Abstract: We present a new algorithm for nonlinear dimensionality reduction that consistently uses global information, and that enables understanding the intrinsic geometry of non-convex manifolds. Compared to methods that consider only local information, our method appears to be more robust to noise. Unlike most methods that incorporate global information, the proposed approach automatically handles non-convexity of the data manifold. We demonstrate the performance of our algorithm and compare it to state-of-the-art methods on synthetic as well as real data.
Abstract: The use of statistical data and of the neural networks, capable of elaborate a series of data and territorial info, have allowed the making of a model useful in the subdivision of urban places into homogeneous zone under the profile of a social, real estate, environmental and urbanist background of a city. The development of homogeneous zone has fiscal and urbanist advantages. The tools in the model proposed, able to be adapted to the dynamic changes of the city, allow the application of the zoning fast and dynamic.
Abstract: This paper aims to propose a novel, robust, and simple method for obtaining a human 3D face model and camera pose (position and orientation) from a video sequence. Given a video sequence of a face recorded from an off-the-shelf digital camera, feature points used to define facial parts are tracked using the Active- Appearance Model (AAM). Then, the face-s 3D structure and camera pose of each video frame can be simultaneously calculated from the obtained point correspondences. This proposed method is primarily based on the combined approaches of Gradient Descent and Powell-s Multidimensional Minimization. Using this proposed method, temporarily occluded point including the case of self-occlusion does not pose a problem. As long as the point correspondences displayed in the video sequence have enough parallax, these missing points can still be reconstructed.
Abstract: The study presents a brief and synthetic discussion of selected conclusions resulting from multidimensional and in-depth empirical studies. Its theoretical part presents the assumptions referring to social responsibility management from the perspective of the specific nature of small enterprise functioning, while the empirical part presents the selected dysfunctions and paradoxes in social responsibility management referring to this group of enterprises. The paper is summarized by a short list of the resulting recommendations.
Abstract: Decision support systems are usually based on
multidimensional structures which use the concept of hypercube.
Dimensions are the axes on which facts are analyzed and form a
space where a fact is located by a set of coordinates at the
intersections of members of dimensions. Conventional
multidimensional structures deal with discrete facts linked to discrete
dimensions. However, when dealing with natural continuous
phenomena the discrete representation is not adequate. There is a
need to integrate spatiotemporal continuity within multidimensional
structures to enable analysis and exploration of continuous field data.
Research issues that lead to the integration of spatiotemporal
continuity in multidimensional structures are numerous. In this paper,
we discuss research issues related to the integration of continuity in
multidimensional structures, present briefly a multidimensional
model for continuous field data. We also define new aggregation
operations. The model and the associated operations and measures
are validated by a prototype.
Abstract: Locality Sensitive Hashing (LSH) is one of the most
promising techniques for solving nearest neighbour search problem in
high dimensional space. Euclidean LSH is the most popular variation
of LSH that has been successfully applied in many multimedia
applications. However, the Euclidean LSH presents limitations that
affect structure and query performances. The main limitation of the
Euclidean LSH is the large memory consumption. In order to achieve
a good accuracy, a large number of hash tables is required. In this
paper, we propose a new hashing algorithm to overcome the storage
space problem and improve query time, while keeping a good
accuracy as similar to that achieved by the original Euclidean LSH.
The Experimental results on a real large-scale dataset show that the
proposed approach achieves good performances and consumes less
memory than the Euclidean LSH.
Abstract: In order to maximize efficiency of an information management platform and to assist in decision making, the collection, storage and analysis of performance-relevant data has become of fundamental importance. This paper addresses the merits and drawbacks provided by the OLAP paradigm for efficiently navigating large volumes of performance measurement data hierarchically. The system managers or database administrators navigate through adequately (re)structured measurement data aiming to detect performance bottlenecks, identify causes for performance problems or assessing the impact of configuration changes on the system and its representative metrics. Of particular importance is finding the root cause of an imminent problem, threatening availability and performance of an information system. Leveraging OLAP techniques, in contrast to traditional static reporting, this is supposed to be accomplished within moderate amount of time and little processing complexity. It is shown how OLAP techniques can help improve understandability and manageability of measurement data and, hence, improve the whole Performance Analysis process.
Abstract: Spatial and mobile computing evolves. This paper
describes a smart modeling platform called “GeoSEMA". This
approach tends to model multidimensional GeoSpatial Evolutionary
and Mobile Agents. Instead of 3D and location-based issues, there
are some other dimensions that may characterize spatial agents, e.g.
discrete-continuous time, agent behaviors. GeoSEMA is seen as a
devoted design pattern motivating temporal geographic-based
applications; it is a firm foundation for multipurpose and
multidimensional special-based applications. It deals with
multipurpose smart objects (buildings, shapes, missiles, etc.) by
stimulating geospatial agents.
