ppxscal/embedding_network huggingface dataset:
Efficient Global Minimization for the Multiphase Chan-Vese Model of Image Segmentation
Authors: Egil Bae, Xue-Cheng Tai
Venue: Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (2009)

The Mumford-Shah model is an important variational image segmentation model. A popular multiphase level set approach, the Chan-Vese model, was developed for this model by representing the phases by several overlapping level set functions. Recently, exactly the same model was also formulated by using binary level set functions. In both approaches, the gradient descent equations had to be solved numerically, a procedure which is slow and has the potential of getting stuck in a local minima. In this work, we develop an efficient and global minimization method for the binary level set representation of the multiphase Chan-Vese model based on graph cuts. If the average intensity values of the different phases are sufficiently evenly distributed, the discretized energy function becomes submodular. Otherwise, a novel method for minimizing nonsubmodular functions is proposed with particular emphasis on this energy function.
Feature Selection for Cancer Classification Based on Support Vector Machine
Authors: Wei Luo, Lipo Wang, Jingjing Sun
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 04 (2009)

Feature selection plays an important role in cancer classification, for gene expression data usually have a large number of dimensions and relatively a small number of samples. In this paper, we use the support vector machine (SVM) for cancer classification. We propose a mixed two-step feature selection method. The first step uses a modified t-test method to select discriminatory features. The second step extracts principal components from the top-ranked genes based on the modified t-test method. We tested our two-step method in three data sets, i.e., the lymphoma data set, the SRBCT data set, and the ovarian cancer data set. The results in all the three data sets show our two-step methods is able to achieve 100% accuracy with much fewer genes than other published results.
Structure of Reversible Cellular Automata
Authors: Jarkko Kari
Venue: Proceedings of the 8th International Conference on Unconventional Computation (2009)

Cellular automata are examples of discrete complex systems where non-trivial global behavior emerges from the local interaction of trivial components. Cellular automata have been studied, among other perspectives, as models of massively parallel computation tightly connected to the microscopic physics. Physics is time reversible and satisfies various conservation laws, and by a careful design these properties can be implemented in cellular automata as well. The concept of time reversibility is important in this context, as irreversible logical operations such as AND and OR are "wasteful" and necessarily lead to dissipation of energy. It is well known that simple cellular automata rules can be computationally universal, and universal cellular automata exist even under the additional constraints of reversibility and conservation laws.In this talk we discuss computational universality in (reversible) cellular automata and the connection to reversible logic. We demonstrate how any globally reversible one-dimensional cellular automaton can be implemented using locally reversible logical gates. Analogous result is true also among two-dimensional cellular automata, while in higher dimensions the question is open. Note that locally reversible cellular automata are the classical counter part of quantum cellular automata, which can be most conveniently defined as infinite, regular arrays of locally unitary operators. Many of the structure results concerning reversible cellular automata can then be naturally extended to hold for the quantum cellular automata as well.
The effect of the microfluidic diodicity on the efficiency of valve-less rectification micropumps using Lattice Boltzmann Method
Authors: Ahmed Fadl, Zongqin Zhang, Sebastian Geller, Jonas T&#x00f6;lke, Manfred Krafczyk, Donna Meyer
Venue: Microsystem Technologies (2009)

The efficiency of the valve-less rectification micropump depends primarily on the microfluidic diodicity (the ratio of the backward pressure drop to the forward pressure drop). In this study, different rectifying structures, including the conventional structures (nozzle/diffuser and Tesla structures), were investigated at very low Reynolds numbers (between 0.2 and 60). The rectifying structures were characterized with respect to their design, and a numerical approach was illustrated to calculate the diodicity for the rectifying structures. In this study, the microfluidic diodicity was evaluated numerically for different rectifying structures including half circle, semicircle, heart, triangle, bifurcation, nozzle/diffuser, and Tesla structures. The Lattice Boltzmann Method (LBM) was utilized as a numerical method to simulate the fluid flow in the microscale. The results suggest that at very low Reynolds number flow, rectification and multifunction micropumping may be achievable by using a number of the presented structures. The results for the conventional structures agree with the reported results.
ICT solutions and labor productivity: evidence from firm-level data
Authors: Juha-Miikka Nurmilaakso
Venue: Electronic Commerce Research (2009)

