ppxscal/embedding_network huggingface dataset:
Operational semantics of goal models in adaptive agents
Authors: Mirko Morandini, Loris Penserini, Anna Perini
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1 (2009)

Several agent-oriented software engineering methodologies address the emerging challenges posed by the increasing need of adaptive software. A common denominator of such methodologies is the paramount importance of the concept of goal model in order to understand the requirements of a software system. Goal models consist of goal graphs representing AND/OR-decomposition of abstract goals down to operationalisable leaf-level goals. Goal models are used primarily in the earlier phases of software engineering, for social modelling, requirements elicitation and analysis, to concretise abstract objectives, to detail them and to capture alternatives for their satisfaction. Although various agent programming languages incorporate the notion of (leaf-level) goal as a language construct, none of them natively support the definition of goal models. However, the semantic gap between goal models used at design-time and the concept of goal used at implementation and execution time represent a limitation especially in the development of self-adaptive and fault-tolerant systems. In such systems, design-time knowledge on goals and variability becomes relevant at run-time, to take autonomous decisions for achieving high level objectives correctly. Recently, unifying operational semantics for (leaf) goals have been proposed [15]. We extend this work to define an operational semantics for the behaviour of goals in goal models, maintaining the flexibility of using different goal types and conditions. We use a simple example to illustrate how the proposed approach effectively deals with the semantic gap between design-time goal models and run-time agent implementations.
On using prototype reduction schemes to enhance the computation of volume-based inter-class overlap measures
Authors: Sang-Woon Kim, B. John Oommen
Venue: Pattern Recognition (2009)

In most pattern recognition (PR) applications, it is advantageous if the accuracy (or error rate) of the classifier can be evaluated or bounded prior to testing it in a real-life setting. It is also well known that if the two class-conditional distributions have a large overlapping volume (almost all the available work on ''overlapping of classes'' deals with the case when there are only two classes), the classification accuracy is poor. This is because if we intend to use the classification accuracy as a criterion for evaluating a PR system, the points within the overlapping volume tend to lead to maximal misclassification. Unfortunately, the computation of the indices which quantify the overlapping volume is expensive. In this vein, we propose a strategy of using a prototype reduction scheme (PRS) to approximately, but quickly, compute the latter. In this paper, we demonstrate, first of all, that this is an extremely expedient proposition. Indeed, we show that by completely discarding (we are not aware of any reported scheme which discards ''irrelevant'' sample (training) points, and which simultaneously attains to an almost-comparable accuracy) the points not included by the PRS, we can obtain a reduced set of sample points, using which, in turn, the measures for the overlapping volume can be computed. The value of the corresponding figures is comparable to those obtained with the original training set (i.e., the one which considers all the data points) even though the computations required to obtain the prototypes and the corresponding measures are significantly less. The proposed method has been rigorously tested on artificial and real-life datasets, and the results obtained are, in our opinion, quite impressive-sometimes faster by two orders of magnitude.
Multi-segmental representations and approximation of set-valued functions with 1D images
Authors: Nira Dyn, Elza Farkhi, Alona Mokhov
Venue: Journal of Approximation Theory (2009)

In this work univariate set-valued functions (SVFs, multifunctions) with 1D compact sets as images are considered. For such a continuous SFV of bounded variation (CBV multifunction), we show that the boundaries of its graph are continuous, and inherit the continuity properties of the SVF. Based on these results we introduce a special class of representations of CBV multifunctions with a finite number of 'holes' in their graphs. Each such representation is a finite union of SVFs with compact convex images having boundaries with continuity properties as those of the represented SVF. With the help of these representations, positive linear operators are adapted to SVFs. For specific positive approximation operators error estimates are obtained in terms of the continuity properties of the approximated multifunction.
Mining International Academic Conference Programs on the Web
Authors: Tomoya Noro, Hidenori Negishi, Takehiro Tokuda
Venue: Proceeding of the 2007 conference on Information Modelling and Knowledge Bases XVIII (2007)

Recently, a large number of international academic conferences have been held all over the world, and the number of conferences has been increasing year by year, which makes it difficult for us to overview the whole situation. On the other hand, explosive growth of the Internet makes it possible for us to obtain various information about conferences from the Web. In this paper, we propose a method for giving an overview of relation among conferences, such as clustering conferences and discovering the conference where active discussion is conducted in certain issues, by constructing a research map from conference information obtained from the Web.
A typed intermediate language for supporting interfaces
Authors: Juan Chen
Venue: Proceedings of the 11th International Workshop on Formal Techniques for Java-like Programs (2009)

