ppxscal/embedding_network huggingface dataset:
Reviewing Data Visualization: an Analytical Taxonomical Study
Authors: José F. Jr. Rodrigues, Agma J. M. Traina, Maria Cristina F. de Oliveira, Caetano Traina,  Jr.
Venue: Proceedings of the conference on Information Visualization (2006)

This paper presents an analytical taxonomy that can suitably describe, rather than simply classify, techniques for data presentation. Unlike previous works, we do not consider particular aspects of visualization techniques, but their mechanisms and foundational vision perception. Instead of just adjusting visualization research to a classification system, our aim is to better understand its process. For doing so, we depart from elementary concepts to reach a model that can describe how visualization techniques work and how they convey meaning.
Tapping the power of text mining
Authors: Weiguo Fan, Linda Wallace, Stephanie Rich, Zhongju Zhang
Venue: Communications of the ACM (2006)

Sifting through vast collections of unstructured or semistructured data beyond the reach of data mining tools, text mining tracks information sources, links isolated concepts in distant documents, maps relationships between activities, and helps answer questions.
Maximum likelihood estimation for array processing in colored noise
Authors: V. Nagesha, S. Kay
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04 (1993)

The problem of direction of arrival estimation of multiple sources in noise with unknown covariance is considered. The noise is modeled as a spatial autoregressive process with unknown parameters. An approximate maximum likelihood estimator (MLE) of the signal and noise parameters is derived. It requires numerical maximization of a compressed likelihood function over the unknown arrival angles. Analytical expressions for the MLEs of the signal covariance and the autoregressive (AR) parameters are given.
On-the-fly TCTL model checking for Time Petri Nets using state class graphs
Authors: Rachid Hadjidj, Hanifa Boucheneb
Venue: Proceedings of the Sixth International Conference on Application of Concurrency to System Design (2006)

This paper shows how to efficiently model check a subclass of TCTL properties for the TPN model, using the so called state class method. The idea is to put the TPN model under analysis in parallel with a special TPN to capture relevant time events to verify a timed property. The special TPN, we call Alarm-clock, has two transitions, with special firing priorities, which can be set to fire at special moments. The verification of a timed property is based on a forward on-the-fly exploration technique, augmented with an abstraction by inclusion to further attenuate the state explosion problem. We prove the decidability of our verification technique for bounded TPN models and give some experimental results to show the effectiveness of our verification technique.
Modular generic programming with extensible superclasses
Authors: Martin Sulzmann, Meng Wang
Venue: Proceedings of the 2006 ACM SIGPLAN workshop on Generic programming (2006)

"Generics for the Masses" (GM) and "Scrap your Boilerplate" (SYB) are generic programming approaches based on some inenious applications of Haskell type classes. To achieve modularity, the GM and SYB approach have been extended by using some experimental language extensions such as abstraction over type classes and recursive instances. Hence, the type class encodings behind the GM and SYB approach become less practical and harder to understand.We show that none of these type class features are necessary if we use the single feature of extensible superclasses, the complement of subclass extension. We formalize type classes with extensible superclasses as the combination of a previously introduced type-passing translation scheme and a general type class framework. Our results shed some new light on the use of type classes to support generic programming.
An empirical analysis and comparison of random testing techniques
Authors: Johannes Mayer, Christoph Schneckenburger
Venue: Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering (2006)

Testing with randomly generated test inputs, namely Random Testing, is a strategy that has been applied succefully in a lot of cases. Recently, some new adaptive approaches to the random generation of test cases have been proposed. Whereas there are many comparisons of Random Testing with Partition Testing, a systematic comparison of random testing techniques is still missing. This paper presents an empirical analysis and comparison of all random testing techniques from the field of Adaptive Random Testing (ART). The ART algorithms are compared for effectiveness using the mean F-measure, obtained through simulation and mutation analysis, and the P-measure. An interesting connection between the testing effectiveness measures F-measure and P-measure is described. The spatial distribution of test cases is determined to explain the behavior of the methods and identify possible shortcomings. Besides this, both the theoretical asymptotic runtime and the empirical runtime for each method are given.
A discussion of leaky prediction based scalable coding
Authors: Yuxin Liu, Zhen Li, P. Salama, E. J. Delp
Venue: Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1 (2003)

