ppxscal/embedding_network huggingface dataset:
Introduction To Numerical Analysis Using MATLAB with CD-ROM, 1st edition
Authors: Rizwan, Butt
Venue: Computer Science (2007)

Numerical analysis is the branch of mathematics concerned with the theoretical foundations of numerical algorithms for the solution of problems arising in scientific applications. Designed for courses in numerical analysis and as a reference for practicing engineers and scientists, this book presents the theoretical concepts of numerical analysis and the practical justification of these methods through computer examples with the latest version of MATLAB. The book addresses a variety of questions ranging from the approximation of functions and integrals to the approximate solution of algebraic, transcendental, differential and integral equations, with particular emphasis on the stability, accuracy, efficiency and reliability of numerical algorithms. The CD-ROM which accompanies the book includes source code, a numerical toolbox, executables, and simulations. FEATURES*Presents each numerical method by first providing examples and geometric motivation, then the steps to perform the computation, and finally the mathematical derivation of the process *Provides short programs in MATLAB that can be used for scientific applications with or without modifications *Shows the visual representation of mathematical concepts in 2D graphics and is compatible with the current MATLAB v.7.5. *Accompanied by a CD-ROM featuring source code, executables, figures, and simulations *Includes an introduction to MATLAB commands *Features an Instructor s Resource Disc for use as a textbook BRIEF TABLE OF CONTENTS 1. Number Systems and Errors. 2. Nonlinear Equations. 3. Systems of Linear Equations. 4. Approximating Functions. 5. Numerical Differentiation and Integration. 6. Ordinary Differential Equations. 7. The Eigenvalue problems. Appendix A. Mathematical Preliminaries. B. Introduction to MATLAB.
On Approximate Range Counting and Depth
Authors: Peyman Afshani, Timothy M. Chan
Venue: Discrete Computational Geometry (2009)

We improve the previous results by Aronov and Har-Peled (SODA&#x2019;05) and Kaplan and Sharir (SODA&#x2019;06) and present a randomized data structure of O(n) expected size which can answer 3D approximate halfspace range counting queries in $O(\log{n\over k})$expected time, where k is the actual value of the count. This is the first optimal method for the problem in the standard decision tree model; moreover, unlike previous methods, the new method is Las Vegas instead of Monte Carlo. In addition, we describe new results for several related problems, including approximate Tukey depth queries in 3D, approximate regression depth queries in 2D, and approximate linear programming with violations in low dimensions.
Experimental Investigation and Implementation of Support for Problem Generation by Presenting Cases
Authors: Kazuaki Kojima, Kazuhisa Miwa
Venue: Proceeding of the 2006 conference on Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding (2006)

The usefulness of cases in creative generation has been widely discussed and well recognized. However, it has been also experimentally indicated that introducing cases can limit human creative generation. In this study, we investigate the effects of case presentation in the task domain of generating mathematical word problems. Mathematical word problems have two essential attributes: surface problem situations and the mathematical structures of solutions, the control of which is recognized as an important issue in learning mathematics. In response, we present problems as cases to participants while controlling those attributes, with the results indicating that such presentation actually provides different constraining effects based on the control to their problem generation. Based on the results, we propose a support system for generating word problems, which presents problems as cases by controlling similarities.
Application of BP Neural Network in Stock Market Prediction
Authors: Bin Fang, Shoufeng Ma
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

Prediction for the change of stock market has been a hot research subject over the years. This thesis has introduced the definition and arithmetic of BP neural network model and established a stock market index prediction model based on the BP neural network model by taking advantage of the self-learning, self-adapting and nonlinear approximate ability. It is shown through empirical research that BP model not only has a rapid velocity of convergence and a high precision of prediction, but also has a certain application value if it is used for the short-term prediction of stock market index.
A novel lexicalized HMM-based learning framework for web opinion mining
Authors: Wei Jin, Hung Hay Ho
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

Merchants selling products on the Web often ask their customers to share their opinions and hands-on experiences on products they have purchased. As e-commerce is becoming more and more popular, the number of customer reviews a product receives grows rapidly. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. In this research, we aim to mine customer reviews of a product and extract highly specific product related entities on which reviewers express their opinions. Opinion expressions and sentences are also identified and opinion orientations for each recognized product entity are classified as positive or negative. Different from previous approaches that have mostly relied on natural language processing techniques or statistic information, we propose a novel machine learning framework using lexicalized HMMs. The approach naturally integrates linguistic features, such as part-of-speech and surrounding contextual clues of words into automatic learning. The experimental results demonstrate the effectiveness of the proposed approach in web opinion mining and extraction from product reviews.
Software development under bounded rationality and opportunism
Authors: Cengiz Erbas, Bahar Celikkol Erbas
Venue: Proceedings of the 2009 ICSE Workshop on Software Development Governance (2009)