Object-oriented languages such as Java and C# provide interfaces to support a restricted form of multiple inheritance. Existing low-level typed intermediate languages for object-oriented languages, however, either do not support interfaces or require non-standard interface implementations. This paper describes a low-level typed intermediate language that can express the standard interface implementation strategies based on interface tables (itables). The language can faithfully model itables, the standard itable-based interface method invocation, and interface cast. The type system is sound and the type checking is decidable.
Creative interactive play for disabled children
Authors: Patrizia Marti, Alessandro Pollini, Alessia Rullo, Leonardo Giusti, Erik Grönvall
Venue: Proceedings of the 8th International Conference on Interaction Design and Children (2009)

The workshop addresses the emerging field of research on robotics, assistive technologies and interaction design promoting play for physically, visually, and hearing impaired children and for emotionally and mentally handicapped children. Interactive devices including toys, pets and educational tools as well as interactive collaborative environments may represent a unique opportunity for disable children to full engage in play and have fun. The Creative Interactive Play workshop presents a collection of innovative interactive technologies and case studies for inclusive play and discusses the challenges and opportunities they can bid to disabled children.
Efficient training methods for conditional random fields
Authors: Andrew K. Mccallum
Venue:  (2008)

Many applications require predicting not a just a single variable, but multiple variables that depend on each other. Recent attention has therefore focused on structured prediction methods, which combine the modeling flexibility of graphical models with the ability to employ complex, dependent features typical of traditional classification methods. Especially popular have been conditional random fields (CRFs), which are graphical models of the conditional distribution over outputs given a set of observed features. Unfortunately, parameter estimation in CRFs requires repeated inference, which can be computationally expensive. Complex graphical structures are increasingly desired in practical applications, but then training time often becomes prohibitive. In this thesis, I investigate efficient training methods for conditional random fields with complex graphical structure, focusing on local methods which avoid propagating information globally along the graph. First, I investigate piecewise training, which trains each of a model's factors separately. I present three views of piecewise training: as maximizing the likelihood in a so-called "node-split graph", as maximizing the Bethe likelihood with uniform messages, and as generalizing the pseudo-moment matching estimator of Wainwright et al. [2003]. Second, I propose piecewise pseudolikelihood, a hybrid procedure which "pseudolikelihood-izes" the piecewise likelihood, and is therefore more efficient if the variables have large cardinality. Piecewise pseudolikelihood performs well even on applications in which standard pseudolikelihood performs poorly. Finally, motivated by the connection between piecewise training and BP, I explore training methods using beliefs arising from stopping BP before convergence. I propose a new schedule for message propagation that improves upon the dynamic schedule proposed recently by Elidan et al. [2006], and present suggestive results applying dynamic schedules to the system of equations that combine inference and learning. I also present two novel families of loopy CRFs, which appear as test cases throughout. First is the dynamic CRF, which combines the factorized state representation of dynamic Bayesian networks with the modeling flexibility of conditional models. The second of these is the skip-chain CRF, which models the fact that identical words are likely to have the same label, even if they occur far apart.
Admission control threshold in cellular relay networks with power adjustment
Authors: Ki-Dong Lee, Byung K. Yi
Venue: EURASIP Journal on Wireless Communications and Networking (2009)

In the cellular network with relays, the mobile station can benefit from both coverage extension and capacity enhancement. However, the operation complexity increases as the number of relays grows up. Furthermore, in the cellular network with cooperative relays, it is even more complex because of an increased dimension of signal-to-noise ratios (SNRs) formed in the cooperative wireless transmission links. In this paper, we propose a new method for admission capacity planning in a cellular network using a cooperative relaying mechanism called decode-and-forward. We mathematically formulate the dropping ratio using the randomness of "channel gain." With this, we formulate an admission threshold planning problem as a simple optimization problem, where we maximize the accommodation capacity (in number of connections) subject to two types of constraints. (1) A constraint that the sum of the transmit powers of the source node and relay node is upper-bounded where both nodes can jointly adjust the transmit power. (2) A constraint that the dropping ratio is upper-bounded by a certain threshold value. The simplicity of the problem formulation facilitates its solution in real-time. We believe that the proposed planning method can provide an attractive guideline for dimensioning a cellular relay network with cooperative relays.
Basic Image Features (BIFs) Arising from Approximate Symmetry Type
Authors: Lewis D. Griffin, Martin Lillholm, Mike Crosier, Justus Sande
Venue: Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision (2009)

