ppxscal/embedding_network huggingface dataset:
An Interactive 3D Exhibition System with Global Illumination for Digital Museum
Authors: Gang Bai, Yue Qi
Venue: Proceedings of the 4th International Conference on E-Learning and Games: Learning by Playing. Game-based Education System Design and Development (2009)

Interactive exhibition of three-dimensional models in digital museums can significantly impact learning and understanding. Current 3D exhibition systems commonly provide viewers with local illumination effects. Interactive exhibition with high realistic rendering quality is becoming a necessity. In order to enhance the sense of reality and immersion, we develop a 3D exhibition system for interactively displaying relic models with global illumination effects. In our system, the precomputed radiance transfer(PRT) method is used for solution to the global illumination effects including diffuse reflection, shadows and inter-reflections. In addition to diffuse reflections, we also extend standard PRT method by blending previously generated textures for heterogeneously colored surfaces. Our system has applied to several cultural relics, of which the results are presented in this paper.
An Asynchronous Encryption Arithmetic Based on Laguerre Chaotic Neural Networks
Authors: Ajin Zou, Xiuchun Xiao
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 04 (2009)

Based on best square approximation theory, new feed-forward neural networks are introduced where hidden units activation functions employ Laguerre orthogonal polynomials. Use these neural networks as the identifier model of the chaotic time series. Then, by varying the chaotic initial value and inputting to the networks, can produce new chaotic series, which are close to the theoretical values. We extract a subsequence as same length as the plaintext from the chaotic series and sort it. At last, by permuting the plaintext according to the sorted results of the subsequence, we can achieve the ciphertext. In the encryption system, the security of it depends completely on the complexity and unpredictability of the chaos. Especially, by varying the chaotic initial value, we can implement asynchronous “one-time pad cipher” encryption. The theoretical analysis and encryption instances proved that our arithmetic is useful, simple and high security, and it also has many advantages that a synchronous system can never achieve.
Global Context Extraction for Object Recognition Using a Combination of Range and Visual Features
Authors: Michael Kemmler, Erik Rodner, Joachim Denzler
Venue: Proceedings of the DAGM 2009 Workshop on Dynamic 3D Imaging (2009)

It has been highlighted by many researchers, that the use of context information as an additional cue for high-level object recognition is important to close the gap between human and computer vision. We present an approach to context extraction in the form of global features for place recognition. Based on an uncalibrated combination of range data of a time-of-flight (ToF) camera and images obtained from a visual sensor, our system is able to classify the environment in predefined places (e.g. kitchen, corridor, office) by representing the sensor data with various global features. Besides state-of-the-art feature types, such as power spectrum models and Gabor filters, we introduce histograms of surface normals as a new representation of range images. An evaluation with different classifiers shows the potential of range data from a ToF camera as an additional cue for this task.
UML Model Refactoring as Refinement: A Coalgebraic Perspective
Authors: Luis Soares Barbosa, Sun Meng
Venue: Proceedings of the 2008 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (2008)

Although increasingly popular, Model Driven Architecture (MDA)still lacks suitable formal foundations on top of which rigorousmethodologies for the description, analysis and transformation ofmodels could be built. This paper aims to contribute in thisdirection: building on previous work by the authors on coalgebraicrefinement for software components and architectures, it discussesrefactoring of models within a coalgebraic semantic framework. Architectures are defined through aggregation based on a coalgebraic semantics for (subsets of)UML. On the other hand, such aggregations, no matter how large and complex they are, can always be dealt with ascoalgebras themselves. This paves the way to a discipline ofmodels' transformations which, being invariant under either behavioural equivalenceor refinement, are able to formally capture a large number of refactoring patterns. The main ideas underlying this research are presented through a detailed example in the context of refactoring of UML class diagrams.
Communicating Mathematics via Pen-Based Interfaces
Authors: Elena Smirnova, Stephen M. Watt
Venue: Proceedings of the 2008 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (2008)

We address the question of how to organize penbased interfaces for mathematical software systems, both for computational mathematics packages and document processing software. Our architecture includes components for ink collection, mathematically-oriented recognizers, portability support and interfaces to applications. We summarize aspects of mathematical handwriting recognition and discuss the methods we have used for individual character recognition and overall expression analysis. We present our pen-based computing environment, Mathink, and give an overview of facilities for training, ink annotation, and testing
Online consumer loyalty: Why e-tailers should seek a high-profile leadership position
Authors: Hsien-Tung Tsai, Heng-Chiang Huang
Venue: Computers in Human Behavior (2009)