Due to the wicked nature of software development, it is imperative to take into consideration bounded rationality and opportunism of the agents involved in, when managing software development projects. These two concepts, which are at the foundations of transaction cost economics, have not yet been studied in relation to software engineering. In this paper, we develop transaction cost economics approach to software engineering, specifically as it relates to governance. This approach enables us better understand the strengths and weaknesses of top-down and bottom-up processes, and outlines a method to be used in choosing amongst alternative governance structures given relevant project characteristics.
Equilibrium dynamic systems intelligence
Authors: Marius-Constantin Popescu, Onisifor Olaru, Nikos Mastorakis
Venue: WSEAS Transactions on Information Science and Applications (2009)

Most work in Artificial Intelligence reviews the balance of classic game theory to predict agent behavior in different positions. In this paper we introduce steady competitive analysis. This approach bridges the gap between the standards of desired paths of artificial intelligence, where a strategy must be selected in order to ensure an end result and a balanced analysis. We show that a strategy without risk level is able to guarantee the value obtained in the Nash equilibrium, by more scientific methods of classical computers. Then we will discuss the concept of competitive strategy and illustrate how it is used in a decentralized load balanced position, typical for network problems. In particular, we will show that when there are many agents, it is possible to guarantee an expected final result, which is a 8/9 factor of the final result obtained in the Nash equilibrium. Finally, we will discuss about extending the above concept in Bayesian game and illustrate its use in a basic structure of an auction.
A hybrid approach to multi-agent decision-making
Authors: Paulo Trigo, Helder Coelho
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

In the aftermath of a large-scale disaster, agents' decisions derive from self-interested (e.g. survival), common-good (e.g. victims' rescue) and teamwork (e.g. fire extinction) motivations. However, current decision-theoretic models are either purely individual or purely collective and find it difficult to deal with motivational attitudes; on the other hand, mental-state based models find it difficult to deal with uncertainty. We propose a hybrid, CvI-JI, approach that combines: i) collective 'versus' individual (CvI) decisions, founded on the Markov decision process (MDP) quantitative evaluation of joint-actions, and ii) joint-intentions (JI) formulation of teamwork, founded on the belief-desire-intention (BDI) architecture of general mental-state based reasoning. The CvI-JI evaluation explores the performance's improvement during the process of learning a coordination policy in a partially observable stochastic domain.
Non-traditional exam seat arrangements
Authors: Cristina Pomales-Garcia, Hector J. Carlo, Tanya M. Ramos-Ortiz, Iza M. Figueroa-Santiago, Sharlyn Garcia-Ortiz
Venue: Computers and Industrial Engineering (2009)

Assuming that seats (chair desks) are identical and can be moved freely within a classroom, this study proposes innovative exam seat arrangements inspired on geometric and architectural patterns, to minimize the possibility of cheating by peaking at a neighbor's exam. Due to a dearth of information regarding legibility in an exam setting, an experiment to investigate the legibility of various characters in different combinations of angles and distances is performed. The results from the experiment are used to identify a legibility region, which is then used to evaluate the proposed arrangements in rectangular classrooms with the same area, but different length-to-width ratios.
Are Parallel BDI Agents Really Better?
Authors: Huiliang Zhang, Shell Ying Huang
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

The traditional BDI agent has 3 basic computational components that generate beliefs, generate intentions and execute intentions. They run in a sequential and cyclic manner. This may introduce several problems. Among them, the inability to watch the environment continuously in dynamic environments may be disastrous and makes an agent less rational --the agent may endanger itself. Two possible solutions are by parallelism and by controlling and managing the 3 components in suitable ways. We examine a parallel architecture with three parallel running components which are the belief manager, the intention generator and the intention executor. The agent built with this architecture will have the ability of performing several actions at once. To evaluate the parallel BDI agent, we compare the parallel agent against four versions of sequential agents where the 3 components of the BDI agent are controlled and managed in different ways and different time resources are allocated to them. Experiments are designed to simulate agents based on the sequential and parallel BDI architectures respectively and the ability of the agents to respond to the same sequences of external events of various priorities are assessed. The comparison results show that the parallel BDI agent has quicker response, react to emergencies immediately and its behaviour is more rational.
What's inside the Cloud? An architectural map of the Cloud landscape
Authors: Alexander Lenk, Markus Klems, Jens Nimis, Stefan Tai, Thomas Sandholm
Venue: Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing (2009)

