ppxscal/embedding_network huggingface dataset:
Spoken Requests for Tourist Information
Authors: Laura Hasler
Venue: Proceedings of the 11th international conference on Text, Speech and Dialogue (2008)

This paper presents an ongoing corpus annotation of speech acts in the domain of tourism, which falls within a wider project on multimodal question answering. An annotation scheme and set of guidelines are developed to mark information about parts of spoken utterances which require a response, distinguishing them from parts of utterances which do not. The corpus used for annotation consists of transcriptions of single-speaker utterances aimed at obtaining tourist information. Interannotator agreement is computed between two annotators to assess the reliability of the guidelines used to facilitate their task.
Matching word images for content-based retrieval from printed document images
Authors: Million Meshesha, C. V. Jawahar
Venue: International Journal on Document Analysis and Recognition (2008)

As large quantity of document images is getting archived by the digital libraries, there is a need for an efficient search strategies to make them available as per users information need. In this paper, we propose an effective word image matching scheme that achieves high performance in the presence of script variability, printing variation, degradation and word-form variants. A novel partial matching algorithm is designed for morphological matching of word form variants in a language. We formulate feature extraction scheme that extracts local features by scanning vertical strips of the word image and combining them automatically based on their discriminatory potential. We present detailed performance analysis of the proposed approach on English, Amharic and Hindi documents.
A Computational Model of Culture-Specific Conversational Behavior
Authors: Dušan Jan, David Herrera, Bilyana Martinovski, David Novick, David Traum
Venue: Proceedings of the 7th international conference on Intelligent Virtual Agents (2007)

This paper presents a model for simulating cultural differences in the conversational behavior of virtual agents. The model provides parameters for differences in proxemics, gaze and overlap in turn taking. We present a review of literature on these factors and show results of a study where native speakers of North American English, Mexican Spanish and Arabic were asked to rate the realism of the simulations generated based on different cultural parameters with respect to their culture.
Document Models
Authors: J. Morbach, R. Hai, B. Bayer, W. Marquardt
Venue: Collaborative and Distributed Chemical Engineering. From Understanding to Substantial Design Process Support: Results of the IMPROVE Project (2008)

In this contribution, a comprehensive document model is presented, which describes the types and dependencies of documents as well as their internal structures and dynamic behavior. Additionally, the contents of documents can be indicated by integrating the document model with a product data model. Due to these properties, the document model provides an adequate basis for a novel class of software tools that support efficient document handling in development processes.
A Streamlined and Generalized Analysis of Chromatin ImmunoPrecipitation Paired-End diTag Data
Authors: Vinsensius B. Vega, Yijun Ruan, Wing-Kin Sung
Venue: Proceedings of the 8th international conference on Computational Science, Part III (2008)

Comprehensive, accurate and detailed maps of transcription factor binding sites (TFBS) help to unravel the transcriptional regulatory relationship between genes and transcription factors. The recently developed sequencing-based genome-wide approach ChIP-PET (Chromatin ImmunoPrecipitation coupled with Paired-End diTag analysis) permits accurate and unbiased mapping of TF-DNA interactions. In this paper we outline a methodical framework to analyze ChIP-PET sequence data to identify most likely binding regions. Mathematical formulations were derived to streamline and strengthen the analysis. We established a more faithful noise distribution estimation that leads to the adaptive threshold scheme. The algorithms were evaluated using three real-world datasets. Using motif enrichment as indirect evidence and additional ChIP-qPCR validations, the overall performance was consistently satisfactory.
Principal Component Analysis Based Probability Neural Network Optimization
Authors: Jie Xing, Deyun Xiao, Jiaxiang Yu
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

Topological structure of Probability Neural Network (PNN) is usually complex when it is trained with large-scale and high-redundant training samples. Aiming this problem, PNN is analyzed and simplified by using probability calculation and multiplication formula. At first, input data of training samples was statistical analyzed by using Principal Component Analysis (PCA). PNN topological structure was optimized based on the statistical results. Subsequently, a complete learning algorithm was provided to avoid the artificial set of smoothing parameters. And Simulated Annealing (SA) coefficient was introduced to increase learning speed and stability. Eventually, the optimized PNN was applied to real problem. The test result validated that the optimized PNN had simpler structure and higher efficiency than typical PNN in the application with large-scale and high-redundant training samples.
Problems in agile trenches
Authors: Mira Kajko-Mattsson
Venue: Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement (2008)

Some agile voices have been raised against extensive documentation. They advocate its sparse use and production. In this paper, we report from agile evolution and maintenance trenches on the lifecycle problems related to system and process documentation. Our report gives an indication of the problems that have been encountered within 18 agile organizations.
Fuzzy Reversed Posynomial Geometric Programming and Its Dual Form
Authors: Bing-Yuan Cao
Venue: Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing (2007)

