ppxscal/embedding_network huggingface dataset:
A Dance Synthesis System Using Motion Capture Data
Authors: Kenichi Takahashi, Hiroaki Ueda
Venue: Knowledge Acquisition: Approaches, Algorithms and Applications: Pacific Rim Knowledge Acquisition Workshop, PKAW 2008, Hanoi, Vietnam, December 15-16, 2008, Revised Selected Papers (2009)

The aim of this system is to synthesize the choreography of the dance motions automatically by using the subjective music impression given by a user, even if the user has no knowledge to the dance at all. In order to show that the system is useful, we perform experiments. In the experiments, the dance data of 16 measures of two musics is synthesized, and the synthesized result of dancing data by the system and the results synthesized by a dance expert are compared.
Phase Self-amending Blind Equalization Algorithm Using Feedforward Neural Network for High-Order QAM Signals in Underwater Acoustic Channels
Authors: Yasong Luo, Zhong Liu, Pengfei Peng, Xuezhi Fu
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

Complex-valued and non-constant modulus signals are widely used in modern high-speed underwater acoustic communication systems. Based on this environment, a complex-valued blind equalization algorithm using feedforward neural network is brought forward. Aiming at the defects that traditional constant modulus equalization algorithm can't rectify the phase deflection, the cost function is reformed and also a new modified constant modulus algorithm is given. Besides, the new algorithm is improved by introducing the square decision technique to achieve better convergence speed and less gurgitation. The results of simulation show that this new equalization algorithm not only has the ability of phase self-amending, but also performs better than traditional algorithm in the ability and speed of convergence in high order QAM communication systems.
Two-Step Tracking by Parts Using Multiple Kernels
Authors: Brais Martínez, Luis Ferraz, Xavier Binefa
Venue: Proceeding of the 2006 conference on Artificial Intelligence Research and Development (2006)

This paper addresses the problem of tracking IR image sequences by using kernel weighted histograms. The work is performed over the basis of the multiple kernel tracking algorithm presented in [3]. We present a new, novel, two-step tracking method which allows a tracking of independent parts of the same object by giving a higher flexibility to the multiple kernel model. This is performed by a progressive approximation of the movement by first estimating the global displacement with a multi-kernel estimator in order to have enough robustness and then, in the second step, the residual displacements of each part. The outcome is a method yet robust to partial occlusions, articulated motions or projectivities over the image with an application to partial occlusion detection and model update.
Effects of magnitude and phase cues on human motor adaptation
Authors: Ali Israr, Hakan Kapson, Volkan Patoglu, Marcia K. O'Malley
Venue: Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2009)

Recent findings have shown that humans can adapt their internal control model to account for the changing dynamics of systems they manipulate. In this paper, we explore the effects of magnitude and phase cues on human motor adaptation. In our experiments, participants excite virtual second-order systems at resonance via a two-degree of freedom haptic interface, with visual and visual plus haptic feedback conditions. Then, we change the virtual system parameters and observe the resulting motor adaptation in catch trials. Through four experimental conditions we demonstrate the effects of magnitude and phase cues on human motor adaptation. First, we show that humans adapt to a nominal virtual system resonant frequency. Second, humans shift to higher and lower natural frequencies during catch trials regardless of feedback modality and force cues. Third, participants can detect changes in natural frequency when gain, magnitude, and phase cues are manipulated independently. Fourth, participants are able to detect changes in natural frequency when the feedback (visual or visual plus haptic) is delayed such that the phase shift between the nominal system and catch trial system is zero. The persistent ability of participants to perform system identification of the dynamic systems which they control, regardless of the cue that is conveyed, demonstrates the human's versatility with regard to manual control situations. We intend to further investigate human motor adaptation and the time for adaptation in order to improve the efficacy of shared control methodologies for training and rehabilitation in haptic virtual environments.
Learning from multirelational data through multiple views
Authors: Hongyu Guo
Venue:  (2008)