This paper analyses the impacts of information and communication technology (ICT) solutions on labor productivity, i.e. revenue per employee. Based on cross-sectional data on 1955 European firms in 2005 and a linear regression model derived from the microeconomic theory of production, the impacts of six common ICT solutions in electronic commerce (e-commerce) cannot be ignored. According to the linear regression analysis, Internet access, standardized data exchange with the trading partners, enterprise resource planning (ERP) system, and customer relationship management (CRM) system contribute significant increases in labor productivity, whereas a website on the Internet, or supply chain management (SCM) system do not result in a significant increase. Especially, Internet access has a significant effect on labor productivity, and the website on the Internet has an insignificant effect.
Performance of Web Caching in High Density Mobile Networks
Authors: Xiaohui Chen, Weidong Wang, Guo Wei
Venue: Proceedings of the 2009 Eigth IEEE/ACIS International Conference on Computer and Information Science (2009)

Our study demonstrated that caching scheme performs better in high density mobile networks than in sporadic circumstance. Based on the analysis of correlation between cache performance and the zipf-like user behaviors, we proposed some possible methods to improve cache performance in high density mobile networks.
Modeling the Score Distributions of Relevant and Non-relevant Documents
Authors: Evangelos Kanoulas, Virgil Pavlu, Keshi Dai, Javed A. Aslam
Venue: Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory (2009)

Empirical modeling of the score distributions associated with retrieved documents is an essential task for many retrieval applications. In this work, we propose modeling the relevant documents' scores by a mixture of Gaussians and modeling the non-relevant scores by a Gamma distribution. Applying variational inference we automatically trade-off the goodness-of-fit with the complexity of the model. We test our model on traditional retrieval functions and actual search engines submitted to TREC. We demonstrate the utility of our model in inferring precision-recall curves. In all experiments our model outperforms the dominant exponential-Gaussian model.
Acquisition of knowledge about uncertainty in the outcome of sensory motor decision tasks
Authors: Julia Trommershauser
Venue: Proceedings of the 2009 IEEE 8th International Conference on Development and Learning (2009)

Recent years have seen a growing number of experiments in which decision making is studied in the context of visuo-motor decision tasks. In these visuo-motor experiments, the outcome of a hand or eye movement is associated with explicit gains and losses (see, e.g. [1]). The results obtained from these studies indicate that human participants typically select surprisingly efficient strategies that come close to maximizing expected gain. For example, under conditions in which the end point variance of speeded hand movements is artificially increased by perturbing the final finger position, humans quickly generate a representation of their modified end point variance and rely on this representation in planning their movements so as to maximize gain [2]. Furthermore, human subjects have been found to implicitly learn the stochasticity of spatially stochastic rewards and losses in only a few hundred trials [3]. These results indicate that humans are able to implicitly estimate the parameters of noisy movement outcomes and of stochastic environmental variables and can use this knowledge to plan their hand movements such as to maximize expected gain. A comparison of performance across these tasks indicates that knowledge about movement uncertainty is learnt within fewer trials than knowledge about the uncertainty in the final target position. This suggests that visual feedback about the hand position is processed and integrated by different mechanisms than visual information about the final target position. 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
An Improved Algorithm for TV-L1 Optical Flow
Authors: Andreas Wedel, Thomas Pock, Christopher Zach, Horst Bischof, Daniel Cremers
Venue: Statistical and Geometrical Approaches to Visual Motion Analysis: International Dagstuhl Seminar, Dagstuhl Castle, Germany, July 13-18, 2008. Revised Papers (2009)

A look at the Middlebury optical flow benchmark [5] reveals that nowadays variational methods yield the most accurate optical flow fields between two image frames. In this work we propose an improvement variant of the original duality based TV-L 1 optical flow algorithm in [31] and provide implementation details. This formulation can preserve discontinuities in the flow field by employing total variation (TV) regularization. Furthermore, it offers robustness against outliers by applying the robust L 1 norm in the data fidelity term.Our contributions are as follows. First, we propose to perform a structure-texture decomposition of the input images to get rid of violations in the optical flow constraint due to illumination changes. Second, we propose to integrate a median filter into the numerical scheme to further increase the robustness to sampling artefacts in the image data. We experimentally show that very precise and robust estimation of optical flow can be achieved with a variational approach in real-time. The numerical scheme and the implementation are described in a detailed way, which enables reimplementation of this high-end method.
Confidence Bias in Situation Awareness
Authors: Ketut Sulistyawati, Yoon Ping Chui
Venue: Proceedings of the 8th International Conference on Engineering Psychology and Cognitive Ergonomics: Held as Part of HCI International 2009 (2009)

In this paper, we explore the concept of confidence bias in Situation Awareness (SA), i.e., the perception of own situational knowledge, a meta-cognition aspect of SA. Two studies were conducted to evaluate the nature of confidence bias across the present and future status, and across individual and team missions, as well as its relation with performance outcome. The results from both studies were consistent. Participants' confidence bias was higher in the future than present status, but did not differ significantly across individual and team missions. Participants who had lower confidence bias were found to have better performance.
Neural network based photometric stereo using illumination planning
Authors: Yuji Iwahori, Wataru Kato, Md. Shoaib Bhuiyan, Robert J. Woodham, Naohiro Ishii
Venue: Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2 (1997)

