ppxscal/embedding_network huggingface dataset:
Capturing Expressive and Indicative Qualities of Conducting Gesture: An Application of Temporal Expectancy Models
Authors: Dilip Swaminathan, Harvey Thornburg, Todd Ingalls, Stjepan Rajko, Jodi James, Ellen Campana, Kathleya Afanador, Randal Leistikow
Venue: Computer Music Modeling and Retrieval. Sense of Sounds: 4th International Symposium, CMMR 2007, Copenhagen, Denmark, August 27-31, 2007. Revised Papers (2008)

Many event sequences in everyday human movement exhibit temporal structure: for instance, footsteps in walking, the striking of balls in a tennis match, the movements of a dancer set to rhythmic music, and the gestures of an orchestra conductor. These events generate prior expectancies regarding the occurrence of future events. Moreover, these expectancies play a critical role in conveying expressive qualities and communicative intent through the movement; thus they are of considerable interest in musical control contexts. To this end, we introduce a novel Bayesian framework which we call the temporal expectancy model and use it to develop an analysis tool for capturing expressive and indicative qualities of the conducting gesture based on temporal expectancies. The temporal expectancy model is a general dynamic Bayesian network (DBN) that can be used to encode prior knowledge regarding temporal structure to improve event segmentation. The conducting analysis tool infers beat and tempo, which are indicative and articulation which is expressive, as well as temporal expectancies regarding beat (ictus and preparation instances) from conducting gesture. Experimental results using our analysis framework reveal a very strong correlation in how significantly the preparation expectancy builds up for staccato vs legato articulation, which bolsters the case for temporal expectancy as cognitive model for event anticipation, and as a key factor in the communication of expressive qualities of conducting gesture. Our system operates on data obtained from a marker based motion capture system, but can be easily adapted for more affordable technologies like video camera arrays.
Machine Vision Application to the Detection of Micro-organism in Drinking Water
Authors: Hernando Fernandez-Canque, Sorin Hintea, Gabor Csipkes, Allan Pellow, Huw Smith
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

The work presented in this paper uses a novel Machine Vision application to detect and identify micro-organism oocysts on drinking water. This new concept of water borne micro-organism detection uses image processing to allow detailed inspection of parasite morphology to nanometre dimensions. The detection results are more reliable than existing manual methods. Combining Normarski Differential Interface Contrast (DIC) and fluorescence microscopy using Fluorescein Isothiocyanate (FITC) and UV filters, the system provides a reliable detection of micro-organisms with a considerable reduction in time, cost and subjectivity over the current labour intensive time consuming manual method.
Data Mining: Algorithms and Problems
Authors: Xindong Wu
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

Data mining, or knowledge discovery in databases (KDD), is an interdisciplinary field that integrates techniques from several research areas including machine learning, statistics, database systems, and pattern recognition, for the analysis of large volumes of possibly complex, highly-distributed and poorly-organized data. The prosperity of the data mining field may attribute to two essential reasons. Firstly, a huge amount of data is collected and stored everyday. On the one hand, along with the continuing development of advanced technologies in many domains, data is generated at enormous speeds. For examples, purchases data at department/grocery stores, bank/credit card transaction data, e-commerce data, Internet traffic data that describes the browsing history of Web users, remote sensor data from agricultural satellites, and gene expression data from microarray technology. On the other hand, the progress made in hardware technology allows today's computer systems to store very large amounts of data. Secondly, with these large volumes of data at hand, the data owners have an imminent intent to turn them into useful knowledge. From a commercial viewpoint, the ultimate goal of the data owners is to gain more and pay less for their business activities. Under the competition pressure, they want to enhance their services, develop cost-effective strategies, and target the right group of potential customers. From a scientific viewpoint, when traditional techniques are infeasible in dealing with the raw data, data mining may help scientists in many ways, such as classifying and segmenting data. By applying the knowledge extracted from data mining, the business analyst may rate customers by their propensity to respond to an offer, the doctor may estimate the probability of an illness re-occurrence, the website publisher may display customized Web pages to individual Web users according to their browsing habit, and the geneticist may discover novel gene-gene interaction patterns. In this talk, we aim to provide a general picture for important data mining steps, topics, algorithms and challenges.
Computer-aided model of the dynamic behavior of the feed drive systems
Authors: Carmen E. Eisinger-Borcia, Doina Marin, Emil M. Videa
Venue: Proceedings of the 9th WSEAS International Conference on International Conference on Automation and Information (2008)

