ppxscal/embedding_network huggingface dataset:
A Spoken Dialog System Speech Interface Based on a Microphone Array
Authors: Gustavo Esteves Coelho, António Joaquim Serralheiro, João Paulo Neto
Venue: Proceedings of the 8th international conference on Computational Processing of the Portuguese Language (2008)

In this paper we present a Spoken Dialog System (SDS) with a Microphone Array (MA). Our goal is to create a hands-free home automation system with a speech interface to control home devices. The MA interface enables to create ubiquitous speech acquisition for the SDS. The implemented system allows any user --- in any position in a room --- to establish a dialog with a virtual butler that is able to control a wide range of home appliances (room lights, air-conditioner, windows shades and hi-fi features). This virtual butler has a 3D animated face that is, while the dialog is engaged, able to steer to the user's position and respond to his/hers commands with synthesized speech. The presented results show that the MA, as distant talk interface, performs quite well and is a step towards a more realistic human-machine interaction.
Direct Support for Model Checking Abstract State Machines by Utilizing Simulation
Authors: Jörg Beckers, Daniel Klünder, Stefan Kowalewski, Bastian Schlich
Venue: Proceedings of the 1st international conference on Abstract State Machines, B and Z (2008)

This paper presents an approach to model checking abstract state machines (ASMs) without the need for translation of the ASM specification into the modeling language of an existing model checker. Instead, our model checker [mc]square uses the simulation capabilities of CoreASM to build the state space, thereby directly supporting ASMs and circumventing a possible loss of expressiveness in a translation process. This enables our approach to present counterexamples and witnesses directly as sequences of ASM states and at the same time supports the major features of CoreASM like distributed ASMs, n-ary functions or extended rule forms. We show the applicability of this approach in a case study that also reveals possible improvements desirable for minimizing the duration needed for building the state space and its memory consumption.
Low-dimensional representations for the simulation and control of complex dynamics
Authors: Zoran Popovic
Venue:  (2008)

Most work in computer simulation has focused on off-line algorithms where each step can take arbitrarily long to compute. This thesis focuses instead on interactive simulations that run in real-time. Besides running quickly, such simulations must allow interaction, remain stable under unexpected user input, and remain "correct" (in some sense) for unbounded periods of time. Further, biomechanical simulation requires near-instantaneous planning without simulating the future. These requirements motivate the development of fundamentally new simulation algorithms. This thesis approaches the interactive simulation problem by developing new, low-dimensional representations of the phenomena being simulated. These representations perform several functions at once. First, they can correlate many degrees of freedom of the underlying phenomenon, allowing us to represent the system with fewer variables. Second, the reduction is constructed so as to allow rapid simulation or control. Finally, the representations allow us to express correctness constraints. Three examples of such simulations are presented, covering fluids, crowds, and human animation. The fluid model enables large, real-time, detailed flows with continuous user interaction, and can handle moving objects immersed in the flow. The crowd model is based on a fluid-like continuum representation, and naturally exhibits emergent phenomena that have been observed in real crowds. Finally, the human model can automatically compute near-optimal human animations using a low-dimensional basis representation of the planning space.
The Diffusion of Perturbations in a Model of Coupled Random Boolean Networks
Authors: Roberto Serra, Marco Villani, Chiara Damiani, Alex Graudenzi, Annamaria Colacci
Venue: Proceedings of the 8th international conference on Cellular Automata for Reseach and Industry (2008)

Deciphering the influence of the interaction among the constituents of a complex system on the overall behaviour is one of the main goals of complex systems science. The model we present in this work is a 2D square cellular automaton whose of each cell is occupied by a complete random Boolean network. Random Boolean networks are a well-known simplified model of genetic regulatory networks and this model of interacting RBNs may be therefore regarded as a simplified model of a tissue or a monoclonal colony. The mechanism of cell-to-cell interaction is here simulated letting some nodes of a particular network being influenced by the state of some nodes belonging to its neighbouring cells. One possible means to investigate the overall dynamics of a complex system is studying its response to perturbations. Our analyses follow this methodological approach. Even though the dynamics of the system is far from trivial we could show in a clear way how the interaction affects the dynamics and the global degree of order.
Integration of Named Entity Information for Chinese Word Segmentation Based on Maximum Entropy
Authors: Ka Seng Leong, Fai Wong, Yiping Li, Ming Chui Dong
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

Word segmentation is an essential process in Chinese information processing. Although related researches were reported and made progresses, the Unknown Named Entity (UNE) problem in segmentation is not fully solved. This usually degrades the accuracy of segmentation in general. In this paper, a model to identify UNEs for improving the overall performance of the segmentation is presented. In order to capture the NE information, functions of characters or words are defined with tags. In addition, useful surrounding contexts are collected from a corpus and used as features. The model is constructed based on Maximum Entropy to handle the UNE identification as tagging problem. Empirical experiments show that the overall accuracy of the segmentation is improved after integrating the UNE identification module into the word segmenter.
Dimensionality Estimation for Self-Organizing Map by Using Spectral Clustering
Authors: Naoyuki Tsuruta, Saleh K. Aly, Sakashi Maeda
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