With an exponent-type membership function serving as conduct fuzzy value, the author builds a model in fuzzy reversed posynomial geometric programming with its dual programming developed. In the application process of fuzzy reversed posynomial geometric programming , he consequently discovers that the former can be changed into a convex parameter geometric programming with respect to ¿,β. Therefore, he can get many nice properties like fuzzy posynomial geometric programming. Besides,he advances a dual theory and algorithm in fuzzy reversed posynomial geometric programming. And finally he uses numerical examples to testify the built model and its method effectively.
Convergence of Gradient Descent Algorithm for a Recurrent Neuron
Authors: Dongpo Xu, Zhengxue Li, Wei Wu, Xiaoshuai Ding, Di Qu
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

Probabilistic convergence results of online gradient descent algorithm have been obtained by many authors for the training of recurrent neural networks with innitely many training samples. This paper proves deterministic convergence of o2ine gradient descent algorithm for a recurrent neural network with nite number of training samples. Our results can be hopefully extended to more complicated recurrent neural networks, and serve as a complementary result to the existing probability convergence results.
Extracting Multi-knowledge from fMRI Data through Swarm-Based Rough Set Reduction
Authors: Hongbo Liu, Ajith Abraham, Hong Ye
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

With the development of brain science, various kinds of new methods and techniques are applied. A mass of Functional Magnetic Resonance Imaging (fMRI) data is collected ceaselessly, which implicates very important information. The useful information need be extracted and translated to intelligible knowledge, which is exigent to develop some methods to analyze them effectively and objectively. In this paper, we attempt to extract multi-knowledge from fMRI Data using rough set approach. We introduce the data pre-processing workflow and methods. A rough set reduction approach is presented based on particle swarm optimization algorithm, which discover the feature combinations in an efficient way to observe the change of positive region as the particles proceed through the search space. The approach supports multi-knowledge extraction. We also illustrate some results using our approach, which is helpful for cognition research.
A Generalized Scanning Technique for Display of Parametrically Defined Surfaces
Authors: Alyn Rockwood
Venue: IEEE Computer Graphics and Applications (1987)

Special attention is given to the adaptation of the technique for efficiency to both polynomial and rational Bezier patches. The resulting algorithms are simple and robust. Their speed depends mainly on the surface area of the object being rendered, not on the number of surface patches. The technique is particularly suitable for rapid display of geometrically complex objects. Since the technique directly renders from the definitions of the surfaces without needing polygons, it can maintain a relatively compact database. Several examples with timing are included. The technique described creates smooth, shaded images for parametrically defined surfaces. It depends on a general surface scan to generate a dense set of points that represents the surface. Hidden surfaces are eliminated by sorting the sample points into a z-buffer and retaining the points nearest the viewer. Alternative schemes are given for computing the surface normal for each point in the z-buffer. Conventional illumination routines can then be applied to determine the intensities at each pixel, although the reflection-mapping technique is preferred for this article.
SYMBIOS: A Semantic Pervasive Services Platform for Biomedical Information Integration
Authors: Myriam Mencke, Ismael Rivera, Juan Miguel Gómez, Giner Alor-Hernandez, Rubén Posada-Gómez, Ying Liu
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

Applying semantic pervasive services to Biomedical research is providing a new breed of intelligent applications which can tackle with the heterogeneity and intrinsic complexity of biomedical information integration. Using semantics leverages the potential of enabling cross-interoperability among a variety of storage and data formats widely distributed both across the Internet and within individual organizations. In this paper, we present SYMBIOS, a fully-fledged biomedical information integration solution based on semantic pervasive services that combine a Service Oriented Architecture (SOA) and semantically-empowered techniques to ascertain biomedical information intelligent integration. We discuss our approach with a proof-of-concept implementation where the breakthroughs and efficiency of integrating the biomedical publications database MEDLine, the Database of Interacting Proteins (DIP) and the Munich Information Center for Protein Sequences (MIPS) has been tested.
The Effects of Theta Burst Transcranial Magnetic Stimulation over the Human Primary Motor and Sensory Cortices on Cortico-Muscular Coherence
Authors: Murat Saglam, Kaoru Matsunaga, Yuki Hayashida, Nobuki Murayama, Ryoji Nakanishi
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