This paper presents a new computer-aided modeling way of the feed drive system (FDS) from modern machine tools [7, 8], based on a general method elaborated especially for multi-bodies branched systems [1, 2, 3]. It was developed in the welcoming frame of Computational Mechanics, suitable to the mathematical simulation of their dynamic behavior, in order to reduce the perturbations provoked by vibrations. The paper presents an 8-DOF new dynamic model of an FDS that allows to obtain the mathematical model as second order nonlinear differential equations with some non-constant coefficients. The flexibility of this mathematical model synergistically completed by the Mathematica® software symbolic calculus capabilities, allow us to determine the values of design parameters that optimize the dynamic behavior, according to predefined criteria.
Cooperative Boolean games
Authors: Paul E. Dunne, Wiebe van der Hoek, Sarit Kraus, Michael Wooldridge
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2 (2008)

We present and formally investigate Cooperative Boolean Games, a new, natural family of coalitional games that are both compact and expressive. In such a game, an agent's primary aim is to achieve its individual goal, which is represented as a propositional logic formula over some set of Boolean variables. Each agent is assumed to exercise unique control over some subset of the overall set of Boolean variables, and the set of valuations for these variables corresponds to the set of actions the agent can take. However, the actions available to an agent are assumed to have some cost, and an agent's secondary aim is to minimise its costs. Typically, an agent must cooperate with others because it does not have sufficient control to ensure its goal is satisfied. However, the desire to minimise costs leads to preferences over possible coalitions, and hence to strategic behaviour. Following an introduction to the formal framework of Cooperative Boolean Games, we investigate solution concepts of the core and stable sets for them. In each case, we characterise the complexity of the associated solution concept, and discuss the surrounding issues. Finally, we present a bargaining protocol for cooperation in Boolean games, and characterise the strategies in equilibrium for this protocol.
Spectrum Bidding in Wireless Networks and Related
Authors: Xiang-Yang Li, Ping Xu, Shaojie Tang, Xiaowen Chu
Venue: Proceedings of the 14th annual international conference on Computing and Combinatorics (2008)

In this paper, we study the spectrum assignment problem for wireless access networks. Opportunistic spectrum usage is a promising technology. However, it could suffer from the selfish behavior of secondary users. In order to improve opportunistic spectrum usage, we propose to combine the game theory with wireless modeling. Several versions of problems are formalized under different assumptions. We design PTAS or efficient approximation algorithms for each of these problems such that overall social benefit is maximized. Finally, we show how to design a truthful mechanism based on all these algorithms.
Exploring organizational commitment from an organizational perspective: organizational learning as a determinant of affective commitment in indian software firms
Authors: Andrea Casey
Venue:  (2008)

This study examined whether organizational learning can predict affective commitment of employees in Indian software firms. Although related literature has indicated that organizational learning impacts organizational commitment, the literature does not include any study that has examined the effect of organizational learning variables on affective commitment. It is important to consider the influence of organizational learning on commitment because the process of socialization involves learning, which in turn is dependent on the availability of information within an organization (Morrison, 1993). The overarching research question that guided this study was, "do employee perceptions of organizational learning as measured by actions in the four organizational learning subsystems, namely, environmental interface, action/reflection, meaning and memory, and dissemination and diffusion predict affective commitment of employees in Indian software firms?" Other research questions examined whether each of the four organizational learning subsystems could predict affective commitment of employees in Indian software firms. Using the learning items of the Organizational Actions Survey and Meyer and Allen's (1997) affective commitment scale, data was collected from 446 individuals employed in 35 Indian software firms using a web-based survey. Factor analysis and regression analysis were used to analyze the data. The level of analysis was at the organizational level. This study found a significant positive correlation between organizational learning and affective commitment (R=.73). Further, each of the four organizational learning subsystems significantly predicted affective commitment of employees in Indian software firms. The framework presented in this study looks at organizational commitment from a workplace learning perspective. Human Resource Development professionals can use organizational learning as a commitment producing strategy by focusing on collective action (learning by doing) and reflection (insightful questioning) embedded in an organizational culture that supports continuous learning and development.
An Effective Method for Initialization of Lloyd-Max's Algorithm of Optimal Scalar Quantization for Laplacian Source
Authors: Zoran Peric, Jelena Nikolic
Venue: Informatica (2007)

