ppxscal/embedding_network huggingface dataset:
The Underwhelming Effects of Location-Awareness of Others on Collaboration in a Pervasive Game
Authors: Nicolas Nova, Fabien Girardin, Gaëlle Molinari, Pierre Dillenbourg
Venue: Proceeding of the 2006 conference on Cooperative Systems Design: Seamless Integration of Artifacts and Conversations -- Enhanced Concepts of Infrastructure for Communication (2006)

In this paper we seek to empirically study the use of location-awareness of others in the context of mobile collaboration. We report on a field experiment carried out using a pervasive game we developed called CatchBob!. Using both quantitative and qualitative data, we show the underwhelming effects of automating location-awareness. Our results indeed shows that automating this process does not necessarily improve the task performance and that it can be detrimental to socio-cognitive processes involved in collaboration such as communication or the modeling of partners' intents. The paper concludes with some potential impacts for location-based application practitioners.
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Authors: Honglak Lee, Roger Grosse, Rajesh Ranganath, Andrew Y. Ng
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks. Scaling such models to full-sized, high-dimensional images remains a difficult problem. To address this problem, we present the convolutional deep belief network, a hierarchical generative model which scales to realistic image sizes. This model is translation-invariant and supports efficient bottom-up and top-down probabilistic inference. Key to our approach is probabilistic max-pooling, a novel technique which shrinks the representations of higher layers in a probabilistically sound way. Our experiments show that the algorithm learns useful high-level visual features, such as object parts, from unlabeled images of objects and natural scenes. We demonstrate excellent performance on several visual recognition tasks and show that our model can perform hierarchical (bottom-up and top-down) inference over full-sized images.
Introducing adaptive assistance in adaptive testing
Authors: Ricardo Conejo, Eduardo Guzmán, José-Luis Pérez-de-la-Cruz, Eva Millán
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

In this paper, we discuss the development of a theoretical framework for introducing adaptive presentation in adaptive testing. To this end, a discussion of some aspects concerning the adaptive selection mechanism for hints is presented. Some axioms that hints must fulfil are also determined, providing a hint validation procedure.
Will mobile learning change language learning?
Authors: Agnes Kukulska-hulme
Venue: ReCALL (2009)

The use of mobile phones and other portable devices is beginning to have an impact on how learning takes place in many disciplines and contexts, including language learning. Learners who are not dependent on access to fixed computers can engage in activities that relate more closely to their current surroundings, sometimes crossing the border between formal and informal learning. This creates the potential for significant change in teaching and learning practices. Taking the broader field of mobile learning as the setting within which developments in mobile-assisted language learning may be understood, the paper argues that an emphasis on mobility can lead to new perspectives and practices. The paper offers reflections on what mobile learning has to offer and considers whether it is likely to change how languages are taught and learnt. ‘Mobile learning’ is not a stable concept; therefore its current interpretations need to be made explicit. Examples of current projects and practices show an affinity between mobile and games-based learning, and can further illuminate what is distinctive and worthwhile about mobile learning.
Math Representation Tools for Facilitating Mathematical Knowledge-Building by Adult Prison-Inmate Learners
Authors: Rod Nason, Any Yeh
Venue: Proceeding of the 2007 conference on Supporting Learning Flow through Integrative Technologies (2007)

In this paper, we describe the enactment, evaluation and revision of a theoretical framework developed to inform the design of ICT-based math representation tools to facilitate the construction of mathematical knowledge by adult prison-inmate learners. The theoretical framework was derived from a review of the research literature from the fields of constructionism, knowledge-building and mathematical problem-solving. The enactment and evaluation was conducted in the context of learning about equivalence of ratio and proportion. The evaluation of the theoretical framework generally confirmed the educational veracity of the theoretical framework. However, the outcomes of the evaluation led to revisions being made to the theoretical framework.
Communication System Design Using DSP Algorithms: With Laboratory Experiments for the TMS320C6713 DSK, 1st edition
Authors: Steven A. Tretter
Venue: Information Technology Transmission, Processing And Storage (2008)