Since their first release in the 1970s, relational databases have been routinely used to collect and organize real-world data&mdash;from financial transactions, marketing surveys, to health informatics observations. Traditional data mining methods expect data in the form of a single table, thus resulting in an inability to deal with such relational repositories. Multirelational Data Mining, on the other hand, aims to discover useful patterns across multiple inter-connected relations in a relational database. To this end, this work focuses on how to build classification models for relational databases through multiple views (feature sets). This study developed four multiple view strategies for mining multirelational data. The thesis firstly introduces the Multi-View Relational Classification (MRC) framework, for constructing hypotheses from sets of attributes of the presented data. The MRC strategy distinguishes itself from existing multirelational mining algorithms by excluding the need to either transform multiple relations into a universal single table or to devise new techniques for direct relational learning. The MRC algorithm offers both predictive performance and efficiency gains over current relational models, when mining diverse relational databases. Secondly, the MRC-IM method extends the MRC approach in order to deal with skew-class multirelational data. Here, the number of examples from one class is much higher than the others and correctly classifying the underrepresented examples is of prime importance. The MRC-IM method offers performance gains over a current relational model not only against majority class instances, but also against underrepresented examples. While the MRC and MRC-IM methods construct an individual view using features within a sole relation, the third multi-view strategy formulated by this work, namely the MRC-Cross approach, enables the search and collection of relevant attributes across multiple relations when constructing individual views. Finally, we present the SESP technique for pre-pruning uninteresting relations of complex relational databases. Through identifying uninteresting views from the MRC framework, our SESP method creates a pruned structure, while minimizing predictive performance loss on the final classification model. The results of this study thus suggest that learning from multiple views sets a new direction for efficiently mining data in many relational forms, including relational databases, graphs and social networks.
Understanding users' perception of speech recognition errors in mobile communication
Authors: Shuang Xu
Venue: International Journal of Mobile Learning and Organisation (2009)

Speech recognition errors remain a problem in the design of voice-based user interfaces. Owing to the limited system resources and constrained input methods, error correction is particularly difficult on mobile devices. This research investigated the users' perception of a proposed multimodal interface design that allows a user to send and receive voice-dictated text messages on cell phones. Task-based interviews were performed to examine the participants' understanding, acceptance and overall satisfaction. Findings indicate that an audio readout significantly improves the users' understanding of the misrecognised messages. An in-depth investigation reveals how users' perception is impacted by speech recognition errors in mobile communication.
Knowledge analysis & application to multimedia content recognition problems
Authors: B. Prabhakaran
Venue:  (2008)

We are living with various kinds of multimedia contents, such as personal images, web-pages and images, personal videos (YouTube) and movies. We have been using the 3D human motion capture techniques in movie and game industry. As hardware techniques for getting multimedia data advanced, many researchers developed new algorithms for automatic multimedia content analysis problems. However, there exists the semantic gap, which is the distance between low-level feature values of multimedia and the high level of human perception. This already well-known semantic gap prevents us from using pre-developed multimedia content recognition algorithms for commercial level application. For reducing this semantic gap, in this dissertation, we analyze the various multimedia data types and find relations between them. For applying knowledge-bases to multimedia content recognition enhancement, we propose extraction methods about knowledge-base and methods for applying the newly extracted knowledge-base effectively to multimedia content recognition problems. First, we analyze the knowledge aspect with 3D human motion capture data since it has more inherent knowledge than video human motion data. We extract the semantic characteristic values from 3D human motions capture data while we reduce the dimensions from 62 to 1 quantization values. Another knowledge extraction is from WordNet, which is hierarchical lexical dictionary. We show that semantic similarity from WordNet would be very useful for removing noisy keywords among annotated keywords for a image. Second, based on this extracted knowledge for enhancing multimedia content recognition problems, we propose several methods how we can apply knowledge-bases to multimedia content recognition problems effectively. For instance, with semantic feature values from 3D human motion capture data, we demonstrate that 3D human motion capture data can be transformed into document-like textual representation, called, HMDoc (Human Motion Document) since we already get the semantic quantization sequences for each 3D human motion capture data. More than this, 3D human motion semantic feature values can be merged with video low-level feature values. This can be built on the traditional machine learning algorithms, such as Hidden Markov Models (HMM). We propose a framework based on HMM for using this 3D human motion semantic feature values as the hidden state sequences. It increases the video human gesture recognition problem&rsquo;s accuracy and decreases the learning time for a HMM model parameters. Another application of extracted knowledge is for image annotation problems. We propose a new research problem, graph-approximating image annotation refinement for applying semantic similarity to decide un-related keywords within polynomial time. So, throughout several quite different multimedia data types, we show how we can use the knowledge-base as another powerful assistance for improving multimedia content recognition problems.
From Response Systems to Distributed Systems for Enhanced Collaborative Learning
Authors: Jeremy Roschelle, Patricia Schank, John Brecht, Deborah Tatar, S. Raj Chaudhury
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)

