ppxscal/embedding_network huggingface dataset:
Exploring Alternative Methods for Error Attribution in Learning Curves Analysis in Intelligent Tutoring Systems
Authors: Adaeze Nwaigwe, Kenneth R. Koedinger, Kurt Vanlehn, Robert Hausmann, Anders Weinstein
Venue: Proceeding of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (2007)

It is becoming a standard technique to use learning curves as part of evaluation of intelligent tutoring systems [1,2,3], but such learning curves require a method for attributing errors. That is, the method must determine for each error a student makes what “knowledge component” in the student model is to blame. To this point, alternative methods for error attribution have not been systematically investigated. We implemented four alternative methods for error attribution --two temporal heuristics and two location-temporal heuristics. We employed two evaluation standards --a statistical standard for measuring model fit and parsimony and the Kappa technique for measuring inter-observer reliability. We looked to see which method better met the “learning-curve standard” that is, led to better prediction of students' changes in error rate over time. Second, we asked if the codes generated by the methods better met the “human-match standard”, that is, were they like error attributions made by human coders. Both evaluation standards led to better results for the location-temporal heuristic methods than the temporal heuristic methods. Interestingly, we found that two of the methods proposed were better at error attribution, according to the learning curve standard, than the original cognitive model of the intelligent tutoring system. Overall, these results suggest that the heuristics proposed and implemented in this paper can generally aid learning curve analysis and perhaps, more generally, the design of student models
Professional Community Server Themes
Authors: Wyatt Preul, Benjamin Tiedt
Venue: Programmer To Programmer (2007)

Wrox's Professional Community Server Themes is one the first guide to the Chameleon theming engine. Inside, Telligent developers Wyatt Preul and Ben Tiedt share their experience with Community Server, and teach readers how to extend their implementation with theming. Professional Community Server Themes is heavy on fully implemented examples, making this a hands-on guide that will get readers customizing the look of their Community Server install quickly. Topics include: *URL Rewriting *Modifying SiteUrls.config through Overrides *Understanding Chameleon *Implementing Blog Themes *Interaction with ASP.NET AJAX *Dynamic Configuration Options *Content Scrubbing *Creating Custom Chameleon Controls and more.
Improving the performance of adaptive optics system using robust compensator
Authors: Reziwan Maimaiti, Noriaki Miura, Toshio Eisaka
Venue: Proceedings of the 9th WSEAS international conference on Robotics, control and manufacturing technology (2009)

This paper discusses the application of a simple and tunable robust compensator for an adaptive optics system, based on the Robust Model Matching (RMM) strategies. We have made some laboratory experiments with the atmospheric simulation instruments. Simulation and experimental results shows that the system with the designed controller has improved significantly comparing with the previous work.
Radius selection algorithms for sphere decoding
Authors: Fei Zhao, Sanzheng Qiao
Venue: Proceedings of the 2nd Canadian Conference on Computer Science and Software Engineering (2009)

The integer least squares problem arises from many applications such as communications, cryptography, and GPS. In this paper, we consider the sphere decoding method in communication applications. One of key issues in sphere decoding is the selection of an initial radius of the search hypersphere. We first present a deterministic radius selection algorithm using the Babai estimate. However, due to the rounding errors in floating-point computation, this method may produce a too small radius and cause sphere decoding to fail to find a solution. In this paper, we perform an error analysis and propose a modified radius selection algorithm by taking computational error into account. Our numerical experiments show that this modified method achieves high success rate without compromising performance.
Community Adaptive Search Engines
Authors: Alfredo Milani, Clement Leung, Alice Chan
Venue: International Journal of Advanced Intelligence Paradigms (2009)

This paper introduces Community Adaptive Search Engines (CASE) for multimedia object retrieval. CASE systems adapt their behaviour depending on the collective feedback of the users in order to eventually converge to the optimal answer. The community adaptive approach uses continuous user feedbacks on the lists of returned objects in order to filter out irrelevant objects and promote the relevant ones. An original dealer&#47;opponent game model for CASE is proposed and an evolutionary approach to solve the CASE game is also presented. Experimental results shows convergence to the optimal solution with acceptable performance for real domain sizes.
Soft Uncoupling of Markov chains for Permeable Language Distinction: A New Algorithm
Authors: Richard Nock, Pascal Vaillant, Frank Nielsen, Claudia Henry
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)