This study takes an approach rooted in information economics to conceptualize and empirically examine the signal-based determinant of online customer loyalty and its antecedents and consequences. The authors test the proposed hypotheses using data obtained from three large online retailers. As an important insight, this research reveals that an e-tailer's competitive equity has a strong, positive effect on relationship investments and loyalty intentions. In addition, using quantile regression analysis, this study clarifies some issues in existing literature by showing that online consumers are motivated by different rules of exchange, depending on their loyalty levels. Finally, the authors discuss the managerial implications of their findings, as well as several important research issues and future research avenues.
Understanding Public Perceptions: Trust and Engagement in ICT-Mediated Services
Authors: Piotr Cofta, Steven Furnell, Hazel Lacohee, Andy Phippen
Venue:  (2009)

Drawing together diverse models and data to present a coherent interdisciplinary view, this complete handbook delivers recommendations in the area of public perception of information and communication technology (ICT) mediated services. This detailed examination not only explores the background, strategies, and business development regarding such themes as internet security, but it also takes a wider approach bycovering important related issues such asidentity and theft, how citizens can come to trust a society that is driven by e-commerce, and the role of government and providers tofind solutions. This ground-breaking manual will allow decision makers, researchers, and industry professionals across the globe to effectively understand the implications of engaging the general public with ICT.
Learning from ambiguously labeled examples
Authors: Eyke Hüllermeier, Jürgen Beringer
Venue: Intelligent Data Analysis (2006)

Inducing a classification function from a set of examples in the form of labeled instances is a standard problem in supervised machine learning. In this paper, we are concerned with ambiguous label classification (ALC), an extension of this setting in which several candidate labels may be assigned to a single example. By extending three concrete classification methods to the ALC setting (nearest neighbor classification, decision tree learning, and rule induction) and evaluating their performance on benchmark data sets, we show that appropriately designed learning algorithms can successfully exploit the information contained in ambiguously labeled examples. Our results indicate that the fundamental idea of the extended methods, namely to disambiguate the label information by means of the inductive bias underlying (heuristic) machine learning methods, works well in practice.
Psychophysiology as a Tool for HCI Research: Promises and Pitfalls
Authors: Byungho Park
Venue: Proceedings of the 13th International Conference on Human-Computer Interaction. Part I: New Trends (2009)

Psychophysiology, an area of psychology that measures individual's physiological responses to refer to one's psychological state, can provide a set of useful measures HCI researchers can take advantage of. However, there are limitations to the method itself and room for misinterpretation. This paper introduces psychophysiology, and also shows how research methods psychophysiology offer can be used for HCI research, advantages and disadvantages of using research tools from psychophysiology. 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
A Framework for Multi-class Learning in Micro-array Data Analysis
Authors: Nicoletta Dessì, Barbara Pes
Venue: Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine (2009)

A large pool of techniques have already been developed for analyzing micro-array datasets but less attention has been paid on multi-class classification problems. In this context, selecting features and quantify classifiers may be hard since only few training examples are available in each single class. This paper demonstrates a framework for multi-class learning that considers learning a classifier within each class independently and grouping all relevant features in a single dataset. Next step, that dataset is presented as input to a classification algorithm that learns a global classifier across the classes. We analyze two micro-array datasets using the proposed framework. Results demonstrate that our approach is capable of identifying a small number of influential genes within each class while the global classifier across the classes performs better than existing multi-class learning methods.
Theta phase synchrony and conscious target perception: Impact of intensive mental training
Authors: Heleen A. Slagter, Antoine Lutz, Lawrence L. Greischar, Sander Nieuwenhuis, Richard J. Davidson
Venue: Journal of Cognitive Neuroscience (2009)