This paper proposes a new approach of neural network based photometric stereo in which all the directions of illumination are close to and is rotationally moved around the viewing direction. Adjacent light sources cause a numerically ill-conditioned problem. ill-conditioning is overcome in two ways. First, many more than three images are acquired. Second, principal components analysis is used as a linear preprocessing technique to determine a reduced dimensionality subspace for use as input. The approach is empirical and uses the radial basis function (RBF) neural network to do non-parametric functional approximation. One neural network maps image irradiance to surface normal. A second network maps surface normal to image irradiance. The two networks are trained using samples from a calibration sphere. Comparison between the actual input and the inversely predicted input is used as a confidence estimate. Further, we introduce the illumination planning, which disposes the information of the cast shadow region from the ill-effected light sources for each point on the object using the confidence estimate. Then, the best combination of NN units with well-effected light sources for each point are used to get the relevant results. The results on real data are also evaluated.
Using graphical examples to motivate the study of recursion
Authors: Ben Stephenson
Venue: Journal of Computing Sciences in Colleges (2009)

Recursion is an important part of a complete computer science education. It is a topic that is often introduced during the first course, and then revisited when tree data structures are introduced and functional languages are discussed. Because recursion is introduced early in the curriculum, the range of problems that can be discussed when it is first encountered is limited. In particular, because students generally haven't been introduced to data structures like trees or efficient sorting algorithms such as quick sort and merge sort, these areas which use recursion to elegantly solve important problems can't be used to motivate its study. This paper describes three graphical problems which can be used to motivate the study of recursion. Each of the examples demonstrates the utility of recursion while being appropriate for students nearing the end of their first term in a computer science course. In particular, these examples can be understood and implemented by students who have a reasonable understanding of loops, function calls, and, for some problems, arrays. They do not require any knowledge of object oriented design, pointers or complex data structures. Furthermore, while iterative solutions exist for each of the problems that we describe, the iterative solution for each problem is more complex than its recursive solution. We believe that this is an additional strength of these examples because it helps convince students that recursion is a tool that will allow them solve some problems more easily.
Vascular Tree Construction with Anatomical Realism for Retinal Images
Authors: Kai-Shung Lin, Chia-Ling Tsai, Michal Sofka, Chih-Hsiangng Tsai, Shih-Jen Chen, Wei-Yang Lin
Venue: Proceedings of the 2009 Ninth IEEE International Conference on Bioinformatics and Bioengineering (2009)

In this paper, we present a method to automatically extract the vessel segments and construct the vascular tree with anatomical realism from a color retinal image. The significance of the work is to assist in clinical studies of diagnosis of cardio-vascular diseases, such as hypertension,which manifest abnormalities in either venous and/or arterial vascular systems. To maximize the completeness of vessel extraction, we introduce vessel connectiveness measure to improve on an existing algorithm which applies multiscale matched filtering and vessel likelihood measure.Vessel segments are grouped using extended Kalman filter to take into consideration continuities in curvature, width,and color changes at the bifurcation or crossover point. The algorithm is tested on five images from the DRIVE database,a mixture of normal and pathological images, and the results are compared with the ground truth images provided by a physician. The preliminary results show that our method reaches an average success rate of 92.1%.
Effects of word confusion networks on voice search
Authors: Junlan Feng, Srinivas Bangalore
Venue: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (2009)

Mobile voice-enabled search is emerging as one of the most popular applications abetted by the exponential growth in the number of mobile devices. The automatic speech recognition (ASR) output of the voice query is parsed into several fields. Search is then performed on a text corpus or a database. In order to improve the robustness of the query parser to noise in the ASR output, in this paper, we investigate two different methods to query parsing. Both methods exploit multiple hypotheses from ASR, in the form of word confusion networks, in order to achieve tighter coupling between ASR and query parsing and improved accuracy of the query parser. We also investigate the results of this improvement on search accuracy. Word confusion-network based query parsing outperforms ASR 1-best based query-parsing by 2.7% absolute and the search performance improves by 1.8% absolute on one of our data sets.
Introduction to Frontiers in Corpus Annotation II: Pie in the Sky
Authors: Adam Meyers
Venue: Proceedings of the Workshop on Frontiers in Corpus Annotations II: Pie in the Sky (2005)

