can be reduced to either a general satiation model (GSa) or a general habit formation model (GHa), our theory also provides approaches to axiomatize both the GSa model and the GHa model. Furthermore, by adding extra preference conditions into our axiomatization framework, we obtain a GHS model with a linear habit formation function and a recursively defined linear satiation function. In the third paper “Hope, Dread, Disappointment, and Elation from Anticipation in Decision Making”, we propose a model to incorporate both anticipation and disappointment into decision making, where we define hope as anticipating a gain and dread as anticipating a loss. In this model, the anticipation for a lottery is a subjectively chosen outcome for a lottery that influences the decision maker’s reference point. The decision maker experiences elation or disappointment when she compares the received outcome with the anticipated outcome. This model captures the trade-off between a utility gain from higher anticipation and a utility loss from higher disappointment. We show that our model contains some existing decision models as its special cases, including disappointment models. We also use our model to explore how a person’s attitude toward the future, either optimistic or pessimistic, could mediate the wealth effect on her risk attitude. Finally, we show that our model can be applied to explain the coexistence of a demand for gambling and insurance and provides unique insights into portfolio choice and advertising decision problems. / text

This dissertation examines what motivates individuals to prefer certain types of product attributes over others. It is proposed that consumer preference regarding product attributes is fundamentally connected to an individual’s future orientation, i.e., how a person perceives, thinks about, and copes with time left in life. Specifically, it is posited that future orientations play key roles in shaping a person’s criteria in product evaluation. Thus, this dissertation seeks to integrate the study of future orientation with research on socio-emotional selectivity influences on consumption. Building on past research, this study proposes a conceptual model including four constructs: future orientations, chronological age, product evaluation, and preferences. An experimental study was used to investigate the research objectives and calibrate and validate the model. The experiment examines the moderating effect of future orientations and chronological age on consumer preference for hedonic vs. utilitarian attributes. The subjects were randomly assigned to one of two future orientations (expansive and limited) and one of two attributes contexts (hedonic and utilitarian). The sample for this study was drawn from consumers in Metropolitan Atlanta, Georgia. The research results will lead to an improved understanding of how preference varies from individual to individual and changes over time. In particular the research will provide insights about the impact of an individual’s future orientation on product attitude. The findings will advance current theory in both the new product evaluation and preference literature and have implications for the practice of marketing at levels of marketing strategy, product development, integrated marketing communications and loyalty programs.

No description available.

Thesis (M.A.)--University of Wyoming, 2007. / Title from PDF title page (viewed on Nov. 24, 2008). Includes bibliographical references.

abstract: Attribute Based Access Control (ABAC) mechanisms have been attracting a lot of interest from the research community in recent times. This is especially because of the flexibility and extensibility it provides by using attributes assigned to subjects as the basis for access control. ABAC enables an administrator of a server to enforce access policies on the data, services and other such resources fairly easily. It also accommodates new policies and changes to existing policies gracefully, thereby making it a potentially good mechanism for implementing access control in large systems, particularly in today's age of Cloud Computing. However management of the attributes in ABAC environment is an area that has been little touched upon. Having a mechanism to allow multiple ABAC based systems to share data and resources can go a long way in making ABAC scalable. At the same time each system should be able to specify their own attribute sets independently. In the research presented in this document a new mechanism is proposed that would enable users to share resources and data in a cloud environment using ABAC techniques in a distributed manner. The focus is mainly on decentralizing the access policy specifications for the shared data so that each data owner can specify the access policy independent of others. The concept of ontologies and semantic web is introduced in the ABAC paradigm that would help in giving a scalable structure to the attributes and also allow systems having different sets of attributes to communicate and share resources. / Dissertation/Thesis / M.S. Computer Science 2014

In a digital world of Flickr, Picasa and Google Images, developing a semantic image represen-tation has become a vital problem. Image processing and computer vision researchers to date, have used several di erent representations for images. They vary from low level features such as SIFT, HOG, GIST etc. to high level concepts such as objects and people. When asked to describe an object or a scene, people usually resort to mid-level features such as size, appearance, feel, use, behaviour etc. Such descriptions are commonly referred to as the attributes of the object or scene. These human understandable, machine detectable attributes have recently become a popular feature category for image representation for various vision tasks. In addition to image and object characteristics, object interactions and back-ground/context information and the actions taking place in the scene form an important part of an image description. It is therefore, essential, to develop an image representation which can e ectively describe various image components and their interactions. Towards this end, we propose a novel image representation, termed Attribute-Graph. An Attribute-Graph is an undirected graph, incorporating both local and global image character-istics. The graph nodes characterise objects as well as the overall scene context using mid-level semantic attributes, while the edges capture the object topology and the actions being per-formed. We demonstrate the e ectiveness of Attribute-Graphs by applying them to the problem of image ranking. Since an image retrieval system should rank images in a way which is compatible with visual similarity as perceived by humans, it is intuitive that we work in a human understandable feature space. Most content based image retrieval algorithms treat images as a set of low level features or try to de ne them in terms of the associated text. Such a representation fails to capture the semantics of the image. This, more often than not, results in retrieved images which are semantically dissimilar to the query. Ranking using the proposed attribute-graph representation alleviates this problem. We benchmark the performance of our ranking algorithm on the rPascal and rImageNet datasets, which we have created in order to evaluate the ranking performance on complex queries containing multiple objects. Our experimental evaluation shows that modelling images as Attribute-Graphs results in improved ranking performance over existing techniques.