Tasks of image recognition become important components for multimodal man-machine interface. For developing feasible components, problems of huge dimensionality and non-linearity must be resolved. We have been applying Self-Organizing Map (SOM) to feature representation stage of lip-reading and face recognition, and have appealed the advantages of SOM for dimensionality reduction and nonlinear feature representation. However, tasks of trial and error to decide the appropriate dimensionality of SOM can be a difficulty for developing image recognition. For this problem, we propose a dimensionality estimation method for SOM by using spectral clustering (SC). SC also has a characteristic of non-linear topographic mapping, and its fruitage suggests the dimensionality of feature space. In the section of experimental results, we will show relations between estimated dimensionalities of SOM and total recognition accuracies. The results emphasize feasibility of this proposed method.
Dominance-Based Rough Set Approach to Reasoning About Ordinal Data
Authors: Roman Słowiński, Salvatore Greco, Benedetto Matarazzo
Venue: Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms (2007)

Dominance-based Rough Set Approach (DRSA) has been proposed by the authors to handle background knowledge about ordinal evaluations of objects from a universe, and about monotonic relationships between these evaluations, e.g. "the larger the mass and the smaller the distance, the larger the gravity" or "the greater the debt of a firm, the greater its risk of failure". Such a knowledge is typical for data describing various phenomena, and for data concerning multiple criteria decision making or decision under uncertainty. It appears that the Indiscernibility-based Rough Set Approach (IRSA) proposed by Pawlak involves a primitive idea of monotonicity related to a scale with only two values: "presence" and "absence" of a property. This is why IRSA can be considered as a particular case of DRSA. Monotonicity gains importance when the binary scale, including only "presence" and "absence" of a property, becomes finer and permits to express the presence of a property to certain degree. This observation leads to very natural fuzzy generalization of the rough set concept via DRSA. It exploits only ordinal properties of membership degrees and monotonic relationships between them, without using any fuzzy connective. We show, moreover, that this generalization is a natural continuation of the ideas given by Leibniz, Frege, Boole, ¿ukasiewicz and Pawlak. Finally, the fuzzy rough approximations taking into account monotonic relationships between memberships to different sets can be applied to case-based reasoning. In this perspective, we propose to consider monotonicity of the type: "the more similar is y to x, the more credible is that y belongs to the same set as x".
Proper Model Selection with Significance Test
Authors: Jin Huang, Charles X. Ling, Harry Zhang, Stan Matwin
Venue: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I (2008)

Model selection is an important and ubiquitous task in machine learning. To select models with the best future classification performance measured by a goal metric, an evaluation metric is often used to select the best classification model among the competing ones. A common practice is to use the same goal and evaluation metric. However, in several recent studies, it is claimed that using an evaluation metric (such as AUC) other than the goal metric (such as accuracy) results in better selection of the correct models. In this paper, we point out a flaw in the experimental design of those studies, and propose an improved method to test the claim. Our extensive experiments show convincingly that only the goal metric itself can most reliably select the correct classification models.
On α-divergence based nonnegative matrix factorization for clustering cancer gene expression data
Authors: Weixiang Liu, Kehong Yuan, Datian Ye
Venue: Artificial Intelligence in Medicine (2008)

Objective: Nonnegative matrix factorization (NMF) has been proven to be a powerful clustering method. Recently Cichocki and coauthors have proposed a family of new algorithms based on the @a-divergence for NMF. However, it is an open problem to choose an optimal @a. Methods and materials: In this paper, we tested such NMF variant with different @a values on clustering cancer gene expression data for optimal @a selection experimentally with 11 datasets. Results and conclusion: Our experimental results show that @a=1 and 2 are two special optimal cases for real applications.
Algorithms for Paraconsistent Reasoning with OWL
Authors: Yue Ma, Pascal Hitzler, Zuoquan Lin
Venue: Proceedings of the 4th European conference on The Semantic Web: Research and Applications (2007)

In an open, constantly changing and collaborative environment like the forthcoming Semantic Web, it is reasonable to expect that knowledge sources will contain noise and inaccuracies. Practical reasoning techniques for ontologies therefore will have to be tolerant to this kind of data, including the ability to handle inconsistencies in a meaningful way. For this purpose, we employ paraconsistent reasoning based on four-valued logic, which is a classical method for dealing with inconsistencies in knowledge bases. Its transfer to OWL DL, however, necessitates the making of fundamental design choices in dealing with class inclusion, which has resulted in differing proposals for paraconsistent description logics in the literature. In this paper, we build on one of the more general approaches which due to its flexibility appears to be most promising for further investigations. We present two algorithms suitable for implementation, one based on a preprocessing before invoking a classical OWL reasoner, the other based on a modification of the KAON2 transformation algorithms. We also report on our implementation, called ParOWL.
Occlusion Processing Method Using Improved Object Contour Extraction Algorithm by Neighboring Edge Search and MER in Simulation
Authors: JeongHee Cha, HyunCheul Shin
Venue: Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology (2008)

