ppxscal/embedding_network huggingface dataset:
Distribution-Free Learning of Bayesian Network Structure
Authors: Xiaohai Sun
Venue: Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II (2008)

We present an independence-based method for learning Bayesian network (BN) structure without making any assumptions on the probability distribution of the domain. This is mainly useful for continuous domains. Even mixed continuous-categorical domains and structures containing vectorial variables can be handled. We address the problem by developing a non-parametric conditional independence test based on the so-called kernel dependence measure, which can be readily used by any existing independence-based BN structure learning algorithm. We demonstrate the structure learning of graphical models in continuous and mixed domains from real-world data without distributional assumptions. We also experimentally show that our test is a good alternative, in particular in case of small sample sizes, compared to existing tests, which can only be used in purely categorical or continuous domains.
The Quadratic Extension Extractor for (Hyper)Elliptic Curves in Odd Characteristic
Authors: Reza Rezaeian Farashahi, Ruud Pellikaan
Venue: Proceedings of the 1st international workshop on Arithmetic of Finite Fields (2007)

We propose a simple and efficient deterministic extractor for the (hyper)elliptic curve $\mathcal{C}$, defined over $\mathbb{F}_{q^2}$, where q is some power of an odd prime. Our extractor, for a given point P on $\mathcal{C}$, outputs the first $\mathbb{F}_{q}$-coefficient of the abscissa of the point P. We show that if a point P is chosen uniformly at random in $\mathcal{C}$, the element extracted from the point P is indistinguishable from a uniformly random variable in $\mathbb{F}_q$.
Computer-Assisted Work Process Modeling in Chemical Engineering
Authors: M. Theißen, R. Hai, W. Marquardt
Venue: Collaborative and Distributed Chemical Engineering. From Understanding to Substantial Design Process Support: Results of the IMPROVE Project (2008)

The transfer project aims at the integrative modeling, analysis, and improvement of a variety of work processes in the life cycle of a chemical product across disciplinary and institutional boundaries. A methodology is elaborated for the creation of conceptual, coarse grained models of work processes originating from empirical field studies in industry and their subsequent enrichment and formalization for computer-based interpretation and processing.
Accessibility Standards Are Not Always Enough: The Development of the Accessibility Passport
Authors: Simon Ball, John Sewell
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

The Accessibility Passport has been developed to enable a dialogue between the specifier, developer, tester (including teacher/tutor) and user (student) of software or learning objects in development. By stating the original brief, the specifier can express what accessibility requirements were, or were not, expected to be included. The developer can outline any accessibility features built in and any user testing undertaken. Crucially users of the software or learning object (teachers and learners) can communicate their experiences back into the development process for future modifications or adaptations.
Interpolation of infinite sequences by entire functions
Authors: Jean-Antoine Désidéri
Venue: Applied Numerical Mathematics (2008)

The study was originally motivated by a theoretical question raised by Warming and Hyett in a famous publication on the Modified Equation Approach [R.F. Warming, B.J. Hyett, The modified equation approach to the stability and accuracy analysis of finite-difference methods, J. Comput Phys. 14 (1974) 159-179]. Their classical accuracy analysis of a finite-difference method applied to a time-dependent problem implicitly relies on the assumption that a function interpolating the numerical values can be expanded, over an indefinite domain, in Taylor's series of the independent variables x and t. Here, we establish constructively that the problem of interpolation of an arbitrary infinite sequence of real numbers by an entire function of x (and possibly t) admits uncountably many solutions. In the case of a single variable, if the values are bounded, the interpolant can be made bounded, and all its derivatives bounded. Besides, the construction is generalized to the interpolation of the values of the function and its derivatives up to an arbitrarily prescribed order (Hermitian interpolation). The proposed interpolant depends on a free parameter @l, and its behavior as @l varies is illustrated by a numerical example.
Extending virtual organizations to improve trust mechanisms
Authors: Ramón Hermoso, Roberto Centeno, Holger Billhardt, Sascha Ossowski
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

Virtual Organizations (VOs) are becoming an increasingly important research topic in the field of Multi-Agent Systems (MAS). The problem of selecting suitable counterparts to interact with is of particular relevance for agents belonging to a VO. This issue has been extensively investigated, applying probability or cognitive approaches, but very few focus has been given to the use of internal organizational structures and the improvement they can provide. In this paper we analyze how organizational structures can support the agent selection process based an trust mechanisms. Furthermore, we present a way to extend VOs automatically (e.g., their role taxonomies) by detecting and identifying new roles. We show that such extensions lead to an improvement of the agents' decisions when employing trust mechanisms that take advantage of organizational structures.
Facial animation by optimized blendshapes from motion capture data
Authors: Xuecheng Liu
Venue: Computer Animation and Virtual Worlds (2008)

