ppxscal/embedding_network huggingface dataset:
A Matrix Factorization Approach for Integrating Multiple Data Views
Authors: Derek Greene, Pádraig Cunningham
Venue: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I (2009)

In many domains there will exist different representations or "views" describing the same set of objects. Taken alone, these views will often be deficient or incomplete. Therefore a key problem for exploratory data analysis is the integration of multiple views to discover the underlying structures in a domain. This problem is made more difficult when disagreement exists between views. We introduce a new unsupervised algorithm for combining information from related views, using a late integration strategy. Combination is performed by applying an approach based on matrix factorization to group related clusters produced on individual views. This yields a projection of the original clusters in the form of a new set of "meta-clusters" covering the entire domain. We also provide a novel model selection strategy for identifying the correct number of meta-clusters. Evaluations performed on a number of multi-view text clustering problems demonstrate the effectiveness of the algorithm.
Near-Real-Time Image Matting with Known Background
Authors: Minglun Gong, Yee-Hong Yang
Venue: Proceedings of the 2009 Canadian Conference on Computer and Robot Vision (2009)

This paper presents a novel matting algorithm for handling cases where foreground objects are in front of complex (non-smooth) but known background. The algorithm is based on a Poisson equation that takes the gradient of the known background into consideration. The quantitative evaluation based on ground truth shows that the matting results obtained using the proposed algorithm is more accurate than the ones generated by the global Poisson matting [9]. The proposed algorithm is also optimized for parallel processing and runs in near-real-time on programmable graphics hardware.
Anatomically-based musculoskeletal modeling: prediction and validation of muscle deformation during walking
Authors: Katja Oberhofer, Kumar Mithraratne, Ngaire S. Stott, Iain A. Anderson
Venue: The Visual Computer: International Journal of Computer Graphics (2009)

Accurate modeling of the musculoskeletal system during motion is a challenging task that has not yet been solved. In this paper, we outline and validate a free-form deformation method called the Host Mesh Fitting (HMF) technique for predicting muscle deformation during walking of a subject-specific musculoskeletal model. 20 lower limb muscles were deformed according to the HMF solution of a surrounding host mesh that resembled the skin boundary, resulting in a realistic walking simulation of the anatomically-based model. The shape changes of five muscles were further validated by comparing the predicted deformations with magnetic resonance image data in two lower limb positions.
The profession of ITComputing's paradigm
Authors: Peter J. Denning, Peter A. Freeman
Venue: Communications of the ACM (2009)

Trying to categorize computing as engineering, science, or math is fruitless; we have our own paradigm.
Fusion Segmentation Algorithm for SAR Images Based on HMT in Contourlet Domain and D-S Theory of Evidence
Authors: Yan Wu, Ming Li, Haitao Zong, Xin Wang
Venue: Proceedings of the International Conference on Computational Science and Its Applications: Part II (2009)

Utilizing the Contourlet's advantages of multiscale, localization, directionality and anisotropy, a new SAR image segmentation algorithm based on hidden Markov tree (HMT) in Contourlet domain and dempster-shafer (D-S) theory of evidence is proposed in this paper. The algorithm extends the hidden Markov tree framework to Contourlet domain and fuses the clustering and persistence of Contourlet transform using HMT model and D-S theory, and then, we deduce the maximum a posterior (MAP) segmentation equation for the new fusion model. The algorithm is used to segment the real SAR images. Experimental results and analysis show that the proposed algorithm effectively reduces the influence of multiplicative speckle noise, improves the segmentation accuracy and provides a better visual quality for SAR images over the algorithms based on HMT-MRF in the wavelet domain, HMT and MRF in the Contourlet domain, respectively.
Encoding Asynchronous Interactions Using Open Petri Nets
Authors: Paolo Baldan, Filippo Bonchi, Fabio Gadducci
Venue: Proceedings of the 20th International Conference on Concurrency Theory (2009)

We present an encoding for (bound) processes of the asynchronous CCS with replication into open Petri nets: ordinary Petri nets equipped with a distinguished set of open places. The standard token game of nets models the reduction semantics of the calculus; the exchange of tokens on open places models the interactions between processes and their environment. The encoding preserves strong and weak CCS asynchronous bisimilarities: it thus represents a relevant step in establishing a precise correspondence between asynchronous calculi and (open) Petri nets. The work is intended as fostering the technology transfer between these formalisms: as an example, we discuss how some results on expressiveness can be transferred from the calculus to nets and back.
XTree: A New XML Keyword Retrieval Model
Authors: Cong-Rui Ji, Zhi-Hong Deng, Yong-Qing Xiang, Hang Yu, Shi-Wei Tang
Venue: Advances in Web and Network Technologies, and Information Management: APWeb/WAIM 2009 International Workshops: WCMT 2009, RTBI 2009, DBIR-ENQOIR 2009, PAIS 2009, Suzhou, China, April 2-4, 2009, Revised Selected Papers (2009)

