ppxscal/embedding_network huggingface dataset:
On very true operators on pocrims
Authors: Radom&#x00ed;r Hala&#x0161;, Michal Botur
Venue: Soft Computing - A Fusion of Foundations, Methodologies and Applications (2009)

Hájek introduced the logic enriching the logic BL by a unary connective vt which is a formalization of Zadeh's fuzzy truth value "very true". algebras, i.e., BL-algebras with unary operations, called vt-operators, which are among others subdiagonal, are an algebraic counterpart of . Partially ordered commutative integral residuated monoids (pocrims) are common generalizations of both BL-algebras and Heyting algebras. The aim of our paper is to introduce and study algebraic properties of pocrims endowed by "very-true" and "very-false"-like operators.
Intelligent Pedagogical Action Selection under Uncertainty
Authors: Selvarajah Mohanarajah, Ray Kemp, Elizabeth Kemp
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

This paper describes a novel design for a Learner Model, which handles the effects of uncertainty formally in Pedagogical Action Selection (PAS) [1]. In our design, a mixture of Dynamic Fuzzy and Bayesian approaches are used for the PAS strategy. We treat the strength of pedagogical actions as a fuzzy variable. We also use the fuzzy logic theory effectively in dynamic prediction.
Pre-estimate on Transport Volume of Container in Xiangjiang Catchment
Authors: Jian-Lan Zhou
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

This paper analyzes systematically common forecasting methods for transport volume of container in Xiangjiang catchment, and applies synthetically quantitative forecasting methods such as regression analysis, traffic method-sharing model, neural networks, etc. The results are proved reasonable after comparing with related data, and may serve as reference for throughout or transport volume pre-estimation of other river catchments. It also suggests that the marine conveyance container transport on Xiangjiang River take several measurements to improve the competitive power in many transport methods.
Dispute prevention and dispute resolution in networked health information technology
Authors: Norman Sondheimer, Ethan Katsh, Lori Clarke, Leon Osterweil, Daniel Rainey
Venue: Proceedings of the 10th Annual International Conference on Digital Government Research: Social Networks: Making Connections between Citizens, Data and Government (2009)

The United States has committed to promote Health Information Technology through Electronic Health Records that will be exchanged among participants in the healthcare enterprise. In anticipation of problems, legal remedies have been established to protect the rights of all participants, especially their rights to protect the privacy of their own information. However, there are certain to be situations where disputes arise among participants that can be resolved without recourse to legal action. This paper pursues the possibility of establishing policies to encourage alternative dispute resolution through online methods integrated into the health information technology processes and lays out areas requiring research.
Faster polynomial multiplication via multipoint Kronecker substitution
Authors: David Harvey
Venue: Journal of Symbolic Computation (2009)

We present several new algorithms for dense polynomial multiplication in Z[x] based on the Kronecker substitution method. Instead of reducing to a single integer multiplication, we reduce to several smaller multiplications. We describe an implementation of multiplication in (Z/nZ)[x] for a word-sized modulus n based on these methods, and compare its performance to that of NTL and Magma.
Minimum Vertex Ranking Spanning Tree Problem on Permutation Graphs
Authors: Ruei-Yuan Chang, Guanling Lee, Sheng-Lung Peng
Venue: Proceedings of the 6th Annual Conference on Theory and Applications of Models of Computation (2009)

The minimum vertex ranking spanning tree problem on graph G is to find a spanning tree T of G such that the minimum vertex ranking of T is minimum among all possible spanning trees of G. In this paper, we propose a linear-time algorithm for this problem on permutation graphs. It improves a previous result that runs in O(n 3) time where n is the number of vertices in the input graph.
Weaving executability into UML class models at PIM level
Authors: Elvinia Riccobene, Patrizia Scandurra
Venue: Proceedings of the 1st Workshop on Behaviour Modelling in Model-Driven Architecture (2009)

Modeling languages that aim to capture PIM level behavior are still a challenge. We propose a high level behavioral formalism based on the Abstract State Machines (ASMs) for the specification and validation of software systems at PIM level. An ASM-based extension of the UML and its Action Semantics is here presented for the construction of executable class models at PIM level and also a model weaving process which makes the execution of such models possible. Our approach is illustrated using an Invoice Order System taken from the literature.
A conversation with David E. Shaw
Authors: CACM Staff
Venue: Communications of the ACM (2009)

Stanford professor Pat Hanrahan sits down with the noted hedge fund founder, computational biochemist, and (above all) computer scientist.
Error analysis and applications of the Fourier-Galerkin Runge-Kutta schemes for high-order stiff PDEs
Authors: N. Vaissmoradi, A. Malek, S. H. Momeni-Masuleh
Venue: Journal of Computational and Applied Mathematics (2009)