Designed for senior electrical engineering students, this textbook explores the theoretical concepts of digital signal processing and communication systems by presenting laboratory experiments using real-time DSP hardware. Originally designed for the Texas Instruments TMS320C6701 Evaluation Module or TMS320C6711 DSK, this new editionupdates the experiments based on the TMS320C6713 (but can easily be adapted to other DSP boards). Each chapter begins with a presentation of the required theory and concludes with instructions for performing experiments to implement the theory. In the process of performing the experiments, students gain experience in working with software tools and equipment commonly used in industry. The primary focus of this book is on communication systems. Algorithms that are particularly suited to DSP implementations are presented. Chapters 1 and 2 introduce the software and hardware tools. Chapter 3 presents FIR and IIR digital filters and Chapter 4 investigates the FFT. Chapters 4 through 8 discuss modulators and demodulators for classical analog modulation methods such as amplitude modulation (AM), double-sideband suppressed-carrier amplitude modulation (DSBSC-AM), single sideband modulation (SSB), and frequency modulation (FM). Chapters 9 through 16 explore digital communication methods leading to the implementation of a complete telephone-line modem. These chapters include shift register pseudo-random binary sequence generators, the RS-232 protocol, pulse amplitude modulation (PAM), quadrature amplitude modulation (QAM) transmitters and receivers, and echo cancellation. Methods for adaptive equalization, carrier recovery, and symbol clock tracking are presented. Chapter 17 gives suggestions for additional experiments.
Generalization risk minimization in empirical game models
Authors: Patrick R. Jordan, Michael P. Wellman
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1 (2009)

Experimental analysis of agent strategies in multiagent systems presents a tradeoff between granularity and statistical confidence. Collecting a large amount of data about each strategy profile improves confidence, but restricts the range of strategies and profiles that can be explored. We propose a flexible approach, where multiple game-theoretic formulations can be constructed to model the same underlying scenario (observation dataset). The prospect of incorrectly selecting an empirical model is termed generalization risk, and the generalization risk framework we describe provides a general criterion for empirical modeling choices, such as adoption of factored strategies or other structured representations of a game model. We propose a principled method of managing generalization risk to derive the optimal game-theoretic model for the observed data in a restricted class of models. Application to a large dataset generated from a trading agent scenario validates the method.
Characteristic morphisms of generalized episturmian words
Authors: Michelangelo Bucci, Aldo de Luca, Alessandro De Luca
Venue: Theoretical Computer Science (2009)

In a recent paper with L.Q. Zamboni, the authors introduced the class of @q-episturmian words. An infinite word over A is standard @q-episturmian, where @q is an involutory antimorphism of A^*, if its set of factors is closed under @q and its left special factors are prefixes. When @q is the reversal operator, one obtains the usual standard episturmian words. In this paper, we introduce and study @q-characteristic morphisms, that is, morphisms which map standard episturmian words into standard @q-episturmian words. They are a natural extension of standard episturmian morphisms. The main result of the paper is a characterization of these morphisms when they are injective. In order to prove this result, we also introduce and study a class of biprefix codes which are overlap-free, i.e., any two code words do not overlap properly, and normal, i.e., no proper suffix (prefix) of any code-word is left (right) special in the code. A further result is that any standard @q-episturmian word is a morphic image, by an injective @q-characteristic morphism, of a standard episturmian word.
On the Dimensions of Data Complexity through Synthetic Data Sets
Authors: Núria Macià, Ester Bernadó-Mansilla, Albert Orriols-Puig
Venue: Proceeding of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence (2008)

This paper deals with the characterization of data complexity and the relationship with the classification accuracy. We study three dimensions of data complexity: the length of the class boundary, the number of features, and the number of instances of the data set. We find that the length of the class boundary is the most relevant dimension of complexity, since it can be used as an estimate of the maximum achievable accuracy rate of a classifier. The number of attributes and the number of instances do not affect classifier accuracy by themselves, if the boundary length is kept constant. The study emphasizes the use of measures revealing the intrinsic structure of data and recommends their use to extract conclusions on classifier behavior and their relative performance in multiple comparison experiments.
Near-Bayesian exploration in polynomial time
Authors: J. Zico Kolter, Andrew Y. Ng
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

We consider the exploration/exploitation problem in reinforcement learning (RL). The Bayesian approach to model-based RL offers an elegant solution to this problem, by considering a distribution over possible models and acting to maximize expected reward; unfortunately, the Bayesian solution is intractable for all but very restricted cases. In this paper we present a simple algorithm, and prove that with high probability it is able to perform &epsilon;-close to the true (intractable) optimal Bayesian policy after some small (polynomial in quantities describing the system) number of time steps. The algorithm and analysis are motivated by the so-called PAC-MDP approach, and extend such results into the setting of Bayesian RL. In this setting, we show that we can achieve lower sample complexity bounds than existing algorithms, while using an exploration strategy that is much greedier than the (extremely cautious) exploration of PAC-MDP algorithms.
Extraction of Children Friendship Relations from Activity Level
Authors: Shigeo Kaneda, Takuya Katsuki, Masahiro Shibata, Hirohide Haga, Mari Ueda, Aki Kono, Kimio Shintani
Venue: Proceeding of the 2008 conference on Knowledge-Based Software Engineering: Proceedings of the Eighth Joint Conference on Knowledge-Based Software Engineering (2008)