Formally, GeoSEMA refers to geospatial, spatio-evolutive and
mobile space constituents where a conceptual geospatial space model
is given in this paper. In addition to modeling and categorizing
geospatial agents, the model incorporates the concept of inter-agents
event-based protocols. Finally, a rapid software-architecture
prototyping GeoSEMA platform is also given. It will be
implemented/ validated in the next phase of our work.
Abstract: In this paper we propose a new approach to constructing the Delaunay Triangulation and the optimum algorithm for the case of multidimensional spaces (d ≥ 2). Analysing the modern state, it is possible to draw a conclusion, that the ideas for the existing effective algorithms developed for the case of d ≥ 2 are not simple to generalize on a multidimensional case, without the loss of efficiency. We offer for the solving this problem an effective algorithm that satisfies all the given requirements. But theoretical complexity of the problem it is impossible to improve as the Worst - Case Optimality for algorithms of solving such a problem is proved.
Abstract: The paper proposes a unified model for multimedia data retrieval which includes data representatives, content representatives, index structure, and search algorithms. The multimedia data are defined as k-dimensional signals indexed in a multidimensional k-tree structure. The benefits of using the k-tree unified model were demonstrated by running the data retrieval application on a six networked nodes test bed cluster. The tests were performed with two retrieval algorithms, one that allows parallel searching using a single feature, the second that performs a weighted cascade search for multiple features querying. The experiments show a significant reduction of retrieval time while maintaining the quality of results.
Abstract: Ant colony optimization (ACO) and its variants are
applied extensively to resolve various continuous optimization
problems. As per the various diversification and intensification
schemes of ACO for continuous function optimization, researchers
generally consider components of multidimensional state space to
generate the new search point(s). However, diversifying to a new
search space by updating only components of the multidimensional
vector may not ensure that the new point is at a significant distance
from the current solution. If a minimum distance is not ensured
during diversification, then there is always a possibility that the
search will end up with reaching only local optimum. Therefore, to
overcome such situations, a Mahalanobis distance-based
diversification with Nelder-Mead simplex-based search scheme for
each ant is proposed for the ACO strategy. A comparative
computational run results, based on nine nonlinear standard test
problems, confirms that the performance of ACO is improved
significantly with the integration of the proposed schemes in the
ACO.
Abstract: Expression data analysis is based mostly on the
statistical approaches that are indispensable for the study of
biological systems. Large amounts of multidimensional data resulting
from the high-throughput technologies are not completely served by
biostatistical techniques and are usually complemented with visual,
knowledge discovery and other computational tools. In many cases,
in biological systems we only speculate on the processes that are
causing the changes, and it is the visual explorative analysis of data
during which a hypothesis is formed. We would like to show the
usability of multidimensional visualization tools and promote their
use in life sciences. We survey and show some of the
multidimensional visualization tools in the process of data
exploration, such as parallel coordinates and radviz and we extend
them by combining them with the self-organizing map algorithm. We
use a time course data set of transitional cell carcinoma of the bladder
in our examples. Analysis of data with these tools has the potential to
uncover additional relationships and non-trivial structures.
Abstract: Among the numerous economic evaluation techniques currently available, Multi-criteria Spatial Analysis lends itself to solving localization problems of property complexes and, in particular, production plants. The methodology involves the use of Geographical Information Systems (GIS) and the mapping overlay technique, which overlaps the different information layers of a territory in order to obtain an overview of the parameters that characterize it. This first phase is used to detect possible settlement surfaces of a new agglomeration, subsequently selected through Analytic Hierarchy Process (AHP), so as to choose the best alternative. The result ensures the synthesis of a multidimensional profile that expresses both the quantitative and qualitative effects. Each criterion can be given a different weight.
Abstract: A novel PDE solver using the multidimensional wave
digital filtering (MDWDF) technique to achieve the solution of a 2D
seismic wave system is presented. In essence, the continuous physical
system served by a linear Kirchhoff circuit is transformed to an
equivalent discrete dynamic system implemented by a MD wave
digital filtering (MDWDF) circuit. This amounts to numerically
approximating the differential equations used to describe elements of a
MD passive electronic circuit by a grid-based difference equations
implemented by the so-called state quantities within the passive
MDWDF circuit. So the digital model can track the wave field on a
dense 3D grid of points. Details about how to transform the continuous
system into a desired discrete passive system are addressed. In
addition, initial and boundary conditions are properly embedded into
the MDWDF circuit in terms of state quantities. Graphic results have
clearly demonstrated some physical effects of seismic wave (P-wave
and S–wave) propagation including radiation, reflection, and
refraction from and across the hard boundaries. Comparison between
the MDWDF technique and the finite difference time domain (FDTD)
approach is also made in terms of the computational efficiency.