The Frontiers in Corpus Annotation workshops are opportunities to discuss the state of the art of corpus annotation in computational linguistics. Corpus annotation has pushed the enitre field in new directions by providing new task definitions and new standards of analysis. At the first Frontiers in Corpus Annotation workshop at HLT-NAACL 2004 we compared assumptions underlying different annotation projects in light of both multilingual applications and the pursuit of merged representations that incorporate the result of various annotation projects.
Inductive learning of pronunciation rules by hypothesis testing and correction
Authors: S. Oakey, R. C. Cawthorn
Venue: Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1 (1981)

This paper describes a system that learns the rules of pronunciation inductively. It begins with a set of 26 rules for single-letter pronunciation. Individual words are presented to it, and the system uses its rule set to hypothesise a pronunciation. This is compared with a dictionary pronunciation, and if any part of the pronunciation is incorrect new rules are created to handle the word as an exception condition. These rules are checked for similarity with others already produced, and where suitable a "general" rule is produced to deal with two or more created rules. The effect is to produce rules that are more and more general, and these approach the general pronunciation rule sets that have been produced manually by other workers.
Optimal Operation Model of Water Supply Network and Application
Authors: Wei Zhang, Zhihong Zou, Huiwen Wang
Venue: Proceedings of the 2009 IITA International Conference on Services Science, Management and Engineering (2009)

Taking customer water quality and electricity consumption of pumping station as objective function, a direct optimal operation model of water supply network was established and solved by using particle swarm algorithm in this paper, then the model determined the optimal running scheme of network when satisfying hydraulic constraints and boundary condition. Comparing the optimal operation model with experience operation, the result showed that the optimal operation model was much better, and particle swarm optimization was feasible to solve the problem in optimal operation of water supply network.
Online word sense disambiguation with structural semantic interconnections
Authors: Roberto Navigli
Venue: Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Posters & Demonstrations (2006)

In this paper we present an online implementation of a knowledge-based Word Sense Disambiguation algorithm called Structural Semantic Interconnections (SSI). We describe the system implementation and the user interface, and discuss the strengths and weaknesses of our approach.
Multimodal Corpus Analysis as a Method for Ensuring Cultural Usability of Embodied Conversational Agents
Authors: Yukiko Nakano, Matthias Rehm
Venue: Proceedings of the 1st International Conference on Human Centered Design: Held as Part of HCI International 2009 (2009)

In this paper we propose the method of multimodal corpus analysis to collect enough empirical data for modeling the behavior of embodied conversational agents. This is a prerequisite to ensure the usability of such complex interactive systems. So far, the development of embodied agents suffers from a lack of explicit usability methods. In most cases, the consideration of usability aspects is constrained to preliminary user tests at the end of the development process.
A Design of Secure Preferential E-Voting
Authors: Kun Peng, Feng Bao
Venue: Proceedings of the 2nd International Conference on E-Voting and Identity (2009)

A secure preferential e-voting scheme is designed in this paper. It is a homomorphic e-voting scheme. It is illustrated that although mix-based voting is a very simple solution to preferential e-voting it is vulnerable to a coercion attack. The coercion attack especially attacks preferential e-voting scheme only outputs the election result and does not reveal any vote, so is invulnerable to the attack. Homomorphism of the employed encryption algorithm is exploited not only to count the votes without revealing them but also to adjust the votes when a new round of counting is needed. Moreover, it achieves all the security properties usually desired in e-voting.
ERP Systems Development: Enhancing Organization's Strategic Control through Monitoring Agents
Authors: Fabio Mulazzani, Barbara Russo, Giancarlo Succi
Venue: Proceedings of the 2009 Eigth IEEE/ACIS International Conference on Computer and Information Science (2009)

Enterprise Resource Planning Systems (ERP) are powerful software packages that enable the integration of a variety of business activities. There are many success stories of ERP systems use and implementation, but there are also significant evidence of failures. So far, there is not enough research that investigates the technical factors that determine the success of use of an ERP system.This paper investigates the case study of a bus company that needs to enhance its ERP system to support the alignment between business processes and strategy. The specific problem of the company was the low level of managerial control over the bus maintenance process, resulting in a poor performance of the process and a consequent low quality of the final service to the customer. The solution to the problem we proposed was the integration of monitoring agents called probes into the ERP system that are able to link the process performances to the strategic goals, objectives or constraints.The introduction of the probes enhances the organization's strategic control by offering a continuous monitoring of the strategic goals achievement, hence increasing the understanding of the company behavior. That has determined the adoption of strategic changes that managed to obtain higher performances of the maintenance process and consequentially an higher quality of the offered service.
Education: Postprocessing in Automated Grading Systems, Part 3
Authors: Peter A. Knipp, S. Raj Chaudhury
Venue: IEEE Design Test (2009)

Postprocessing provides crucial flexibility when instructors use automated systems to assess student performance in science laboratory courses.