ppxscal/embedding_network huggingface dataset:
Initial Steps in Designing and Delivering Training to Enable Managers to Use the SL Environment to Support Organizational Decision-Making
Authors: M. Susan Wurtz, Dan Power
Venue: Proceeding of the 2008 conference on Collaborative Decision Making: Perspectives and Challenges (2008)

Using virtual worlds such as the Second Life environment for Decision Support activities will require large investments in training. This paper argues that following the processes described by Robert F. Mager for basing the design of instructional strategies and materials on thorough preliminary analyses (Goal Analysis, Performance Problem Analysis, Skills Hierarchy Analysis and Participant Assessment), will ensure that the training is effective and efficient and that several common pitfalls will be avoided. These analyses are themselves a decision support system in that the analyses will suggest that certain training expectations and processes will be effective but the analyses do not dictate every aspect of the design of instructional strategies and materials. Designing effective training in this area will include beta testing and/or control group experimentation. Several questions for testing and/or experimentation are posed.
A hybrid multiagent approach for global trajectory optimization
Authors: Massimiliano Vasile, Marco Locatelli
Venue: Journal of Global Optimization (2009)

In this paper we consider a global optimization method for space trajectory design problems. The method, which actually aims at finding not only the global minimizer but a whole set of low-lying local minimizers (corresponding to a set of different design options), is based on a domain decomposition technique where each subdomain is evaluated through a procedure based on the evolution of a population of agents. The method is applied to two space trajectory design problems and compared with existing deterministic and stochastic global optimization methods.
PVS metamodel
Authors: Lukman Ab. Rahim
Venue: Proceedings of the 8th WSEAS International Conference on Software engineering, parallel and distributed systems (2009)

In translating Object Constraint Language (OCL) to Prototype Verification System (PVS) using model transformation technique, the problem is the absence of PVS metamodel. The PVS metamodel is crucial to the transformation because it defines the elements that may involves in it. This paper proposes a PVS metamodel. The metamodel is created based on the main language construct of PVS where each PVS language construct is represented by element in the metamodel. An example of how the metamodel is used in the transformation is also discussed.
Wavelet filter design based on the lifting scheme and its application in lossless image compression
Authors: Tilo Strutz
Venue: WSEAS Transactions on Signal Processing (2009)

The description of filter banks using lifting structures does not only benefit low-complexity implementation in software or hardware, but is also advantageous for the design of filter banks because of the guaranteed perfect reconstruction property. This paper proposes a new design method for wavelet filter banks, which is explained based on a single lifting structure suitable for 9/7 filter pairs. The filters are derived directly, the factorisation of known filters is not necessary. In addition, it is shown that the signal boundaries can be treated with little computational efforts. The modification of the standard design constraints leads to families of related filter pairs with varying characteristics. It includes a filter bank that can be implemented in integer arithmetic without divisions, shows better performance than the standard 9/7 filter bank for lossless image compression and competitive performance when applied in lossy compression.
Concurrent access control for multi-user and multi-processor systems based on trust relationships
Authors: Isaac Agudo, Carmen Fernandez-Gago, Javier Lopez
Venue: Concurrency and Computation: Practice Experience (2009)

Concurrent access control is an old problem in many fields in Computer Science. It has been solved in many languages and systems, using mechanisms like monitors or priority queues. Nowadays computers implement multi-core capabilities. This means that they are virtually capable of execution of processes in parallel. This requires new techniques and open new issues in the field of concurrent access control. Moreover, most operating systems are multi-user; thus, we have to focus on a multi-processor multi-user scenario. Trust becomes a paramount aspect when building distributed applications; the same applies on a lower scale in modern computers. We propose the use of a trust graph that keeps record of the trust relationships of the system and helps in deciding on concurrent access requests. The information encoded in the graph will be used both in order to decide on the access requests and to order granted requests in terms of their associated trust level. Copyright &copy; 2009 John Wiley & Sons, Ltd.
Kernel methods and component analysis for pattern recognition
Authors: Jason C. Isaacs
Venue:  (2007)

Kernel methods, as alternatives to component analysis, are mathematical tools that provide a higher dimensional representation, for feature recognition and image analysis problems. In machine learning, the kernel trick is a method for converting a linear classification learning algorithm into non-linear one, by mapping the original observations into a higher-dimensional space so that the use of a linear classifier in the new space is equivalent to a non-linear classifier in the original space. In this dissertation we present the performance results of several continuous distribution function kernels, lattice oscillation model kernels, Kelvin function kernels, and orthogonal polynomial kernels on select benchmarking databases. In addition, we develop methods to analyze the use of these kernels for projection analysis applications; principal component analysis, independent component analysis, and optimal projection analysis. We compare the performance results with known kernel methods on several benchmarks. Empirical results show that several of these kernels outperform other previously suggested kernels on these data sets. Additionally, we develop a genetic algorithm-based kernel optimal projection analysis method which, through extensive testing, demonstrates a ten percent average improvement in performance on all data sets over the kernel principal component analysis projection. We also compare our kernels methods for kernel eigenface representations with previous techniques. Finally, we analyze the benchmark databases used here to determine whether we can aid in the selection of a particular kernel that would perform optimally based on the statistical characteristics of each database.
Learning Bayesian network parameters under incomplete data with domain knowledge
Authors: Wenhui Liao, Qiang Ji
Venue: Pattern Recognition (2009)