Without prior knowledge, distinguishing different languages may be a hard task, especially when their borders are permeable. We develop an extension of spectral clustering ---a powerful unsupervised classification toolbox ---that is shown to resolve accurately the task of soft language distinction. At the heart of our approach, we replace the usual hard membership assignment of spectral clustering by a soft, probabilistic assignment, which also presents the advantage to bypass a well-known complexity bottleneck of the method. Experiments with a readily available system display the potential of the method, which brings a visually appealing soft distinction of languages that may define altogether a whole corpus.
MINION: A Fast, Scalable, Constraint Solver
Authors: Ian P. Gent, Chris Jefferson, Ian Miguel
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)

We present Minion, a new constraint solver. Empirical results on standard benchmarks show orders of magnitude performance gains over state-of-the-art constraint toolkits. These gains increase with problem size --MINION delivers scalable constraint solving. MINION is a general-purpose constraint solver, with an expressive input language based on the common constraint modelling device of matrix models. Focussing on matrix models supports a highly-optimised implementation, exploiting the properties of modern processors. This contrasts with current constraint toolkits, which, in order to provide ever more modelling and solving options, have become progressively more complex at the cost of both performance and usability. MINION is a black box from the user point of view, deliberately providing few options. This, combined with its raw speed, makes MINION a substantial step towards Puget's 'Model and Run' constraint solving paradigm.
Trusting the cloud
Authors: Christian Cachin, Idit Keidar, Alexander Shraer
Venue: ACM SIGACT News (2009)

More and more users store data in "clouds" that are accessed remotely over the Internet. We survey well-known cryptographic tools for providing integrity and consistency for data stored in clouds and discuss recent research in cryptography and distributed computing addressing these problems.
Comparison of GIS-based methodologies for the landslide susceptibility assessment
Authors: Paolo Magliulo, Antonio Di Lisio, Filippo Russo
Venue: Geoinformatica (2009)

In this paper, two simple GIS-based methodologies have been used to assess the landslide susceptibility in a basin located in Southern Italy. The methodologies at issue, based on the spatial distribution of landslides and/or of causal factors, are bivariate statistics-based and expert-based, respectively. The spatial distribution of both the landslides and the causal factors has been investigated by integrating pre-existing and original data, which have been processed using ArcView GIS 3.2 software. The obtained results, consisting of landslide susceptibility maps have been compared and discussed. The bivariate statistics-based method has provided the most satisfying results. On the contrary, the expert-based method has provided a classification of the study area in terms of landslide susceptibility which does not completely fit with the surveyed spatial distribution of the landslides.
System of generalized resolvent equations with corresponding system of variational inclusions
Authors: Rais Ahmad, J. C. Yao
Venue: Journal of Global Optimization (2009)

The purpose of this paper is to introduce a new system of generalized resolvent equations with corresponding system of variational inclusions in uniformly smooth Banach spaces. We establish an equivalence relation between system of generalized resolvent equations and system of variational inclusions. The iterative algorithms for finding the approximate solutions of system of generalized resolvent equations are proposed. The convergence of approximate solutions of system of generalized resolvent equations obtained by the proposed iterative algorithm is also studied.
Self-management for neural dynamics in brain-like information processing
Authors: Benjamin Dittes, Alexander Gepperth, Antonello Ceravola, Jannik Fritsch, Christian Goerick
Venue: Proceedings of the 6th international conference on Autonomic computing (2009)

Neural dynamics coupled by adaptive synaptic information transmission provide a very powerful tool for biologically inspired visual processing systems[4]. Currently, progress is limited by the computing time needed to evaluate the underlying equations and by the high number of parameters necessary to tune to achieve the desired system performance. In this contribution we apply Autonomic Computing techniques to overcome these limitations. We approach the computing time problem with an error model of the differential equations allowing for self-optimization of the evaluation step size and the parameter problem with a self-configuration heuristics to keep neural activation in working range. We show the equivalence of system behavior compared to the case without self-management, the performance gain achieved by the self-optimization and the stability achieved by the self-configuration.
Improving throughput by tuning carrier sensing in 802.11 wireless networks
Authors: Qiang Shen, Xuming Fang, Rongsheng Huang, Pan Li, Yuguang Fang
Venue: Computer Communications (2009)