The information processing capacity of the human mind is limited, as is evidenced by the attentional blink-a deficit in identifying the second of two targets (T1 and T2) presented in close succession. This deficit is thought to result from an overinvestment of limited resources in T1 processing. We previously reported that intensive mental training in a style of meditation aimed at reducing elaborate object processing, reduced brain resource allocation to T1, and improved T2 accuracy [Slagter, H. A., Lutz, A., Greischar, L. L., Francis, A. D., Nieuwenhuis, S., Davis, J., et al. Mental training affects distribution of limited brain resources. PloS Biology,5, e138, 2007]. Here we report EEG spectral analyses to examine the possibility that this reduction in elaborate T1 processing rendered the system more available to process new target information, as indexed by T2-locked phase variability. Intensive mental training was associated with decreased cross-trial variability in the phase of oscillatory theta activity after successfully detected T2s, in particular, for those individuals who showed the greatest reduction in brain resource allocation to T1. These data implicate theta phase locking in conscious target perception, and suggest that after mental training the cognitive system is more rapidly available to process new target information. Mental training was not associated with changes in the amplitude of T2-induced responses or oscillatory activity before task onset. In combination, these findings illustrate the usefulness of systematic mental training in the study of the human mind by revealing the neural mechanisms that enable the brain to successfully represent target information.
An Integrated Methodology for Workpiece-Fixture System Stiffness Calculation and Error Control
Authors: Fa-ping Zhang, Ji-ping Lu, Yan Yan, Hou-fang Sun, Shahid I. Butt
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 02 (2009)

The deformation error for the machining of thin-walled part has a great influence on the machining quality of the part. However the traditional methodologies are still unsuccessful in studying the influence between the workpiece-fixture system’s stiffness and machining errors of part. In this paper a framework to curb the machining errors based on the deformation of workpiece-fixture system is provided. By the quasic static force analysis of workpiece, a new method to compute the stiffness of workpiece-fixture system is presented and the contact analysis in Finite Element Analysis （FEA）and multi-nonlinear calculation are avoided. Further, the analytic model for machining errors caused by the deformation of workpiece-fixture system is given. Based on the FEA and neural networks, cutting parameters have been optimized under the given machining errors caused by the deformation of workpiece-fixture system，so that the errors can be controlled. Finally, a case study has been used to validate the proposed model.
EM works for pronoun anaphora resolution
Authors: Eugene Charniak, Micha Elsner
Venue: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (2009)

We present an algorithm for pronoun-anaphora (in English) that uses Expectation Maximization (EM) to learn virtually all of its parameters in an unsupervised fashion. While EM frequently fails to find good models for the tasks to which it is set, in this case it works quite well. We have compared it to several systems available on the web (all we have found so far). Our program significantly outperforms all of them. The algorithm is fast and robust, and has been made publically available for downloading.
IGDSS Consultation Model of Investment Decision for Highway
Authors: Lei Jian, Wang Xuancang
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 02 (2009)

In order to improve the veracity of investment decision for highway and reduce risk. For the existing shortages of model for highway investment decision, an IGDSS consultation model for investment decision for highway based on GDSS was put forward, which took the form of consultation and used CSCW based on Web, in addition, the module of intelligence expert has been added. By using the model to analyze, results shows that it overcome some shortages of traditional meetings and is an effective manner of decision-making
Convergence of non-autonomous discrete-time Hopfield model with delays
Authors: Lifen Yuan, Zhaohui Yuan, Yigang He
Venue: Neurocomputing (2009)

This paper is concerned with boundedness, convergence of solution of a class of non-autonomous discrete-time delayed Hopfield neural network model. Using the inequality technique, we obtain some sufficient conditions ensuring the boundedness of solutions of the discrete-time delayed Hopfield models in time-varying situation. Then, by exploring intrinsic features between non-autonomous system and its asymptotic equations, several novel sufficient conditions are established to ensure that all solutions of the networks converge to the solution of its asymptotic equations. Especially, for case of asymptotic autonomous system or asymptotic periodic system, we obtain some sufficient conditions ensuring all solutions of original system convergent to equilibrium or periodic solution of asymptotic system, respectively. An example is provided for demonstrating the effectiveness of the global stability conditions presented. Our results are not only presented in terms of system parameters and can be easily verified but also are less restrictive than previously known criteria.
Solving the auction-based task allocation problem in an open environment
Authors: David Sarne, Sarit Kraus
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 1 (2005)

