ppxscal/embedding_network huggingface dataset:
Stability Based Sparse LSI/PCA: Incorporating Feature Selection in LSI and PCA
Authors: Dimitrios Mavroeidis, Michalis Vazirgiannis
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

The stability of sample based algorithms is a concept commonly used for parameter tuning and validity assessment. In this paper we focus on two well studied algorithms, LSI and PCA, and propose a feature selection process that provably guarantees the stability of their outputs. The feature selection process is performed such that the level of (statistical) accuracy of the LSI/PCA input matrices is adequate for computing meaningful (stable) eigenvectors. The feature selection process "sparsifies" LSI/PCA, resulting in the projection of the instances on the eigenvectors of a principal submatrix of the original input matrix, thus producing sparse factor loadings that are linear combinations solely of the selected features. We utilize bootstrapping confidence intervals for assessing the statistical accuracy of the input sample matrices, and matrix perturbation theory in order to relate the statistical accuracy to the stability of eigenvectors. Experiments on several UCI-datasets verify empirically our approach.
Craniofacial Superimposition Based on Genetic Algorithms and Fuzzy Location of Cephalometric Landmarks
Authors: Oscar Ibáñez, Oscar Cordón, Sergio Damas, Jose Santamaría
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

Craniofacial superimposition is the second stage of a complex forensic technique that aims to identify a missing person from a photograph (or video shot) and the skull found. This specific task is devoted to find the most appropriate pose of the skull to be projected onto the photograph. The process is guided by a number of landmarks identified both in the skull (craniometric landmarks) and in the photograph (cephalometric landmarks). In this contribution we extend our previous genetic algorithm-based approach to the problem by considering the uncertainty involved in the location of the cephalometric landmarks. This proposal is tested over two real cases solved by the Physical Anthropology lab at the University of Granada (Spain).
Computer Virus Evolution Model Inspired by Biological DNA
Authors: Yu Zhang, Tao Li, Renchao Qin
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

There has been considerable interest in computer viruses since they appeared. Firstly, computer viruses make software developers pay more attention to security and develop anti-virus technology more carefully. Secondly, computer viruses are vital simulation platforms to study biological viruses. Moreover, computer viruses (a form of artificial life) are helpful to enhance the understanding of the philosophy of life. Therefore, computer viruses and their evolutions are worth thoroughly studying in scientific research sense. To further investigate the evolution of computer viruses, a DNA-based model for computer viruses evolution, which draws inspiration from biological DNA and artificial life, is proposed. The formal definition of computer virus is introduced, and the evolution operators which include selection, mutation, and recombination operator are presented. The simulation experiments were conducted and the results indicate that computer viruses have enormous capabilities of self-propagation and self-evolution.
Building rural wireless networks: lessons learnt and future directions
Authors: David Lloyd Johnson, Kobus Roux
Venue: Proceedings of the 2008 ACM workshop on Wireless networks and systems for developing regions (2008)

Providing connectivity to under-serviced rural areas comes with a unique set of challenges such as the high cost of installing equipment, lack of reliable power, skill shortages and high cost of providing Internet connectivity which is mostly satellite based. The recent emergence of low-cost commodity wireless 802.11 devices and the use of mesh networking as a key enabling technology for rural areas could see a new wave of connectivity in these areas. The paper presents pilot deployments of low-cost wireless rural networks in South Africa and Zambia that are showing very encouraging results in which houses, schools and clinics are connected on shoestring budgets. Some key areas for future development are also discussed such the use of IPv6, power saving mechanisms for battery operated routers, and support for real-time flow over mesh networks.
3D Cerebral Cortical Morphometry in Autism: Increased Folding in Children and Adolescents in Frontal, Parietal, and Temporal Lobes
Authors: Suyash P. Awate, Lawrence Win, Paul Yushkevich, Robert T. Schultz, James C. Gee
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

This paper presents a systematic evaluation of cortical folding, or complexity, in autism. It introduces two novel measures to analyze folding in a specific region of interest, which, unlike traditional measures, produce an intuitive easily-interpretable description of folding and inform the nature of folding change by incorporating local surface-patch orientation. This study reports new findings of increased cortical folding in autistics in the frontal, parietal, and temporal lobes, as compared to controls. These differences are stronger in children than adolescents. The paper validates part of the findings using the new measures based on comparisons with traditional measures. Unlike studies in the literature, this paper reports new findings, via a fully 3D folding analysis on all brain lobes, based on the consensus of virtually all 6 folding measures used (2 new, 4 traditional) via rigorous statistical permutation testing. In these ways, this paper not only strengthens some previous clinical findings, but also extends the state of the art in autism research.
A Multi-cellular Developmental System in Continuous Space Using Cell Migration
Authors: Nicolas Bredeche
Venue: Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats (2008)