As more and more data are represented and stored by XML format, how to query XML data has become an increasingly important research issue. Keyword search is a proven user-friendly way of querying HTML documents, and it is well suited to XML trees as well. However, it is still an open problem in XML keyword retrieval that which XML nodes are meaningful and reasonable to a query, how to find these nodes effectively and efficiently. In recent years, many XML keyword retrieval models have been presented to solve the problem, such as XRANK and SLCA. These models usually return the most specific results and discard most ancestral nodes. There may not be sufficient information for users to understand the returned results easily. In this paper, we present a new XML keyword retrieval model, XTree, which can cover every keyword node and return the comprehensive result trees. For XTree model, we propose Xscan algorithm for processing keyword queries and GenerateTree for constructing results. We analytically and experimentally evaluate the performances of our algorithms, and the experiments show that our algorithms are efficient.
Acoustic interaction design through "audemes": experiences with the blind
Authors: Mexhid Ferati, Steve Mannheimer, Davide Bolchini
Venue: Proceedings of the 27th ACM international conference on Design of communication (2009)

This paper presents and discusses design decisions for an acoustic edutainment application for blind users called AEDIN (Acoustic EDutainment INterface), comprising audio elements used as navigational and thematic landmarks in touch-screen computers. We tested designs with blind and visually impaired teenagers. Preliminary results demonstrated the efficacy of AEDIN as an easy-to-learn and memorize architecture, and a potentially fun interface. The paper illustrates the lessons learned from the design and evaluation experience and contextually outlines new research directions for aural communication design.
Unsupervised Segmentation Method for Color Image Based on MRF
Authors: Yimin Hou, Xiangmin Lun, Wei Meng, Tao Liu, Xiaoli Sun
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 01 (2009)

The paper proposes an unsupervised color image segmentation method based on Markov Random Field (MRF). The method involves intensity Euclidean Distance and spatial position information of the pixels in the neighborhood potential function of MRF. Therefore, the traditional potential function of MRF segmentation method is improved. Transforms the segmentation to a Maximum A Posteriori (MAP) problem which is solved by the Iterative Conditional Model (ICM). Uses the Fuzzy C-means to initialize the classification in the rang of specified class number. The optimal class number was chosen according to Minimum Message Length (MML) criterion to complete an unsupervised segmentation. In the experiments, synthetic and real images are used in the procedure and the results show that the proposed method is more effective than the classical methods.
Block Division for 3D Head Shape Clustering
Authors: Jianwei Niu, Zhizhong Li, Song Xu
Venue: Proceedings of the 2nd International Conference on Digital Human Modeling: Held as Part of HCI International 2009 (2009)

In our previous Three Dimensional (3D) anthropometric shape clustering study, block-division technique is adopted. The objective of this study was to examine the sensitivity of clustering results on block-division. Such a block-division technique means to divide each 3D surface into a predefined number of blocks. Then by using a block-distance measure, each surface is converted into a block-distance based vector. Finally, k-means clustering is performed on the vectors to segment a population into several groups. Totally 447 3D head samples have been analyzed in the case study. The influence of block division number on clustering was evaluated by using One-way ANOVA. No significant difference was found between the three block division alternatives. This means the adopted method is robust to block division.
TEXTNN-A MATLAB program for textural classification using neural networks
Authors: Emilson Pereira Leite, Carlos Roberto de Souza Filho
Venue: Computers Geosciences (2009)

A new MATLAB code that provides tools to perform classification of textural images for applications in the geosciences is presented in this paper. The program, here coined as textural neural network (TEXTNN), comprises the computation of variogram maps in the frequency domain for specific lag distances in the neighborhood of a pixel. The result is then converted back to spatial domain, where directional or omni-directional semivariograms are extracted. Feature vectors are built with textural information composed of semivariance values at these lag distances and, moreover, with histogram measures of mean, standard deviation and weighted-rank fill ratio. This procedure is applied to a selected group of pixels or to all pixels in an image using a moving window. A feed-forward back-propagation neural network can then be designed and trained on feature vectors of predefined classes (training set). The training phase minimizes the mean-squared error on the training set. Additionally, at each iteration, the mean-squared error for every validation is assessed and a test set is evaluated. The program also calculates contingency matrices, global accuracy and kappa coefficient for the training, validation and test sets, allowing a quantitative appraisal of the predictive power of the neural network models. The interpreter is able to select the best model obtained from a k-fold cross-validation or to use a unique split-sample dataset for classification of all pixels in a given textural image. The performance of the algorithms and the end-user program were tested using synthetic images, orbital synthetic aperture radar (SAR) (RADARSAT) imagery for oil-seepage detection, and airborne, multi-polarized SAR imagery for geologic mapping, and the overall results are considered quite positive.
MRI Tissue Classification and Bias Field Estimation Based on Coherent Local Intensity Clustering: A Unified Energy Minimization Framework
Authors: Chunming Li, Chenyang Xu, Adam W. Anderson, John C. Gore
Venue: Proceedings of the 21st International Conference on Information Processing in Medical Imaging (2009)

