1,050 research outputs found
Multivariate volume visualization through dynamic projections
pre-printWe propose a multivariate volume visualization framework that tightly couples dynamic projections with a high-dimensional transfer function design for interactive volume visualization. We assume that the complex, high-dimensional data in the attribute space can be well-represented through a collection of low-dimensional linear subspaces, and embed the data points in a variety of 2D views created as projections onto these subspaces. Through dynamic projections, we present animated transitions between different views to help the user navigate and explore the attribute space for effective transfer function design. Our framework not only provides a more intuitive understanding of the attribute space but also allows the design of the transfer function under multiple dynamic views, which is more flexible than being restricted to a single static view of the data. For large volumetric datasets, we maintain interactivity during the transfer function design via intelligent sampling and scalable clustering. Using examples in combustion and climate simulations, we demonstrate how our framework can be used to visualize interesting structures in the volumetric space
BUILDING DSS USING KNOWLEDGE DISCOVERY IN DATABASE APPLIED TO ADMISSION & REGISTRATION FUNCTIONS
This research investigates the practical issues surrounding the development and
implementation of Decision Support Systems (DSS). The research describes the traditional
development approaches analyzing their drawbacks and introduces a new DSS development
methodology. The proposed DSS methodology is based upon four modules; needs' analysis,
data warehouse (DW), knowledge discovery in database (KDD), and a DSS module.
The proposed DSS methodology is applied to and evaluated using the admission and
registration functions in Egyptian Universities. The research investigates the organizational
requirements that are required to underpin these functions in Egyptian Universities. These
requirements have been identified following an in-depth survey of the recruitment process in
the Egyptian Universities. This survey employed a multi-part admission and registration DSS
questionnaire (ARDSSQ) to identify the required data sources together with the likely users
and their information needs. The questionnaire was sent to senior managers within the
Egyptian Universities (both private and government) with responsibility for student
recruitment, in particular admission and registration.
Further, access to a large database has allowed the evaluation of the practical suitability of
using a data warehouse structure and knowledge management tools within the decision
making framework. 1600 students' records have been analyzed to explore the KDD process,
and another 2000 records have been used to build and test the data mining techniques within
the KDD process.
Moreover, the research has analyzed the key characteristics of data warehouses and explored
the advantages and disadvantages of such data structures. This evaluation has been used to
build a data warehouse for the Egyptian Universities that handle their admission and
registration related archival data. The decision makers' potential benefits of the data
warehouse within the student recruitment process will be explored.
The design of the proposed admission and registration DSS (ARDSS) will be developed and
tested using Cool: Gen (5.0) CASE tools by Computer Associates (CA), connected to a MSSQL
Server (6.5), in a Windows NT (4.0) environment. Crystal Reports (4.6) by Seagate will
be used as a report generation tool. CLUST AN Graphics (5.0) by CLUST AN software will
also be used as a clustering package.
Finally, the contribution of this research is found in the following areas:
A new DSS development methodology;
The development and validation of a new research questionnaire (i.e. ARDSSQ);
The development of the admission and registration data warehouse;
The evaluation and use of cluster analysis proximities and techniques in the KDD process
to find knowledge in the students' records;
And the development of the ARDSS software that encompasses the advantages of the
KDD and DW and submitting these advantages to the senior admission and registration
managers in the Egyptian Universities.
The ARDSS software could be adjusted for usage in different countries for the same purpose,
it is also scalable to handle new decision situations and can be integrated with other systems
MEG, PSYCHOPHYSICAL AND COMPUTATIONAL STUDIES OF LOUDNESS, TIMBRE, AND AUDIOVISUAL INTEGRATION
Natural scenes and ecological signals are inherently complex and understanding of their perception and processing is incomplete. For example, a speech signal contains not only information at various frequencies, but is also not static; the signal is concurrently modulated temporally. In addition, an auditory signal may be paired with additional sensory information, as in the case of audiovisual speech. In order to make sense of the signal, a human observer must process the information provided by low-level sensory systems and integrate it across sensory modalities and with cognitive information (e.g., object identification information, phonetic information). The observer must then create functional relationships between the signals encountered to form a coherent percept. The neuronal and cognitive mechanisms underlying this integration can be quantified in several ways: by taking physiological measurements, assessing behavioral output for a given task and modeling signal relationships. While ecological tokens are complex in a way that exceeds our current understanding, progress can be made by utilizing synthetic signals that encompass specific essential features of ecological signals.