For realistic simulation, it is essential to determine the occlusion areas of the virtual object produces after registering real image and virtual object exactly. In this paper, we proposed the new method to solve occlusions using improved object contour extraction by neighboring edge search and MER(Minimum Enclosing Rectangle). We acquire the detailed contour of complex objects by proposed algorithms and extract the three dimensional information of the position using picking algorithm. We use MER to determine the occlusion area for performance improvement. In the experiment, we compared proposed algorithms with existed method and proved the effectiveness in the environment which partial occlusions happen.
Image Data Source Selection Using Gaussian Mixture Models
Authors: Soufyane Allali, Daniel Blank, Wolfgang Müller, Andreas Henrich
Venue: Adaptive Multimedial Retrieval: Retrieval, User, and Semantics: 5th International Workshop, AMR 2007, Paris, France, July 5-6, 2007 Revised Selected Papers (2007)

In peer-to-peer (P2P) networks, computers with equal rights form a logical (overlay) network in order to provide a common service that lies beyond the capacity of every single participant. Efficient similarity search is generally recognized as a frontier in research about P2P systems. In literature, a variety of approaches exist. One of which is data source selection based approaches where peers summarize the data they contribute to the network, generating typically one summary per peer. When processing queries, these summaries are used to choose the peers (data sources) that are most likely to contribute to the query result. Only those data sources are contacted.In this paper we use a Gaussian mixture model to generate peer summaries using the peers' local data. We compare this method to other local unsupervised clustering methods for generating peer summaries and show that a Gaussian mixture model is promising when it comes to locally generated summaries for peers without the need for a distributed summary computation that needs coordination between peers.
Property Testing on k-Vertex-Connectivity of Graphs
Authors: Yuichi Yoshida, Hiro Ito
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I (2008)

We present an algorithm for testing the k-vertex-connectivity of graphs with given maximum degree. The time complexity of the algorithm is independent of the number of vertices and edges of graphs. A graph G with n vertices and maximum degree at most d is called &epsilon;-far from k-vertex-connectivity when at least $\frac{\epsilon dn}{2}$ edges must be added to or removed from G to obtain a k-vertex-connected graph with maximum degree at most d. The algorithm always accepts every graph that is k-vertex-connected and rejects every graph that is &epsilon;-far from k-vertex-connectivity with a probability of at least 2/3. The algorithm runs in ${O\left(d\left(\frac{c}{\epsilon d}\right)^{k}\log\frac{1}{\epsilon d}\right)}$ time (c > 1 is a constant) for given (k - 1)-vertex-connected graphs, and ${O\left(d\left(\frac{ck}{\epsilon d}\right)^{k}\log\frac{k}{\epsilon d}\right)}$ time (c > 1 is a constant) for given general graphs. It is the first constant-time k-vertex-connectivity testing algorithm for general k &ge; 4.
The Nearest Real Polynomial with a Real Multiple Zero in a Given Real Interval
Authors: Hiroshi Sekigawa
Venue: Computer Mathematics: 8th Asian Symposium, ASCM 2007, Singapore, December 15-17, 2007. Revised and Invited Papers (2008)

Given f ¿ ¿[x] and a closed real interval I, we provide a rigorous method for finding a nearest polynomial with a real multiple zero in I, that is, $\tilde{f}\in\mathbb{R}[x]$ such that $\tilde{f}$ has a multiple zero in I and $\|f - \tilde{f}\|_\infty$, the infinity norm of the vector of coefficients of , is minimal. First, we prove that if a nearest polynomial exists, there is a nearest polynomial $\tilde{g}\in\mathbb{R}[x]$ such that the absolute value of every coefficient of $f-\tilde{g}$ is $\|f - \tilde{f}\|_\infty$ with at most one exceptional coefficient. Using this property, we construct h ¿ ¿[x] such that a zero of h is a real multiple zero ¿ ¿ I of $\tilde{g}$. Furthermore, we give a rational function whose value at ¿ is $\|f - \tilde{f}\|_\infty$.
Feature Combination for Better Differentiating Anger from Neutral in Mandarin Emotional Speech
Authors: Tsang-Long Pao, Yu-Te Chen, Jun-Heng Yeh, Yun-Maw Cheng, Charles S. Chien
Venue: Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction (2007)