In this paper we analyze the process of allocating tasks to self-interested agents in uncertain changing open environments. The allocator in our model is responsible for the performance of dynamically arriving tasks using a second price reverse auction as the allocation protocol. Since the agents are self-interested (i.e. each agent attempts to maximize its own revenue), previous models concerning cooperative agents aiming for a joint goal are not applicable. Thus the main challenge is to identify a set of equilibrium strategies - a stable solution where no agent can benefit from changing its strategy given the other agents' strategies - for any specific environmental settings. We formulate the model and discuss the difficulty in extracting the agents' equilibrium strategies directly from the model's equations. Consequently we propose an efficient algorithm to accurately approximate the agents' equilibrium strategies. A comparative illustration through simulation of the system performance in a closed and open environments is given, emphasizing the advantage of the allocator operating in the latter environment, reaching results close to those obtained by a central enforceable allocation.
Research on Transient Air Fuel Ratio Control of Gasoline Engines
Authors: Yao Ju-Biao
Venue: Proceedings of the 2009 International Forum on Information Technology and Applications - Volume 01 (2009)

Investigate a kind of air fuel ratio control strategy that combined the modified Elman neural network and the traditional PI (proportional plus integral) controller. The neural network was used to estimate the air fuel ratio signal without transfer delay, which input signals consist of the crankshaft rotate speed, the throttle position, and the injection duration. The traditional PI controller was used to control the transient air fuel ratio by using the estimated signal. To verify the validity of the control strategy, a single cylinder gasoline engine model was built with GT (Grand Touring) -Power. Based on using GT-Power/Simulink, the simulation model for controlling the air fuel ratio of the gasoline engine was built.
Beginning Joomla! Web Site Development
Authors: Cory Webb
Venue:  (2009)

By programmers for programmers-the essential beginner's guide to building websites with Joomla! Want to build and maintain dynamic websites without having to learn HTML and CSS? Joomla! open-source web content management system and this beginner's guide are all you need. This book walks you step-by-step through the process of building a website with Joomla!, providing detailed instruction in Wrox's practical, programmer-to- programmer style. The book explores key concepts and shows how each concept relates to the development of an actual real-world web site you can access online. Joomla! is an open-source web content management system used to build and maintain dynamic web sites without using specific web languages like HTML and Cascading Style Sheets (CSS) Walks you step by step through the process of building a website with Jooma! and features plenty of examples of actual websites that illustrate concepts Topics include configuration; managing content, core modules, plugins, menus, and extensions; building custom templates; and performing troubleshooting; also provides advanced tips and tricks, and more From configuration and management to troubleshooting and advanced tips, this is the book to get you up and running with Joomla! 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
On upper bounds for the tail distribution of geometric sums of subexponential random variables
Authors: Andrew Richards
Venue: Queueing Systems: Theory and Applications (2009)

The approach used by Kalashnikov and Tsitsiashvili for constructing upper bounds for the tail distribution of a geometric sum with subexponential summands is reconsidered. By expressing the problem in a more probabilistic light, several improvements and one correction are made, which enables the constructed bound to be significantly tighter. Several examples are given, showing how to implement the theoretical result.
An application of latent semantic analysis to word sense discrimination for words with related and unrelated meanings
Authors: Juan Pino, Maxine Eskenazi
Venue: Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications (2009)

We present an application of Latent Semantic Analysis to word sense discrimination within a tutor for English vocabulary learning. We attempt to match the meaning of a word in a document with the meaning of the same word in a fill-in-the-blank question. We compare the performance of the Lesk algorithm to Latent Semantic Analysis. We also compare the performance of Latent Semantic Analysis on a set of words with several unrelated meanings and on a set of words having both related and unrelated meanings.
k/K-Nearest Neighborhood Criterion for Improvement of Locally Linear Embedding
Authors: Armin Eftekhari, Hamid Abrishami-Moghaddam, Massoud Babaie-Zadeh
Venue: Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns (2009)

Spectral manifold learning techniques have recently found extensive applications in machine vision. The common strategy of spectral algorithms for manifold learning is exploiting the local relationships in a symmetric adjacency graph, which is typically constructed using k -nearest neighborhood (k-NN) criterion. In this paper, with our focus on locally linear embedding as a powerful and well-known spectral technique, shortcomings of k-NN for construction of the adjacency graph are first illustrated, and then a new criterion, namely k/K-nearest neighborhood (k/K-NN) is introduced to overcome these drawbacks. The proposed criterion involves finding the sparsest representation of each sample in the dataset, and is realized by modifying Robust-SL0, a recently proposed algorithm for sparse approximate representation. k/K-NN criterion gives rise to a modified spectral manifold learning technique, namely Sparse-LLE, which demonstrates remarkable improvement over conventional LLE through our experiments.