Bayesian networks (BNs) have gained increasing attention in recent years. One key issue in Bayesian networks is parameter learning. When training data is incomplete or sparse or when multiple hidden nodes exist, learning parameters in Bayesian networks becomes extremely difficult. Under these circumstances, the learning algorithms are required to operate in a high-dimensional search space and they could easily get trapped among copious local maxima. This paper presents a learning algorithm to incorporate domain knowledge into the learning to regularize the otherwise ill-posed problem, to limit the search space, and to avoid local optima. Unlike the conventional approaches that typically exploit the quantitative domain knowledge such as prior probability distribution, our method systematically incorporates qualitative constraints on some of the parameters into the learning process. Specifically, the problem is formulated as a constrained optimization problem, where an objective function is defined as a combination of the likelihood function and penalty functions constructed from the qualitative domain knowledge. Then, a gradient-descent procedure is systematically integrated with the E-step and M-step of the EM algorithm, to estimate the parameters iteratively until it converges. The experiments with both synthetic data and real data for facial action recognition show our algorithm improves the accuracy of the learned BN parameters significantly over the conventional EM algorithm.
ECLIPS Extended Collaborative integrated LIfe cycle Planning System
Authors: A. Peyraud, E. Jacquet-Lagreze, G. Merkuryeva, S. Timmermans, C. Verlhac, V. de Vulpillieres
Venue: Proceeding of the 2008 conference on Collaborative Decision Making: Perspectives and Challenges (2008)

ECLIPS is a European research project, partially funded by the European Commission in the context of its Research Framework Programme 6. Six partners participate in this research project: MÖBIUS (Belgium), EURODECISION (France), LoQutus (Belgium), the Technical University of RIGA (Latvia), Huntsman Advanced Materials (Germany), PLIVA-Lachema Diagnostika (Czech Republic). For more information about ECLIPS we recommend to visit the project web site www.eclipsproject.com. The overall goal of this project is to extend supply chain expertise to recent evolutions: globalisation, products diversification, and shortening of products life cycles. We consider that any life cycle can be divided into three phases: introduction, maturity and end-of-life. Three main issues are considered: Improve the statistical prediction of the demand at the beginning and at the end of a product life. Increase the profit during maturity phase by making cyclic the production at all levels of the process. From a pure mathematical point of view, Multi-Echelon Cyclic Planning induces an additional cost. However, simplification of production management and increase of the manufacturing efficiency should counterbalance this cost. More generally, to improve the whole life cycle management of products in supply chain, including switches between the three phases.
Iris Image Analysis Based on Affinity Propagation Algorithm
Authors: Huabiao Xiao, Ping Guo
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

Biometric identification is getting more and more popular as the demand of security increases. Iris is a promising biometric because of its stability and uniqueness. In this paper, a novel iris analysis method is proposed by using Affinity Propagation (AP) Algorithm. AP algorithm is a new clustering method by transforming the input matrix of similarity between pairs of data points. The proposed method is evaluated using the Chinese Academy of Sciences-Institute of Automation (CASIA) iris image database, the similarity between two irises is measured by their negative Hamming Distance. Experiments indicate that the algorithm has excellent solution in iris analysis, when AP algorithm is used as preprocess in an iris identification system, it can greatly decrease the time complexity of iris recognition.
Authenticated indexing for outsourced spatial databases
Authors: Yin Yang, Stavros Papadopoulos, Dimitris Papadias, George Kollios
Venue: The VLDB Journal &mdash; The International Journal on Very Large Data Bases (2009)

In spatial database outsourcing, a data owner delegates its data management tasks to a location-based service (LBS), which indexes the data with an authenticated data structure (ADS). The LBS receives queries (ranges, nearest neighbors) originating from several clients/subscribers. Each query initiates the computation of a verification object (VO) based on the ADS. The VO is returned to the client that can verify the result correctness using the public key of the owner. Our first contribution is the MR-tree, a space-efficient ADS that supports fast query processing and verification. Our second contribution is the MR*-tree, a modified version of the MR-tree, which significantly reduces the VO size through a novel embedding technique. Finally, whereas most ADSs must be constructed and maintained by the owner, we outsource the MR- and MR*-tree construction and maintenance to the LBS, thus relieving the owner from this computationally intensive task.
Oracle Identity Management: Governance, Risk and Compliance, 2nd Edition Implementing Multinational Regulatory Compliance
Authors: Marlin B. Pohlman
Venue:  (2007)

