Efficient Monte Carlo methods for multi-dimensional learning with classifier chains

Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems. Since these classes are often strongly correlated, modeling the dependencies between them allows MDC methods to improve their performance – at the expense of an increased computational cost. In this paper we focus on the classifier chains (CC) approach for modeling dependencies, one of the most popular and highest-performing methods for multi-label classification (MLC), a particular case of MDC which involves only binary classes (i.e., labels). The original CC algorithm makes a greedy approximation, and is fast but tends to propagate errors along the chain. Here we present novel Monte Carlo schemes, both for finding a good chain sequence and performing efficient inference. Our algorithms remain tractable for high-dimensional data sets and obtain the best predictive performance across several real data sets.

A new perspective on service quality: A multi-dimensional, hierarchical scale

This study offers a new perspective on the nature, content and structure of perceived service quality. The Nordic and Gap schools of quality assessment are integrated with recent advances in the literature to develop and test a multidimensional, hierarchical scale. The scale provides a framework for assessing service quality within a high involvement, high contact, ongoing service environment. Empirical results indicated that service quality conforms to a multidimensional, hierarchical structure consisting of four primary dimensions, which in turn comprise nine sub-dimensions. The results obtained extend our understanding of service evaluation and have important implications for service providers seeking to improve the quality of the services they provide.

A Bayesian modeling approach to multi-dimensional sentiment distributions prediction

Sentiment analysis has long focused on binary classification of text as either positive or negative. There has been few work on mapping sentiments or emotions into multiple dimensions. This paper studies a Bayesian modeling approach to multi-class sentiment classification and multidimensional sentiment distributions prediction. It proposes effective mechanisms to incorporate supervised information such as labeled feature constraints and document-level sentiment distributions derived from the training data into model learning. We have evaluated our approach on the datasets collected from the confession section of the Experience Project website where people share their life experiences and personal stories. Our results show that using the latent representation of the training documents derived from our approach as features to build a maximum entropy classifier outperforms other approaches on multi-class sentiment classification. In the more difficult task of multi-dimensional sentiment distributions prediction, our approach gives superior performance compared to a few competitive baselines. © 2012 ACM.

Data Independence in the Multi-dimensional Numbered Information Spaces

The concept of data independence designates the techniques that allow data to be changed without affecting the applications that process it. The different structures of the information bases require corresponded tools for supporting data independence. A kind of information bases (the Multi-dimensional Numbered Information Spaces) are pointed in the paper. The data independence in such information bases is discussed.

Developing of Distributed Virtual Laboratories for Smart Sensor System Design Based on Multi-Dimensional Access Method

In the article it is considered preconditions and main principles of creation of virtual laboratories for computer-aided design, as tools for interdisciplinary researches. Virtual laboratory, what are offered, is worth to be used on the stage of the requirements specification or EFT-stage, because it gives the possibility of fast estimating of the project realization, certain characteristics and, as a result, expected benefit of its applications. Using of these technologies already increase automation level of design stages of new devices for different purposes. Proposed computer technology gives possibility to specialists from such scientific fields, as chemistry, biology, biochemistry, physics etc, to check possibility of device creating on the basis of developed sensors. It lets to reduce terms and costs of designing of computer devices and systems on the early stages of designing, for example on the stage of requirements specification or EFT-stage. An important feature of this project is using the advanced multi-dimensional access method for organizing the information base of the Virtual laboratory.

On Multi-Dimensional Random Walk Models Approximating Symmetric Space-Fractional Diffusion Processes

Mathematics Subject Classification: 26A33, 47B06, 47G30, 60G50, 60G52, 60G60.