This paper presents a labor-saving method to construct optimal facial animation blendshapes from given blendshape sketches and facial motion capture data. At first, a mapping function is established between target &ldquo;Marker Face&rdquo; and performer's face by RBF interpolating selected feature points. Sketched blendshapes are transferred to performer's &ldquo;Marker Face&rdquo; by using motion vector adjustment technique. Then, the blendshapes of performer's &ldquo;Marker Face&rdquo; are optimized according to the facial motion capture data. At last, the optimized blendshapes are inversely transferred to target facial model. Apart from that, this paper also proposes a method of computing blendshape weights from facial motion capture data more accurately. Experiments show that expressive facial animation can be acquired. Copyright &copy; 2008 John Wiley & Sons, Ltd.
On sharp inequalities of Simpson type and Ostrowski type in two independent variables
Authors: Zhongxue Lü
Venue: Computers Mathematics with Applications (2008)

In this paper, two sharp inequalities are derived. The first is a sharp Simpson inequality and the second is a sharp inequality of Ostrowski type in two independent variables.
Diversity Maintenance Mechanism for Multi-Objective Genetic Algorithms Using Clustering and Network Inversion
Authors: Tomoyuki Hiroyasu, Kenji Kobayashi, Masashi Nishioka, Mitsunori Miki
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

One of the major issues in applying multi-objective genetic algorithms to real-world problems is how to reduce the large number of evaluations. The simplest approach is a search with a small population size. However, the diversity of solutions is often lost with such a search. To overcome this difficulty, this paper proposes a diversity maintenance mechanism using clustering and Network Inversion that is capable of preserving diversity by relocating solutions. In addition, the proposed mechanism adopts clustering of training data sets to improve the accuracy of relocation. The results of numerical experiments on test functions and diesel engine emission and fuel economy problems showed that the proposed mechanism provided solutions with high diversity even when the search was performed with a small number of solutions.
Adaptive User Modelling and Recommendation in Constrained Physical Environments
Authors: Fabian Bohnert
Venue: Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems (2008)

Visitors to physical educational environments, such as museums, are often overwhelmed by the information available in the space they are exploring. They are confronted with the challenge of finding personally interesting items to view in the available time. Electronic mobile guides can provide guidance and point to relevant information by identifying and recommending items that match a visitor's interests. However, recommendation generation in physical spaces has challenges of its own. Factors such as the spatial layout of the environment and suggested order of item access must be taken into account, as they constrain the recommendation process. This research investigates adaptive user modelling and personalisation approaches that consider such and other constraints.
On Detection of Malicious Users Using Group Testing Techniques
Authors: My Tra Thai, Ying Xuan, Incheol Shin, Taieb Znati
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems (2008)

Despite decades of research, there have not been developed concrete defense solutions for most of current attacks to Internet services, let alone new attack types. An essential problem to overcome is that malicious traffic can be similar to legitimate ones. Thus a more fundamental model which should be based on the overall performance of servers/subnets without inspecting each traffic must be remedied. Based on this observation, we propose a novel system framework, called Detection of Malicious Users (DMU) which attempts to solve various attack types. Motivated by DMU, we introduce a new theoretical model, called Size Constraint Group Testing (SCGT). Several algorithms based on SCGT for various networking scenarios are proposed. We also provide several fundamental results on SCGT, revealing some necessary conditions to obtain an O(1) detection time algorithm.
A Cost-Sensitive Approach to Feature Selection in Micro-Array Data Classification
Authors: Andrea Bosin, Nicoletta Dessì, Barbara Pes
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

In analyzing gene expression data from micro-array, a major challenge is the definition of a feature selection criterion to judge the goodness of a subset of features with respect to a particular classification model. This paper presents a cost-sensitive approach feature selection that focuses on two fundamental requirements: (1) the quality of the features in order to promote the classifier accuracy and (2) the cost of computation due to the complexity that occurs during training and testing the classifier. The paper describes the approach in detail and includes a case study for a publicly available micro-array dataset. Results show that the proposed process yields state-of-art performance and uses only a small fraction of features that are generally used in competitive approaches on the same dataset.
Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies
Authors: Nikos Komodakis, Georgios Tziritas, Nikos Paragios
Venue: Computer Vision and Image Understanding (2008)