In this paper, we focus on the leaky prediction based scalable coding (LPSC) structure and present a general framework for LPSC. We demonstrate the similarity between LPSC and motion compensation based multiple description coding scheme. We show that since the information contained in the enhancement layer in LPSC is actually a mismatch between two descriptions for each frame, it cannot be guaranteed that the enhancement layer always achieves superior reconstruction quality beyond that achieved by the base layer. We derive three reconstructions for each frame under the LPSC framework, and propose a maximum-likelihood (ML) estimation scheme for LPSC video reconstruction at the decoder. This generally achieves superior decoded video quality than both the enhancement layer and the base layer.
Coherent atomic beam splitter control for nano-scale atom wave packet lithography
Authors: S. V. Borisenok, Y. V. Rozhdestvensky
Venue: Proceedings of the 2003 International Conference on Physics and Control - Volume 3 (2003)

Atomic beam splitter needs for the practical realization of nano-scale lithography by coherent scattering of the atoms. We consider the different types of atomic beam splitters, which could be used for the creation of the space structures with nano-scale period. We concentrate to a few main schemes of an interaction between an atom beam and optical radiation, which can produce a large angle splitting of atom beam into only two main momentum components: multi Bragg scattering an atom wave packet in standing wave; coherent splitting by scattering of an atom wave packet in standing wave with modulated intensity, when the intensity of the standing wave changes harmonically. We discuss also the experimental possibility of an atom selection in internal states for obtaining of space periodical structure with high clearance.
Phase multistability of self-modulated oscillations
Authors: A. M. Nekrasov, O. V. Sosnovtseva
Venue: Proceedings of the 2003 International Conference on Physics and Control - Volume 2 (2003)

The paper examines the type of multistability that can arise in the coupling of two oscillators when the systems individually display a selfmodulation being the typical waveform when fast dynamics modulated by a slow dynamics. The investigation is based on a phase reduction method and on the calculation of phase maps for vanishing and finite coupling strengths, respectively. Various phase-locked patterns are observed. In the presence of a frequency mismatch, the two-parameter bifurcation analysis reveals a set of synchronization regions inserted one into the other. Numerical examples using a generator with inertial nonlinearity and a biologically motivated model of nephron autoregulation are given.
Achieving structural and composable modeling of complex systems
Authors: David I. August, Sharad Malik, Li-Shiuan Peh, Vijay Pai, Manish Vachharajani, Paul Willmann
Venue: International Journal of Parallel Programming (2005)

This paper describes a recently released, structural and composable modeling system called the Liberty Simulation Environment (LSE). LSE automatically constructs simulators from system descriptions that closely resemble the structure of hardware at the chosen level of abstraction. Component-based reuse features allow an extremely diverse range of complex models to be built easily from a core set of component libraries. This paper also describes the makeup and initial experience with a set of such libraries currently undergoing refinement. With LSE and these soon-to-be-released component libraries, students will be able to learn about systems in a more intuitive fashion, researchers will be able to collaborate with each other more easily, and developers will be able to rapidly and meaningfully explore novel design candidates.
Piecewise linear autoregressions through threshold decomposition
Authors: E. A. Heredia, G. R. Arce
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04 (1993)

In practical applications, signals very often come from nonlinear systems and exhibit features such as limit cycles, bifurcations, time irreversibility, and others that cannot be reproduced by linear models. The authors introduce a method to develop piecewise linear approximations to nonlinear autoregressive processes. The method is based on the threshold decomposition algorithm which can provide multivariate linear-spline characterization of functions. Signal models obtained through piecewise linear autoregressions (PARs) constitute better and more robust representations of unknown nonlinear operators, as is shown using examples from time series prediction.
Electronic commerce and the strategic management of deep-sea container shipping companies: an exploratory survey analysis
Authors: Eladio Peñaloza, Mary Brooks, Sunny Marche
Venue: Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet (2006)

Despite the increased adoption of electronic commerce (EC) among container shipping companies, the role of EC practice on their strategic management is poorly understood. This study takes an interdisciplinary approach to examine the uses, motivations, barriers and strategic relevance of EC in the container shipping industry in 1992 and 2002. To test five hypotheses, a 41-question survey was sent to 297 shipping companies, yielding an 11.1% response rate. The research found that the role of EC became more strategic and relevant for the identification of business goals. This change, however, could not be explained solely by the desire of companies to exploit EC externally with clients, but also by their ability to master EC internally. The role of EC was more strategic in companies where service is a key element of the company's competitive strategy and EC is regarded as a competitive necessity.
Combining kernels for classification
Authors: Tony Jebara
Venue:  (2006)