This paper presents a new energy minimization method for simultaneous tissue classification and bias field estimation of magnetic resonance (MR) images. We first derive an important characteristic of local image intensities -- the intensities of different tissues within a neighborhood form separable clusters, and the center of each cluster can be well approximated by the product of the bias within the neighborhood and a tissue-dependent constant. We then introduce a coherent local intensity clustering (CLIC) criterion function as a metric to evaluate tissue classification and bias field estimation. An integration of this metric defines an energy on a bias field, membership functions of the tissues, and the parameters that approximate the true signal from the corresponding tissues. Thus, tissue classification and bias field estimation are simultaneously achieved by minimizing this energy. The smoothness of the derived optimal bias field is ensured by the spatially coherent nature of the CLIC criterion function. As a result, no extra effort is needed to smooth the bias field in our method. Moreover, the proposed algorithm is robust to the choice of initial conditions, thereby allowing fully automatic applications. Our algorithm has been applied to high field and ultra high field MR images with promising results.
Representation for the Lagrangian numerical differentiation formula involving elementary symmetric functions
Authors: Honghuan Bai, Aimin Xu, Feng Cui
Venue: Journal of Computational and Applied Mathematics (2009)

By using elementary symmetric functions, this paper presents an explicit representation for the Lagrangian numerical differentiation formula as well as the error estimate for local approximation. And we also point out that the numerical differentiation formula constructed by Li [J.P. Li, General explicit difference formulas for numerical differentiation, J. Comput. Appl. Math. 183 (2005) 29-52] is actually a special case of the Lagrangian numerical differentiation formula to approximate the values of the derivatives at the nodes.
Finding Similar or Diverse Solutions in Answer Set Programming
Authors: Thomas Eiter, Esra Erdem, Halit Erdoğan, Michael Fink
Venue: Proceedings of the 25th International Conference on Logic Programming (2009)

We study finding similar or diverse solutions of a given computational problem, in answer set programming, and introduce offline methods and online methods to compute them using answer set solvers. We analyze the computational complexity of some problems that are related to finding similar or diverse solutions, and show the applicability and effectiveness of our methods in phylogeny reconstruction.
Applying pervasive technologies to create economic incentives that alter consumer behavior
Authors: Tetsuo Yamabe, Vili Lehdonvirta, Hitoshi Ito, Hayuru Soma, Hiroaki Kimura, Tatsuo Nakajima
Venue: Proceedings of the 11th international conference on Ubiquitous computing (2009)

Economic incentives are a powerful way of shaping consumer behavior towards more commercially efficient and environmentally sustainable patterns. In this paper, we explore the idea of combining pervasive computing techniques with electronic payment systems to create activity-based micro-incentives. Users who consume additional resources by e.g., occupying an air-conditioned space instead of a normal space are levied additional micro-payments. In an alternative approach, consumers who choose to save resources are rewarded with micro-rebates off the price of a service. As a result, the cost of using a service corresponds more closely with the resources used, leading market mechanisms to allocate resources efficiently. A key challenge is designing incentive mechanisms that alter consumer behavior in the desired fashion. We introduce four incentive models, and present evaluation results suggesting that consumers make different decisions depending on which model is used.
Distributed Fusion Estimation Based on Pseudo-measurement for Multi-sensor System
Authors: Jin Xue-bo, Du Jing-jing, Wang Lei-lei
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 01 (2009)

By considering the relation between local fusion estimation and fusion center, in this paper the estimation from local fusion nodes is regarded as a pseudo-measurement. Then the distributed estimation algorithm is turned to be two-level centralized fusion estimation and the new optimal distributed fusion estimation algorithm is obtained with Kalman filtering form, which in general only centralized estimation method has. Simulations show the developed algorithm has the excellent estimation performance. By the developed algorithm, the distributed multisensor system can be unified with centralized system and make it possible that applying the abundant research result of centralized system to distributed multisensor system.
Feedback-Based Energy-aware Scheduling Algorithm for Hard Real-time Tasks
Authors: Dong-song Zhang, Shi-yao Jin, Tong Wu, Hua-wei Li
Venue: Proceedings of the 2009 IEEE International Conference on Networking, Architecture, and Storage (2009)