In this paper an exact and complete analysis of the Lloyd-Max's algorithm and its initialization is carried out. An effective method for initialization of Lloyd-Max's algorithm of optimal scalar quantization for Laplacian source is proposed. The proposed method is very simple method of making an intelligent guess of the starting points for the iterative Lloyd-Max's algorithm. Namely, the initial values for the iterative Lloyd-Max's algorithm can be determined by the values of compandor's parameters. It is demonstrated that by following that logic the proposed method provides a rapid convergence of the Lloyd-Max's algorithm.
Asymptotic Convergence Properties of Entropy Regularized Likelihood Learning on Finite Mixtures with Automatic Model Selection
Authors: Zhiwu Lu, Xiaoqing Lu, Zhiyuan Ye
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

In finite mixture modelling, it is crucial to select the number of components for a data set. We have proposed an entropy regularized likelihood (ERL) learning principle for the finite mixtures to solve this model selection problem under regularization theory. In this paper, we further give an asymptotic analysis of the ERL learning, and find that the global minimization of the ERL function in a simulated annealing way (i.e., the regularization factor is gradually reduced to zero) leads to automatic model selection on the finite mixtures with a good parameter estimation. As compared with the EM algorithm, the ERL learning can go across the local minima of the negative likelihood and keep robust with respect to initialization. The simulation experiments then prove our theoretic analysis.
Visualizing Non-subordination and Multidominance in Tree Diagrams: Testing Five Syntax Tree Variants
Authors: Leonie Bosveld-De Smet, Mark Vries
Venue: Proceedings of the 5th international conference on Diagrammatic Representation and Inference (2008)

In linguistics, it is quite common to use tree diagrams for immediate constituent analysis of sentences. Traditionally, these trees are binary and two-dimensional. However, phenomena such as coordination and right node raising, have led to the view that a simple hierarchical approach of sentences is inadequate: some linguistic phenomena rather seem to involve non-subordination and multiple dependencies. The central question of the present research is this: what are workable alternative tree-like diagrams that can accommodate to this view? An experiment has been set up to test five different types of tree visualizations, including three-dimensional trees. Subjects were asked to respond to various questions concerning coordination and (non-constituent) right node raising constructions, and to mark their preference for each tree visualization. This paper will discuss the representation problems, and present the experiment and its results. It turned out that the tree most rich in information was the least usable one, whereas the tree, most close to the traditional syntax tree, but with colour enrichment, performed best.
Homomorphic Encryption and Signatures from Vector Decomposition
Authors: Tatsuaki Okamoto, Katsuyuki Takashima
Venue: Proceedings of the 2nd international conference on Pairing-Based Cryptography (2008)

This paper introduces a new concept, distortion eigenvector space; it is a (higher dimensional) vector space in which bilinear pairings and distortion maps are available. A distortion eigenvector space can be efficiently realized on a supersingular hyperelliptic curve or a direct product of supersingular elliptic curves. We also introduce an intractable problem (with trapdoor) on distortion eigenvector spaces, the higher dimensional generalization of the vector decomposition problem (VDP). We define several computational and decisional problems regarding VDP, and clarify the relations among them. A trapdoor bijective function with algebraically rich properties can be obtained from the VDP on distortion eigenvector spaces. This paper presents two applications of this trapdoor bijective function; one is multivariate homomorphic encryption as well as a two-party protocol to securely evaluate 2DNF formulas in a higher dimensional manner, and the other is various types of signatures such as ordinary signatures, blind signatures, generically (selectively and universally) convertible undeniable signatures and their combination.
The Burroughs laboratory computer
Authors: G. G. HOBERG
Venue: Papers and discussions presented at the Dec. 10-12, 1951, joint AIEE-IRE computer conference: Review of electronic digital computers (1951)

In early 1950 the Research Division of the Burroughs Adding Machine Company developed a need for a computing installation of moderate size which would, among other objectives: 1. Serve as a proving ground for new ideas and devices. 2. Provide data on large-scale-computer reliability. 3. Offer a means for indoctrinating a large number of inexperienced people in the various phases of realizing and operating a digital computer. 4. Produce useful solutions of engineering problems associated with a research program. 5. Solve business problems in a manner which would simulate their handling by contemplated smaller and relatively special-purpose commercial machines. 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
Surface-Based Structural Group Analysis of fMRI Data
Authors: Grégory Operto, Cédric Clouchoux, Rémy Bulot, Jean-Luc Anton, Olivier Coulon
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