An integrating factor mixed with Runge-Kutta technique is a time integration method that can be efficiently combined with spatial spectral approximations to provide a very high resolution to the smooth solutions of some linear and nonlinear partial differential equations. In this paper, the novel hybrid Fourier-Galerkin Runge-Kutta scheme, with the aid of an integrating factor, is proposed to solve nonlinear high-order stiff PDEs. Error analysis and properties of the scheme are provided. Application to the approximate solution of the nonlinear stiff Korteweg-de Vries (the 3rd order PDE, dispersive equation), Kuramoto-Sivashinsky (the 4th order PDE, dissipative equation) and Kawahara (the 5th order PDE) equations are presented. Comparisons are made between this proposed scheme and the competing method given by Kassam and Trefethen. It is found that for KdV, KS and Kawahara equations, the proposed method is the best.
Efficient approximate entity extraction with edit distance constraints
Authors: Wei Wang, Chuan Xiao, Xuemin Lin, Chengqi Zhang
Venue: Proceedings of the 35th SIGMOD international conference on Management of data (2009)

Named entity recognition aims at extracting named entities from unstructured text. A recent trend of named entity recognition is finding approximate matches in the text with respect to a large dictionary of known entities, as the domain knowledge encoded in the dictionary helps to improve the extraction performance. In this paper, we study the problem of approximate dictionary matching with edit distance constraints. Compared to existing studies using token-based similarity constraints, our problem definition enables us to capture typographical or orthographical errors, both of which are common in entity extraction tasks yet may be missed by token-based similarity constraints. Our problem is technically challenging as existing approaches based on q-gram filtering have poor performance due to the existence of many short entities in the dictionary. Our proposed solution is based on an improved neighborhood generation method employing novel partitioning and prefix pruning techniques. We also propose an efficient document processing algorithm that minimizes unnecessary comparisons and enumerations and hence achieves good scalability. We have conducted extensive experiments on several publicly available named entity recognition datasets. The proposed algorithm outperforms alternative approaches by up to an order of magnitude.
Using FAQ as a Case Base for Intelligent Tutoring
Authors: Demetrius Ribeiro Lima, Marta Costa Rosatelli
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

This paper presents an Intelligent Tutoring System that was designed and integrated to a web-based distance learning environment. In this system, the Frequently Asked Questions is the knowledge base of a Case-Based Reasoning module that is used to retrieve situations similar to the ones currently presented by the student, i.e., to search for answers to the students' queries.
Translating medical terminologies through word alignment in parallel text corpora
Authors: Louise Deléger, Magnus Merkel, Pierre Zweigenbaum
Venue: Journal of Biomedical Informatics (2009)

Developing international multilingual terminologies is a time-consuming process. We present a methodology which aims to ease this process by automatically acquiring new translations of medical terms based on word alignment in parallel text corpora, and test it on English and French. After collecting a parallel, English-French corpus, we detected French translations of English terms from three terminologies-MeSH, SNOMED CT and the MedlinePlus Health Topics. We obtained respectively for each terminology 74.8%, 77.8% and 76.3% of linguistically correct new translations. A sample of the MeSH translations was submitted to expert review and 61.5% were deemed desirable additions to the French MeSH. In conclusion, we successfully obtained good quality new translations, which underlines the suitability of using alignment in text corpora to help translating terminologies. Our method may be applied to different European languages and provides a methodological framework that may be used with different processing tools.
Self-scaled conjugate gradient training algorithms
Authors: A. E. Kostopoulos, T. N. Grapsa
Venue: Neurocomputing (2009)