The experiments presented here cover five aspects of complex signal processing using approximations of ecological signals : (i) auditory integration of complex tones comprised of different frequencies and component power levels; (ii) audiovisual integration approximating that of human speech; (iii) behavioral measurement of signal discrimination; (iv) signal classification via simple computational analyses and (v) neuronal processing of synthesized auditory signals approximating speech tokens. To investigate neuronal processing, magnetoencephalography (MEG) is employed to assess cortical processing non-invasively. Behavioral measures are employed to evaluate observer acuity in signal discrimination and to test the limits of perceptual resolution. Computational methods are used to examine the relationships in perceptual space and physiological processing between synthetic auditory signals, using features of the signals themselves as well as biologically-motivated models of auditory representation. Together, the various methodologies and experimental paradigms advance the understanding of ecological signal analytics concerning the complex interactions in ecological signal structure
Cognitive models of pilot categorization and prioritization of flight-deck information
In the past decade, automated systems on modern commercial flight decks have increased dramatically. Pilots now regularly interact and share tasks with these systems. This interaction has led human factors research to direct more attention to the pilot's cognitive processing and mental model of the information flow occurring on the flight deck. The experiment reported herein investigated how pilots mentally represent and process information typically available during flight. Fifty-two commercial pilots participated in tasks that required them to provide similarity ratings for pairs of flight-deck information and to prioritize this information under two contextual conditions. Pilots processed the information along three cognitive dimensions. These dimensions included the flight function and the flight action that the information supported and how frequently pilots refer to the information. Pilots classified the information as aviation, navigation, communications, or systems administration information. Prioritization results indicated a high degree of consensus among pilots, while scaling results revealed two dimensions along which information is prioritized. Pilot cognitive workload for flight-deck tasks and the potential for using these findings to operationalize cognitive metrics are evaluated. Such measures may be useful additions for flight-deck human performance evaluation
Extending Two-Dimensional Knowledge Management System Theory with Organizational Activity Systems\u27 Workflow Dynamics
Between 2005 and 2010 and across 48 countries, including the United States, an increasing positive correlation emerged between national intellectual capital and gross domestic product per capita. The problem remains organizations operating with increasingly complex knowledge networks often lose intellectual capital resulting from ineffective knowledge management practices. The purpose of this study was to provide management opportunities to reduce intellectual capital loss. The first research question addressed how an enhanced intelligent, complex, and adaptive system (ICAS) model could clarify management\u27s understanding of organizational knowledge transfer. The second research question addressed how interdisciplinary theory could become more meaningfully infused to enhance management practices of the organization\u27s knowledge ecosystem. The nature of this study was phenomenological to gain deeper understanding of individual experiences related to knowledge flow phenomena. Data were collected from a single historical research dataset containing 11 subject interviews and analyzed using Moustakas\u27 heuristic framework. Original interviews were collected in 2012 during research within a military unit, included in this study based on theme alignment. Organizational, knowledge management, emergent systems, and cognition theories were synthesized to enhance understandings of emergent ICAS forces. Individuals create unique ICAS flow emergent force dynamics in relation to micro- and macro-meso sensemaking and sensegiving. Findings indicated individual knowledge work significantly shapes emergent ICAS flow dynamics. Collectively enhancing knowledge stewardship over time could foster positive social change by improving national welfare
Facility Location Decision for Global Entrepreneurial Small-to-Medium Enterprises Using Similarity Coefficient-based Clustering Algorithms
Decisions on location selection are critical for the survival of small-to-medium entrepreneurial organizations from the time they are established until later stages of operation and expansion. The selection of location for small and medium entrepreneurial businesses requires a selection strategy that incorporates relevant factors, quantifies these factors and develops a methodology that analyzes data for better decision-making. In the era of globalization where borders have become easier to transcend, many small ventures tend to choose more attractive international markets as a potential location for their operations where they can obtain higher returns on their investment. Thus, significant changes in the location decision process of the small and medium entrepreneurial companies have received great attention in the literature about small firms with global orientation as a response to the international entrepreneurship phenomenon. Therefore, consideration should be given to factors and attributes that reinforce the appeal of the international market to new businesses. These factors and attributes will provide the decision maker with an effective methodology for data analysis that will provide a framework for decision-making in the selection of locations for the entrepreneurial organization.