As structural and surface-based analyses gain interest for activation detection, morphometry and intersubject matching purposes, this paper proposes a method to perform structural group analyses directly on the cortical surface. Scale-space blobs are extracted from surface-based functional maps and matched across subjects. The process aims at identifying activations within a population despite the various effects due to variability. Results of the method are presented with simulated activations and with data from a somatotopy protocol.
Case Studies in Model Manipulation for Scientific Computing
Authors: Jacques Carette, Spencer Smith, John Mccutchan, Christopher Anand, Alexandre Korobkine
Venue: Proceedings of the 9th AISC international conference, the 15th Calculemas symposium, and the 7th international MKM conference on Intelligent Computer Mathematics (2008)

The same methodology is used to develop 3 different applications. We begin by using a very expressive, appropriate Domain Specific Language, to write down precise problem definitions, using their most natural formulation. Once defined, the problems form an implicit definition of a unique solution. From the problem statement, our model, we use mathematical transformations to make the problem simpler to solve computationally. We call this crucial step "model manipulation." With the model rephrased in more computational terms, we can also derive various quantities directly from this model, which greatly simplify traditional numeric solutions, our eventual goal. From all this data, we then use standard code generation and code transformation techniques to generate lower-level code to perform the final numerical steps. This methodology is very flexible, generates faster code, and generates code that would have been all but impossible for a human programmer to get correct.
Turn to the Left or to the West: Verbal Navigational Directions in Relative and Absolute Frames of Reference
Authors: Toru Ishikawa, Mika Kiyomoto
Venue: Proceedings of the 5th international conference on Geographic Information Science (2008)

This study examined how people use verbal route directions given in relative and absolute frames of reference in real-world navigation, particularly differences or similarities in cognitive load posed by the two frames of reference. Participants, Japanese speakers, walked the first set of five routes with relative (or absolute) directions and the second set of five routes with absolute (or relative) directions. For the first set of routes, participants performed equivalently on navigating the routes with relative and absolute directions, showing that they can somehow adapt to either way of thinking about space. But for the second set of routes, participants did better with relative directions than with absolute directions, showing that switching from a nonpreferred to a preferred frame of reference was easier than switching the other way around. In particular, participants with a poor sense of direction found the latter to be difficult. This asymmetric pattern of performance, depending on which frame of reference was used first, shows that the cognitive loads of processing information given in the two frames of reference are not the same. Concerning configurational understanding of the routes, participants did equally well with relative and absolute verbal directions, showing that they constructed equivalent mental images. These results were replicated in the second experiment of text comprehension, in which participants only read verbal directions and did spatial tasks. Implications of these results for the design of navigational aids were discussed.
An Auction-Based System for Workforce Resource Allocation
Authors: Nadim Haque, Botond Virginas, Mathias Kern, Gilbert Owusu
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

In this paper, we look at the problem of workforce redeployments where we consider the deployment of field engineers within a company organisation. Traditional methods for solving such problems have focused primarily on using centralised decision makers and optimisation algorithms which compute results in a solely centralised manner. Now, auctions have shown to have been used successfully for allocating resources in various applications and systems. Thus, in this paper, we propose a system that makes use of auctions as a mechanism for allocating the resources (field engineers) between different geographical parts of an organisation.
6DOF Haptic Cooperative Virtual Prototyping over High Latency Networks
Authors: Mathieu Gautier, Claude Andriot, Pierre Ehanno
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

In this paper, we propose a networked shared 6-DOF haptic cooperation system, based on several distributed physical simulations dedicated to CAD objects simulation. The simulations are linked together with a spring-damper coupling scheme. This system achieves good local responsiveness and enables remote users to perform complex virtual prototyping tasks.A preliminary test was conducted on this system using a complex industrial virtual prototyping scenario. This test required a good coordination between participants. These first experimentations give promising results to carry on prototyping task over large delayed networks.
A Rule-Based Recommender System for Online Discussion Forums
Authors: Fabian Abel, Ig Ibert Bittencourt, Nicola Henze, Daniel Krause, Julita Vassileva
Venue: Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems (2008)

In this paper we present a rule-based personalization framework for encapsulating and combining personalization algorithms known from adaptive hypermedia and recommender systems. We show how this personalization framework can be integrated into existing systems by example of the educational online board Comtella-D, which exploits the framework for recommending relevant discussions to the users. In our evaluations we compare different recommender strategies, investigate usage behavior over time, and show that a small amount of user data is sufficient to generate precise recommendations.