This article presents some efficient training algorithms, based on conjugate gradient optimization methods. In addition to the existing conjugate gradient training algorithms, we introduce Perry's conjugate gradient method as a training algorithm [A. Perry, A modified conjugate gradient algorithm, Operations Research 26 (1978) 26-43]. Perry's method has been proven to be a very efficient method in the context of unconstrained optimization, but it has never been used in MLP training. Furthermore, a new class of conjugate gradient (CG) methods is proposed, called self-scaled CG methods, which are derived from the principles of Hestenes-Stiefel, Fletcher-Reeves, Polak-Ribiere and Perry's method. This class is based on the spectral scaling parameter introduced in [J. Barzilai, J.M. Borwein, Two point step size gradient methods, IMA Journal of Numerical Analysis 8 (1988) 141-148]. The spectral scaling parameter contains second order information without estimating the Hessian matrix. Furthermore, we incorporate to the CG training algorithms an efficient line search technique based on the Wolfe conditions and on safeguarded cubic interpolation [D.F. Shanno, K.H. Phua, Minimization of unconstrained multivariate functions, ACM Transactions on Mathematical Software 2 (1976) 87-94]. In addition, the initial learning rate parameter, fed to the line search technique, was automatically adapted at each iteration by a closed formula proposed in [D.F. Shanno, K.H. Phua, Minimization of unconstrained multivariate functions, ACM Transactions on Mathematical Software 2 (1976) 87-94; D.G. Sotiropoulos, A.E. Kostopoulos, T.N. Grapsa, A spectral version of Perry's conjugate gradient method for neural network training, in: D.T. Tsahalis (Ed.), Fourth GRACM Congress on Computational Mechanics, vol. 1, 2002, pp. 172-179]. Finally, an efficient restarting procedure was employed in order to further improve the effectiveness of the CG training algorithms. Experimental results show that, in general, the new class of methods can perform better with a much lower computational cost and better success performance.
P3T: A System to Support Preparing and Performing Peer Tutoring
Authors: Emily Ching, Chih-Ti Chen, Chih-Yueh Chou, Yi-Chan Deng, Tak-Wai Chan
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

Peer tutoring has been proven an effective way to engage students in active learning. This paper presents a system called P3T that supports peer tutoring in a classroom where every student is equipped with TabletPC or laptop with wireless capability. P3T structures peer tutoring by scaffolding both tutors and tutees to prepare for the tutorial session and facilitating their elaborations during their face-to-face tutoring. The rationales of the evolving design of P3T prototype are given and discussed in this paper.
Improving Ontology Matching Using Meta-level Learning
Authors: Kai Eckert, Christian Meilicke, Heiner Stuckenschmidt
Venue: Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications (2009)

Despite serious research efforts, automatic ontology matching still suffers from severe problems with respect to the quality of matching results. Existing matching systems trade-off precision and recall and have their specific strengths and weaknesses. This leads to problems when the right matcher for a given task has to be selected. In this paper, we present a method for improving matching results by not choosing a specific matcher but applying machine learning techniques on an ensemble of matchers. Hereby we learn rules for the correctness of a correspondence based on the output of different matchers and additional information about the nature of the elements to be matched, thus leveraging the weaknesses of an individual matcher. We show that our method always performs significantly better than the median of the matchers used and in most cases outperforms the best matcher with an optimal threshold for a given pair of ontologies. As a side product of our experiments, we discovered that the majority vote is a simple but powerful heuristic for combining matchers that almost reaches the quality of our learning results.
Drawing graphs with non-uniform vertices
Authors: David Harel, Yehuda Koren
Venue: Proceedings of the working conference on Advanced visual interfaces (2002)

The vertices of most graphs that appear in real applications are non-uniform. They can be circles, ellipses, rectangles, or other geometric elements of varying shapes and sizes. Unfortunately, current force directed methods for laying out graphs are suitable mostly for graphs whose vertices are zero-sized and dimensionless points. It turns out that naively extending these methods to handle non-uniform vertices results in serious deficiencies in terms of output quality and performance. In this paper we try to remedy this situation by identifying the special characteristics and problematics of such graphs and offering several algorithms for tackling them. The algorithms can be viewed as carefully constructed extensions of force-directed methods, and their output quality and performance are similar.
NavEx: Providing Navigation Support for Adaptive Browsing of Annotated Code Examples
Authors: Michael Yudelson, Peter Brusilovsky
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

This paper presents NavEx, an adaptive environment for accessing interactive programming examples. NavEx implements a specific kind of adaptive navigation support known as adaptive annotation. The classroom study of NavEx confirmed that adaptive navigation support can visibly increase student motivation to work with non-mandatory educational content. NavEx boosted the overall amount of work and the average length of a session. In addition, various features of NavEx were highly regarded by the students.
Retrospective analysis of RoboCup rescue simulation agent teams
Authors: Harith Siddhartha, Rahul Sarika, Kamal Karlapalem
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 (2009)

We introduce the Score Vector, a new evaluation scheme for the agent teams participating in RoboCup Rescue Simulation Competitions. It is a vector of parameters which monitor the game and provide analysis of the game at a microscopic level, and we show its robustness by thorough testing in various scenarios. We further explore the possibility of designing new scenarios to aptly challenge the teams.
Using domain knowledge for ontology-guided entity extraction from noisy, unstructured text data
Authors: Sergey Bratus, Anna Rumshisky, Rajendra Magar, Paul Thompson
Venue: Proceedings of The Third Workshop on Analytics for Noisy Unstructured Text Data (2009)