Drawing inferences from large, heterogeneous data sets requires a theoretical framework that is capable of representing, for example, DNA and protein sequences, protein structures, microarray expression data, various types of interaction networks, etc. Recently, a class of algorithms known as kernel methods has emerged as a powerful framework for combining diverse types of data. The power and current popularity of kernel methods stem in part from their ability to handle diverse forms of structured inputs, including vectors, graphs, and strings. The support vector machine (SVM) algorithm is the most popular kernel method, due to its theoretical underpinnings and strong empirical performance on a wide variety of classification tasks. Recently, several methods have been proposed for combining kernels from heterogeneous data sources. Specifically, several recently described extensions allow the SVM to assign relative weights to various data sets, depending upon their utilities in performing a given classification task. However, all of these methods produce stationary combinations; i.e., the relative weights of the various kernels do not vary among input examples. In this work, we describe, implement and validate a method for combining multiple kernels in a nonstationary fashion, where the kernel function combination varies depending on the input. The approach uses a large-margin latent-variable generative probabilistic model within the maximum entropy discrimination (MED) framework. In this method, parameter estimation is rendered tractable by variational bounding and an iterative optimization procedure. Here, we propose an MED Hilbert space Gaussian mixture model, in which each component is implicitly mapped via a Mercer kernel function, and we show that the support vector machine is a special case of this model. The mixture model allows us to combine a given set of kernels in a nonlinear and nonstationary manner, while avoiding overfitting by regularization. We also derive an efficient sequential minimal optimization algorithm for discriminative parameter estimation. We empirically investigate the performance of the SVM and its variants on numerous multi-kernel learning tasks, ranging from illustrative synthetic data sets, to commonly used benchmark data sets, to the real-world computational biology problem of protein function annotation. In the majority of cases, and without any particular tuning of the algorithm, our new technique outperforms existing methods.
Methods for achieving susceptibility to consensus for conflict profiles
Authors: Ngoc Thanh Nguyen
Venue: Journal of Intelligent Fuzzy Systems: Applications in Engineering and Technology (2006)

A conflict profile represents a conflict situation and is understood as a set of opinions of agents or experts, who are asked to solve some common problem. Because of the autonomy of experts or agents the elements of a conflict profile often differ from each other. Thus their reconciling is needed for determining a proper opinion or solution of the problem. This proper opinion or solution is called a consensus. Susceptibility to consensus is a very important feature of conflict situations. One should say that a conflict situation just "incubates" to a consensus if it is possible to determine an agreement which is suitable for all conflict participants. We say that such conflict is susceptible to consensus. In this paper we present two methods for achieving susceptibility for conflict profiles. The first of them relies on modification of a profile by adding o removing elements so that it becomes susceptible to consensus. The second method allows determining a fuzzy weight function for the profile elements to achieve a new consensus susceptible profile. Some algorithms in the second methods are also worked out.
Born to Compute
Authors: Francis Sullivan
Venue: Computing in Science and Engineering (2006)

By now, almost all readers of this magazine have come in contact with Sudoku puzzles. Many of you have already become addicted, and I'd guess a few brave souls have even started a 12-step plan in a desperate attempt to kick the habit and become useful members of society once again. Why is that?
Design of Gabor Filter Banks for Iris Recognition
Authors: Chia-Te Chou, Sheng-Wen Shih, Duan-Yu Chen
Venue: Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia (2006)

In this paper, we propose a systematic approach to design Gabor filter banks for iris feature extraction. The spectra of normalized iris images were analyzed and the most informative band (MIB) was located. When tuning the filter parameters, the pass bands of the filter banks are confined to be within the MIB. It turns out that with the MIB constraint the number of orientation parameters of the Gabor filters can be reduced to one. Furthermore, we show that the spatial locations of the Gabor filters can be arbitrarily and uniformly selected provided that the conditions of the Nyquist sampling theorem are satisfied. The main difficulty of designing filter banks for iris feature extraction is that there are too many free parameters to be determined, e.g., there will be 6N parameters to be designed for N filters. However, after analyzing the possible factors influencing the iris feature extraction process, we found that the free parameters of the Gabor filter banks can be reduced from 6N to three. By using the genetic algorithm to tune the filter parameters, the performance of our recognition system has been gradually improved. The experimental results show that the best equal error rate of our system is about 0.05% computed with the CASIA database.
A distributed approach for assistive service provision in pervasive environment
Authors: Mahmoud Ghorbel, Mounir Mokhtari, Stéphane Renouard
Venue: Proceedings of the 4th international workshop on Wireless mobile applications and services on WLAN hotspots (2006)