Innovators need a common, reusable framework for implementing patterns of collaborative learning on wireless handheld devices. Our project explores the application of an architecture from distributed system research, “tuple spaces,” to a wide variety of collaborative learning scenarios. We are testing this approach with documented scenarios from prior work published by our team and others. We are also designing new scenarios that leverage the architecture to engage learners optimally in cognitive, collaborative work. Our research explores the conceptual alignment, programming complexity, and classroom usability of this approach.
Discovery of Narrativity on the WWW based on Perspective Information Access
Authors: Mina Akaishi, Nicolas Spyratos, Koichi Hori, Yuzuru Tanaka
Venue: Proceeding of the 2006 conference on Information Modelling and Knowledge Bases XVII (2006)

We propose a framework for discovering narrative relationships between objects on the WWW based on perspective information access. Today people can access Web resources easily. However, it is difficult to reach information that satisfies users' requests. Search engines are the most popular tools to find information, if a user can specify appropriate keywords to express the concrete contents of the required information. However, our target users are persons looking for new information concerned with other information, even when the relationship among them is not yet known. To support such users, we propose a perspective information access framework that shows paths to reach the required information. Such perspective path corresponds to narrativity between the source and the destination information.
Group Key Agreement for Ad Hoc Networks
Authors: Lijun Liao
Venue:  (2007)

Over the last 30 years the study of group key agreement has stimulated much work. And as a result of the increased popularity of ad hoc networks, some approaches for the group key establishment in such networks are proposed. However, they are either only for static group or the memory, computation and communication costs are unacceptable for ad-hoc networks. In this thesis some protocol suites from the literature (2d-cube, 2d-octopus, Asokan-Ginzboorg, CLIQUES, STR and TGDH) shall be discussed. We have optimized STR and TGDH by reducing the memory, communication and computation costs. The optimized version are denoted by uSTR and uTGDH respectively. Based on the protocol suites uSTR and uTGDH we present a Tree-based group key agreement Framework for Ad-hoc Networks (TFAN). TFAN is especially suitable for ad-hoc networks with limited bandwidth and devices with limited memory and computation capability. To simulate the protocols, we have implemented TFAN, uSTR and uTGDH with J2ME CDC. The TFAN API will be described in this thesis.
Handling Pronouns Intelligently
Authors: Anna Maria di Sciullo
Venue: Proceeding of the 2005 conference on New Trends in Software Methodologies, Tools and Techniques: Proceedings of the fourth SoMeT_W05 (2005)

Intelligent information processing systems, including search, question answering, and summarization systems, must be able to handle pronouns, i.e. elements that do not have independent reference. We thus, focus on pronominal anaphora resolution (Mitkov [20], Kennedy and Boguraev [18], Lappin and Leass [19]), and identify the main features of an intelligent software design that includes the identification of minimal domains of interpretation based on asymmetric relations. We show that the identification of domains of interpretation based on asymmetric agreement (Di Sciullo [9]) opens new avenues in intelligent systems design.
A Fourier spectral method for the Navier-Stokes equations with volume penalization for moving solid obstacles
Authors: Dmitry Kolomenskiy, Kai Schneider
Venue: Journal of Computational Physics (2009)

An efficient numerical scheme to compute flows past rigid solid bodies moving through viscous incompressible fluid is presented. Solid obstacles of arbitrary shape are taken into account using the volume penalization method to impose no-slip boundary condition. The 2D Navier-Stokes equations, written in the vorticity-streamfunction formulation, are discretized using a Fourier pseudo-spectral scheme. Four different time discretization schemes of the penalization term are proposed and compared. The originality of the present work lies in the implementation of time-dependent penalization, which makes the above method capable of solving problems where the obstacle follows an arbitrary motion. Fluid-solid coupling for freely falling bodies is also implemented. The numerical method is validated for different test cases: the flow past a cylinder, Couette flow between rotating cylinders, sedimentation of a cylinder and a falling leaf with elliptical shape.
Stronger CDA strategies through empirical game-theoretic analysis and reinforcement learning
Authors: L. Julian Schvartzman, Michael P. Wellman
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1 (2009)

We present a general methodology to automate the search for equilibrium strategies in games derived from computational experimentation. Our approach interleaves empirical game-theoretic analysis with reinforcement learning. We apply this methodology to the classic Continuous Double Auction game, conducting the most comprehensive CDA strategic study published to date. Empirical game analysis confirms prior findings about the relative performance of known strategies. Reinforcement learning derives new bidding strategies from the empirical equilibrium environment. Iterative application of this approach yields strategies stronger than any other published CDA bidding policy, culminating in a new Nash equilibrium supported exclusively by our learned strategies.
Exploring children's investigation of data outliers
Authors: Juan Pablo Hourcade, Keith B. Perry
Venue: Proceedings of the 8th International Conference on Interaction Design and Children (2009)