Just as written language is a sequence of elementary alphabet, speech is a sequence of elementary acoustic symbols. Speech signals convey more than spoken words. The additional information conveyed in speech includes gender information, age, accent, speaker's identity, health, prosody and emotion [1].
A New Approach to Analyse Neighbourhood Relations in 2D Polygonal Networks
Authors: Lourenço Bandeira, Pedro Pina, José Saraiva
Venue: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications (2008)

A new algorithm to identify the neighbours of polygons on 4-connected raster images of 2D polygonal networks is presented in this paper. It is mainly based on mathematical morphology operators and can be indistinctively applied to networks of tri and tetravalent nature (with three and four edges at each vertex, respectively). The algorithm is tested on a varied set of polygonal terrains occurring on the surface of Mars and its computational performance compared to other approaches.
Bidding and Voting Strategy for Energy Efficient Collaborative Target Classification in Wireless Sensor Networks
Authors: Xue Wang, Daowei Bi, Liang Ding, Sheng Wang
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

Recent advances in wireless communications and micro-electro-mechanical systems have fostered the development of wireless sensor networks (WSNs) which consist of tiny, low-cost and low-power sensor nodes. Individual sensor nodes only have local views of the environment, but global views can be obtained by collaborative processing between densely deployed sensor nodes. Energy efficiency is critical for battery powered WSNs to prolong network lifetime. It is very challenging to concurrently achieve energy efficiency and collaborative processing in WSNs. Inspired by some commercial practices, we proposed the bidding and voting strategy (BVS), and apply it to vehicular target classification in WSNs. In BVS, bidding efficiently selects sensor nodes for collaborative classification while voting improves classification decision accuracy by combining local decisions. Vehicular targets are classified by means of support vector machine (SVM). Simulation experiments with real world data are conducted and the results show the proposed strategy can guarantee target classification accuracy while achieve energy efficiency.
Evaluation of Information Visualization Tools Using the NFR Approach
Authors: Pushpa Kumar, Nary Subramanian, Kang Zhang
Venue: Proceedings of the 10th international conference on Visual Information Systems: Web-Based Visual Information Search and Management (2008)

Determination of quality of information visualization (infovis) tools will assist practitioners in industry and academia to choose the appropriate tools for their needs. Infovis tools are widely used to visualize data-intensive applications so that quick decisions may be taken. In this paper we use a novel technique, the NFR Approach, to determine quality of infovis tools. The NFR Approach considers visualizability as an important non-functional requirement (NFR) for infovis tools and evaluates visualizability to determine the quality of these tools. The chief artifact that the NFR Approach employs for evaluation is the softgoal interdependency graph (SIG) wherein factors affecting visualizabil-ity are related to tool features by means of contributions that come in four flavors. Well-defined propagation rules help determine a qualitative score for visualizability based on the contributions. The chief advantages of the NFR Approach include rationale for the scores, flexibility to accommodate different definitions for visualizability, and historical record-keeping. The NFR Approach is validated by applying the Approach to three infovis tools --- Prefuse, Tom Sawyer, and GnuPlot.
Robust Neural Networks Control for Uncertain Systems with Time-Varying Delays and Sector Bounded Perturbations
Authors: Qing Zhu, Shumin Fei, Tao Li, Tianping Zhang
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

In this paper, a robust neural networks adaptive control scheme is proposed for the stabilization of uncertain linear systems with time-varying delay and bounded perturbations. The uncertainty is assumed to be unknown continuous function without norm-bounded restriction. The perturbation is sector-bounded. Combined with liner matrix inequality method, neural networks and adaptive control, the control scheme ensures the stability of the close-loop system for any admissible uncertainty.
TTCN-3 Test Data Analyser Using Constraint Programming
Authors: Diana Vega, George Din, Ina Schieferdecker
Venue: Proceedings of the 2008 19th International Conference on Systems Engineering (2008)

This paper presents the idea of analyzing and refining the test data variance computation as a method to determine the quality of TTCN-3 tests. TTCN-3 as the only internationally standardized testing language is being used extensively by industry. A large amount of functional test specifications are written in this language. For this reason, a sensible issue is the quality analysis of these specifications. Data variance characterizes the test data distribution over the test system interface. TTCN-3 template distance constitutes the criterion to quantify the variance: "similar"and "different enough" test data. Due to the high flexibility of the language, the stimuli templates to be compared may not only contain concret evalues but also variables, function calls, parameters, values which are known at runtime only, etc. Therefore, we combine Constraint Programming (CP) with static analysis of TTCN-3 test suites targeting a realistic template solving. We name template constraint an expression whose value (or domain restrictions) can only be determined by looking at the execution paths in the analyzed test behavior. This method leads to a better computation of the data distance and, thus, to a better refinement of similarity classes. The approach is illustrated by an example which shows how data varies when the distance is computed taking into account the template constraints.