In this paper, we study the impacts of physical carrier sensing and channel rate on the throughput of 802.11 wireless networks with chain topology. Firstly, we show that by adopting different carrier sensing thresholds for the RTS and CTS transmissions, the blocking problem caused by exposed terminals can be greatly alleviated. In 802.11 wireless networks with this modification, the spatial reuse ratio under certain channel rates can be increased to 13, which is the highest value to our best knowledge. Secondly, in multi-rate networks, we demonstrate that 13 is still the best value of spatial reuse ratio in terms of maximizing the achievable data rate under certain conditions. Thirdly, this paper proposes a new method to address the intra-flow contention by decreasing the carrier sensing threshold of the source node. This method requires less response time than that of the traditional method which adjusts the backoff window size. Finally, extensive simulations are implemented in NS2 and the results show that our scheme significantly improves the throughput of 802.11 wireless networks with chain topology.
How should context-escaping closures proceed?
Authors: Dave Clarke, Pascal Costanza, Éric Tanter
Venue: International Workshop on Context-Oriented Programming (2009)

Context-oriented programming treats execution context explicitly and provides means for context-dependent adaptation at runtime. This is achieved, for example, using dynamic layer activation and contextual dispatch, where the context consists of a layer environment of a stack of active layers. Layers can adapt existing behaviour using proceed to access earlier activated layers. A problem arises when a call to proceed is made from within a closure that escapes the layer environment in which it was defined. It is not clear how to interpret proceed when the closure is subsequently applied in a different environment, because the layers it implicitly refers to (such as the original layer and/or the remaining layers) may no longer be active. In this paper, we describe this problem in detail and present some approaches for dealing with it, though ultimately we leave the question open.
Identification of structurally important amino acids in proteins by graph-theoretic measures
Authors: Tammy M. K. Cheng, Yu-En Lu, Pietro Lió
Venue: Proceedings of the KDD-09 Workshop on Statistical and Relational Learning in Bioinformatics (2009)

Identifying key residues important for maintaining a protein structure is a non-trivial problem in Computational Biology. In this paper, we present results based on a graph model representing protein structures. This model considers the structure as residue-residue interactions in order to capture protein stability. We propose the application of approximate minimum vertex cover algorithms (MVC) as a novel approach for identifying the structurally important residues, which we shall refer to as key residues. We establish that MVC based algorithms captures the essence of protein structural stability by correlation analysis with &Delta;&Delta;G, the change of protein free energies due to amino acid variations. We also benchmark our approach with popular approaches for analyzing large complex networks --- betweenness, and Eigenvector centrality. Our findings are such that they do not correlate well with &Delta;&Delta;G. We give explanations from the free energy point of view, which shall benefit future development measures for protein structure stability.
Capitalization of knowledge from projects
Authors: Chaker Djaiz, Davy Monticolo, Nada Matta
Venue: Proceeding of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering (2006)

The knowledge engineering offers a rational framework allowing a representation of knowledge obtained through the experiences. This technique found a great application in knowledge management and especially to capitalize knowledge. In fact, the rational representation of knowledge allows their exploitation and their re-use. It is a necessary condition to allow a re-use and a knowledge appropriation. The knowledge management must take into account this dimension, since its first concern is to make knowledge persistent, ready to be re-used. In this paper, we study the traces classifications of the design project achievements in order to have a knowledge aggregation and to thus provide a representation of handled knowledge, directives and competences organization as well as negotiation strategies and cooperative problems solving.
Get Your Word Processor into use... Quickly!
Authors: Norma Sollers
Venue:  (2007)

Have you found a modern word processor difficult to learn? Or has it otherwise felt time consuming using the manuals? If that's your experience, how about trying another kind of a guide? Nowadays, office applications (such as word processors and spreadsheet programs) include a huge array of features and tools. In fact, there are so many such features that it has become a problem finding out how to master the most basic! For example, for a novice it's often difficult to quickly get to a level where you can do something relevant from the beginning to the end, like creating a simple but tidy document. On the other hand, tutorials which come with a program often contain too much detail and are far too complicated. Norma Sollers wrote this new guide as a tutorial to enable anyone to put a word processor into good use with ease. When writing the guide, the experience of many years in teaching word processors has been drawn upon. The guide covers all the basic tasks in word processing, with the most important goal being to quickly be able to do things on your own. In addition, the tasks are planned and the guide written so that they are suitable for beginners, the elderly and children... in other words, for everyone who wants to master the basics of a word processor! This tutorial guide is based on the freely available OpenOffice 2 Writer software, but the principles are applicable to any word processor. 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
Chaos and Graphics: Optimal rotation alignment of 3D objects using a GPU-based similarity function
Authors: Michael Martinek, Roberto Grosso
Venue: Computers and Graphics (2009)