This paper introduces a novel multi-cellular developmental system where cells are placed in a continuous space. Cells communicate by diffusing and perceiving substances in the environment and are able to migrate around following affinities with substance gradients. The optimization process is performed using Echo State neural networks on the problem of minimizing tile size variations in the context of a tiling problem. Experimental results show that problem complexity only impacts the number of substances used, rather than the number of cells, which implies some sort of scalability with regards to the size of the phenotype. Symmetry breaking and robustness are addressed by adding noise as an intrinsic property of the model. A (positive) side effect is that the resulting model produces very robust solutions with efficient self-healing behavior in the presence of perturbations never met before.
Performance in Industrial Holonic Systems
Authors: Vadim Ermolayev, Wolf-Ekkehard Matzke
Venue: Proceedings of the 3rd international conference on Industrial Applications of Holonic and Multi-Agent Systems: Holonic and Multi-Agent Systems for Manufacturing (2007)

We are glad to present the papers of the special session on Performance in Industrial Holonic Systems (Pi-HolS 2007) at the 3d International Conference on Industrial Applications of Holonic and Multi-Agent Systems (HoloMAS 2007). The session brought together researchers and practitioners in the areas of holonic and multi-agent systems, knowledge engineering, and performance management to share their views and experience in design, development, deployment, and application of technologies for measuring, assessing and optimizing the performance of industrial systems based on holonic principles.
Labeling Nodes of Automatically Generated Taxonomy for Multi-type Relational Datasets
Authors: Tao Li, Sarabjot S. Anand
Venue: Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery (2008)

Automatic Taxonomy Generation organizes a large dataset into a hierarchical structure so as to facilitate people's navigation and browsing actions. To better summarize the content of each node as well as to reflect the distinctiveness between sibling ones, meaningful labels need to be assigned to all the nodes within a derived taxonomy. Current research only focuses on labeling taxonomies that are built from a corpora of textual documents. In this paper we address the problem of labeling taxonomies built for multi-type relational datasets. A novel measure is proposed to quantitatively evaluate the homogeneity of each node and the heterogeneity of its sibling nodes using information-theoretical techniques, based on which the labels of taxonomic nodes are determined. We perform some experiments on a real dataset to prove the effectiveness of our method.
The equational theory of prebisimilarity over basic CCS with divergence
Authors: Luca Aceto, Silvio Capobianco, Anna Ingolfsdottir, Bas Luttik
Venue: Information Processing Letters (2008)

This paper studies the equational theory of prebisimilarity, a bisimulation-based preorder introduced by Hennessy and Milner in the early 1980s, over basic CCS with the divergent process @W. It is well known that prebisimilarity affords a finite ground-complete axiomatization over this language; this study proves that this ground-complete axiomatization is also complete in the presence of an infinite set of actions. Moreover, in sharp contrast to this positive result, it is shown that prebisimilarity is not finitely based over basic CCS with the divergent process @W when the set of actions is finite and nonempty.
Mining Top-n Local Outliers in Constrained Spatial Networks
Authors: Chongsheng Zhang, Zhongbo Wu, Bo Qu, Hong Chen
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

Outlier mining, also called outlier detection, is a challenging research issue in data mining with important applications as intrusion detection, fraud detection and medical analysis. From the perspective of data, previous work on outlier mining have involved in various types of data such as spatial data, time series data, trajectory data, and sensor data. However, few of them have considered a constrained spatial networks data in which each object must reside or move along a certain edge. In fact, in such special constrained spatial network data environments, previous outlier definitions and the according mining algorithms could work neither properly nor efficiently. In this paper we introduce a new definition of density-based local outlier in constrained spatial networks that considers for each object the outlier-ness with respect to its k nearest neighbors. Moreover , to detect outliers efficiently, we propose a fast cluster-and-bound algorithm that first cluster on each individual edge, then estimate the outlying degree of each cluster and prune those that could not contain top-n outliers, therefore constraining the computation of outliers to only very limited objects. Experiments on synthetic data sets demonstrate the scalability, effectiveness and efficiency of our methods.
Hadamard Conjugation for the Kimura 3ST Model: Combinatorial Proof Using Path Sets
Authors: Michael D. Hendy, Sagi Snir
Venue: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) (2008)