In this paper we introduce a novel method to address minimization of static and dynamic MRFs. Our approach is based on principles from linear programming and, in particular, on primal-dual strategies. It generalizes prior state-of-the-art methods such as @a-expansion, while it can also be used for efficiently minimizing NP-hard problems with complex pair-wise potential functions. Furthermore, it offers a substantial speedup - of a magnitude 10 - over existing techniques, due to the fact that it exploits information coming not only from the original MRF problem, but also from a dual one. The proposed technique consists of recovering pair of solutions for the primal and the dual such that the gap between them is minimized. Therefore, it can also boost performance of dynamic MRFs, where one should expect that the new pair of primal-dual solutions is closed to the previous one. Promising results in a number of applications, and theoretical, as well as numerical comparisons with the state of the art demonstrate the extreme potentials of this approach.
Stereo audio source separation based on time--frequency masking and multilevel thresholding
Authors: Maximo Cobos, José J. López
Venue: Digital Signal Processing (2008)

Source separation and up-mixing in real commercial music recordings is a challenging problem. In the last few years, some algorithms have provided interesting results, but the problem remains unsolved. In this paper we describe a method for separating the sources present in a two channel mixture based on the panning coefficients used in the stereo mixdown. The sources are separated by estimating time-frequency masks using the multilevel extension of the Otsu thresholding algorithm used in image segmentation. A refinement step is also carried out for extraction and reassignment of inter-source residuals. Examples of application and performance evaluation are also discussed.
The CereVoice Characterful Speech Synthesiser SDK
Authors: Matthew P. Aylett, Christopher J. Pidcock
Venue: Proceedings of the 7th international conference on Intelligent Virtual Agents (2007)

CereProc® Ltd. have recently released a beta version of a commercial unit selection synthesiser featuring XML control of speech style. The system is freely available for academic use and allows fine control of the rendered speech as well as full timings to interface with avatars and other animation.
Faster Algebraic Algorithms for Path and Packing Problems
Authors: Ioannis Koutis
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I (2008)

We study the problem of deciding whether an n-variate polynomial, presented as an arithmetic circuit G, contains a degree k square-free term with an odd coefficient. We show that if G can be evaluated over the integers modulo 2 k + 1 in time t and space s, the problem can be decided with constant probability in O((kn + t)2 k ) time and O(kn + s) space. Based on this, we present new and faster algorithms for two well studied problems: (i) an O *(2 mk ) algorithm for the m-set k-packing problem and (ii) an O *(23k/2) algorithm for the simple k-path problem, or an O *(2 k ) algorithm if the graph has an induced k-subgraph with an odd number of Hamiltonian paths. Our algorithms use poly(n) random bits, comparing to the 2 O(k) random bits required in prior algorithms, while having similar low space requirements.
Improving the Run-Time Performance of Multi-class Support Vector Machines
Authors: Andrey Sluzhivoy, Josef Pauli, Volker Rölke, Anastasia Noglik
Venue: Proceedings of the 30th DAGM symposium on Pattern Recognition (2008)

In this paper we propose three approaches to speed up the prediction phase of multi-class Support Vector Machines (SVM). For the binary classification the method of partial sum estimation and the method of orthonormalization of the support vector set are introduced. Both methods rely on an already trained SVM and reduce the amount of necessary computations during the classification phase. The predicted result is always the same as when using the standard method. No limitations on the training algorithm, on the kernel function or on the kind of input data are implied. Experiments show that both methods outperform the standard method, though the orthonormalization method delivers significantly better results. For the multi-class classification we have developed the pairwise classification heuristics method, which avoids a lot of unnecessary evaluations of binary classifiers and obtains the predicted class in a shorter time. By combining the orthonormalization method with the pairwise classification heuristics, we show that the multi-class classification can be performed considerably faster compared to the standard method without any loss of accuracy.
Multi-class Protein Fold Recognition Through a Symbolic-Statistical Framework
Authors: Marenglen Biba, Floriana Esposito, Stefano Ferilli, Teresa M. Basile, Nicola Mauro
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

Protein fold recognition is an important problem in molecular biology. Machine learning symbolic approaches have been applied to automatically discover local structural signatures and relate these to the concept of fold in SCOP. However, most of these methods cannot handle uncertainty being therefore not able to solve multiple prediction problems. In this paper we present an application of the symbolic-statistical framework PRISM to a multi-class protein fold recognition problem. We compare the proposed approach to a symbolic-only technique and show that the hybrid framework outperforms the symbolic-only one in terms of predictive accuracy in the multiple prediction problem.
A Globally Asymptotically Stable Plasticity Rule for Firing Rate Homeostasis
Authors: Prashant Joshi, Jochen Triesch
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