We consider detection of local image symmetry using linear filters. We prove a simple criterion for determining if a filter is sensitive to a group of symmetries. We show that derivative-of-Gaussian (DtG) filters are excellent at detecting local image symmetry. Building on this, we propose a very simple algorithm that, based on the responses of a bank of six DtG filters, classifies each location of an image into one of seven Basic Image Features (BIFs). This effectively and efficiently realizes Marr's proposal for an image primal sketch. We summarize results on the use of BIFs for texture classification, object category detection, and pixel classification.
Finding inconsistency for UML-based composition at program level
Authors: Hector Miguel Chavez, Wuwei Shen
Venue: Proceedings of the 2009 ICSE Workshop on Modeling in Software Engineering (2009)

In this paper, we study how to enforce the composition based on the Unified Modeling Language (UML 2.0) at the program level. We develop a novel technique for automatically detecting the inconsistency for composition between a class diagram and its implementation program using a software model checker. Specifically, given the attributes that are intended to implement composition in a UML class diagram, our approach checks the composition property in two steps. First, our approach systematically generates all valid object diagrams, i.e. valid input program states. Second, after a method to destroy the owner object is called on each object diagram, our approach checks whether all external links to the owned objects have been removed. Central to this approach is how to prune away the large search space that includes all valid input program states.
A Biometric Menagerie Index for Characterising Template/Model-Specific Variation
Authors: Norman Poh, Josef Kittler
Venue: Proceedings of the Third International Conference on Advances in Biometrics (2009)

An important phenomenon influencing the performance of a biometric experiment, attributed to Doddington et al (1998), is that the match scores (whether under genuine or impostor matching) are strongly dependent on the model or template from which the match scores have been derived. Although there exist studies to classify the characteristic of the template/model, as well as the query data, into animal names such as sheep, goats, wolves and lambs --- so-called Doddington's menagerie, or higher semantic categories considering simultaneously both genuine and impostor match scores, due to Yager and Dunstone (2008), there is currently absence of means to characterise the extent of Doddington's menagerie. This paper aims to design such an index, called the biometric menagerie index (BMI). It is defined as the ratio of the between-client variance and the expectation of the total variance. BMI has three desirable properties. First, it is invariant to shifting and scaling of the match scores. Second, its value lies between zero and one, with zero implying the absence of Doddington's menagerie effect, and one signifying its strong presence. Third, it is experimentally verified that BMI generalizes to different choices of impostor population. Our findings based on the XM2VTS benchmark score database suggest the followings: First, the BMI of genuine match scores is generally higher than that of the impostor match scores. Second, two different matching algorithms observing the same biometric data may have significantly different BMI values, hence suggesting that the biometric menagerie is algorithm-dependent.
Learning with Weighted Transducers
Authors: Corinna Cortes, Mehryar Mohri
Venue: Proceeding of the 2009 conference on Finite-State Methods and Natural Language Processing: Post-proceedings of the 7th International Workshop FSMNLP 2008 (2009)

Weighted finite-state transducers have been used successfully in a variety of natural language processing applications, including speech recognition, speech synthesis, and machine translation. This paper shows how weighted transducers can be combined with existing learning algorithms to form powerful techniques for sequence learning problems.
Latent classification models for binary data
Authors: Helge Langseth, Thomas D. Nielsen
Venue: Pattern Recognition (2009)

One of the simplest, and yet most consistently well-performing set of classifiers is the naive Bayes models (a special class of Bayesian network models). However, these models rely on the (naive) assumption that all the attributes used to describe an instance are conditionally independent given the class of that instance. To relax this independence assumption, we have in previous work proposed a family of models, called latent classification models (LCMs). LCMs are defined for continuous domains and generalize the naive Bayes model by using latent variables to model class-conditional dependencies between the attributes. In addition to providing good classification accuracy, the LCM has several appealing properties, including a relatively small parameter space making it less susceptible to over-fitting. In this paper we take a first step towards generalizing LCMs to hybrid domains, by proposing an LCM for domains with binary attributes. We present algorithms for learning the proposed model, and we describe a variational approximation-based inference procedure. Finally, we empirically compare the accuracy of the proposed model to the accuracy of other classifiers for a number of different domains, including the problem of recognizing symbols in black and white images.
Plenary lecture 3: signal processing - key element in designing an accurate machining forces measuring device
Authors: Mihaiela Iliescu
Venue: Proceedings of the 8th WSEAS international conference on Signal processing, robotics and automation (2009)