Under a stochastic model of molecular sequence evolution the probability of each possible pattern of a characters is well defined. The Kimura's three-substitution-types (K3ST) model of evolution, allows analytical expression for these probabilities of by means of the Hadamard conjugation as a function of the phylogeny T and the substitution probabilities on each edge of TM . In this paper we produce a direct combinatorial proof of these results, using pathset distances which generalise pairwise distances between sequences. This interpretation provides us with tools that were proved useful in related problems in the mathematical analysis of sequence evolution.
Who Works Together in Agent Coalition Formation?
Authors: Vicki H. Allan, Kevin Westwood
Venue: Proceedings of the 11th international workshop on Cooperative Information Agents XI (2007)

Coalitions are often required for multi-agent collaboration. In this research, we consider tasks that can only be completed with the combined efforts of multiple agents using approaches which are both cooperative and competitive. Often agents forming coalitions determine optimal coalitions by looking at all possibilities. This requires an exponential algorithm and is not feasible when the number of agents and tasks is large. We propose agents use a two step process of first determining the task, and secondly, the agents that will be solicited to help complete the task. We describe polynomial time heuristics for each decision. We measure four different agent types using the described heuristics. We explore diminishing choices and performance under various parameters.
An Efficient Characteristic Method for the Magnetic Induction Equation with Various Resistivity Scales
Authors: Jiangguo (James) Liu
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

In this paper, we develop an efficient characteristic finite element method (FEM) for solving the magnetic induction equation in magnetohydrodynamics (MHD). We carry out numerical experiments on a two dimensional test case to investigate the influence of resistivity at different scales. In particular, our numerical results exhibit how the topological structure and energy of the magnetic field evolve for different scales of resistivity. Magnetic reconnection can also be observed in the numerical experiments.
An adaptive Huber method with local error control, for the numerical solution of the first kind Abel integral equations
Authors: L. K. Bieniasz
Venue: Computing (2008)

In contrast to the existing plethora of adaptive numerical methods for differential and integro-differential equations, there seems to be a shortage of adaptive methods for purely integral equations with weakly singular kernels, such as the first kind Abel equation. In order to make up this deficiency, an adaptive procedure based on the product-integration method of Huber is developed in this work. In the procedure, an a posteriori estimate of the dominant expansion term of the local discretisation error at a given grid node is used to determine the size of the next integration step, in a way similar to the adaptive solvers for ordinary differential equations. Computational experiments indicate that in practice the control of the local errors is sufficient for bringing the true global errors down to the level of a prescribed error tolerance. The lower limit of the acceptable values of the error tolerance parameter depends on the interference of machine errors, and the quality of the approximations available for the method coefficients specific for a given kernel function.
A SIP-Based Session Mobility Management Framework for Ubiquitous Multimedia Services
Authors: Chung-Ming Huang, Chang-Zhou Tsai
Venue: Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing (2008)

This paper proposes a SIP-based session mobility management framework for achieving session handoff that is transparent to the underlying wireless or wired technology between network domains. Session mobility is concerned with a presentation session changing its attachment device from one client device to the other client device and still continuing the presentation session. This concept requires migrating related information of the current service execution, including service content, service data, etc, from one device to another device. Since different devices and their attached networks have different capabilities and performance, media streaming service quality should be adapted when the session handoff occurs. MPEG-21 efforts for multimedia adaptation to bring suitable multimedia services to users. MPEG-21 Digital Item Adaptation provides normative XML descriptions for handling multimedia content adaptation, but does not specify relations to existing technologies for transport mechanisms. Thus, we adopt the MPEG-21 DIA for multimedia session and usage environment description in our proposed SIP-based session mobility management framework.
Plenary lecture VI: adaptive sliding mode control for an interior permanent-magnet synchronous motor
Authors: Arash Kiyoumarsi
Venue: Proceedings of the 10th WSEAS International Conference on Automatic Control, Modelling & Simulation (2008)

An adaptive sliding controller will be proposed for the control of an interior permanent-magnet (IPM) synchronous motor-drive. A backstepping-like design is used to deal with the mismatched-uncertainties problem and the function approximation method is employed to transform the uncertainties into finite combinations of orthonormal basis functions. Adaptive laws are derived based on the Lyapunov-like design.
Computer-aided optimal designs for improving neural network generalization
Authors: Sébastien Issanchou, Jean-Pierre Gauchi
Venue: Neural Networks (2008)