In this paper, we address the challenging task of finding the best alignment between two 3D objects by solving a global optimization problem in the space of rotations SO(3). The objective function to be optimized is a newly developed rotation-variant similarity measure, which is obtained directly from the object's geometry and is entirely implemented on the GPU. By exploiting the modern GPU's parallel architecture, we can process considerably greater amounts of data than a CPU implementation can do in the same amount of time. This allows us to create a similarity measure which combines speed and accuracy. The actual problem of rotation alignment is then solved by finding the global maximum of this similarity function in the space of rotations. A special rotation representation allows for an efficient local optimization on the manifold SO(3). Furthermore, unwanted local maxima can be avoided by a heuristic global optimization procedure which exploits rotational symmetry. Due to this common sense heuristics, the global search can be gradually reduced to a lower-dimensional problem up to a 1D line search to handle objects with high rotational symmetry. We show that our method is superior to existing normalization techniques such as PCA and provides a high degree of precision despite remarkably short runtimes.
Network+ Guide to Networks, 5th edition
Authors: Tamara Dean
Venue:  (2009)

Completely revised and updated, this text provides thorough preparation for CompTIAs Network+ examination, and acts as a perfect text for an introduction to networking course.
Online dictionary learning for sparse coding
Authors: Julien Mairal, Francis Bach, Jean Ponce, Guillermo Sapiro
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

Sparse coding---that is, modelling data vectors as sparse linear combinations of basis elements---is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on learning the basis set, also called dictionary, to adapt it to specific data, an approach that has recently proven to be very effective for signal reconstruction and classification in the audio and image processing domains. This paper proposes a new online optimization algorithm for dictionary learning, based on stochastic approximations, which scales up gracefully to large datasets with millions of training samples. A proof of convergence is presented, along with experiments with natural images demonstrating that it leads to faster performance and better dictionaries than classical batch algorithms for both small and large datasets.
VAST 2007 Contest TexPlorer
Authors: Chi-Chun Pan, Anuj R. Jaiswal, Junyan Luo, Anthony Robinson, Prasenjit Mitra, Alan M. MacEachren, Ian Turton
Venue: Proceedings of the 2007 IEEE Symposium on Visual Analytics Science and Technology (2007)

TexPlorer is an integrated system for exploring and analyzing vast amount of text documents. The data processing modules of TexPlorer consist of named entity extraction, entity relation extraction, hierarchical clustering, and text summarization tools. Using timeline tool, tree-view, table-view, and concept maps, TexPlorer provides visualizations from different aspects and allows analysts to explore vast amount of text documents efficiently.
Investing Online For Dummies, 6th edition
Authors: Matt Krantz
Venue: For Dummies Business & Personal Finance (2007)

Investors are becoming a lot more independent these days. With the stock market sinking and soaring from day to day, retirement plans becoming less certain, and a longer life expectancy in retirement, more people are looking for some control over their investments. If youre one of them, Investing Online For Dummies, 6th Edition might be just what youre looking for. The Internet brings a world of investment resources to your desktop, but how do you find your way through the dizzying array of investment possibilities? The Dummies Way, of course! This book helps you set reasonable expectations, assess your risk tolerance, choose an asset allocation, analyze and select investments, and maximize your financial success. Youll discover how to Determine how much you can afford to invest Choose an online broker Research, trade, and track investments online Measure the potential return of a stock Maximize your investment knowledge and build a profitable portfolio Buy bonds online Understand options, commodities, and IPOs If youve decided to take control of your investments but dont want to make it your full-time job, Investing Online For Dummies, 6th Edition gives you the important information that will also give you confidence. Completely updated to cover all the new online tools and resources, it even provides some bonus assistance on the companion Web site. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
An Automated Agent for Bilateral Negotiation with Bounded Rational Agents with Incomplete Information
Authors: Raz Lin, Sarit Kraus, Jonathan Wilkenfeld, James Barry
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)

Many day-to-day tasks require negotiation, mostly under conditions of incomplete information. In particular, the opponent's exact tradeoff between different offers is usually unknown. We propose a model of an automated negotiation agent capable of negotiating with a bounded rational agent (and in particular, against humans) under conditions of incomplete information. Although we test our agent in one specific domain, the agent's architecture is generic; thus it can be adapted to any domain as long as the negotiators' preferences can be expressed in additive utilities. Our results indicate that the agent played significantly better, including reaching a higher proportion of agreements, than human counterparts when playing one of the sides, while when playing the other side there was no significant difference between the results of the agent and the human players.