When manufacturing parts, specially by machining process, it is often necessary to have reliable information on machining forces involved. So, systems and devices for measuring these forces have to be used, this paper presenting an innovative one, which, has also been patented. One of the most important aspects in designing such a device is represented by good signal processing, so that, for instance, specific calibrating equations to be appropriate determined. Checking obtained results into real machining conditions represents a final challenge.
New research on public deliberation and information technology
Authors: Peter Muhlberger
Venue: Proceedings of the 10th Annual International Conference on Digital Government Research: Social Networks: Making Connections between Citizens, Data and Government (2009)

This is a proposal for a panel to examine new research programs into the uses of information technology (IT) tools in combination with public deliberation for improving public input into government at all levels. A better public input process is needed to address intractable national policy issues and local "wicked" planning problems. Both proponents of IT and of deliberation contend that their approaches can promote changes in public input that would be beneficial for addressing such difficult issues. These changes include educating and motivating the public, improving the synthesis and coordination of public views on an issue, and conferring greater legitimacy to policy decisions. Several new research efforts are broaching the possibility that IT and deliberation approaches can be especially helpful in combination. This panel will explore the research questions, methodologies, research experiences, and findings of these valuable efforts. The panel seeks to cross-fertilize the diverse approaches in this area, raise audience awareness of and interest in this domain of research, and disseminate knowledge of pitfalls in this area of research.
Fiber Optical Parametric Amplifiers, Oscillators and Related Devices, 1st edition
Authors: Michel E. Marhic
Venue:  (2007)

This is the first book to provide comprehensive coverage of the theory and practice of OPAs and related devices, including fiber optical parametric oscillators (OPOs). After introducing the field, the theory and techniques behind all types of fiber OPAs are covered starting from first principles - topics include the scalar and vector OPA theory; the nonlinear Schrodinger equation; OPO theory; and quantum noise figure of fiber OPAs. Challenges of making fiber OPAs practical for a number of applications are discussed, and a survey of the state-of-the-art in feasibility demonstrations and performance evaluations is provided. The capabilities and limitations of OPAs; the potential applications for OPAs and OPOs, and prospects for future developments in the field are discussed. Theoretical tools developed in this text can also be applied to other areas of nonlinear optics. This is a valuable resource for researchers, advanced practitioners, and graduate students in optoelectronics. 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
EML3D: An XML Based Markup Language for 3D Object Manipulation in Second Life
Authors: Arturo Nakasone, Helmut Prendinger
Venue: Proceedings of the 10th International Symposium on Smart Graphics (2009)

Recently, virtual worlds like "Second Life" have received a lot of attention not only as environments that promote social interaction, but also as media in which scientific research can be performed effectively. In this latter aspect, they offer several advantages over traditional 3D visual applications, such as the capacity of supporting true collaborative endeavors in which real people, through the use of graphical avatars, can simultaneously discuss and analyze data, thereby enhancing their collaborative experience. Unfortunately, processing capabilities of current virtual worlds are limited when trying to visualize this data as 3D objects. External applications to solve this problem have been developed, but their functionality is obscure and they require good understanding in programming languages. To overcome these inconveniences for common users, we have developed EML3D (Environment Markup Language 3D), a markup language that provides a tool to manipulate virtual objects in Second Life in an easy and stable way.
Towards improving fuzzy clustering using support vector machine: Application to gene expression data
Authors: Anirban Mukhopadhyay, Ujjwal Maulik
Venue: Pattern Recognition (2009)

Recent advancement in microarray technology permits monitoring of the expression levels of a large set of genes across a number of time points simultaneously. For extracting knowledge from such huge volume of microarray gene expression data, computational analysis is required. Clustering is one of the important data mining tools for analyzing such microarray data to group similar genes into clusters. Researchers have proposed a number of clustering algorithms in this purpose. In this article, an attempt has been made in order to improve the performance of fuzzy clustering by combining it with support vector machine (SVM) classifier. A recently proposed real-coded variable string length genetic algorithm based clustering technique and an iterated version of fuzzy C-means clustering have been utilized in this purpose. The performance of the proposed clustering scheme has been compared with that of some well-known existing clustering algorithms and their SVM boosted versions for one simulated and six real life gene expression data sets. Statistical significance test based on analysis of variance (ANOVA) followed by posteriori Tukey-Kramer multiple comparison test has been conducted to establish the statistical significance of the superior performance of the proposed clustering scheme. Moreover biological significance of the clustering solutions have been established.