In this article we propose a new insight into the field of feed-forward neural network modeling. We considered the framework of the nonlinear regression models to construct computer-aided D-optimal designs for this class of neural models. These designs can be seen as a particular case of active learning. Classical algorithms are used to construct local approximate and local exact D-optimal designs. We observed that the so-called generalization of a neural network (the equivalent term, ''predictive ability'', is more familiar to statisticians) is improved when the D-efficiency of the chosen ''learning set design'' increases. We thus showed that the D-efficiency criterion can be the basis for a better strategy for the neural network learning phase than the standard uniform random strategy encountered in this field. Our proposition is based on two possible strategies: a One-Step Strategy or a Full Sequential Strategy. Intensive Monte Carlo simulations with an academic example show that the D-optimal ''learning set design'' strategies proposed lead to a substantial improvement in the use of neural network models.
Analyzing PETs for Enterprise Operations
Authors: Stuart Shapiro, Aaron Powell
Venue: Proceedings of the 8th international symposium on Privacy Enhancing Technologies (2008)

Enterprises (large, often highly distributed organizations) in both the private and public sectors are increasingly recognizing the need to comprehensively address privacy risk. This entails, as it does for information security, a systematic combination of people, processes, and technology. However, while establishing roles and processes governing the management of personally identifiable information (PII) can be done fairly readily (assuming availability of the necessary expertise and experience), finding and deploying appropriate enterprise technology-- commercial or open source--is proving more problematic. A technological model targeted at enterprises (i.e., data stewards) differs from one targeted at individuals (i.e., data subjects). However, the privacy-enhancing technology research community has tended to focus more on the latter than on the former. Furthermore, various enterprise technologies exist with capabilities that can support privacy, even if not specifically intended to do so. We have adopted the term privacy-enabling technologies (PETs) to denote the expansiveness of this field.
Detection of Basal Cell Carcinoma Based on Gaussian Prototype Fitting of Confocal Raman Spectra
Authors: Seong-Joon Baek, Aaron Park, Sangki Kang, Yonggwan Won, Jin Young Kim, Seung You Na
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

Confocal Raman spectroscopy is known to have strong potential for providing noninvasive dermatological diagnosis of skin cancer. According to the previous work, various well known methods including maximum a posteriori probability classifier (MAP), linear classifier using minimum squared error (MSE) and multi layer perceptron networks classifier (MLP) showed competitive results for basal cell carcinoma (BCC) detection. The experimental results are hard to interpret, however, since the classifiers uses global features obtained by principal component analysis (PCA). In this paper, we propose a method that can identify which regions of the spectra are discriminating for BCC detection. For the purpose, 5 and 7 Gaussian prototypes were built located on the typical peak position of BCC and normal (NOR) tissue spectra respectively. Every spectrum is approximated by a linear combination of the Gaussian prototypes. Decision tree is then applied to identify which prototypes are important for the detection of BCC. Among 12 prototypes, 5 discriminating prototypes were selected and the associated weights were used as an input feature vector. According to the experiments involving 216 confocal Raman spectra, support vector machines (SVM) gave 97.4% sensitivity, which confirms that the peak regions corresponding to the selected features are significant for BCC detection and the proposed fitting method is effective.
Toward a Rigorous Variation of Coppersmith's Algorithm on Three Variables
Authors: Aurélie Bauer, Antoine Joux
Venue: Proceedings of the 26th annual international conference on Advances in Cryptology (2007)

In 1996, Coppersmith introduced two lattice reduction based techniques to find small roots in polynomial equations. One technique works for modular univariate polynomials, the other for bivariate polynomials over the integers. Since then, these methods have been used in a huge variety of cryptanalytic applications. Some applications also use extensions of Coppersmith's techniques on more variables. However, these extensions are heuristic methods. In the present paper, we present and analyze a new variation of Coppersmith's algorithm on three variables over the integers. We also study the applicability of our method to short RSA exponents attacks. In addition to lattice reduction techniques, our method also uses Gröbner bases computations. Moreover, at least in principle, it can be generalized to four or more variables.
A Stylized Binarization Procedure for Black/White Comics Using HSV Region Extension
Authors: Dong-Sung Ryu, Hwan-Gue Cho
Venue: Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology (2008)

Generally most of the black/white comics color in human's skin as white, while the dark region is filled with the irregular but regular patterns like hatching. Note that it is not enough for simple threshold method to perform this work. In this paper, we propose a simple and straightforward binarization procedure which can generate black/white comics from the video frame image. Our procedure is decoupled into two processes: region extension and binarization. First, the region extension based on mean shift segmentation is responsible to simplify and classify colors on the image. Second, we implement the dynamic binarization method to consider color information in HSV color model. Our novel black/white cartooning procedure was so successful to render comic cuts from a well-known cinema in a resonable time and manual intervention.