In this comprehensive work, Dr. Pohlman leverages his experience as both a corporate board member and corporate governance solution implementer to provide a mechanism for promoting corporate accountability and stewardship of personally identifiable information within daily business operations. This guidebook enables both the implementer and the corporate board member to select, configure, and audit the flow of user information throughout the business, aligning the information flow with local laws and regulations. Prior to this effort, Dr. Pohlman co-authored the Oracle Unified Method, an iterative and incremental development process framework developed by Oracle. Extending the work a step further, he aligns the deployment of identity management systems with multinational legal mandates and IT governance frameworks to create a roadmap for achieving successful implementation in the area of Identity Management and Governance, Risk & Compliance. 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
Time-Varying Matrix Square Roots Solving via Zhang Neural Network and Gradient Neural Network: Modeling, Verification and Comparison
Authors: Yunong Zhang, Yiwen Yang, Ning Tan
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

A special kind of recurrent neural networks (RNN) with implicit dynamics has recently been proposed by Zhang et al, which could be generalized to solve online various time-varying problems. In comparison with conventional gradient neural networks (GNN), such RNN (or termed specifically as Zhang neural networks, ZNN) models are elegantly designed by defining matrix-valued indefinite error functions. In this paper, we generalize and investigate the ZNN and GNN models for online solution of time-varying matrix square roots. In addition, software modeling techniques are investigated to model and simulate both neural-network systems. Computer-modeling results verify that superior convergence and efficacy could be achieved by such ZNN models in this time-varying problem solving, as compared to the GNN models.
Teaching Thinking Skills in E-Learning --Application of The Bloom's Taxonomy
Authors: Tse Yew Lee, Tiffany Ho Peiwen
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)

Educational institutions worldwide are recognizing that teaching core subjects alone is not sufficient to equip students for the knowledge economy. To be prepared for the demands of the knowledge economy, students “need to know how to use their knowledge and skills-by thinking critically, applying knowledge to new situations, analyzing information, comprehending new ideas, communicating, collaborating, solving problems, and making decisions.” [1]. One important skill students need to have is 'thinking skills'. Thinking skills is defined as “thinking creatively, making decisions, solving problems, seeing things in the mind's eye, knowing how to learn and reasoning.” [2]. In 1956, Benjamin Bloom and his team of educational psychologists, developed a hierarchy, popularly referred to as “Bloom's Taxonomy”, to categorize 6 levels of thinking. In this paper, we will briefly explain this taxonomy, using examples from the Learning Objects developed by ITE to illustrate how we design and develop online learning activities that are aimed at helping students to develop thinking skills.
MPLS Label Stacking on the Line Network
Authors: Jean-Claude Bermond, David Coudert, Joanna Moulierac, Stéphane Perennes, Hervé Rivano, Ignasi Sau, Fernando Solano Donado
Venue: Proceedings of the 8th International IFIP-TC 6 Networking Conference (2009)

All-Optical Label Switching (AOLS) is a new technology that performs forwarding without any Optical-Electrical-Optical conversions. The most promising scheme to manage the control plane of these optical networks is Generic MultiProtocol Label Switching (GMPLS). In this paper, we study the problem of routing a set of requests in GMPLS networks with the aim of minimizing the number of labels required to ensure the forwarding. In order to spare the label space, we consider label stacking, allowing the configuration of GMPLS tunnels. We study particularly this network design problem when the network is a line. We provide an exact algorithm for the case in which all the requests have a common source and present some approximation algorithms and heuristics when an arbitrary number of sources are distributed over the line. We analyze by simulations the performance of our proposed algorithms and compare them with previous ones.
Theoretical Photochemistry of the Photochromic Molecules Based on Density Functional Theory Methods
Authors: Ivan A. Mikhailov, Artëm E. Masunov
Venue: Proceedings of the 9th International Conference on Computational Science (2009)