Children learn to fit into society by group interaction through which they are greatly influenced by their relations with friends. Although teachers need to observe them to assist their growth and social progress and support the development of their personalities, only experienced teachers can watch children while simultaneously providing high-quality guidance. To resolve this problem, this paper proposes a mathematical and objective method that assists teachers with observation. It uses the numerical data of activity levels recorded by pedometers from which we make tree diagrams called dendrograms based on hierarchical clustering with recorded activity levels. We also calculate the children's “breadth” and “depth” of friend relations using more than one dendrogram. After the authors recorded the children's activity levels at a kindergarten for two months to evaluate our proposed method, we found that the results generally coincided with teacher remarks and evaluations of the children.
Sorting Signed Permutations by Inversions in O(nlogn) Time
Authors: Krister M. Swenson, Vaibhav Rajan, Yu Lin, Bernard M. Moret
Venue: Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology (2009)

The study of genomic inversions (or reversals) has been a mainstay of computational genomics for nearly 20 years. After the initial breakthrough of Hannenhalli and Pevzner, who gave the first polynomial-time algorithm for sorting signed permutations by inversions, improved algorithms have been designed, culminating with an optimal linear-time algorithm for computing the inversion distance and a subquadratic algorithm for providing a shortest sequence of inversions--also known as sorting by inversions. Remaining open was the question of whether sorting by inversions could be done in O(nlogn) time.In this paper, we present a qualified answer to this question, by providing two new sorting algorithms, a simple and fast randomized algorithm and a deterministic refinement. The deterministic algorithm runs in time O(nlogn + kn), where k is a data-dependent parameter. We provide the results of extensive experiments showing that both the average and the standard deviation for k are small constants, independent of the size of the permutation. We conclude (but do not prove) that almost all signed permutations can be sorted by inversions in O(nlogn) time.
HyperSea: towards a spatial hypertext environment for web 2.0 content
Authors: Georgios D.P. Styliaras, Sotiris P. Christodoulou
Venue: Proceedings of the 20th ACM conference on Hypertext and hypermedia (2009)

In this paper, we present HyperSea, an environment for importing, organizing and interacting with web 2.0 content. The environment is based mainly on previous research on hypertext systems, spatial hypertext and it tries to overcome presentation limitations of today's popular web 2.0 applications. Content is structured as islands and nodes which may be interlinked and characterized by various levels of visual cues, according to its type and origin. As the resulting content is structured, HyperSea may support alternative views and search operations over it. We present an extensive case-study for illustrating functionality and we organize some future work.
Mathematical modelling of the ultrasonic non-destructive control for the composite materials
Authors: Constantin Petriceanu, Catalin Gheorghe Amza
Venue: Proceedings of the 10th WSEAS international conference on Mathematical and computational methods in science and engineering (2008)

This article presents the realization of a specialized software application for ultrasonic non-destructive testing of heterogeneous composite materials. Starting with the propagation of plane ultrasonic waves inside an infinite length layer and by using the Debye's series developing formalism it will determine the conception of a mathematical model adequate to the proposed purpose. The use of this software for an adapted programming language C++Builder5, it will facilitate the further manipulation of the less skilled computer users, the created interface being easy to handle. The results obtained in such way were compared to those obtained through experiments, having as consequence that the mathematical model corresponds to the physical reality and can be successfully used in the determination of mechanical properties or of the integrity state's of a heterogeneous composite material.
Fuzzy Chance-Constrained Goal Programming Model and Algorithm of Oilfield Measures
Authors: Jiekun Song, Jiepeng Song, Yu Zhang, Zaixu Zhang, Shuiqing Fan
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

Measures programming is an important way for improving the effect of measures of oil fields. Based on the theory of fuzzy chance-constrained goal programming, a fuzzy chance-constrained goal programming model is put forward for measures programming of Oilfield, which uses the increasing production and cost as objectives. The process of its hybrid intelligent algorithm including fuzzy simulation, genetic algorithm and neural network is provided, and the sensitivity analysis on uncertain factors to determine the influence on the optimal result can also be carried on. Finally, an example is given to illustrate the validity of the model and approach.
Microsoft Windows Vista: Mastering the Vista User Experience, 1st edition
Authors: J. Peter Bruzzese
Venue:  (2008)