We propose an integrated Cloud computing stack architecture to serve as a reference point for future mash-ups and comparative studies. We also show how the existing Cloud landscape maps into this architecture and identify an infrastructure gap that we plan to address in future work.
An Intention-oriented Model of Copyright Law for e-Learning: International Semantic Mapping of Copyright Laws Based on A Copyright Ontology
Authors: Wenhuan Lu, Mitsuru Ikeda
Venue: Proceeding of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation (2005)

Global sharing of e-Learning resources shows a rising tendency. Copyright issue is very important in exchanging e-Learning resources among different countries. However there is a lack of systemic knowledge management methodology for the issue. This study aims to develop a methodology and design an IT framework to provide semantical information on the mapping among multinational copyright laws. For this propose, we have been adopting ontological engineering approach for appropriately representing similarity among multinational laws. In this research, we focus on the “intention behind a law” because the semantic similarity among the laws can be well recognized based on the intention. Copyright law ontology provides well-organized concept to clarify the commonality and the difference among multinational laws. In this paper, we intend to focus on “intention-oriented” multinational copyright law model (“iMCM”), which can formulate domain principle of law. After giving an overview of iMCM, we detail an illustrative example to show how to represent the intention behind the law using common concept defined in ontology.
Modelling of pressure gradient in the space behind the projectile
Authors: Ludek Jedlička, Stanislav Beer, Miroslav Vídeňka
Venue: Proceedings of the 7th WSEAS international conference on System science and simulation in engineering (2008)

The article deals with modelling of pressure distribution of propellant gases in the space behind the projectile of ordinary ballistic system. The number of existing models describing the pressure gradient is analyzed and confronted with results of experimental firings carried out on the high performance anti-aircraft cannon of calibre of 30 mm.
Evaluation Methods for Ordinal Classification
Authors: Lisa Gaudette, Nathalie Japkowicz
Venue: Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence (2009)

Ordinal classification is a form of multi-class classification where there is an inherent ordering between the classes, but not a meaningful numeric difference between them. Little attention has been paid as to how to evaluate these problems, with many authors simply reporting accuracy, which does not account for the severity of the error. Several evaluation metrics are compared across a dataset for a problem of classifying user reviews, where the data is highly skewed towards the highest values. Mean squared error is found to be the best metric when we prefer more (smaller) errors overall to reduce the number of large errors, while mean absolute error is also a good metric if we instead prefer fewer errors overall with more tolerance for large errors.
Wavelet Neural Networks and Support Vector Machine for Financial Distress Prediction Modelling: The Chinese Case
Authors: Hongshan Yao
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

Wavelet neural networks (WNN) and support vector machine (SVM) are two advanced methods which are fit for classification. A comparative analysis of the two methods was conducted based on Chinese firms. The results show WNN has good classification effect. Wavelet decomposition has been demonstrated to be an effective tool for recognizing the firms' feature. Also the study applied SVM to the same estimation sample and test sample. The results show SVM is much superior to WNN for small sample learning.
Eliciting truthful answers to multiple-choice questions
Authors: Nicolas Lambert, Yoav Shoham
Venue: Proceedings of the tenth ACM conference on Electronic commerce (2009)

Motivated by the prevalence of online questionnaires in electronic commerce, and of multiple-choice questions in such questionnaires, we consider the problem of eliciting truthful answers to multiple-choice questions from a knowledgeable respondent. Specifically, each question is a statement regarding an uncertain future event, and is multiple-choice -- the responder must select exactly one of the given answers. The principal offers a payment, whose amount is a function of the answer selected and the true outcome (which the principal will eventually observe). This problem significantly generalizes recent work on truthful elicitation of distribution properties, which itself generalized a long line of work in elicitation of complete distributions. We provide necessary and sufficient conditions for the existence of payments that induce truthful answers, and give a characterization of those payments. We also study in greater details the common case of questions with ordinal answers, and illustrate our results with several examples of practical interest.
Combining Domain-Independent Planning and HTN Planning: The Duet Planner
Authors: Alfonso Gerevini, Ugur Kuter, Dana Nau, Alessandro Saetti, Nathaniel Waisbrot
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