Recent studies proposed a new paradigm of repetitive transcranial magnetic stimulation (rTMS), "theta burst stimulation" (TBS); to primary motor cortex (M1) or sensory cortex (S1) can influence cortical excitability in humans. Particularly it has been shown that TBS can induce the long-lasting effects with the stimulation duration shorter than those of conventional rTMSs. However, in those studies, effects of TBS over M1 or S1 were assessed only by means of motor- and/or somatosensory-evoked-potentials. Here we asked how the coherence between electromyographic (EMG) and electroencephalographic (EEG) signals during isometric contraction of the first dorsal interosseous muscle is modified by TBS. The coherence magnitude localizing for the C3 scalp site, and at 13-30Hz band, significantly decreased 30-60 minutes after the TBS on M1, but not that on S1, and recovered to the original level in 90-120 minutes. These findings indicate that TBS over M1 can suppress the cortico-muscular synchronization.
Web Usage Mining Via Fuzzy Logic Techniques
Authors: Víctor H. Escobar-Jeria, María J. Martín-Bautista, Daniel Sánchez, María-Amparo Vila
Venue: Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing (2007)

With the increment of users and information on the Web, mining processes inspired in the traditional data mining ones have been developed. This new recent area of investigation is called Web Mining. Within this area, we study the analysis of web log files in what is called Web Usage Mining. Different techniques of mining to discover usage patterns from web data can be applied in Web Usage Mining. We will also study in a more detailed way applications of Fuzzy Logic in this area. Specially, we apply fuzzy association rules to web log files, and we give initial traces about the application of Fuzzy Logic to personalization and user profile construction.
Sampling Optimization Trade-Offs for Long-Term Monitoring of Gamma Dose Rates
Authors: S. J. Melles, G. B. Heuvelink, C. J. Twenhöfel, U. Stöhlker
Venue: Proceeding sof the international conference on Computational Science and Its Applications, Part I (2008)

This paper applies a recently developed optimization method to examine the design of networks that monitor radiation under routine conditions. Annual gamma dose rates were modelled by combining regression with interpolation of the regression residuals using spatially exhaustive predictors and an anisotropic variogram of the residuals. Locations of monitoring stations were optimized by minimizing the spatially averaged regression kriging standard deviation. Results suggest that the current network design is near optimal in terms of interpolation error in predicted gamma dose rates. When the network was thinned to fewer stations, spatial optimization was more effective at reducing the interpolation error. Given that some EU countries are considering reducing station density in border regions, the analysis reported here may be useful in guiding which stations can be removed.
Conceptualizing Interactions with Matchmakers and Front-Agents Using Formal Verification Methods
Authors: Amelia Bădică, Costin Bădică
Venue: Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications (2008)

In this note we propose a rigorous analysis of interactions of requesters and providers with matchmakers and front-agents based on formal specification and verification using FSP process algebra and FLTL temporal logic.
A Concealed t-out-of-n Signer Ambiguous Signature Scheme with Variety of Keys
Authors: Ya-Fen Chang, Chin-Chen Chang, Pei-Yu Lin
Venue: Informatica (2007)

In 2004, Abe et al. proposed a threshold signer-ambiguous signature scheme from variety of keys. Their scheme is a generalized case of the ring signature scheme, and it allows the key types to be based on the trapdoor one-way permutations (TOWP) or sigma-protocols including Schnorr's signature scheme. However, the signed message is public for all, which may result in disputes. In this paper, we present a novel threshold signer-ambiguous signature scheme, having the signed message concealed and keeping who the receivers are secret from variety of keys.
Modeling and Simulation for Security Risk Propagation in Critical Information Systems
Authors: Young-Gab Kim, Dongwon Jeong, Soo-Hyun Park, Jongin Lim, Doo-Kwon Baik
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

Existing risk propagation models are limited and inadequate for the analysis of cyber attacks caused by various threats to information systems, because of their limited focus only on one specific threat, such as a single virus or worm. Therefore, we herein propose a risk propagation model based on the Markov process, which can be applied to diverse threats to information systems. Furthermore, simulations including in case a threat occurs related with other threats are performed using five scenarios to verify the proposed model.
Automatic Segmentation of the Liver in CT Using Level Sets Without Edges
Authors: J. F. Garamendi, N. Malpica, J. Martel, E. Schiavi
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I (2007)

Liver volumetry is a required step for the planning of liver surgery and resection. It is generally based on Computerized tomography images, and segmentation of the liver is the most important step of the process. We propose an automatic segmentation algorithm based on a geometric level set method which provides an accurate segmentation of the liver, and requires no a priori information. We show results on different datasets, with and without a contrast agent. The segmentation is compared to manual delineation by a radiologist with good results.
Statistical Properties of Overlapping Ambiguities in Chinese Word Segmentation and a Strategy for Their Disambiguation
Authors: Wei Qiao, Maosong Sun, Wolfgang Menzel
Venue: Proceedings of the 11th international conference on Text, Speech and Dialogue (2008)