How can neural circuits maintain stable activity states when they are constantly being modified by Hebbian processes that are notorious for being unstable? A new synaptic plasticity mechanism is presented here that enables a neuron to obtain homeostasis of its firing rate over longer timescales while leaving the neuron free to exhibit fluctuating dynamics in response to external inputs. Mathematical results demonstrate that this rule is globally asymptotically stable. Performance of the rule is benchmarked through simulations from single neuron to network level, using sigmoidal neurons as well as spiking neurons with dynamic synapses.
Robust Dynamic Human Activity Recognition Based on Relative Energy Allocation
Authors: Nam Pham, Tarek Abdelzaher
Venue: Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems (2008)

This paper develops an algorithm for robust human activity recognition in the face of imprecise sensor placement. It is motivated by the emerging body sensor networks that monitor human activities (as opposed to environmental phenomena) for medical, entertainment, health-and-wellness, training, assisted-living, or entertainment reasons. Activities such as sitting, writing, and walking have been successfully inferred from data provided by body-worn accelerometers. A common concern with previous approaches is their sensitivity with respect to sensor placement. This paper makes two contributions. First, we explicitly address robustness of human activity recognition with respect to changes in accelerometer orientation. We develop a novel set of features based on relative activity-specific body-energy allocation and successfully apply them to recognize human activities in the presence of imprecise sensor placement. Second, we evaluate the accuracy of the approach using empirical data from body-worn sensors.
A Low Complexity Channel Estimation Algorithm Based on Barker Code for IEEE802.11
Authors: Anding Wang, Yuyang Qiu, Lili Lin
Venue: Proceedings of the 2008 ISECS International Colloquium on Computing, Communication, Control, and Management - Volume 02 (2008)

In this paper we propose a computationally efficient channel estimation algorithm for the IEEE802.11 system which is based on the Barker code. The method exploits the correlation results of the Barker code matched filter at the receiver and estimates the channel impulse response with the strong regulatory of them. In order to be optimal, this estimator performs the combination of the initial estimation linearly to correct the results. Moreover the average of multiple symbols is attached to reduce the noise influence. As a result, it has a significantly lower complexity and better tracing capability than previously methods. Simulation results confirm the excellent estimation performance of our method, which is thus an attractive add-on to WLAN receivers in high-mobility scenarios.
Communities in Large Networks: Identification and Ranking
Authors: Martin Olsen
Venue: Algorithms and Models for the Web-Graph: Fourth International Workshop, WAW 2006, Banff, Canada, November 30 - December 1, 2006. Revised Papers (2007)

We study the problem of identifying and ranking the members of a community in a very large network with link analysis only, given a set of representatives of the community. We define the concept of a community justified by a formal analysis of a simple model of the evolution of a directed graph. We show that the problem of deciding whether a non trivial community exists is NP complete. Nevertheless, experiments show that a very simple greedy approach can identify members of a community in the Danish part of the web graph with time complexity only dependent on the size of the found community and its immediate surroundings. The members are ranked with a "local" variant of the PageRank algorithm. Results are reported from successful experiments on identifying and ranking Danish Computer Science sites and Danish Chess pages using only a few representatives. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Semiring Lattice Parsing Applied to CYK
Authors: Salvador España Boquera, Jorge Gorbe Moya, Francisco Zamora Martínez
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I (2007)

Context-Free Grammars play an important role in the pattern recognition research community. Word graphs provide a compact representation of the ambiguous alternatives generated during many pattern recognition, machine translation and other NLP tasks. This paper generalizes the framework for string parsing based on semirings and hypergraphs to the case of lattice parsing. This framework is the basis for the implementation of a parsing interface in a dataflow software architecture where modules send and receive word graphs in a serialized form using a protocol which allows the easy generation, filtering and parsing of word graphs. An implementation of the CYK algorithm is presented as an example. Experimental results are reported to demonstrate the proposed method.
Approximate Control Design for Solar Driven Sensor Nodes
Authors: Clemens Moser, Lothar Thiele, Davide Brunelli, Luca Benini
Venue: Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control (2008)

This paper addresses power management of wireless sensor nodes which receive their energy from solar cells. In an outdoor environment, the future available energy is estimated and used as input to a receding horizon controller. We want to maximize the utility of the sensor application given the time-varying amount of solar energy. In order to avoid real-time optimization, we precompute off-line an explicit state feedback solution. However, it is a well-known problem of the optimal feedback solution that the computational complexity grows very quickly, which is particularly unfavourable for sensor nodes. A new method to derive approximate solutions to a multiparametric linear programming problem is presented. The resulting control laws substantially reduce the on-line complexity in terms of computational and storage demand. We show that a sensor node's performance is not necessary decreased due to suboptimality of the control design.