In spite of the increasing amounts of digitized data that will be available to children when they grow up, little attention has been paid toward preparing them for the process of exploring data, researching related information, synthesizing findings, and presenting them to peers. In this paper we present our observations on activities conducted with 10-12 year old children that involved identifying outliers using an information visualization tool, researching the reasons behind the outlying data, and synthesizing and presenting results using a media authoring tool. Our observations suggest children in this age group can make use of information visualization tools designed for a general adult population, but that great difficulties remain in conducting online research, and in integrating data exploration, online research, and presentation of results.
Railway Signal and Paraconsistency
Authors: Kazumi Nakamatsu, Jair M. Abe
Venue: Proceeding of the 2005 conference on Advances in Logic Based Intelligent Systems: Selected Papers of LAPTEC 2005 (2005)

In this paper, we propose a complete lattice structure that expresses a paraconsistent view for railway signal knowledge. In the complete lattice, the meanings of the railway signal information regarding to railway interlocking are denoted well. Also, we have introduce a simulation system in which a paraconsistent logic program EVALPSN (Extended Vector Annotated Logic Program with Strong Negation) based on the railway signal lattice is used to express interlocking information.
Five Questions ... For George Siemens
Authors: Lisa Neal Gualtieri
Venue: eLearn (2009)

George Siemens is the author of Knowing Knowledge and the recently released Handbook of Emerging Technologies for Learning. He is also associate director of research and development with the Learning Technologies Centre at the University of Manitoba and is the founder and president of Complexive Systems Inc., a learning lab focused on helping organizations develop integrated learning structures to meet the needs of global strategy execution. Lisa Gualtieri interviewed him about educational systems.
Democratic citizenship community: a social network to promote e-deliberative process
Authors: Cristiano Maciel, Licínio Roque, Ana Cristina Bicharra Garcia
Venue: Proceedings of the 10th Annual International Conference on Digital Government Research: Social Networks: Making Connections between Citizens, Data and Government (2009)

Social networks, such as virtual communities, permit connections between citizen and government. Integrating consultative and deliberative environments for popular participation in democratic issues and creating virtual communities for warranting better relationship among members make it possible to model decision-making processes. For this reason, the Democratic Citizenship Community (DCC) was developed, based on the Government-Citizen Interactive Model. The DCC has interaction and communication resources such as citizens' profiles, debate, voting, information library, socialization space and users' help. This research analyzes the DCC social network in a pilot case study and presents initial discussions about this system.
Parameterized Complexity of Coloring Problems: Treewidth versus Vertex Cover
Authors: Jiří Fiala, Petr A. Golovach, Jan Kratochvíl
Venue: Proceedings of the 6th Annual Conference on Theory and Applications of Models of Computation (2009)

We compare the fixed parameter complexity of various variants of coloring problems (including List Coloring, Precoloring Extension, Equitable Coloring, L(p,1)-Labeling and Channel Assignment) when parameterized by treewidth and by vertex cover number. In most (but not all) cases we conclude that parametrization by the vertex cover number provides a significant drop in the complexity of the problems.
Evaluating the service quality of software providers appraised in CMM/CMMI
Authors: Rodrigo Pinheiro Santos, Kathia Marçal Oliveira, Wander Pereira Silva
Venue: Software Quality Control (2009)

Recently, several companies have decided to adopt maturity models such as the CMM/CMMI to ensure quality software processes. The state year report of the Software Engineering Institute (SEI) showed that more than three thousand CMMI appraisals have been conducted since 2002. Many of these were performed at software providers, i.e. companies that develop software for other companies. Although the costs of the implementation and appraisal of CMM/CMMI are high for the software providers, there is no formal study investigating whether this investment pays off or, in other words, whether their customers are measurably satisfied with the quality of the service provided. This article presents the results of a formal evaluation of customer perception of the service quality offered by the software providers appraised in CMM/CMMI. We developed an instrument based on a widely used service quality evaluation model (SERVQUAL) and applied this instrument to several customers of software providers appraised in CMM/CMMI. The results show a considerable discrepancy between customers' expectations and their perceptions of the services provided.
Formation control of multi-agent systems with heterogeneous communication delays
Authors: Cheng-Lin Liu, Yu-Ping Tian
Venue: International Journal of Systems Science (2009)

In this article, the formation control is investigated for a network of second-order dynamic agents with heterogeneous communication delays. The desired stationary formation is achieved by introducing diverse self-delay for each agent. In addition, a delay-dependent formation control algorithm is proposed to achieve the desired moving formation. Based on the frequency-domain analysis and matrix theory, sufficient conditions are obtained for the multi-agent systems asymptotically converging to desired stationary and moving formations, respectively. Simulation results illustrate the correctness of the results.