Domain-specific knowledge is often recorded by experts in the form of unstructured text. For example, in the medical domain, clinical notes from electronic health records contain a wealth of information. Similar practices are found in other domains. The challenge we discuss in this paper is how to identify and extract part names from technicians repair notes, a noisy unstructured text data source from General Motors' archives of solved vehicle repair problems, with the goal to develop a robust and dynamic reasoning system to be used as a repair adviser by service technicians. In the present work, we discuss two approaches to this problem. We present an algorithm for ontology-guided entity disambiguation that uses existing knowledge sources such as domain-specific ontologies and other structured data. We illustrate its use in automotive domain, using GM parts ontology and the unit structure of repair manuals text to build context models, which are then used to disambiguate mentions of part-related entities in the text. We also describe extraction of part names with a small amount of annotated data using Hidden Markov Models (HMM) with shrinkage, achieving an f-score of approximately 80%. Next we used linear-chain Conditional Random Fields (CRF) in order to model observation dependencies present in the repair notes. Using CRF did not lead to improved performance, but a slight improvement over the HMM results was obtained by using a weighted combination of the HMM and CRF models.
Analysis of Nonlinear Dynamic Structure for the Shanghai Stock Exchange Index
Authors: Yu Dong, Hu Song
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

In this paper,we investigate the statistical properties of the Shanghai stock exchange index(SSEI). A GARCH-M(3,4) model and a TARCH-M(3,4) model successfully capture non-linear structure and asymmetries in the conditional mean and conditional variance. The TARCH-M(3,4) model is better in term of forecasting performance.
A repartitioning hypergraph model for dynamic load balancing
Authors: Umit V. Catalyurek, Erik G. Boman, Karen D. Devine, Doruk Bozdağ, Robert T. Heaphy, Lee Ann Riesen
Venue: Journal of Parallel and Distributed Computing (2009)

In parallel adaptive applications, the computational structure of the applications changes over time, leading to load imbalances even though the initial load distributions were balanced. To restore balance and to keep communication volume low in further iterations of the applications, dynamic load balancing (repartitioning) of the changed computational structure is required. Repartitioning differs from static load balancing (partitioning) due to the additional requirement of minimizing migration cost to move data from an existing partition to a new partition. In this paper, we present a novel repartitioning hypergraph model for dynamic load balancing that accounts for both communication volume in the application and migration cost to move data, in order to minimize the overall cost. The use of a hypergraph-based model allows us to accurately model communication costs rather than approximate them with graph-based models. We show that the new model can be realized using hypergraph partitioning with fixed vertices and describe our parallel multilevel implementation within the Zoltan load balancing toolkit. To the best of our knowledge, this is the first implementation for dynamic load balancing based on hypergraph partitioning. To demonstrate the effectiveness of our approach, we conducted experiments on a Linux cluster with 1024 processors. The results show that, in terms of reducing total cost, our new model compares favorably to the graph-based dynamic load balancing approaches, and multilevel approaches improve the repartitioning quality significantly.
Reasoning in Argumentation Frameworks Using Quantified Boolean Formulas
Authors: Uwe Egly, Stefan Woltran
Venue: Proceeding of the 2006 conference on Computational Models of Argument: Proceedings of COMMA 2006 (2006)

This paper describes a generic approach to implement propositional argumentation frameworks by means of quantified Boolean formulas (QBFs). The motivation to this work is based on the following observations: Firstly, depending on the underlying deductive system and the chosen semantics (i.e., the kind of extension under consideration), reasoning in argumentation frameworks can become computationally involving up to the fourth level of the polynomial hierarchy. This makes the language of QBFs a suitable target formalism since decision problems from the polynomial hierarchy can be efficiently represented in terms of QBFs. Secondly, several practicably efficient solvers for QBFs are currently available, and thus can be used as black-box engines in potential implementations of argumentation frameworks. Finally, the definition of suitable QBF modules provides us with a tool box in order to capture a broad range of reasoning tasks associated to formal argumentation.
Dialogue-Learning Correlations in Spoken Dialogue Tutoring
Authors: Kate Forbes-Riley, Diane Litman, Alison Huettner, Arthur Ward
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

We examine correlations between dialogue characteristics and learning in two corpora of spoken tutoring dialogues: a human-human corpus and a human-computer corpus, both of which have been manually annotated with dialogue acts relative to the tutoring domain. The results from our human-computer corpus show that the presence of student utterances that display reasoning, as well as the presence of reasoning questions asked by the computer tutor, both positively correlate with learning. The results from our human-human corpus show that the introduction of a new concept into the dialogue by students positively correlates with learning, but student attempts at deeper reasoning do not, and the human tutor's attempts to direct the dialogue negatively correlate with learning.