Abstract: A complex valued neural network is a neural network, which consists of complex valued input and/or weights and/or thresholds and/or activation functions. Complex-valued neural networks have been widening the scope of applications not only in electronics and informatics, but also in social systems. One of the most important applications of the complex valued neural network is in image and vision processing. In Neural networks, radial basis functions are often used for interpolation in multidimensional space. A Radial Basis function is a function, which has built into it a distance criterion with respect to a centre. Radial basis functions have often been applied in the area of neural networks where they may be used as a replacement for the sigmoid hidden layer transfer characteristic in multi-layer perceptron. This paper aims to present exhaustive results of using RBF units in a complex-valued neural network model that uses the back-propagation algorithm (called 'Complex-BP') for learning. Our experiments results demonstrate the effectiveness of a Radial basis function in a complex valued neural network in image recognition over a real valued neural network. We have studied and stated various observations like effect of learning rates, ranges of the initial weights randomly selected, error functions used and number of iterations for the convergence of error on a neural network model with RBF units. Some inherent properties of this complex back propagation algorithm are also studied and discussed.
Abstract: This study examined the underlying dimensions of
brand equity in the chocolate industry. For this purpose, researchers
developed a model to identify which factors are influential in
building brand equity. The second purpose was to assess brand
loyalty and brand images mediating effect between brand attitude,
brand personality, brand association with brand equity. The study
employed structural equation modeling to investigate the causal
relationships between the dimensions of brand equity and brand
equity itself. It specifically measured the way in which consumers’
perceptions of the dimensions of brand equity affected the overall
brand equity evaluations. Data were collected from a sample of
consumers of chocolate industry in Iran. The results of this empirical
study indicate that brand loyalty and brand image are important
components of brand equity in this industry. Moreover, the role of
brand loyalty and brand image as mediating factors in the intention of
brand equity are supported. The principal contribution of the present
research is that it provides empirical evidence of the
multidimensionality of consumer based brand equity, supporting
Aaker´s and Keller´s conceptualization of brand equity. The present
research also enriched brand equity building by incorporating the
brand personality and brand image, as recommended by previous
researchers. Moreover, creating the brand equity index in chocolate
industry of Iran particularly is novel.
Abstract: Biological, psychological and social experiences and
perceptions of healthcare services in patients medically diagnosed of
coronary heart disease were investigated using a sample of 10
participants whose responses to the in-depth interview questions
were analyzed based on inter-and-intra-case analyses. The results
obtained revealed that advancing age, single status, divorce and/or
death of spouse and the issue of single parenting negatively impacted
patients- biopsychosocial experiences. The patients- experiences of
physical signs and symptoms, anxiety and depression, past serious
medical conditions, use of self-prescribed medications, family
history of poor mental/medical or physical health, nutritional
problems and insufficient physical activities heightened their risk of
coronary attack. Collectivist culture served as a big source of relieve
to the patients. Patients- temperament, experience of different
chronic life stresses/challenges, mood alteration, regular drinking,
smoking/gambling, and family/social impairments compounded their
health situation. Patients were satisfied with the biomedical services
rendered by the healthcare personnel, whereas their psychological
and social needs were not attended to. Effective procedural treatment
model, a holistic and multidimensional approach to the treatment of
heart disease patients was proposed.
Abstract: In this study, participants with adjustment disorder with depressed mood (aged 18-54 years) with mild depression (N=18), severe depression (N=12) were compared with healthy controls (N=20) on the Multidimensional Aptitude Battery (MAB) a cognitive performance test. Using One Way Analysis of Variance and Matched Sample t-test. The results of the analysis shows that severely depressed participants performed poorly on the cognitive performance test relative to controls, however there were no significant differences on the cognitive performance test scores between the severely depressed and the mildly depressed. In addition, performance on the non-verbal performance subtest was poorer than that of the verbal subtest, suggesting that depression affects the executive functions of the person.
Abstract: The data is available in abundance in any business
organization. It includes the records for finance, maintenance,
inventory, progress reports etc. As the time progresses, the data keep
on accumulating and the challenge is to extract the information from
this data bank. Knowledge discovery from these large and complex
databases is the key problem of this era. Data mining and machine
learning techniques are needed which can scale to the size of the
problems and can be customized to the application of business. For
the development of accurate and required information for particular
problem, business analyst needs to develop multidimensional models
which give the reliable information so that they can take right
decision for particular problem. If the multidimensional model does
not possess the advance features, the accuracy cannot be expected.
The present work involves the development of a Multidimensional
data model incorporating advance features. The criterion of
computation is based on the data precision and to include slowly
change time dimension. The final results are displayed in graphical
form.
Abstract: In the multi objective optimization, in the case when generated set of Pareto optimal solutions is large, occurs the problem to select of the best solution from this set. In this paper, is suggested a method to order of Pareto set. Ordering the Pareto optimal set carried out in conformity with the introduced distance function between each solution and selected reference point, where the reference point may be adjusted to represent the preferences of a decision making agent. Preference information about objective weights from a decision maker may be expressed imprecisely. The developed elicitation procedure provides an opportunity to obtain surrogate numerical weights for the objectives, and thus, to manage impreciseness of preference. The proposed method is a scalable to many objectives and can be used independently or as complementary to the various visualization techniques in the multidimensional case.