In this research, the most frequent and critical attributes to select the best location for the entrepreneurial firms (globally) are extracted from relevant literature. Then, a similarity-based cluster analysis approach is introduced to quantify these attributes based on the existing data of economic metrics, such as technological advancement, expenditures on education, expenditures on research and development, the quality of the labor force, unemployment rates, domestic competitiveness, etc. Subsequently, the resulting outcomes are used to identify groups of prospective sites that fit the needs of the entrepreneurial firm. Last, the validity of the adopted methodology will be tested via numerical examples
Mathematical statistics vs machine learning: towards an intelligent modeling framework for soil and plant growth processes
Mestrado de dupla diplomação com a Kuban State Agrarian UniversityThe work described in this dissertation focuses on the methods for analyzing MS and ML that are used in PF.
The purpose of the work is to investigate these methods on their practical application to a specific set of data.
In the course of the work, the following tasks were completed: the current state of affairs in the field of PF was investigated, the theoretical foundations of the methods of MS and ML were investigated, which were subjected to practical tests on a specific set of data. Conclusions were drawn about the advantages and disadvantages of these methods. A selection of works of scientists engaged in research on the introduction of a specific set of nutrients into the soil was also investigated.
The most important contributions to this work are the practical application of various methods of analysis, as well as the design of a DST designed to help farmers integrate PF into their pilot training farms.O trabalho descrito nesta dissertação versa sobre mĂ©todos e tĂ©cnicas no âmbito da EstatĂstica Matemática e de ML usados para efeitos de previsĂŁo de colheitas e tratamento de solos em agricultura de precisĂŁo.
O objetivo do trabalho Ă© investigar esses mĂ©todos em sua aplicação prática a um conjunto especĂfico de dados.
No decorrer do trabalho, foram realizadas as seguintes tarefas: investigou-se a situação atual no campo da agricultura de precisĂŁo, investigaram-se os fundamentos teĂłricos dos mĂ©todos e tĂ©cnicas da estatĂstica matemática e de ML. Estes mĂ©todos e tĂ©cnicas foram submetidos a testes práticos em um conjunto especĂfico de dados. Foram tiradas conclusões sobre as vantagens e desvantagens desses mĂ©todos: Uma seslção de trabalhos cientĂficos relacionados com a investigação sobre a introdução de um conjunto especĂfico de nutrientes no solo foram tambĂ©m investigados.
As contribuições mais importantes para este trabalho sĂŁo a aplicação prática de vários mĂ©todos de análise, bem como o projeto de uma ferramenta de apoio Ă decisĂŁo projetada para ajudar os agricultores a integrar a agricultura de precisĂŁo nas suas propriedades agrĂcolas
Visualization of Jacques Lacan’s Registers of the Psychoanalytic Field, and Discovery of Metaphor and of Metonymy. Analytical Case Study of Edgar Allan Poe’s “The Purloined Letter”
We start with a description of Lacan’s work that we then take into our analytics methodology. In a first investigation, a Lacan-motivated template of the Poe story is fitted to the data. A segmentation of the storyline is used in order to map out the diachrony. Based on this, it will be shown how synchronous aspects, potentially related to Lacanian registers, can be sought. This demonstrates the effectiveness of an approach based on a model template of the storyline narrative. In a second and more comprehensive investigation, we develop an approach for revealing, that is, uncovering, Lacanian register relationships. Objectives of this work include the wide and general application of our methodology. This methodology is strongly based on the “letting the data speak” Correspondence Analysis analytics platform of Jean-Paul Benzécri, that is also the geometric data analysis, both qualitative and quantitative analytics, developed by Pierre Bourdieu
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