Comparative study of dissolved organic matter from groundwater and surface water in the Florida coastal Everglades using multi-dimensional spectrofluorometry combined with multivariate statistics

Dissolved organic matter (DOM) in groundwater and surface water samples from the Florida coastal Everglades were studied using excitation–emission matrix fluorescence modeled through parallel factor analysis (EEM-PARAFAC). DOM in both surface and groundwater from the eastern Everglades S332 basin reflected a terrestrial-derived fingerprint through dominantly higher abundances of humic-like PARAFAC components. In contrast, surface water DOM from northeastern Florida Bay featured a microbial-derived DOM signature based on the higher abundance of microbial humic-like and protein-like components consistent with its marine source. Surprisingly, groundwater DOM from northeastern Florida Bay reflected a terrestrial-derived source except for samples from central Florida Bay well, which mirrored a combination of terrestrial and marine end-member origin. Furthermore, surface water and groundwater displayed effects of different degradation pathways such as photodegradation and biodegradation as exemplified by two PARAFAC components seemingly indicative of such degradation processes. Finally, Principal Component Analysis of the EEM-PARAFAC data was able to distinguish and classify most of the samples according to DOM origins and degradation processes experienced, except for a small overlap of S332 surface water and groundwater, implying rather active surface-to-ground water interaction in some sites particularly during the rainy season. This study highlights that EEM-PARAFAC could be used successfully to trace and differentiate DOM from diverse sources across both horizontal and vertical flow profiles, and as such could be a convenient and useful tool for the better understanding of hydrological interactions and carbon biogeochemical cycling.

Coupled symplectic maps as models for subdiffusive processes in disordered Hamiltonian lattices

Acknowledgements One of us (T. B.) acknowledges many interesting discussions on coupled maps with Professor C. Tsallis. We are also grateful to the anonymous referees for their constructive feedback that helped us improve the manuscript and to the HPCS Laboratory of the TEI of Western Greece for providing the computer facilities where all our simulations were performed. C. G. A. was partially supported by the “EPSRC EP/I032606/1” grant of the University of Aberdeen. This research has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES - Investing in knowledge society through the European Social Fund.

Coupled symplectic maps as models for subdiffusive processes in disordered Hamiltonian lattices

Acknowledgements One of us (T. B.) acknowledges many interesting discussions on coupled maps with Professor C. Tsallis. We are also grateful to the anonymous referees for their constructive feedback that helped us improve the manuscript and to the HPCS Laboratory of the TEI of Western Greece for providing the computer facilities where all our simulations were performed. C. G. A. was partially supported by the “EPSRC EP/I032606/1” grant of the University of Aberdeen. This research has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES - Investing in knowledge society through the European Social Fund.

Multi-dimensional summarization in cyber-physical society

Text summarization has been studied for over a half century, but traditional methods process texts empirically and neglect the fundamental characteristics and principles of language use and understanding. Automatic summarization is a desirable technique for processing big data. This reference summarizes previous text summarization approaches in a multi-dimensional category space, introduces a multi-dimensional methodology for research and development, unveils the basic characteristics and principles of language use and understanding, investigates some fundamental mechanisms of summarization, studies dimensions on representations, and proposes a multi-dimensional evaluation mechanism. Investigation extends to incorporating pictures into summary and to the summarization of videos, graphs and pictures, and converges to a general summarization method. Further, some basic behaviors of summarization are studied in the complex cyber-physical-social space. Finally, a creative summarization mechanism is proposed as an effort toward the creative summarization of things, which is an open process of interactions among physical objects, data, people, and systems in cyber-physical-social space through a multi-dimensional lens of semantic computing. The insights can inspire research and development of many computing areas.

Development and Application of a Multi-dimensional Strategy for Measuring Fidelity of Implementation of a Districtwide Intervention

This symposium describes a multi-dimensional strategy to examine fidelity of implementation in an authentic school district context. An existing large-district peer mentoring program provides an example. The presentation will address development of a logic model to articulate a theory of change; collaborative creation of a data set aligned with essential concepts and research questions; identification of independent, dependent, and covariate variables; issues related to use of big data that include conditioning and transformation of data prior to analysis; operationalization of a strategy to capture fidelity of implementation data from all stakeholders; and ways in which fidelity indicators might be used.