Mechanism of photoswitching in diarylethenes involves the light-initiated symmetry-allowed disrotatory electrocyclic reaction. Here we propose a computationally inexpensive Density Functional Theory (DFT) based method that is able to produce accurate potential surfaces for the excited states. The method includes constrained optimization of the geometry for the ground and two excited singlet states along the ring-closing reaction coordinate using the Slater Transition State method, followed by single-point energy evaluation. The ground state energy is calculated with the broken-symmetry unrestricted Kohn-Sham formalism (UDFT). The first excited state energy is obtained by adding the UDFT ground state energy to the excitation energy of the pure singlet obtained in the linear response Time-Dependent (TD) DFT restricted Kohn-Sham formalism. The excitation energy of the double excited state is calculated using a recently proposed (Mikhailov, I. A.; Tafur, S.; Masunov, A. E. Phys. Rev. A 77, 012510, 2008) a posteriori Tamm-Dancoff approximation to the second order response TD-DFT.
Plenary lecture XIV: some remarks over the MLS method
Authors: Razvan Raducanu
Venue: Proceedings of the 10th WSEAS international conference on Mathematical and computational methods in science and engineering (2008)

The present paper presents a possible implementation of the Element Free Galerkin method to the study of fracture of Mindlin- Reissner plates. The novelty of this paper resides on the fact that it uses an enriched meshless method (Moving Least Square) around the crack tip in the theory of Mindlin-Reissner plates. This method is especially designed to characterize in a better way the behavior of cracks.
The role of knowledge sharing and trust in new product development outsourcing
Authors: Amy Y. Chou
Venue: International Journal of Information Systems and Change Management (2008)

The idea of forming strategic alliances with suppliers while developing a new product has been recognised as an effective strategy for hedging the risk in a dynamic business environment. To implement such strategy, new product development (NPD) team needs to understand the factor that may affect the suppliers' interest of involving in NPD. This study proposes a research model to study such emergent area and to explore the relationships among trust, knowledge sharing, adoption of collaborative information technology and supplier's involvement in NPD process.
Tools for Authoring a Dialogue Agent that Participates in Learning Studies
Authors: Pamela W. Jordan, Brian Hall, Michael Ringenberg, Yui Cue, Carolyn Rosé
Venue: Proceeding of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (2007)

TuTalk supports the rapid development of dialogue agents for learning applications. It enables an experimenter to create a dialogue agent with either minimal or no programming and provides the infrastructure needed for testing hypotheses about dialogue. Our main goals in developing this tool were to provide 1) an authoring interface and language for setting up the domain knowledge and resources needed to support the agent and 2) a plug-and-play type of system that facilitates the integration of new modules and experimentation with different core modules. In this paper we describe the authoring tool and the usability studies that have shaped its design, the dialogue that is supported and features of the authoring language and their usage history.
Causality information and proactive cancellation mechanisms
Authors: Malolan Chetlur, Philip A. Wilsey
Venue: Concurrency and Computation: Practice Experience (2009)

Time Warp distributed simulations can enter a catastrophic state of cascading rollbacks where out of order (premature) event executions propagate faster than the corrective measures (anti-messages) designed to terminate them. In this paper, a distributed, proactive cancellation mechanism designed to avoid this situation is presented. We also present a version of total clocks called Plausible Total Clocks (PTC). PTC employs a compact time vector of event counter range to address the scalability issues typically suffered by total clock solutions. That is, PTC consists of constant size vectors and are independent of the number of simulation objects in the simulation. The events generated due to an out of order execution are proactively cancelled by determining its causality relation with the already annihilated events. We present a proof of correctness of the distributed cancellation mechanism using PTC and also show that the catastrophic states are avoided with this proactive cancellation mechanism. This cancellation mechanism assumes first-input&ndash;first-output communication layer, static inter-connection topology, and logical processes consisting of multiple simulation objects. Copyright &copy; 2009 John Wiley & Sons, Ltd.
Hybrid and custom data structures: evolution of the data structures course
Authors: Daniel J. Ernst, Daniel E. Stevenson, Paul J. Wagner
Venue: ACM SIGCSE Bulletin (2009)

The topic of data structures has historically been taught with two major focuses: first, the basic definition and implementation of a small set of basic data structures (e.g. list, stack, queue, tree, graph), and second, the usage of these basic data structures as provided by a data structures framework in solving larger application problems. We see a further evolution of data structures to include new generations of hybrid and custom data structures, implying that our students must not only understand how to use these new data structures but that they continue to understand low-level implementation issues so that they can develop the next generation of data structures needed in the future. We suggest that the data structures course evolve to reflect these new generations of data structures.
A Distributed Facilitation Framework
Authors: Abdelkader Adla, Pascale Zarate, Jean-Luc Soubie
Venue: Proceeding of the 2008 conference on Collaborative Decision Making: Perspectives and Challenges (2008)

Most meetings are perceived to be extremely unproductive in terms of efficiently utilizing the participants' time and effectively achieving the meeting objectives. Indeed, meetings consume a great deal of time and effort in organizations. These problems occur frequently because effective guidelines or procedures are not used. To overcome these problems, we propose in this paper a framework for distributed facilitation incorporating a model of the decision making processes. In this framework many group facilitation tasks are automated, at least partially to increase the ability of the facilitator to monitor and control the meeting process.