In this paper, we propose assistive service provision architecture in pervasive environments. This architecture is based on a distributed approach and ensures service design, deployment and discovery aspects. In order to perform those aspects, several constraints are supported by this architecture such as the context of the pervasive environment, the user profile and the terminal profile. Delivered services are dedicated to dependent people (elderly and people with disabilities). Our strategy is based on our previous work on smart home for people with disabilities. We also present the OSGi-based implementation of our architecture. This implementation is a first step to experiment and validate our approach. We are aiming at having evaluations in real conditions The rapid development of information and communication technologies is opening a large variety of new services and aids not only for valid people, but also for dependent people due to physical/cognitive restriction or the elderly. Combining technical aids and mobile technology allows those people to benefit from an independent living in both usual and temporary living environments.
Refinement verification of the lazy caching algorithm
Authors: Wim H. Hesselink
Venue: Acta Informatica (2006)

The lazy caching algorithm of Afek et&#x00a0;al. (ACM Trans. Program. Lang. Syst. 15, 182&#x2013;206, 1993) is a protocol that allows the use of local caches with delayed updates. It results in a memory model that is not atomic (linearizable) but only sequentially consistent as defined by Lamport. In Distributed Computing 12 (1999), specifying and proving sequential consistency for the lazy caching algorithm was made into a benchmark for verification models. The present note contains such a specification and proof. It provides a simulation from the implementation to the abstract specification. The concrete verification only relies on the state space and the next-state relation. All behavioural aspects are treated in theories independent of the specific algorithm. The proofs of the underlying theories and of the concrete algorithm have been verified with the proof assistant PVS.
Students' perception of knowledge activation on a guided collaborative problem solving organizer
Authors: Wei-Chen Hung, James Lockard
Venue: Proceedings of the 7th international conference on Learning sciences (2006)

While problem solving may seem like an intuitive process as demonstrated by experts, there are steps in the process that are clearly identifiable and can be systematically applied. To understand a problem and to develop effective problem-solving skills, students must learn to identify learning issues and locate, evaluate, and apply resources relevant to those issues. The purpose of this study was to investigate the design and impact of an innovative learning and performance-support system to facilitate students' development of metacognition and problem solving skills. Detailed descriptions of the theoretical and design frameworks are provided and discussed. A field test including survey and group interviews was conducted to initially study the impact of this innovative design approach. Findings related to the design and how the system supports problem solving activities are discussed, and future research suggestions are offered.
Data Mining Application to Syndrome Differentiation in Traditional Chinese Medicine
Authors: Wang Bo, Zhang Ming-Wei, Zhang Bin, Wei Wei-Jie
Venue: Proceedings of the Seventh International Conference on Parallel and Distributed Computing, Applications and Technologies (2006)

Traditional Chinese Medicine is special for western people. Its diagnosis and treatment depend on syndrome. Syndrome is composed of some symptoms, and each symptom demonstrates different values in different syndrome. In this paper, we first describe approach of syndrome and symptom in TCM. Then, a hierarch model of syndrome differentiation in Traditional Chinese Medicine is proposed. According to the model, data mining model is designed to complete it. Given special data mining schema and character of high dimensional data sets, we introduce hypergraph in cluster and attributes combination in association procedure. Finally, the result of model in syndrome differentiation of Traditional Chinese Medicine is given.
ITSUMO: an Intelligent Transportation System for Urban Mobility
Authors: Bruno Castro da Silva, Robert Junges, Denise de Oliveira, Ana L. C. Bazzan
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

This paper presents an overview of ITSUMO, a microscopic traffic simulator based on cellular automata. The implementation uses agent technologies with a bottom-up philosophy in mind. We give an overview of the system and some details of its modules (data, simulation, driver and information/visualization).
Globally Optimal Grouping for Symmetric Boundaries
Authors: Joachim S. Stahl, Song Wang
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (2006)

Many natural and man-made structures have a boundary that shows certain level of bilateral symmetry, a property that has been used to solve many computer-vision tasks. In this paper, we present a new grouping method for detecting closed boundaries with symmetry. We first construct a new type of grouping token in the form of a symmetric trapezoid, with which we can flexibly incorporate various boundary and region information into a unified grouping cost function. Particularly, this grouping cost function integrates Gestalt laws of proximity, closure, and continuity, besides the desirable boundary symmetry. We then develop a graph algorithm to find the boundary that minimizes this grouping cost function in a globally optimal fashion. Finally, we test this method by some experiments on a set of natural and medical images.