Despite the recent advances in planning for classical domains, the question of how to use domain knowledge in planning is yet to be completely and clearly answered. Some of the existing planners use domain-independent search heuristics, and some others depend on intensively-engineered domain-specific knowledge to guide the planning process. In this paper, we describe an approach to combine ideas from both of the above schools of thought. We present Duet, our planning system that incorporates the ability of using hierarchical domain knowledge in the form of Hierarchical Task Networks (HTNs) as in SHOP2 [14] and using domain-independent local search techniques as in LPG [8]. In our experiments, Duet was able to solve much larger problems than LPG could solve, with only minimal domain knowledge encoded in HTNs (much less domain knowledge than SHOP2 needed to solve those problems by itself).
Unexpected high temperature chemical freezing during slow isochoric cooling
Authors: Radu Dan Rugescu
Venue: Proceedings of the 10th WSEAS international conference on Mathematical and computational methods in science and engineering (2008)

An observation is presented regarding the chemical freezing during a slow calorimeter cooling, after the combustion of solid propellants with excess fuel elements into a vacuum. The residual combustion gas within the calorimeter, mainly consisting of CO2, CO, H2O, H2 and N2, although cooled down to the room temperature, presents a chemical composition that meets almost exactly the value encountered at a very high temperature, namely around 1674±36°K. It appears abnormal that, during the very slow cooling which follows the isochoric combustion, when any chemical freezing seems mostly improbable, such a freezing occurs however. With utmost regularity, the gases refuse completely to follow the equilibrium below this high 1700K limit. The fact is known at much lower temperatures for the isobaric combustion with oxygen in excess, still there are doubts that it had been previously observed for calorimeter products involving the entire water-gas reaction. Numerical simulation of combustion and nozzle expansion processes got a high accuracy level when this process was included in the computational codes. Recent temperature measurements confirm with good accuracy that this freezing temperature is well below the maximal combustion temperature within the calorimeter, but still very high. Detailed investigations are envisaged through the national grant proposal INTECH by a consortium of professional research teams.
Translation of Overlay Models of Student Knowledge for Relative Domains Based on Domain Ontology Mapping
Authors: Sergey Sosnovsky, Peter Dolog, Nicola Henze, Peter Brusilovsky, Wolfgang Nejdl
Venue: Proceeding of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (2007)

The effectiveness of an adaptive educational system in many respects depends on the precision of modeling assumptions it makes about a student. One of the well-known challenges in student modeling is to adequately assess the initial level of student's knowledge when s/he starts working with a system. Sometimes potentially handful data are available as a part of user model from a system used by the student before. The usage of external user modeling information is troublesome because of differences in system architecture, knowledge representation, modeling constraints, etc. In this paper, we argue that the implementation of underlying knowledge models in a sharable format, as domain ontologies-along with application of automatic ontology mapping techniques for model alignment-can help to overcome the “new-user” problem and will greatly widen opportunities for student model translation. Moreover, it then becomes possible for systems from relevant domains to rely on knowledge transfer and reuse those portions of the student models that are related to overlapping concepts.
The Content Analysis of Social Presence and Collaborative Learning Behavior Patterns in a Computer-Mediated Learning Environment
Authors: Hyo-Jeong So
Venue: Proceeding of the 2005 conference on Towards Sustainable and Scalable Educational Innovations Informed by the Learning Sciences: Sharing Good Practices of Research, Experimentation and Innovation (2005)

The purpose of the present study was to examine social presence and collaborative learning behavior patterns in asynchronous discussion forums using the content analysis method which has become a popular qualitative research approach in distance education. Two online discussion forums of students who enrolled in a distance course at a major university in the United States were analyzed by two coders using coding schemes. The inter-coder reliability measures were.87 for Holsti's CR and.91 for Cohen's Kappa. The results revealed that students showed affective, interactive, and cohesive behaviors of social presence to help each other in the process of collaborative learning. Additionally, this study found that while students actively exchanged resources and feedback, there was a lack of elaborating and challenging behaviors. In conclusion, it was argued that distance educators should consider instructional methods to increase the level of social presence in order to facilitate collaborative learning processes. This study will be beneficial to those who are interested in examining the effect of the social dimensions of learning in a distance learning environment, and also those who are interested in employing qualitative content analysis methods.
Sketch-Based Interface for Crowd Animation
Authors: Masaki Oshita, Yusuke Ogiwara
Venue: Proceedings of the 10th International Symposium on Smart Graphics (2009)

In this paper, we propose a novel interface for controlling crowd animation. Crowd animation is widely used in movie production and computer games. However, to make an intended crowd animation, a lot of agent model parameters have to be tuned through trial and error. Our method estimates crowd parameters based on a few example paths given by a user through a sketch-based interface. The parameters include guiding paths, moving speed, distance between agents, and adjustments of the distance (crowd regularity). Based on the computed parameters, a crowd animation is generated using an agent model. We demonstrate the effectiveness of our method through our experiments.