You have Microsoft Windows Vistanow get the most out of it, faster and more easily than ever before! J. Peter Bruzzese shows you how to maximize your Vista productivity and fun with 100 quick video lessons. Learn Vistas most powerful features and techniques visually: just watch! Youll master Vista at your own pace--and get specific answers and solutions immediately, whenever you need them! These practical videos cover Vistas new Aero user interface, security, networking, Internet Explorer 7, Windows Media Player 11, Windows Media Center, mobile computing, administration, backup/recovery, troubleshooting, and plenty more! For anyone who wants to master Microsoft Windows Vista fastfrom novices to experienced power users and administrators! Looking for a better way to master todays rapidly changing technologies? Want expert help, but dont have the time or energy to read a book? Cant find classroom training worth the money? Discover LiveLessons: self-paced, personal video instruction from the worlds leading experts. LiveLessons is an eight-hour video course organized into bite-sized, self-contained sessions--youll learn key skills in as little as five minutes! Each session begins with well-defined learning objectives and ends with comprehensive summaries, which help you track your progress. Follow along as your instructor shows exactly how to get great results in your real-world environment. Expert Windows author and trainer J. Peter Bruzzese presents easy, practical video introductions to Vistas most powerful new capabilities: everything from Aero to Windows Media Center, security to advanced administration! Click play to follow along as Bruzzese walks you through Vistas most important new featuresall you need to do is watch! 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
SEEC --A Software Error Estimation Method for Multi-component Software Projects
Authors: Timo Kuusela, Tuomas Mäkilä
Venue: Proceeding of the 2006 conference on Knowledge-Based Software Engineering: Proceedings of the Seventh Joint Conference on Knowledge-Based Software Engineering (2006)

In this article, some existing error estimation methods are presented and their suitability for modern software development projects is analyzed. Based on this analysis, a new software error estimation method SEEC (Software Error Estimation by Component-wise comparison), specifically designed for multi-site, multi-component software projects, is introduced. The required activities and the mathematical formulation of the new method are presented. Finally, the integration of the SEEC method to a software development process is illustrated with the SPEM process modeling language.
Finger force of exploratory movements is adapted to the compliance of deformable objects
Authors: Lukas R. Kaim, Knut Drewing
Venue: Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2009)

In this experiment we investigated the influence of stimulus properties on exploratory movement parameters in active touch. More precisely, we investigated whether and to what extent variations in stimulus compliance influence the executed finger force and velocity of the exploratory movements. Therefore, we varied the compliance of silicon rubber stimuli with deformable surfaces. Participants freely explored pairwise presented stimuli with a bare finger, and were asked to select the softer one. We found that both exploratory force and velocity depended on the compliance of the stimulus. Our results suggest in particular that observers strategically adapt their maximum finger force to the expected softness of the stimulus.
A Versatile Hyper-Ellipsoidal Basis Function for Function Approximation in High Dimensional Space
Authors: Saichon Jaiyen, Chidchanok Lursinsap, Suphakant Phimoltares
Venue: Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks (2009)

This paper presents a versatile hyper-ellipsoidal basis function for function approximation in a given high dimensional space. This hyper-ellipsoidal basis function can be translated and rotated to cover the data based upon the distribution of data in a given high dimensional space. Based on this function, we propose a one-pass hyper-ellipsoidal learning algorithm for which any new incoming data can be fed for learning without involving the previously learned one. This learning algorithm is used to adjust the parameters of the versatile hyper-ellipsoidal basis function. In addition, we propose the hyper-ellipsoidal basis function (HEBF) neural network that uses the one-pass hyper-ellipsoidal neural learning algorithm. The structure of this neural network is similar to the radial basis function (RBF) neural networks. The hidden neurons in the HEBF neural network can be increased or decreased during learning process. The number of the hidden neurons in the network can be grown based on geometric growth criterion and can be reduced by merging the two hidden neurons into a new hidden neuron based on merging criterion during learning process. The merging process can be done independently without considering the learned data set.
Managing uncertainty in group recommending processes
Authors: Luis M. Campos, Juan M. Fernández-Luna, Juan F. Huete, Miguel A. Rueda-Morales
Venue: User Modeling and User-Adapted Interaction (2009)

While the problem of building recommender systems has attracted considerable attention in recent years, most recommender systems are designed for recommending items to individuals. The aim of this paper is to automatically recommend a ranked list of new items to a group of users. We will investigate the value of using Bayesian networks to represent the different uncertainties involved in a group recommending process, i.e. those uncertainties related to mechanisms that govern the interactions between group members and the processes leading to the final choice or recommendation. We will also show how the most common aggregation strategies might be encoded using a Bayesian network formalism. The proposed model can be considered as a collaborative Bayesian network-based group recommender system, where group ratings are computed from the past voting patterns of other users with similar tastes.