Overlapping ambiguity is a major ambiguity type in Chinese word segmentation. In this paper, the statistical properties of overlapping ambiguities are intensively studied based on the observations from a very large balanced general-purpose Chinese corpus. The relevant statistics are given from different perspectives. The stability of high frequent maximal overlapping ambiguities is tested based on statistical observations from both general-purpose corpus and domain-specific corpora. A disambiguation strategy for overlapping ambiguities, with a predefined solution for each of the 5,507 pseudo overlapping ambiguities, is proposed consequently, suggesting that over 42% of overlapping ambiguities in Chinese running text could be solved without making any error. Several state-of-the-art word segmenters are used to make comparisons on solving these overlapping ambiguities. Preliminary experiments show that about 2% of the 5,507 pseudo ambiguities which are mistakenly segmented by these segmenters can be properly treated by the proposed strategy.
Component-Based Abstraction and Refinement
Authors: Juncao Li, Xiuli Sun, Fei Xie, Xiaoyu Song
Venue: Proceedings of the 10th international conference on Software Reuse: High Confidence Software Reuse in Large Systems (2008)

In this paper, we present a comprehensive approach to model check- ing component-based systems (including software, hardware, and embedded systems) through abstraction and refinement. This approach is based on assume-guarantee compositional reasoning and features two synergistic techniques: (1) an automatic algorithm to component-based abstraction and (2) a mechanized assistant for abstraction refinement. The key insight to the abstraction algorithm is that a verified property is a natural abstraction of a component. The abstraction algorithm automatically determines which component properties can be included in the abstraction for verifying a system property by determining whether the assumptions of the component properties hold in the context of the system. If the abstraction fails to establish the system property, the refinement assistant determines the causes of the failure, e.g., why a component property is not included, and provides automatic remedies or requests manual remedies. This approach has been applied in component-based hardware/software co-verification of embedded systems. Case studies have shown that this approach is very effective in abstracting component-based embedded systems and guiding abstraction refinement.
An Enhanced Accessory Pathway Localization Method for Efficient Treatment of Wolff-Parkinson-White Syndrome
Authors: Sándor M. Szilágyi, László Szilágyi, Levente K. Görög, Constantin T. Luca, Dragoş Cozma, Gabriel Ivanica, Zoltán Benyó
Venue: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications (2008)

This paper presents an analysis of the Arruda accessory pathway localization method for patients suffering from Wolff-Parkinson-White syndrome, with modifications to increase the overall performance. The Arruda method was tested on a total of 79 cases, and 91.1 % localization performance was reached. After a deeper analysis of each decision point of the Arruda localization method, we considered that the lead aVF is not as relevant as other used leads (I, II, III, V1). The branch of the decision tree, which evaluates the left ventricle positions, was entirely replaced using different decision criteria based of the same biological parameters. The modified algorithm significantly improves the localization performance in the left ventricle. The overall performance reaches 94.9 %. A high localization performance of non-invasive methods is relevant because it can enlighten the necessary invasive interventions, and also reduces the discomfort caused to the patient.
A Generic Diffusion Kernel for Semi-supervised Learning
Authors: Lei Jia, Shizhong Liao
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks (2008)

In this paper, we present a generic diffusion kernel for graph-based semi-supervised learning, whose kernel matrix is generated with a Taylor expansion on the generating function of diffusion similarity matrix. The generic diffusion kernel subsumes common known diffusion kernels, and provides a kernel framework for semi-supervised learning. Specifically, we first present the definition of diffusion similarity matrix, and lay the theoretical foundation for our approach. Then we derive the 2- and d-diffusion kernels, and naturally extend them to the generic diffusion kernel. Further we prove that small eigenvalues of the generic diffusion kernel correspond to smooth eigenvectors over the graph. This property is critical for the construction of generic diffusion kernels. Experiments on simulated and benchmark databases demonstrate that the generic diffusion kernel is sound and effective.
Nearest Local Hyperplane Rules for Pattern Classification
Authors: Gábor Takács, Béla Pataki
Venue: Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing (2007)

Predicting the class of an observation from its nearest neighbors is one of the earliest approaches in pattern recognition. In addition to their simplicity, nearest neighbor rules have appealing theoretical properties, e.g. the asymptotic error probability of the plain 1-nearest-neighbor (NN) rule is at most twice the Bayes bound, which means zero asymptotic risk in the separable case. But given only a finite number of training examples, NN classifiers are often outperformed in practice. A possible modification of the NN rule to handle separable problems better is the nearest local hyperplane (NLH) approach. In this paper we introduce a new way of NLH classification that has two advantages over the original NLH algorithm. First, our method preserves the zero asymptotic risk property of NN classifiers in the separable case. Second, it usually provides better finite sample performance.