In this paper, we propose a new algorithm for Dynamic Voltage Scaling (DVS) based on feedback control mechanism. Besides handling a periodic tasks, our algorithm significantly reduces the energy consumption of periodic tasks and consequently the energy consumption of the whole task set. The results show that, comparing to On-Line Dynamic Voltage Scaling (the best algorithm to our knowledge), our approach improves the energy savings by 10% on most task sets tested and by 5% on average.
Joint unsupervised coreference resolution with Markov logic
Authors: Hoifung Poon, Pedro Domingos
Venue: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2008)

Machine learning approaches to coreference resolution are typically supervised, and require expensive labeled data. Some unsupervised approaches have been proposed (e.g., Haghighi and Klein (2007)), but they are less accurate. In this paper, we present the first unsupervised approach that is competitive with supervised ones. This is made possible by performing joint inference across mentions, in contrast to the pairwise classification typically used in supervised methods, and by using Markov logic as a representation language, which enables us to easily express relations like apposition and predicate nominals. On MUC and ACE datasets, our model outperforms Haghigi and Klein's one using only a fraction of the training data, and often matches or exceeds the accuracy of state-of-the-art supervised models.
An investigation into computational recognition of children's jokes
Authors: Julia M. Taylor, Lawrence J. Mazlack
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

The purpose of this paper is to describe an investigation into an ontology-based computational recognition of children's jokes. While humor has been studied for centuries computational humor has received very little attention: This may in part be due to the difficulty of the task: at the very least, it requires formal methods for humor generation/recognition and "being able to produce/interpret natural language, being capable of subtle and flexible inferences, and having a vast store of knowledge about the real world." [Ritchie, 2004]. There are some humor generators (see Ritchie [2004] for a review) and a handful of humor recognizers. Yet, "If computers are ever going to communicate naturally and effectively with humans, they must be able to use humor." [Binsted, 2006]. We are interested in recognition, not generation of humor. Recognition of all verbally expressed humor is an overly broad task. To narrow the task, only jokes for young children are considered. The reduction of the domain size to young children's jokes is expected to decrease the complexity and sophistication of the language to be analyzed. This in turn decreases the knowledge that needs to be captured for text interpretation.
Approximate inference of Bayesian networks through edge deletion
Authors: Julie Thornton
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 4 (2005)

In this paper, we introduce two new algorithms for approximate inference of Bayesian networks that use edge deletion techniques. The first reduces a network to its maximal weight spanning tree using the Kullback-Leibler information divergence as edge weights, and then runs Pearl's algorithm on the resulting tree for linear-time inference. The second algorithm deletes edges from the triangulated graph until the biggest clique in the triangulated graph is below a desired bound, thus placing a polynomial time bound on inference. When tested for efficiency, these two algorithms perform up to 10,000 times faster than exact techniques. See www.cis.ksu.edu/~jas3466/research.html for more information.
A Policy Model for Secure Information Flow
Authors: Adedayo O. Adetoye, Atta Badii
Venue: Foundations and Applications of Security Analysis: Joint Workshop on Automated Reasoning for Security Protocol Analysis and Issues in the Theory of Security, ARSPA-WITS 2009, York, UK, March 28-29, 2009, Revised Selected Papers (2009)

When a computer program requires legitimate access to confidential data, the question arises whether such a program may illegally reveal sensitive information. This paper proposes a policy model to specify what information flow is permitted in a computational system. The security definition, which is based on a general notion of information lattices, allows various representations of information to be used in the enforcement of secure information flow in deterministic or nondeterministic systems. A flexible semantics-based analysis technique is presented, which uses the input-output relational model induced by an attacker's observational power, to compute the information released by the computational system. An illustrative attacker model demonstrates the use of the technique to develop a termination-sensitive analysis. The technique allows the development of various information flow analyses, parametrised by the attacker's observational power, which can be used to enforce what declassification policies.
Feature subsumption for opinion analysis
Authors: Ellen Riloff, Siddharth Patwardhan, Janyce Wiebe
Venue: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (2006)

Lexical features are key to many approaches to sentiment analysis and opinion detection. A variety of representations have been used, including single words, multi-word Ngrams, phrases, and lexico-syntactic patterns. In this paper, we use a subsumption hierarchy to formally define different types of lexical features and their relationship to one another, both in terms of representational coverage and performance. We use the subsumption hierarchy in two ways: (1) as an analytic tool to automatically identify complex features that outperform simpler features, and (2) to reduce a feature set by removing unnecessary features. We show that reducing the feature set improves performance on three opinion classification tasks, especially when combined with traditional feature selection.